diff --git a/.flake8 b/.flake8
new file mode 100644
index 00000000..7e9ce2e9
--- /dev/null
+++ b/.flake8
@@ -0,0 +1,11 @@
+[flake8]
+exclude =
+    .git,
+    .claude,
+    __pycache__,
+    build,
+    dist,
+    docs/build,
+    *.egg-info,
+    .venv,
+    venv
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 00000000..fac305a0
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,25 @@
+## What and why
+
+<!-- What does this change do, and why is it needed? Link the issue or
+planning/ file it addresses. -->
+
+## How it was verified
+
+<!-- Tests added, commands run, simulations or fits checked. -->
+
+## Checklist
+
+See [CONTRIBUTING.md](https://github.com/harmslab/tfscreen/blob/main/CONTRIBUTING.md) for details on each item.
+
+- [ ] Branch is up to date with `upstream/main`
+- [ ] New and changed code has full unit test coverage, including branches and error paths
+- [ ] Bug fixes include a test that fails without the fix
+- [ ] Slow tests are marked `@pytest.mark.slow`
+- [ ] `pytest tests/tfscreen --runslow` passes locally
+- [ ] `diff-cover coverage.xml --compare-branch=upstream/main` reports 100%
+- [ ] `flake8 . --count --select=E9,F63,F7,F82` reports no errors
+- [ ] Public functions, classes and methods have numpydoc docstrings
+- [ ] New components, CLIs and YAML keys follow the conventions in `CLAUDE.md`
+- [ ] `CLAUDE.md` is updated if the change alters how the code works
+- [ ] `CHANGELOG.md` has an entry under `[Unreleased]` for any user-visible change
+- [ ] No data, checkpoints or other large files are committed
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 360c936f..225f4a29 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -12,6 +12,11 @@ on:
         type: boolean
         default: false
 
+# A newer push to the same branch or pull request cancels the older run.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   test:
     name: Python ${{ matrix.python-version }} / ${{ matrix.os }}
@@ -20,7 +25,11 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest]
-        python-version: ["3.11", "3.12", "3.13"]
+        python-version: ["3.12", "3.13", "3.14"]
+        # The coverage job runs this one, plus the slow tests.
+        exclude:
+          - os: ubuntu-latest
+            python-version: "3.12"
 
     steps:
       - uses: actions/checkout@v4
@@ -29,6 +38,7 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
+          cache: pip
 
       - name: Install package and test dependencies
         run: pip install -e ".[test]"
@@ -40,5 +50,47 @@ jobs:
         env:
           NUMBA_DISABLE_JIT: "1"
         run: >
-          pytest tests/tfscreen
+          pytest -n auto tests/tfscreen
           ${{ (github.event_name == 'workflow_dispatch' && inputs.run_slow_tests) && '--runslow' || '' }}
+
+  coverage:
+    name: Coverage
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          # diff-cover compares against main, so it needs the full history.
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: pip
+
+      - name: Install package and test dependencies
+        run: pip install -e ".[test]"
+
+      # Slow tests are included so code tested only by them counts as covered.
+      # pytest-cov combines the workers' data; settings come from pyproject.toml
+      # (branch coverage, source = tfscreen only, so planning/, tests/ and
+      # docs/ are never measured).
+      - name: Run unit tests under coverage
+        env:
+          NUMBA_DISABLE_JIT: "1"
+        run: pytest -n auto --runslow --cov --cov-report= tests/tfscreen
+
+      - name: Report total coverage
+        run: |
+          coverage xml
+          coverage report --fail-under=90
+
+      # Every line and branch a pull request adds or changes in src/ must be
+      # covered. Files outside coverage.xml are not judged.
+      - name: Check coverage of changed lines
+        if: github.event_name == 'pull_request'
+        run: >
+          diff-cover coverage.xml
+          --compare-branch=origin/${{ github.base_ref }}
+          --fail-under=100
diff --git a/.gitignore b/.gitignore
index 6321bc4b..e350fc49 100644
--- a/.gitignore
+++ b/.gitignore
@@ -205,3 +205,45 @@ cython_debug/
 marimo/_static/
 marimo/_lsp/
 __marimo__/
+
+# Local test/lint/coverage output from run_all_tests.sh
+reports/
+docs/badges/
+
+# Lab data for development; never committed (C10)
+planning/dev-data/
+planning/results/
+# Run outputs downloaded into study directories (lab-data fits, multi-GB
+# simulations); the study's scripts and README stay tracked
+planning/studies/recipe/
+planning/studies/full-size-sim/sim_*/
+planning/studies/full-size-sim/pilot_*/
+planning/studies/full-size-sim-*/
+planning/studies/m-bias-factorial/m_bias/
+planning/studies/m-bias-factorial/m_subsets/
+planning/studies/m-bias-factorial/m_subsets_real/
+planning/studies/m-bias-factorial/m_subsets_poisson/
+planning/studies/m-bias-factorial/held_s2/
+planning/studies/m-bias-factorial/held_kanR_*/
+planning/studies/m-bias-factorial/held_all_*/
+planning/studies/m-bias-factorial/held_mix_*/
+planning/studies/m-bias-factorial/kanR_only_*/
+planning/studies/m-bias-factorial/jk_seed*/
+planning/studies/m-bias-factorial/km_draws/
+planning/studies/m-bias-factorial/sim_realistic_seed2/
+planning/studies/m-bias-factorial/sim_realistic_s2/
+planning/studies/m-bias-factorial/real_depth/
+planning/studies/m-bias-factorial/sim_realistic_nocong/
+planning/studies/two-stage-validation/run_*/
+planning/studies/m-bias-factorial/stage2_*/
+
+.DS_Store 
+
+planning/studies/x-vs-binding/out/
+planning/studies/within-tube-m/out/
+planning/studies/within-tube-m/runs/
+planning/studies/relative-m/runs/
+planning/studies/joint-fit-sim/runs/
+planning/studies/joint-fit-sim/out/
+planning/studies/joint-fit-real/runs/
+planning/studies/joint-fit-real/out/
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 396fa180..467f845d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -29,7 +29,1469 @@ fall into two kinds:
 
 ## [Unreleased]
 
-Nothing yet.
+## [0.5.0] - 2026-10-24
+
+The release for the first manuscript on the 2026 screen: someone in the lab
+can take a screen from per-tube read counts to per-genotype *X* curves with
+95% intervals through one command, `tfs-fit-two-stage`, documented in
+"Analyzing a screen" (`docs/source/screen-analysis.rst`). On simulations of
+that screen the deep genotypes' intervals cover the truth 0.88-0.91 of the
+time up to 1e5 reads and 0.76-0.81 above. tfscreen now needs Python 3.12 or
+newer.
+
+The entries come in two groups. The first covers 2026-10-03 to
+2026-10-24: the two-stage fit, its validation and results table, tube checks
+and corrections, dependency floors, and the experimental joint
+growth/binding pieces, which the two-stage fit refuses. The second, from
+"Changes up to 2026-10-03" on, is the release as summarized then.
+
+### Fixed
+
+- **A ΔΔG CSV column can supply two structures.** `load_ddG_prior_csv`
+  failed ("The truth value of a Series is ambiguous") whenever one file
+  column fed two structures: a `column_map` such as `{HD: H}` for a model,
+  or, with no model given, a column kept as its own structure and copied to
+  another. The numeric check now looks at each source column once
+  (`theta/thermo/io.py`).
+- **`--shared_shift` works under pandas 3.** `shared_shift.row_shifts`
+  wrote wt's zero into the array from `Series.to_numpy()`, which pandas
+  3's copy-on-write returns as a read-only view (`ValueError: assignment
+  destination is read-only`), so `tfs-predict-theta` and
+  `tfs-predict-epistasis` with `--shared_shift` failed, and so did the
+  two-stage fit's predict step. It now takes a copy. CI (pandas 3) had
+  failed these 7 tests on every job since the shift was added; locally
+  (pandas 2) they passed. `tests/conftest.py` now turns copy-on-write on
+  under pandas 2, so the local suite behaves as CI's does.
+
+- **`tfs-fit-two-stage`'s k-shift refits stay at stage 2's MAP.** They
+  restarted at Adam's default step size (1e-3), fell about 6.7e5 nats off
+  stage 2's MAP and re-descended along the dk_geno population against wt,
+  so on the real data X moved +0.32 even with k unchanged and the shift
+  measured where the optimizer stopped
+  (`planning/studies/two-stage-validation/`, run 1). The refits now start
+  at `two_stage.kshift_step_size` (default 1e-5) with the dk_geno
+  population hyperparameters held at stage 2's MAP
+  (`two_stage.dk_geno_hypers`, `pin_dk_geno_hypers`), and a third refit
+  with k unchanged (`two_stage.kshift_control`, on by default) checks the
+  procedure: `kshift/drift.csv` gives each refit's change in X from stage
+  2's MAP, and the command warns when the control moves more than a
+  quarter of the shift. Existing `kshift/` directories are reused as
+  they are; delete them (and `predict/`) to rerun.
+
+- **`tfs-fit-two-stage` with a pinned k or m in stage 1.** A pinned k or
+  m is a deterministic site, which a MAP checkpoint does not hold, so
+  `tfs-extract-params` skipped both and stage 1 failed writing `km.csv`.
+  It now falls back to `two_stage.km_from_map`, which reads the MAP's
+  parameters and takes a pinned condition's prior location.
+
+- **`total_num_reads` is the total over all replicates.** `tfs-simulate`
+  passed every replicate the full total and each split it over its own
+  tubes, so a simulation had N times the configured reads, and with a
+  design a replicate with few tubes got far more reads per tube (2.5e9 for
+  the dev-data design's two-tube replicate). Each sequenced replicate now
+  gets its share by tube count (`simulate_cli.replicate_read_totals`).
+  Multi-replicate simulations now produce the reads their config states,
+  1/N of what they produced before.
+- **Simulated wt keeps its reference curve under `hill_geno`.** The
+  simulation sampler drew wt's phenotype category and perturbation like any
+  genotype's, so wt could come out flat ("never binds", about 5% of seeds)
+  or shifted; a relative fit gauges X on wt, and the full-size simulation's
+  flat wt stretched the X truth 3,600-fold. `thermo_to_growth` now sets wt
+  to the `wt_*` SimPriors curve unless a wt override is given
+  (`_pin_wt_reference`). Simulations with `hill_geno` change for wt only.
+- **`tfs-configure-model --set_priors` works under pandas 3.** The priors
+  CSV's `value` column mixes numbers and text, which pandas 3 reads as a
+  string dtype that refused the new float (`TypeError: Invalid value ... for
+  dtype 'str'`). `priors_edit.apply_priors_updates` reads it as object. This
+  failed every CI job from 2026-10-04 on; pandas 2 was unaffected.
+- `python -m tfscreen.tfmodel.scripts.sample_posterior_cli` ran the command
+  twice (a duplicated `__main__` block). The `tfs-sample-posterior` console
+  script was unaffected.
+
+### Changed
+
+- **Dependency floors and a `cuda12` extra.** `pyproject.toml` gives
+  every dependency a lower bound, the oldest versions the suite has passed
+  with (pandas 2.3, numpy 2.3, jax 0.11.2, numpyro 0.22, among others);
+  there are no upper bounds. `pip install -e ".[cuda12]"` installs GPU jax
+  on Linux, where pip's plain jax is CPU-only. A fresh install on the
+  newest releases (pandas 3.0.6, numpy 2.5.4, jax 0.12.0) passed the full
+  suite. The README, quickstart and "Analyzing a screen" page say how to
+  install.
+- **tfscreen needs Python 3.12 or newer** (was 3.11): jax 0.11 and newer,
+  the new floor, have no 3.11 wheels. CI tests 3.12, 3.13 and 3.14.
+- **`tfs-fit-two-stage` no longer adds k's uncertainty by default.** A new
+  `two_stage.kshift` setting (default false) skips the jackknife and the
+  k-shift refits, and predict runs without `--shared_shift`; `tfs_x.csv`'s
+  `k_shift` is then blank. Validation run 2 found the repaired shift a
+  third or less of the true one on simulations
+  (`planning/studies/two-stage-validation/`). Without it, the deep
+  genotypes' 95% intervals covered 0.88-0.91 up to 1e5 reads and
+  0.76-0.81 above. The whole fit now takes about half a day. Set
+  `kshift: true` for the old behavior, which `--step jackknife` and
+  `--step kshift` still run. The example SLURM chain follows the setting.
+- **`tfs-fit-two-stage` refuses the experimental joint-fit pieces and the
+  marker warp.** `read_two_stage_config` now fails on `binding_df`, a
+  theta growth or binding noise model other than `zero` (such as
+  `genotype_offset`), the titrant-shift, binding-offset and warp priors in
+  `set_priors`, and a `growth_priors` table whose `m_warp` column turns the
+  warp on. A warp was fitted in stage 1 and silently dropped in stage 2,
+  whose pins carry only k and m. Those components' docstrings now say they
+  are experimental.
+- **`binding_weight` defaults to 1.** Each binding observation now counts
+  once, the likelihood of the data as measured. The old default,
+  `N_growth_rows / N_binding_rows`, made the binding data weigh as much as
+  the whole growth dataset, so any
+  mismatch between binding and growth distorted the growth fit; it was one
+  reason binding was split from growth on 2026-09-26. A config stores its
+  resolved weight, so one written before this change keeps it; pass the
+  ratio as `--binding_weight` to reproduce the old default in a new one.
+  The count-likelihood, congression-calibration, svi-overconfidence and
+  real-data-fit studies already set 1. Joint-fit plan step 3
+  (`planning/joint-fit-return.md`).
+
+- CI runs the tests in parallel (`pytest-xdist`, `-n auto`) and the coverage
+  job measures through `pytest-cov`; `pytest-xdist` and `pytest-cov` joined
+  the `test` extra. The Ubuntu / Python 3.12 matrix job is gone, since the
+  coverage job runs the same suite there plus the slow tests. A newer push
+  to a branch cancels its older run.
+- The fit's progress line counts plateau windows from 1: `Plateau: 1/3` in
+  the first window that could stall, `3/3` in the window that decides a
+  step-size cut or a stop.
+
+### Added
+
+- **"Analyzing a screen" docs page** (`docs/source/screen-analysis.rst`):
+  the real-screen route from per-tube counts to `tfs_x.csv`, with tube
+  checks and corrections, the two-stage YAML, what each step does, cost,
+  the results table's columns, what *X* is and what its intervals carry
+  and leave out. The index, quickstart, README and `fitting.rst` point to
+  it; `fitting.rst` marks the joint growth/binding model experimental.
+  `examples/tfmodel/` gains `growth_priors_loose.csv` (named by
+  `two_stage.yaml`, which until now pointed at a file that was not there)
+  and a SLURM chain, `two_stage_submit.sh` with `two_stage_step.srun`.
+- **`tfs-fit-two-stage` writes one results table, `{out_dir}/tfs_x.csv`.**
+  A new last step, `summary`: one row per genotype and concentration with
+  X's quantiles (with the shared k shift), the shift itself (`k_shift`),
+  the genotype's number of mutations, its reads in total and per library
+  (counted once into `{out_dir}/genotype_reads.csv`), `in_training_data`,
+  and the marker-consistency `consistency_z` and `inconsistent` (NaN when
+  the check did not run). It is rewritten on every run, so it picks up a
+  consistency step run later (`two_stage.genotype_reads`, `num_mutations`,
+  `x_summary`).
+- **`tfs-configure-model --congression_sets`.** The congression mixture's
+  fixed co-resident draws (entry n-1 = number of sets with n co-residents;
+  default 12 3 1) were an orchestrator setting with no flag; now settable
+  and written to the config, refused without `transformation_model
+  mixture`. Each set is a cell class grown through the whole model, so
+  cost grows with the total (140 sets took about twice the default's wall
+  time on a small stage 1). On the joint-fit simulation the mixture's
+  approximation is the largest remaining source of stage 1's m bias
+  (`planning/studies/joint-fit-sim/`, run 8).
+
+- **`hill_geno` holds any population SD.** `theta_logit_low_hyper_scale_fixed`,
+  `theta_logit_delta_hyper_scale_fixed` and `theta_log_hill_K_hyper_scale_fixed`
+  join `theta_log_hill_n_hyper_scale_fixed`: > 0 makes that SD a
+  deterministic site at the held value with no guide parameters, as in
+  `hill_relative`. Set with `tfs-configure-model --set_priors`. On the
+  joint-fit simulation's stage 1, the learned SDs (tight HalfNormal priors,
+  log K's at 0.1) and the population locations' priors compressed the
+  singles' theta range and steepened m by 14%
+  (`planning/studies/joint-fit-sim/`, run 5).
+
+- **Simulator pieces for the joint fit.** A linear growth condition takes
+  `warp_a` and `warp_x0` (`growth_linkage.warp_theta`, the fit's `m_warp`
+  in numpy; truth `warp_log_a`, `warp_x0` in
+  `tfs_sim_growth_parameters.csv`); the top-level `growth_titrant_scale`
+  (s) makes growth see each curve at s times the nominal titrant while
+  binding sees the nominal one (`library_prediction` scales the growth
+  concentrations of the simulator's data, so the prior draw, wt's
+  reference and the overrides all follow; truth log s in
+  `tfs_sim_growth_titrant_scale.csv`); `binding_data.genotype_offset_sd`
+  adds one Normal offset per binding genotype to its whole curve
+  (`binding_data.apply_genotype_offsets`; truth in
+  `tfs_sim_binding_offsets.csv`). All off by default, so existing configs
+  simulate as before. Study: `planning/studies/joint-fit-sim/`, joint-fit
+  plan step 5.
+
+- **`tfs-extract-params` writes linear growth's warp.** With `m_warp` on,
+  `{out_prefix}_warp_log_a.csv` and `{out_prefix}_warp_x0.csv` hold each
+  warped condition's steepness and threshold (the extraction context now
+  carries the condition_growth priors, so only warped conditions get rows).
+  The warp runs with `hill_geno`'s absolute theta and the congression
+  mixture's partition-function rules, acting on each cell class's theta;
+  tested with the titrant shift and the binding offsets together. Joint-fit
+  plan step 4(c) (`planning/joint-fit-return.md`).
+
+- **`theta_binding_noise: genotype_offset`.** Each binding genotype's
+  binding theta is its curve plus one offset of its own, the same at every
+  titrant concentration, delta_g = tau * z_g with z_g ~ Normal(0, 1) and
+  tau ~ HalfNormal(0.2), learned unless `offset_scale_fixed` > 0 holds it.
+  Growth never sees it, so the shared curve is the in vivo one; a genotype
+  whose in vitro binding differs from its in vivo occupancy (M42I), or
+  whose curve carries its day's normalization error, disagrees alone
+  instead of pulling the growth parameters. The offsets live in a plate
+  over the binding genotypes only, indexed by a new
+  `BindingData.binding_ordinal` (binding-tensor order; the batch's indices
+  in binding-only mode), so a full library carries no prior-only offsets.
+  Subset and chunked predictions remap the plate by binding genotype label
+  (`analysis/prediction.py`); `tfs-extract-params` writes
+  `{out_prefix}_offset.csv` (per binding genotype) and
+  `{out_prefix}_offset_scale.csv`, and now runs the binding noise
+  component's extraction specs. Joint-fit plan step 4(b)
+  (`planning/joint-fit-return.md`).
+
+- **A titrant scale between binding and growth (`hill_geno`).** With
+  `theta_growth_log_conc_shift_scale` > 0 (a priors row, e.g.
+  `tfs-configure-model --set_priors theta_growth_log_conc_shift_scale=1`),
+  one global site `theta_growth_log_conc_shift` (log s) ~ Normal(loc,
+  scale) is sampled and growth sees each genotype's curve at s times the
+  nominal titrant concentration, while binding sees the nominal one: the
+  in vivo IPTG need not equal the nominal, and the curve is in binding
+  units. Applied in `generative/model.py` (`growth_titrant`) to the growth
+  data the theta component sees, including the congression mixture's
+  population and `tfs-predict-growth`'s; extracted as
+  `{out_prefix}_growth_log_conc_shift.csv`. Off by default (scale 0, no
+  site), so existing models trace unchanged. `tfs-predict-theta` still
+  reports the curve at the concentrations asked for, in binding units.
+  Joint-fit plan step 4(a) (`planning/joint-fit-return.md`).
+
+- **`tfs-process-binding`: a lab binding table to a `binding_df`.** One
+  row per measurement (genotype, protein and titrant concentrations in
+  molar by default, theta, replicate and date) becomes one row per
+  genotype and IPTG concentration at a chosen protein concentration (2 uM
+  by default), with the titrant in mM. Concentrations are rounded to six
+  significant digits before grouping, since the lab's values carry
+  floating-point residue that differs between days and would otherwise
+  split replicates of one point. Measurements within a replicate are
+  averaged first; `theta_std` is the standard error of the replicate mean
+  from one replicate SD pooled over every replicated point at that protein
+  concentration (most points have one replicate), or from `--theta_std`.
+  Writes `{out_prefix}.csv` for `tfs-configure-model --binding_df` and a
+  summary with replicate counts (`process_raw/binding.py`; joint-fit plan
+  step 2, `planning/joint-fit-return.md`).
+
+- **`tfs-check-tubes`: flag tubes whose composition disagrees with their
+  titrant label.** A routine QC of the growth table from
+  `tfs-process-counts`, for any titrant. Within each library x condition
+  panel, every tube's centered log-ratio composition is fit robustly as a
+  row (replicate x time) effect plus a per-row-scaled concentration profile,
+  scored on the axis from the lowest concentration's profile (0) to the
+  highest's (1), and tested within its row by a leave-one-out isotonic
+  order test and a level test against its label's expected score. Tube
+  totals and read depth cancel, and rows missing concentrations are not
+  shifted, unlike the row centering of the study script
+  (`planning/studies/relative-m/tube_qc.py`). Writes `{out_prefix}.csv`, one
+  row per tube with `score`, `expected`, `order_z`, `deviation_z` and
+  `flagged` (`process_raw/tube_qc.py::score_tubes`). On the dev data it
+  flags four tubes of pheS+4CP replicate 2, t_sel 82, and nothing else.
+
+- **`tfs-correct-tubes`: documented corrections to a tube table.** A YAML
+  corrections file kept with the experiment's data names each correction
+  (`id`, `action` swap/relabel/exclude, `tubes`, `reason`, `by`, `date`,
+  optional `evidence`), and the command writes a new tube table for
+  `tfs-process-counts` with a `correction_id` column, a long-form
+  `{out_prefix}_changes.csv`, and a provenance file holding the
+  corrections and the SHA-256 of the inputs, evidence files and output.
+  A correction moves only the design columns
+  (`tube_corrections.DESIGN_COLUMNS`); the sample name, its count file,
+  OD600 and totals stay with the physical sample, which is right for a
+  bench swap and a sample-sheet swap alike. It refuses unknown tubes, a
+  tube in two corrections, a swap across libraries or one that changes
+  nothing, a relabel of a non-design column, a result where two tubes
+  share a library and design, an empty reason, by or date, and an output
+  that would overwrite an input. Prompted by the pheS+4CP replicate 2,
+  t_sel 82 row of the dev data (`planning/studies/relative-m/`, run 3).
+  Example: `examples/process_raw/corrections.yaml`.
+
+- **A per-condition warp of theta in `linear` growth.** `m_warp` (bool, or
+  per condition through a 0/1 `--growth_priors` column) makes a
+  condition's growth follow f(theta) = (s(theta) - s(0)) / (s(1) - s(0)),
+  s a logistic with steepness a and threshold x0 sampled per warped
+  condition (priors `warp_log_a_*`, `warp_x0_*`); f(0) = 0 and f(1) = 1,
+  so wt's gauge and m keep their meaning, and f is linear as a -> 0. For
+  selection markers that read occupancy through differently shaped maps
+  (dev data: kanR switch-like, pheS graded;
+  `planning/studies/relative-m/`).
+- **A titrant-dependent k in `linear` growth.** The prior
+  `k_titrant_loglinear_scale`, set with `tfs-configure-model
+  --set_priors`, adds a growth shift shared by every genotype to the
+  selection phase of selection conditions, one slope in log
+  concentration per selection condition; 0 at no titrant, 0 (off) by
+  default. The dev data's flat genotypes grow faster at 1 mM IPTG than
+  at none under both selections (`planning/studies/within-tube-m/`). A
+  free shift at every concentration was tried and dropped: it is not
+  identified when every genotype has its own curve.
+- **Hold wt's curve in `hill_relative`.** Priors `theta_wt_hill_K_fixed`
+  (a titrant concentration) and `theta_wt_hill_n_fixed`, set with
+  `tfs-configure-model --set_priors`, hold wt's Hill K and n when > 0
+  (default 0, free). wt's curve sets the X gauge, and a one-library fit
+  of the dev data collapsed it below 1 uM IPTG.
+- **`--shared_shift` for `tfs-predict-theta` and `tfs-predict-epistasis`.**
+  A table of X shifts (`genotype`, `titrant_name`, `titrant_conc`,
+  `shift`) shared by every non-wt genotype: each posterior draw gets one
+  standard-normal multiple of it before quantiles (or epistasis) are taken
+  (`tfmodel/analysis/shared_shift.py`), so each genotype's interval carries
+  the uncertainty while differences between genotypes keep only what does
+  not cancel; wt (fixed by the X gauge) does not move. Needs a posterior
+  with draws. Written for the two-stage fit's k uncertainty, which moves
+  every genotype's X nearly alike.
+- **`tfs-fit-two-stage`**: the two-stage fit of a growth-only relative
+  model as one command (`planning/two-stage-fit.md`). In its output
+  directory it runs stage 1 (wt, spikes and singles with the congression
+  mixture, `max` rule, lambda fitted with the measured value as its prior),
+  a delete-d jackknife of stage 1 (30 refits, 20% of tubes dropped,
+  stratified by condition and IPTG), stage 2 (the full library with every
+  condition's k and m pinned at stage 1; MAP, Laplace, X), two stage 2
+  refits at k plus and minus one jackknife SD (the X shift per SD), X (and
+  optionally epistasis) with that shift as one shared offset per draw, and
+  a marker-consistency check (stage 2 on one library alone; per-genotype
+  z-scores; on by default, `--no_consistency`). The YAML's top-level keys
+  are `tfs-configure-model`'s arguments; a `two_stage` block holds the rest
+  (`examples/tfmodel/two_stage.yaml`). Steps skip finished work, `--step`
+  runs one and `--jackknife_task i` one jackknife subsample (for a cluster
+  array).
+- **`tfmodel/two_stage.py`**: the two-stage fit's pieces as functions
+  (stage-1 genotypes, stratified delete-d jackknife subsamples of tubes,
+  jackknife k/m standard errors and k's main direction, pinned growth-prior
+  tables, the X shift table), ported from `planning/studies/m-bias-factorial/`
+  and checked to reproduce its numbers; also chunked growth-table filters
+  and the marker-consistency statistic. `tfs-fit-two-stage` chains them.
+
+- **Per-condition k/m pins in `linear` growth.** `m_pinned` and `k_pinned`
+  take one flag per `condition_rep` as well as a single bool, so one
+  marker's k and m can be held at an independent estimate while the
+  other's are fitted. `tfs-configure-model --growth_priors` takes 0/1
+  `k_pinned`/`m_pinned` columns (indexed rows in the priors CSV, read back
+  as a tuple; flags that all agree collapse to the bool). The free
+  conditions are sampled as `condition_growth_{k,m}_free` and
+  `condition_growth_{k,m}` is a deterministic site holding every condition,
+  so it is read from a posterior file, not a MAP checkpoint. Configs
+  without per-condition pins trace exactly as before.
+
+- **Merged transformants, per-origin skew.** The simulator draws
+  single-plasmid cells as clone counts per genotype, one weighted row each
+  (`_sim_transform_collapsed`), so `transform_sizes` can be realistic (the
+  dev-data calibration has 6.7e7 double transformants) at one row per
+  genotype; founder sampling and demographic growth add up exactly over
+  merged clones. `max_congressed_cells` caps the multi-plasmid cells kept
+  one by one. `lib_assembly_skew_sigma` takes one value per library origin.
+  The random stream changed, so a seed no longer reproduces a simulation
+  made before this.
+- **`unassigned_read_fraction`** simulate key (one number or one per
+  library): that share of each tube's reads goes to `__unknown__`, as in
+  real count files, where the dev data's kanR library lost 66% of its
+  reads. The raw count files and the growth table carry it like real data.
+
+- **Tube-offset diagnostic in `tfs-summarize-fit`** (pipeline plan step 7).
+  `tfs-extract-params` now writes the per-tube sample offsets,
+  `{out_prefix}_sample_offset_offset.csv` (`level`) or `_delta_k.csv`
+  (`normal`), one row per tube labeled by its design, plus their SD
+  (`sample_offset/_tubes.py`). `tfs-summarize-fit` reports them in prior SDs
+  and, per condition, their Spearman trend with titrant and with time
+  (`tfmodel/analysis/tube_offsets.py`), writes `_tube_offsets.csv`,
+  `_tube_offset_trends.csv` and `_tube_offsets.pdf`, and flags a fit whose
+  offsets trend (BH q < 0.05) in the JSON's new `tube_offsets` block, so a
+  fit in the offset mode is not accepted silently.
+- **`cfu0` per library** in the simulate config (`{library: cells}`), so
+  separately grown libraries can start at their own densities, and
+  **`tfs-simulate --no_write_growth`**, which skips the direct growth table
+  (several GB at a real library's size; `tfs-process-counts` rebuilds it from
+  the raw files). The full-size simulation study calibrates `cfu0` per
+  library from a pilot run.
+
+- **Simulation designs** (`design` key in the simulate config,
+  `simulate/build_sample_dataframes.read_design`): a tube table in the
+  format `tfs-process-counts` reads, in place of `condition_blocks`, so a
+  simulation follows a real experiment's layout exactly (irregular times,
+  replicates sampled differently). Each replicate gets its own tubes and the
+  raw output keeps the design's tube names. Used by the full-size simulation
+  study (`planning/studies/full-size-sim/`).
+
+- **`tfs-simulate` writes the raw experiment** (`simulate/raw_output.py`,
+  pipeline plan step 6): one counts file per sequenced tube in
+  `tfs-process-fastq`'s format (`{out_prefix}_counts/`), the tube table
+  (`{out_prefix}_tubes.csv`) and, with an `od600` block, the OD600 table and
+  a copy of the calibration, and prints the `tfs-process-counts` command
+  that processes them. A simulation now goes through exactly the commands
+  real data do; the output matches `tfs_sim_growth.csv` row for row.
+  `--no_write_raw` skips it. `examples/simulate-and-analyze/run.sh` and the
+  quickstart now fit the processed table.
+
+- **MAP convergence on the exact loss** (`RunInference.full_batch_loss`,
+  `run_optimization(exact_loss=None)`, `ConvergenceMonitor.end_window(...,
+  exact_loss=)`). A MAP fit (and the staged MAP's stages, the SVI pre-MAP
+  and `tfs-prefit-calibration`) now computes the exact full-batch negative
+  log joint at the end of each convergence window and judges the window by
+  a line through the recent exact losses. On the dev-data screen the
+  mini-batch loss's trend SE was about 5e4 nats per window, so a descent of
+  1e5 nats per window read as a stall and every MAP was cut to its final
+  step size while still descending; the reference fit and the staged fit
+  stopped at points 6e3 nats apart that the final losses ranked the wrong
+  way round. The convergence CSV gains a `loss_exact` column. SVI keeps the
+  mini-batch test.
+
+- **Staged MAP for level tube offsets** (`tfmodel/inference/staged_map.py`,
+  `tfs-fit-model --stage_offsets auto|on|off --staged_step_size`). A cold
+  MAP with `sample_offset: level` could settle with the tube offsets near
+  ±2.8, carrying the population's growth; the real-data fit avoided that by
+  hand over six runs. A fresh level-offset MAP now runs three MAPs itself:
+  the offsets held at 0, the offsets alone with everything else held there,
+  and the joint MAP from that point at step size 1e-4. Each stage writes its
+  own checkpoint, convergence record and `_params.npz`
+  (`{out_prefix}_stage1_*`, `{out_prefix}_stage2_*`); a finished stage is
+  reused and an interrupted one resumes. `HeldModel` wraps a model with
+  sites held at fixed values (a `numpyro.handlers.condition`). Dev-data
+  validation (`planning/studies/staged-map/`): it avoids the offset mode,
+  which the cold MAP fell into (4.8e5 nats worse), but run to convergence
+  the offset mode scores 7.9e4 nats better than the staged MAP; it is the
+  model's preferred optimum, with IPTG-structured offsets and unphysical
+  k/m. Filed: `planning/offset-mode-growth-transition.md`.
+
+### Changes up to 2026-10-03
+
+A modeling release and an interface release. The model changes (the
+congression mixture, the count likelihood, the growth-only relative fit, tube
+offsets, OD600 tube totals, the convergence monitor and the block and
+arrowhead Laplace) are listed from "Added" onward below the interface
+entries. The interface entries come first: the command-line tools were made
+consistent, three hand-edited steps of the real-data fit became flags, and
+the documentation was rewritten to match the code. Many commands changed
+their arguments; old scripts and run templates need updating.
+
+### Added
+
+- **Provenance on every run** (`tfscreen.util.provenance`). Every `tfs-*`
+  command prints the tfscreen version, git commit (with a flag for
+  uncommitted changes) and command line when it starts, and writes them to
+  `{out_prefix}_provenance.json` when it takes `--out_prefix`. The
+  `tfs-configure-model` YAML has a `provenance:` block and fit checkpoints a
+  `provenance` entry. Study READMEs no longer need to infer commits from file
+  times.
+- **`tfs-process-counts` reads OD600.** `--od600_file` (one reading per
+  tube), `--od600_calibration_file` (a `tfs-calibrate-od600` calibration) and
+  `--tube_volume_mL` give each tube's `sample_cfu` and SD
+  (`od600.tube_totals_from_od600`), with the calibration curve's shared error
+  (`sample_cfu_curve_std`) kept apart from the reading's own
+  (`sample_cfu_reading_std`). A reading below the detection threshold is an
+  error. Supplied `sample_cfu` still works. Several libraries go through in
+  one call, as before, now tested against separate runs.
+- **`tfs-configure-model --set_priors name=value ...`** sets any scalar prior
+  by full row name or unique dotted suffix (`sigma_fixed=0.17`,
+  `theta_log_hill_n_hyper_scale_fixed=0.5`); unknown or ambiguous names are
+  errors. Replaces `sed` on the priors CSV.
+- **`tfs-configure-model --growth_priors`** (per-condition linear k/m priors
+  from a table) and **`--growth_priors_wt_rates`** (k and m from wt
+  monoculture rates on the relative-X gauge, SD floored by
+  `--growth_priors_sd_floor`), for models the pre-fit does not calibrate.
+  Replaces `set_growth_priors.py`. Shared helpers in
+  `tfscreen.tfmodel.priors_edit`, which `tfs-prefit-calibration` now uses too.
+- **`tfs-sample-posterior --laplace auto|full|arrowhead|blocks|point`** and
+  `--laplace_max_params` (default 20,000). `auto` uses the full Laplace up
+  to that many MAP parameters and the arrowhead Laplace above. The arrowhead
+  writes the Schur-complement directions it held at the MAP to
+  `{out_prefix}_held_directions.csv` (`RunInference.held_shared_directions`).
+- **CLI reference page** `docs/source/cli.rst`, generated from the commands'
+  own parsers by `docs/scripts/make_cli_reference.py`;
+  `tests/tfscreen/util/cli/test_cli_reference.py` fails when it is stale.
+  `generalized_main.capture_parser` returns a command's parser without
+  running it.
+- **Per-argument `--help`.** `generalized_main` takes each argument's help
+  from the function's numpydoc `Parameters` entry and shows defaults; the
+  program name is the command's own.
+- `examples/process_raw/`: a synthetic tube table, OD600 table and per-tube
+  count files (`make_example_data.py`) in the formats `tfs-process-counts`
+  reads.
+- Documentation: a pipeline page (reads to posterior, one command per step),
+  and the old analysis page split into fitting, model and downstream pages.
+
+### Changed
+
+- **A switch that defaults to on is turned off with `--no_<name>`.**
+  Previously `--<name>` silently inverted it: `tfs-sample-prior --noise`
+  turned noise off, `tfs-build-empirical --drop_railed` stopped dropping and
+  `tfs-process-counts --verbose` made it quiet. `--<name>` now does nothing.
+- **A flag whose default is None reads a string** unless the command types
+  it. `tfs-summarize-fit --out_prefix` and `tfs-process-fastq
+  --max_num_reads` crashed with "invalid NoneType value" whenever they were
+  passed.
+- `tfs-process-fastq library_config f1_fastq f2_fastq [--out_dir counts]`:
+  the library YAML comes first and the output directory is a flag (was
+  `f1 f2 out_dir run_config`). `--num_workers` defaults to -1.
+- `tfs-process-counts sample_file counts_dir [--out_prefix]` writes
+  `{out_prefix}.csv` (default `tfs_growth`, or `tfs_presplit` with
+  `--presplit`); the output path was a positional. A missing `library`
+  column defaults to `default`. The `obs_file` column is no longer written.
+- `tfs-simulate config_file [--out_prefix tfs_sim]` writes
+  `{out_prefix}_{name}.csv` (was positional `output_dir` and
+  `--output_prefix tfs_sim_`). Default file names are unchanged.
+- `tfs-configure-model` refuses a library whose spiked genotype has no growth
+  data (`check_spikes_in_data`); `--allow_missing_spikes` restores the old
+  report-only behavior.
+- `tfs-sample-posterior`: `--laplace` replaces `--map_point`,
+  `--laplace_blocks` and `--laplace_shared`. A small MAP is unchanged (full
+  Laplace); a MAP above 20,000 parameters now gets the arrowhead Laplace by
+  default instead of a dense Hessian that does not fit in memory.
+- `tfs-build-empirical growth_df [--binding_df] [--seed]
+  [--growth_calibration_file] [--base_growth_df]` (was `growth_file seed
+  --binding_file --calibration_file --base_growth_file`); `--seed` is
+  required only when the pre-fit runs.
+- `tfs-fit-genotypes growth_df growth_calibration_file`, default prefix
+  `tfs_fit_genotypes` (was `growth_file calibration_file`, `tfs_mle`).
+- `tfs-predict-growth --seed` (was `--subset_seed`).
+- `ModelOrchestrator`'s `theta_growth_noise` defaults to `zero`, as
+  `tfs-configure-model` does (was `logit_normal`), so the Python API and the
+  CLI build the same model by default. Saved configs record the component,
+  so they read back unchanged.
+- `tfs-setup-grid` and `tfs-setup-sim-grid` take `--out_dir` (was
+  `--out_prefix`, which named a directory); `grid_summary.json` records
+  `out_dir`.
+- Docstrings corrected where they described old behavior: MAP predictions
+  give a `q0.5` column, not `point_est`; default quantiles are the bare
+  `q<level>` ladder; `tfs-build-empirical` writes
+  `<prefix>_phenotype_model.json`; `tfs-summarize-fit`'s default prefix is
+  `{run_dir}/summary/tfs_summarize`.
+
+### Removed
+
+- `tfs-process-presplit`: use `tfs-process-counts --presplit`.
+- `tfs-diagnose-nan` (SVI component guide only, the one hand-written argparse
+  CLI), `tfs-subset-genotypes` (its output no longer fit the configure and
+  fit flow), `tfs-report-cfu0` and `tfs-summarize-sbc` (superseded by
+  `tfs-summarize-calibration`; `error_calibration.summarize_sbc` remains as a
+  library function).
+- `coverage_prob` and the `param_std` argument of
+  `analysis.stats_test_suite`. Its only caller, `tfs-summarize-fit`, never
+  passed uncertainties, so the statistic was always null; coverage is in
+  the summary's calibration outputs.
+
+### Fixed
+
+- The first spiked sequence in the example library configs
+  (`examples/process_raw/library_config.yaml`,
+  `examples/simulate/simulate_config.yaml`,
+  `examples/simulate-empirical/simulate_config.yaml`) carried a stray H74A
+  codon, so it decoded to H74A/K84L instead of the K84L its comment names.
+  The `examples/process_raw` count files were regenerated.
+- The presplit table now carries `library`. `tfs-process-presplit` wrote
+  `replicate, condition_pre, genotype, ln_cfu, ln_cfu_std`, and
+  `tfs-configure-model --presplit_df` refused it ("Missing columns:
+  library"), so processed presplit data could not enter the model.
+- `tfs-summarize-fit` finds a posterior written as `{out_prefix}.h5`, the
+  name `tfs-sample-posterior` uses. It looked only for `*_posterior.h5`, so
+  any prefix other than the default `tfs_posterior` lost the trajectory
+  plots. `*_ground_truth.h5` prior draws are not taken for a posterior.
+
+### Added
+
+- **`tfs-sample-posterior --laplace_blocks`** (`RunInference.get_laplace_posteriors(block_genotypes=True)`,
+  `block_laplace_factors`): a per-genotype Laplace at a MAP. The shared
+  parameters (growth k and m, hyperparameters, tube offsets) stay at the
+  MAP. Given them, each genotype's parameters couple to no other genotype,
+  so the Hessian splits into one small block per genotype (9-11
+  parameters). B Hessian-vector products per genotype chunk give every
+  block at once (`--genotype_chunk_size`), so it runs on a full library,
+  where the full Hessian (about 2M parameters) cannot. It checks that the
+  blocks really separate, and refuses a model where they do not. On the
+  relative-fit run-4 MAPs, X 95% coverage was 0.93 (Poisson) and 0.84
+  (realistic), against 0.98 / 0.94 for the full Laplace and 0.81 / 0.76 for
+  the low-rank guide. It leaves out the k/m uncertainty (coverage 0).
+
+- **`tfs-sample-posterior --laplace_blocks --laplace_shared`**
+  (`get_laplace_posteriors(block_shared=True)`, `arrowhead_laplace_factors`):
+  the block Laplace with the shared parameters' uncertainty. The Hessian is
+  an arrowhead: genotype blocks, each genotype's coupling to the shared
+  parameters, and the shared block. One more Hessian-vector product per
+  shared parameter per chunk gives all of it. The shared parameters are drawn
+  from their marginal (the Schur complement) and each genotype from its
+  conditional given them. That is the full Laplace, and it matched the
+  dense inverse Hessian exactly on a toy model, chunked or not. Two
+  departures, both for MAPs short of an optimum: a floored direction of a
+  genotype block carries no coupling, and a negative direction of the Schur
+  complement is held at the MAP and named in the log. On the relative-fit
+  run-4 MAPs, X 95% coverage was 0.97 (Poisson) and 0.94 (realistic), with
+  0.95-0.98 above 1000 reads. k/m coverage was 0.92 / 1.0 and 0.50 / 0.42,
+  close to the full Laplace's 0.92 / 0.92 and 0.58 / 0.50.
+
+- **`theta: hill_relative` can hold every population SD.**
+  `theta_X_low_hyper_scale_fixed`, `theta_X_delta_hyper_scale_fixed` and
+  `theta_log_hill_K_hyper_scale_fixed` join the log(n) field below (above
+  0: held, a deterministic site with no guide parameters). On the dev-data
+  growth-only MAP the learned SDs ran to 3.6, 4.3 and 11, against about
+  0.45, 0.45 and 0.7 among genotypes with more than 1e4 reads, so the MAP
+  shrank nothing and its X at 100 reads or fewer was noise.
+
+### Fixed
+
+- **`test_nan_explosion` passes under numpyro 0.22.** numpyro 0.22 checks
+  distribution arguments by default (0.19 did not), so `svi.init` refused
+  the test's NaN starting loc with a `ValueError` before the optimization
+  loop's own NaN check ran. Every CI runner failed (numpyro 0.22.0, jax
+  0.11.2), while local runs on numpyro 0.19.0 passed. The test now fits
+  under `validation_enabled(False)`
+  (`tests/tfscreen/tfmodel/inference/test_run_optimization.py`). A fit run
+  on numpyro 0.22 that starts from a non-finite point now stops at init with
+  numpyro's `ValueError`, not tfscreen's "model exploded" `RuntimeError`.
+
+- **The Laplace path no longer applies the mini-batch likelihood scale at
+  full batch.** `scale_vector` is built for the configured batch size, so a
+  model configured with `batch_size` below the library weighted every
+  growth likelihood by `num_genotype / batch_size` (53x on the dev-data
+  screen at 4096) even when the Hessian was taken over the whole library.
+  `RunInference._unscaled_batch` removes it. The full Laplace had only run
+  on simulations with `batch_size` at or above the library, so no result
+  was affected.
+
+- **`theta: hill_geno` and `hill_relative` take
+  `theta_log_hill_n_hyper_scale_fixed`.** Above 0, the population SD of
+  log(hill_n) is held at that value (a deterministic site, no guide
+  parameters) instead of learned. Set `theta.theta_log_hill_n_hyper_scale_fixed`
+  in the priors CSV; default 0 keeps it learned. On the dev-data screen
+  the learned SD ran to about 19 in every MAP fit, joint and growth-only.
+  n spread from 0.01 to 13, and in 42% of genotypes the curve changed more
+  between 0 and the lowest nonzero IPTG (0.1 µM) than across the whole
+  measured range. The data show no such step: for the binding genotypes
+  the fit predicted a 0.4-0.55 ln-unit gain relative to wt in kanR+kan,
+  and the counts show 0.00-0.08.
+
+- **`condition_growth: linear` takes per-condition `m_scale_plus` and
+  `m_scale_minus`.** Like `k_scale`, each may now be a per-condition array:
+  indexed rows in the priors CSV, joined to the conditions by
+  `condition_rep`. Each condition takes the entry its `+`/`-` flag names.
+  Before this, every selection condition shared one m prior width. That
+  could not express a tight prior from monoculture data for kan alongside
+  a loose one for pheS, which the growth-only dev-data fits need.
+  Scalars behave as before.
+
+- **`tfs-fit-model --init_from <params.npz>`** starts a fit at a MAP
+  point, the `{site}_auto_loc` arrays of an earlier fit's
+  `*_params.npz`, wherever they name a site of this model. They take
+  precedence over the guesses, and other sites start at their guesses. So
+  a model can be seeded from a simpler one, for example a
+  `sample_offset: level` fit from a `zero` fit. The option is refused with
+  `checkpoint_file`. Before this, guesses were the only way in, and
+  `read_configuration` silently drops guess rows for names outside the
+  configured guesses, including sites such as `condition_growth_k` and the
+  tube offsets. On the dev-data screen the level-offset MAP had fallen into
+  a mode with offsets of ±2.8. Holding the no-offset MAP's point and
+  fitting only small per-tube offsets beat it by 1.2e5 nats of count
+  likelihood (`planning/dev-data/real_fit`, gitignored).
+
+- **`sample_offset: level` takes `sigma_fixed`.** Above 0, the SD of the
+  per-tube offsets is held at that value (a deterministic site, no guide
+  parameters) instead of learned; set `growth.sample_offset.sigma_fixed` in
+  the priors CSV. On the first real-data fit the learned SD went from its
+  0.2 prior scale to 0.53, and the offsets (-2.7 to +3.0, smooth in IPTG
+  and time) carried the population's growth in place of k and m. Default 0
+  keeps the learned SD.
+
+- **`tfs-sample-posterior --map_point`** writes a MAP checkpoint's point
+  itself (one sample, `RunInference.get_map_posteriors`) instead of a
+  Laplace posterior, whose full Hessian is out of reach on a full library
+  (about 2 million parameters for the dev-data screen).
+- **`tfs-sample-posterior --skip_growth_observations`** leaves the
+  per-observation growth sites (`growth_pred`, `growth_obs`) out of the
+  posterior file. They were 94% of its size on a 300-genotype subset and
+  would be about 100 GB per site at 500 draws on the full library;
+  `tfs-predict-growth` recomputes growth from the parameter draws.
+
+- **Compressed tables load by their inner extension.** `read_dataframe`
+  reads `.csv.gz`, `.tsv.bz2` and the like as CSV or TSV, with pandas
+  inferring the compression. They used to fall through to the slow
+  delimiter-sniffing reader. A full-library growth table from
+  `tfs-process-counts` is 5 GB as CSV and 0.7 GB gzipped.
+
+- **`linear` growth takes `k_pinned`.** Like `m_pinned`, it holds the
+  per-condition baseline k at `k_loc` (a deterministic site with no guide
+  parameters), set by `condition_growth.k_pinned` in the priors CSV. It is
+  for conditional fits at a fixed draw of k and m, such as the two-stage fit
+  in `planning/studies/svi-overconfidence/`. It is not meant for production
+  fits.
+
+### Fixed
+
+- **`ln_cfu0` extraction labeled rows with other genotypes' values.** The
+  model holds `ln_cfu0` as a (replicate, condition_pre, genotype) array, but
+  the extract spec indexed its flattened values with `map_ln_cfu0`, which
+  numbers only the combinations present in the data. Wherever a genotype
+  was missing from a replicate or library, every later row took another
+  cell's value: on an SVI-overconfidence run (2 of 200 cells missing) three
+  of four blocks correlated 0.09-0.25 with the presplit data they were fit
+  to, against 0.999 once labeled correctly. Fits were unaffected; the
+  `*_ln_cfu0.csv` outputs of `tfs-extract-params`, and anything downstream
+  of them, were wrong. Both `ln_cfu0` components now key rows by array
+  position (`hierarchical.ln_cfu0_extract_spec`).
+
+- **Simulated binding observations are no longer clipped to [0, 1].**
+  `generate_binding_df` and `generate_library_binding_df` clipped noisy
+  `theta_obs` to [0, 1], while the fit's binding likelihood is an unclipped
+  Normal, so the clipped anchors near 0 and 1 were biased (10-17% of the
+  observations in the SVI-overconfidence grids). `binding_data.clip_theta_obs:
+  true` reproduces the old simulations; the random draws are unchanged, so
+  only the formerly clipped values differ.
+
+- **SVI hyperparameter scales no longer start frozen.** The component
+  guide's hyperparameter scales are constrained `greater_than(1e-4)`, and
+  the guide start capped every scale at `guide_init_scale`, also 1e-4. They
+  started on the bound, `-inf` in unconstrained space, and never moved: in
+  every component-guide SVI fit since 2026-09-27 each hyperparameter's guide
+  SD stayed at 1e-4. The convergence monitor reported them as an infinite
+  drift, so conditional fits never stopped before their cap.
+  `initialization.component_guide_init` now starts a bounded scale at least
+  `init_scale` above its bound (`component_guide_map` records the bound).
+  Study grids fit with the component guide since then (count-likelihood v4,
+  relative-fit v3, SVI-overconfidence v2 and two-stage) carry the bug.
+- **Laplace posteriors no longer blow up along a MAP's negative-curvature
+  directions.** `get_laplace_posteriors` floored Hessian eigenvalues at
+  1e-3, a variance of 1000 in unconstrained units. A MAP stopped short of
+  its optimum always has a few negative eigenvalues, and where they touched
+  the growth slopes m the Laplace draws of m spread 50x wider than their
+  posterior. Eigenvalues are now floored at the prior's curvature along
+  each eigenvector (`laplace_eigenvalue_floor`, prior SDs from
+  `site_unconstrained_prior_sds` at the MAP), so no direction is wider than
+  the prior. On seed 8 of the two-stage grid the SD of m fell from 0.018 to
+  0.0005.
+
+- **Breaking: `ln_cfu0: hierarchical_factored` is refused across libraries.**
+  It shares each genotype's starting abundance across every pre-condition of
+  a replicate. kanR and pheS are transformed and grown up separately, so
+  that is wrong for them. On simulations the fit pushed the per-genotype
+  difference, SD 0.17-0.29 ln units, into a confident per-genotype theta
+  error: 95% coverage above 1000 reads was 0.09, against 0.80 with
+  `hierarchical`. The orchestrator now refuses the factored model when a
+  replicate's pre-conditions come from different libraries. The study grids
+  and `examples/simulate-and-analyze/run.sh` now use `hierarchical`, and
+  their earlier calibration numbers carry the mismatch.
+
+- **Breaking: `ln_cfu0: hierarchical_factored` has a non-centered tube
+  offset.** It used to draw `tube_offset ~ Normal(0, tube_scale)` directly.
+  The 4 offsets (replicate x pre-condition) trade against the genotype
+  baselines, so they could sit at 0. The joint density then grew without
+  bound as `tube_scale` went to 0, so no MAP existed: the pre-MAP put the
+  scale at 5e-5 against a prior median of 0.34. The component now samples
+  `tube_offset_z ~ N(0, 1)` and keeps `tube_offset = tube_scale *
+  tube_offset_z` as a deterministic site. The guide's parameters are now
+  `ln_cfu0_tube_offset_z_locs/scales`, so checkpoints and guesses files
+  written before do not load. Reconfigure and refit.
+- **A runaway loss is no longer called converged.** The convergence monitor
+  records the run's starting loss. A window whose loss falls below -1000
+  times its magnitude now ends the run as `diverged`, not converged
+  (`convergence.RUNAWAY_LOSS_FACTOR`). A full-covariance guide had run to a
+  loss of -8e23 and was reported as converged.
+
+- **NUTS starts where it should.** `RunInference.run_nuts` passed
+  `initialize_model`'s `ParamInfo` tuple to `init_to_value`. No site
+  matched it, so every chain started at a uniform draw in [-2, 2] on the
+  unconstrained scale. `tfs-fit-model --analysis_method nuts` now starts
+  the chains at the MAP warm-up's point (`--pre_map_num_epoch`, as SVI
+  does), falling back to prior medians. It also passes the full data set
+  through `get_batch`, prints the worst split R-hat and least n_eff, and
+  takes `--nuts_dense_mass`.
+
+- **SVI no longer throws away the pre-MAP solution.** The component guide
+  started with every scale at 0.1 and a multiplicative 0.1 jitter on every
+  starting value. The scale is in each parameter's own units, so for growth
+  rates (per minute) and ln_cfu levels it was enormous. SVI started about
+  1000x above the pre-MAP's loss, count and `lncfu` fits alike. It then
+  re-descended for tens of thousands of epochs into other optima: a
+  sign-flipped mirror mode, a halved slope `m`, or the whole library
+  shifted against wt. `tfs-fit-model --guide_init_scale` now defaults to
+  1e-4 (`DEFAULT_GUIDE_INIT_SCALE`). It also sets the autoguides'
+  `init_scale` for a fresh fit, instead of numpyro's 0.1. SVI widens the
+  scales itself. `--init_param_jitter` and
+  `RunInference.run_optimization(init_param_jitter=...)` now default to 0.
+  SVI calibration results from before this fix (the count-likelihood and
+  relative-fit grids) need a rerun.
+
+### Added
+
+- **`tfs-calibrate-od600` (roadmap step 3).** Fits an OD600-to-CFU/mL
+  calibration from a lab's own two experiments: repeated OD600 readings of
+  a dilution series (reading noise and detection threshold) and plate
+  counts of cultures whose OD600 was read (CFU/mL from colonies, dilution
+  and plated volume, with counting and pipetting error). Writes the
+  polynomial's coefficients with their full covariance (the curve's error
+  is shared by every tube, so callers get it apart from the reading noise),
+  the reading noise, the detection threshold and the calibrated range, plus
+  a diagnostic PDF and CSVs. The method, file format and apply/invert
+  functions are in `tfscreen.process_raw.od600`; documented CSV inputs;
+  synthetic example in `examples/od600/`. It reproduces the lab
+  notebook's coefficients to 2e-9 when fed the notebook's CFU/mL, but the
+  notebook did not divide by the plated volume, so its constants (and the
+  CFU totals made with them) are 10x low.
+
+### Changed
+
+- **Breaking: the simulator's `od600.calibration` is a
+  `tfs-calibrate-od600` file.** The lab notebook's format (`A_CFU`,
+  `OD600_PCT_STD`, ...) is refused with a pointer to the new tool.
+  `examples/simulate/od600_calibration.yaml` is replaced by
+  `examples/od600/od600_calibration.yaml`, made by the tool from synthetic
+  data at a realistic scale (8e8 CFU/mL per OD600 unit, not the old
+  example's 8e7). `od600_in_range` now means at or below the top of the
+  calibrated OD600 range.
+
+- **Breaking: `tfs-configure-model` defaults to the count likelihood.**
+  `--growth_likelihood` now defaults to `counts` and
+  `--sample_offset_model` to `level` (was `lncfu` and `zero`). On the
+  count-likelihood simulation grid (`planning/studies/count-likelihood/`)
+  counts cut theta RMSE by 35-60% and dk_geno RMSE by 40-85% at about the
+  same coverage. The growth file then needs read counts, each tube's total
+  reads and its total cells (see `--growth_likelihood`), and
+  `--growth_noise_model` must stay `zero`: a call that passed
+  `--growth_noise_model normal_kt` without a likelihood now fails, and
+  should add `--growth_likelihood lncfu --sample_offset_model zero` to keep
+  its old model. `ModelOrchestrator` keeps `lncfu`/`zero` as its own
+  defaults, so a config written before the count likelihood existed (no
+  `growth_likelihood` key) still reads back as the model it was.
+  `tfs-build-empirical` inherits the new defaults.
+
+### Added
+
+- **Relative-X fit (`theta: hill_relative`; roadmap step 5).** A theta
+  component that fits growth alone on a wt-relative scale X instead of
+  claiming absolute occupancy (growth fixes it only up to an affine map):
+  hill_geno's per-genotype curve with real-valued baselines, gauged so that
+  wt's X is 1 at the low and 0 at the high gauge concentration
+  (`theta_gauge_conc`, an orchestrator setting and `tfs-configure-model
+  --theta_gauge_conc c_lo c_hi`; default the lowest and highest measured
+  concentration, recorded in the config). wt keeps its K and n free. It
+  refuses binding data, a learned activity, `logit` rescaling, non-linear
+  condition growth, theta noise and, under the congression mixture, any
+  theta rule but `max`. Outputs are labelled: deterministic sites
+  `theta_X_low`/`theta_X_high`, a `theta_scale = X` column from
+  `tfs-predict-theta`; `tfs-predict-epistasis` and `tfs-extract-epistasis`
+  allow only `--scale add` on X, and the former writes no `in_regime`.
+  `tfs-summarize-fit` puts simulated theta and `growth_k`/`growth_m` truth
+  on the X gauge, and `tfs-summarize-calibration` gives such rows
+  `theta_regime = X`. Prediction on a genotype subset keeps wt (the gauge
+  needs it) and drops it from the output. Validation grid:
+  `planning/studies/relative-fit/`.
+- **Count likelihood (`growth_likelihood: counts`; roadmap step 7).** Growth
+  can be observed as read counts instead of `ln_cfu`: each genotype's reads
+  in a tube are negative binomial with mean
+  `depth * exp(ln_cfu_pred - ln(tube total))` and variance
+  `mu (1 + phi) + mu^2 inv_r` (both learned; study 0b found real counts 5-18x
+  Poisson), with no pseudocount (`generative/observe/growth_counts.py`). The
+  log-pmf is computed by Loader's algorithm (`CountNegativeBinomial`),
+  accurate to ~1e-4 nats in float32 where numpyro's negative binomial loses
+  several nats per observation at 1e4-1e6 reads. `tfs-configure-model
+  --growth_likelihood counts` (requires `growth_noise_model zero`); the
+  growth file needs `counts`, each tube's total reads (`sample_reads`, or
+  `adjusted_counts`/`frequency` to derive it) and total cells
+  (`sample_ln_cfu` or `sample_cfu`). Presplit data are still observed as
+  `ln_cfu`.
+- **`sample_offset: level`.** One ln_cfu offset per tube, shared by every
+  genotype in it, with a learned constant SD (the tube's composition offset
+  and any error in its supplied total), in place of `normal`'s
+  time-scaled growth-rate offset. `tfs-configure-model` now exposes
+  `--sample_offset_model` (it was always `zero` before).
+- **`counts_to_lncfu` writes `sample_reads`**, each tube's total reads
+  (`__unknown__` included, no pseudocounts).
+- **Simulator sampling noise and OD600 (roadmap step 4).** Optional
+  simulate-config keys, all off by default so existing configs simulate
+  exactly as before: `founder_sampling` (Poisson founders per clone per
+  tube), `demographic_growth` (birth-death noise given the founders),
+  `shared_transformation` (one library assembly and transformation for all
+  replicates, as from one glycerol stock), `pcr_template_molecules` and
+  `pcr_amplification_cv` (a template bottleneck with PCR jackpotting before
+  sequencing). An `od600` block gives every tube one simulated OD600 reading
+  through an OD600-to-CFU calibration (`simulate/od600.py`), optionally
+  feeds the pipeline the OD600-derived total instead of the truth, adds
+  OD-only replicates, and writes `tfs_sim_od600.csv`.
+  `selection_experiment` gains `shared_state` and `sequence`.
+  `examples/simulate/od600_calibration.yaml` is a synthetic calibration for
+  examples; `od600.calibration` is a `tfs-setup-sim-grid` path key.
+- **Growth-only models (no binding data).** `ModelOrchestrator` accepts
+  `binding_df=None` alongside growth data, and `tfs-configure-model` takes
+  `--binding_df` as an optional flag (at least one of `--binding_df` and
+  `--growth_df` is required). A growth-only model has no binding tensors,
+  no binding likelihood and no `theta_binding_noise` component; it refuses
+  a `binding_weight` or a non-`zero` binding noise model, and
+  `tfs-prefit-calibration` refuses a growth-only config.
+  `tfs-summarize-fit` then plots trajectories for wt, the spiked genotypes
+  and ten other genotypes chosen with a fixed seed, instead of every
+  genotype. Joint growth + binding models are unchanged (identical log
+  density at a fixed seed). Step 1 of `planning/analysis-roadmap.md`.
+- **Soft-min congression dk rules (`congression_dk_rule` `softmin`, `min`).**
+  A congressed cell's dk_geno can now follow the whole soft-min family
+  `dk_cell = -(1/alpha) log sum_g x_g exp(-alpha dk_g)`, not only its
+  `alpha -> 0` limit: `dilution` (still the default, the share-weighted
+  mean), `softmin` (finite alpha, from the new `congression_dk_alpha`, in
+  units of 1/dk) and `min` (`alpha -> inf`, the worst variant sets the
+  cell's cost). Available in the simulator (`simulate/cell_rules.py`,
+  config keys `congression_dk_rule`/`congression_dk_alpha`) and the fit's
+  `mixture` transformation (`ModelOrchestrator` settings written to the
+  config and carried as static `GrowthData` fields; `tfs-configure-model
+  --congression_dk_rule`/`--congression_dk_alpha`). Alpha is required by
+  `softmin` and refused by the other rules. Congression physics plan,
+  step 5.
+- **Limit on simulated Hill coefficients (`theta_sim_priors.max_hill_n`).**
+  hill_mut's simulation adds per-mutation and horseshoe-epistasis effects on
+  log(hill_n), so double mutants could reach Hill coefficients in the
+  hundreds (270 in the congression calibration study's library, with 50 of
+  483 genotypes above 4). An optional `max_hill_n` in hill_mut's `SimPriors`
+  sets larger draws to the limit, in both `hill_mut.simulate` and
+  `binding_params.build_theta_gc_override_hill_mut` (the path used when
+  spiked binding curves come from a params file). Off by default, so
+  existing configs simulate as before.
+
+- **Guide selection for SVI (`tfs-fit-model --guide_type`).** With
+  `analysis_method=svi`, the variational family can now be any numpyro
+  autoguide as well as the component guide (still the default):
+  `delta`, `auto_normal`, `auto_diagonal_normal`, `auto_multivariate_normal`
+  or `auto_low_rank_multivariate_normal`. Numpyro class names such as
+  `AutoNormal` are accepted, case-insensitively. `--guide_rank` sets the rank
+  of `auto_low_rank_multivariate_normal`, and `--guide_init_scale` sets an
+  autoguide's initial scale. An option the chosen guide does not take is
+  refused before any fitting starts, as is any guide option with
+  `analysis_method` `map` or `nuts`.
+  - `RunInference.setup_svi` takes `guide_type`, `guide_kwargs` and
+    `init_values` (constrained site values an autoguide's location starts
+    from, via `init_to_value`). The registry is `AUTOGUIDES`/`GUIDE_TYPES` in
+    `inference/run_inference.py`, with `resolve_guide_type` and
+    `check_guide_kwargs`.
+  - An autoguide starts from the pre-MAP solution when `pre_map_num_epoch > 0`,
+    and from the configured guesses otherwise.
+  - Checkpoints record `guide_type` and `guide_kwargs`.
+    `restore_svi_from_checkpoint` rebuilds that guide; a checkpoint written
+    before this change is treated as a component-guide checkpoint.
+    `tfs-fit-model` refuses to resume a checkpoint with a different guide.
+  - `tfs-sample-posterior` sends `delta` checkpoints to the Laplace
+    approximation and every other guide to guide sampling. It routes by the
+    recorded `guide_type`, because `AutoNormal` parameters share AutoDelta's
+    `{site}_auto_loc` names. Older checkpoints still fall back to that name
+    check.
+  - `get_posteriors` drops autoguide auxiliary sites (names starting with
+    `_`, such as `AutoContinuous`'s `_auto_latent`) before the forward pass.
+
+- **`tfs-summarize-calibration`** (`analysis/calibration_grid.py`) pools
+  posterior calibration across a `tfs-setup-sim-grid` grid. It reads each
+  run's `combo.json` and the `tfs-summarize-fit` outputs in its `summary/`
+  directory, and reports coverage at several interval levels, calibration
+  error and bias, PIT uniformity (KS), interval width, and median RMSE and
+  Pearson r against the simulated truth. Rows are per run x quantity x
+  stratum: whether the genotype has binding data, its purity (`spike`: only
+  a spiked sequence encodes it, `bulk_fraction` 0; `bulk`: only the bulk
+  sub-libraries, `bulk_fraction` 1; `mixed`: both, like wt and the spiked
+  single mutants), and, for theta, whether the true value is resolvable or
+  saturated. Arms (runs sharing every grid variable except `--replicate_keys`)
+  are averaged, and `--baseline key=value ...` pairs each run with the
+  baseline run fit to the same simulated data. Unfinished runs are listed in
+  `{out_prefix}_run_status.csv`. No thresholds or grades. New console script:
+  reinstall to register it.
+
+- **`GrowthData.bulk_fraction`**: each genotype's fraction of cells from the
+  bulk (co-transformed) sub-libraries, built by `ModelOrchestrator` from the
+  library composition table (legacy `spiked_genotypes`: 0 for spiked, 1
+  otherwise; `__unknown__` counts as bulk; a genotype missing from the table
+  is an error). This separates congression purity from the `ln_cfu0` prior
+  class, which previously shared one flag. Not yet used by the model, so fits
+  are unchanged.
+
+- **Library-ordered dk_geno and activity from their components.** Every
+  `dk_geno` (`fixed`, `pinned`, `hierarchical_geno`) and `activity`
+  (`fixed`, `hierarchical_geno`, `horseshoe_geno`, `hierarchical_mut`,
+  `horseshoe_mut`) component's `define_model` and `guide` take
+  `return_population=False`. When True they return `(tensor, population)`,
+  where `population` holds every genotype's value in library order, computed
+  from the same draw (no new sample sites), or `None` when the latents arrived
+  as a batch-sized substitution. The shared helper is
+  `generative/components/_population.py::per_genotype`. New
+  `GrowthData.external_dk_population` / `external_activity_population`
+  fields are the fallback for prediction. Groundwork for the congression
+  mixture's co-resident lookup; nothing requests the population yet, so fits
+  are unchanged.
+
+- **Fixed co-resident sets for the congression mixture.** `ModelOrchestrator`
+  draws, once per genotype, K co-resident plasmid sets
+  (`_draw_coresident_sets`) into `GrowthData.coresident_idx`
+  (`(num_genotype, K, N_max)` library indices, -1 = empty slot) and
+  `GrowthData.coresident_n` (co-residents per set). New settings
+  `congression_sets` (default `[12, 3, 1]`: 12 sets with one co-resident,
+  3 with two, 1 with three) and `congression_seed` (default 0) are written
+  to the config's `components` block, so a refit reproduces the same draws.
+  Co-residents are drawn from the bulk share of the pool,
+  `pool_fraction * bulk_fraction`, over genotypes with growth data
+  (`__unknown__` excluded); the legacy `spiked_genotypes` path uses a
+  uniform pool over non-spiked genotypes. Not yet used by the model, so fits
+  are unchanged.
+
+### Changed
+
+- **`tfs-configure-model`: `binding_df` is now the `--binding_df` flag**,
+  no longer a positional argument (breaking for command-line callers;
+  `configure_model(binding_path, ...)` from Python still works).
+  `examples/simulate-and-analyze/run.sh`, the congression-calibration
+  study's `run.srun` and the docs are updated.
+- **Breaking: a congressed cell's theta now follows a partition function
+  (`congression_theta_rule: homodimer`), and TF activity defaults to 1.**
+  The congression mixture (`transformation: mixture`) and the simulator
+  took a co-transformed cell's theta from its highest-theta plasmid. Both
+  now default to the homodimer partition function over equal plasmid
+  shares, `logit(theta_cell) = log sum_g x_g exp(l_g)` with `x_g = 1/M`;
+  `heterodimer` (`2 log sum_g x_g exp(l_g / 2)`) and the old `max` remain
+  selectable. The fit's rule is a new `ModelOrchestrator` setting
+  (`congression_theta_rule`, written to the config) and
+  `tfs-configure-model --congression_theta_rule`; the simulator key keeps
+  its name. The partition-function rules mix the variants' occupancies and
+  require TF activity 1: the simulator refuses otherwise, and
+  `ModelOrchestrator` refuses a mixture model with these rules and a
+  learned activity component. To match, the default activity component is
+  now `fixed` (was `horseshoe_geno`) in `ModelOrchestrator` and
+  `tfs-configure-model`. Configs that set neither key change behavior;
+  set `congression_theta_rule: max` and/or `activity_model: horseshoe_geno`
+  to keep the old ones. The example simulate configs now use `homodimer`.
+  See `planning/congression-physics-plan.md`, step 4.
+
+- **Gradient clipping is off by default in `tfs-fit-model` and
+  `tfs-prefit-calibration` (SVI, MAP, pre-MAP, prefit).** Every
+  congression-calibration fit on the step-3.5 grids ran to `max_num_epochs`,
+  and the lambda-1 seed-1 fits "blew up" to 4-5 times their best loss.
+  numpyro's `ClippedAdam` (the only optimizer, `adam_clip_norm=1.0`) clips each
+  gradient *element*. These losses have gradients of 1e3-1e6 per element, so
+  every element was clipped every step and each update followed the sign of
+  one draw's gradient; the fit settled where those signs balance, not where
+  the expected gradient vanishes. The ELBO was heavy-tailed: one binding
+  genotype (a step-like curve, `hill_n` 24.6, measured with SD 0.001) cost
+  ~2.4e5 nats whenever a guide draw crossed its step. At the sign-balance
+  point 5-90% of draws crossed it, depending on the run, and a step-size cut
+  cannot move such a point. The "blow-ups" were the block medians flipping
+  once that share passed 50%; the parameters barely moved. Resumed unclipped
+  from the blown-up checkpoints, the crossings fall to ~0 and the loss to
+  below the clipped runs' best, and a fresh unclipped fit converges (46k
+  steps). Study: `planning/studies/congression-calibration/diagnosis/`.
+  - The default optimizer is numpyro `Adam` (`inference/run_inference.py`,
+    `adam_optimizer`). `--adam_clip_norm` still selects `ClippedAdam`.
+    Step-size cuts keep whichever was chosen. Both share their state, so
+    checkpoints written under clipping resume unclipped. Checkpoints now
+    record `adam_clip_norm`.
+  - Results of fits made with the clip, notably the step-3.5 baseline and
+    lambda-profile grids, are biased toward whatever the clipped
+    equilibrium favored, and should be rerun.
+- **The convergence stop also needs an unskewed loss, and posterior SDs used
+  to measure parameter movement are floored at 1% of the prior SD.**
+  - The loss test reads block medians, which ignore rare large penalties that
+    dominate the mean, the actual objective. Each window now also reports its
+    mean and its skew, `(mean - median) / (1.4826 MAD)` about the trend line
+    (`convergence.loss_skew`). At the final step size a window with skew above
+    `MAX_LOSS_SKEW` (3) is not a plateau, so such a run is reported rather
+    than called converged. Skew never forces a step-size cut. Benign ELBO noise
+    stays below ~1 (at most 0.4 on every recorded clean trace); the clipped
+    calibration runs gave 29-37. The statistic cannot see penalties carried
+    by most steps, but then the loss level shows them.
+  - The mean-field component guide collapses the guide SDs of hierarchical
+    scales (hill_mut `theta_sigma_d_*`, `theta_epi_tau`) to ~4e-4, far below
+    any honest posterior width. Measured in those units, a location creeping
+    ~2e-4 per window read as 0.3-0.5 SDs of movement at step size 1e-6
+    forever. The SD used is now at least `PRIOR_SD_FLOOR` (0.01) times the
+    site's prior SD in the location's unconstrained units
+    (`initialization.site_unconstrained_prior_sds`), for the component guide,
+    AutoNormal and the AutoContinuous guides.
+  - `{out_prefix}_convergence.csv` gains `loss_mean`, `loss_skew` and
+    `loss_skewed`. A resume rewrites an older file under the new columns.
+
+- **Breaking: convergence and step size in `tfs-fit-model` and
+  `tfs-prefit-calibration`.** The stop rule divided the window-to-window loss
+  change by the improvement since the start of the run. On the step-3.5
+  simulations the start-up loss was ~500 times the final loss, so fits stopped
+  (and printed "converged") while the loss was still falling 20-30% per 1000
+  epochs; after a resume the same rule effectively never fired. The rule
+  before that (change relative to the loss itself) never fired on a noisy
+  ELBO. Both measured against an arbitrary scale instead of the loss's noise.
+  - Optimization now runs in windows of `--convergence_window_steps` optimizer
+    steps (default 2000, at least 10 epochs). A window's loss is *improving*
+    if a line through 20 block medians drops by more than `--convergence_z`
+    (3) standard errors and more than `--loss_rtol` (1e-6) of the loss. Each
+    parameter's trend is measured in posterior SDs (guide scale), prior SDs
+    (MAP) or log units (positive parameters), beyond noise and the step size's
+    resolution; it is *moving* above `--param_tolerance` (0.05). See
+    `inference/convergence.py`.
+  - The step size is now cut by `--adam_step_size_cut` (0.1) after
+    `--patience` (3) windows without loss improvement, down to
+    `--adam_final_step_size`. This replaces the exponential decay over
+    `max_num_epochs`, which collapsed the pre-MAP's step size within its own
+    1000 epochs and made resumes depend on `max_num_epochs`. At the final step
+    size, `--patience` windows with neither loss improvement nor parameter
+    movement end the run. `--max_num_epochs` is only a cap, and a run that
+    reaches it says it has not converged and names the parameter still
+    moving.
+  - Removed flags: `--convergence_tolerance`, `--convergence_window` (replaced
+    by `--convergence_window_steps`, in optimizer steps) and
+    `--convergence_check_interval`. `--patience` now counts windows (default
+    3, was 10 checks). `--init_param_jitter` was removed from
+    `tfs-prefit-calibration` (it never affected the MAP). Old templates that
+    pass the removed flags now fail at argument parsing.
+  - New output `{out_prefix}_convergence.csv`, one row per window (loss, drop,
+    standard error, parameter moving most, step size, decision).
+    `{out_prefix}_losses.txt` is now `epoch,loss,step,step_size`, with the
+    loss the median of each block. Checkpoints record the step size and the
+    monitor state; a resume continues at that step size.
+  - `RunInference.run_optimization` takes `convergence_window_steps`,
+    `patience`, `convergence_z`, `loss_rtol`, `param_tolerance`,
+    `final_step_size` and `step_size_cut`, in place of `convergence_tolerance`,
+    `convergence_window` and `convergence_check_interval`.
+- **SVI and MAP start where they were meant to.** AutoDelta (MAP, the SVI
+  pre-MAP, `tfs-prefit-calibration`) ignored the configured guesses and
+  started at prior medians. The component guide ignored guesses keyed by site
+  name, and the pre-MAP result reached it under names it does not use
+  (`{site}_auto_loc`), so SVI never started from the pre-MAP. Now
+  `RunInference.site_values` collects site values from guesses and MAP
+  results, MAP runs start from them, and `RunInference.component_guide_start`
+  maps them onto the component guide's `{site}_loc(s)` parameters
+  (`inference/initialization.py`). The component guide's scales now start at
+  most `--guide_init_scale` (default 0.1; was the prior width, 1.0), as the
+  numpyro autoguides do. `--pre_map_num_epoch` is now a cap for a warm-up
+  that stops when it converges (default 10000, was a fixed 1000). On the smoke
+  data SVI starts ~18 times lower in loss, converges in 40k instead of 64k
+  steps, and ends ~185 nats lower.
+
+- **Breaking: congression is now an observable-level mixture
+  (`transformation: mixture`).** The old `empirical` component replaced each
+  bulk genotype's theta by the expected maximum over co-resident plasmids and
+  grew one trajectory with the genotype's own dk_geno. Averaging theta cannot
+  represent a mixture of growth rates, and in simulation that correction
+  removed almost none of the congression error once dk_geno varies
+  (`planning/studies/congression-estimator/`). The `mixture` component splits
+  each genotype's cells into a clean class and K congressed classes built from
+  the fixed co-resident sets: a congressed cell takes theta and TF activity
+  from its highest-theta plasmid (at each titrant concentration) and the mean
+  dk_geno of its plasmids (dilution). Every class is grown through the usual
+  `theta_rescale` → `calculate_growth` → `growth_transition` pipeline on a
+  leading class axis, and the classes are mixed as
+  `ln_cfu = ln_cfu0 + logsumexp_c(log w_c + G_c)`, with
+  `w_cong = bulk_fraction * (1 - P(M = 1))` split over the sets by
+  `P(M = n + 1)`, where a transformant carries `M` ~ zero-truncated
+  Poisson(lambda) plasmids that share its abundance (as in the simulator, so
+  the fit's lambda is the simulator's `transformation_poisson_lambda`; the
+  zero class of cells that never took up a plasmid is not observed). Theta noise acts on
+  the genotype's own theta before the classes are built; `theta_growth_pred`
+  is still the genotype's own theta.
+  - `empirical` and `logit_norm` are removed and refused by name with a
+    message; configs must switch to `transformation: mixture` (fits are not
+    comparable).
+  - New transformation interface: `define_model`/`guide` sample lambda only;
+    `cell_classes(focal, population, params, data)` returns the classes;
+    `NEEDS_POPULATION` replaces `NEEDS_FULL_POPULATION_THETA`.
+  - `GrowthData.congression_mask` is removed; purity is `bulk_fraction`.
+  - The `categorical_geno`/`empirical` incompatibility check is gone (fixed in
+    step 3.3a).
+  - A `mixture` model with bulk genotypes but an empty co-resident pool is
+    refused when built.
+  - `predict()` passes library-wide references and the fitted model's
+    co-resident sets to the genotype-subset model: dk_geno and activity are
+    posterior medians of their deterministic sites; theta is the theta
+    component evaluated at posterior-median parameters over the whole library
+    on the *prediction's* concentration grid (which can differ from the fit's). A raw MAP
+    checkpoint with a `mixture` model now raises (run `tfs-sample-posterior`)
+    instead of warning and predicting without a background.
+  - Growth tensors grow by a factor of 1 + K (K = 16 by default); use
+    mini-batching or a smaller `congression_sets` on large libraries.
+
+- **Breaking: the empirical pipeline's congression de-attenuation (Stage 1.5)
+  is retired.** `--congression_lambda` is removed from `tfs-fit-genotypes`
+  and `tfs-build-empirical`, together with `--library_config` and
+  `--save_theta_history` on `tfs-fit-genotypes` and the
+  `*_params_deattenuated.csv`, `*_theta_history.csv` and
+  `*_stage1p5_fits.csv` outputs. It corrected theta alone with the
+  expected-maximum operator the fit dropped above, and it used Poisson rather
+  than zero-truncated co-resident weights, a spiked/bulk split rather than
+  `bulk_fraction`, and no dk_geno dilution. Empirical distributions are now
+  built from uncorrected per-genotype fits. `genotype_fit/congression.py`,
+  `simulate/empirical/congression.py` and
+  `transformation/_congression.py` are deleted. A replacement is filed as
+  `planning/empirical-mixture-refit.md`.
+
+- **Test reports and badges are no longer committed.** `reports/` and
+  `docs/badges/` are gitignored and untracked; `run_all_tests.sh` now writes
+  everything, badges included, under `reports/`, and stops on the first
+  failing step. `README.rst` shows the live GitHub Actions status badge for
+  `tests.yml` instead. A new `.flake8` excludes `.claude/` (agent worktrees),
+  build and venv directories, which made up most of `reports/flake.txt`.
+
+- **Co-transformed cells in the simulator now grow by cell-level physics.**
+  A cell carrying several plasmids used to get the min/max/mean/... of its
+  plasmids' whole `k*t` (`multi_plasmid_combine_fcn`, per marker library).
+  Because `dk_geno` varies more across genotypes than the whole theta signal,
+  that let `dk_geno` decide which plasmid set the cell's growth. Now the
+  cell's theta and TF activity come from `congression_theta_rule` (`max`:
+  the highest-theta plasmid at each condition), its `dk_geno` from
+  `congression_dk_rule` (`dilution`: share-weighted mean), and its `k_pre`,
+  `k_sel` and `k*t` from the same growth model, theta rescale, growth
+  transition and tube noise as any genotype. Single-plasmid cells are
+  unchanged; at lambda = 0 output is identical to before.
+  - **Breaking:** `multi_plasmid_combine_fcn` is removed and a config that
+    sets it is refused with a message naming the new keys. Both new keys are
+    optional (defaults `max` and `dilution`). Example configs updated.
+  - With `transformation_poisson_lambda > 0`, `selection_experiment` needs
+    the config's `growth` block and phenotype columns `theta`, `activity`,
+    `dk_geno`, and checks that they reproduce the phenotype's `k_pre`/`k_sel`
+    (`_check_cell_components`), so the two paths cannot drift apart.
+  - New `simulate/cell_rules.py` (`THETA_RULES`, `DK_RULES`). New
+    `thermo_to_growth.growth_rates`/`growth_rate_one_condition` are the one
+    theta/activity/dk_geno -> k formula, used per genotype and per cell;
+    they replace `_apply_growth_params`.
+
+### Fixed
+
+- **Step-size cuts during a slow descent.** The convergence monitor cut the
+  step size after `patience` windows each without a significant loss trend,
+  even when those windows together were clearly still falling: on the
+  count-likelihood v2 grid, 20 of 23 runs made their first cut mid-descent
+  (pooled t = 4-14), and one (run 0012) was cut into a wrong optimum at
+  twice its twin's ELBO. Before a cut or a stop the monitor now fits one
+  line through all the stalled windows (`convergence.pooled_loss_trend`) and
+  starts the count over if that is a significant descent. Fits spend longer
+  at the large step size; more may reach `max_num_epochs`. New
+  `pooled_loss_t` column in `{out_prefix}_convergence.csv`; checkpoints
+  carry the stalled windows.
+- **NaN from horseshoe priors far in the tail.** The slab-regularized
+  horseshoe scale `sqrt(c2 lam^2 / (c2 + tau^2 lam^2))` and numpyro's
+  HalfCauchy log density both square the local scale; a draw near 1e20
+  overflowed float32 and turned the fit to NaN (hill_mut epistasis for
+  low-read doubles, whose guide local scales widened to ~11 in log space;
+  count-likelihood v2 run 0007). New `components/_horseshoe.py`
+  (`regularized_scale`, a `HalfCauchy` with a `hypot`-based log density),
+  used by hill_mut, activity `horseshoe_mut`, `thermo/horseshoe.py` and the
+  thermo `PK`/`PnnC`/`PddG` variants; same values, no overflow.
+- **`tfs-prefit-calibration` took its Hessian at the wrong point.**
+  `RunInference.compute_hessian_sigmas` treated the MAP values it was given
+  as unconstrained, but its caller passes `svi.get_params(...)`, which
+  AutoDelta returns constrained; every positive site (hyper-scales, noise
+  scales, the count likelihood's `phi`) was evaluated at `exp(value)`. The
+  pre-fit's Hessian-derived `k_scale`/`m_scale_plus` were therefore off
+  (on a count-likelihood grid run, k sigmas of 0.0012-0.0029 against 0.0003-0.0007 at the
+  right point, so the written `k_scale` was ~0.0028 where the 0.002 floor
+  now applies), and on the count likelihood `phi` overflowed and wrote
+  NaN scales that crashed the fit. It now maps the values to unconstrained
+  space first. `tfs-sample-posterior` and `tfs-extract-params` read the
+  optimizer state (already unconstrained) and were not affected.
+- **Count likelihood NaN for genotypes predicted near extinction.** The
+  negative binomial's concentration `mu / phi` underflowed float32 when the
+  expected reads fell below about `e^-40` (reached by genotypes with zero
+  reads everywhere, whose likelihood keeps rising as `mu` falls), and the
+  log-pmf's gradient and Hessian went to NaN; SVI then exploded within its
+  first 250 steps. The concentration now has a floor of `e^-30`
+  (`growth_counts._LOG_C_FLOOR`), which changes only rows expecting under
+  ~1e-12 reads.
+- **Prediction with a per-tube `sample_offset`.** `tfs-predict-growth` (and
+  anything calling `analysis.prediction.predict` on a new time or
+  concentration grid) failed to reshape the per-tube offsets of
+  `sample_offset: normal` (and now `level`) onto the prediction grid's
+  tubes. Per-tube offsets are now set to zero there: a prediction is for a
+  typical tube.
+- **Absent prior groups survive the priors CSV.** `write_configuration`
+  wrote a `None` prior group (growth priors of a binding-only model, binding
+  priors of a growth-only one) as a `None` row that reloaded as NaN; it is
+  now left out and reloads as `None` (`configuration_io._extract_scalars`,
+  `_update_dataclass`).
+- **MAP parameter movement silently lost all its prior-SD units on models with
+  a horseshoe slab.** `initialization.site_prior_sds` did not catch the
+  `ZeroDivisionError` numpyro raises for the variance of an `InverseGamma`
+  with Python-float concentration 2 (the slab prior with the default
+  `slab_df` 4). The whole calculation then fell back to unconstrained units
+  for every parameter ("Could not compute prior SDs for MAP parameter
+  movement" in the pre-MAP log of every hill_mut epistasis fit). An undefined
+  variance now skips only its own site.
+
+- **Library enumeration counted every codon of a tile as a site, flooding
+  partially degenerate libraries with wt.** `LibraryManager._prepare_indexes`
+  (`genetics/library_manager.py`) indexed every block in a tile, so each
+  non-degenerate codon (and each out-of-frame flank) added one wt copy to every
+  single-mutant sub-library, and each such codon paired with a degenerate one
+  added a full single-mutant expansion to the double-mutant sub-libraries. A
+  site is now a codon whose designed sequence differs from wt (a degenerate or
+  explicit mutant codon). Fully degenerate tiles, including the real design and
+  `examples/process_raw`/`examples/simulate`, enumerate exactly as before.
+  Partially degenerate ones change a lot: in
+  `planning/studies/congression-calibration/simulate_config.yaml`, `single-1`
+  goes from 46 sequences (30 wt) to 16 (0 wt), `single-2` from 67 (36 wt) to
+  32 (1 wt), and `double-1-2` from 3082 (1080 wt) to 512 (0 wt), and wt's share
+  of the co-resident pool drops from 0.39 to 0.003 (0.59 to 0.015 for
+  `examples/simulate-and-analyze`). Everything built on the enumeration moves
+  with it for such libraries: the simulator's transformation draws,
+  `tfs-report-cfu0`, and `tfs-configure-model`'s library snapshot
+  (`pool_fraction`, `bulk_fraction`) and hence the fit's co-resident sets.
+  Simulations and fits made from partially degenerate configs should be
+  regenerated. Genotype names are unchanged. `tfs-process-fastq` no longer
+  expects the exact wt DNA sequence unless a site's expansion or a spiked
+  sequence contains it (it used to come from the non-degenerate codons). A
+  tile with no
+  site is now an error, and a design that encodes no wt at all (no site's
+  codon encodes the wt amino acid and no spiked sequence is wt) warns, since wt
+  is then not an expected genotype.
+
+- **The SVI loss file and epoch checkpoints were numbered from the end of the
+  pre-MAP**, because the step counter carried over from the pre-MAP to SVI on
+  the same `RunInference`; the SVI `losses.txt` also lost its header. A run
+  started without a state now resets the counter.
+
+- **`tfs-setup-sim-grid` grids could not be moved, and nested file paths were
+  not resolved.** Each run's `tfs_sim_config.yaml` and rendered template
+  referred to input files by a path relative to their original location, so
+  moving the grid (on or off a cluster, to another partition) broke every run.
+  Nested file keys (`binding_data.*.choose_by` params files,
+  `empirical.phenotype_model`) were not resolved at all and so needed the run
+  template to copy the file in. Setup now copies every input file into
+  `<out_prefix>/inputs/` once and each run refers to it as
+  `../inputs/<name>`, so the grid directory is self-contained. The file-path
+  keys are listed in `_SIM_PATH_KEYS`
+  (`simulate/scripts/setup_sim_grid_cli.py`), nested ones included; the
+  `stratified`/`random` `choose_by` keywords are left alone, and template
+  variables naming a file are copied the same way. Two different files with
+  the same name get a numeric suffix, and a changed file never overwrites a
+  copy that existing runs use. Setup fails before writing anything if an input
+  file is missing or is a directory, or if a config value outside
+  `_SIM_PATH_KEYS` names an existing file. The congression-calibration study's
+  `run.srun` no longer copies `hill_params.csv`, and its grid YAMLs drop the
+  `hill_params_file` template block.
+- **`tfs-setup-grid` grids could not be moved.** Each run's
+  `tfs_configure_config.yaml` and rendered template referred to the input
+  files (`growth_df`, `binding_df`, `library_config`, ...) by a path relative
+  to their original location, so moving the grid directory (on or off a
+  cluster, to another partition) broke every run. Setup now copies every input
+  file into `<out_prefix>/inputs/` once and each run refers to it as
+  `../inputs/<name>`, so the grid directory is self-contained. The inputs are
+  the `configure_model` file arguments in `_PATH_KEYS`
+  (`tfmodel/scripts/setup_grid_cli.py`) and template variables naming a file;
+  the written config names the copy everywhere it recorded the file,
+  `library.source` included. The priors/guesses/library CSVs are per-run
+  outputs and are unchanged. Two different files with the same name get a
+  numeric suffix, and a changed file never overwrites a copy that existing
+  runs use. Setup now fails before writing anything if an input file is
+  missing or is a directory (previously a missing data file quietly skipped
+  the combination), if a `configure_model` value outside `_PATH_KEYS` names a
+  file, or if the template does not render. The staging helpers moved from
+  `setup_sim_grid_cli.py` into `util/grid_utils.py` (`InputStager`,
+  `check_no_outside_paths`, `stage_template_vars`, `render_run_template`),
+  and both grid CLIs use them; the unused `relativize_*` path helpers were
+  removed.
+- **Growth prediction failed on every model built from a library file.**
+  `tfs-predict-growth`, and everything else that goes through
+  `prediction.predict` (prior-predictive growth, the genotype trajectory
+  plots), raised. `prediction.copy_orchestrator` rebuilt the prediction model from
+  `orchestrator.settings`, which carries both `library_file` and the spiked
+  list derived from it, and `ModelOrchestrator` refuses the pair. It now
+  drops the derived list when `library_file` is set, as
+  `write_configuration` does. Configs from `tfs-configure-model
+  --library_config` (the only current path) were affected; legacy
+  `spiked_genotypes` configs were not.
+
+- **Per-genotype latents scrambled across genotypes under autoguides.**
+  Several components sampled their per-genotype latents in a plate sized
+  `data.batch_size`, so every numpyro autoguide -- including the `AutoDelta`
+  behind MAP, pre-MAP and `tfs-prefit-calibration` -- held one parameter per
+  batch *position* rather than per genotype. The full-batch index is
+  binding-first and reshuffled every step, so this happened even without
+  mini-batching: binding genotypes kept their positions, but every bulk
+  genotype's latents were overwritten by whichever genotype landed in that
+  position. The component SVI guide was not affected, since it holds
+  library-sized parameters and slices them itself.
+  - `activity/hierarchical_geno`, `activity/horseshoe_geno`,
+    `dk_geno/hierarchical_geno`, `ln_cfu0/hierarchical`,
+    `ln_cfu0/hierarchical_factored`, `theta/categorical_geno` and
+    `noise/logit_normal` now sample these latents in a library-sized
+    `{name}_genotype_plate` and slice them to the batch with
+    `data.batch_idx`. The latent priors and guides that were wrapped in
+    `scale_vector` no longer are; only the likelihood is scaled.
+  - The theta-noise components (`zero`, `beta`, `logit_normal`) take an
+    optional `data` argument, which `generative/model.py` now passes.
+  - `noise/beta` is a documented exception: its `{name}_dist` latent is the
+    noisy theta itself and remains batch-shaped.
+  - `RunInference.get_map_posteriors` and `get_laplace_posteriors` now raise
+    on a MAP checkpoint whose genotype-indexed latents are not library-sized,
+    instead of clipping the indices and reusing one genotype's value for
+    another. **MAP checkpoints written before this fix must be refit.**
+  - `draw_prior` no longer returns plate sites as if they were latents.
+- **New `tfmodel/inference/batch_safety.py`** detects batch-dependent latents
+  by tracing a model at two batch sizes (abstractly, under `jax.eval_shape`).
+  `tests/tfscreen/tfmodel/inference/test_batch_safety.py` runs it across the
+  component registry. `orchestrator_latent_dimension` gives the size of the
+  flattened latent vector an `AutoContinuous` guide works in.
+- **`RunInference.run_optimization` refuses unsafe autoguide fits.** When the
+  guide is any numpyro `AutoGuide` (including the `AutoDelta` behind MAP,
+  pre-MAP and `tfs-prefit-calibration`), it first runs the batch-safety check
+  and raises a `ValueError` naming the offending sites -- for example, a MAP
+  fit with `theta_growth_noise=beta`. The component guide is not checked. For
+  an `AutoMultivariateNormal` guide it also reports the latent dimension and
+  the memory the dense covariance needs, and warns above 4 GB.
+
+- **`tfs-summarize-fit` could pick the wrong config or losses file in a grid
+  run directory.** It matched `*_config.yaml` and `*_losses.txt`, which there
+  also match the simulate config and the prefit/pre-MAP losses; the right file
+  won only by alphabetical order. It now takes the config that has `data` and
+  `components` sections, and ignores `*_prefit_losses.txt` and
+  `*_premap_losses.txt`.
+
+- **Simulator counted a co-transformed cell once per plasmid.**
+  `_sim_sequencing` and `_calc_genotype_cfu0`
+  (`simulate/selection_experiment.py`) gave every plasmid in a cell that
+  cell's whole abundance, so a cell carrying n plasmids contributed n cells'
+  worth of reads and `ln_cfu_0`. Bulk genotypes (transformed with
+  `transformation_poisson_lambda`) were inflated relative to the monoclonal
+  spikes by `E[n | n >= 1] = lambda / (1 - exp(-lambda))` (1.19 at
+  lambda = 0.357), so a genotype's simulated spike/bulk make-up depended on
+  lambda. A cell's abundance is now split among its plasmids (1/n each; new
+  helper `_plasmid_shares`), so each cell counts once and the simulated
+  composition matches the design `pool_fraction`/`bulk_fraction` from
+  `genetics.library_composition_table` at any lambda. **Simulated read counts
+  and `ln_cfu_0` change for any config with lambda > 0.** Growth itself
+  (`_sim_growth`) is unchanged.
+
+- **Batch-order bug in three more components.** `activity/hierarchical_mut`
+  and `activity/horseshoe_mut` sliced activity to the batch only when
+  `batch_size < num_genotype`, and `theta/_simple` indexed its per-genotype
+  theta by the batch-relative `geno_theta_idx` alone. The full-batch training
+  index is binding-first and reshuffled every step, so at full batch these
+  returned library-ordered values for reordered batch positions: bulk
+  genotypes got other genotypes' activity (or, for `_simple` with
+  per-genotype curves, theta). The shape-based batch-safety check could not
+  see it. All three now slice through `batch_idx`. Fits using
+  `hierarchical_mut`/`horseshoe_mut` activity without mini-batching should be
+  rerun. The pre-fit's `_simple` model contains only binding genotypes, whose
+  index is library order and never reshuffled, so pre-fit results are
+  unaffected.
+  - New `find_batch_order_mismatches` /
+    `find_orchestrator_batch_order_mismatches` in
+    `inference/batch_safety.py`: trace two orderings of the full batch with
+    the latents held fixed and check that the batch-positional predictions
+    follow the index. `test_batch_safety.py` runs it across the component
+    registry and the epistasis and binding-only variants.
+
+- **`theta/categorical_geno` full-population theta.** The component sliced
+  its per-genotype latent to the batch when sampling, so the full-population
+  theta `generative/model.py` builds for the congression background (by
+  calling `run_model` with `batch_idx = geno_theta_idx = arange(num_genotype)`)
+  came back in batch order at full batch and indexed past the batch under
+  mini-batching, repeating the last genotype. The empirical correction's
+  background CDF is order-free, so full-batch fits were unaffected; fits
+  combining `categorical_geno`, `transformation=empirical` and mini-batching
+  used a corrupted background and should be rerun. theta now stays
+  library-sized and `run_model` indexes it through
+  `batch_idx[geno_theta_idx]` (the `hill_geno` pattern). The `theta_theta`
+  deterministic site is now stored for every genotype.
+  - New `tests/tfscreen/tfmodel/generative/test_population_theta.py`
+    checks, for every theta component (model and guide; shuffled full batch
+    and mini-batch), that the full-population theta is library-ordered.
+
+- **Posterior files depended on `forward_batch_size`.** `get_posteriors`,
+  `get_laplace_posteriors` and `get_map_posteriors` run the model over
+  genotype chunks, and sliced every genotype-indexed latent to the chunk
+  first. Components that index library-sized parameters by library position
+  (`hill_geno`, `thermo.*`, and `categorical_geno`) then read past the sliced
+  arrays for every chunk after the first, and JAX clamped the index instead
+  of raising. So for any library larger than one chunk (512 genotypes by
+  default), the deterministic sites written for genotypes past the first
+  chunk (`theta_growth_pred`, `growth_pred`, and everything
+  `tfs-predict-theta`/`-growth` derive from them) were wrong. The forward pass
+  now receives library-sized latents whole, as in training, and slices only
+  batch-positional latents (`noise/beta`) and the saved output
+  (`RunInference._batch_positional_latents`). Posterior files for libraries
+  larger than `forward_batch_size` should be regenerated with
+  `tfs-sample-posterior`.
+  - New `tests/tfscreen/tfmodel/inference/test_forward_chunking.py` checks
+    that posterior and MAP posterior files are identical at
+    `forward_batch_size` 512 and 2.
 
 ## [0.4.4] - 2026-09-22
 
@@ -677,7 +2139,8 @@ genotypes are handled.
 - **Fitting routines** (`tfscreen.fitting`): OLS, WLS, GLS, GEE, GLM, NLS, matrix NLS/WLS, and Kalman / unscented Kalman filter estimators.
 - Walkthrough notebook (`notebooks/tfscreen-walkthrough/`).
 
-[Unreleased]: https://github.com/harmslab/tfscreen/compare/v0.4.4...HEAD
+[Unreleased]: https://github.com/harmslab/tfscreen/compare/v0.5.0...HEAD
+[0.5.0]: https://github.com/harmslab/tfscreen/compare/v0.4.4...v0.5.0
 [0.4.4]: https://github.com/harmslab/tfscreen/compare/v0.4.3...v0.4.4
 [0.4.3]: https://github.com/harmslab/tfscreen/compare/v0.4.2...v0.4.3
 [0.4.2]: https://github.com/harmslab/tfscreen/compare/v0.4.1...v0.4.2
diff --git a/CLAUDE.md b/CLAUDE.md
index 29637e29..0fd24059 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -29,6 +29,10 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 ~/miniconda3/bin/coverage run --branch -m pytest tests/tfscreen --runslow
 ```
 
+### Dependencies
+
+`pyproject.toml` floors every dependency at the oldest versions the suite has passed with (no caps: CI installs the newest). No lock or constraints file lives in the repo (user, 2026-10-20: not at the top level beside `src`, `docs`, `tests`); an analysis records its own `pip freeze`. tfscreen needs Python 3.12+ (jax 0.11 dropped 3.11); CI tests 3.12, 3.13 and 3.14. The `cuda12` extra is GPU jax on Linux, where pip's plain jax is CPU-only. `planning/studies/fresh-install/fresh_install.srun` checks a clean install on a cluster GPU node.
+
 ### Linting
 ```bash
 # Fatal errors only
@@ -40,34 +44,37 @@ flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127
 
 ### CLI Entry Points
 ```
-tfs-process-fastq          # FASTQ → read counts
-tfs-process-counts         # counts → ln_cfu DataFrames
-tfs-process-presplit       # Process pre-split count files
-tfs-configure-model        # Generate YAML config template (+ library composition snapshot)
+tfs-process-fastq          # FASTQ → read counts (library_config f1 f2 --out_dir; library YAML first)
+tfs-process-counts         # per-tube counts → growth table (tube table + counts dir; tube totals from --od600_file/--od600_calibration_file/--tube_volume_mL or supplied sample_cfu; every library in one call; --presplit writes the presplit table)
+tfs-calibrate-od600        # Fit an OD600-to-CFU/mL calibration from a dilution series of replicate readings + plate counts (process_raw/od600.py)
+tfs-check-tubes            # Tube QC on the growth table: each tube's apparent titrant from its genotype composition (centered log-ratio, robust row effect + per-row-scaled concentration profile, so tube totals cancel and incomplete rows are not shifted); flags tubes out of order in their replicate x time row (leave-one-out isotonic order test + level test vs the label's expected score, --flag_z noise SDs), a likely tube/sample-sheet swap; rows of < 3 tubes are never flagged; process_raw/tube_qc.py
+tfs-correct-tubes          # Apply a YAML corrections file (swap/relabel/exclude, each with id, reason, by, date, evidence) to a tube table before tfs-process-counts; writes a new table (+ correction_id), {out_prefix}_changes.csv and provenance with input hashes; only DESIGN_COLUMNS move, measurements stay with the physical sample (process_raw/tube_corrections.py)
+tfs-process-binding        # Lab binding table (one row per measurement) -> binding_df at one protein concentration (--protein_conc, default 2 uM): concentrations rounded to 6 significant digits so days line up, replicates (--replicate_cols, default biorep,date) averaged, theta_std = pooled replicate SD / sqrt(n) or --theta_std; titrant written in mM (process_raw/binding.py)
+tfs-configure-model        # Generate YAML config template (+ library composition snapshot); --set_priors name=value ... sets scalar priors (held SDs, sigma_fixed); --growth_priors / --growth_priors_wt_rates set per-condition linear k/m priors where the pre-fit does not run, and --growth_priors's 0/1 k_pinned/m_pinned columns clamp chosen conditions (priors_edit.py); a library spike with no growth data is an error unless --allow_missing_spikes
 tfs-prefit-calibration     # Pre-fit linking function via MAP
-tfs-fit-model              # Main hierarchical Bayesian inference
-tfs-fit-genotypes          # Per-genotype MLE fits of the growth model (+ optional congression de-attenuation; spiked genotypes come from --library_config)
-tfs-sample-posterior       # Draw posterior samples from fitted model
+tfs-fit-model              # Main hierarchical Bayesian inference (--guide_type component|delta|auto_normal|auto_diagonal_normal|auto_multivariate_normal|auto_low_rank_multivariate_normal; --guide_rank, --guide_init_scale; convergence: --convergence_window_steps, --patience, --convergence_z, --loss_rtol, --param_tolerance, --adam_step_size -> --adam_final_step_size by --adam_step_size_cut; --adam_clip_norm opts back into elementwise gradient clipping, off by default; --init_from <params.npz> starts at an earlier MAP's {site}_auto_loc values, e.g. a level-offset fit from a zero-offset fit, since guesses cannot name sites outside the configured guesses; --stage_offsets auto|on|off, --staged_step_size: a fresh MAP with level tube offsets runs the staged MAP, inference/staged_map.py)
+tfs-fit-two-stage          # The two-stage relative fit as one command (stage 1 wt+spikes+singles with the congression mixture -> jackknife of stage 1 -> stage 2 full library with k/m pinned -> X (the jackknife and k-shift refits that add k's uncertainty as a shared per-draw shift are off by default, two_stage.kshift: false, since validation run 2 found the shift a third or less of the true one; when on: refits from stage 2's MAP at kshift_step_size 1e-5, dk_geno population held at stage 2's MAP, a k-unchanged control refit, kshift/drift.csv) -> marker-consistency check -> summary: {out_dir}/tfs_x.csv, X quantiles + k_shift + reads (total, per library) + num_mutations + consistency z/flag per genotype x concentration, the users' table); YAML = tfs-configure-model's args + a two_stage block (examples/tfmodel/two_stage.yaml); resumable, --step, --jackknife_task for arrays, --no_consistency; tfmodel/two_stage.py, planning/two-stage-fit.md
+tfs-fit-genotypes          # Per-genotype MLE fits of the growth model (no congression correction)
+tfs-sample-posterior       # Draw posterior samples from fitted model (MAP: --laplace auto|full|arrowhead|blocks|point; auto = full up to --laplace_max_params 20000 MAP parameters, arrowhead above; arrowhead writes {out_prefix}_held_directions.csv; blocks holds the shared parameters at the MAP; point = the MAP point, no Hessian; --skip_growth_observations: leave out growth_pred/growth_obs, most of the file on a large library)
 tfs-sample-prior           # Draw prior predictive samples
-tfs-extract-params         # Extract parameters from checkpoint
+tfs-extract-params         # Extract parameters from checkpoint (+ {out_prefix}_model_params.csv: learned model-side pyro.params, e.g. the thermo PnnC NN weights)
 tfs-predict-growth         # Predict growth from fitted model
-tfs-predict-theta          # Predict operator occupancy
-tfs-predict-epistasis      # Joint second-order epistasis from the theta posterior (per-draw ep, then quantiles; captures cross-genotype posterior covariance the marginal tfs-extract-epistasis path drops)
+tfs-predict-theta          # Predict operator occupancy (--shared_shift adds one shared per-draw X shift, the two-stage fit's k uncertainty; analysis/shared_shift.py)
+tfs-predict-epistasis      # Joint second-order epistasis from the theta posterior (per-draw ep, then quantiles; captures cross-genotype posterior covariance the marginal tfs-extract-epistasis path drops; --shared_shift as tfs-predict-theta)
 tfs-cat-response           # Fit categorical response curves
 tfs-extract-epistasis      # Calculate second-order epistasis from a long-form observable table (--scale add|mult|logit; --scale_constant rescales the transform before epistasis, e.g. -RT to put logit onto a free-energy scale)
 tfs-compare-runs           # Cross-run agreement statistics for any quantile-summarized estimate -- predicted features (theta/growth/epistasis) or fitted parameters (log_hill_K/dk_geno/growth_k/k_ref) -- across N runs (seeds / k-fold dropouts). Reports raw numbers (rms_sd, overdispersion + p/q, n_present/n_runs); no thresholds, no grades. --index_by picks the entity, --group_by breaks it out further, --match_by overrides key detection
-tfs-diagnose-nan           # Diagnose NaN issues in inference
-tfs-simulate               # Simulate a full experiment
-tfs-report-cfu0            # Report average ln_cfu0 by genotype class from a simulate config
+tfs-simulate               # Simulate a full experiment (config_file --out_prefix tfs_sim → {out_prefix}_{name}.csv; also the raw experiment, {out_prefix}_counts/ + _tubes.csv [+ _tube_od600.csv, _od600_calibration.yaml], for tfs-process-counts, whose command it prints; --no_write_raw skips it; --no_write_growth skips the multi-GB direct growth table; cfu0 may be a {library: cells} map; total_num_reads is the total over all replicates, split by tube count (replicate_read_totals; until 2026-10-08 every replicate got the full total); unassigned_read_fraction (scalar or per library) sends that share of each tube's reads to __unknown__; single-plasmid transformants are merged into one weighted row per genotype (_sim_transform_collapsed, exact for founder/demographic noise), so transform_sizes can be realistic; with an od600 block, tubes read below the detection threshold are dropped from the direct growth table as from the raw tube table (raw_output.undetectable_tubes; logged), so both routes hold the same tubes (until 2026-10-11 the direct table kept them), and max_congressed_cells caps the multi-plasmid cells; lib_assembly_skew_sigma may be per origin; thermodynamic theta models also write {out_prefix}_mutation_parameters.csv and, with multi-mutants, _pair_parameters.csv: the per-mutation d_*/ddG_* and per-pair epi_* truth; library_genotypes (list file) / library_subsample ({origin: N}, needs library_subsample_seed) restrict the library before anything is simulated, see **Library subsample**)
 tfs-build-empirical # Fit real data → empirical phenotype-generating distribution (Stages 1-2)
 tfs-setup-sim-grid         # Set up grid of simulation runs
 tfs-setup-grid             # Set up grid of model configs
 tfs-summarize-grid         # Summarize grid results
-tfs-summarize-fit          # Summarize a fitted model
-tfs-summarize-sbc          # Summarize simulation-based calibration runs
-tfs-subset-genotypes       # Subset genotype data
+tfs-summarize-fit          # Summarize a fitted model (and, with tube offsets, the offset-mode check: offsets in prior SDs, Spearman trend vs titrant/time per condition, BH-flagged; tfmodel/analysis/tube_offsets.py)
+tfs-summarize-calibration  # Pool posterior calibration (coverage, PIT, width, RMSE) across a tfs-setup-sim-grid grid, by arm and stratum; --baseline pairs runs fit to the same simulated data
 ```
 
+Removed in 0.5.0: `tfs-process-presplit` (now `tfs-process-counts --presplit`), `tfs-diagnose-nan`, `tfs-subset-genotypes`, `tfs-report-cfu0`, `tfs-summarize-sbc` (the library function `error_calibration.summarize_sbc` remains). `docs/source/cli.rst` is the generated reference of every command's flags.
+
 ## Architecture
 
 ### Data Flow
@@ -110,10 +117,13 @@ The hierarchical Bayesian inference engine. Key files:
 - **`generative/model.py`** — Numpyro probabilistic model definition. The generative model is:
   `ln_cfu = ln_cfu0 + (k_pre + dk_geno + m_pre·A·θ)·t_pre + (k_sel + dk_geno + m_sel·A·θ)·t_sel`
   where θ = operator occupancy, A = per-genotype TF activity, dk_geno = pleiotropic growth effect.
+  With `transformation: mixture` each genotype's cells are split into classes (clean, plus K congressed classes built from fixed co-resident sets) with cell-level θ/A/dk_geno (θ by `congression_theta_rule`, default the homodimer partition function over equal plasmid shares; dk_geno by `congression_dk_rule`, default dilution); every class is grown through the same pipeline on a leading class axis and the classes are mixed as `ln_cfu = ln_cfu0 + logsumexp_c(log w_c + G_c)`. The transformation component's `cell_classes(focal, population, params, data)` builds the classes (`single`: one class); `NEEDS_POPULATION` makes `jax_model` supply library-ordered θ/activity/dk_geno (`return_population=True`, or `GrowthData.external_*_population` in prediction).
 
 - **`model_orchestrator.py`** — `ModelOrchestrator`: top-level class orchestrating data loading, inference, and prediction.
 
-- **`inference/run_inference.py`** — `RunInference`: coordinates JAX/Numpyro sampling (SVI or NUTS) and MAP estimation (optax).
+- **`inference/run_inference.py`** — `RunInference`: coordinates JAX/Numpyro sampling (SVI or NUTS) and MAP estimation (optax). **Guide selection:** `setup_svi(guide_type=..., guide_kwargs=..., init_values=...)` takes any name in `GUIDE_TYPES` — `component` (the guide assembled from the model components) or a numpyro autoguide from the `AUTOGUIDES` registry (snake_case of the class name, `delta` = `AutoDelta`; CamelCase class names accepted via `resolve_guide_type`). `tfs-fit-model --guide_type` exposes it for `analysis_method=svi`; an autoguide's location starts at the pre-MAP point (`init_to_value`), and `init_params` (component-guide param names) is not passed to it. Checkpoints record `guide_type`/`guide_kwargs`; `restore_svi_from_checkpoint` rebuilds that guide (legacy checkpoints → `component`) and `tfs-sample-posterior` routes `delta` → Laplace, everything else → guide sampling. `run_optimization` refuses any autoguide (AutoDelta included) when `inference/batch_safety.py` finds batch-dependent latents. Because per-genotype latents are library-sized, the `AutoContinuous` guides (incl. `auto_multivariate_normal`) are valid under mini-batching; the dense MVN just costs O(D²) memory (reported, warned above `_DENSE_GUIDE_WARN_GB`).
+
+- **`inference/convergence.py`** — when to cut the step size and when to stop; see **Convergence and step size** below.
 
 - **`tensors/tensor_manager.py`** — `TensorManager`: maps ragged per-genotype observations into JAX-compatible tensors.
 
@@ -121,18 +131,36 @@ The hierarchical Bayesian inference engine. Key files:
 
 - **`generative/components/`** — Pluggable model components selected via the YAML config's `components:` section (registry key noted in parens where it differs from the directory name):
   - `activity/`: `fixed`, `hierarchical_geno`, `hierarchical_mut`, `horseshoe_geno`, `horseshoe_mut`
-  - `growth/` (registry key `condition_growth`): `linear`, `power`, `saturation`. Their per-condition prior loc/scale fields accept a scalar (broadcast to all conditions) **or** a per-condition array; the pre-fit calibration writes per-condition arrays to pin the baselines (see **Per-condition growth priors** below). Each declares `get_scale_bounds()`.
+  - `growth/` (registry key `condition_growth`): `linear`, `power`, `saturation`. `linear` also takes `m_warp` (bool or per-condition tuple, a 0/1 `--growth_priors` column: that condition's growth follows a logistic warp f(theta), f(0) = 0 and f(1) = 1, steepness a and threshold x0 sampled per warped condition; for markers whose occupancy-to-growth maps differ in shape, `planning/studies/relative-m/`; extracted as `warp_log_a`/`warp_x0` per warped condition; runs with `hill_geno` and the mixture's partition rules, acting on each cell class's theta), `k_titrant_loglinear_scale` (> 0: a selection-phase k shift linear in log titrant concentration from a tenth of the lowest nonzero one, in selection conditions only, shared by every genotype, 0 at no titrant; needs the full growth-tensor condition map; a free shift per concentration was dropped as unidentified, since it trades with m along wt's curve; `planning/studies/within-tube-m/`). Their per-condition prior loc/scale fields (in `linear`, `m_scale_plus`/`m_scale_minus` too) accept a scalar (broadcast to all conditions) **or** a per-condition array; the pre-fit calibration writes per-condition arrays to pin the baselines (see **Per-condition growth priors** below). Each declares `get_scale_bounds()`.
   - `growth_transition/`: `instant`, `memory`, `baranyi`, `baranyi_k`, `baranyi_tau`, `two_pop`
-  - `transformation/`: `empirical`, `logit_norm`, `single`
-  - `theta/`: `categorical_geno`, `hill_geno`, `hill_mut`; thermodynamic partition-function variants under `theta/thermo/` (lac dimer and MWC dimer, with/without unfolded state, PK/PnnC/PddG parameterizations)
+  - `transformation/`: `single`, `mixture` (`empirical`/`logit_norm` were removed; `ModelOrchestrator` refuses them by name with a message, `_RETIRED_TRANSFORMATIONS`). The old E[max] θ operator (`_congression.py`) is gone, along with Stage 1.5 of the empirical pipeline that used it
+  - `theta/`: `categorical_geno`, `hill_geno` (with `theta_growth_log_conc_shift_scale` > 0, a global log s site `theta_growth_log_conc_shift`: growth sees each curve at s times nominal titrant, binding at nominal, applied in `model.py`'s `growth_titrant` to the growth data only, including the mixture population and `prediction._population_theta`; off by default; `tfs-predict-theta` reports the curve in binding units; joint-fit plan step 4a), `hill_relative` (wt-relative X, see **Relative-X fit** below), `hill_mut`; `hill_geno`/`hill_relative` take `theta_log_hill_n_hyper_scale_fixed` (> 0 holds the log(n) population SD; `hill_geno` also `theta_{logit_low,logit_delta,log_hill_K}_hyper_scale_fixed`, joint-fit-sim run 5; learned, a MAP on real data ran it to about 19, and n near 0.01 hid each genotype's response below the lowest nonzero concentration, in the 0-titrant tubes); `hill_relative` also takes `theta_{X_low,X_delta,log_hill_K}_hyper_scale_fixed` (the dev-data MAP ran them to 3.6, 4.3 and 11 against about 0.45, 0.45 and 0.7 among well-measured genotypes, so it shrank nothing; `planning/dev-data/real_fit/make_init_hs.py` rescales a MAP's non-centered offsets for a warm start at the held values); thermodynamic partition-function variants under `theta/thermo/` (lac dimer and MWC dimer, with/without unfolded state, PK/PnnC/PddG parameterizations; in the U1 PddG/PnnC models mutations shift each folded structure by ΔΔG_X and leave U alone (ΔΔG_U = 0), so with K_U = [U]/[H] the per-mutation `d_ln_K_U` (C12: `d_ln_K_u`) is ΔΔG_H (`_PROJ_U`, `_project_unfolding`; `epi_ln_K_U`/`epi_ln_K_u` per pair), the names PK uses for its free shift; until 2026-10-10 they held ln_K_U at wt for every genotype, which moved U with H and made every mutation stability-neutral; like `hill_mut`, all twelve skip their per-mutation sites when `num_mutation == 0`, in `define_model`, `guide` and `prior.sample_ddG`, since numpyro plates need size > 0, so a wt-only library draws; **physics** (`theta/thermo/mwc.py`, shared by all twelve): the standard MWC dimer, two independent equivalent effector sites per conformation, P_X = (1 + K_x_e·e)² with K_x_e per site (M⁻¹); cooperativity only from the H/L equilibrium. Until 2026-10-11 C12 used Sochor's 1 + K·e + K²·e² (K2/K1 = 1, not 1/4) and C4 a one-step L + 2E with an M⁻² K_E. M3 = C12 (K_h_l, K_h_o, K_h_e, K_l_o, K_l_e); M2 = C4 is M3 with K_h_e = K_l_o = 0 (ln_K_op ↔ K_h_o, ln_K_HL ↔ K_h_l, ln_K_E ↔ K_l_e per site), computed by the same `mwc.mwc_theta` (`theta_c4`/`theta_c12`), so the nesting is exact; the C4/K3 labels are historical. Bookkeeping: TF ≫ operator, so operator-bound TF is out of the TF mass balance and there is no `op_total` anywhere (removed from priors, ThetaParam, build_calc_df, extraction); h = r/F(e), r = tf_total/2 (monomer units), F = B0 + B1·e + B2·e² (B0 = 1 + K_h_l + K_u, B1 = 2(K_h_e + K_h_l·K_l_e), B2 = K_h_e² + K_h_l·K_l_e²), W = h·(K_h_o·P_H + K_h_l·K_l_o·P_L), θ = W/(1+W); free effector from the cubic B2x³ + (B1 + B2(2r − e))x² + (B0 + B1(r − e))x − B0·e = 0. **All of it in log space** (since 2026-10-11; `mwc.log_W`, `mwc_theta_log`): log-sum-exp for every partition-function sum, log P = 2·softplus(ln K + log e), θ = sigmoid(log W), so no ln K is exponentiated and any finite ln K gives a finite θ in [0, 1] in float32 with the right limits. Before, θ came from exp(ln K): K² overflows float32 above ln K 44, and Laplace draws of the PddG pilot fits (ln K to ±60) wrote NaN θ for whole genotypes (15-71 per run). The free effector is solved for log e by safeguarded Newton (`_solve_log_free_effector`: bracket [x_lo, log e_total], rtsafe bisection fallback, `NEWTON_ITERATIONS` = 16; 12 converged exactly in float64 over ln K uniform on ±150) on a residual that stays conditioned on both sides of the equivalence point e_total = 2r (`_mass_balance`: log(e + r·n̄) − log e_total for e_total ≤ r, log(e + c⁻) − log(c⁺ + r·(2 − n̄)) above, c = e_total − 2r); at e_total ≈ 2r with huge K_E θ is genuinely ill-posed at float precision, so tests avoid comparing there. Derivatives of the root come from the implicit function theorem (`jax.custom_jvp`), not from the iterations, so gradients and the Laplace Hessian are exact and finite at extreme values. `mwc_theta`, `free_effector` and `theta_saturated` keep their linear-K signatures (tests) and log their inputs. `tfs-predict-theta` warns (`warn_nonfinite_theta`) if any output row is still NaN or infinite. Projection rows (`mwc.PROJ_C4/PROJ_C12/PROJ_U_*`, imported as each PddG/PnnC `_PROJ`): effector rows are per site, (ΔΔG_X − ΔΔG_XE2)/2, so C4 Δln K_E = (ΔΔG_L − ΔΔG_LE2)/2. **Defaults**: tf_total 1e-5 M monomer; C12 keeps Sochor's K_h_l 6.3, K_h_o 4.2e8, K_l_o 0.1 but not his K_l_e/K_h_e ≈ 13.6, which left the default wt at θ_sat 0.64 (residual HE2 repression at 1e-5 M TF). Since 2026-10-10 ρ = K_l_e/K_h_e = `MWC_EFFECTOR_SELECTIVITY` ≈ 79.6 is solved exactly from W_sat = r(K_h_o + K_h_l·K_l_o·ρ²)/(1 + K_h_l·ρ²) for `CALIBRATION_THETA_SATURATED` = 0.05 (`effector_selectivity_for_theta_saturated`), then K_l_e = Sochor's 7.6e5 × `MWC_EFFECTOR_SCALE` ≈ 0.393 (≈ 3.0e5) and K_h_e = K_l_e/ρ (≈ 3.7e3), the scale chosen for an 80 µM midpoint; C4 uses the same K_h_l, K_h_o and calibrates ln K_E (≈ 12.41); both give a default wt midpoint (halfway between θ(0) and `mwc.theta_saturated`) of 80 µM with depletion. Default wt curves: C12 θ 0.997 → 0.05, EC10/50/90 27/80/339 µM, Hill-like n 1.73; C4 0.997 → 0, 29/80/229 µM, n 2.14. Prior-predictive fraction of wt draws (PK wt priors, U0, 200k) with Δθ(0→1 mM) ≥ 0.5 / ≥ 0.2 / Δθ(0→10 mM) ≥ 0.5: C12 0.61/0.71/0.65 (0.42/0.52/0.43 with Sochor's ρ), C4 0.72/0.81/0.87. **units**: all twelve read `titrant_conc` in mM and multiply it by `theta_conc_unit_scale` (1e-3) so the equations run in M. Old priors CSVs/checkpoints are not supported (the legacy `theta_conc_unit_scale` reader is gone; an old priors CSV with a `theta_op_total_M` or other stale row now fails to load, see **CSV (de)serialization**). Tests: `theta/thermo/test_mwc.py` (brute-force species sum, Newton vs brentq, exact nesting with depletion, 80 µM midpoints, C12 θ_sat 0.05, projection rows), `test_conc_units.py`)
   - `theta_rescale/`: `passthrough`, `logit`
   - `dk_geno/`: `fixed`, `hierarchical_geno`, `pinned`
-  - `noise/` (theta observation noise; registry keys `theta_growth_noise`: `zero`/`beta`/`logit_normal`, `theta_binding_noise`: `zero`/`beta`)
+  - `noise/` (theta observation noise; registry keys `theta_growth_noise`: `zero`/`beta`/`logit_normal`, `theta_binding_noise`: `zero`/`beta`/`genotype_offset`; `genotype_offset`: one additive theta offset per binding genotype, constant over titrant, `tau * z_g`, tau learned (HalfNormal(0.2)) or held by `offset_scale_fixed`; plate `{name}_binding_plate` over the binding genotypes only, indexed by `BindingData.binding_ordinal` (arange in joint models, the batch indices binding-only); prediction remaps a `*_binding_plate` site by binding genotype label; joint-fit plan step 4b)
   - `growth_noise/`: `zero`, `normal_kt`
-  - `ln_cfu0/`: `hierarchical`, `hierarchical_factored`
-  - `sample_offset/`: `zero`, `normal`
+  - `ln_cfu0/`: `hierarchical` (one starting abundance per replicate x pre-condition x genotype; use this), `hierarchical_factored` (one genotype baseline per replicate shared by every pre-condition, plus a per-tube offset; tube offsets non-centered, `tube_offset = tube_scale * tube_offset_z`, because the centered form had an unbounded density as `tube_scale` went to 0). The orchestrator refuses `hierarchical_factored` when a replicate's pre-conditions come from different libraries (`_check_factored_ln_cfu0`): kanR and pheS are transformed and grown up separately (user, 2026-09-28), so their starting abundances differ per genotype. On simulations the fit pushed that difference into a confident per-genotype theta error: 95% coverage 0.09 above 1000 reads, against 0.80 with `hierarchical` (`planning/studies/svi-overconfidence/`). Every study grid used the factored model until then.
+  - `sample_offset/`: `zero`, `normal` (per-tube growth-rate offset, scaled by elapsed time), `level` (per-tube ln_cfu offset, constant prior; `sigma_fixed` > 0 holds its SD instead of learning it: learned, it grew to a free level per tube on the first real-data fit and absorbed the population's growth). Both extract one row per measured tube, labeled from the growth tensor's `{dim}_idx` columns in the components' reshape order (`_tubes.py`; `test_tubes.py` checks the labels against the model's own tensor)
+
+- **`generative/observe/`** — *not* under `components/`, and not swappable via YAML. Holds the four observation-likelihood layers (`binding`, `growth`, `presplit`, `base_growth`), registered under flat `model_registry` keys `observe_binding`/`observe_growth`/`observe_presplit`/`observe_base_growth`. `ModelOrchestrator` wires in `observe_binding` whenever binding data were supplied and (unless `binding_only`) the growth observer chosen by `growth_likelihood` (`lncfu` → `observe_growth`, Student-t on `ln_cfu`; `counts` → `observe_growth_counts`, see **Count likelihood** below), plus `observe_presplit`/`observe_base_growth` only when the corresponding data (`presplit_df`/`base_growth_df`) was supplied — these are parallel, independently-gated observers, not alternative choices for one axis. **Growth-only models** (`binding_df=None`, `tfs-configure-model` without `--binding_df`) have `data.binding = None`, `priors.binding = None`, no `theta_binding_noise` component and no binding sites in the model (`jax_model` gates them on `data.binding is not None`, static structure); the orchestrator refuses a `binding_weight` or a non-`zero` `theta_binding_noise` without binding, and `tfs-prefit-calibration` refuses a growth-only config. Absent prior groups are left out of the priors CSV (`configuration_io._extract_scalars` / `_update_dataclass` skip `None`), so they reload as `None`, not NaN.
+
+### Count likelihood (`growth_likelihood: counts`)
+
+Roadmap step 7. **The `tfs-configure-model` default** (with `sample_offset: level`), since the count-likelihood study (2026-09-27: theta RMSE 35-60% lower than `lncfu` at similar coverage). `ModelOrchestrator`'s own defaults stay `lncfu`/`zero` on purpose: configs are read back through it, and one written before step 7 has no `growth_likelihood` key, so changing the orchestrator default would silently turn old `lncfu` configs into count models (tests with synthetic `ln_cfu` tables also rely on it). `observe/growth_counts.py` observes reads directly: `reads ~ NegBin(mu, var = mu (1 + phi) + mu^2 inv_r)` with `log mu = ln_sample_reads + ln_cfu_pred - sample_ln_cfu` (predicted frequency times the tube's reads), learned `growth_phi`/`growth_inv_r` (LogNormal priors, medians 5 and 0.01), no pseudocount. The log-pmf is `CountNegativeBinomial`, Loader's algorithm (R's `dnbinom_mu`: `bd0` deviance + `stirlerr`): numpyro's own negative binomial loses several nats per observation to float32 cancellation at 1e4-1e6 reads; keep any rewrite of it tested against scipy at high depth (`test_observe_growth_counts.py`), and guard `jnp.where` branches so the unused one cannot overflow (NaN gradients). The concentration has a floor (`c + e^-30`, `_LOG_C_FLOOR`): without it `mu / phi` underflows for genotypes predicted near extinction (all-zero genotypes drift there) and the gradient/Hessian go NaN; `test_finite_value_gradient_and_hessian` covers log mu down to -200. Data: `GrowthData.counts`/`ln_sample_reads`/`sample_ln_cfu` (same layout as `ln_cfu`, None for `lncfu`; sliced by `tensors/batch.py`) and the static `GrowthData.growth_likelihood`, built by `model_orchestrator._add_count_columns` (tube reads from `sample_reads`, else `adjusted_counts / frequency`, about 1% high from pseudocounts, the same for every genotype in a tube; tube totals from `sample_ln_cfu` or `sample_cfu`). The orchestrator refuses `growth_noise` other than `zero` with counts. Pair it with `sample_offset: level` (one constant-prior ln_cfu offset per tube), since an error in a tube's supplied total otherwise moves every genotype in it. Presplit is still observed as `ln_cfu`. Validation grid: `planning/studies/count-likelihood/`.
+
+### OD600 calibration (`tfs-calibrate-od600`)
+
+Roadmap step 3. `process_raw/od600.py` holds the method (C10: the lab's data never enter the repo): `reading_noise`/`detection_threshold` from repeated readings of a dilution series (largest relative SD; midway between the two most dilute means), `plate_counts_to_cfu` (`colonies * dilution / plated_volume_mL`, Poisson counting + one pipetting error per step), `fit_polynomial` (weighted least squares, covariance scaled by the reduced chi-square as in `curve_fit`), `calibrate`, `write_calibration`/`read_calibration`/`check_calibration` (`kind: od600_to_cfu_per_mL`, coefficients, full covariance, `reading_rel_sd`, `detection_threshold`, `calibrated_od600_range`; the lab notebook's `A_CFU`... format is refused with a pointer), `od600_to_cfu_per_mL`, `cfu_per_mL_error_components` (curve SD `sqrt(J C J^T)`, shared by every tube (C11), kept apart from the per-reading SD) and `cfu_per_mL_to_od600` (bisection). It reproduces the lab notebook's coefficients to 2e-9 given the notebook's CFU/mL, but the notebook never divided by the plated volume (0.1 mL), so its constants are 10x low; the tool divides.
+
+`tube_totals_from_od600(od600_df, cal, tube_volume_mL)` turns one reading per tube into `sample_cfu` (CFU/mL times the culture volume: totals are cells per tube), `sample_cfu_std`, `sample_cfu_curve_std`, `sample_cfu_reading_std` and `od600_in_calibrated_range`; a reading below the detection threshold is an error (kept deliberately: dropping a real tube is the lab's call, and the simulator's raw and direct outputs both leave such tubes out before this step). `tfs-process-counts` calls it (`add_tube_totals`) when given `--od600_file` (or an `od600` column) with `--od600_calibration_file` and `--tube_volume_mL`, and refuses OD600 plus supplied totals. This is roadmap/pipeline step 1 (P3: the OD600 table stays a first-class input so the population model can replace the totals step). Synthetic inputs in `examples/process_raw/` (`make_example_data.py`).
+
+### Relative-X fit (`theta: hill_relative`)
 
-- **`generative/observe/`** — *not* under `components/`, and not swappable via YAML. Holds the four observation-likelihood layers (`binding`, `growth`, `presplit`, `base_growth`), registered under flat `model_registry` keys `observe_binding`/`observe_growth`/`observe_presplit`/`observe_base_growth`. `ModelOrchestrator` wires in `observe_binding` and (unless `binding_only`) `observe_growth` unconditionally, plus `observe_presplit`/`observe_base_growth` only when the corresponding data (`presplit_df`/`base_growth_df`) was supplied — these are parallel, independently-gated observers, not alternative choices for one axis.
+Roadmap step 5. Growth alone fixes an occupancy-like variable only up to an affine map (`k`/`m` absorb it), so `theta/hill_relative.py` fits a wt-relative growth variable X: hill_geno's curve with real-valued baselines (`X_low`, `X_delta`), wt gauged to `X = 1` at `c_lo` and `X = 0` at `c_hi` (`gauge_baselines`, from wt's own K and n; `data.growth.theta_gauge_log_conc`). `theta_gauge_conc` is an orchestrator setting (`tfs-configure-model --theta_gauge_conc`; default the measured min/max, resolved and written to the config; refused for other theta components). `model_orchestrator._check_relative_theta` refuses binding data, activity other than `fixed`, `theta_rescale` other than `passthrough`, non-`linear` condition growth, theta noise other than `zero` and, with `mixture`, any `congression_theta_rule` but `max` (the only rule invariant to an increasing map; `mixture`'s `max` branch does not clip). The module's `THETA_SCALE = "X"` is how downstream code tells: `tfs-predict-theta` adds `theta_scale = X`, `extract_theta_epistasis`/`tfs-extract-epistasis` allow only `scale='add'` (no `in_regime`), `tfs-summarize-fit` maps simulated truth to the gauge (`hill_relative.x_scale_truth`/`x_scale_growth_truth`, activity from `*_sim_parameters.csv`), `calibration_grid` labels those rows `theta_regime = X`. `prediction.copy_orchestrator` always keeps wt in a genotype subset (its K/n set the gauge) and `predict` drops it again. `tfs-prefit-calibration` refuses it (growth-only). **For a screen the route is settled: the two-stage fit** (`tfs-fit-two-stage`, documented in `docs/source/screen-analysis.rst`; `planning/two-stage-fit.md`): a single full-library MAP compresses X about 1.45-fold through the doubles' pull on m, so k and m come from a stage 1 of wt, spikes and singles. The rest of this paragraph is the history of the single-fit comparison, kept for the reasons behind it. Growth alone constrains mostly `m·(X_g - X_wt)`, so the posterior is a curved ridge along `m·X` that only wt's absolute growth change (via the tube totals) pins. Runs 2 and 3 of `planning/studies/relative-fit/` (2026-09-28) found the mean-field component guide sliding along it (smaller |m|, stretched X) while the low-rank guide (`--guide_type auto_low_rank_multivariate_normal`) recovered X, and MAP + Laplace blowing up in 5 of 6 runs. Both comparisons ran with bugs fixed on 2026-09-29: every component-guide hyperparameter scale frozen at its 1e-4 bound (2026-09-27 16:48 to 2026-09-29; the low-rank autoguide was unaffected) and the flat 1e-3 Laplace eigenvalue floor (the blowups); the learned log(hill_n) spread was also free then. The svi-overconfidence study's relative arm, in the same window on another library, found the two guides about equal (X 95% coverage 0.68 vs 0.74, RMSE 0.071 vs 0.072). Every guide collapses k and m. The post-fix rerun (`planning/studies/relative-fit/grid_v4.yaml`, run 4, 2026-10-01, 30 runs) confirmed the guide verdict: the component guide is biased even with the fixes (X RMSE 0.29-0.30, 95% coverage 0.06 Poisson / 0.38 realistic), and the low-rank guide recovers X (RMSE 0.07 / 0.12, coverage 0.81 / 0.76); rank 20 matches numpyro's default rank. MAP + floored Laplace now works and covers X and k/m best, but with the widest intervals and the noisiest point. Two-stage is the best calibrated under Poisson noise (0.91) and no better than low-rank under realistic noise (0.78). Every guide collapses k and m. So: the low-rank guide, rank 20, expecting X intervals to undercover somewhat and k/m intervals to be too narrow. On a full library the low-rank guide slid along the ridge (dev data, 3.3e5 nats worse than the MAP), and the MAP plus the arrowhead Laplace (below) covered best on run 4 (X 0.97 / 0.94, k/m about the full Laplace's), so that is the full-library route. Validation grid: `planning/studies/relative-fit/`.
+
+### Structural inputs (`thermo_data`, `thermo_options`)
+
+`theta/thermo/io.py` reads `thermo_data` for the PddG (ΔΔG CSV) and PnnC (LigandMPNN HDF5) models. `load_thermo_data(path, mut_labels, pair_labels, theta_name, thermo_options)` is the one entry point (orchestrator growth and binding paths, `sim_data_class.build_sim_data`): a `.PddG` component reads the CSV keeping only its `STRUCTURE_NAMES` (via `required_struct_names`, from the registry) in that order, a `.PnnC` component reads the HDF5 (structures in its `structure_names` order; the C12 PnnC permutes them into `STRUCTURE_NAMES`, the C4 PnnC requires that order), and with no or another component the extension decides (`.csv` keeps every column in file order). Whatever the file order, everything per structure downstream (`theta_ddG`, `theta_ddG_prior_mean`, `theta_ddG_sigma`, the `ddG_*` truth and extracted tables) is in `STRUCTURE_NAMES` order; see **Per-structure ΔΔG**. `thermo_options` (`normalize_thermo_options`; `components.thermo_options` in a fit config, `tfs-configure-model --thermo_ddG_units/--thermo_temperature_K/--thermo_column_map/--thermo_residue_offset/--thermo_max_missing_fraction`; a top-level `thermo_options` block in a simulate YAML, in `SIMULATE_KNOWN_KEYS`; the simulate-only `ddG_truth_sd` is a separate top-level key, see **Per-structure ΔΔG**): `ddG_units` `kT` (default, values used as-is) / `kcal/mol` / `kJ/mol`, divided by RT once at load at `temperature_K` (298.15; 1 kcal/mol = 1.6878 kT); `column_map` `{model_name: csv_column}` or `"HD=HO,..."` (`mut` mappable too); `residue_offset` added to the file's residue numbers (CSV labels and HDF5 `residue_nums`); `max_missing_fraction` (0.1). None/absent keeps the pre-2026-10-10 reading (`test_default_ddG_units_leave_the_draw_unchanged`: default and `kT` give the same draw). Sign convention: ΔΔG_X adds to state X's free energy (positive destabilises X), Δln K_{A→B} = ΔΔG_A − ΔΔG_B, ΔΔG_U = 0. CSV: mutation column `mut`/`Mutation`/`mutation`, `Unnamed: *` index columns dropped, extra columns ignored with a printed note, missing required columns listed in the error, non-single-mutation labels skipped; prints `ΔΔG for n/M library mutations`; raises on zero coverage, on a file wt residue that disagrees with the library's at the same (offset) position, on two wt residues at one file position, on duplicate labels; zero-fills absent/blank mutations and warns (list truncated at 10) above `max_missing_fraction`. The wt/zero-coverage errors search offsets ±50 and suggest the `residue_offset` that lines the wt residues up. HDF5: any library residue absent from a structure is an error (no neutral feature value), with coverage counts; an optional per-group `wt_aa` dataset (now written by `scripts/generate_struct_ensemble.py` from the PDB) is checked like the CSV's wt. Until 2026-10-10 the loader had none of this: kcal/mol went straight into ln K, absent mutations (e.g. a 0-based file against a 1-based library) were silently 0, the PddG `_get_struct_perm` demanded exact column-set equality and `mut` exactly. Now the four PddG `_get_struct_perm` accept extra structures and use `len(STRUCTURE_NAMES)` instead of `data.num_struct`; PnnC still requires its exact names. The one real-ish file, `notebooks/other-data/ddG_ensemble_clean_with_effector.csv` (main checkout, untracked): `Unnamed: 0`, `Mutation`, `unfolded` (all 0) and 12 states, 0-based numbering (V0 = LacI residue 1; a near-copy of chain A sits at 1000-1338, within 0.12, and goes unused), units not recorded; it serves the O2_C12 PddG models with `residue_offset: 1` and the O2_C4 ones only with `column_map: {HD: HO}` as well. Its H/HO, L/LO, HE2/HOE2, LE2/LOE2 columns are identical, so it carries no operator-binding information (every d_ln_K_h_o/l_o prior mean is 0).
 
 ### Adding a New Model Component
 
@@ -141,9 +169,46 @@ The hierarchical Bayesian inference engine. Key files:
 3. Register it in `tfmodel/generative/registry.py` under the appropriate category key
 4. Add a test in `tests/tfscreen/tfmodel/components/<category>/`
 5. For a new `condition_growth` component: make its per-condition prior loc/scale fields accept scalar-or-array (broadcast-then-index by the condition plate) and implement `get_scale_bounds()` so the pre-fit can pin its per-condition baselines — see **Per-condition growth priors** above.
+6. Per-genotype latents must be **mini-batch safe**: sample them in a library-sized plate (`pyro.plate(f"{name}_genotype_plate", data.num_genotype)`), then slice with `[..., data.batch_idx]` whenever the value is library-sized (a smaller value is an already-sliced substitution from the posterior forward pass). Never size a latent's plate with `data.batch_size`, and do not wrap latent priors/guides in `scale_vector` (only the likelihood is scaled). A batch-sized latent silently aliases genotypes onto batch positions under every numpyro autoguide, including `AutoDelta` (MAP, pre-MAP, `tfs-prefit-calibration`) — even at full batch, because the full-batch index is binding-first and reshuffled each step. Slice **unconditionally**: never `if data.batch_size < data.num_genotype`, and never index a library-ordered array by the batch-relative `geno_theta_idx` alone (translate through `data.batch_idx[data.geno_theta_idx]`, the `hill_geno` pattern). At full batch the index is a permutation, not the identity, so skipping the slice pairs batch positions with the wrong genotypes while every shape stays right. `inference/batch_safety.py` checks both failure modes: `find_batch_dependent_latents` traces at two batch sizes (shape; `RunInference.run_optimization` runs it before fitting with any autoguide and refuses an unsafe model), and `find_batch_order_mismatches` traces two orderings of the full batch with the latents held fixed and checks that `growth_pred`/`theta_growth_pred`/`binding_pred`/`theta_binding_pred` permute with the index (order). `tests/tfscreen/tfmodel/inference/test_batch_safety.py` enforces both registry-wide. Known exception: `noise/beta`'s `{name}_dist` (its latent is the noisy θ itself).
+
+7. A new `dk_geno` or `activity` component must accept `return_population=False` in `define_model` and `guide` and, when True, return `(tensor, population)`: the library-ordered per-genotype values from the same draw, or `None` if the latents arrived batch-sized. Build both with `generative/components/_population.py::per_genotype` (default path = the old slice-then-compute, so no extra cost when not requested). The congression mixture looks co-resident genotypes up in these by library index; `tests/tfscreen/tfmodel/generative/components/test_component_population.py` checks every registered variant.
+
+8. Horseshoe priors use `components/_horseshoe.py`: its `HalfCauchy` (log density via `hypot`, finite far in the tail) and `regularized_scale(lam, tau, c2)` (the slab-regularized `sqrt(c2 lam^2 / (c2 + tau^2 lam^2))` without squaring `lam`). Never write `lam ** 2` there: a mean-field guide can widen a local scale until a draw near 1e20 squares to inf, and the whole fit goes NaN (count-likelihood grid v2, run 0007, hill_mut epistasis on low-read doubles).
+
+9. Guide sites should follow the `{site}_loc(s)` / `{site}_scale(s)` naming convention (a Normal or LogNormal guide whose location/scale params are named after the site). The MAP warm-up hands its solution to the component guide through it (`inference/initialization.py`); `tests/tfscreen/tfmodel/inference/test_initialization.py::test_component_guides_follow_loc_scale_convention` fails for a new variant that does not, unless it is added to `_KNOWN_UNMAPPED` with a reason.
 
 (This applies to `components/` categories. `generative/observe/` is not a `<category>/<variant>` registry entry — see above.)
 
+### Convergence and step size
+
+`RunInference.run_optimization` is the one optimization loop (SVI, MAP, the SVI pre-MAP warm-up, `tfs-prefit-calibration`). It runs in tumbling **windows** of `convergence_window_steps` optimizer steps (default 2000; raised to ≥ `MIN_WINDOW_EPOCHS` = 10 epochs so mini-batched genotypes are each visited several times; split into `PARAM_BLOCKS` = 8 blocks). `inference/convergence.py` holds the decisions as host-side numpy, testable on synthetic traces (`tests/tfscreen/tfmodel/inference/test_convergence.py`, including a recorded real-data and a simulated trace in `loss_traces/`).
+
+- **Loss test** (`loss_trend`): OLS line through the medians of 20 blocks of the window's per-step losses → drop per window ± SE. *Improving* iff `drop > z·SE` and `drop > loss_rtol·|loss|`. Judged against the loss's own noise: never against the start-up transient (the old rule's `|Δ|/(loss_start − loss_best)` stopped runs ~500× above their final loss and never stopped after a resume) nor against `|loss|` (the rule before that, which never fired on a noisy ELBO). A rising or oscillating loss is *not* improving.
+- **Exact loss (MAP)**: `run_optimization(exact_loss=None)` (auto: on for `AutoDelta`, so MAP, every staged-MAP stage, the SVI pre-MAP and the prefit) computes `RunInference.full_batch_loss` at each window end: `-log_density` over genotype chunks of the full batch (chunk = batch size) with the mini-batch scale removed, the chunk-repeated part (library-sized latents' priors, unsliced binding data) measured as `f(a) + f(b) - f(a, b)` on one-genotype batches and counted once (`test_full_batch_loss.py` checks chunk invariance with binding data). `ConvergenceMonitor.end_window(..., exact_loss=)` then judges the window by a line through the last `patience + 1` exact losses (drop > `z`·SE and > `loss_rtol`·|loss|; SE = the optimizer's jitter), skips the skew check and the separate pooled check, and records `loss_exact` in the convergence CSV. Why: on the dev data (218k genotypes, batch 4096) the mini-batch trend SE was ~5e4 nats per window, so a 1e5-per-window descent read as a stall and every MAP was cut to 1e-6 by ~40k steps while still descending (`planning/studies/staged-map/`). SVI keeps the mini-batch test.
+- **Loss skew** (`loss_skew`, reported by `loss_trend`): `(mean − median) / (1.4826·MAD)` of the window's losses about the trend line. Medians hide rare huge penalties that dominate the mean (the objective); benign ELBO noise, skewed or heavy-tailed, stays < ~1 (all recorded clean traces ≤ 0.4), rare penalties give 10–40. At the floor a window with skew > `MAX_LOSS_SKEW` (3) is not a plateau; skew never forces a cut (a smaller step size does not remove penalties). Blind once penalized steps are the majority (median and MAD follow them) — then the loss level shows it instead.
+- **Parameter test** (`param_drift`): each tracked param's block means (accumulated on the device inside the `lax.scan` carry — no per-step host transfer) → per-element trend over the window, in **normalizer** units (`RunInference._param_normalizer_specs`): paired guide scale for a location (`_loc`/`_locs`/`_auto_loc` ↔ `_scale`/`_scales`/`_auto_scale`; the posterior SD), derived SD for AutoContinuous `auto_loc`, prior SD for an AutoDelta location on a real site, and 1 for positive/bounded params (log/logit units, so relative). Excess = `|drift| − z·SE − floor`, where `floor = MIN_STEP_COHERENCE (0.01) · step_size · window` is the optimizer's resolution (Adam moves ~step size per step whatever the gradient; without the floor, jitter of a location whose guide scale collapsed below the step size reads as hundreds of SDs of drift). The posterior SD is floored at `PRIOR_SD_FLOOR` (0.01) × the site's prior SD in the location's unconstrained units (`initialization.site_unconstrained_prior_sds`: exact SD for real sites, robust MC `IQR/1.349` of prior draws otherwise; `RunInference._posterior_sd_floors` maps component-guide locations via `{site}_loc(s)`, AutoNormal via `{site}_auto_loc`, and AutoContinuous `auto_loc` via numpyro's `_ravel_dict` latent order): the mean-field component guide collapses hierarchical scales' guide SDs to ~4e-4 (hill_mut `theta_sigma_d_*_scale`, `theta_epi_tau_scale`), and in those units a negligible creep never settles. Summarized per array (max if ≤ 100 elements, else 99th percentile); *moving* iff any summary > `param_tolerance` (0.05). `scale_tril`/`cov_factor` are not tracked.
+- **Decisions** (`ConvergenceMonitor.end_window`): above the floor step size, `patience` consecutive windows without loss improvement → cut (`step_size_cut`, default 0.1), unless the stalls pooled are a descent: before a cut or a stop, `pooled_loss_trend` fits one line through the block medians of all `patience` stalled windows (same `z`/`loss_rtol`), and a significant drop resets the stall count (`pooled_loss_t` in the convergence CSV; the stalled windows' medians are kept in checkpoints). A single window misses a slow, noisy descent: on the count-likelihood v2 grid, 20 of 23 first cuts came during a descent that pooled to t = 4-14, one (run 0012) into a wrong optimum (`loss_traces/sim_svi_counts_noisy_descent_run0012.npy`). **Parameters and skew never block a cut**: some directions never settle (a horseshoe local scale's MAP drifts to 0 forever at ~no loss change) and holding the step size high for them only wastes time. At `final_step_size`, `patience` consecutive windows with no loss improvement, no parameter movement and no loss skew → converged; a still-moving parameter or a skewed loss keeps the run going and is named in the log, so a slide, degenerate direction or hidden penalty is reported, never called converged. `max_num_epochs` is only a cap. A hierarchical MAP generally has no finite optimum (hyper-scale funnel), so MAP/pre-MAP runs often end at the cap — the honest outcome.
+- **Optimizer** (`run_inference.adam_optimizer`): plain numpyro `Adam` by default; `adam_clip_norm` (every CLI and `setup_svi`) opts into `ClippedAdam`. **Do not re-enable clipping by default.** `ClippedAdam` clips each gradient *element* to ±`clip_norm`; these losses have gradients of 1e3–1e6 per element, so every element is clipped every step and Adam follows each draw's gradient *sign* — its fixed point is where the per-draw signs balance, not where the expected gradient is zero. With a heavy-tailed ELBO (step-like binding curve M42C/K84N, `hill_n` 24.6, measured with SD 0.001; a guide draw crossing it costs ~2.4e5 nats) that held 5–90% of draws in violation on every congression-calibration run (2026-09-25; the "blow-ups" were block medians flipping once the rate passed 50%, and step-size cuts cannot move a sign-balance point). Unclipped, the same fits drop to ~0 violations and converge (`planning/studies/congression-calibration/diagnosis/`). The two optimizers share their state, so old checkpoints resume unclipped.
+- **Step-size cuts** swap `svi.optim` for a new optimizer of the same kind (clipped or not, read off the current `svi.optim`) at the smaller constant step (the optimizer state is step-size independent) and re-trace the scan (a fresh closure, so no stale compiled step size). `setup_svi` records `_step_size`/`_adam_clip_norm` (the latter written to checkpoints); a step-size *schedule* is still accepted but refuses cuts. The old `optax.exponential_decay` over `max_num_epochs` is gone (it collapsed the pre-MAP's step size within its 1000 epochs and tied resumes to `max_num_epochs`).
+- **Runaway loss** (`RUNAWAY_LOSS_FACTOR` = 1e3): a window whose loss falls below -1000 times the magnitude of the run's first block median ends the run as `diverged` (not converged; `monitor.diverged`, kept in checkpoints with `reference_loss`). It means the objective is unbounded: a hierarchical density singularity (the old centered `hierarchical_factored` tube offset), or float32 error in a dense guide's own log density (`auto_multivariate_normal` on the small SVI-overconfidence library, where `log q` of its own draws reached -4e22 through an ill-conditioned `scale_tril`).
+- **Records**: `{out_prefix}_convergence.csv` (one row per window: loss (median), drop ± SE, t, `loss_mean`/`loss_skew`/`loss_skewed`, worst param and its excess/drift, step size, plateau count, decision; a resume rewrites an older file under the current columns, `_migrate_convergence_columns`); `{out_prefix}_losses.txt` is `epoch,loss,step,step_size` (loss = block median). Checkpoints carry `step_size` and `convergence` (the monitor's `state_dict`); a resume continues at that step size and plateau count (a checkpoint without them resumes at `adam_step_size`). A run started with `svi_state=None` resets `_current_step` to 0 (the pre-MAP no longer shifts SVI's epoch numbering).
+
+**Starting points** (`inference/initialization.py`). Configured guesses are keyed mostly by *site* name and partly by component-guide param name; AutoDelta and the component guide both ignored site-keyed guesses (AutoDelta started at prior medians; the MAP → component-guide hand-off matched no names, so the pre-MAP was discarded). Now `RunInference.site_values` turns any mix of guesses / `{site}_auto_loc` MAP params / guide locations into constrained site values; MAP (`_run_map`, prefit, pre-MAP) starts there through `setup_svi(init_values=...)` (fallback `init_to_median` for AutoDelta); `RunInference.component_guide_start` maps site values onto the component guide via the `{site}_loc(s)`/`{site}_scale(s)` convention (LogNormal locations take the log; each match verified against the traced distribution) and caps every mapped scale at `guide_init_scale` (`fit_model_cli.DEFAULT_GUIDE_INIT_SCALE`, 1e-4; also the autoguides' `init_scale` for a fresh fit). A scale param with a lower-bounded constraint (the hyperparameter scales' `greater_than(1e-4)`) starts at least `guide_init_scale` above the bound: starting on it is `-inf` unconstrained, and the param never moves (every hyperparameter scale stayed frozen at 1e-4 from 2026-09-27 to 2026-09-29). The scale is one number in every site's unconstrained units, so it must be small: at the old 0.1 (numpyro's autoguide default) an SD of 0.1 on a growth rate per minute is 17 ln units over a selection, SVI started ~1000x above the pre-MAP's loss and re-descended into other optima (mirror mode, halved m, the library shifted against wt; relative-fit grid, 2026-09-27). SVI widens the scales itself (about e-fold per 1000 steps at step size 1e-3). `init_param_jitter` defaults to 0 for the same reason (it is multiplicative, so 0.1 moves an ln_cfu0 location near 15 by ~1.5 ln units). `test_default_guide_start_stays_near_its_point` checks the start on a count model; a fit's first logged SVI loss should sit near its pre-MAP loss. `tests/tfscreen/tfmodel/inference/test_initialization.py` checks the convention registry-wide; exceptions: horseshoe_geno (`activity_*_offset_*` param names) and prior-guided noise latents. A new component guide should follow the convention. `read_configuration` keeps only guess rows whose names are in the orchestrator's `init_params`; it silently drops any others, such as a bare `condition_growth_k` or `sample_offset_offset` row. To start at a point the guesses cannot name, use `tfs-fit-model --init_from <params.npz>`: an earlier MAP's `{site}_auto_loc` arrays, which `site_values` ranks first, plus its model-side params (every other array; see **Model-side params**). The dev-data level-offset MAP is the case: Adam moves every parameter about one step size per step, so in the first window it carried the tube offsets and k/m (k prior SD 0.002 per minute) by whole units into a ±2.8 offset mode. The step-size cuts then froze it there. Starting from the no-offset MAP plus small per-tube offsets was 1.2e5 nats better (`planning/dev-data/real_fit`).
+
+**Staged MAP** (`inference/staged_map.py`, pipeline plan step 3). `tfs-fit-model` builds that start itself: a fresh MAP (no `--checkpoint_file`, no `--init_from`) of a model with `sample_offset: level` runs (1) the MAP with `sample_offset_offset` held at 0 (and a learned `sample_offset_sigma` held at `sigma_prior_scale`: with every offset at 0 its MAP is 0 and the density unbounded), files `{out_prefix}_stage1_*`; (2) the offsets alone, every other site held at stage 1's MAP (`{out_prefix}_stage2_*`; the tubes do not couple given the rest, so this is `small_offsets.py`); (3) the joint MAP from stage 1 + stage 2's offsets at `--staged_step_size` 1e-4, writing `{out_prefix}_*`. A stage is an ordinary `_run_map` on `HeldModel(orchestrator, held)`, whose `jax_model` is `numpyro.handlers.condition` on the held constrained values (as jax arrays: the model indexes per-genotype values with traced batch indices); the AutoDelta then has no parameters for them. Fit a `HeldModel` with autoguides only (its `jax_model_guide` is the wrapped model's, read by `site_values` for names). A stage whose `_params.npz` exists is reused on rerun; one with only a checkpoint resumes. `--stage_offsets auto` (default) stages only that case; SVI's pre-MAP is not staged. Validation on the dev data: `planning/studies/staged-map/`. **The offset mode is the objective's better optimum, not only a trap** (run 2, 2026-10-05, every MAP converged on the exact loss): the reference fit run to convergence moved into it and beat the staged MAP by 7.9e4 nats, with offsets that follow IPTG in the selection conditions (kanR selection +2 to -4 from 0 to 1 mM, pheS the reverse), constant over time, and unphysical k/m. The staged MAP keeps the physical basin only by stopping in a local optimum. Working hypothesis (IPTG is added at `-t_pre`, so markers are expressed before selection): a selection-onset transient that `instant` cannot express. Filed as `planning/offset-mode-growth-transition.md`, taken up after pipeline steps 6 and 7.
+
+**MAP values: constrained or not.** AutoDelta's `svi.get_params(state)` (what `run_optimization` returns and `write_params` saves) is *constrained*; `svi.optim.get_params(state.optim_state)` (what `tfs-sample-posterior`, `tfs-extract-params` and `checkpoint_io` read from checkpoints) is *unconstrained*. `compute_hessian_sigmas` takes the former, `get_laplace_posteriors`/`get_map_posteriors`/`_map_params_to_constrained` the latter. Mixing them up silently evaluates positive sites at `exp(value)` (the pre-fit did until 2026-09-27).
+
+**Model-side params** (`inference/model_params.py`). A few components register learned point estimates with `pyro.param` inside the *model*, not only in a guide: the thermo PnnC structure-MLP weights (`theta/thermo/nn.py`, `theta_nn_{struct}_{W1,b1,W2,b2}`). Until 2026-10-11 the PddG/PnnC per-structure ddG scale was one too (`theta_ddG_sigma_s`); it is now the sample site `theta_ddG_sigma` (see **Per-structure ΔΔG**), so it is an ordinary MAP site, in the Laplace Hessian and in the guides. They are empirical-Bayes estimates that live in the param store next to the guide's params, under their bare names (constrained in `svi.get_params` and `_params.npz`, unconstrained in a checkpoint's optimizer state). Anything that runs the model after a fit must hand them over, or every one falls back to its init value (NN = 0). Until 2026-10-10 none did: a PnnC posterior's prior mean was the untrained NN's 0 instead of the MAP's (off by up to 0.99 on a C4_U0 PnnC fit). Now `RunInference.model_params(params, unconstrained=...)` picks them out of any param dict (by tracing the model's param sites), and the post-fit entry points (`get_map_posteriors`, `get_laplace_posteriors`, `get_posteriors`, `compute_hessian_sigmas`) set `RunInference.fixed_model_params` and run `hold_model_params(model, params)` (substitute plus `block`, so no outer `substitute` or param store can replace them). The Laplace holds them fixed at the MAP: they are not in the Hessian (`num_params` counts `_auto_loc` only). Posterior files carry them in the `model_params` HDF5 group (absent for models without such params; iterate samples with `model_params.sample_keys`); `analysis/prediction.predict` (`tfs-predict-growth`) reads that group, or the bare names of a raw MAP `_params.npz`. `tfs-extract-params` writes them to `{out_prefix}_model_params.csv` (`parameter`, `index`, `value`). Hand-offs carry them too: `--init_from` reads every non-`_auto_loc` array of the npz, the pre-MAP passes them to SVI (component guide and autoguides alike, as `init_params`), the staged MAP holds them at stage 1's values in stage 2 (`HeldModel(..., params=)`) and starts stage 3 from them, and the window's exact loss (`full_batch_loss`) evaluates the model at their current values. Not covered: NUTS fits the model with them at their init values (no optimizer), and its pre-MAP's values are not handed to it.
+
+**Subset predictions keep the fitted mutation index** (`analysis/prediction.py`). `predict(genotypes=...)` rebuilds the model on the subset (`copy_orchestrator`), and `tfs-predict-growth` always does this in genotype chunks (auto-sized batch, `--genotypes_file`). Per-mutation and per-pair sites (`*_mutation_plate`, `*_pair_plate` and the like: hill_mut, activity `hierarchical_mut`/`horseshoe_mut`, all 12 `thermo.*` models, and the structure data built from the labels) are indexed by `mut_labels`/`pair_labels`. Until 2026-10-10 the rebuilt model derived those labels from the subset alone, so the fitted arrays landed on the wrong mutations and pairs; the predict loop only remaps plates named after a growth-tensor dimension. hill_mut chunked predictions were off by up to 1.6 ln_cfu on a real run, and PddG/PnnC subsets crashed on a shape mismatch. Now `copy_orchestrator` passes the fit's labels as `ModelOrchestrator(mut_labels=, pair_labels=)` (not a setting, never written to a config), `build_mut_geno_matrix(..., mut_labels=, pair_labels=)` builds the subset's genotype columns in that fixed index space (absent mutations get zero rows; a mutation or pair outside it is a `ValueError`), and `predict` traces the subset model and refuses any posterior site whose shape does not match it, so a plate it cannot remap fails loudly instead of misindexing. `tests/tfscreen/tfmodel/analysis/test_prediction_mutation_subsets.py` checks chunked and subset predictions against the full one.
+
+**Block Laplace.** `get_laplace_posteriors(block_genotypes=True)` (`tfs-sample-posterior --laplace blocks`) holds the shared parameters at the MAP and gives each genotype the Laplace of its own parameters. Given the shared parameters the genotypes do not couple (checked: every cross-genotype Hessian entry was 0 on a relative-fit run, wt included, since the X gauge fixes only wt's own baselines), so `block_laplace_factors` gets every B x B block from B Hessian-vector products whose probe is 1 on one slot of every genotype, over genotype chunks, and refuses a model whose blocks leak. Each block is floored like the full Laplace. Both Laplace paths take the Hessian on `_unscaled_batch`, the full batch with the mini-batch scale removed: `scale_vector` carries `num_genotype / batch_size` even at full batch. On relative-fit run 4 the block Laplace covered X 0.93 / 0.84 (Poisson / realistic) and k/m 0. The MAP point's X is poor at low depth under realistic noise (RMSE 1.3 at <= 100 reads, against 0.22 for the low-rank guide's point; the MAP's hyperscales run away, so nothing shrinks it), but the intervals are wide enough to cover there.
+
+**Arrowhead Laplace.** `block_shared=True` (`--laplace arrowhead`; what `--laplace auto` picks above 20,000 MAP parameters) keeps the shared parameters' uncertainty: `_genotype_hessian_parts` adds one probe per shared element (its response on a chunk's genotype rows is their coupling; on the shared rows it is summed over chunks, less `(chunks - 1)` times the chunk-invariant part, measured as `H(a) + H(b) - H(a + b)` on two one-genotype batches since every chunk's potential carries the priors and any unsliced binding data). `arrowhead_laplace_factors` then draws the shared parameters from the Schur complement and each genotype from its conditional, the full Laplace (`test_arrowhead_laplace_matches_full_laplace`). Two departures for MAPs short of an optimum, both needed: a floored direction of a genotype block carries no coupling (a short dev-data MAP floored 167 of 308 blocks, and the kept couplings subtracted 6e10 from m's curvature of 1e10), and a negative direction of the Schur complement is held at the MAP, named in the log and recorded on `RunInference.held_shared_directions` (`tfs-sample-posterior` writes it to `{out_prefix}_held_directions.csv`) (floored at the prior, the k/dk_geno slide gave k its prior SD and X widths of 1-3 on run 4). On run 4 it covered X 0.97 / 0.94 and k/m 0.92 / 1.0 (Poisson) and 0.50 / 0.42 (realistic), about the full Laplace's.
+
+**Laplace floor.** `get_laplace_posteriors` floors each Hessian eigenvalue at the prior's curvature along its eigenvector (`laplace_eigenvalue_floor`; prior SDs from `site_unconstrained_prior_sds` on one trace with every latent at its MAP, `trace_model_sites(..., unconstrained=)`; `LAPLACE_MIN_EIGENVALUE` = 1e-3 where no prior SD is usable, named by site in a warning), so no direction of the Laplace is wider than the prior. The full Laplace computes the prior SDs before the Hessian. If they cannot be computed it is an error (`RuntimeError`), unless `allow_flat_floor=True` (`tfs-sample-posterior --allow_flat_laplace_floor`), which warns and floors everything at 1e-3. Until 2026-10-11 the supports came from a first trace with every site drawn from the prior, and the failure was a one-line print: on the PddG pilot fits the held `sigma_s` widened the ddG prior, the draw (ln K −38 to 61) gave NaN θ and ln_cfu, the count likelihood refused it (`CountNegativeBinomial ... invalid log_mu`), and all four runs fell back to the flat floor, turning 16-38 negative eigenvalues into directions with SD ~30 (wt ln K_op 95% 14.9-24.6 against PK's 19.4-20.4, medians moved up to 28 kT). **Structure traces** (`initialization.structure_trace`: seeded trace with numpyro argument validation off) are used wherever a trace only reads site shapes, supports or distributions: `trace_model_sites`, `model_param_constraints`, the site-transform traces in `run_inference`, and the two traces in `analysis/prediction.predict` (the same refusal stopped `tfs-summarize-fit`'s trajectory plots). Validation never runs under jit anyway; eagerly it rejects a prior draw at an observation nothing evaluates. The old flat 1e-3 floor turned a MAP's negative eigenvalues (a hierarchical MAP stopped at its epoch cap always has a few) into variances of 1000 that leaked into whatever they touched: on the two-stage grid the draws of the growth slope m spread 50x (seed 8).
+
 ### Per-condition growth priors
 
 The `condition_growth` components (`linear`/`power`/`saturation`) carry a per-condition **additive baseline** (`k` for linear/power, `min` for saturation) that is only jointly identified with the shared per-genotype `dk_geno`: the growth likelihood `g = k_condition + dk_geno + A·m·θ` is invariant to `k += C, dk_geno −= C`. With only weak/rare anchors (wt's pinned `dk_geno=0`, `base_growth`) the whole system slides by a global constant `C`, inflating all condition baselines and `k_ref` and making genotypes with constrained `dk_geno` (notably wt) badly mis-fit their `ln_cfu`. The fix is to pin each condition baseline with a per-condition prior — a prior on the 4 baseline params acts at full strength, unlike the abundance-diluted genotype anchors.
@@ -151,12 +216,12 @@ The `condition_growth` components (`linear`/`power`/`saturation`) carry a per-co
 Mechanism, spanning three files:
 
 - **Components** (`generative/components/growth/*.py`): `ModelPriors` loc/scale fields accept a scalar (broadcast, the default/back-compat) or a length-`num_condition_rep` array; `define_model`/`guide` broadcast-then-index by the condition plate. Each component declares `get_scale_bounds() → {suffix: {floor, ceiling, scale_field}}`, giving the pre-fit per-parameter scale floors (tight for the baseline term, looser for e.g. `power`'s log-exponent `n`).
-- **CSV (de)serialization** (`configuration_io.py`): the priors CSV supports per-condition **indexed rows** (`flat_index` + `condition_rep`/`replicate` label columns) alongside scalar rows. On load, indexed rows are **name-joined** to the model's `map_condition_rep` order (`_read_priors_flat`/`_assemble_condition_array`) and **fail fast** on an unknown or missing condition. A fresh `tfs-configure-model` still writes scalar rows (2-column CSV, legacy path).
+- **CSV (de)serialization** (`configuration_io.py`): the priors CSV supports per-condition **indexed rows** (`flat_index` + `condition_rep`/`replicate` label columns) alongside scalar rows. On load, indexed rows are **name-joined** to the model's `map_condition_rep` order (`_read_priors_flat`/`_assemble_condition_array`) and **fail fast** on an unknown or missing condition. A fresh `tfs-configure-model` still writes scalar rows (2-column CSV, legacy path). A priors row whose name is not a prior of the configured model is an error that lists every such row and says to regenerate the file with `tfs-configure-model` (`_update_dataclass` → `_check_unknown_keys`, all components). Known names are every dataclass field (including one left at None and the static tuples, which are kept, not overwritten) and one-level `{field}.<sub>` entries of a dict field (`pinned`). Until 2026-10-10 such rows were silently ignored, so a priors file written for an older model loaded and its rows were read with the meanings they have now. Guesses are not checked this way: `read_configuration` keeps only the guess rows the model's `init_params` names, on purpose (see **Starting points**). `test_configuration_io_helpers.py::TestUnknownPriorRows` also checks every registered component's own rows round-trip.
 - **Pre-fit** (`prefit_calibration_cli.py`): after the MAP calibration, `_build_csv_updates` writes each `condition_growth` site's per-condition MAP **loc** array into the priors CSV (the baseline pin), and `_build_hessian_scale_updates` writes a tight **scalar** scale (floored via `get_scale_bounds()`). `_apply_priors_updates` expands a scalar prior row into per-condition indexed rows tagged with `condition_rep`. `growth_transition` sites keep warm-start-only behavior.
 
 Net flow: `configure` (scalar) → `prefit` (per-condition `k_loc` indexed rows + tight scalar `k_scale`) → `fit` (loads per-condition priors that hold the baselines, closing the slide). This is the primary mechanism; the `base_growth_data`/`k_ref` anchor (below) is complementary but insufficient alone because `k_ref` is itself free to slide.
 
-**Hard clamp on `m` (`tfs-prefit-calibration --pin_m`).** A soft Normal prior on the slope `m` — however tight — is only a KL penalty in SVI, and the growth likelihood over many observations can override it (empirically m walked ~5σ off even a 0.0005-scale pin). `linear`'s `ModelPriors.m_pinned` (bool, static) makes `m` a `deterministic` site clamped to its per-condition `m_loc` instead of a sampled site (guide drops the `m` variational params); `--pin_m` sets `condition_growth.m_pinned=1` in the priors CSV. `m` is safe to clamp because its calibration MAP loc is unbiased (dk_geno is uncorrelated with θ). **`k` is intentionally never clamped this way**: it carries real per-experiment tube-noise variance (`tube_noise_sigma`; mirrored by `k_scale_floor`) and sits in the additive k/dk_geno slide, so it keeps a floored soft prior — pin its loc, not its scale.
+**Hard clamp on `m` (`tfs-prefit-calibration --pin_m`).** A soft Normal prior on the slope `m` — however tight — is only a KL penalty in SVI, and the growth likelihood over many observations can override it (empirically m walked ~5σ off even a 0.0005-scale pin). `linear`'s `ModelPriors.m_pinned` (bool, static) makes `m` a `deterministic` site clamped to its per-condition `m_loc` instead of a sampled site (guide drops the `m` variational params); `--pin_m` sets `condition_growth.m_pinned=1` in the priors CSV. `m` is safe to clamp because its calibration MAP loc is unbiased (dk_geno is uncorrelated with θ). **`k` is not clamped this way in production fits**: it carries real per-experiment tube-noise variance (`tube_noise_sigma`; mirrored by `k_scale_floor`) and sits in the additive k/dk_geno slide, so it keeps a floored soft prior — pin its loc, not its scale. Both flags also take one value per condition (a tuple; indexed 0/1 rows in the priors CSV, from `--growth_priors` columns `k_pinned`/`m_pinned`): the pinned conditions are clamped, the free ones are sampled as `{name}_{k,m}_free`, and `{name}_{k,m}` is a deterministic site over every condition (`configuration_io._static_flag` turns the rows into a hashable tuple; uniform flags collapse to the bool, so old configs trace unchanged). `planning/studies/m-bias-factorial/` pins kanR's k and m at the no-doubles fit while pheS's are fitted. `ModelPriors.k_pinned` exists for conditional fits at a fixed draw of (k, m): the two-stage fit (`planning/studies/svi-overconfidence/two_stage.py`) refits everything else by SVI at Laplace draws of k and m and pools the conditional posteriors, which carries the k/m uncertainty the guides collapse into each genotype's interval.
 
 ### Key Abstractions
 
@@ -172,15 +237,19 @@ See **YAML Standards** below for the full conventions. The tfmodel config (`tfs_
 
 `tfs-configure-model` takes `--library_config`, the library YAML that describes the screened library — **the same file handed to `tfs-process-fastq`**. It is required whenever `growth_df` is given (a binding-only model has no congression correction and no ln_cfu0 latents, so it refuses one). It replaced the old `--spiked` list, which could not express the central fact that the spiked controls are *also* encoded in the bulk sub-libraries: wt and each spiked single mutant arrive both as a monoclonal spike and as bulk library members, and they cannot be told apart by sequence.
 
-Keys read (via `LibraryManager`): `reading_frame`, `first_amplicon_residue`, `wt_seq`, `degen_sites`, `tiles`, `tile_combos`, `spiked_seqs`, plus `library_mixture`. Everything else (a full simulate config, say) is ignored.
+Keys read (via `LibraryManager`): `reading_frame`, `first_amplicon_residue`, `wt_seq`, `degen_sites`, `tiles`, `tile_combos`, `spiked_seqs`, plus `library_mixture` and the subsample keys (`library_genotypes`, `library_subsample`, `library_subsample_seed`, `library_subsample_depth`; see **Library subsample**). Everything else (a full simulate config, say) is ignored.
 
 Flow: `genetics.library_composition_table(library_config)` (`genetics/library_design.py`) →  one row per genotype with `is_wt`, `in_spiked_origin`, `pool_fraction` and `bulk_fraction` → written to `{out_prefix}_library.csv`. The **snapshot CSV is what `tfs-fit-model` reads**, never the YAML; this mirrors the priors/guesses CSV pattern and makes the table the place to override the design's numbers. `ModelOrchestrator(library_file=...)` derives today's `spiked_genotypes` from `in_spiked_origin` and exposes the full table as `orchestrator.library_df`. `spiked_genotypes` is still accepted directly (and is what `tfs-prefit-calibration` uses when it clears both keys for its calibration-subset model), but the two are mutually exclusive, and a config carrying `spiked_genotypes` is the legacy path.
 
 **Two checks, two different guarantees.** The *genetics* keys are attestable: they determine the genotype names, so `tfs-configure-model` fails fast if any genotype in `growth_df`/`presplit_df`/`base_growth_df` is absent from the library (`check_genotypes_in_library`; `__unknown__` excepted). A residue-numbering shift mismatches essentially every mutant and is caught immediately. `library_mixture` is **declared-only**: no upstream artifact ever saw it, so nothing can cross-check it. It is recorded verbatim in the config's `library` block, and that is the whole defense — the values should be the best available estimate of the realized pool, not just the intended design.
 
-A library-derived spiked genotype that has no growth data is **reported, not fatal** (spikes can drop out of a real dataset), unlike the hand-supplied `spiked_genotypes` list, which still raises.
+A library-derived spiked genotype that has no growth data is an **error** at configure time (`configure_model_cli.check_spikes_in_data`; the real-data fit's processing config named three spikes, one with the wrong codon, and nothing caught it), unless `--allow_missing_spikes`, which reports it (spikes can drop out of a real dataset). The orchestrator itself still only reports it.
+
+`pool_fraction`/`bulk_fraction` are computed and snapshotted. `bulk_fraction` is carried into the model as `GrowthData.bulk_fraction` (library-sized float per genotype, indexed by `batch_idx`; built by `ModelOrchestrator._build_bulk_fraction`: the table's value, `__unknown__` → 1.0, any other missing genotype raises; legacy `spiked_genotypes` → 0 spiked / 1 otherwise) and is the `f_g` of the `mixture` transformation: a genotype's congressed fraction is `f_g (1 - P(M = 1))`, with `M` ~ zero-truncated Poisson(λ) plasmids per transformant (a cell's abundance is shared among its plasmids, so abundance by co-resident count n is `P(M = n + 1)`; λ is the simulator's `transformation_poisson_lambda`). Purity (`bulk_fraction`) and the `ln_cfu0` prior class (`ln_cfu0_spiked_mask`, from `in_spiked_origin`) are separate fields; the old binary `congression_mask` is gone. The orchestrator also draws each genotype's fixed co-resident plasmid sets for the mixture — `GrowthData.coresident_idx`/`coresident_n`, via `_draw_coresident_sets`, from the bulk share `pool_fraction * bulk_fraction` of genotypes with growth data (legacy: uniform over non-spiked); `congression_sets` (default `[12, 3, 1]`; `tfs-configure-model --congression_sets`, mixture only) and `congression_seed` are orchestrator settings written to the config, so draws are reproducible. `congression_theta_rule` (default `homodimer`; also `heterodimer`, `max`; `tfs-configure-model --congression_theta_rule`) is a third such setting, carried into the model as the static `GrowthData.congression_theta_rule`; the partition-function rules require `activity='fixed'` (the default activity component; the orchestrator refuses a mixture with these rules and a learned activity). `congression_dk_rule` (default `dilution`; also `softmin`, which needs `congression_dk_alpha` > 0, and `min`; `tfs-configure-model --congression_dk_rule`/`--congression_dk_alpha`) sets a congressed cell's dk_geno the same way (static `GrowthData.congression_dk_rule`/`congression_dk_alpha`).
 
-`pool_fraction`/`bulk_fraction` are computed and snapshotted but **not yet consumed by the model** — `bulk_fraction` is the `f_g` the observable-level congression mixture will use (see `future/congression-physics-plan.md`), and the current backend still treats spiked as a binary via `congression_mask`/`ln_cfu0_spiked_mask`.
+### Library subsample (`genetics/library_subsample.py`)
+
+Four optional library-YAML keys, read by `tfs-simulate` (and `run_simulation`) and by `library_composition_table` (so `tfs-configure-model --library_config` snapshots the same subset), never by `LibraryManager` itself or `tfs-process-fastq`: `library_genotypes` (text file, one genotype per line, relative to the YAML's own directory: `build_library`/`library_composition_table`/`library_prediction` take `base_dir`, default the YAML's dir for a path; simulate_cli passes the config file's dir; every unlisted genotype is dropped from every origin; a listed genotype outside the library is an error), `library_subsample` (`{origin: N}`, N random non-protected genotypes per origin; `spiked` refused, N >= 1), `library_subsample_seed` (required with `library_subsample`, deliberately not `seed`: configure never sees `tfs-simulate --seed`; each origin draws from `default_rng([seed, crc32(origin)])` and keeps a prefix of one permutation, so N=500 ⊂ N=1000 and adding an origin leaves the others' draws alone) and `library_subsample_depth`. wt and every spiked-origin genotype are protected (kept in every origin, not counted in N); a `double-x-y` origin's lower-order members are drawn like any other; an origin left empty is an error. `subsample_library_df` keeps whole genotypes (all their sequences) and renormalizes `weight` per origin; it returns a per-origin summary (genotypes/sequences, full and kept). **Depth** (`subsample_design`): `full_library` (default) multiplies each origin's `library_mixture` and `transform_sizes` by its kept fraction of sequences and `total_num_reads`/`pcr_template_molecules` by the kept pool share (the per-sequence rule of `scale_library_design`), leaving `cfu0` (tube totals/OD600 realistic; cells per kept genotype ×1/share); `as_written` changes nothing (the subsample is the library). simulate_cli builds the library only when `has_subsample` (otherwise `library_prediction` builds it as before, so mocks of it keep working), prints `format_subsample_summary`, and runs `library_prediction(..., library_df=)`, the replicates, read split and presplit with the updated `sim_cf`; `{out_prefix}_input-config.yaml` stays the config as given. `library_composition_table` uses the same depth rule, so kept genotypes have the simulation's composition; configure records `subsample_record` (list file by basename) as `library.subsample`. Keys are in `SIMULATE_KNOWN_KEYS`; `library_genotypes` is in `_SIM_PATH_KEYS`. `tfs-setup-grid` needs nothing: configure reads the original library YAML at setup. Pilot that motivated it: `research/projects/2026-10-07_lasr-bgmp/pilot/2026-10-10_structural-prior-pilot` (`scripts/tfs.py` monkeypatched `get_libraries`; `make_configs.py` did the depth scaling by hand). Tests: `tests/tfscreen/genetics/test_library_subsample.py`, `tests/tfscreen/simulate/test_library_subsample_sim.py`, grid staging in both grid CLI tests.
 
 ### Pre-fit model accounting (`tfmodel/model_stats.py`)
 
@@ -211,13 +280,23 @@ The simulation pipeline generates ground-truth phenotypes for a synthetic TF scr
 | `genotype_theta_df` | Long-form theta predictions (one row per genotype × titrant_conc) |
 | `parameters_df` | One row per genotype; per-genotype Hill/theta params + dk_geno + activity |
 | `binding_theta_df` | Theta at binding concentrations for calibration genotypes (`None` if not configured) |
+| `library_binding_theta_df` | Only with `return_binding_theta=True` (what `tfs-simulate` passes), always last: theta of every library genotype at the binding concentrations (`None` without `binding_data`); `library_binding` reads it |
+| `mutation_truth` | Only with `return_mutation_truth=True` (what `tfs-simulate` passes): `{"mutation": df or None, "pair": df or None}`, see below |
 
 The core calculation happens in `thermo_to_growth` (`simulate/thermo_to_growth.py`):
 
 1. **Prior-predictive theta sampling** — `sample_theta_prior` draws a `theta_gc` matrix of shape `(G, C)` where G = number of library genotypes and C = number of unique titrant concentrations. The genotype order here matches the library (`sim_data`) order.
 2. **theta_gc_override injection** — specific rows of `theta_gc` can be replaced before any further computation (see below).
 3. **Growth rate calculation** — theta is mapped to growth rates via the configured `growth_params`.
-4. **parameters_df assembly** — per-genotype Hill parameters extracted from the `theta_param` pytree, then patched with `theta_params_override` (see below).
+4. **parameters_df assembly** — per-genotype Hill parameters extracted from the `theta_param` pytree, then patched with `theta_params_override` (see below). `_theta_param_to_df` takes both (T, G) fields (one column, `_T{t}` suffixes when T > 1) and (G,) fields. The thermodynamic models' titrant-independent constants (`ln_K_op`, `ln_K_HL`, `ln_K_U` for C4; `ln_K_h_l`, `ln_K_h_o`, `ln_K_l_o`, `ln_K_u` for C12) are (G,); until 2026-10-10 they were dropped, so only `ln_K_E`/`ln_K_h_e`/`ln_K_l_e` had truth.
+
+### Per-mutation and per-pair truth (`tfs_sim_mutation_parameters.csv`, `tfs_sim_pair_parameters.csv`)
+
+`ThetaParam` carries only assembled per-genotype values. For components drawn on the prior-predictive path (all thermodynamic models), `sample_theta_prior(..., return_deterministic=True)` traces the draw (the trace does not change it) and `thermo_to_growth._mutation_truth_tables` turns the deterministic sites into two tables: one row per mutation with every `theta_d_*` site (prefix dropped, so `d_ln_K_op` etc.) plus, for PddG/PnnC, `ddG_{structure}` from `theta_ddG` (the per-state truth draw, see **Per-structure ΔΔG**); and one row per pair (canonical `A/B` label) with every `theta_epi_*` site. Column names are the `get_extract_specs` names, so they join `*_params_d_*.csv` on `mutation` and `*_params_epi_*.csv` on `pair`. `tfs-simulate` writes them when present; hill_geno/hill_mut go through `simulate()` (no trace) and write neither. The `ddG_*` columns are labelled with the component's `STRUCTURE_NAMES` (the order `theta_ddG` is in); until 2026-10-10 they took `sim_data.struct_names`, the CSV's column order, so a CSV whose columns were not in model order mislabelled them.
+
+`tfs-summarize-fit` (`_summarize_params`) joins a `*_params_d_X.csv` / `*_params_epi_X.csv` table to these files when they have a `d_X` / `epi_X` column (pair labels standardized on both sides), and otherwise falls back to the per-genotype truth: sim[mutation] − sim[wt] and the additive epistasis of the per-genotype values (the hill_mut path). `tfs-summarize-calibration` reads the resulting `ref` columns with no change of its own. The per-mutation file still scores a mutation whose single mutant is not in the library.
+
+**Per-structure ΔΔG** (`theta/thermo/prior.py`). PddG/PnnC put the structural prior on the constants: per state `theta_ddG_sigma` (σ_s, S) ~ LogNormal(`theta_ddG_sigma_loc` 0, `theta_ddG_sigma_scale` 1.2; median 1 kT, 95% 0.095-10.5 kT; priors fields of all 8 modules, defaults `prior.DEFAULT_DDG_SIGMA_LOC/SCALE`), then per mutation `theta_dlnK` (M × C, sample site, mut plate dim -1, event C) ~ MultivariateNormal(P·μ_m, P·diag(σ_s²)·Pᵀ), μ the per-state prior mean (`theta_ddG_prior_mean`, M × S deterministic: the CSV values for PddG, the MLP output for PnnC), P = each module's `DLNK_PROJ` (`prior.projection(mwc.PROJ_*, mwc.PROJ_U_*)`: the folded rows incl. the per-site effector rows, then for U1 the unfolding row Δln K_U = ΔΔG_H; C = 3/4 for C4 U0/U1, 5/6 for C12), so the constants come out in `d_ln_K_*` order with `d_ln_K_U`/`d_ln_K_u` last. This is the distribution the per-state model ΔΔG ~ N(μ, σ_s) induces on what the data see. `scale_tril` is the Cholesky factor from a QR of (P·diag σ)ᵀ, once per evaluation, shared over the M mutations (`constant_scale_tril`); everything is in `STRUCTURE_NAMES` order (PddG and the C12 PnnC permute the data's columns into it, the C4 PnnC requires it). Why (2026-10-11, after c4f03d42): that commit sampled per-state `theta_ddG` ~ N(μ, σ_s) and projected it; in U0 every constant is a difference of states, so an equal shift of all states (P's null vector (1,…,1)) changes nothing, and joint MAP used it to zero one state's residuals and collapse its σ_s (C4_U0, true σ 0.3 kT: σ_LE2 0.001, ΔΔG RMSE 0.28). With the prior on Δln K there is no per-state latent and one σ_s → 0 keeps the covariance PD (the null vector has no zero entry); same problem now gives σ_s 0.28-0.32 on every state, Δln K RMSE 0.0497 (per-state form 0.0512, flat 0.0502). U1: P square and invertible, a pure change of variables (constant Jacobian), same MAP as the per-state form to 1e-6 (`test_ddG_constant_prior.py`). σ_s identifiability: σ_s enters only via PΣPᵀ; every σ_s is identified in all four families (the rank-one p_s p_sᵀ are independent: C4_U0 σ_H² = cov(K_op, K_HL), σ_L² = −2·cov(K_HL, K_E), HD/LE2 from the K_op/K_E variances; C12_U0 H and L each own off-diagonal terms, HO/LO/HE2/LE2 a variance each; `prior.identifiable_sigma`, `TestSigmaIdentifiability`). Remaining MAP limit (docstring): with weak data a σ_s can still drift toward 0 (density unbounded only where the covariance turns singular with all residuals inside it), so a σ_s near 0 from MAP means 'not resolved'. **Held σ_s** (`theta_ddG_sigma_fixed`, kT, default 0, a `pytree_node=False` field of all 8 ModelPriors, read by `prior.ddG_sigma_fixed(priors)` like hill's `_fixed()`): > 0 makes `sample_dlnK(..., sigma_fixed=)` record `theta_ddG_sigma` as a deterministic (S,) of the held value (the MVN uses it) and `guide_dlnK(..., sigma_fixed=)` register no σ_s params or site; AutoDelta/Laplace then have no σ_s, posteriors still carry the deterministic site, so `ddG_sigma` is still extracted (every row the held value) and the summary truth join still works; `get_guesses` keeps its σ_s entry (no sample site matches it). One scalar for every state, always kT (not converted by `ddG_units`); for the simulation-study oracle arm. Set with `tfs-configure-model --set_priors theta_ddG_sigma_fixed=1.69` (full name `theta.theta_ddG_sigma_fixed`). Tests: `theta/thermo/test_ddG_sigma_fixed.py`. **Post-hoc per-state ΔΔG**: deterministic `theta_ddG` (M × S, `prior.posthoc_ddG`): U1 = P⁻¹·Δln K exactly (ΔΔG_U = 0); U0 = the conditional mean μ + ΣPᵀ(PΣPᵀ)⁻¹(Δln K − Pμ), whose common mode is the table's, not the data's (reproduces the constants exactly; Δln K = Pμ gives μ in every family). Score per-state ΔΔG only for U1. Component guides call `prior.guide_dlnK` (`theta_ddG_sigma_locs/_scales` LogNormal; `theta_dlnK_locs/_scales` Normal(...).to_event(1), (M, C), locs start at P·μ for PddG, 0 for PnnC); `get_guesses` uses `prior.dlnK_guesses` (Δln K = P·μ, σ_s = 1 kT). AutoDelta/Laplace see `theta_dlnK` and σ_s as ordinary sites (Hessian included). `predict_unmeasured` is unchanged: it assembles from the `d_ln_K_*` posteriors, which for a library mutation without data are its prior (P·μ); a mutation outside `mut_labels` is still NaN. The pairwise horseshoe (`horseshoe.sample_pair_ddG`) stays per state and non-centered on purpose: its scales are sample sites with proper heavy-tailed priors (finite MAP optimum), and a centered horseshoe would give MAP an unbounded spike at τλ̃ → 0 that zeros weakly measured pairs; in U0 its common-mode direction is unconstrained by the data and regularized only by the shrinkage (docstring). `prior.ddG_extract_specs` (called by all 8 `get_extract_specs`) writes one table per structure, `ddG_{structure}` (from the post-hoc site) and `ddG_prior_mean_{structure}`, keyed by `mutation`, plus one `ddG_sigma` table keyed by `structure`; it uses the spec key `out_names` (`extraction._extract_param_est`), which names a spec's output tables when several read slices of one site. `tfs-summarize-fit` joins a direct table named for a per-mutation truth column (`ddG_H`) on `mutation`, and scores a prior-mean table against the same truth (`ddG_prior_mean_H` vs `ddG_H`: how well the structural prior alone predicts it) without a calibration, since it is a point (its quantiles coincide; `tfs-summarize-calibration` still lists it, so read only its rmse and pearson_r). For a U0 fit (`_ddG_per_state_identified(config_yaml)`, from the theta module's `DLNK_PROJ`) the `ddG_{structure}` summary is still written but with the truth in `ref_unidentified` (no `ref`, so `tfs-summarize-calibration` skips it) and `per_state_identified = False`, no plot, no calibration, and one printed note: score U0 fits on `d_ln_K_*`. `_summarize_ddG_sigma` joins `*_params_ddG_sigma.csv` to `*_sim_ddG_sigma.csv` on `structure`. This is the with/without-structural-priors comparison: a PddG/PnnC fit's `ddG_*` (U1) and `d_ln_K_*`, against a PK fit of the same simulation, which has no ΔΔG and is scored on `d_ln_K_*` (PddG/PnnC simulations write the `d_ln_K_*` truth too; PK's per-titrant `d_ln_K_E` rows join the per-mutation truth, which is uniform across effectors). For a PK fit, summarize prints one note that the truth's `ddG_*` have no table instead of warning per structure. Until 2026-10-10 nothing extracted `theta_ddG`, so the `ddG_*` truth was written but never scored. **Truth-noise dial** (`ddG_truth_sd`, a top-level simulate key in `SIMULATE_KNOWN_KEYS`, not in `thermo_options` so the fit-side reader never sees it and a grid varies it as an ordinary top-level variable): the simulator conditions the σ_s site (`numpyro.handlers.condition` inside `seed`, so the conditioned site draws no key), draws the per-state truth ΔΔG = prior mean + σ_s·z (`prior.per_state_truth`, key `fold_in(rng_key, 7919)`) and conditions `theta_dlnK` on P·ΔΔG in a second traced draw with the same key (`sample_theta._per_state_truth_conditions`; σ_s from the first draw, so an unconditioned call keeps its one σ_s draw), so σ_s is the SD of truth − table and the constants have exactly the model's MVN (`test_constant_covariance_matches_the_model`). `sample_theta.ddG_truth_conditions(sd, theta_component, thermo_options)` turns it into `{"theta_ddG_sigma": (S,)}` in kT, in `STRUCTURE_NAMES` order: scalar or `{structure: sd}` naming every structure, in `ddG_units` (converted with `ddG_to_kT_factor`, like the table), finite and >= 0 (0 gives truth = table exactly: the ΔΔG Normal is built with `validate_args=False` for this), refused for non-PddG/PnnC components and with `phenotype_source: empirical`. Absent = 1 kT per structure (`DEFAULT_DDG_TRUTH_SD`; chosen over drawing σ_s from its prior so the truth noise is always a known number). `library_prediction` always passes it for PddG/PnnC as `thermo_to_growth(theta_conditions=)` → `sample_theta_prior(conditions=)` (a name that is not a sample site is an error); a direct `sample_theta_prior` call without conditions draws σ_s from its prior. `sample_theta_prior(return_deterministic=True)` returns the deterministic sites plus the sample sites in `_TRUTH_SAMPLE_SITES` (`theta_ddG_sigma`), with `theta_ddG` replaced by the per-state truth draw (the model's post-hoc site cannot recover the U0 common shift). The truth tables: `ddG_{structure}` columns of `mutation_parameters` and, from `_ddG_sigma_truth_table`, `tfs_sim_ddG_sigma.csv` (`structure`, `ddG_sigma`, kT); `thermo_to_growth`'s truth dict has keys `mutation`, `pair`, `ddG_sigma`. The fit side is untouched (σ_s is a free sample site there), and `theta_ddG_prior_mean` stays the unperturbed table. Both 2026-10-11 changes moved the seeded PddG/PnnC truth (σ_s a site and ΔΔG drawn (M, S) rather than (S, M) offsets; then the redraw through `theta_dlnK`); the truth files keep their schema (`ddG_{structure}`, `d_ln_K_*`, `ddG_sigma`). For PnnC the sim's prior mean is the untrained MLP's output, 0. Tests: `tests/tfscreen/simulate/test_ddG_truth_sd.py`.
 
 ### Binding data and calibration genotypes
 
@@ -225,11 +304,15 @@ The optional `binding_data` YAML block configures calibration genotypes for whic
 
 **`spiked_binding`** — clean, monoclonal (congression-free) controls; **pool = `spiked_seqs`**.
 - `choose_by: <file>` — the named genotypes get the file's Hill params as their true phenotype (see the measured-params bullets below). `num` is forbidden with a file.
-- `choose_by: stratified|random` — assign diverse (greedy-maximin) / random prior-predictive theta curves to `num` spiked genotypes (default all); `wt` keeps its natural reference. Their theta at *growth* concentrations is injected via `theta_gc_override` so growth matches binding.
+- `choose_by: stratified|random`, Hill and other non-thermodynamic components — assign diverse (greedy-maximin) / random prior-predictive theta curves to `num` spiked genotypes (default all); `wt` keeps its natural reference. Their theta at *growth* concentrations is injected via `theta_gc_override` so growth matches binding. Their `parameters_df` truth is not patched (no `theta_params_override`), so it still describes the replaced draw.
+- `choose_by: stratified|random`, thermodynamic components (`thermo.*`, `library_prediction._spiked_keeps_own_theta`) — no override: the spikes keep their own model-generated theta, so binding theta, growth theta and the K truth all come from one draw (pasting a pool curve over a genotype whose theta follows from shared per-mutation K's would leave its K truth describing a curve that was never simulated). `stratified`/`random` only choose *which* `num` spikes are measured (`_select_own_theta_spiked`: greedy maximin over the spikes' own binding curves, or a random draw), after `thermo_to_growth`. Until 2026-10-10 this path built a wt-only stratified pool, which the thermodynamic models could not draw (a numpyro plate of size 0).
 
 **`library_binding`** — in-library controls drawn from the **bulk** (congression-affected growth); **pool = library genotypes NOT in `spiked_seqs`**. Omit to disable.
-- These are regime-matched to the bulk: on the fit side they are simply extra `binding_df` rows and, because they are *not* in `--spiked`, they get `congression_mask=True` automatically. No fit-side change.
+- These are regime-matched to the bulk: on the fit side they are simply extra `binding_df` rows and, because they are not spiked, their growth gets the bulk `bulk_fraction` automatically (binding itself is never congressed). No fit-side change.
 - **Pre/post-sim split** (`simulate/library_binding_data.py::generate_library_binding_df`): the `file` path injects the genotypes' phenotype *pre-sim* (in `library_prediction`, via the same override machinery), but the binding *measurement* — and, for `stratified`/`random`, the *selection* — happen **post-growth-sim** so selection is restricted to genotypes that actually survived with growth data (guaranteeing `num` usable anchors). `file`-specified genotypes that don't survive are **warned** and dropped. `simulate_cli.py` writes the selected set to `tfs_sim_library_binding.csv`.
+- **Binding theta is evaluated, not looked up.** `thermo_to_growth(..., binding_titrant_conc=...)` returns (last) the theta of every genotype at the binding concentrations (`_theta_at_concs`: the component's own `run_model` on the drawn `theta_param` with `sim_data_class.with_titrant_conc`; genotypes overridden with complete Hill `theta_params_override` follow those params; overridden ones without are NaN). `library_prediction(..., return_binding_theta=True)` passes it on, and `generate_library_binding_df(..., theta_df=...)` selects and measures from it, so `library_binding` works for any theta component and at binding concentrations that are not growth concentrations. Without `theta_df` it falls back to the Hill columns of `parameters_df` (until 2026-10-10 the only path, so `library_binding` raised `KeyError: 'theta_low'` for the thermodynamic models).
+
+**Noise** (`binding_data.noise`): Gaussian on `theta_obs`, not clipped to [0, 1] (the fit's binding likelihood is an unclipped Normal; clipped anchors biased the absolute theta scale). `binding_data.clip_theta_obs: true` reproduces simulations made before 2026-09-29.
 
 **Validation** (`library_prediction._validate_binding_config`, fail-fast): `spiked_binding` file genotypes must be ⊆ `spiked_seqs`; `library_binding` file genotypes must be disjoint from `spiked_seqs`; `num` + a file is an error; a file requires a Hill theta component; `spiked_binding.num` ∈ `[1, num_spiked]`; `library_binding` stratified/random requires `num`.
 
@@ -260,10 +343,20 @@ The optional `base_growth_data` YAML block generates a simulated `base_growth_df
 - Each growth model uses different YAML parameter names than the fit's extract names, and the module hand-maintains the mapping (`b,m` → `growth_k,growth_m` for `linear`; `b,a,n` → `growth_k,growth_m,growth_n` for `power`; `kmin,kmax` → `growth_min,growth_max` for `saturation`) by comparing `simulate/growth/growth_linkage.py`'s numpy formulas against the corresponding JAX formulas in `generative/components/growth/*.py`. If a new `condition_growth` component is added, this mapping must be extended too, or its parameters won't get ground-truth comparison in `tfs-summarize-fit`.
 - On the `tfmodel` side, `tfmodel/scripts/summarize_fit_cli.py::_summarize_condition_growth_params` and `_summarize_k_ref` join these two files against the fit's extracted params (on `condition_rep`, and trivially for the single-row `k_ref`, respectively) to annotate them with a `ref` column, mirroring `_summarize_params`'s genotype-keyed comparison but for growth's condition-keyed/global-scalar parameters.
 
+### Joint-fit simulator keys (joint-fit plan step 5)
+
+All off by default. A `linear` growth entry takes `warp_a` (> 0) and `warp_x0` (default 0.5): k = b + A·m·f(θ), f the fit's `m_warp` logistic (`simulate/growth/growth_linkage.warp_theta`), applied per cell in `selection_experiment` too; truth `warp_log_a` = log a and `warp_x0` in `tfs_sim_growth_parameters.csv`. Top-level `growth_titrant_scale` (s; in `SIMULATE_KNOWN_KEYS`): `library_prediction` replaces sim_data's growth concentrations by s × nominal (`with_titrant_conc`) right after building it, so the prior draw, `_pin_wt_reference`, the measured-params and empirical overrides and (through a scaled `sample_df` copy) the spiked stratified pool all evaluate growth theta at s × c, while `_theta_at_concs` and the binding pools use the nominal binding concentrations; `tfs_sim_genotype_theta.csv` is therefore the theta growth sees, and the Hill truth in `tfs_sim_parameters.csv` is in binding units, like the fit's curve; truth log s in `tfs_sim_growth_titrant_scale.csv`. `binding_data.genotype_offset_sd`: one Normal offset per binding genotype added to every theta_obs of it (`binding_data.apply_genotype_offsets`, after spiked and library binding are assembled; no draw at 0), truth `tfs_sim_binding_offsets.csv`. Tests: `tests/tfscreen/simulate/test_joint_fit_sim.py`.
+
+### Sampling noise (roadmap step 4)
+
+Optional simulate-config keys, all off by default (a config without them simulates byte-for-byte as before): `founder_sampling` (`_sim_growth`: each tube gets a Poisson number of cells per transformant clone, drawn per tube), `demographic_growth` (given founders, Gamma(n0, e^kt) growth or Binomial(n0, e^kt) death; needs `founder_sampling`), `shared_transformation` (one library assembly and transformation shared by all replicates through the `shared_state` dict `simulate_cli` passes to `selection_experiment`; without it each replicate redraws both, unlike the real protocol's single glycerol stock), `pcr_template_molecules` / `pcr_amplification_cv` (`_sim_sequencing`: reads drawn from a multinomial set of template molecules, each amplified by a Gamma factor; count variance grows by about `(reads/templates)(1 + cv^2)`). `selection_experiment(..., sequence=False)` simulates totals (and OD600) without reads, for OD-only replicates. Real counts are 5-18x Poisson at 100-3,000 reads (`planning/studies/noise-anatomy/`); matching that needs templates on the order of reads per tube / 10.
+
 ### theta_gc_override and theta_params_override
 
 These two dicts are the mechanism by which binding data is "pinned" into the growth simulation.
 
+Neither is used for spiked stratified/random binding with the thermodynamic components, whose spikes keep their own theta (see the binding section).
+
 **`theta_gc_override`** (`dict[str, np.ndarray]`):
 - Keys are genotype strings; values are 1-D arrays of theta at the growth titrant concentrations (sorted-unique order from `sample_df`).
 - Applied in `thermo_to_growth` *after* prior-predictive sampling but *before* noise and all downstream computations.
@@ -288,26 +381,29 @@ These two dicts are the mechanism by which binding data is "pinned" into the gro
 | `simulate/base_growth_data.py` | Generates simulated direct growth-rate calibration data (`base_growth_data` YAML block) and the single-row `k_ref` ground-truth echo |
 | `simulate/growth_parameters_output.py` | Generates per-condition `condition_growth` ground truth (`tfs_sim_growth_parameters.csv`) from the `growth` YAML block |
 | `simulate/presplit_data.py` | Generates simulated pre-split (t = -t_pre) data (`presplit_data` YAML block; `generate_presplit_df`) |
+| `simulate/od600.py` | Simulated OD600 per tube (`od600` YAML block): inverts a `tfs-calibrate-od600` calibration (read/apply/invert live in `process_raw/od600.py`) to get each tube's true OD600, adds reading noise, flags detectable/in-calibrated-range, and (`sample_cfu_from_od600`) runs it forward to the lab's estimate. `simulate_cli` writes `tfs_sim_od600.csv` for every replicate, including `num_od_only_replicates` extra unsequenced ones |
 | `simulate/sample_theta.py` | `sample_theta_prior` (prior-predictive) and `sample_theta_stratified` (greedy maximin) |
 | `simulate/sim_data_class.py` | `SimData` container and `build_sim_data` factory |
-| `simulate/build_sample_dataframes.py` | Constructs sample/timepoint DataFrames from simulation config |
-| `simulate/selection_experiment.py` | Models the selection experiment (growth + sequencing) |
+| `simulate/build_sample_dataframes.py` | Constructs sample/timepoint DataFrames from simulation config: `build_sample_dataframes` (the `condition_blocks` grid) or, with the `design` key, `read_design`/`design_conditions` (a tube table in tfs-process-counts format; `library_prediction` simulates the union of its conditions and `simulate_cli` gives each replicate exactly its own tubes, carrying the design's tube names into the raw output as `design_sample`; refused with `condition_blocks` or `od600.num_od_only_replicates`) |
+| `simulate/selection_experiment.py` | Models the selection experiment (transformation, growth, sequencing). A co-transformed cell splits its abundance among its plasmids (`_plasmid_shares`) and has one growth rate built from cell-level physics (`_cell_kt`); `_check_cell_components` fails fast if the phenotype's `k_pre`/`k_sel` can't be rebuilt from its theta/activity/dk_geno |
+| `simulate/cell_rules.py` | Pluggable rules combining a co-transformed cell's plasmids: `THETA_RULES` (`homodimer`, the default: `logit θ_cell = log Σ x_g exp(l_g)` over equal shares; `heterodimer`: `2 log Σ x_g exp(l_g/2)`; both require activity 1 and raise otherwise; `max`: highest-θ plasmid sets θ and activity) and `DK_RULES`, the soft-min family `dk_cell = -(1/alpha) log sum_g x_g exp(-alpha dk_g)` (`dilution`, the default: share-weighted mean, alpha -> 0; `softmin`: finite `congression_dk_alpha`; `min`: the worst variant, alpha -> inf), chosen by `congression_theta_rule`/`congression_dk_rule`. The fit's mixture uses the same rules (`mixture.cell_classes`, `THETA_EPS` shared). Growth is then `thermo_to_growth.growth_rate_one_condition`, the same formula used per genotype. Never combine whole growth rates across plasmids |
 | `simulate/scripts/simulate_cli.py` | `tfs-simulate` CLI entry point; runs `library_prediction` + `selection_experiment` across replicates and writes all output CSVs, delegating the optional `binding_data`/`presplit_data`/`base_growth_data` blocks to their respective generator modules above |
-| `simulate/scripts/report_cfu0_cli.py` | `tfs-report-cfu0` CLI entry point; reuses `library_prediction` + `selection_experiment` (same pattern as `simulate_cli.py`) across `num_replicates` to report mean `ln_cfu_0` and surviving-genotype counts by class (`wt`/`spiked`/`single`/`double`), for tuning `transform_sizes`/`library_mixture`/`cfu0` against observed real-library values |
+| `simulate/raw_output.py` | `write_raw_experiment`: the simulated experiment in the raw formats real data come in (pipeline plan step 6), called by `tfs-simulate`. One `counts_tubeNNNN.csv` per sequenced tube in `tfs-process-fastq`'s format (every library genotype, zeros included, plus `__unknown__` = 0), the tube table (design columns only; `sample_cfu`/`sample_cfu_std` without an `od600` block), and with one the OD600 table and a copy of the calibration; tubes read below the detection threshold are left out (`undetectable_tubes`, which `simulate_cli` also applies to the direct growth table and to the in-library binding survivors; `tfs_sim_od600.csv` keeps every reading and presplit is unaffected; `test_undetectable_tubes.py` checks both routes give the same tubes). Fixed-width tube names keep the `counts*{sample}*.csv` glob unique. `tfs-process-counts` on these files reproduces `tfs_sim_growth.csv` row for row (`test_raw_output.py`); ln_cfu differs only by the pseudocounts of library genotypes never seen, which a real counts file lists |
 | `simulate/run_simulation.py` | Simpler, non-CLI orchestrator (`run_simulation`); does not support `binding_data`/`presplit_data`/`base_growth_data` |
 
 ### Empirical phenotype pipeline (`simulate/empirical/`)
 
 An alternative to prior-predictive phenotype sampling: instead of drawing theta from made-up priors, fit **real** screen data to an empirical phenotype-generating distribution and resample from it, so the simulated library's phenotype *distribution* matches reality while ground truth stays known. Targets `hill_geno` + linear growth; asserts `A ≡ 1` (repressor: blocks or not, leaky binding absorbed into `theta_low`). Three stages:
 
-**Module location note.** Stage 1 (per-genotype MLE fit) and Stage 1.5 (congression de-attenuation) live in `tfmodel/genotype_fit/` (`fit.py`, `congression.py`) — they are a general per-genotype inference engine for the growth model, exposed standalone as **`tfs-fit-genotypes`** (`tfmodel/scripts/fit_genotypes_cli.py`). `simulate/empirical/fit_phenotypes.py` and `.../congression.py` are back-compat re-export shims. `tfs-fit-genotypes` takes `growth_file` + `calibration_file` (positional; a prefit priors CSV or wide `condition_rep,growth_k,growth_m`) and writes `<prefix>_params.csv` (raw fit), `<prefix>_theta.csv` (predicted θ vs `[genotype,titrant_name,titrant_conc]`), and — with `--congression_lambda` — `<prefix>_params_deattenuated.csv` + a `theta_deattenuated` column (and, with `--save_theta_history`, the fixed-point trajectory `<prefix>_theta_history.csv`). Reusable helpers `fits_to_results_df(fits)` (rebuild the params table from any fits dict) and `predict_theta(fits, growth_df)` back both this CLI and `tfs-build-empirical`. **`<prefix>_stage1_fits.csv` from `tfs-build-empirical` is the RAW (pre-de-attenuation) fit; the de-attenuated fits are now also written to `<prefix>_stage1p5_fits.csv` when `--congression_lambda` is given.** The Stage-1.5 correction is inherently a *population* operation (the background CDF couples all bulk genotypes), even though the per-genotype MLE fits themselves are independent/parallel.
+**Module location note.** Stage 1 (per-genotype MLE fit) lives in `tfmodel/genotype_fit/fit.py` — a general per-genotype inference engine for the growth model, exposed standalone as **`tfs-fit-genotypes`** (`tfmodel/scripts/fit_genotypes_cli.py`). `simulate/empirical/fit_phenotypes.py` is a back-compat re-export shim. `tfs-fit-genotypes` takes `growth_df` + `growth_calibration_file` (positional; a prefit priors CSV or wide `condition_rep,growth_k,growth_m`) and writes `<prefix>_params.csv` and `<prefix>_theta.csv` (predicted θ, column `theta_raw`, vs `[genotype,titrant_name,titrant_conc]`). Reusable helpers `fits_to_results_df(fits)` (rebuild the params table from any fits dict) and `predict_theta(fits, growth_df)` back both this CLI and `tfs-build-empirical`.
+
+**No congression correction (Stage 1.5 retired, 2026-09-24).** The Stage 1 fits of bulk genotypes carry the congression bias (small in the measured regime, about 0.05 ln units), and Stage 2 builds the distribution from them as-is. The old Stage 1.5 de-attenuated θ with the θ-only `E[max]` operator the main fit dropped in step 3.3c (Poisson rather than zero-truncated co-resident weights, binary spiked/bulk purity, no dk dilution); `--congression_lambda` was removed from `tfs-fit-genotypes` and `tfs-build-empirical`. A proper replacement (an iterated per-genotype refit under the same observable-level mixture as the fit) waits on a review of the whole empirical pipeline (`planning/empirical-mixture-refit.md`).
 
 - **Stage 1** (`fit_phenotypes.py`): per-genotype MLE of the growth model on a real `ln_cfu` DataFrame (derives `ln_cfu_std` from the processed `ln_cfu_var` via `get_scaled_cfu`, like `model_orchestrator`), calibration (`growth_k`/`growth_m` per `condition_rep`) frozen. Fits `(dk_geno, theta_low, theta_high, log_hill_K, hill_n)` in transformed coords (logit theta bounds, log n) so `run_least_squares` returns covariance in the space Stage 2 needs. Per-genotype independent (no cross-genotype coupling); weak `dk_geno` Tikhonov prior; ±16 logit clamp. Returns `GenotypeFit(estimate, covariance)` per genotype. The fits are embarrassingly parallel — `num_workers` (`-1` = `cpu_count-1`) runs them over a `ProcessPoolExecutor`; the parallel path strips the genotype `Categorical` first (it carries all categories on every group → O(N²) pickling). Worker startup pays a ~2s JAX import, so parallelism is a wash for tiny/fast runs and near-linear for large real libraries. `_hill_theta` is numerically identical to `hill_geno.run_model` and `binding_params._hill_theta` (verified — same `_ZERO_CONC_SENTINEL`).
 - **Stage 2** (`population.py`): measurement-error EM (`z_i~N(mu,Σ)`, `y_i~N(z_i,S_i)`) that **deconvolves estimation noise** (`Cov(y)=Σ+mean(S_i)`), so it recovers a narrower population than a naive KDE on the point estimates. `fit_population()` → `PopulationModel` (single MV-Normal in transformed space; `wt_ref` field holds wt's actual Stage-1 fit; `.sample(n)` → natural-space DataFrame; `.save()`/`.load()`).
-- **Stage 1.5** (`congression.py`, optional; between Stage 1 and Stage 2, gated by `--congression_lambda`): de-attenuates the **bulk** genotypes' theta curves for co-transformation. Reuses the inference's own θ-level operator `transformation._congression.update_thetas` (the `E[max(x,M)]` **dominant-max occupancy** map — the tightest-bound operator sets effective θ, so `E[max]` only, never the min variant — with an empirical background CDF). `correct_theta_matrix` is the fixed point: `θ_true ← θ_obs`; iterate `θ_true += gain·(θ_obs − update_thetas(θ_true; background=θ_true))` per-concentration until converged (the background *is* the corrected population, hence the iteration). `deattenuate_congression` evaluates each bulk genotype's Stage-1 Hill at the growth `titrant_conc` grid, corrects, and refits Hill (dk_geno and the Stage-1 covariance are left untouched — a deliberate bias-only correction; Stage 2's estimation-noise deconvolution still runs after). λ passes straight through **unconverted**: a focal barcode is size-biased into its cell, so co-residents are Poisson(λ) at the *same* zero-truncated `transformation_poisson_lambda` (`_sim_transform` uses `zero_truncated_poisson` + i.i.d. plasmid draws). Spiked genotypes are congression-free → excluded from correction and the background CDF, and pass through unchanged. This is the θ-level analogue of the simulator's growth-level congression (agree to first order, exact at λ→0); spiked-only vs bulk distribution is the external check.
 - **Stage 3** (`resample.py`): `resample_phenotypes` draws one i.i.d. phenotype per genotype (wt pinned to `dk_geno=0` + `wt_ref` Hill); `make_empirical_overrides` reuses `build_theta_gc_override_hill_geno` → `(theta_gc_override, theta_params_override, dk_geno_override)`. `thermo_to_growth` gained a `dk_geno_override` param (the one growth-path change; `theta_params_override` only patches θ columns). `build_empirical_binding_theta` rebuilds spiked `binding_theta_df` from resampled params (selection from the `binding_data` config; θ from resampled Hill, not `sample_theta_stratified`).
 
-Integration: `library_prediction` gains a `phenotype_source: empirical` branch (needs an `empirical: {phenotype_model: <path>}` block pointing at the single self-contained `<out_prefix>_phenotype_model.json`; `_resolve_phenotype_model_path` accepts that path with/without `.json` or the bare `<out_prefix>`, and the fit prints the absolute path — use it so no file-copying is needed) that resamples all genotypes, injects the overrides, forces `theta_component=hill_geno` (warns + ignores any other value; the resampled phenotypes are per-genotype Hill curves and the discarded prior draw must match the `parameters_df` schema — this also avoids running/overflowing an e.g. `hill_mut` draw), drops the ignored `theta_priors`/`theta_sim_priors`, and forces `activity=fixed/1`. `phenotype_source`/`empirical` are in `selection_experiment.SIMULATE_KNOWN_KEYS`. Resampled ground truth flows into `parameters_df`/`genotype_theta_df` automatically (no new return value); `library_binding` regenerates for free (reads `parameters_df`). `tfs-build-empirical` (`simulate/scripts/build_empirical_cli.py`) is a **one-command orchestrator**: given the experimental inputs (`growth_file` and `seed` positional; `--binding_file` required; optional `--spiked_file`/`--base_growth_file`/`--thermo_data`/`--congression_lambda`/`--num_workers`) it internally calls `configure_model` (linear + hill_geno defaults — no model choices exposed, since here they'd only be wrong) then `run_prefit_calibration` (MAP-calibrates per-condition k/m), then Stages 1-2 (plus optional Stage 1.5 congression de-attenuation when `--congression_lambda` is given), saving the deliverable `<prefix>_phenotype_model.json` (one self-contained, human-readable file = the generating distribution; `PopulationModel.save`/`.load`) + the diagnostic `<prefix>_stage1_fits.csv`, plus the `<prefix>_configure_*`/`<prefix>_prefit_*` intermediates. The configure/prefit imports are lazy (the heavy JAX stack loads only on this path). The MAP prefit is the slow step, so `--calibration_file` skips configure+prefit and reuses a calibration for fast Stage-1/2 iteration — either a prefit **priors CSV** (read via `fit_phenotypes.read_calibration`, which pivots the `growth.condition_growth.k_loc`/`growth.condition_growth.m_loc` per-`condition_rep` rows — prefit writes these via `_csv_row_name` = `growth.{component}.{field}` — to wide k/m) or a wide `(condition_rep, growth_k, growth_m)` CSV. This **complements** the fully-synthetic prior path (the *accuracy* benchmark) as a *realism* benchmark.
+Integration: `library_prediction` gains a `phenotype_source: empirical` branch (needs an `empirical: {phenotype_model: <path>}` block pointing at the single self-contained `<out_prefix>_phenotype_model.json`; `_resolve_phenotype_model_path` accepts that path with/without `.json` or the bare `<out_prefix>`, and the fit prints the absolute path — use it so no file-copying is needed) that resamples all genotypes, injects the overrides, forces `theta_component=hill_geno` (warns + ignores any other value; the resampled phenotypes are per-genotype Hill curves and the discarded prior draw must match the `parameters_df` schema — this also avoids running/overflowing an e.g. `hill_mut` draw), drops the ignored `theta_priors`/`theta_sim_priors`, and forces `activity=fixed/1`. `phenotype_source`/`empirical` are in `selection_experiment.SIMULATE_KNOWN_KEYS`. Resampled ground truth flows into `parameters_df`/`genotype_theta_df` automatically (no new return value); `library_binding` regenerates for free (reads `parameters_df`). `tfs-build-empirical` (`simulate/scripts/build_empirical_cli.py`) is a **one-command orchestrator**: given the experimental inputs (`growth_df` positional; `--binding_df` and `--seed` required; optional `--library_config`/`--base_growth_df`/`--thermo_data`/`--num_workers`) it internally calls `configure_model` (linear + hill_geno defaults — no model choices exposed, since here they'd only be wrong) then `run_prefit_calibration` (MAP-calibrates per-condition k/m), then Stages 1-2, saving the deliverable `<prefix>_phenotype_model.json` (one self-contained, human-readable file = the generating distribution; `PopulationModel.save`/`.load`) + the diagnostic `<prefix>_stage1_fits.csv`, plus the `<prefix>_configure_*`/`<prefix>_prefit_*` intermediates. The configure/prefit imports are lazy (the heavy JAX stack loads only on this path). The MAP prefit is the slow step, so `--growth_calibration_file` skips configure+prefit and reuses a calibration for fast Stage-1/2 iteration — either a prefit **priors CSV** (read via `fit_phenotypes.read_calibration`, which pivots the `growth.condition_growth.k_loc`/`growth.condition_growth.m_loc` per-`condition_rep` rows — prefit writes these via `_csv_row_name` = `growth.{component}.{field}` — to wide k/m) or a wide `(condition_rep, growth_k, growth_m)` CSV. This **complements** the fully-synthetic prior path (the *accuracy* benchmark) as a *realism* benchmark.
 
 ## Categorical response assessment (`analysis/cat_response/`)
 
@@ -396,6 +492,58 @@ overconfident ones, exactly what Axis 2 exists to detect.
 arguments are direct CSV paths (`rep1.csv rep2.csv`); a single argument is a
 manifest file (one path per line) *unless* it ends in `.csv`.
 
+## Planning (`planning/`)
+
+Ideas, active plans and studies live in `planning/` (called `future/` before
+2026-09-23); `planning/README.md` has the conventions. **Read
+`planning/findings-ledger.md` before proposing a plan or study on the
+growth/binding model, lambda, the k/dk_geno slide or m:** it lists what is
+established, refuted and open, with sources (2026-10-18: a week of
+joint-fit work had re-found July and early-October results). Ideas and plans are one
+Markdown file each with a YAML header whose `status` (`idea`/`active`/
+`promoted`/`dropped`/`done`) tells them apart. An active plan keeps its step
+list current. Studies live in `planning/studies/<slug>/` (a `README.md` with
+question, decision fed, how to run, inputs, commit and results, plus the
+script); once a plan cites a study it is frozen. `dev/` is untracked scratch;
+anything a plan cites moves into `planning/studies/`. **The current
+priority is `planning/release-0.5.0.md`** (active, 2026-10-19): the
+close-out checklist for 0.5.0 (never tagged; the version stays 0.5.0),
+the release Natalie's manuscript uses, so that someone in the lab can
+take a screen from counts to X with reasonable intervals through
+`tfs-fit-two-stage`. Its decisions are settled (repair the k shift or ship
+without it; no marker warp; the reversed pheS row excluded; the measured
+lambda prior; kanR's m from the tube totals); validation round 2 runs in
+`planning/studies/two-stage-validation/` (run 2). The paper's analysis,
+thermodynamic models with a floating protein concentration, and
+experimental design come after it. `planning/joint-fit-return.md` is
+paused (binding cannot yet set X's scale; ledger), and
+`planning/relative-m.md` is an idea. The other active plans are
+`planning/congression-physics-plan.md`, `planning/analysis-roadmap.md`
+(steps 2, 6, 7b, 8 and 9 left; outcome so far in
+`planning/analysis-roadmap-summary.md`) and `planning/experiment-pipeline.md`
+(one experiment file from raw data to posterior; steps 0-4, 6 and 7 done,
+step 3 as machinery; step 5 (orchestrator) deferred; next, step 8, a
+science prioritization that takes up
+`planning/offset-mode-growth-transition.md`). The single-MAP real-data
+recipe is `planning/studies/real-data-fit/fit/run_recipe.srun` (it applies
+`planning/dev-data/tube_corrections.yaml` with `tfs-correct-tubes` before
+`tfs-process-counts`); the two-stage fit has replaced it for X. The full-size
+simulation (`planning/studies/full-size-sim/`) is step 8's baseline: X
+undercovers on a full library, worse with read depth, because the k/m/
+dk_geno shared errors are held at the MAP by the arrowhead Laplace.
+`planning/deep-coverage.md` (active, 2026-10-09) works on that: honest
+intervals for the well-measured genotypes from this experiment's data,
+starting with k/m priors from the wt monoculture rates
+(`planning/studies/km-anchor/`). The full-library MAP compresses X about
+1.45-fold (each double pulls m steeper); the fix is a two-stage fit
+(stage 1: wt, spikes and singles with the congression mixture, which
+congression's pheS bias needs; stage 2: the full library with every
+condition's k and m pinned there), with k's uncertainty from a stage-1
+jackknife added as one X shift shared by every genotype
+(`planning/studies/m-bias-factorial/`). `planning/two-stage-fit.md`
+(active) makes it one command; its pieces are in `tfmodel/two_stage.py`. `planning/design-noise-sweep.md` (idea)
+holds the next screen's noise-source sweep.
+
 ## YAML Standards
 
 All YAML files in this codebase follow these conventions. Apply them when creating or modifying any YAML file.
@@ -453,10 +601,13 @@ template:
 - Multi-key variants (multiple keys in one dict) always travel together and are never split.
 - `simulate` / `configure_model` variables go to the config; `template` variables go to the Jinja2 template only.  To share a variable, list it in both sections.
 - Variable names in blocks: `snake_case`.
+- File paths are the grid tool's job, never the run template's, and a grid output directory must stay movable as a unit (on/off a cluster, onto a data partition). Both grid CLIs (output directory `--out_dir`) copy every input file into `<out_dir>/inputs/` once (`grid_utils.InputStager`; a numeric suffix keeps two different files with the same name apart, and a changed file never overwrites an existing copy) and each run refers to it as `../inputs/<name>`. Relative paths resolve against the grid YAML's directory (overrides, `configure_model` variables and template variables) or, for `tfs-setup-sim-grid`, the base config's directory (base config values). A template variable naming an existing file is copied the same way. Setup fails, before writing anything, on a missing or directory input, on a config value outside the known path keys that names an existing file (`grid_utils.check_no_outside_paths`), or on a template that does not render.
+- `tfs-setup-sim-grid`: the config file paths are the key paths in `_SIM_PATH_KEYS` (`setup_sim_grid_cli.py`; `library_genotypes` and nested keys such as `binding_data.*.choose_by` and `empirical.phenotype_model` included, `choose_by` keywords excepted). Add any new file-valued simulate key there.
+- `tfs-setup-grid`: the inputs are the `configure_model` file arguments in `_PATH_KEYS` (`setup_grid_cli.py`: `binding_df`, `growth_df`, `presplit_df`, `base_growth_df`, `thermo_data`, `library_config`). `tfs-configure-model` reads the originals; the written config is then pointed at the copies wherever it recorded them (`data.*`, `components.thermo_data`/`presplit_df`/`base_growth_df`, `library.source`), and setup fails on any other path in it that names a file outside the grid. The priors/guesses/library CSVs are per-run outputs next to the config and stay as they are. Data paths are read relative to the working directory, so a run is launched from its own directory. Add any new file-valued `configure_model` argument to `_PATH_KEYS`.
 
 ### Shared grid utilities
 
-Both grid CLIs import from `tfscreen.util.grid_utils` for run-name generation, Jinja2 environment setup, and config-path rewriting.  Add new reusable grid helpers there rather than duplicating them.
+Both grid CLIs import from `tfscreen.util.grid_utils` for run-name generation, Jinja2 environment setup and input staging (`InputStager`, `check_no_outside_paths`, `stage_template_vars`, `render_run_template`).  Add new reusable grid helpers there rather than duplicating them.
 
 ### `examples/` directory
 
@@ -464,6 +615,7 @@ Both grid CLIs import from `tfscreen.util.grid_utils` for run-name generation, J
 |------|---------|
 | `simulate/simulate_config.yaml` | Canonical well-commented simulate config reference |
 | `simulate/simulate_grid.yaml` | Example simulate grid for `tfs-setup-sim-grid` |
+| `od600/` | Synthetic `tfs-calibrate-od600` inputs (`replicates.csv`, `plate_counts.csv`, from `make_example_data.py`) and the calibration made from them (`od600_calibration.yaml`, used by the simulate example's `od600` block); not any lab's real calibration |
 | `simulate/run.sh` | Jinja2 shell template rendered into each simulate grid run subdir |
 | `simulate-and-analyze/simulate_config.yaml` | Combined simulate + analyze workflow config |
 | `simulate-empirical/simulate_config.yaml` | Simulate config that resamples phenotypes from a `tfs-build-empirical` model (`phenotype_source: empirical`) |
@@ -471,19 +623,24 @@ Both grid CLIs import from `tfscreen.util.grid_utils` for run-name generation, J
 | `simulate-and-analyze/run.sh` | Jinja2 shell template for simulate-and-analyze runs |
 | `tfmodel/grid.yaml` | Example tfmodel grid for `tfs-setup-grid` |
 | `tfmodel/run.srun` | Jinja2 SLURM template rendered into each tfmodel grid run subdir |
+| `tfmodel/two_stage.yaml` | Example `tfs-fit-two-stage` configuration (tfs-configure-model's arguments plus the `two_stage` block) |
+| `tfmodel/growth_priors_loose.csv` | The loose per-condition k/m priors `two_stage.yaml` names (the 2026 screen's) |
+| `tfmodel/two_stage_submit.sh`, `tfmodel/two_stage_step.srun` | Submit `tfs-fit-two-stage` as chained SLURM jobs (jackknife as an array, absolute paths); edit the account/partition in the `.srun`. Documented in `docs/source/screen-analysis.rst`, the real-screen recipe |
 | `process_raw/library_config.yaml` | Minimal library config for `tfs-process-fastq` and `tfs-configure-model --library_config` (genetics keys + `library_mixture`) |
+| `process_raw/` (`make_example_data.py`, `tube_table.csv`, `od600.csv`, `counts/`) | Synthetic tube table, OD600 table and per-tube count files in the formats `tfs-process-counts` reads; run with `../od600/od600_calibration.yaml` |
+| `process_raw/corrections.yaml` | Example `tfs-correct-tubes` corrections file (one swap, one relabel, one exclusion) for `tube_table.csv` |
 
 ### `process_raw` YAML
 
-`tfs-process-fastq` accepts a `run_config` argument that is passed to `LibraryManager`. It reads only the library genetics keys: `reading_frame`, `wt_seq`, `degen_sites`, `tiles`, `expected_5p`, `expected_3p`, `tile_combos`, `spiked_seqs`. All other keys are ignored.
+`tfs-process-fastq` takes the library YAML as its first positional (`library_config`), passed to `LibraryManager`. It reads only the library genetics keys: `reading_frame`, `wt_seq`, `degen_sites`, `tiles`, `expected_5p`, `expected_3p`, `tile_combos`, `spiked_seqs`. All other keys are ignored.
 
-**Required practice**: maintain **one** `run_config.yaml` per experiment and pass that same file to `tfs-simulate`, `tfs-process-fastq` and `tfs-configure-model --library_config`. Nothing in the pipeline checks that the three saw the same file: the genotype-set check in `tfs-configure-model` catches a genetics mismatch, but `library_mixture` (which only `tfs-configure-model` and `tfs-simulate` read) can silently drift. Use `examples/process_raw/library_config.yaml` only when you need a minimal standalone library config (e.g. for processing real data without a matching simulation); note it carries `library_mixture` as well as the genetics keys.
+**Required practice**: maintain **one** library YAML per experiment and pass that same file to `tfs-simulate`, `tfs-process-fastq` and `tfs-configure-model --library_config`. Nothing in the pipeline checks that the three saw the same file: the genotype-set check in `tfs-configure-model` catches a genetics mismatch, but `library_mixture` (which only `tfs-configure-model` and `tfs-simulate` read) can silently drift. Use `examples/process_raw/library_config.yaml` only when you need a minimal standalone library config (e.g. for processing real data without a matching simulation); note it carries `library_mixture` as well as the genetics keys.
 
 ### Terminology
 
 - **Condition**: unique growth setting (marker + selection) — same avg growth rate for same genotype
-- **Sample**: one experimental tube (replicate + condition)
-- **Timepoint**: one aliquot (replicate + condition + time)
+- **Tube = sequenced sample = timepoint**: after `presplit` the culture is split into one tube per condition × titrant × planned timepoint (a grid: rows = timepoints, columns = IPTG); pre-growth and selection both happen in the tubes; a timepoint is taken by pulling every tube in its row and reading its OD600, spinning it down and freezing it; sequencing comes later. Total CFU per tube comes from OD600 through a lab-specific calibration (not in this repo). There are no aliquots from a shared tube (stopping the shaker to pipette disturbed the trajectories more than tube-to-tube noise does). So anything tube-level (growth environment, OD600 reading, PCR) is independent across timepoints, not a trajectory-wide effect. Rationale: `docs/source/process-raw.rst`, "One tube per time-point"
+- **Sample** (in `sample_df`/`sample_ln_cfu`): one sequenced tube, i.e. one replicate × condition × titrant × time. Older text sometimes says "sample" for a replicate × condition; read it per this entry
 - **theta (θ)**: operator occupancy — fraction of operators bound by TF
 - **A**: per-genotype TF activity (multiplied by theta to scale occupancy)
 - **dk_geno**: pleiotropic growth effect of mutation independent of TF activity
@@ -510,23 +667,31 @@ All scripts use `generalized_main` from `tfscreen.util.cli.generalized_main`. Th
 - Parameters **without** a default → positional (required) arguments
 - Parameters **with** a default (including `None`) → `--flag` arguments
 
+- A bool that defaults to False is a `--name` switch; one that defaults to True is turned off with `--no_name` (`--name` is accepted and does nothing). Never invert a flag's meaning.
+- A `None` default reads a string unless `manual_arg_types` gives a type (`int`, `float`, or a list type with `manual_arg_nargs`).
+- The numpydoc docstring is the `--help`: the text before `Parameters` is the description, each parameter's entry is its help, and defaults are appended unless the entry already says "default". Document every parameter.
+- `generalized_main` returns None (console scripts run `sys.exit(main())`; a returned value would exit 1), prints the run's provenance (`util/provenance.py`: version, commit, dirty flag, command) and writes `{out_prefix}_provenance.json` when the function takes `out_prefix`. Configs (`provenance:` block) and fit checkpoints embed `get_provenance()`.
+- After changing a CLI signature or docstring, run `python docs/scripts/make_cli_reference.py`; `tests/tfscreen/util/cli/test_cli_reference.py` fails while `docs/source/cli.rst` is stale.
+
 Positional argument order (use only what the script needs):
-1. `config_file` — path to YAML config
-2. `posterior_file` — path to posteriors `.h5`/`.npz`
+1. `config_file` — path to YAML config (`tfs-process-fastq`: the library YAML)
+2. `param_file` — a posterior `.h5` or a MAP checkpoint `.pkl` (`tfs-sample-posterior` takes `checkpoint_file`)
 3. `data_file` — path to a long-form observable CSV (e.g. `tfs-cat-response`
-   takes `data_file x_obs y_obs`; `tfs-extract-epistasis` takes `data_file y_obs`)
+   takes `data_file x_obs` with `--y_obs`; `tfs-extract-epistasis` takes `data_file` with `--y_obs`)
+
+Shared names: `--seed` for every seed, `--num_workers` (-1 = all CPUs but one), data inputs named as `tfs-configure-model` names them (`growth_df`, `binding_df`, `presplit_df`, `base_growth_df`), the library YAML is always `library_config`.
 
 ### `--library_config`
 
-`tfs-configure-model`, `tfs-fit-genotypes` and `tfs-build-empirical` take the library YAML as `--library_config`. It is an exception to the positional-if-required rule: `tfs-configure-model` requires it only when `growth_df` is given, and `growth_df` is itself a flag, so a positional would be wrong for the binding-only call. Validate it in the function body instead.
+`tfs-configure-model` and `tfs-build-empirical` take the library YAML as `--library_config`. It is an exception to the positional-if-required rule: `tfs-configure-model` requires it only when `growth_df` is given, and `growth_df` is itself a flag, so a positional would be wrong for the binding-only call. Validate it in the function body instead. The same holds for the data: `tfs-configure-model` takes `--binding_df` and `--growth_df` as flags and requires at least one (both: joint model; binding only: binding-only model; growth only: growth-only model).
 
 ### Output flag
 
-Always `--out_prefix` (never `--out_root`, `--out`, or `--output_file`). The function parameter must also be named `out_prefix`.
+Always `--out_prefix` (never `--out_root`, `--out`, `--output_file` or `--output_prefix`). The function parameter must also be named `out_prefix`, and outputs are `{out_prefix}.ext` or `{out_prefix}_<name>.ext`. A command whose output is a directory of files takes `--out_dir` instead (`tfs-process-fastq`, `tfs-setup-grid`, `tfs-setup-sim-grid`).
 
 ### File-backed list arguments
 
-When a list of genotypes, titrant names, or concentrations is needed, the `_cli` wrapper takes file-path strings (one value per line, `#` comments allowed). Use `manual_arg_types` in `generalized_main` to override the `NoneType` inferred from `default=None`. The shared helper `_read_lines(path)` lives in `tfscreen.util.cli`.
+When a list of genotypes, titrant names, or concentrations is needed, the `_cli` wrapper takes file-path strings (one value per line, `#` comments allowed). Use `manual_arg_types` in `generalized_main` to override the `NoneType` inferred from `default=None`. The shared helper `read_lines(path)` lives in `tfscreen.util.cli`.
 
 ### `in_training_data` column
 
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000..bbb8584d
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,210 @@
+# Contributing to tfscreen
+
+Thanks for helping. This file covers how to propose a change and what a pull
+request needs before it can merge.
+
+## Before you start
+
+For anything larger than a bug fix, open an issue first and describe what you
+want to change and why. The model is a coupled system, and a change that looks
+local often touches the priors, the guides, the simulator and the CLIs at once.
+A short discussion up front saves rework.
+
+Ideas, active plans and the studies behind them live in `planning/`. Check
+there before starting work on the model; your idea may already have a plan or
+a recorded reason it was set aside. See `planning/README.md`.
+
+## Workflow
+
+We develop on forks and merge into `main` through pull requests. Nobody pushes
+directly to `main`.
+
+1. Fork `harmslab/tfscreen` on GitHub and clone your fork.
+2. Add the main repository as `upstream`:
+
+   ```bash
+   git remote add upstream git@github.com:harmslab/tfscreen.git
+   ```
+
+3. Create a branch for each change, named for what it does
+   (`fix-presplit-offset`, `add-baranyi-guide`). Keep one logical change per
+   branch.
+4. Before opening the pull request, bring your branch up to date with
+   `upstream/main` and rerun the tests.
+5. Open a pull request into `harmslab/tfscreen:main`. The pull request
+   template asks what changed, why, and how you verified it, and gives a
+   checklist of everything below. Work through it before asking for review.
+
+`main` is a protected branch. GitHub enforces the following:
+
+- Every change goes through a pull request. Direct pushes and force pushes to
+  `main` are blocked.
+- A pull request needs one approving review.
+- All CI checks must pass: the test matrix and the coverage job.
+- The branch must be up to date with `main` before it can merge. If `main`
+  moves while your pull request is open, merge or rebase onto it and let CI
+  run again.
+- Pushing new commits dismisses earlier approvals, so a reviewer has to
+  approve the final version. Finish your changes before asking for a last
+  review.
+
+## Development setup
+
+tfscreen needs Python 3.12 or later. Install it in editable mode with the test
+dependencies:
+
+```bash
+pip install -e ".[test]"
+```
+
+## Tests
+
+Every pull request needs full unit test coverage of the code it adds or
+changes. In practice that means:
+
+- Every new function, branch and error path has a test that exercises it.
+  We measure branch coverage, not just line coverage.
+- A bug fix comes with a test that fails before the fix and passes after it.
+- Tests mirror the source layout: `src/tfscreen/foo/bar.py` is tested in
+  `tests/tfscreen/foo/test_bar.py`.
+- Tests that take more than a few seconds get `@pytest.mark.slow`. They are
+  skipped by default and run with `--runslow`.
+- End-to-end pipeline checks belong in `tests/smoke-tests/`.
+
+`tests/conftest.py` sets `NUMBA_DISABLE_JIT=1`, so no prefix is needed. Run the
+unit tests:
+
+```bash
+pytest tests/tfscreen
+```
+
+Add `-n auto` to run them in parallel (`pytest-xdist`, in the `test` extra).
+
+Check coverage, including slow tests. Coverage settings (branch coverage,
+source) live in `pyproject.toml`, so the same commands work locally and in CI:
+
+```bash
+pytest -n auto --runslow --cov --cov-report= tests/tfscreen
+```
+
+```bash
+coverage report
+```
+
+To see what CI will say about the lines your branch changes:
+
+```bash
+coverage xml
+```
+
+```bash
+diff-cover coverage.xml --compare-branch=upstream/main
+```
+
+Some tests check rules across the whole component registry, for example that
+every per-genotype latent is mini-batch safe and that guides follow the
+`{site}_loc` / `{site}_scale` naming convention. If one of these fails for a
+new component, fix the component rather than the test.
+
+CI runs the unit tests on Linux and macOS for Python 3.12, 3.13 and 3.14, plus
+a lint check for fatal errors. A separate coverage job runs the tests with
+`--runslow` and fails if either check misses:
+
+- Every line a pull request adds or changes must be covered by a test
+  (`diff-cover --fail-under=100`). diff-cover counts lines, not branches, so
+  check the branch columns of `coverage report` for your files yourself.
+- Total coverage of the package must not drop below the floor set in
+  `.github/workflows/tests.yml`. Raise the floor when coverage improves.
+
+Run the same lint locally:
+
+```bash
+flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+```
+
+## Code conventions
+
+`CLAUDE.md` is the detailed reference for the codebase. It is written for AI
+coding tools but serves human contributors equally well. The parts you are
+most likely to need:
+
+- **New model components:** the checklist under "Adding a new model
+  component", including registration and mini-batch safety.
+- **CLI scripts:** the "CLI standards" section. Every `tfs-*` script uses
+  `generalized_main`, lives in `<name>_cli.py`, writes with `--out_prefix`
+  and is registered in `pyproject.toml`. Its docstring is its `--help`, one
+  entry per parameter. After changing any CLI signature or docstring,
+  regenerate the reference page with `python docs/scripts/make_cli_reference.py`;
+  `tests/tfscreen/util/cli/test_cli_reference.py` fails until you do.
+- **YAML files:** the "YAML standards" section.
+- **Quantile outputs:** bare `q<level>` column names, no feature prefix.
+
+### Docstrings
+
+Docstrings follow the
+[numpydoc](https://numpydoc.readthedocs.io/en/latest/format.html) convention.
+Every public function, class and method gets a full docstring: a one-line
+summary, an extended description where needed, then `Parameters`, `Returns`
+and `Raises` sections as they apply. Give each parameter's type and shape,
+for example `numpy.ndarray of shape (num_genotype,)`, and say what units it
+is in when that is not obvious.
+
+```python
+def detect_match_keys(dfs, value_columns=(), match_by=None):
+    """
+    Determine the columns that make a row "the same row" across runs.
+
+    Parameters
+    ----------
+    dfs : list of pandas.DataFrame
+        All tables that must share the key.
+    value_columns : iterable of str, optional
+        Columns holding estimates, excluded from the key.
+    match_by : list of str, optional
+        Explicit key columns. Overrides auto-detection.
+
+    Returns
+    -------
+    list of str
+        The match key columns.
+
+    Raises
+    ------
+    ValueError
+        If the key is not unique within a run.
+    """
+```
+
+Private helpers (leading underscore) may use a one-line docstring when the
+behavior is simple.
+
+### Validation
+
+Configuration errors should fail fast with a clear message. Do not silently
+drop or coerce a value the user supplied.
+
+### Keeping `CLAUDE.md` current
+
+If your change alters how the code works, update `CLAUDE.md` in the same pull
+request so it stays accurate.
+
+## Changelog
+
+Record every user-visible change in `CHANGELOG.md` under `[Unreleased]`, in
+the same pull request as the change. The file follows
+[Keep a Changelog](https://keepachangelog.com/en/1.1.0/). Cite files and
+functions rather than commit hashes.
+
+## Commits
+
+Write commit messages with an imperative subject line of 72 characters or
+fewer ("Add soft-min congression dk rule", not "Added..."). Use the body to say
+why the change was made and what it affects. Use American spelling.
+
+Do not commit data, checkpoints, notebooks of exploratory work or other large
+files. `dev/` is untracked scratch space.
+
+## License
+
+By contributing, you agree that your contributions are licensed under the
+project's license (see `LICENSE`).
diff --git a/README.rst b/README.rst
index e93afdff..cddd6a11 100644
--- a/README.rst
+++ b/README.rst
@@ -2,11 +2,15 @@
 tfscreen
 ========
 
+.. image:: https://github.com/harmslab/tfscreen/actions/workflows/tests.yml/badge.svg?branch=main
+   :target: https://github.com/harmslab/tfscreen/actions/workflows/tests.yml
+   :alt: Tests
+
 `tfscreen` is a Python library for simulating and analyzing high-throughput screens of transcription
 factor (TF) function. It is designed to infer the energetic effects of mutations on conformations
-in the TF energy landscape — information that enables interpretable predictive models of how
-arbitrary combinations of mutations affect the TF response curve (operator binding versus allosteric
-effector).
+in the TF energy landscape. Those effects make it possible to build interpretable models that
+predict how any combination of mutations changes the TF response curve, operator binding against
+allosteric effector.
 
 The core strategy is to measure operator occupancy for thousands of double-mutant cycles across
 multiple allosteric effector concentrations. A single set of energetic effects must explain all
@@ -18,14 +22,17 @@ Occupancy is inferred from *E. coli* growth in a dual-marker selection scheme: *
 resistance, growth enhanced when expressed) and *pheS** (4-chloro-L-phenylalanine sensitivity,
 growth reduced when expressed) respond in opposite directions to a shift in occupancy. Libraries
 of TF variants are grown in different effector/selection combinations and variant frequencies are
-followed over time by direct sequencing. A hierarchical Bayesian model jointly fits the growth
-data and direct binding measurements, learning the relationship between occupancy and growth rate
-along the way.
+followed over time by direct sequencing. A hierarchical Bayesian model fits the read counts,
+alone or jointly with direct binding measurements, and learns the relationship between occupancy
+and growth rate along the way.
 
 Documentation
 -------------
 
-Full documentation is available at https://tfscreen.readthedocs.io.
+Full documentation is available at https://tfscreen.readthedocs.io. To analyze a screen, start
+with the "Analyzing a screen" page: per-tube read counts to per-genotype response curves with
+intervals, through one command, ``tfs-fit-two-stage``. The pipeline page walks from sequencing
+reads to a posterior, and the CLI reference lists every ``tfs-*`` command and flag.
 
 Installation
 ------------
@@ -35,3 +42,7 @@ Clone the repository and install in editable mode::
     git clone https://github.com/harmslab/tfscreen
     cd tfscreen
     pip install -e .
+
+On a Linux machine with a CUDA 12 GPU, use ``pip install -e ".[cuda12]"``
+(plain ``jax`` from pip runs on the CPU only). tfscreen needs Python 3.12 or
+newer.
diff --git a/docs/badges/coverage-badge.svg b/docs/badges/coverage-badge.svg
deleted file mode 100644
index 7d57d56b..00000000
--- a/docs/badges/coverage-badge.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="114" height="20" role="img" aria-label="coverage: 96.16%"><title>coverage: 96.16%</title><filter id="blur"><feGaussianBlur stdDeviation="16"/></filter><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="114" height="20" rx="3"/></clipPath><g clip-path="url(#r)"><rect width="61" height="20" fill="#555"/><rect x="61" width="53" height="20" fill="#4b0"/><rect width="114" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><g transform="scale(.1)"><g aria-hidden="true" fill="#010101"><text x="315" y="150" fill-opacity=".8" filter="url(#blur)" textLength="510">coverage</text><text x="315" y="150" fill-opacity=".3" textLength="510">coverage</text></g><text x="315" y="140" textLength="510">coverage</text></g><g transform="scale(.1)"><g aria-hidden="true" fill="#010101"><text x="865" y="150" fill-opacity=".8" filter="url(#blur)" textLength="430">96.16%</text><text x="865" y="150" fill-opacity=".3" textLength="430">96.16%</text></g><text x="865" y="140" textLength="430">96.16%</text></g></g></svg>
\ No newline at end of file
diff --git a/docs/badges/tests-badge.svg b/docs/badges/tests-badge.svg
deleted file mode 100644
index 934192a8..00000000
--- a/docs/badges/tests-badge.svg
+++ /dev/null
@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="74" height="20" role="img" aria-label="tests: 4420"><title>tests: 4420</title><filter id="blur"><feGaussianBlur stdDeviation="16"/></filter><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="74" height="20" rx="3"/></clipPath><g clip-path="url(#r)"><rect width="37" height="20" fill="#555"/><rect x="37" width="37" height="20" fill="#4b0"/><rect width="74" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><g transform="scale(.1)"><g aria-hidden="true" fill="#010101"><text x="195" y="150" fill-opacity=".8" filter="url(#blur)" textLength="270">tests</text><text x="195" y="150" fill-opacity=".3" textLength="270">tests</text></g><text x="195" y="140" textLength="270">tests</text></g><g transform="scale(.1)"><g aria-hidden="true" fill="#010101"><text x="545" y="150" fill-opacity=".8" filter="url(#blur)" textLength="270">4420</text><text x="545" y="150" fill-opacity=".3" textLength="270">4420</text></g><text x="545" y="140" textLength="270">4420</text></g></g></svg>
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diff --git a/docs/manuscript/2026-04-22_methods-draft.md b/docs/manuscript/2026-04-22_methods-draft.md
new file mode 100644
index 00000000..f01c50d9
--- /dev/null
+++ b/docs/manuscript/2026-04-22_methods-draft.md
@@ -0,0 +1,102 @@
+# Computational Methods (Rough Draft)
+
+## Overview
+
+We measured ln colony-forming units (ln CFU) as a function of IPTG concentration under two selective conditions. We aimed to use these data to infer the fractional occupancy (θ) of LacI bound to the operator upstream of both reporter genes. The full model comprised a three-level stack: a biophysical Hill model mapped binding parameters to fractional occupancy; a multiple-transformation correction adjusted that occupancy for plasmid copy number heterogeneity; and a linking function mapped corrected occupancy to observed ln CFU over time.
+
+## Selective Conditions
+
+We used two selection schemes to jointly constrain the occupancy inference. The first used *pheS* (4-chlorophenylalanine sensitivity) to measure **repression**: cells grew best when the LacI repressor was bound to the operator, blocking expression of the *pheS* susceptibility gene. The second used kanamycin resistance (*kanR*) to measure **induction**: cells grew best when the repressor was displaced from the operator, permitting KanR expression. Because these two conditions responded in opposite directions to occupancy, fitting them simultaneously allowed robust inference of θ across the dynamic range of repressor function.
+
+## Data
+
+We jointly fit two types of data. The first were high-throughput growth measurements: 209,000 mutant cycles covering approximately 210,000 distinct genotypes, with ln CFU measured across a structured set of conditions. Each genotype yielded up to 60 observations per replicate. We observed each genotype at 8 IPTG concentrations between 0 and 1 mM using each of the two reporters (kanR and pheS) under appropriate selection (kanamycin and 4CP). We also included 4 unselected controls (0 mM and 1 mM IPTG paired with each reporter in the absence of selection agent, allowing use to measure pre-selection growth rates). Together, this yielded 20 total conditions, each measured at 3 time points. We performed the full experiment in biological duplicate, yielding 120 observations per genotype.
+
+The second data type was low-throughput *in vitro* binding measurements: fractional occupancy of selected LacI variants binding to the same operator sequence as we used *in vivo*, measured by fluorescence anisotropy in at least biological triplicate. We used the standard deviation across replicates the uncertainty on each θ measurement. We used these data in the model to anchor the Hill model parameters to physical binding affinities.
+
+To assess inference quality, we spiked four additional genotypes with known binding properties into the library: the double mutants M42I/H74A, M42I/K84L, and H74A/K84L, and the triple mutant M42I/H74A/K84L. We did not provide their independently measured θ values to the model, allowing us to compare the inferred Hill parameters against ground-truth measurements as a post-hoc validation.
+
+## Sequencing and CFU Computation
+
+We derived the high-throughput growth data from deep sequencing of pooled library samples. We performed sequencing at the University of Oregon GC3F facility using 150 bp paired-end reads on the NovaSeq platform, yielding approximately 2.5 billion read pairs per biological replicate. We multiplexed samples using barcoding, and the sequencing facility demultiplexed them prior to analysis.
+
+We performed read assignment using tfscreen, mapping reads to the *lacI* reference sequence via a direct sequencing strategy. We assigned a base position an "n" if its PHRED quality score was below 15. For positions in the center of the gene with overlapping forward and reverse reads, we called bases hierarchically: if reads agreed, we used their consensus; if both reads disagreed and neither was "n", we assigned "n" to both; if one read had a non-n call and the other was "n", we assigned the non-n call. We matched reads against the known library sequences, allowing up to one base mismatch with an expected sequence, and discarded reads equidistant from two or more library members as ambiguous.
+
+We converted reads to per-genotype frequencies within each sample (defined by IPTG concentration, selection condition, biological replicate, and time point). We retained a genotype for analysis only if we observed it more than 15 times across all 60 conditions within a biological replicate, ensuring sufficient coverage for reliable frequency estimation. We added a pseudocount of one to all read counts for numerical stability. To convert frequencies to absolute CFU, we estimated the total CFU for each sample from OD$_{600}$ measurements taken in biological triplicate and averaged across replicates. We converted OD$_{600}$ to CFU using a second-order polynomial calibration curve derived from 25 carefully measured samples spanning the relevant density range, obtaining colony counts by serial dilution plating. We computed absolute per-genotype CFU as the product of the genotype frequency and the total CFU for that sample, and log-transformed the result to give ln CFU.
+
+We propagated uncertainty on ln CFU from two independent sources: counting noise in the sequencing frequencies and variability in the total CFU estimate across triplicates. Because ln CFU = ln(frequency) + ln(sample CFU), the variance on ln CFU is the sum of the relative variances of its two components (delta method):
+
+$$\sigma^2_{\ln \mathrm{CFU}} = \frac{\sigma^2_f}{f^2} + \frac{\sigma^2_C}{C^2}$$
+
+The frequency variance $\sigma^2_f = f(1-f)/N$ followed from the binomial distribution, where $f$ was the estimated frequency and $N$ was the total read count for that sample. The CFU variance $\sigma^2_C$ was the squared standard deviation of the total CFU across the three biological replicates. We propagated this combined $\sigma^2_{\ln \mathrm{CFU}}$ into the likelihood as the scale parameter on the growth model distribution.
+
+## Biophysical Model
+
+We modeled fractional occupancy using a scaled Hill equation:
+
+$$\theta(\mathrm{[IPTG]}) = \theta_\mathrm{min} + \Delta\theta \cdot \frac{[\mathrm{IPTG}]^n}{[\mathrm{IPTG}]^n + K^n}$$
+
+where $\Delta\theta = \theta_\mathrm{max} - \theta_\mathrm{min}$ was the change in occupancy from zero to saturating IPTG, $K$ was the binding constant, and $n$ was the Hill coefficient. We inferred the four parameters ($K$, $n$, $\theta_\mathrm{min}$, $\theta_\mathrm{max}$) in the hierarchical Bayesian framework described below.
+
+## Multiple-Transformation Correction
+
+Before passing the Hill-model occupancy θ to the linking function, we applied a correction for plasmid copy number heterogeneity. In a high-throughput transformation, most cells take up a single plasmid copy, but a minority acquire two or more. We empirically characterized this distribution and found it followed a zero-truncated Poisson with parameter λ (predominantly one copy, rarely two, vanishingly rarely three).
+
+Because LacI is a repressor, alleles with higher operator affinity will exhibit a dominant phenotype when present alongside a lower-affinity allele in the same cell. Multiple transformation thus confounded our inference: if a given genotype's frequency changed over the course of selection, it could reflect its own fitness or the fact that it was co-transformed with a dominant plasmid. This effect systematically biased our estimates of low occupancies ($\theta \rightarrow 0$), because the lower the value of $\theta$, the higher the probability that it was masked by a co-transformed variant with higher affinity.
+
+We applied the correction as follows. At each iteration of the model, we built an empirical distribution of θ values across all genotypes in the current batch. For each genotype's θ, we computed the probability — given λ and this empirical distribution — that an observed cell carried a second, distinct plasmid whose θ masked the genotype's own fitness. Genotypes with high θ had a low probability of being masked; those with low θ were more susceptible. We computed the corrected θ by integrating over this sampling probability. [*Equation to be added.*]
+
+We parameterized the correction with a single λ, using a prior informed by an independent empirical measurement of co-transformation rates. To measure λ, we transformed bacteria with an equimolar mixture of 8 plasmids that differed slightly in size but shared a single restriction site. We extracted plasmids from 107 individual colonies, digested them with the shared restriction enzyme, and resolved the products by gel electrophoresis; the number of bands per lane directly reported the plasmid copy number for that colony. We fit a zero-truncated Poisson to these counts by maximum likelihood, yielding λ = 0.4 (range 0.1–0.6). Zero-truncation was necessary because cells that failed to take up any plasmid were lethal under selection and therefore unobservable. We used the resulting estimate and its uncertainty as the prior on λ in the full model.
+
+## Linking Function
+
+We defined a *linking function* that predicted ln CFU as a function of time given the corrected θ. The core assumption--borne out experimentally--was that growth rate scaled linearly with θ. Cells experienced two sequential growth phases: a pre-selection phase (duration $t_\mathrm{pre}$) followed by a selective phase measured at multiple durations $t_\mathrm{sel}$. The full model was:
+
+$$\ln \mathrm{CFU} = \ln \mathrm{CFU}_0 + \bigl(\theta \cdot m_\mathrm{pre} + b_\mathrm{pre} + \Delta k_\mathrm{geno}\bigr)\, (t_\mathrm{pre} + \tau) + \bigl(\theta \cdot m_\mathrm{sel} + b_\mathrm{sel} + \Delta k_\mathrm{geno}\bigr)\,(t_\mathrm{sel} - \tau)$$
+
+The parameters were:
+
+- $\ln \mathrm{CFU}_0$: initial colony count for a given genotype
+- $\theta \in [0, 1]$: corrected fractional occupancy of the operator (0 = fully unoccupied, 1 = fully occupied)
+- $m_\mathrm{pre}$, $b_\mathrm{pre}$: slope and intercept mapping θ to growth rate during pre-selection
+- $m_\mathrm{sel}$, $b_\mathrm{sel}$: analogous slope and intercept during selection
+- $\Delta k_\mathrm{geno}$: pleiotropic fitness effect of a mutation, independent of its effect on operator occupancy (for example, a destabilizing mutation may impose a metabolic cost from misfolded protein regardless of its DNA-binding phenotype)
+- $\tau$: lag parameter describing the delay in growth rate adjustment when transitioning from pre-selection to selective conditions (see below)
+
+## Lag Model
+
+We evaluated three functional forms for the lag: an instantaneous transition model (no lag), a model with a sigmoidal transition integrated over time, and a memory model in which the lag depended on current occupancy. We selected the model based on posterior predictive checks, with visual inspection confirming a clear winner. The memory model performed substantially better than the alternatives [*quantitative comparison to be added*]:
+
+$$\tau = \tau_0 + \frac{k_1}{k_2 + \theta}$$
+
+When θ was large (repressor tightly bound, downstream gene expression suppressed), the denominator was large and the lag was shorter — the cell required little time to adjust because the gene product was already scarce. When θ was small (operator largely unoccupied, gene product abundant), the lag was longer because the existing product had to be diluted or turned over before growth under the new condition stabilized.
+
+## Parameter Inference
+
+### Likelihood
+
+We evaluated two likelihood terms simultaneously. We fit the growth data with a Student-t likelihood, which provided robustness to occasional outliers; an equivalent Normal likelihood yielded similar results. We fit the in vitro fluorescence anisotropy binding data with a Normal likelihood. In both cases, we set the observation variance to the experimental uncertainty in the $\ln \mathrm{CFU}$ and $\theta$ values. The tight experimental uncertainties on the in vitro measurements produced a sharp likelihood that effectively pinned the Hill model parameters to physically measured binding affinities without requiring those measurements to function as priors.
+
+### Prior structure
+
+Model parameters fell into two categories. Parameters that appeared once across the entire dataset — the linking function growth terms ($m_\mathrm{pre}$, $b_\mathrm{pre}$, $m_\mathrm{sel}$, $b_\mathrm{sel}$), the lag terms ($\tau_0$, $k_1$, $k_2$), and the co-transformation rate (λ) — we assigned simple, weakly informative priors. We additionally informed the prior on λ using the externally measured co-transformation rates described above.
+
+Parameters that varied across genotypes — $\Delta k_\mathrm{geno}$ and the Hill model parameters ($K$, $n$, $\theta_\mathrm{min}$, $\theta_\mathrm{max}$) — we assigned hierarchical priors, with separate hyperparameter distributions learned for single mutants and double mutants. This distinction reflected the experimental design: we spiked single mutants into the library at higher initial frequencies than double mutants, so the two classes were expected to have different $\ln \mathrm{CFU}_0$ distributions. For all hierarchical parameters, we drew per-genotype values from a Normal distribution whose location and scale were themselves treated as random variables: we placed a Normal hyperprior on the location and a HalfNormal hyperprior on the scale, and learned these hyperparameters jointly from all genotypes within each class.
+
+We assigned wild type and the four reference genotypes (WT, M42I, H74A, K84L) — spiked in at substantially higher concentrations than the bulk library — individual simple priors on $\ln \mathrm{CFU}_0$ rather than drawing them from either hierarchical class.
+
+### Fitting procedure
+
+We performed MAP optimization using NumPyro's AutoDelta guide in two steps: first, a constrained fit in which we optimized only growth parameters (τ and the pre-selection slope) while holding all other parameters at simple initial values; then an unconstrained MAP over all parameters simultaneously. This initialization strategy provided stable starting points for the stochastic phase.
+
+We then ran SVI using NumPyro's clipped Adam optimizer with an exponentially decaying step size schedule, starting at $10^{-3}$ and decaying to $10^{-6}$. Each SVI step used 2 ELBO particles and operated on a minibatch of 1,024 genotypes: 1,020 drawn from the library and the 4 reference genotypes included in every batch. Because we oversampled the reference genotypes relative to their true library frequency, we used JAX's built-in importance weighting to correct their contribution to the ELBO.
+
+We declared convergence within a run when the mean ELBO changed by less than $10^{-6}$ over a sliding window of 100 epochs, sustained for at least 10 consecutive windows (patience counter). [*Total epoch counts to be added.*] We assessed convergence across runs by comparing posterior parameter estimates across multiple random seeds. [*Cross-seed convergence thresholds to be added.*]
+
+### Outputs
+
+After SVI, we extracted posterior distributions for each model parameter and derived point estimates and 95% credibility intervals. The primary outputs reported per genotype were the Hill model parameters: binding constant $K$, Hill coefficient $n$, unoccupied-state baseline $\theta_\mathrm{min}$, and bound-state baseline $\theta_\mathrm{max}$.
+
+## Implementation
+
+We implemented the model in Python using the standard scientific computing stack. We performed probabilistic modeling and inference with NumPyro and ran all calculations on NVIDIA A100 GPUs via the University of Oregon TALAPAS high-performance computing cluster. The implementation is available as the open-source package **tfscreen** at https://github.com/harmslab/tfscreen.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 10.txt b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 10.txt
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+Actually one more clarification. We used simple priors for log CFU0 for wild type
+and for spiked genotypes and then we used hierarchical priors in two
+different classes. One set of hierarchical priors for single mutants and
+a separate set of hierarchical priors for double mutants. That's
+because the single mutants by design had a higher initial frequency in the
+library than the double mutants so we wanted to capture that in our main
+analysis.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 11.txt b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 11.txt
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+So let's add a few more experimental details. So another detail is that we
+spiked in an additional four genotypes that we did not include measured values
+of theta four, but we did have the measured theta so we can basically check
+the quality of our inference by asking whether or not these values had good
+estimates. So those genotypes that we included with measured theta but we
+didn't include it in the fit were double mutants of M42I H74A, H74I M40, sorry, H74A M42I, sorry, let's
+start with this. So we've got M42I H74A, M42I K84L, and then H74A K84L, and then the
+triple mutant with all three. So those were included as spiked samples where
+we knew their binding constants or their K values but we didn't actually put them
+into the estimate. Another control that we did was making sure that we measured
+lambda with some frequency and the way that we did that is we transformed
+bacteria with eight different plasmids that differed slightly in size but all
+shared a single shared restriction enzyme. We then transformed using exactly the
+same transformation protocol that we did by even mixture of these plasmids,
+extracted plasmids from single colonies, digested the resulting plasmids using the
+restriction enzyme, and then random on a gel allowing us to just count how many
+plasmids were present by the number of bands that came out on the gel. We did
+this for 107 different transformations allowing us to estimate our lambda. We
+did that estimate using a maximum likelihood fit of a truncated Poisson
+distribution to these values, these data, allowing us to estimate both the lambda
+which ended up being 0.4 and the uncertainty on the lambda. So lambda
+basically had ranged from 0.1 up to about 0.6. So we used those as priors,
+those values from the experiment as priors on our lambda that we
+estimated from the actual experiment itself. We had to use zero truncated
+Poisson because we're not able to observe cells that don't pick up plasmids,
+right? So if a cell doesn't pick up a plasmid, it would die in our selection.
+And further, there's a whole bunch of reasons that a cell would have zero
+plasmids. So it's actually two different processes. So all we can see are cells
+that pick up one, two, three, etc. or more plasmids. Okay, so that's information
+about different genotypes about the lambda. I think that's all I'm going to include in this book.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 3.txt b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 3.txt
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+So I'm thinking through how to write up the computational methods section for an upcoming
+paper we're writing about the lack repressor and lack repressor high throughput screen.
+So I'm going to just give some random texts, thinking out loud, blathering, talking, and
+then let's turn this into a rough draft of the method.
+So the output from our experiment is a collection of measured log CFU as a function of IPTG
+concentration and as a function of different selective conditions.
+Our two selective conditions are phi s and canamycin.
+The phi s condition is scans, measures repression because cells will grow best when the lack
+repressor is bound to the operator and the canamycin selector measures induction because
+cells will grow best when they're inducing the repressor or they're making the repressor
+come off the operator.
+The goal is to use this combined data to infer the fractional occupancy of the transcription
+factor bound to the operator that's in front of both can and sorry can r and phi s.
+The way that we go about this is by defining a linking function.
+The linking function maps allows us to predict the fractional sorry the log CFU as a function
+of time given the occupancy of the transcription factor.
+So the core idea of the linking function is that it consists of a map between growth rate
+and fractional occupancy that can then be fit using a hierarchical Bayesian approach.
+And so overall the math looks like this, we'll see how good the math transcription is here
+for Claude, it's the log CFU goes like the log CFU not so the initial CFU for that genotype
+plus parentheses, the fractional occupancy which is theta times the slope plus the intercept
+plus delta k geno that whole block is in parentheses times the time for the precondition plus tau
+at another term that's going to be theta times the slope under selection plus the intercept
+under selection plus delta k geno again and parentheses on that and then parentheses
+t selection minus tau and parentheses.
+This is our core linking model, you'll notice there are theta terms, those are the fractional
+occupancies that map to the growth rate in a linear sense.
+So basically the slope times theta is how much does increasing theta between zero and
+one change the growth rate and then the intercept terms here are just the growth rate when the
+operator is completely unoccupied.
+Finally delta k geno is the pleiotropic effect of mutations, so if a mutation occurs how
+does that affect growth rate independent of fractional occupancy, this captures the fact
+that say a mutation that causes the protein to become destabilized might incur a metabolic
+fitness effect just because it's a poorly folded protein.
+The tau term is the latency or the lag when we're going from our pre-growth conditions
+to our selective conditions, sorry call them our pre-selection conditions to our pre-growth
+conditions, well sorry let's try that again.
+T pre is the time that our cells spend in the pre-selection conditions, T cell is the
+amount of time the protein spent in our selective conditions, we're going to measure the log
+CFU at different T cell.
+The tau term describes the lag in the growth rate going from the pre-condition to the selective
+condition.
+We tried a number of different models for tau here, we ended up with the following that
+tau, the lag, equals tau naught plus k1 over parenthesis k2 plus theta in parenthesis,
+so this tau is basically saying that the amount of time that it takes to lag actually depends
+on the occupancy, so if something is very tightly occupied, it is repressing expression,
+the change will be relatively rapid because you have a larger number on the bottom, whereas
+if tau is very zero, it's going to be as fast as possible because there's a large amount
+of the product that was expressed.
+So that defines the whole linking function, letting us link theta to log CFU.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 4.txt b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 4.txt
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+The next part of the model is the biophysical model for transcription vector binding.
+So for this what we're doing is we take the standard hill model with a binding constant,
+a cooperativity parameter, a bound baseline parameter, and an unoccupied baseline parameter,
+and we fit that to each of the fractional occupancy data.
+We do this in a hierarchical Bayesian context where we are inferring the prior distributions
+for all four of these parameters from the data together.
+The last piece of the mapping for the total function is actually a correction that occurs
+between that biophysical fractional occupancy calculated with the hill model and the theta
+that we feed into our linking function.
+That's actually a correction for multiple transformations.
+So one of the challenges in a high-throughbrit screen like this is that when we transform
+bacteria, most of them pick up one copy of a plasmid, but some pick up more than one.
+We empirically measure this and found that our cells pick up a zero truncated Poisson
+distribution, pick up most of the time one, a very small fraction of the time two, and
+even a smaller fraction of the time three plasmids.
+What this correction parameter does is it actually captures the fact that when we look
+at the growth rate of a cell or the frequency of a different genotypes going up in the population,
+it could be going up because it, in fact, is higher fitness or because that plasmid was
+co-transformed with another plasmid that has high fitness, because the amount of protein
+in each cell is higher than the amount of DNA, and because this is a repressor, things
+that have a higher binding affinity will always win the exhibit a dominance phenotype.
+And so what we need to be able to do is correct for that dominance by asking when a frequency
+of a gene is going up, is it going up because it's being dragged out by another gene, and
+is it going down because it's being dragged down by another gene.
+So to correct for this, we have a sampling from the, we take each theta and then we ask
+what's the probability that it's sampling from another theta, and we use that to effectively
+correct that theta.
+So then if we stack all of these models together, we'll start from the bottom, we have a biophysical
+model of transcription factor occupancy with things like binding constants and hill coefficients.
+Those are things we want to learn.
+We take those values, we run them, and we're sampling hierarchically from their priors.
+We then take that theta, that occupancy, and we feed it through a correction that accounts
+for multiple transformation.
+In this case, it has a single parameter lambda that we learned from all of our data all at
+once with a prior that we set from externally measured co-transformation lambda, so it puts
+on control parameter.
+And then we take this corrected theta and we feed it through our linking function.
+We then learn those parameters to describe the whole data.
+So that's the entire model of stack.
+Let's go through the priors that are on each of these model parameters, and so for the
+linking function, the growth parameters, so the slope in the precondition, the intercept
+in the precondition, the slope in the selection condition, and the growth in the selection
+condition, these terms are all used simple priors.
+That is because they only occur once, there's one across the whole data set, so there's
+no way to learn a collection, learn a hyper, sorry, no way to learn parameters for some
+hyperdistribution from this, right?
+It's just a single simple parameter.
+Likewise, this is true for the tau terms, tau naught, k1, and k2.
+These use simple priors because they are single terms that are used for the entire data set.
+If we look at the correction for multiple transformations, again we have a single simple
+prior on lambda because there's one lambda for the entire data set, so we set that prior
+and then go from there.
+But then pretty much everything else uses a hierarchical priors situation where we're
+learning the prior distribution from the data.
+So we set a broad hyper-prior distribution and then we learn the actual distribution
+from the data.
+So the terms that are used with this kind of prior approach are the delta k genome,
+the log CFU naught, the hill coefficient parameters, so k and then the baseline parameters
+for that all use a hierarchical Bayesian model.
+One last note on the nature of the priors, there's a special case.
+We have several genotypes that we spike in at high concentrations.
+These include wild type as well as a set of controls where we know what their binding
+measurements are.
+These spiked genotypes actually use a standard single prior for log CFU naught, their initial
+concentration, because their initial concentrations are much different than the rest of the population
+and at the same time have their own values, so they're sampled from their own simple priors.
+So what kinds of data do we do our fitting on?
+So this is this big complicated model, how do we learn it?
+So we feed in two types of data into the model.
+The first are the data that I already mentioned, these are growth data that we collect in high
+throughput.
+For our experiment we have 209 mutant cycles corresponding to, I think we get a number,
+but something like 210,000 different genotypes that we measure growth as a function of time
+in different IPTG, canamison, and 4CP concentrations.
+We then take those data and we complement it with some low throughput data which we used
+to measure bindings.
+We measured the fractional occupancy of different genotypes of the lacropressor to the same
+operator in vitro using fluorescence and isotropy.
+We then put both of those data into the hierarchical model and then attempt to find parameters or
+sample parameters that line up with this.
+So how do we actually do that sampling process?
+We use a two-stage process.
+We first start with a simple maximum aposterior I run, where we run a quick optimization to
+get good guesses for the initial parameters.
+We do this actually in two stages.
+We do one fix, fit where we fit our growth parameters, things like tau and the pre-selection
+slope.
+So we do a map run on those, pinning every other parameter and fitting a very simple
+binding model to everything else, sorry, a simple model, and the idea there is to really
+get good initial guesses for those terms.
+We then do unconstrained map on all parameters at once to move away from whatever our starting
+guesses are, to much better starting guesses.
+And then we follow that up with stochastic variance, sorry, stochastic variational inference
+SVI, which we then run until we see convergence for multiple things.
+We do this multiple times with different seeds to make sure that we see convergent results
+on our final outputs.
+But at the end of the day, the key thing that we're pulling out are these hill coefficients
+K, N, and then the baselines, how much is bound in the absence of IPTG, how much is
+bound in the presence of IPTG.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 5.txt b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 5.txt
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+I'm now going to talk about some of the computational architecture that we used for this.
+So we implemented our model inside of a Python framework using the standard scientific computing
+stack.
+The model itself was implemented in Jack's NumPyro with all of the tools that come with
+that.
+We ran all of our calculations on NVIDIA A100 GPUs that were available in the University
+of Oregon TALAPAS computing cluster.
+I think that pretty much covers sort of the Python sort of computational architecture
+there.
+Yeah, I guess that's actually about it.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 6.txt b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 6.txt
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+Some other thoughts about what to mention in here.
+We actually ended up measuring 60 observations in our, sorry, in our experimental pipeline.
+We get 60 different observations for each genotype.
+So that is because we're covering eight different IPTG concentrations with selection, with canomycin
+or with 4CP.
+So that's 16.
+We then measure four different controls.
+So zero millimolar IPTG and one millimolar IPTG with the fee S, excuse me, with the fee
+S or the can R in the absence of selection.
+So those are basically getting the pre-growth rates or the growth rates without actually
+adding selection.
+So that brings us to 20.
+And then we measure three different time points for each one.
+So as the cells progressed over time, we get three observations.
+So that gives us 60 different condition observations.
+We then pulled out genotype frequency information for each of those.
+So each genotype has 60 possible observations.
+We then did the entire experiment in biological duplicate.
+So now we have two sets of independent measurements, giving us 120 observations per genotype.
+That we are trying to ultimately use to constrain four parameters, the hill coefficient, K,
+N, and then the baseline betas.
+So that's what going is experiment.
+Let's also talk about how we pulled parameters out.
+So after we do the SVI runs, what we do is we then extract posterior distributions for
+each of the different parameters.
+Pull from those posterior distributions to get credibility intervals for each of these.
+And so that gives us a 95% credibility region for the binding constant for each of those.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 7.txt b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 7.txt
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+I guess we should also note that this implementation of our library, TF screen, that is the letter
+T, the letter F, S-C-R-E-E-N, lowercase, we implemented TF screen and it is available
+on github at HTTPS github.com slash harm's lab slash TF screen so anybody can use it.
+I think that's all that's important with that.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 8.txt b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 8.txt
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+All right, so let's go through these questions that we need to add.
+So what is the Hill model equation?
+The Hill model equation is the theta with zero IPTG plus delta theta, that's the change
+in theta upon binding, upon going to fully saturating IPTG.
+That's multiplied by the IPTG concentration raised to the N over the IPTG concentration
+raised to the N plus the equilibrium constant raised to the N, where N is the hill coefficient.
+The multiple transformation correction math, I'm going to skip for now, but I'll get to
+that in a second.
+I'm going to add a block that just says there's some math to be done here, let me know.
+For the likelihood and noise model, we actually use the student T probability distribution
+to account for some possible outliers.
+We also actually tried it with normal distribution and saw no huge difference between the two.
+We did not actually add specifically, in that we estimated the variance on the distribution
+directly.
+We did not add any specific noise.
+We have our deterministic model of binding, which we then use to attempt to deterministically
+reproduce the binding and the growth data without actually adding a specific noise model
+to that.
+We just infer the variance on the probability distribution as our noise model.
+For the lag model selection, we basically did posterior predictive checks formally,
+but visually it was very clear that this was the best of the models that we tried.
+The models that we tried were an instantaneous transition model, so assuming no lag, a baryony
+transition model, which basically has a integrated sigmoidal curve that gives rise to the amount
+of time that it takes for a transition.
+Then the memory model that we described, that was the best one by far.
+I can add a note, actually, to the text to say we're going to add some numbers to support
+that, but for now we're just going to say we did the posterior predictive and visual
+inspection checks.
+How are the logs, if you can compute it from sign sequencing data, yes, let's add that
+to the top.
+The way that this is done is we did noviceek high throughput sequencing, which gave us
+approximately 2.5 billion reads per bioreplicate using barcoding to be able to pool samples
+into single reads.
+We demultiplexed those reads using standard, actually I'll describe the reads really quickly,
+so we did 150 base pair paired end reads on noviceek, so that's 2.5 billion paired end
+reads per run.
+We demultiplexed from our sequencing core itself, all sequencing was done at the UOGC3F,
+that is University of Oregon Genome, actually I don't know what I was saying before, but
+it's GC3F Gamma Charlie 3 Foxtrot, the UOGC3F facility, so they ran our sequencing for us
+and then demultiplexed.
+Once we had the demultiplexed reads, we then assigned reads using the TF screen library.
+We used a direct sequencing strategy here, so we were asking could we map our reads back
+onto our reference gene, the lacropressor gene, and also ask if we could do base calling.
+We did the base calling in a sort of multi-level strategy.
+We basically took every base in the read and we looked at its Q score and a Q score less
+than 15 was assigned to an N at that position.
+We then asked whether this was compatible with a unique gene in our library, so we specified
+what was in our library to begin with and asked could we match it to that library to
+do our read calling, and we basically allowed no more than one mismatch with the existing
+library and if there was ambiguity, meaning that it was one base away from more than one
+library, remember we threw the read out, so that was our primary quality control.
+We then took this collection of reads and on a per genotype basis asked if we saw that
+genotype more than 15 times within a bio-replicant across all of the different read conditions.
+This is basically asking whether we truly saw this genotype somewhere in the library.
+Once we had that, that gave us a raw count per position, for a raw count per genotype
+per condition.
+We then converted these counts into frequencies, so within a given population, so a given IPDG
+concentration, bio-replicant, pre-selection condition, and post-selection vision that
+we could uniquely define each tube that it came from effectively.
+We then calculated the frequency of each gene using a pseudo-count strategy.
+We added a pseudo-count of one for numerical stability.
+We then took these frequencies and multiplied the frequencies by the overall CFU port for
+that condition.
+We got the CFU for that condition by actually doing the experiment in a biological triplicate
+and measuring the OD600 at each bioreplicant and then taking the average across the bioreplicates
+to give us an estimate for how the OD600 changed as a function of time in these samples.
+We converted the OD600 into a log CFU using a careful plate calibration and polynomial
+fitting strategy where we measured OD600 very closely for 25 different conditions, and then
+we did serial dilution plating to measure how that mapped to colony forming units.
+That allowed us to define a second-order polynomial that allowed us to map from the OD600 to the
+CFUs.
+Now we have the total CFUs for each tube, and we have the frequency of each genotype
+in that.
+We multiply those numbers together to get us the absolute CFU for that genotype under
+those conditions.
+Finally, the last step is to do log conversion to convert those into log CFU as a function
+of time.
+Then with the In vivo binding data, how did they enter the likelihood?
+The way that this worked is we actually had two observations, so we had two likelihood
+terms.
+One likelihood term using a student T for the growth data.
+Our second likelihood term is a normal distribution for the binding curves, both go in at the
+same time.
+We did not use these to set priors.
+The priors were previously existing.
+We did have those.
+We had very tight uncertainties on these values experimentally, so the likelihood function
+was quite sharp around the correct theta for each of these in vitro binding data.
+Another feature of this is that we made sure, since we're using SVI, we're having to sample,
+we used a batch size of 1024.
+We found empirically that that gave us the best outcomes.
+We're looking at 1,024 genotypes per run.
+We also included the genotypes which measured theta in every single batch.
+Actually, I'll say what those genotypes are.
+The genotypes were wild type, M42I, H74A, K84L.
+We included those in every single batch, those four genotypes.
+We had 1,020 library genotypes and these four in every single genotype.
+Then we used JAX's internal tools to weight them correctly so that we got a correct likelihood
+weight.
+We were basically oversampling on these genotypes so we make sure they have a lower weight in
+the final analysis.
+Then I'll get to the SVI conversion criteria later, but at some level the primary convergence
+criteria was an elbow plateau and then making sure that we saw parameter stability across
+different parameter seeds.
+Those were the two major mechanisms we used.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 9.txt b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 9.txt
new file mode 100644
index 00000000..45ef45c9
--- /dev/null
+++ b/docs/manuscript/verbal-transcripts/2026-04-22_University of Oregon 9.txt	
@@ -0,0 +1,31 @@
+All right some quick notes on the transformation correction which I kind of
+left as vague before. What we did is we empirically built a distribution of
+Thetas at every iteration of the model and then integrated this set of theta
+values. We then sampled from that distribution and so we could say all
+right for a given theta that we have what's the probability given the lambda
+and this empirical distribution what's the probability that my particular theta
+is being masked by another one. So if theta is very very high for example the
+probability of being masked is very low. If theta is very very low the
+probability of being masked is higher. Conditioned of course on that lambda the
+Poisson parameter there we go brain is falling apart here. Okay so that's how we
+did that there I'm actually gonna have to pull up the equation later which I
+don't have so again put a pin in that saying we have to add that that data to
+that. The other thing I wanted to add into this sort of extra bit is some of
+the parameters that we used in the actual SVI run so we use the atom
+optimizer which is built into
+sorry we used the clipped atom optimizer for SVI that is built into NumPyro and
+we use the auto delta optimizer for our map estimates. When we did our run we
+used a exponential decay in our step sizes we started with an atom step size
+of one times ten to the minus three and reached to a one time ten to the minus
+six step size. We did this with two elbow particles and did this for some
+number of epochs. I actually don't have the number of epochs on me. We used a
+convergence criteria for having the elbow score changed by less than ten to
+the minus six over a sliding window of a hundred epochs so we could pair the
+first hundred epochs the average elbow and the first hundred epochs the average
+elbow and the next set of hundred epochs. We then asked did they both or they
+both was the difference between them less than ten to the minus six and then
+we did this actually with a patience counterbase. We had to see that for at
+least ten times in a row before we decided that the system was converged so
+that was looking for convergence during each run. We then looked for convergence
+the level of the posterior parameters and I'm actually gonna have to give you the
+numbers for that later so we're gonna leave that now off that now.
diff --git a/docs/manuscript/verbal-transcripts/2026-04-23_predict-efffecs-mutation-protein.txt b/docs/manuscript/verbal-transcripts/2026-04-23_predict-efffecs-mutation-protein.txt
new file mode 100644
index 00000000..9e68a892
--- /dev/null
+++ b/docs/manuscript/verbal-transcripts/2026-04-23_predict-efffecs-mutation-protein.txt
@@ -0,0 +1,5 @@
+So what would it take to predict the effects of mutations on protein function? There are many tools that exist that can predict, say, a mutation that disrupts protein function in their tools that exist that, say, give me sequences that are compatible with a given protein structure. And there are even tools that, for example, can try to map binding to particular interaction. can predict how mutation changes a particular binding interaction. But when you think about protein function, it's a broader phenomenon. A good example of this would be a transcription factor, that binds to DNA and either recruits or inhibits the recruitment of transcriptional machinery, in response to chemical signals. So it has to we have to talk about its function, we have to integrate minimally how good is it at bonding to the small molecule that affects its binding activity to the DNA? And how old does it bind to the DNA itself? Um, if we think about tools that exist to do that, uh, we might imagine uh, training some kind of machine learning model on a bunch of uh, geentific variants of a protein. And what that might allow us to do is make some predictions. If I introduce a mutation at this position, it changes the function in this way. But there are a couple of real problems with that. Uh, the 2 major ones are that often the output of a hydrobit screen is a single number for a protein. So what we're training on isn't a function exactly. We're training on some proxy for function, so maybe, uh, fitness and bacterium or protein stability. And the second problem is that we can never build sequence libraries that are big enough to actually do the sampling. The sequence libraries are really stuck with single mutations and a smattering sample of combined mutations. If mutations just added up, could some additively to give function without any epistasis, this wouldn't be a problem. But of course, epistasis is everywhere. The effect of one mutation changes the effect of another. So, even if we train an amazing model that allows us to predict the effects of single mutations, based on some high rubid data, as soon as we start combining mutations, the ability of that model to function is going to decay. We think we're in a really good place to take a next step towards actualizing the dream of building a model that protects protein function. And we can do that by leveraging existing powerful tools. And specifically, we can do this for transcription factors. So, the reason transcription factors are an attractive starting point is, first of all, that their function can be described in terms of protein stability. What do I mean by that? Well, if we think about a transcription factor as a collection of different confirmations that are all exchanging with one another, and let's make this specific. Let's imagine that this is a repressor that binds to DNA in the absence of its ligand. You have a collection of different confirmations. You've got the bound to DNA form in the repress state. The off of DNA in unrepressed state without liggin bound. And then maybe you have an effector bound confirmation. It's not bound to DNA, and you could even formally have the other combinations, right? There's an innumerable set of protein confirmations that you could use to describe the function of this protein. Or to model the function. And then when you think about the function itself, let's say that's the occupancy of the transcription factor binding site on the genome, the operator, as a function of its allosteric effector. You can calculate that, right? If you know the structures and you know the equilibrium constants, sorry, I guess you don't need instructions. But if you know the equilibrium constants for the system and you know the concentrations of the components, you can then calculate the function, given that information. So, what we could put this down to is what we really are trying to learn for a transcription factor system is what are the equilibrium constants describing the system? How do mutations alter those equilibrium constants? Now, we could just think about these as terms to learn, just trying to learn the equilibrium constants. Um, and, uh, when we start adding up mutations, we're adding a plug, equilibrium constants, uh, maybe we have to add some epistasis in there, but we're trying to think about it at that term. But I would argue that that's not the right level of granularity thing about this problem. And the reason for that is twofold. The 1st is that an equilibrium constant folds together multiple things. I'll talk about in a second. And the 2nd is if we just have a naked equilibrium constant, it's difficult to bring to bear other information on that, right? Um, Let's talk about, to get to both of those. Let's think about what an equilibrium constant actually is. At the end of the day, an equilibrium constant could be written as E to the minus delta G not prime over RT. And what's the delta in that delta G? It's the difference in the free energy of 2 confirmations at standard state. Right? And so if we think about this in terms of, say, a binding reaction, how we often think about this, this would just be what's the difference in the energy of the unbound, the protein and the ligand unbound versus them bound together. That's a structural question, right? A structural, and there's entry involved, so you write entry points, but it's like, the, the microstate ensemble for the unbalanced state versus the balance state, what are the differences between those two? Um, If we think about a reaction, it doesn't involve binding, say, a protein is transitioning between, is, say, a high affinity DNA form and a low DNA affinity form. Now we're just talking about the literal difference in those 2 energies. That's cool, because what that means, if equilibrium constants are what we need to learn how the function, how the function changes, and those can be related to structure on one hand and function on another, we can then think about a combined experiment where we bring together, uh, information about function on one side, and we bring in information about structure on the other, to then ultimately learn these equilibrium constants to describe the system with high precision. And so let's make this concrete for the lack of pressor. So the lack of pressor exists in two different forms, a high affinity form, and a low affinity form for DNA, HD. And this can be approximated. People have shown with experimental things. We're just talking about the equilibrium population of the species, this can be approximated as basically H form, and H form bound to DNA, L form, and L form bound to its effector, allosteric vector. In this case, IPDG. This means that we have four pieces of structural information, four different confirmations we have to worry about, that we can then use to learn the output infrastructure.  
+  
+So if the task then is to connect structure to equilibrium constant in the equilibrium constant function, to function, what do we need on the structure side? So to do that, there are fantastic new machine learning tools that allow you to ask, basically, the question, is a sequence compatible with a given structure? So, thermal, MPNN, ligand, NPN are, uh, sorry, protein, MPNN, and ligand, MPNN are 2 really good examples of this. You can feed in a structure in a sequence, and get out a series of features that at some level map the difference between those two. And when we were thinking about a confirmational ensemble, like the lack of pressor or an energy landscape, we have, say, 4 different conformations that each must be compatible with the same sequence to define the function. The other thing that's been shown can be successfully done there is transfer learning, where you take the output of one of these neural networks, say, protein and BNN. And then I reweight it, put a neural network in between it and you're observable to reweight it to reproduce, say, binding free energy. So, what we have here is a clear route forward to take sequences as input and structures, sorry, structures is inputs, and map them towards equilibrium constants, right? Calculate the compatibility of a sequence with a structure, turn that into an energy for that structure, take the differences in those energies for the different structures. Those give you the equilibrium constants, within allow you to calculate the function. The question, then, is, what is the training data that goes on the other side? And you think about the problem here. If we just imagine that we measure for individual point mutes, the output of an occupancy versus binding curves, right? So on the X axis, you have IPG, and the Y axis you have occupancy. This is already a challenging measurement to do experimentally, but in principle, it can be done, and it's been done for 100s of thousands of variants, not millions, but a few thousands of variants. If we have that information for individual mutants, the question is, do we have enough information to train equilibrium poses? From a formal number of parameters perspective, maybe, right? We have a binding curve, let's say, you've got 10 data points in that curve. For each curve, you're trying to learn 4 parameters for each of the Delta Gs, which turns into 3 equilibrium constants, maybe even describe the system that way. Problem, though, is that's really actually a pretty challenging statistical problem to do. Um, because there's a degeneracy here, right? You introduce mutation and it shifts the equilibrium to the left. You don't know if it shifted the equilibrium to the left because it's stabilized to the balance state or it destabilized the unbound state, right? So a shift in equilibrium, you don't know what structural change it has. That is where a recent insight that we had actually comes into play, unless you fill in this hole in the middle. And the thing that we discovered was, we reason about, was that you can get interesting coupling between the effects of mutations, or epistasis, due to redistribution of the conformational ensemble. And so the basic idea is that if you have the observation that led to it, if you have two mutations that have no effect on their own, but together, they give a completely different effect. one explanation for that is they're having different effects on different confirmations. Really good example would be destabilizing mutations, right? Mutation one can be destabilizing, it's destabilizing the native state, but not so much that the native state isn't civil one you see. Mutation 2 could be destabilizing. It's destabilizing the native state, but not so much that you can't see it. When you put them down together, they're both destabilized native states extensively, and you reveal the unfolded state as a new ground state in the system. So, what you're seeing there is a rerating the ensemble between the folded and the unfolded state. But really, this could happen between any collection of confirmations, right? We could replace the unfolded state with a new functional form, right? So this new functional form comes about when it happens, and it's actually something we've observed experimentally over in evolution. The question, though, then, that we have is, what is it? What does that have to do with our inference? Well, if mutations can lead to coupling by redistributing the ensemble, and that leads to apostasis, sorry, if mutations that redistribute the ensemble lead to apostasis, maybe we could use apostasis to learn about the conformational ensemble, and thus learn about these equilibrium constants. And what we found, actually, we can show this in a pretty straightforward way, that there's, if you just think about the types of coupling that might happen in a system like this, there's a few different possibilities. mutations have some sort of through space, through structure coupling, that happens in one structure. That will be reflected as epistasis in the baselines when you're basically all one structure and the other. Um, Uh, you could also have epistasis that changes as a function of, uh, allosteric vector. That reflects ensemble redistribution. And so, what our premise is, is that if we measure mutant cycles as a function of IPTG, and measure fractional occupancy, we're going to be getting the data that we need to actually train the model to learn the equilibrium constants describing the system. So that, uh, we can then, the input to this would be a finally, a trained model where you put in a sequence on the one side. It runs through structural information, tracks, train tracks, and structural information to give you equilibrium constants. Those equilibrium constants can then be used to calculate function. So sequence goes to function. And what we're trying to do then is demonstrate that's true. So what are some preliminary data that we have? The preliminated it that we have is twofold. The 1st is we've developed this massive experiment, where we have measured 60 different data points for 209, sorry, 120 data points. So 2 bio replicates of 60 for 209,000 different mutant cycles inside the lack of pressor. So these are, we know that wild type effect, the effects of the individual mutations and the pairways effect. And we've measured this at 8 different IPG concentrations, under 2 selective markers, so a positive conditional selective marker, and a negative conditional selective marker. And we have a sophisticated linking function that links from bacterial growth to binding. So, we could think about this too, like, pretty good approximation. We have measured the binding profile as a function of IPDG for 209,000 variants of lack of pressure. So that's a really great piece of data, because we can look at this and we can actually pull out things like, oh, look, there's apostasis as a function of concentration. Uh, that reflects redistribution of the ensemble, for we can see things like contacts. Um, the, uh, so that's a really great piece of input. Uh, the other preliminary data that we have is, uh, we decided to, we're going to, what we're going to do in this, uh, calculation is actually adapt, uh, an approach that was pioneered by thermal MPNN, which does transfer learning to take, uh, protein of PNN and train it on thermodynamic values. We're going to do the same thing to, instead of learning equilibrium that, you're going to learn equilibrium constants as a hidden variable that's reproducing our binding measurements. But before we even did that, we said, well, let's just, this is effectively an energetic confirmational stability thing. introduce mutations, change the stability of the different confirmations, and that's from 5 there. So we ran that model and showed that we can actually reproduce our data, quantitative. It's not perfect, but with a very simple model, we can reproduce binding. So with these two pieces of information, we're now positioned to bolt this whole system together, and use transfer learning approach to reweight each of the energies here, and to take the outputs from multiple runs of ligand and PNN and actually predict function.  
+  
+So let's actually go through the computational pipeline that we're envisioning. We take each of the 4 structures in the lack repressor ensemble, the high affinity form, the high affinity form bound to DNA, the low affinity form, and the low affinity form bound to IPTG. We take those 4 structures and we feed them individually through ligand MPNN. This generates a fixed collection of features that are then going to be the input to our inputs. And we then label those sequences, those things with protein sequences. I need to actually think through how to do this. Yeah, overall idea. I'm actually sure how to do the sequence mapping, so we'll come back to that. But the idea would be we can use feature one, our reference structure, as an input. We're going to hold that. And then the other structures will just take as the difference. So, structure 2 features minus structure one features. Structure 3 features minus structure one features. So what we have is a collection of features, major features, and then perturbations to those features depending on different structures. Um. For each training sequence, we then label those features according to their sequence identity. That's the part that I'm actually realizing I'm a little fuzzy on, so I've got to think through. Um, and run that through a, all of those features, feature one, Delta feature 2, Delta feature 3, et cetera, through a light attention block. And what that will do is take this, these high dimensional features and their differences and reduce it down to a manageable, small number of values. A scalar vector or a vector of scalars. Um, and then we take those features and we run them through a multi-layer perceptron, another small neural network. And the idea with that is it will reduce these to a collection of free energies. And so, uh, the free energies that we will get out of this are what is the pre-energy of the, um, 1st structure. And then what are the changes in the free energy for each of the individual structures? So what's the difference in energy between, uh, one, that's different to energy between, uh, 2 and one, cetera? We can then take those free energies that we calculate. Um, and we can then uh, take the differences in those energies to give us equilibrium constants. So, for example, if structure one is the high affinity form, uh, not bound to DNA, and structure 2 is a high energy, uh, energy affinity form bound to DNA. Uh, the, uh, We would get the energy change, the energy for the wild type structure, H form, and then the difference in the energy when you bring them together into the HD form. of a balance form. That, Delta G, not prime, can then be converted into an equilibrium concept. What is the binding constant for the DNA protein interaction, which can then be fed forward?  
diff --git a/docs/scripts/make_cli_reference.py b/docs/scripts/make_cli_reference.py
new file mode 100644
index 00000000..303a8e2f
--- /dev/null
+++ b/docs/scripts/make_cli_reference.py
@@ -0,0 +1,116 @@
+"""
+Write docs/source/cli.rst: the --help of every tfs-* command.
+
+The page is generated from the same parsers the commands build
+(``generalized_main``), so the flags it lists are the flags the code takes.
+Rerun after changing any CLI signature or docstring:
+
+    python docs/scripts/make_cli_reference.py
+
+``tests/tfscreen/util/cli/test_cli_reference.py`` fails when the page is stale.
+"""
+
+import importlib
+import os
+import sys
+import tomllib
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+ROOT = os.path.dirname(os.path.dirname(HERE))
+OUT = os.path.join(ROOT, "docs", "source", "cli.rst")
+
+HEADER = """\
+.. This file is generated by docs/scripts/make_cli_reference.py. Do not edit.
+
+=============
+CLI reference
+=============
+
+Every ``tfs-*`` command, as its ``--help`` prints it. Arguments without a
+leading ``--`` are positional and required. A switch that is on by default is
+turned off with ``--no_<name>``. Every command prints its provenance (tfscreen
+version, git commit, command line) when it starts and, if it takes
+``--out_prefix``, writes it to ``{out_prefix}_provenance.json``.
+
+The commands in pipeline order are described in :doc:`pipeline`.
+
+.. contents::
+   :local:
+   :depth: 1
+
+"""
+
+# Pipeline order; any command not listed here goes at the end, alphabetically.
+ORDER = [
+    "tfs-process-fastq",
+    "tfs-calibrate-od600",
+    "tfs-process-counts",
+    "tfs-configure-model",
+    "tfs-prefit-calibration",
+    "tfs-fit-model",
+    "tfs-sample-posterior",
+    "tfs-extract-params",
+    "tfs-predict-theta",
+    "tfs-predict-growth",
+    "tfs-predict-epistasis",
+    "tfs-extract-epistasis",
+    "tfs-cat-response",
+    "tfs-compare-runs",
+    "tfs-summarize-fit",
+    "tfs-fit-genotypes",
+    "tfs-sample-prior",
+    "tfs-simulate",
+    "tfs-build-empirical",
+    "tfs-setup-sim-grid",
+    "tfs-setup-grid",
+    "tfs-summarize-grid",
+    "tfs-summarize-calibration",
+]
+
+
+def entry_points():
+    with open(os.path.join(ROOT, "pyproject.toml"), "rb") as fh:
+        scripts = tomllib.load(fh)["project"]["scripts"]
+    names = [n for n in ORDER if n in scripts]
+    names += sorted(n for n in scripts if n not in ORDER)
+    return [(n, scripts[n]) for n in names]
+
+
+def help_text(name, target):
+    from tfscreen.util.cli.generalized_main import capture_parser
+    module, func = target.split(":")
+    main = getattr(importlib.import_module(module), func)
+    parser = capture_parser(main)
+    parser.prog = name
+    return parser.format_help()
+
+
+def render():
+    # fixed width so the page does not depend on the terminal
+    os.environ["COLUMNS"] = "88"
+    parts = [HEADER]
+    for name, target in entry_points():
+        text = help_text(name, target)
+        parts.append(f"{name}\n{'-' * len(name)}\n\n.. code-block:: text\n\n")
+        parts.append("".join(f"   {line}".rstrip() + "\n"
+                             for line in text.splitlines()))
+        parts.append("\n")
+    return "".join(parts).rstrip() + "\n"
+
+
+def main():
+    text = render()
+    check = "--check" in sys.argv
+    if check:
+        with open(OUT) as fh:
+            if fh.read() != text:
+                sys.exit("docs/source/cli.rst is stale; run "
+                         "python docs/scripts/make_cli_reference.py")
+        return
+    with open(OUT, "w") as fh:
+        fh.write(text)
+    print(f"Wrote {OUT}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/source/analysis.rst b/docs/source/analysis.rst
deleted file mode 100644
index 1d8b32e4..00000000
--- a/docs/source/analysis.rst
+++ /dev/null
@@ -1,463 +0,0 @@
-========
-Analysis
-========
-
-The ``tfscreen.tfmodel`` module provides a hierarchical Bayesian model that
-infers per-genotype and operator occupancy
-(*θ*) from bacterial growth data and direct binding measurements.
-
-Standard Workflow
-=================
-
-A complete analysis run consists of the following steps. The example commands
-assume all input files are in the current working directory and use the
-default output-file naming convention (``tfs_configure_*``, ``tfs_fit_model_*``,
-etc.). The :download:`example run.srun <../../examples/tfmodel/run.srun>`
-shows a complete Slurm script for a cluster run.
-
-Step 1: Configure Model (``tfs-configure-model``)
---------------------------------------------------
-
-Validates the input data, maps categorical labels to numerical indices, selects
-model components, and writes the configuration files:
-
-* ``{out_prefix}_config.yaml`` — main configuration read by all downstream steps
-* ``{out_prefix}_priors.csv`` — prior distribution settings for all parameters
-* ``{out_prefix}_guesses.csv`` — initial-value guesses for array parameters
-* ``{out_prefix}_library.csv`` — per-genotype library composition resolved from
-  ``--library_config`` (growth models only)
-
-``binding_df`` (direct binding measurements) is the only required argument.
-``growth_df`` is optional; omitting it configures a binding-only model. See
-:doc:`model-inputs` for what each input contributes to the fit, how much
-data is useful, and the exact file format for ``binding_df``, ``growth_df``,
-``base_growth_df``, ``presplit_df``, and ``transformation_lambda``.
-
-.. code-block:: bash
-
-    tfs-configure-model binding.csv \
-        --growth_df growth.csv \
-        --library_config run_config.yaml \
-        --out_prefix tfs_configure
-
-``--library_config`` is the library YAML describing the screened library — the
-**same file** passed to ``tfs-process-fastq``. It is required whenever
-``--growth_df`` is given. It tells the model which genotypes came in as
-monoclonal spikes (and so are congression-free), replacing the older
-hand-written ``--spiked`` list; because a spiked control is usually also
-encoded in the bulk sub-libraries, the resolved table records each genotype's
-expected ``pool_fraction`` and ``bulk_fraction`` rather than a yes/no flag.
-``tfs-configure-model`` fails if any genotype in the data is absent from the
-library, which catches a residue-numbering or wt-sequence mismatch between the
-library description and the reads it was called against.
-
-Key component flags (see :ref:`model-components` for the full list):
-
-* ``--condition_growth_model`` — how growth rate depends on TF occupancy (default: ``linear``)
-* ``--growth_transition_model`` — pre-to-selection phase lag model (default: ``instant``)
-* ``--activity_model`` — per-genotype TF activity prior (default: ``horseshoe_geno``)
-* ``--theta_model`` — operator occupancy parameterisation (default: ``hill_geno``)
-* ``--dk_geno_model`` — pleiotropic growth-effect prior (default: ``hierarchical_geno``)
-* ``--transformation_model`` — multi-plasmid congression correction (default: ``single``, i.e. no correction)
-
-Step 2: Pre-fit Calibration (``tfs-prefit-calibration``)
----------------------------------------------------------
-
-Runs a fast MAP fit on a simplified version of the model to calibrate the priors
-for the ``condition_growth`` and ``growth_transition`` components. The
-calibration fit is restricted to the intersection of genotypes and titrant
-conditions present in both the growth and binding data.
-
-After convergence the script updates the production ``{out_prefix}_priors.csv``
-and ``{out_prefix}_guesses.csv`` files in place (a ``.bak`` backup is written
-first), giving the full production fit a warm start.
-
-Diagnostic artefacts from the calibration MAP run are also written (default
-prefix ``tfs_prefit``):
-
-* ``tfs_prefit_params.npz`` — MAP point estimates in constrained space.
-* ``tfs_prefit_checkpoint.pkl`` — optimizer checkpoint (can be passed to
-  ``--checkpoint_file`` to resume an interrupted calibration run).
-* ``tfs_prefit_losses.txt`` — per-epoch loss history.
-
-.. code-block:: bash
-
-    tfs-prefit-calibration tfs_configure_config.yaml \
-        --seed 42 \
-        --convergence_tolerance 0.00001
-
-Step 3: Fit Model (``tfs-fit-model``)
---------------------------------------
-
-Performs the main parameter estimation. Three inference methods are available
-via ``--analysis_method``:
-
-* **map** — Maximum A Posteriori optimisation (Adam). Fast; produces a point
-  estimate. Use ``tfs-sample-posterior`` afterwards to obtain uncertainty
-  estimates via a Laplace approximation.
-* **svi** (default) — Stochastic Variational Inference. Automatically runs a
-  short MAP pre-pass (``--pre_map_num_epoch``) before the full variational fit.
-  Produces a full approximate posterior; posterior samples are drawn after
-  convergence.
-* **nuts** — No-U-Turn Sampler (exact MCMC). Slowest; most accurate.
-
-The example below matches the MAP configuration used in the example ``run.srun``:
-
-.. code-block:: bash
-
-    tfs-fit-model \
-        tfs_configure_config.yaml \
-        --seed 42 \
-        --analysis_method map \
-        --adam_step_size 1e-6 \
-        --convergence_check_interval 100 \
-        --convergence_window 50 \
-        --checkpoint_interval 100 \
-        --max_num_epochs 100000000 \
-        --pre_map_num_epoch 100000 \
-        --convergence_tolerance 0.0005 \
-        --patience 5
-
-Key outputs (with default ``--out_prefix tfs_fit_model``):
-
-* ``tfs_fit_model_checkpoint.pkl`` — optimizer checkpoint; resume with ``--checkpoint_file``
-* ``tfs_fit_model_params.npz`` — MAP/SVI parameter point estimates
-
-Step 4: Sample Posterior (``tfs-sample-posterior``)
-----------------------------------------------------
-
-Draws posterior samples from a checkpoint produced by ``tfs-fit-model``. The
-checkpoint type is detected automatically:
-
-* **MAP checkpoint** — constructs a Laplace (Hessian-based) Gaussian
-  approximation at the MAP point, then draws samples.
-* **SVI checkpoint** — draws directly from the fitted variational distribution
-  (resumes with 0 additional optimisation epochs).
-* **NUTS checkpoint** — reconstructs posteriors from the saved MCMC samples.
-
-.. code-block:: bash
-
-    tfs-sample-posterior \
-        tfs_configure_config.yaml \
-        tfs_fit_model_checkpoint.pkl \
-        --num_posterior_samples 1000 \
-        --sampling_batch_size 10 \
-        --seed 42
-
-Output (default ``--out_prefix tfs_posterior``):
-
-* ``tfs_posterior.h5`` — posterior samples for all latent variables; passed to
-  the prediction steps below.
-
-Step 5: Extract Parameters (``tfs-extract-params``)
-----------------------------------------------------
-
-Extracts interpretable parameter summaries from a posterior ``.h5`` file and
-writes one CSV per parameter group.
-
-.. code-block:: bash
-
-    tfs-extract-params \
-        tfs_configure_config.yaml \
-        tfs_posterior.h5
-
-Outputs (default ``--out_prefix tfs_params``):
-
-* ``tfs_params_log_hill_K.csv`` — per-genotype log₁₀(*Kd*) with posterior quantiles.
-* ``tfs_params_theta_low.csv``, ``tfs_params_theta_high.csv`` — per-genotype
-  lower and upper occupancy plateaux.
-* ``tfs_params_dk_geno.csv`` — per-genotype pleiotropic growth effect.
-* Additional CSVs depending on the components selected during configuration
-  (e.g. ``tfs_params_d_log_hill_K.csv`` for per-mutation effects when using
-  ``hill_mut``; ``tfs_params_epi_*.csv`` for epistasis terms when
-  ``--epistasis`` is set).
-
-**Quantile columns**
-
-Each CSV contains metadata columns (typically ``genotype`` and
-``titrant_name``) followed by 17 quantile columns named ``q{level}``:
-
-.. code-block:: text
-
-    q0.001  q0.005  q0.01  q0.025  q0.05  q0.1  q0.159  q0.25
-    q0.5    q0.75   q0.841 q0.9    q0.95  q0.975 q0.99  q0.995  q0.999
-
-``q0.5`` is the posterior median (the recommended point estimate).  ``q0.025``
-and ``q0.975`` span the 95% credible interval; ``q0.159`` and ``q0.841``
-span the ±1 σ interval under a normal approximation.
-
-When ``tfs-extract-params`` is given a MAP checkpoint (``.pkl``) instead of
-a posterior file, only a single ``q0.5`` column is written (the MAP point
-estimate, not a true posterior quantile).
-
-Step 6: Predict Growth (``tfs-predict-growth``)
-------------------------------------------------
-
-Predicts ln(CFU) from the fitted model. By default, predictions are produced at
-every (genotype, replicate, condition, titrant_name, titrant_conc, time)
-combination present in the training data.
-
-.. code-block:: bash
-
-    tfs-predict-growth \
-        tfs_configure_config.yaml \
-        tfs_posterior.h5
-
-Output (default ``--out_prefix tfs_growth_pred``):
-
-* ``tfs_growth_pred.csv`` — one row per prediction point; quantile columns
-  (``median``, ``lower_95``, ``upper_95``, etc.) plus ``in_training_data``.
-
-To add predictions at novel genotypes or concentrations, use
-``--genotypes_file`` or ``--titrant_concs_file`` (plain-text files, one value
-per line). Pass ``--only_files`` to skip training-data combinations and predict
-only at the file-specified inputs.
-
-Step 7: Predict Theta (``tfs-predict-theta``)
-----------------------------------------------
-
-Predicts operator occupancy *θ* as a function of titrant concentration.
-
-.. code-block:: bash
-
-    tfs-predict-theta \
-        tfs_configure_config.yaml \
-        tfs_posterior.h5 \
-        --genotypes_file predict_genotypes.txt
-
-Output (default ``--out_prefix tfs_theta_pred``):
-
-* ``tfs_theta_pred.csv`` — one row per (genotype, titrant_name, titrant_conc)
-  with posterior quantile columns and an ``in_training_data`` flag.
-
-``predict_genotypes.txt`` is a plain-text file with one genotype per line
-(e.g. ``M42I/K84L`` or ``wt``). Genotypes not seen during training can be
-predicted using the mutation-additivity model when the chosen ``theta_model``
-supports it (e.g. ``hill_mut``).
-
-Step 8: Categorise Response (``tfs-cat-response``)
----------------------------------------------------
-
-Fits categorical response curve models to a ``y_obs``-vs-``x_obs`` curve and
-selects the best-fitting model per group by AIC weight. Groups are defined by
-the ``genotype`` column plus any ``--group_by`` columns (mirroring
-``tfs-extract-epistasis``). For the *θ*-vs-titrant output of
-``tfs-predict-theta``, pass the concentration column as ``x_obs`` and a theta
-column (e.g. ``q0.5``) as ``y_obs``.
-
-.. code-block:: bash
-
-    tfs-cat-response \
-        tfs_theta_pred.csv \
-        titrant_conc \
-        q0.5 \
-        --group_by titrant_name \
-        --num_workers -1
-
-If ``--y_std`` is omitted and the ``q0.841``/``q0.159`` quantile columns are
-present, the per-row sigma is taken as ``(q0.841 - q0.159) / 2``; otherwise the
-fit is unweighted.
-
-The per-group fits are embarrassingly parallel. ``--num_workers`` defaults to
-``-1`` (use ``os.cpu_count() - 1`` processes); pass ``1`` to run serially or a
-positive integer to pin the worker count.
-
-Output (default ``--out_prefix tfs_cat_response``):
-
-* ``tfs_cat_response.csv`` — one row per group with ``best_model``, AIC
-  weights, and fitted parameters for every model.
-* ``tfs_cat_response_<model>.csv`` — one file per model with that model's
-  parameter table and per-group fit statistics.
-* ``tfs_cat_response_predictions.csv`` — predicted curves for every group and
-  model.
-
-Step 9: Summarise Fit (``tfs-summarize-fit``)
-----------------------------------------------
-
-Collects the outputs of the completed run, computes prediction quality
-statistics, and writes diagnostic plots and tables to a ``summary/``
-subdirectory.  It can be run after Steps 5–7 are complete and does not
-require the full posterior file to be retained.
-
-.. code-block:: bash
-
-    tfs-summarize-fit out/
-
-For a full guide to every output file — including how to read the theta
-correlation plots, calibration curves, and parameter-recovery scatter
-plots — see :doc:`summarize-fit`.
-
----
-
-.. _model-components:
-
-Model Components
-================
-
-``tfs-configure-model`` accepts ``--<axis>_model`` flags to select from a
-registry of pluggable sub-models for each aspect of the generative process.
-
-Condition Growth (``--condition_growth_model``)
------------------------------------------------
-
-Maps operator occupancy to per-condition growth rates: *k = b + A·m·θ*.
-
-* **linear** (default) — shared hierarchical prior for *m* and *b* across conditions
-* **power** — power-law relationship; incompatible with ``--theta_rescale_model logit``
-* **saturation** — saturating (Michaelis-Menten-like) relationship; incompatible with ``logit``
-
-Growth Transition (``--growth_transition_model``)
---------------------------------------------------
-
-Models the lag phase when bacteria switch from pre-selection to selection medium.
-
-* **instant** (default) — no lag; genotypes immediately adopt the new growth rate
-* **memory** — occupancy-dependent lag time
-* **baranyi** — Baranyi–Roberts lag model
-* **baranyi_k** — Baranyi model parameterised through growth rate *k*
-* **baranyi_tau** — Baranyi model parameterised through lag time *τ*
-* **two_pop** — two-population lag model
-
-Initial Population (``--ln_cfu0_model``)
------------------------------------------
-
-Models the starting genotype frequencies in each replicate.
-
-* **hierarchical** (default) — shared global prior on ln(CFU\ :sub:`0`)
-* **hierarchical_factored** — factored hierarchical prior
-
-Pleiotropic Growth Effect (``--dk_geno_model``)
-------------------------------------------------
-
-Models the growth-rate offset attributable to each genotype independent of TF
-occupancy (*dk_geno*).
-
-* **hierarchical_geno** (default) — per-genotype effects drawn from a global prior
-* **fixed** — no genotype-specific pleiotropic effects
-* **pinned** — genotype-specific effects pinned to externally supplied values
-
-Activity (``--activity_model``)
----------------------------------
-
-Models the per-genotype scalar *A* that multiplies the occupancy contribution
-to growth.
-
-* **horseshoe_geno** (default) — sparse horseshoe prior over genotypes
-* **hierarchical_geno** — standard hierarchical prior over genotypes
-* **horseshoe_mut** — sparse horseshoe prior, decomposed by mutation
-* **hierarchical_mut** — hierarchical prior, decomposed by mutation
-* **fixed** — all genotypes share the wildtype activity (*A* = 1)
-
-Occupancy (``--theta_model``)
-------------------------------
-
-Parameterises fractional operator occupancy *θ* as a function of titrant
-concentration.
-
-* **hill_geno** (default) — Hill equation with per-genotype *Kd* and *n*
-* **categorical_geno** — independent *θ* at each titrant concentration
-* **hill_mut** — Hill equation with per-mutation additive effects on *Kd* and *n*
-* **thermo.\*** — thermodynamic partition-function models (see
-  ``configure_model_cli.py`` docstring for the full set of registry keys)
-
-Transformation Correction (``--transformation_model``)
--------------------------------------------------------
-
-Corrects for congression (multiple plasmids entering one cell during
-transformation).
-
-* **empirical** (default) — empirical correction curve
-* **logit_norm** — logit-normal correction
-* **single** — no correction (assumes exactly one plasmid per cell)
-
-Theta Rescale (``--theta_rescale_model``)
-------------------------------------------
-
-Rescales *θ* before it enters the condition-growth model.
-
-* **passthrough** (default) — identity; *θ* ∈ [0, 1]
-* **logit** — maps *θ* → log(*θ*/(1−*θ*)); expands dynamic range at extremes.
-  Incompatible with the ``power`` and ``saturation`` growth models.
-
-Noise Models
-------------
-
-Additional noise components (default ``zero`` for all):
-
-* ``--theta_growth_noise_model``: ``zero`` (default), ``beta``, ``logit_normal``
-* ``--theta_binding_noise_model``: ``zero`` (default), ``beta``
-* ``--growth_noise_model``: ``zero`` (default), ``normal_kt`` (learns a global
-  growth-rate noise term *σ_k* in quadrature with *ln_cfu_std*)
-
----
-
-.. _model-naming:
-
-Model Naming Conventions
-========================
-
-Model component names follow a set of conventions that encode what each component does.
-
-Level of Parameterisation
---------------------------
-
-Models that infer one parameter per *genotype* carry a ``_geno`` suffix; models that
-decompose effects at the *mutation* level carry a ``_mut`` suffix. Models with no
-natural per-mutation alternative (e.g. ``fixed``) have no suffix.
-
-Examples: ``hierarchical_geno``, ``horseshoe_mut``, ``hill_geno``, ``hill_mut``.
-
-Thermodynamic Theta Models
----------------------------
-
-Operator-occupancy (*θ*) models derived from an explicit partition function use a
-three-part dot-separated name::
-
-    thermo.{MODEL}.{PRIOR}
-
-*MODEL* encodes the partition-function topology with four underscore-separated fields:
-
-.. list-table::
-   :header-rows: 1
-   :widths: 10 60
-
-   * - Field
-     - Meaning
-   * - ``O``
-     - Oligomeric state (e.g. ``O2`` = homodimer)
-   * - ``C``
-     - Number of conformational states
-   * - ``K``
-     - Number of independent equilibrium constants
-   * - ``U``
-     - Unfolded state: ``U0`` = folded only, ``U1`` = folding equilibrium
-
-A trailing letter (``a``, ``b``, …) disambiguates topologically distinct models
-that share the same O/C/K/U counts. These letters carry no ordering; ``a`` simply
-means "first registered variant."
-
-Currently implemented models:
-
-* ``O2_C4_K3_U0_a`` — four-state lac-repressor homodimer (no unfolding)
-* ``O2_C4_K3_U1_a`` — same with an explicit folding/unfolding equilibrium
-* ``O2_C12_K5_U0_a`` — full MWC two-state homodimer (no unfolding)
-* ``O2_C12_K5_U1_a`` — same with an explicit folding/unfolding equilibrium
-
-*PRIOR* describes how the equilibrium constants are parameterised:
-
-.. list-table::
-   :header-rows: 1
-   :widths: 10 60
-
-   * - Name
-     - Description
-   * - ``PK``
-     - Independent normal prior on each log-*K*
-   * - ``PddG``
-     - Priors informed by estimated ΔΔG values
-   * - ``PnnC``
-     - Neural network predicting per-conformation ΔΔG values
-   * - ``PnnK``
-     - Neural network predicting log-*K* values directly (planned)
-
-Full example names: ``thermo.O2_C4_K3_U0_a.PK``, ``thermo.O2_C12_K5_U1_a.PnnC``.
diff --git a/docs/source/cli.rst b/docs/source/cli.rst
new file mode 100644
index 00000000..91442d28
--- /dev/null
+++ b/docs/source/cli.rst
@@ -0,0 +1,2223 @@
+.. This file is generated by docs/scripts/make_cli_reference.py. Do not edit.
+
+=============
+CLI reference
+=============
+
+Every ``tfs-*`` command, as its ``--help`` prints it. Arguments without a
+leading ``--`` are positional and required. A switch that is on by default is
+turned off with ``--no_<name>``. Every command prints its provenance (tfscreen
+version, git commit, command line) when it starts and, if it takes
+``--out_prefix``, writes it to ``{out_prefix}_provenance.json``.
+
+The commands in pipeline order are described in :doc:`pipeline`.
+
+.. contents::
+   :local:
+   :depth: 1
+
+tfs-process-fastq
+-----------------
+
+.. code-block:: text
+
+   usage: tfs-process-fastq [-h] [--out_dir OUT_DIR] [--phred_cutoff PHRED_CUTOFF]
+                            [--min_read_length MIN_READ_LENGTH]
+                            [--allowed_num_flank_diffs ALLOWED_NUM_FLANK_DIFFS]
+                            [--allowed_diff_from_expected ALLOWED_DIFF_FROM_EXPECTED]
+                            [--print_raw_seq] [--max_num_reads MAX_NUM_READS]
+                            [--chunk_size CHUNK_SIZE] [--num_workers NUM_WORKERS]
+                            library_config f1_fastq f2_fastq
+
+   Count the protein genotypes observed in a pair of fastq files.
+
+   Writes counts_<f1 name>.csv and stats_<f1 name>.csv into
+   out_dir. tfs-process-counts finds each tube's counts file by its
+   sample name, so name the fastq files after the tubes.
+
+   positional arguments:
+     library_config        The library YAML (the same file later passed to tfs-configure-
+                           model --library_config), a dict of its keys, or a
+                           LibraryManager. Only the genetics keys are read; see
+                           LibraryManager.
+     f1_fastq              forward-read fastq/fastq.gz file
+     f2_fastq              reverse-read fastq/fastq.gz file of the same tube
+
+   options:
+     -h, --help            show this help message and exit
+     --out_dir OUT_DIR     output directory, created if absent (default: 'counts')
+     --phred_cutoff PHRED_CUTOFF
+                           assign any base with phred < cutoff to "N" (default: 10)
+     --min_read_length MIN_READ_LENGTH
+                           toss reads where the final readable sequence is less than this
+                           long (default: 50)
+     --allowed_num_flank_diffs ALLOWED_NUM_FLANK_DIFFS
+                           allow up to allowed_num_diffs differences between the read and
+                           expected_5p and expected_3p when looking for them. (default:
+                           1)
+     --allowed_diff_from_expected ALLOWED_DIFF_FROM_EXPECTED
+                           allow up to allowed_diff_from_expected differences between the
+                           read and the expected library sequences when calling the
+                           sequence (default: 2)
+     --print_raw_seq       if True, print the foward and reverse sequences to standard
+                           out. These are written with random 10 letter prefixes in the
+                           order they are encountered. This only writes out correctly
+                           oriented sequences, but does not alter the sequences in any
+                           way. Setting this to True with a high number of
+                           allowed_num_flank_diffs reveals sequences that are somewhat
+                           close to expected and can be used for troubleshooting. The
+                           sequences include the 5' and 3' flank regions.
+     --max_num_reads MAX_NUM_READS
+                           only read the first max_num_reads. if None, read whole files.
+                           (default: None)
+     --chunk_size CHUNK_SIZE
+                           break the reads into chunks of chunk_size reads for processing
+                           by their own worker (default: 10000)
+     --num_workers NUM_WORKERS
+                           number of worker processes; -1 (or None) uses os.cpu_count() -
+                           1 (default: -1)
+
+tfs-calibrate-od600
+-------------------
+
+.. code-block:: text
+
+   usage: tfs-calibrate-od600 [-h] [--out_prefix OUT_PREFIX] [--degree DEGREE]
+                              [--pipette_rel_error PIPETTE_REL_ERROR]
+                              [--detection_threshold DETECTION_THRESHOLD]
+                              replicate_file plate_count_file
+
+   Fit an OD600-to-CFU/mL calibration and write it for the pipeline.
+
+   A calibration depends on the plate reader, plate, volume and strain, so
+   each lab makes its own from two small experiments done with the same
+   handling as production samples.
+
+   positional arguments:
+     replicate_file        CSV/TSV/Excel of repeated OD600 readings of a dilution series,
+                           one row per reading: dilution (relative to the undiluted
+                           culture), od600. Gives the reading noise (the largest relative
+                           SD across dilutions) and the detection threshold (midway
+                           between the mean readings of the two most dilute samples).
+     plate_count_file      CSV/TSV/Excel of plate counts of cultures whose OD600 was
+                           read, one row per plate: od600, colonies, dilution (total
+                           dilution factor before plating), plated_volume_mL,
+                           num_dilutions and plating_steps (for the pipetting error).
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Writes {out_prefix}.yaml (the calibration, read by the rest of
+                           the pipeline), {out_prefix}_replicates.csv (reading noise by
+                           dilution), {out_prefix}_plate_counts.csv (CFU/mL, its SD, the
+                           fitted curve and the standardized residual) and
+                           {out_prefix}.pdf. (default: 'tfs_od600')
+     --degree DEGREE       Degree of the polynomial of CFU/mL in OD600 (default 2).
+     --pipette_rel_error PIPETTE_REL_ERROR
+                           Relative error of one pipetting step (default 0.02).
+     --detection_threshold DETECTION_THRESHOLD
+                           Override the detection threshold estimated from the dilution
+                           series. (default: None)
+
+tfs-process-counts
+------------------
+
+.. code-block:: text
+
+   usage: tfs-process-counts [-h] [--out_prefix OUT_PREFIX] [--od600_file OD600_FILE]
+                             [--od600_calibration_file OD600_CALIBRATION_FILE]
+                             [--tube_volume_mL TUBE_VOLUME_ML] [--presplit]
+                             [--counts_glob_prefix COUNTS_GLOB_PREFIX]
+                             [--min_genotype_obs MIN_GENOTYPE_OBS]
+                             [--pseudocount PSEUDOCOUNT] [--no_verbose]
+                             sample_file counts_dir
+
+   Turn per-tube genotype counts into the table tfs-configure-model reads.
+
+   The tube table has one row per sequenced tube: a unique sample name,
+   its library, and the design columns (replicate, condition_pre,
+   t_pre, condition_sel, t_sel, titrant_name,
+   titrant_conc), which are carried through to the output. Every library
+   is processed in one call; genotypes are filtered and filled in within
+   each library.
+
+   Each tube needs its total CFU. Give its OD600 (--od600_file, or an
+   od600 column in the tube table) with --od600_calibration_file and
+   --tube_volume_mL, and the totals and their SDs are computed here:
+   sample_cfu_curve_std is the calibration curve's error, shared by every
+   tube, and sample_cfu_reading_std is the reading's own. Or supply them
+   in the tube table as sample_cfu and sample_cfu_std.
+
+   Writes {out_prefix}.csv. Without --presplit it is the growth table
+   for tfs-configure-model --growth_df: one row per genotype and tube
+   with counts, sample_reads, frequency, ln_cfu and
+   ln_cfu_var. With --presplit it is the presplit table for
+   --presplit_df (library, replicate, condition_pre,
+   genotype, ln_cfu, ln_cfu_std).
+
+   positional arguments:
+     sample_file           Tube table (CSV, TSV or Excel), one row per sequenced tube.
+     counts_dir            Directory of tfs-process-fastq count files. Each tube's file
+                           must be the only one matching
+                           {counts_glob_prefix}*{sample}*.csv.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Output prefix. Default tfs_growth, or tfs_presplit with
+                           --presplit.
+     --od600_file OD600_FILE
+                           Table of one OD600 reading per tube (columns sample, od600).
+                           (default: None)
+     --od600_calibration_file OD600_CALIBRATION_FILE
+                           OD600-to-CFU/mL calibration written by tfs-calibrate-od600.
+                           (default: None)
+     --tube_volume_mL TUBE_VOLUME_ML
+                           Culture volume of one tube in mL. Required with OD600.
+                           (default: None)
+     --presplit            Write the presplit table instead of the growth table. The tube
+                           table then needs replicate and condition_pre.
+     --counts_glob_prefix COUNTS_GLOB_PREFIX
+                           File-name prefix of the count files. (default: 'counts')
+     --min_genotype_obs MIN_GENOTYPE_OBS
+                           Drop genotypes with fewer total reads than this within a
+                           library. (default: 10)
+     --pseudocount PSEUDOCOUNT
+                           Added to every genotype's count before taking frequencies.
+                           (default: 1)
+     --no_verbose          Turn off (on by default): Print which count file was matched
+                           to each tube.
+
+tfs-configure-model
+-------------------
+
+.. code-block:: text
+
+   usage: tfs-configure-model [-h] [--binding_df BINDING_DF] [--growth_df GROWTH_DF]
+                              [--presplit_df PRESPLIT_DF]
+                              [--base_growth_df BASE_GROWTH_DF] [--out_prefix OUT_PREFIX]
+                              [--condition_growth_model CONDITION_GROWTH_MODEL]
+                              [--growth_transition_model GROWTH_TRANSITION_MODEL]
+                              [--ln_cfu0_model LN_CFU0_MODEL]
+                              [--dk_geno_model DK_GENO_MODEL]
+                              [--activity_model ACTIVITY_MODEL]
+                              [--theta_model THETA_MODEL]
+                              [--transformation_model TRANSFORMATION_MODEL]
+                              [--transformation_lambda TRANSFORMATION_LAMBDA TRANSFORMATION_LAMBDA]
+                              [--congression_theta_rule CONGRESSION_THETA_RULE]
+                              [--congression_dk_rule CONGRESSION_DK_RULE]
+                              [--congression_dk_alpha CONGRESSION_DK_ALPHA]
+                              [--congression_sets CONGRESSION_SETS [CONGRESSION_SETS ...]]
+                              [--theta_rescale_model THETA_RESCALE_MODEL]
+                              [--theta_growth_noise_model THETA_GROWTH_NOISE_MODEL]
+                              [--theta_binding_noise_model THETA_BINDING_NOISE_MODEL]
+                              [--growth_noise_model GROWTH_NOISE_MODEL]
+                              [--sample_offset_model SAMPLE_OFFSET_MODEL]
+                              [--growth_likelihood GROWTH_LIKELIHOOD]
+                              [--theta_gauge_conc THETA_GAUGE_CONC THETA_GAUGE_CONC]
+                              [--library_config LIBRARY_CONFIG]
+                              [--growth_shares_replicates] [--epistasis]
+                              [--thermo_data THERMO_DATA]
+                              [--thermo_ddG_units THERMO_DDG_UNITS]
+                              [--thermo_temperature_K THERMO_TEMPERATURE_K]
+                              [--thermo_column_map THERMO_COLUMN_MAP]
+                              [--thermo_residue_offset THERMO_RESIDUE_OFFSET]
+                              [--thermo_max_missing_fraction THERMO_MAX_MISSING_FRACTION]
+                              [--batch_size BATCH_SIZE] [--binding_weight BINDING_WEIGHT]
+                              [--set_priors SET_PRIORS [SET_PRIORS ...]]
+                              [--growth_priors GROWTH_PRIORS]
+                              [--growth_priors_wt_rates GROWTH_PRIORS_WT_RATES]
+                              [--growth_priors_sd_floor GROWTH_PRIORS_SD_FLOOR]
+                              [--allow_missing_spikes] [--skip_model_stats]
+
+   Build and write the YAML configuration files needed by tfs-fit-model.
+
+   Constructs a ModelOrchestrator from the supplied data and model-component choices,
+   then writes {out_prefix}_config.yaml (the main configuration),
+   {out_prefix}_priors.csv (prior distributions for all parameters),
+   {out_prefix}_guesses.csv (initial-value guesses for array parameters) and,
+   for a growth model, {out_prefix}_library.csv (the per-genotype library
+   composition resolved from library_config).
+
+   At least one of growth_df and binding_df is required. With both, the
+   joint growth + binding model is configured. With only binding_df, a
+   binding-only model infers theta directly from the binding measurements.
+   With only growth_df, a growth-only model infers theta from growth alone
+   (no binding likelihood, so theta_binding_noise_model must stay 'zero'
+   and binding_weight unset; tfs-prefit-calibration needs binding and
+   refuses such a config).
+
+   options:
+     -h, --help            show this help message and exit
+     --binding_df BINDING_DF
+                           Path to the binding data CSV file (theta vs. titrant
+                           measurements per genotype). When omitted, a growth-only model
+                           is configured. (default: None)
+     --growth_df GROWTH_DF
+                           Path to the growth data CSV file (ln_cfu measurements per
+                           genotype, replicate, and timepoint). When omitted, a binding-
+                           only model is configured. (default: None)
+     --presplit_df PRESPLIT_DF
+                           Path to the pre-split (t = -t_pre) sequencing-observation CSV
+                           file. Provides a direct constraint on ln_cfu0 for genotypes it
+                           covers. See data_class.PreSplitData. (default: None)
+     --base_growth_df BASE_GROWTH_DF
+                           Path to a CSV of direct, reference-condition growth-rate
+                           measurements (columns: genotype, rate, rate_std) for a subset
+                           of genotypes (wt at minimum). Anchors the new k_ref latent
+                           scalar to dk_geno via rate_obs ~ Normal(k_ref + dk_geno,
+                           rate_std), resolving an identifiability confound between
+                           condition_growth's k/m and dk_geno's hierarchical
+                           hyperparameters. See model_orchestrator._read_base_growth_df
+                           and generative/model.py's base_growth_obs block. (default:
+                           None)
+     --out_prefix OUT_PREFIX
+                           Prefix for the output files ({out_prefix}_config.yaml,
+                           {out_prefix}_priors.csv, {out_prefix}_guesses.csv and, for a
+                           growth model, {out_prefix}_library.csv). Default
+                           'tfs_configure'.
+     --condition_growth_model CONDITION_GROWTH_MODEL
+                           Model to use to describe growth under different conditions
+                           (e.g., pheS+4CP). Allowed values are 'linear' (default),
+                           'power', or 'saturation'.
+     --growth_transition_model GROWTH_TRANSITION_MODEL
+                           Model to use to describe the transition between the pre-
+                           selection and selection phases. Allowed values are 'instant'
+                           (default), 'memory', 'baranyi', 'baranyi_k', 'baranyi_tau', or
+                           'two_pop'.
+     --ln_cfu0_model LN_CFU0_MODEL
+                           Model to use to describe ln_cfu0, the initial populations of
+                           genotypes in each replicate. Allowed values are 'hierarchical'
+                           (default) or 'hierarchical_factored'. The latter shares each
+                           genotype's baseline across pre-conditions and is refused when
+                           they come from different libraries (kanR and pheS are grown up
+                           separately).
+     --dk_geno_model DK_GENO_MODEL
+                           Model to use to describe dk_geno, the pleiotropic effect of a
+                           genotype on growth, independent of occupancy. Allowed values
+                           are 'hierarchical_geno' (default), 'fixed', or 'pinned'
+                           (dk_geno fixed to externally supplied per-genotype values for
+                           a subset of genotypes).
+     --activity_model ACTIVITY_MODEL
+                           Model to use to describe activity, a scalar multiplied against
+                           occupancy that defines how strongly a genotype alters
+                           transcription given its occupancy. Allowed values are 'fixed'
+                           (default; activity 1 for every genotype), 'hierarchical_geno',
+                           'horseshoe_geno', 'hierarchical_mut', or 'horseshoe_mut'.
+     --theta_model THETA_MODEL
+                           Model to use to describe theta, the fractional occupancy of a
+                           genotype on the transcription factor binding site. Allowed
+                           values are 'hill_geno' (default), 'categorical_geno',
+                           'hill_mut', and the thermodynamic partition-function models
+                           (pass the exact registry key, e.g. 'thermo.O2_C4_K3_U0_a.PK',
+                           'thermo.O2_C4_K3_U0_a.PnnC', 'thermo.O2_C4_K3_U0_a.PddG',
+                           'thermo.O2_C12_K5_U0_a.PK', 'thermo.O2_C12_K5_U0_a.PnnC',
+                           'thermo.O2_C12_K5_U0_a.PddG', and their O2_C4_K3_U1_a /
+                           O2_C12_K5_U1_a unfolded equivalents). 'hill_relative' fits
+                           growth alone on a wt-relative scale X instead of theta (wt's X
+                           is 1 at the low and 0 at the high gauge concentration,
+                           theta_gauge_conc). It takes no binding_df, and requires
+                           activity 'fixed', theta_rescale 'passthrough', linear
+                           condition growth, theta_growth_noise 'zero' and, with the
+                           'mixture' transformation, congression_theta_rule 'max'.
+     --transformation_model TRANSFORMATION_MODEL
+                           Model for congression. Allowed values are 'single' (default;
+                           one plasmid per cell) or 'mixture' (clean and congressed cells
+                           mixed at the observable level). 'empirical' and 'logit_norm'
+                           were removed.
+     --transformation_lambda TRANSFORMATION_LAMBDA TRANSFORMATION_LAMBDA
+                           (mean, std) -- the experimentally measured congression lambda,
+                           in linear space (e.g. (0.36, 0.05)). Required when
+                           transformation_model is 'mixture'; forbidden when it is
+                           'single'. Used to moment-match a LogNormal prior for the
+                           transformation's lambda parameter, replacing the manual step
+                           of hand-editing the priors/guesses CSVs with rescaled log-
+                           space values. (default: None)
+     --congression_theta_rule CONGRESSION_THETA_RULE
+                           How the 'mixture' transformation builds a congressed cell's
+                           theta from its plasmids' thetas: 'homodimer' (default),
+                           'heterodimer' or 'max'. The homodimer and heterodimer rules
+                           require activity_model 'fixed'. Ignored by 'single'.
+     --congression_dk_rule CONGRESSION_DK_RULE
+                           How the 'mixture' transformation builds a congressed cell's
+                           dk_geno from its plasmids': 'dilution' (default; the share-
+                           weighted mean), 'softmin' (with congression_dk_alpha) or 'min'
+                           (the worst variant sets the cost). Ignored by 'single'.
+     --congression_dk_alpha CONGRESSION_DK_ALPHA
+                           alpha for congression_dk_rule 'softmin' (> 0, in units of
+                           1/dk): dk_cell = -(1/alpha) log sum_g x_g exp(-alpha dk_g).
+                           Required by 'softmin', refused by the other rules. (default:
+                           None)
+     --congression_sets CONGRESSION_SETS [CONGRESSION_SETS ...]
+                           With the 'mixture' transformation, the number of fixed co-
+                           resident sets drawn per genotype, by co-resident count: entry
+                           n-1 is the number of sets with n co-resident plasmids (the
+                           mixture's quadrature over a congressed cell's partners). None
+                           keeps the orchestrator's default, 12 3 1 (16 sets). Each set
+                           is one cell class grown through the whole model, so the cost
+                           of a fit grows with the total. Written to the config.
+     --theta_rescale_model THETA_RESCALE_MODEL
+                           Rescaling applied to theta before it enters the growth model.
+                           Allowed values are 'passthrough' (default, identity) or
+                           'logit' (maps theta to log(theta/(1-theta)), expanding the
+                           dynamic range at both extremes).
+     --theta_growth_noise_model THETA_GROWTH_NOISE_MODEL
+                           Model to use for stochastic experimental noise in theta
+                           measured by bacterial growth. Allowed values are 'zero'
+                           (default), 'beta', or 'logit_normal'.
+     --theta_binding_noise_model THETA_BINDING_NOISE_MODEL
+                           Model to use for stochastic experimental noise in theta
+                           measured by binding. Allowed values are 'zero' (default),
+                           'beta' or 'genotype_offset' (one offset per binding genotype,
+                           the same at every titrant concentration, with a learned scale:
+                           an in vivo/in vitro difference or a curve's day-normalization
+                           error).
+     --growth_noise_model GROWTH_NOISE_MODEL
+                           Model for additive growth-rate noise. 'zero' (default) adds no
+                           noise; 'normal_kt' learns a global sigma_k that inflates the
+                           observation scale in quadrature with ln_cfu_std, capturing
+                           biological variability in growth rates not explained by theta
+                           or dk_geno.
+     --sample_offset_model SAMPLE_OFFSET_MODEL
+                           Per-tube offset shared by every genotype in a tube. 'level'
+                           (default) adds one ln_cfu offset per tube with a learned,
+                           constant SD (the tube's composition offset and any error in
+                           its supplied total, including the tube's own growth noise);
+                           'zero' adds none; 'normal' adds a per-tube growth-rate offset
+                           scaled by elapsed time. With growth_likelihood 'counts', an
+                           error in a tube's supplied total otherwise moves every
+                           genotype in it.
+     --growth_likelihood GROWTH_LIKELIHOOD
+                           How growth data are observed: 'counts' (default; negative
+                           binomial on the read counts, with a learned dispersion whose
+                           variance has a part proportional to the mean and a quadratic
+                           part; no pseudocount) or 'lncfu' (Student-t on ln_cfu).
+                           'counts' needs a 'counts' column, each tube's total reads
+                           ('sample_reads', or 'adjusted_counts' and 'frequency' to
+                           derive it) and total cells ('sample_ln_cfu', or 'sample_cfu'
+                           with its uncertainty), and growth_noise_model 'zero'. It cut
+                           theta RMSE by 35-60% against 'lncfu' on simulations
+                           (planning/studies/count-likelihood/).
+     --theta_gauge_conc THETA_GAUGE_CONC THETA_GAUGE_CONC
+                           c_lo c_hi: the titrant concentrations at which theta_model
+                           'hill_relative' pins wt's X to 1 and 0. Default: the lowest
+                           and highest concentration in growth_df. Refused by other theta
+                           models.
+     --library_config LIBRARY_CONFIG
+                           Path to the library YAML describing the screened library --
+                           the same file handed to tfs-process-fastq. Required whenever
+                           growth_df is given (a binding-only model has no congression
+                           correction and no ln_cfu0 latents, so it does not need one).
+                           Read keys: reading_frame, first_amplicon_residue, wt_seq,
+                           degen_sites, tiles, tile_combos, spiked_seqs and
+                           library_mixture; any other keys (a full simulate config, say)
+                           are ignored. Genotypes encoded by a spiked sequence become the
+                           spiked set, replacing the old hand-supplied --spiked list. The
+                           resolved per-genotype table is written to
+                           {out_prefix}_library.csv and is what tfs-fit-model reads.
+                           (default: None)
+     --growth_shares_replicates
+                           Whether replicates should share the same parameters for the
+                           growth and growth transition models. Default is False.
+     --epistasis           Whether to model pairwise epistatic interactions between
+                           mutations in any mutation-level model (hill_mut,
+                           hierarchical_mut). When True, each pair of mutations present
+                           in the same genotype gets an independent epistasis term. When
+                           False (default), effects are purely additive at the mutation
+                           level.
+     --thermo_data THERMO_DATA
+                           Path to the structural/thermodynamic data file. Required when
+                           theta_model is a thermo-based model; ignored otherwise. For
+                           PnnC models this must be the HDF5 file produced by
+                           scripts/generate_struct_ensemble.py. For PddG models this must
+                           be a CSV file with a mut column and one column per structure
+                           containing pre-computed ΔΔG prior means; the required
+                           structure columns depend on the topology -- O2_C4_* models use
+                           (H, HD, L, LE2) while O2_C12_* models use (H, HO, L, LO, HE2,
+                           LE2). Mutation or mutation may name the mutation column, an
+                           unnamed index column is dropped and other columns are ignored.
+                           ΔΔG_X shifts state X's free energy (positive destabilises X).
+                           The loader prints how many library mutations the file covers
+                           and fails on zero coverage or on wt residues that disagree
+                           with the library. (default: None)
+     --thermo_ddG_units THERMO_DDG_UNITS
+                           Unit of the PddG CSV's ΔΔG values: kT (default; used as-is),
+                           kcal/mol or kJ/mol (divided by RT at load).
+     --thermo_temperature_K THERMO_TEMPERATURE_K
+                           Temperature for that conversion. Default 298.15.
+     --thermo_column_map THERMO_COLUMN_MAP
+                           model_name=csv_column pairs, comma-separated (e.g. HD=HO),
+                           naming the CSV column that supplies a model structure.
+                           (default: None)
+     --thermo_residue_offset THERMO_RESIDUE_OFFSET
+                           Added to the file's residue numbers (CSV labels or HDF5
+                           residue_nums) to give the library numbering, e.g. 1 for a
+                           0-based file. Default 0.
+     --thermo_max_missing_fraction THERMO_MAX_MISSING_FRACTION
+                           Warn when more than this fraction of library mutations has no
+                           ΔΔG in the CSV. Default 0.1. These five are stored in the
+                           config as components.thermo_options.
+     --batch_size BATCH_SIZE
+                           Mini-batch size for SVI. Defaults to 1024. Set to None to use
+                           the full dataset as a single batch.
+     --binding_weight BINDING_WEIGHT
+                           Multiplicative scale applied to the binding log-likelihood.
+                           None (default) is 1: each binding observation counts once, the
+                           likelihood of the data as measured. Until 2026-10-16 the
+                           default was N_growth_rows / N_binding_rows, which made the
+                           binding data count as much as the whole growth dataset; pass
+                           that ratio to reproduce an older configuration. The resolved
+                           value (never None) is saved in the YAML, so a config written
+                           before the change keeps its weight.
+     --set_priors SET_PRIORS [SET_PRIORS ...]
+                           name=value pairs that set scalar priors in
+                           {out_prefix}_priors.csv, so the file is never edited by hand.
+                           A name is a full row name
+                           (theta.theta_log_hill_n_hyper_scale_fixed) or a unique dotted
+                           suffix of one (theta_log_hill_n_hyper_scale_fixed,
+                           sample_offset.sigma_fixed). An unknown or ambiguous name is an
+                           error. Common uses: sigma_fixed=0.17 holds the level tube
+                           offsets' SD; theta_log_hill_n_hyper_scale_fixed=0.5 holds the
+                           population SD of log(hill_n) (and, for hill_relative,
+                           theta_X_low_hyper_scale_fixed,
+                           theta_X_delta_hyper_scale_fixed,
+                           theta_log_hill_K_hyper_scale_fixed); for the PddG/PnnC thermo
+                           models, theta_ddG_sigma_fixed=1.69 holds every structure's ΔΔG
+                           scale σ_s at 1.69 kT. (default: None)
+     --growth_priors GROWTH_PRIORS
+                           CSV of per-condition growth priors for linear condition
+                           growth: a condition_rep column (and replicate, if conditions
+                           are not shared across replicates) plus any of k_loc, k_scale,
+                           m_loc, m_scale, and the 0/1 flags k_pinned, m_pinned, which
+                           hold that condition's k or m at its loc instead of fitting it
+                           (say, one marker's k and m held at an independent estimate
+                           while the other's are fitted). Conditions or values it leaves
+                           out keep the defaults. Use it where tfs-prefit-calibration
+                           does not run (a growth-only model); the pre-fit overwrites
+                           k_loc and m_loc.
+     --growth_priors_wt_rates GROWTH_PRIORS_WT_RATES
+                           CSV of wt monoculture growth rates (condition_sel,
+                           titrant_conc, rate_mean, rate_sd, num_replicates), for
+                           theta_model 'hill_relative' only. For each listed condition, k
+                           = the rate at the high gauge concentration and m = the rate at
+                           the low one minus k, each with the standard error of the mean
+                           as SD (floored at growth_priors_sd_floor). List only the
+                           conditions where the monoculture stands for the library's wt.
+                           Overrides growth_priors for the conditions it lists. (default:
+                           None)
+     --growth_priors_sd_floor GROWTH_PRIORS_SD_FLOOR
+                           Smallest k and m prior SD (per minute) derived from
+                           growth_priors_wt_rates. Default 0.002.
+     --allow_missing_spikes
+                           By default, a spiked genotype named in library_config with no
+                           growth data is an error: it usually means the counts were
+                           called with a different library config. Pass this to only
+                           report it (a spike can drop out of a real experiment).
+     --skip_model_stats    Skip the pre-fit parameter/observation accounting. By default
+                           a summary is printed to stdout and written to
+                           {out_prefix}_model_stats.csv (one row per latent) and
+                           {out_prefix}_model_stats.json (headline counts, coverage,
+                           anchors, warnings). See tfmodel.model_stats. The accounting
+                           traces the model abstractly, so it costs no meaningful time or
+                           memory; skip it only if a component's trace misbehaves.
+
+tfs-prefit-calibration
+----------------------
+
+.. code-block:: text
+
+   usage: tfs-prefit-calibration [-h] [--seed SEED] [--checkpoint_file CHECKPOINT_FILE]
+                                 [--out_prefix OUT_PREFIX]
+                                 [--adam_step_size ADAM_STEP_SIZE]
+                                 [--adam_final_step_size ADAM_FINAL_STEP_SIZE]
+                                 [--adam_step_size_cut ADAM_STEP_SIZE_CUT]
+                                 [--adam_clip_norm ADAM_CLIP_NORM]
+                                 [--elbo_num_particles ELBO_NUM_PARTICLES]
+                                 [--convergence_window_steps CONVERGENCE_WINDOW_STEPS]
+                                 [--patience PATIENCE] [--convergence_z CONVERGENCE_Z]
+                                 [--loss_rtol LOSS_RTOL]
+                                 [--param_tolerance PARAM_TOLERANCE]
+                                 [--checkpoint_interval CHECKPOINT_INTERVAL]
+                                 [--max_num_epochs MAX_NUM_EPOCHS]
+                                 [--epoch_checkpoint_interval EPOCH_CHECKPOINT_INTERVAL]
+                                 [--k_scale_floor K_SCALE_FLOOR]
+                                 [--m_scale_floor M_SCALE_FLOOR]
+                                 [--k_scale_ceiling K_SCALE_CEILING]
+                                 [--m_scale_ceiling M_SCALE_CEILING]
+                                 [--hessian_chunk_size HESSIAN_CHUNK_SIZE] [--pin_m]
+                                 config_file
+
+   Run the calibration pre-fit and update the production priors / guesses
+   CSVs in place.
+
+   The script:
+
+   1. Reads the production config_file to obtain the production
+      ModelOrchestrator (so we know which priors / guesses files to
+      update later) and the production data paths.
+   2. Filters the growth and binding inputs to their shared
+      (genotype, titrant_name, titrant_conc) cells.
+   3. Builds an in-process calibration ModelOrchestrator whose
+      condition_growth and growth_transition components match
+      the production choices but whose other components are pinned /
+      collapsed (see module docstring).
+   4. Runs MAP, then computes per-site Hessian sigmas at the MAP
+      point.
+   5. Writes .bak copies of the production priors and guesses CSVs
+      and overwrites only the rows that belong to condition_growth
+      or growth_transition.
+
+   positional arguments:
+     config_file           Path to the *production* YAML configuration file. The pre-fit
+                           does not write a calibration YAML — it only updates the
+                           existing production CSVs.
+
+   options:
+     -h, --help            show this help message and exit
+     --seed SEED           Random seed for reproducibility. Required unless resuming from
+                           a checkpoint. (default: None)
+     --checkpoint_file CHECKPOINT_FILE
+                           Path to a previously written pre-fit checkpoint to resume
+                           from. (default: None)
+     --out_prefix OUT_PREFIX
+                           Prefix for calibration MAP artefacts ({out_prefix}_params.npz,
+                           {out_prefix}_checkpoint.pkl, etc.). Defaults to "tfs_prefit".
+                           These are diagnostic outputs; the user-facing artefact is the
+                           in-place update of the production CSVs.
+     --adam_step_size ADAM_STEP_SIZE
+                           Starting step size for the Adam optimizer (default 1e-3).
+     --adam_final_step_size ADAM_FINAL_STEP_SIZE
+                           Smallest step size for the Adam optimizer (default 1e-6). The
+                           step size is cut by adam_step_size_cut each time the fit
+                           plateaus, and the run converges at the first plateau at this
+                           size.
+     --adam_step_size_cut ADAM_STEP_SIZE_CUT
+                           Factor applied to the step size at each cut (default 0.1).
+     --adam_clip_norm ADAM_CLIP_NORM
+                           Clip each gradient element to +/- this value (numpyro
+                           ClippedAdam). None (default) disables clipping: when gradients
+                           are much larger than the clip, an elementwise clip makes Adam
+                           follow the gradient's sign, and its fixed point is biased by
+                           rare large ELBO penalties.
+     --elbo_num_particles ELBO_NUM_PARTICLES
+                           Number of particles for ELBO estimation (default 2).
+     --convergence_window_steps CONVERGENCE_WINDOW_STEPS
+                           Optimizer steps per convergence window (default 2000; at least
+                           10 epochs).
+     --patience PATIENCE   Consecutive windows without loss improvement required for a
+                           step-size cut, and (at the final step size) without loss
+                           improvement or parameter movement required for convergence
+                           (default 3).
+     --convergence_z CONVERGENCE_Z
+                           Standard errors of change attributed to noise (default 3).
+     --loss_rtol LOSS_RTOL
+                           Loss changes per window below this fraction of the loss count
+                           as no change (default 1e-6).
+     --param_tolerance PARAM_TOLERANCE
+                           Parameter movement per window, beyond noise, still counted as
+                           no change (default 0.05), in prior SDs (or log/logit units for
+                           positive or bounded parameters).
+     --checkpoint_interval CHECKPOINT_INTERVAL
+                           Frequency (in epochs) between checkpoint writes (default 10).
+     --max_num_epochs MAX_NUM_EPOCHS
+                           Maximum number of MAP optimization epochs (default 100000); a
+                           cap only.
+     --epoch_checkpoint_interval EPOCH_CHECKPOINT_INTERVAL
+                           Frequency (in epochs) to write numbered epoch checkpoints to a
+                           checkpoints/ subdirectory (default 0). Set to 0 or None to
+                           disable.
+     --k_scale_floor K_SCALE_FLOOR
+                           Minimum prior scale applied to the baseline growth-rate (k/b)
+                           parameters after Hessian estimation (default
+                           _DEFAULT_K_SCALE_FLOOR = 0.002). Represents the irreducible
+                           day-to-day variability in growth rates that the Hessian cannot
+                           capture. For real data, estimate this by running the prefit on
+                           multiple independent calibration replicates and measuring the
+                           spread of MAP estimates.
+     --m_scale_floor M_SCALE_FLOOR
+                           Minimum prior scale applied to the theta-slope (m) parameters
+                           after Hessian estimation (default _DEFAULT_M_SCALE_FLOOR =
+                           0.001).
+     --k_scale_ceiling K_SCALE_CEILING
+                           Maximum prior scale for k after Hessian estimation (default
+                           _DEFAULT_K_SCALE_CEILING = 0.1, matching the linear component
+                           default). Prevents degenerate Hessian directions — which arise
+                           from the b / ln_cfu0 near-degeneracy in the prefit calibration
+                           — from producing a scale larger than the original production
+                           prior and thereby *loosening* the constraint.
+     --m_scale_ceiling M_SCALE_CEILING
+                           Maximum prior scale for m_scale_plus after Hessian estimation
+                           (default _DEFAULT_M_SCALE_CEILING = 0.01, matching the linear
+                           default).
+     --hessian_chunk_size HESSIAN_CHUNK_SIZE
+                           Number of Hessian rows computed per device batch (default 64).
+                           Reduce if device runs out of memory during Hessian
+                           computation.
+     --pin_m               When set, hard-clamp the production model's per-condition
+                           slope m to its calibration MAP loc by writing
+                           condition_growth.m_pinned into the priors CSV (default False).
+                           Use this instead of an ultra-tight m_scale when you want m
+                           held exactly at the calibration value — a soft prior can be
+                           overridden by the growth likelihood. Only affects m; k keeps
+                           its floored soft prior.
+
+tfs-fit-model
+-------------
+
+.. code-block:: text
+
+   usage: tfs-fit-model [-h] [--seed SEED] [--checkpoint_file CHECKPOINT_FILE]
+                        [--analysis_method ANALYSIS_METHOD] [--guide_type GUIDE_TYPE]
+                        [--guide_rank GUIDE_RANK] [--guide_init_scale GUIDE_INIT_SCALE]
+                        [--out_prefix OUT_PREFIX] [--adam_step_size ADAM_STEP_SIZE]
+                        [--adam_final_step_size ADAM_FINAL_STEP_SIZE]
+                        [--adam_step_size_cut ADAM_STEP_SIZE_CUT]
+                        [--adam_clip_norm ADAM_CLIP_NORM]
+                        [--elbo_num_particles ELBO_NUM_PARTICLES]
+                        [--convergence_window_steps CONVERGENCE_WINDOW_STEPS]
+                        [--patience PATIENCE] [--convergence_z CONVERGENCE_Z]
+                        [--loss_rtol LOSS_RTOL] [--param_tolerance PARAM_TOLERANCE]
+                        [--checkpoint_interval CHECKPOINT_INTERVAL]
+                        [--max_num_epochs MAX_NUM_EPOCHS]
+                        [--forward_batch_size FORWARD_BATCH_SIZE]
+                        [--pre_map_num_epoch PRE_MAP_NUM_EPOCH]
+                        [--init_param_jitter INIT_PARAM_JITTER]
+                        [--nuts_num_warmup NUTS_NUM_WARMUP]
+                        [--nuts_num_samples NUTS_NUM_SAMPLES]
+                        [--nuts_num_chains NUTS_NUM_CHAINS]
+                        [--nuts_target_accept_prob NUTS_TARGET_ACCEPT_PROB]
+                        [--nuts_dense_mass]
+                        [--epoch_checkpoint_interval EPOCH_CHECKPOINT_INTERVAL]
+                        [--init_from INIT_FROM] [--stage_offsets STAGE_OFFSETS]
+                        [--staged_step_size STAGED_STEP_SIZE]
+                        config_file
+
+   Fit the joint hierarchical model using a previously generated configuration file.
+
+   This function extracts estimates of transcription factor fractional occupancy (theta)
+   and other latent parameters using Stochastic Variational Inference (SVI) or maximum a
+   posteriori (MAP) approaches based on the config.
+
+   Optimization (SVI, MAP and the pre-MAP warm-up) runs in windows of
+   convergence_window_steps optimizer steps.  After patience
+   consecutive windows without a significant improvement in the loss
+   (against its own noise), the step size is cut by adam_step_size_cut.
+   Once it has reached adam_final_step_size, the run stops after
+   patience consecutive windows in which the loss has not improved and no
+   parameter has moved (against its posterior or prior width).  Each window
+   is recorded in {out_prefix}_convergence.csv.
+
+   positional arguments:
+     config_file           Path to a YAML configuration file to load settings from.
+
+   options:
+     -h, --help            show this help message and exit
+     --seed SEED           Random seed for reproducibility. Must be provided if not
+                           loading from a checkpoint. (default: None)
+     --checkpoint_file CHECKPOINT_FILE
+                           Path to a checkpoint file to resume SVI from, or None to start
+                           fresh. A resumed run continues at the checkpoint's step size
+                           and convergence stage. (default: None)
+     --analysis_method ANALYSIS_METHOD
+                           Method for inference. Allowed values are 'svi' (default),
+                           'map', or 'nuts'. Case-insensitive. Posterior sampling is not
+                           performed; call tfs-sample-posterior after fitting.
+     --guide_type GUIDE_TYPE
+                           Variational family for analysis_method 'svi' (default
+                           'component', the guide assembled from the model components).
+                           Also accepts the numpyro autoguides 'auto_normal',
+                           'auto_diagonal_normal', 'auto_multivariate_normal',
+                           'auto_low_rank_multivariate_normal' and 'delta'; numpyro class
+                           names such as 'AutoNormal' work too. Every guide's location
+                           starts at the pre-MAP solution when pre_map_num_epoch > 0,
+                           otherwise at the configured guesses. A resumed checkpoint must
+                           use the guide it was written with.
+     --guide_rank GUIDE_RANK
+                           Covariance rank for 'auto_low_rank_multivariate_normal'
+                           (numpyro's default when omitted).
+     --guide_init_scale GUIDE_INIT_SCALE
+                           Initial scale of the variational distribution, in each site's
+                           unconstrained units (default 1e-4, DEFAULT_GUIDE_INIT_SCALE).
+                           For 'component' it caps every guide scale at the start; for an
+                           autoguide it is numpyro's init_scale for a fresh fit (a
+                           resumed fit keeps its checkpoint's). SVI widens the scales
+                           itself; a large start throws the pre-MAP point away. Not
+                           accepted by 'delta'.
+     --out_prefix OUT_PREFIX
+                           Prefix for all output files (default 'tfs_fit_model'):
+                           {out_prefix}_checkpoint.pkl, {out_prefix}_params.npz,
+                           {out_prefix}_losses.txt, {out_prefix}_convergence.csv, the
+                           pre-MAP's {out_prefix}_premap_* files, and, for NUTS only, the
+                           posterior HDF5.
+     --adam_step_size ADAM_STEP_SIZE
+                           Starting step size for the Adam optimizer (default 1e-3).
+     --adam_final_step_size ADAM_FINAL_STEP_SIZE
+                           Smallest step size (default 1e-6). Set equal to adam_step_size
+                           for a constant step size.
+     --adam_step_size_cut ADAM_STEP_SIZE_CUT
+                           Factor applied to the step size at each cut (default 0.1).
+     --adam_clip_norm ADAM_CLIP_NORM
+                           Clip each gradient element to +/- this value (numpyro
+                           ClippedAdam). None (default) disables clipping: when gradients
+                           are much larger than the clip, an elementwise clip makes Adam
+                           follow the gradient's sign, and its fixed point is biased by
+                           rare large ELBO penalties.
+     --elbo_num_particles ELBO_NUM_PARTICLES
+                           Number of particles for ELBO estimation (default 2).
+     --convergence_window_steps CONVERGENCE_WINDOW_STEPS
+                           Optimizer steps per convergence window (default 2000; at least
+                           10 epochs, so with mini-batching every genotype is visited
+                           several times per window).
+     --patience PATIENCE   Consecutive windows without loss improvement required for a
+                           step-size cut, and (at the final step size) without loss
+                           improvement or parameter movement required for stopping
+                           (default 3).
+     --convergence_z CONVERGENCE_Z
+                           Standard errors of change attributed to noise (default 3).
+     --loss_rtol LOSS_RTOL
+                           Loss changes per window below this fraction of the loss count
+                           as no change (default 1e-6); matters only for
+                           (near-)deterministic losses.
+     --param_tolerance PARAM_TOLERANCE
+                           Parameter movement per window, beyond noise, still counted as
+                           no change (default 0.05), in posterior SDs (SVI), prior SDs
+                           (MAP), or log/logit units (positive or bounded parameters).
+     --checkpoint_interval CHECKPOINT_INTERVAL
+                           Frequency (in epochs) between checkpoints (default 10).
+     --max_num_epochs MAX_NUM_EPOCHS
+                           Maximum number of SVI or MAP epochs (default 100000); a cap
+                           only.
+     --forward_batch_size FORWARD_BATCH_SIZE
+                           When getting NUTS posteriors, calculate forward predictions in
+                           batches of this size (default 512).
+     --pre_map_num_epoch PRE_MAP_NUM_EPOCH
+                           Maximum number of epochs of the MAP warm-up run before SVI or
+                           NUTS (default 10000; 0 skips it). The warm-up stops earlier
+                           when it converges. SVI's guide and NUTS's chains start at its
+                           point. Not used by 'map'.
+     --init_param_jitter INIT_PARAM_JITTER
+                           Multiplicative jitter on the component guide's starting
+                           parameters (default 0, none). Not used by autoguides or MAP.
+                           Leave it off when starting from a pre-MAP: it scales with each
+                           value, so 0.1 moves an ln_cfu0 location near 15 by about 1.5
+                           ln units.
+     --nuts_num_warmup NUTS_NUM_WARMUP
+                           Number of NUTS warmup steps (default 500). Only used if
+                           analysis_method is 'nuts'.
+     --nuts_num_samples NUTS_NUM_SAMPLES
+                           Number of NUTS posterior samples to draw (default 500). Only
+                           used if analysis_method is 'nuts'.
+     --nuts_num_chains NUTS_NUM_CHAINS
+                           Number of MCMC chains (default 1). Only used if
+                           analysis_method is 'nuts'.
+     --nuts_target_accept_prob NUTS_TARGET_ACCEPT_PROB
+                           Target acceptance probability for NUTS step-size adaptation
+                           (default 0.9). Only used if analysis_method is 'nuts'.
+     --nuts_dense_mass     Adapt a dense mass matrix for NUTS instead of a diagonal one
+                           (default False). Worth it for correlated posteriors of up to a
+                           few thousand latents; with a diagonal one the relative fit ran
+                           at NUTS's maximum tree depth. Only used if analysis_method is
+                           'nuts'.
+     --epoch_checkpoint_interval EPOCH_CHECKPOINT_INTERVAL
+                           Frequency (in epochs) to write numbered epoch checkpoints to a
+                           checkpoints/ subdirectory alongside out_prefix (default 1000).
+                           Files are named {epoch:07d}_checkpoint.pkl. Set to 0 or None
+                           to disable. Raises FileExistsError if a target file already
+                           exists.
+     --init_from INIT_FROM
+                           A *_params.npz written by a MAP fit ({site}_auto_loc arrays)
+                           to start from instead of the configured guesses, wherever it
+                           names a site of this model; other sites start at their
+                           guesses. The fit may be of a different model: starting a
+                           sample_offset: level fit from a zero fit's point, say (the npz
+                           can carry sample_offset_offset_auto_loc to set the offsets
+                           too). Refused with checkpoint_file, which sets the start
+                           itself. (default: None)
+     --stage_offsets STAGE_OFFSETS
+                           'auto' (default), 'on' or 'off'. A MAP with level tube offsets
+                           (sample_offset: level) started cold can settle in a mode where
+                           the offsets carry the population's growth. Staged, it runs
+                           three MAPs: the offsets held at 0 ({out_prefix}_stage1_*), the
+                           offsets alone with everything else held there
+                           ({out_prefix}_stage2_*), and the joint MAP from that point at
+                           staged_step_size ({out_prefix}_*). A finished stage is reused
+                           when the command is rerun; an interrupted one resumes. 'auto'
+                           stages a fresh MAP of a level-offset model (no
+                           checkpoint_file, no init_from); 'on' requires one; 'off' fits
+                           it in one go.
+     --staged_step_size STAGED_STEP_SIZE
+                           Starting step size of the staged MAP's joint stage (default
+                           1e-4): Adam moves every parameter by about a step size per
+                           step, and a larger step can carry the start back into the
+                           offset mode.
+
+tfs-sample-posterior
+--------------------
+
+.. code-block:: text
+
+   usage: tfs-sample-posterior [-h] [--out_prefix OUT_PREFIX] [--seed SEED]
+                               [--num_posterior_samples NUM_POSTERIOR_SAMPLES]
+                               [--sampling_batch_size SAMPLING_BATCH_SIZE]
+                               [--forward_batch_size FORWARD_BATCH_SIZE]
+                               [--hessian_chunk_size HESSIAN_CHUNK_SIZE]
+                               [--laplace LAPLACE]
+                               [--laplace_max_params LAPLACE_MAX_PARAMS]
+                               [--skip_growth_observations]
+                               [--genotype_chunk_size GENOTYPE_CHUNK_SIZE]
+                               [--allow_flat_laplace_floor]
+                               config_file checkpoint_file
+
+   Draw posterior samples from an existing MAP, SVI, or NUTS checkpoint.
+
+   Three checkpoint types are handled automatically:
+
+   1. NUTS checkpoint: runs the forward model over the saved MCMC samples and
+      writes posterior predictives.
+   2. MAP checkpoint (AutoDelta guide): forms a Laplace (Gaussian) approximation
+      via the Hessian of the log-joint at the MAP point, then draws samples.
+   3. SVI checkpoint (component guide): restores the fitted variational state
+      and draws posterior samples directly from the guide.
+
+   The .h5 file written by this command can be passed directly to
+   tfs-predict-growth, tfs-predict-theta, and tfs-extract-params to obtain
+   full posterior uncertainty (quantile columns) on any quantity of interest.
+   This is the recommended path for obtaining uncertainty estimates from a
+   MAP-fitted model: fit with tfs-fit-model, sample here, then predict.
+
+   positional arguments:
+     config_file           Path to the YAML configuration file used when fitting the
+                           model.
+     checkpoint_file       Path to the checkpoint .pkl file produced by tfs-fit-model or
+                           tfs-prefit-calibration.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Prefix for the posterior output file (default
+                           'tfs_posterior'). Posterior samples are written to
+                           {out_prefix}.h5.
+     --seed SEED           Random seed used when constructing the RunInference object
+                           (default 0). For SVI and MAP checkpoints the PRNG state is
+                           restored from the checkpoint, so this value has no effect on
+                           the posterior samples.
+     --num_posterior_samples NUM_POSTERIOR_SAMPLES
+                           Number of posterior samples to draw (default 10000). Not used
+                           for NUTS checkpoints (all MCMC samples are used directly).
+     --sampling_batch_size SAMPLING_BATCH_SIZE
+                           Parameter sampling batch size (default 100). Not used for
+                           NUTS.
+     --forward_batch_size FORWARD_BATCH_SIZE
+                           Forward-model batch size for posterior predictives (default
+                           512).
+     --hessian_chunk_size HESSIAN_CHUNK_SIZE
+                           Number of Hessian rows computed per device batch for MAP
+                           checkpoints (default 64). Reduce if the Hessian computation
+                           hits device OOM.
+     --laplace LAPLACE     How a MAP checkpoint becomes a posterior (ignored for SVI and
+                           NUTS): - auto (default): full up to laplace_max_params
+                           parameters, arrowhead above. - full: the Laplace from the
+                           dense Hessian, O(D^2) memory. Out of reach on a full library
+                           (millions of parameters). - arrowhead: the full Laplace
+                           computed per genotype. The Hessian is an arrowhead (per-
+                           genotype blocks, their coupling to the shared parameters, the
+                           shared block); the shared parameters (growth k and m,
+                           hyperparameters, tube offsets) are drawn from their marginal
+                           and each genotype from its conditional. Runs on a full
+                           library. A negative direction of the shared block's Schur
+                           complement is held at the MAP; those directions are written to
+                           {out_prefix}_held_directions.csv, since they have no interval.
+                           - blocks: per-genotype blocks only, the shared parameters held
+                           at the MAP. Leaves out the k/m uncertainty. - point: the MAP
+                           point itself, one sample, no Hessian.
+     --laplace_max_params LAPLACE_MAX_PARAMS
+                           Largest number of MAP parameters for which auto picks the full
+                           Laplace (default 20000).
+     --skip_growth_observations
+                           Leave the per-observation growth sites (growth_pred,
+                           growth_obs) out of the file (default False). They are most of
+                           its size (94% on a 300-genotype subset; about 100 GB per site
+                           at 500 samples on a 200,000-genotype library), and tfs-
+                           predict-growth recomputes growth from the parameter samples
+                           rather than reading them.
+     --genotype_chunk_size GENOTYPE_CHUNK_SIZE
+                           Genotypes per Hessian-vector-product pass for the arrowhead
+                           and blocks Laplace (default None, all at once). Lower it on
+                           device OOM.
+     --allow_flat_laplace_floor
+                           If the prior SDs that floor the Laplace's Hessian eigenvalues
+                           cannot be computed, warn and floor every eigenvalue at a flat
+                           1e-3 instead of stopping with an error (default False). The
+                           flat floor turns a negative eigenvalue into a direction with
+                           SD ~30 in unconstrained units, so use it only to get a
+                           posterior out of a model whose prior SDs fail for a reason you
+                           understand.
+
+tfs-extract-params
+------------------
+
+.. code-block:: text
+
+   usage: tfs-extract-params [-h] [--out_prefix OUT_PREFIX] config_file param_file
+
+   Extract parameters from a checkpoint or posterior file and write to CSV.
+
+   If param_file ends with .pkl it is treated as a MAP (AutoDelta)
+   checkpoint: parameters are converted to constrained space and written with
+   a single q0.5 column.
+
+   Otherwise it is treated as a posterior samples .h5 or .npz file
+   produced by tfs-sample-posterior, and 17 quantile columns are written:
+   q0.001, q0.005, q0.01, q0.025, q0.05, q0.1,
+   q0.159, q0.25, q0.5, q0.75, q0.841, q0.9,
+   q0.95, q0.975, q0.99, q0.995, q0.999.
+
+   Writes one CSV per parameter group named {out_prefix}_{param_name}.csv
+   (e.g. tfs_params_activity.csv, tfs_params_theta.csv; thermo PddG/PnnC
+   also give one ddG_{structure} and ddG_prior_mean_{structure}
+   table per structure, both deterministic sites that need a posterior
+   file, plus one ddG_sigma table of the per-structure SD σ_s keyed by
+   structure; the ΔΔG is computed from the fitted constants,
+   thermo/prior.py). A model with its own learned
+   pyro.param sites (the thermo PnnC NN weights) also gets
+   {out_prefix}_model_params.csv: one row per element (parameter,
+   index, value).
+
+   positional arguments:
+     config_file           Path to the YAML configuration file used when fitting the
+                           model.
+     param_file            Path to a MAP checkpoint .pkl file, or a posterior .h5/.npz
+                           file produced by tfs-sample-posterior.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Prefix for output CSV files. Default 'tfs_params'.
+
+tfs-predict-theta
+-----------------
+
+.. code-block:: text
+
+   usage: tfs-predict-theta [-h] [--out_prefix OUT_PREFIX]
+                            [--genotypes_file GENOTYPES_FILE]
+                            [--titrant_names_file TITRANT_NAMES_FILE]
+                            [--titrant_concs_file TITRANT_CONCS_FILE] [--only_files]
+                            [--num_samples NUM_SAMPLES]
+                            [--genotype_batch_size GENOTYPE_BATCH_SIZE]
+                            [--shared_shift SHARED_SHIFT] [--shift_seed SHIFT_SEED]
+                            config_file param_file
+
+   Predict operator occupancy (theta) from a fitted hierarchical model.
+
+   By default predicts theta at all (genotype, titrant_name, titrant_conc)
+   combinations present in the training data, unioned with any genotypes or
+   titrant (name, conc) pairs supplied via file arguments.  Pass --only_files
+   to predict exclusively at the file-specified inputs and skip training-data
+   combinations.
+
+   titrant_names_file and titrant_concs_file together define a paired
+   (name, conc) grid that is unioned with the training titrant grid (or
+   replaces it when --only_files is set).  They must always be provided
+   together or both omitted.
+
+   When any requested genotype was not seen during training,
+   extract_theta_unmeasured is used.
+
+   A boolean column 'in_training_data' is added to the output: 1 if the
+   (genotype, titrant_name, titrant_conc) triple was in the training data,
+   0 otherwise.
+
+   A relative theta component (hill_relative) predicts the wt-relative
+   growth variable X, not theta; its output gets a column theta_scale
+   with the value X so nothing downstream takes it for an occupancy.
+
+   positional arguments:
+     config_file           Path to the YAML configuration file.
+     param_file            Path to a posterior .h5 file produced by tfs-sample-posterior,
+                           or a MAP checkpoint .pkl file produced by tfs-fit-model. When
+                           a .pkl file is supplied the output has a single q0.5 column
+                           (the MAP point) and no uncertainty. To obtain uncertainty
+                           estimates from a MAP fit, first run tfs-sample-posterior on
+                           the .pkl checkpoint; it builds a Laplace approximation and
+                           writes a .h5 file. Passing that .h5 here gives the full ladder
+                           of bare q<level> quantile columns (q0.025, q0.5, q0.975, ...).
+                           NUTS and SVI checkpoints are not supported directly; run tfs-
+                           sample-posterior first.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Prefix for the output CSV file. Written to {out_prefix}.csv.
+                           Default 'tfs_pred_theta'.
+     --genotypes_file GENOTYPES_FILE
+                           Plain-text file with one genotype per line (slash-separated
+                           mutations, e.g. 'M42I/K84L', or 'wt'). These genotypes are
+                           unioned with all training genotypes unless --only_files is
+                           set. May include genotypes not seen during training when the
+                           theta component supports predict_unmeasured. Default None.
+     --titrant_names_file TITRANT_NAMES_FILE
+                           Plain-text file with one titrant name per line. Must be
+                           provided together with titrant_concs_file (or both omitted).
+                           The resulting (name, conc) pairs are unioned with the training
+                           titrant grid unless --only_files is set. A single name is
+                           broadcast across all concentrations in titrant_concs_file.
+                           Default None.
+     --titrant_concs_file TITRANT_CONCS_FILE
+                           Plain-text file with one concentration per line. Must be
+                           provided together with titrant_names_file (or both omitted).
+                           Default None.
+     --only_files          If True, predict only at the genotypes and titrant (name,
+                           conc) pairs supplied via file arguments, ignoring training-
+                           data combinations. Default False.
+     --num_samples NUM_SAMPLES
+                           Number of joint posterior samples to include as sample_0 …
+                           sample_N-1 columns alongside the quantile columns. 0 (default)
+                           writes quantiles only.
+     --genotype_batch_size GENOTYPE_BATCH_SIZE
+                           When predicting unmeasured genotypes, process this many at a
+                           time to cap the memory used by the epistasis pair-indicator
+                           matrix (batch_size × N_pair × 4 bytes). Smaller values reduce
+                           peak memory at the cost of more iterations. Default 2000.
+     --shared_shift SHARED_SHIFT
+                           CSV of a shift shared by every non-wt genotype (columns
+                           genotype, titrant_name, titrant_conc, shift), written by the
+                           two-stage fit: the X change from one standard deviation of
+                           stage 1's k. Each posterior draw gets one standard-normal
+                           multiple of it, the same for every row, before quantiles are
+                           taken, so each genotype's interval carries k's uncertainty
+                           while differences between genotypes keep only what does not
+                           cancel. wt (fixed by the X gauge) does not move. Needs a
+                           posterior file with draws (not a MAP checkpoint) and training
+                           genotypes only. Default None.
+     --shift_seed SHIFT_SEED
+                           Seed for the shared per-draw multiples. Default 0.
+
+tfs-predict-growth
+------------------
+
+.. code-block:: text
+
+   usage: tfs-predict-growth [-h] [--out_prefix OUT_PREFIX]
+                             [--genotypes_file GENOTYPES_FILE]
+                             [--titrant_names_file TITRANT_NAMES_FILE]
+                             [--titrant_concs_file TITRANT_CONCS_FILE] [--only_files]
+                             [--num_samples NUM_SAMPLES]
+                             [--num_marginal_samples NUM_MARGINAL_SAMPLES]
+                             [--genotype_batch_size GENOTYPE_BATCH_SIZE]
+                             [--subset_genotypes] [--seed SEED]
+                             config_file param_file
+
+   Predict growth signal (ln_cfu) from a fitted hierarchical model.
+
+   By default predicts at all (genotype, titrant_name, titrant_conc,
+   replicate, t_pre, t_sel) combinations present in the training data, unioned
+   with any genotypes or concentrations supplied via file arguments.  Pass
+   --only_files to predict exclusively at the file-specified inputs and skip
+   training-data combinations.
+
+   titrant_names_file is a post-prediction row filter (restrict-only): it
+   narrows which titrant names appear in the output but does not affect which
+   concentrations are predicted.  It is not subject to union semantics.
+
+   A boolean column 'in_training_data' is added to the output: 1 if the
+   (genotype, titrant_name, titrant_conc) triple was in the training data,
+   0 otherwise.
+
+   positional arguments:
+     config_file           Path to the YAML configuration file.
+     param_file            Path to a posterior .h5 file produced by tfs-sample-posterior,
+                           or a MAP checkpoint .pkl file produced by tfs-fit-model. When
+                           a .pkl file is supplied the output has a single q0.5 column
+                           (the MAP point) and no uncertainty. To obtain uncertainty
+                           estimates from a MAP fit, first run tfs-sample-posterior on
+                           the .pkl checkpoint; it builds a Laplace approximation and
+                           writes a .h5 file. Passing that .h5 here gives the full ladder
+                           of bare q<level> quantile columns (q0.025, q0.5, q0.975, ...).
+                           NUTS and SVI checkpoints are not supported directly; run tfs-
+                           sample-posterior first.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Prefix for the output CSV file. Written to {out_prefix}.csv.
+                           Default 'tfs_pred_growth'.
+     --genotypes_file GENOTYPES_FILE
+                           Plain-text file with one genotype per line (slash-separated
+                           mutations, e.g. 'M42I/K84L', or 'wt'). These genotypes are
+                           unioned with all training genotypes unless --only_files is
+                           set. Default None.
+     --titrant_names_file TITRANT_NAMES_FILE
+                           Plain-text file with one titrant name per line. Filters output
+                           rows to the named titrants (restrict-only; does not affect
+                           which concentrations are predicted). If None, all titrant
+                           names are included. (default: None)
+     --titrant_concs_file TITRANT_CONCS_FILE
+                           Plain-text file with one concentration per line. These
+                           concentrations are unioned with all training concentrations
+                           unless --only_files is set. Default None.
+     --only_files          If True, predict only at the genotypes and concentrations
+                           supplied via file arguments, ignoring training-data
+                           combinations. Default False.
+     --num_samples NUM_SAMPLES
+                           Number of joint posterior samples to include as sample_0 …
+                           sample_N-1 columns alongside the quantile columns. 0 (default)
+                           writes quantiles only.
+     --num_marginal_samples NUM_MARGINAL_SAMPLES
+                           Number of posterior samples to run through the model when
+                           computing quantiles. If None, all available samples are used.
+                           (default: None)
+     --genotype_batch_size GENOTYPE_BATCH_SIZE
+                           Maximum number of genotypes per predict() call. The genotype
+                           list is split into chunks of this size, each chunk is
+                           predicted separately, and the results are concatenated.
+                           Reduces peak memory at the cost of one JAX re-compilation per
+                           batch. If None (default), a batch size is estimated
+                           automatically from the available device memory and the per-
+                           genotype tensor cost; pass an explicit value to override.
+     --subset_genotypes    If True, predict only a single memory-fit block of genotypes
+                           instead of every genotype. The block size is the auto-sized
+                           (or explicit) genotype_batch_size; the block always includes
+                           the binding genotypes, the spiked genotypes, and any genotypes
+                           supplied via genotypes_file, with the remainder of the block
+                           filled by a random sample of the other genotypes. Intended for
+                           quickly assessing the input/output ln_cfu correlation without
+                           paying for a full prediction sweep. Default False.
+     --seed SEED           Seed for the random draw used by subset_genotypes, making the
+                           sampled block reproducible. Ignored unless subset_genotypes is
+                           True. Default None (non-deterministic draw).
+
+tfs-predict-epistasis
+---------------------
+
+.. code-block:: text
+
+   usage: tfs-predict-epistasis [-h] [--out_prefix OUT_PREFIX]
+                                [--genotypes_file GENOTYPES_FILE]
+                                [--titrant_names_file TITRANT_NAMES_FILE]
+                                [--titrant_concs_file TITRANT_CONCS_FILE] [--only_files]
+                                [--scale SCALE] [--scale_constant SCALE_CONSTANT]
+                                [--regime_eps REGIME_EPS] [--regime_ci REGIME_CI]
+                                [--shared_shift SHARED_SHIFT] [--shift_seed SHIFT_SEED]
+                                config_file param_file
+
+   Predict second-order epistasis from the joint posterior of a fitted model.
+
+   For every double mutant, epistasis is calculated on its mutant cycle (the
+   double, its two single-mutant parents, and the wildtype).  In contrast to
+   running tfs-extract-epistasis on a tfs-predict-theta table -- which uses the
+   per-genotype marginal theta estimates and assumes the four corners are
+   independent -- this command draws theta for all genotypes from the same
+   posterior sample, computes epistasis within each draw, and then reports
+   quantiles across draws.  The uncertainty therefore reflects the true
+   posterior covariance between the corners of each cycle.
+
+   Epistasis is computed independently at each condition (titrant_name,
+   titrant_conc).  Output columns are 'genotype', 'titrant_name',
+   'titrant_conc', one 'q<level>' column per quantile (e.g. 'q0.5', 'q0.025',
+   'q0.975' -- the library-wide quantile-output convention), and a trailing
+   'in_regime' flag (int 0/1), written to {out_prefix}.csv.
+
+   'in_regime' marks whether the estimate is backed by data or by model
+   extrapolation.  It is 1 only when all four corners of the mutant cycle (wt,
+   both singles, double) have their theta posterior (central 95% interval by
+   default) inside the resolvable band [regime_eps, 1 - regime_eps].  When a
+   corner's theta is near saturation (0 or 1), logit(theta) is compressed and
+   the growth data constrain it weakly, so the epistasis there leans on the
+   theta-model's functional form and cross-genotype posterior covariance --
+   treat in_regime == 0 rows as model-conditional.  (This checks posterior mass
+   only; it does not separately test whether the growth signal exceeds the
+   growth noise.)
+
+   Only genotypes seen during training are supported (the joint sample matrix
+   is built from the training theta curves); requesting an out-of-training
+   genotype is an error.
+
+   positional arguments:
+     config_file           Path to the YAML configuration file.
+     param_file            Path to a posterior .h5 file produced by tfs-sample-posterior,
+                           or a MAP checkpoint .pkl file produced by tfs-fit-model. A
+                           .pkl provides a single point estimate, so every q<level>
+                           column collapses to that value (no uncertainty); run tfs-
+                           sample-posterior first to obtain a Laplace posterior and real
+                           quantiles.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Prefix for the output CSV file. Written to {out_prefix}.csv.
+                           Default 'tfs_pred_epistasis'.
+     --genotypes_file GENOTYPES_FILE
+                           Plain-text file with one genotype per line (slash-separated
+                           mutations, e.g. 'M42I/K84L', or 'wt'). Restricts the analysis
+                           to cycles whose genotypes appear here (unioned with all
+                           training genotypes unless --only_files is set). All genotypes
+                           must have been seen during training. Default None.
+     --titrant_names_file TITRANT_NAMES_FILE
+                           Plain-text file with one titrant name per line. Must be
+                           provided together with titrant_concs_file (or both omitted).
+                           The resulting (name, conc) pairs are unioned with the training
+                           titrant grid unless --only_files is set. A single name is
+                           broadcast across all concentrations in titrant_concs_file.
+                           Default None.
+     --titrant_concs_file TITRANT_CONCS_FILE
+                           Plain-text file with one concentration per line. Must be
+                           provided together with titrant_names_file (or both omitted).
+                           Default None.
+     --only_files          If True, use only the genotypes and titrant (name, conc) pairs
+                           supplied via file arguments, ignoring training-data
+                           combinations. Default False.
+     --scale SCALE         Epistatic scale. 'logit' (default) computes additive epistasis
+                           of logit(theta) -- the natural scale for an occupancy in [0,
+                           1]. 'add': (Y11 - Y10) - (Y01 - Y00). 'mult': (Y11 / Y10) /
+                           (Y01 / Y00). A relative fit (theta 'hill_relative') gives X,
+                           not theta: it takes only 'add', and its output has no
+                           'in_regime' column.
+     --scale_constant SCALE_CONSTANT
+                           Constant applied to the transform before the difference-of-
+                           differences; multiplies the reported epistasis. Default 1.0.
+                           Mainly a unit conversion for scale='logit': since logit(theta)
+                           = -dG/RT, passing -RT (e.g. -0.6159 for kcal/mol at 310.15 K)
+                           reports epistasis as an interaction free energy. Has no effect
+                           on (and is rejected for) scale='mult'.
+     --regime_eps REGIME_EPS
+                           Theta resolution floor for the 'in_regime' flag. A cycle
+                           corner counts as resolvable when its theta posterior sits in
+                           [regime_eps, 1 - regime_eps]. Default 0.01 (matching a 1%-in-
+                           theta resolution, whose logit band is +/- ~4.6). Must be in
+                           [0, 0.5).
+     --regime_ci REGIME_CI
+                           Central posterior-mass fraction required inside the band for a
+                           corner to count as in-regime. Default 0.95 (checks the theta
+                           2.5-97.5% interval). Must be in (0, 1).
+     --shared_shift SHARED_SHIFT
+                           CSV of a shift shared by every non-wt genotype (genotype,
+                           titrant_name, titrant_conc, shift), written by the two-stage
+                           fit: the X change from one standard deviation of stage 1's k.
+                           Each joint draw gets one standard-normal multiple of it before
+                           epistasis is computed. The cycle's wt corner does not move, so
+                           an epistasis on X (X_ab - X_a - X_b + X_wt) keeps one net copy
+                           of the shift. Needs a posterior file with draws. Default None.
+     --shift_seed SHIFT_SEED
+                           Seed for the shared per-draw multiples. Default 0.
+
+tfs-extract-epistasis
+---------------------
+
+.. code-block:: text
+
+   usage: tfs-extract-epistasis [-h] [--y_obs Y_OBS] [--out_prefix OUT_PREFIX]
+                                [--y_std Y_STD] [--group_by GROUP_BY [GROUP_BY ...]]
+                                [--scale SCALE] [--scale_constant SCALE_CONSTANT]
+                                [--keep_extra] [--logit_eps LOGIT_EPS]
+                                data_file
+
+   Calculate second-order epistasis for pairs of mutations.
+
+   Reads a long-form CSV (one row per genotype, with genotypes in the format
+   'MUT1/MUT2') and, for every double mutant, builds a mutant cycle from its
+   two single-mutant parents and the wildtype. Epistasis is calculated on the
+   requested observable and written, one row per double mutant, to
+   {out_prefix}.csv.
+
+   positional arguments:
+     data_file             Path to the input CSV. Must contain a 'genotype' column, the
+                           column named by y_obs, and (if given) the columns named by
+                           y_std and group_by.
+
+   options:
+     -h, --help            show this help message and exit
+     --y_obs Y_OBS         Name of the column holding the observable for which epistasis
+                           is calculated (e.g. 'fitness', 'dG'). If None (default) and
+                           the input has a 'q0.5' column (as written by tfs-predict-
+                           theta), 'q0.5' is used.
+     --out_prefix OUT_PREFIX
+                           Prefix for the output CSV file, written to {out_prefix}.csv.
+                           Default 'tfs_epistasis'.
+     --y_std Y_STD         Name of the column holding the standard error of y_obs. If
+                           given, the epistasis error (ep_std) is propagated and written.
+                           If None (default) and both 'q0.841' and 'q0.159' are present,
+                           the standard error is taken as (q0.841 - q0.159) / 2.
+     --group_by GROUP_BY [GROUP_BY ...]
+                           One or more column names that define a unique experimental
+                           condition. Epistasis is calculated independently within each
+                           condition. If omitted, the whole table is treated as a single
+                           condition. (default: None)
+     --scale SCALE         Epistatic scale. "add" (default): (Y11 - Y10) - (Y01 - Y00).
+                           "mult": (Y11 / Y10) / (Y01 / Y00). "logit": additive epistasis
+                           of logit(Y) — requires an observable in (0, 1), such as
+                           theta/occupancy.
+     --scale_constant SCALE_CONSTANT
+                           Constant applied to the transformed observable before
+                           epistasis is taken; multiplies ep_obs (and abs() multiplies
+                           ep_std). Default 1.0. Mainly a unit conversion for
+                           scale="logit": since logit(theta) = -dG/RT, passing -RT (e.g.
+                           -0.6159 for kcal/mol at 310.15 K) reports epistasis as an
+                           interaction free energy. The caller owns the sign,
+                           temperature, and gas constant/units. Has no effect on (and is
+                           rejected for) scale="mult".
+     --keep_extra          If True, retain all columns from the input CSV in the output.
+                           If False (default), keep only the identifier columns and the
+                           calculated epistasis values.
+     --logit_eps LOGIT_EPS
+                           Only used when scale="logit". Observations are clamped to
+                           [logit_eps, 1 - logit_eps] before the transform. Default 1e-9.
+
+tfs-cat-response
+----------------
+
+.. code-block:: text
+
+   usage: tfs-cat-response [-h] [--y_obs Y_OBS] [--out_prefix OUT_PREFIX] [--y_std Y_STD]
+                           [--group_by GROUP_BY [GROUP_BY ...]]
+                           [--models MODELS [MODELS ...]] [--alpha ALPHA]
+                           [--select_by SELECT_BY] [--adequacy_alpha ADEQUACY_ALPHA]
+                           [--curvy_cutoff CURVY_CUTOFF] [--rope_cutoff ROPE_CUTOFF]
+                           [--rope_multiplier ROPE_MULTIPLIER] [--write_all_predictions]
+                           [--num_workers NUM_WORKERS]
+                           data_file x_obs
+
+   Classify each group's response curve using categorical response models.
+
+   Reads a long-form CSV and fits one or more response models to every group.
+   Groups are defined by the 'genotype' column plus any --group_by columns. For
+   each group the best model is selected per --select_by (default the 'shape'
+   classifier), then graded against zero on the observed data (per-point
+   sig_nonzero + a per-curve data-based nonzero test with a Benjamini-Hochberg
+   FDR correction) to assign the 'fittable' bool.
+
+   Writes:
+     - {out_prefix}.csv             one row per group; all models' weights and
+                                    parameter estimates, plus the assessment
+                                    rollups (nonzero_p/q, omnibus_p/q,
+                                    n_nonzero, all_equiv_zero, fittable).
+     - {out_prefix}_{model}.csv     one file per model; that model's parameter
+                                    table and per-group fit statistics.
+     - {out_prefix}_predictions.csv best-model predicted curves (all models
+                                    when --write_all_predictions).
+     - {out_prefix}_assessment.csv  per (group, x) best-model assessment.
+
+   positional arguments:
+     data_file             Path to the input CSV. Must contain a 'genotype' column,
+                           x_obs, y_obs, and (if given) y_std and every column in
+                           group_by.
+     x_obs                 Name of the column holding the independent variable (e.g.
+                           'titrant_conc').
+
+   options:
+     -h, --help            show this help message and exit
+     --y_obs Y_OBS         Name of the column holding the observable (e.g. 'q0.5'). If
+                           None (default) and the input has a 'q0.5' column (as written
+                           by tfs-predict-theta), 'q0.5' is used.
+     --out_prefix OUT_PREFIX
+                           Prefix for the output CSV files. Default 'tfs_cat_response'.
+     --y_std Y_STD         Name of the column holding per-row uncertainty (standard
+                           deviation). If None (default) and both 'q0.841' and 'q0.159'
+                           are present, sigma is computed as (q0.841 - q0.159) / 2;
+                           otherwise the fit is unweighted.
+     --group_by GROUP_BY [GROUP_BY ...]
+                           Additional column(s) that, together with 'genotype', define a
+                           group. If omitted, groups are defined by 'genotype' alone.
+                           (default: None)
+     --models MODELS [MODELS ...]
+                           Response models to fit. If None (default), the curated
+                           DEFAULT_MODELS set is used (flat, linear_log, repressor,
+                           inducer, bell_peak_log, bell_dip_log). Pass explicit names to
+                           reach any model in MODEL_LIBRARY, including the raw-x and
+                           biphasic variants.
+     --alpha ALPHA         Significance level with two roles: the per-point sig_nonzero
+                           test, and the nonzero_q threshold used to call a curve 'real'.
+                           Default 0.05.
+     --select_by SELECT_BY
+                           Model-selection strategy. "shape" (default) is the liberal
+                           shape classifier: it gates flat-vs-curvy on the flat fit's
+                           residual autocorrelation and names the curvy shape by best R2
+                           (defaults to the physical SHAPE_MODELS vocabulary -- no
+                           linear, includes biphasic). "aicc" selects the lowest-AICc
+                           model. "adequacy" keeps the AICc pick unless flagged, then
+                           escalates to a no-simpler adequate model (never demotes).
+     --adequacy_alpha ADEQUACY_ALPHA
+                           Runs-test threshold for the shape_status diagnostic and, when
+                           select_by="adequacy", for escalation. Default 0.05.
+     --curvy_cutoff CURVY_CUTOFF
+                           Only used when select_by="shape": flat-vs-curvy gate on the
+                           flat fit's residual-autocorrelation p-value (larger = more
+                           curves called curvy). Sweep it and visually inspect. Default
+                           0.1.
+     --rope_cutoff ROPE_CUTOFF
+                           ROPE half-width separating confident_zero from indeterminate.
+                           If None (default), auto-derived as rope_multiplier *
+                           median(observed y_std) -- a detectability threshold that
+                           rarely fires confident_zero; pass a fixed, biologically-
+                           meaningful value to make it fire.
+     --rope_multiplier ROPE_MULTIPLIER
+                           Multiplier used when rope_cutoff is auto-derived. Default 2.0.
+     --write_all_predictions
+                           If True, write every fit model's predicted curve rather than
+                           only the best model's. Default False.
+     --num_workers NUM_WORKERS
+                           Number of parallel worker processes. 1 runs serially; -1 (the
+                           default) uses os.cpu_count() - 1; N uses N processes.
+
+tfs-compare-runs
+----------------
+
+.. code-block:: text
+
+   usage: tfs-compare-runs [-h] [--out_prefix OUT_PREFIX] [--reference REFERENCE]
+                           [--index_by INDEX_BY] [--group_by GROUP_BY [GROUP_BY ...]]
+                           [--match_by MATCH_BY [MATCH_BY ...]] [--y_obs Y_OBS]
+                           [--y_std Y_STD] [--point_quantile POINT_QUANTILE]
+                           [--sigma_quantiles SIGMA_QUANTILES SIGMA_QUANTILES]
+                           [--no_aggregate]
+                           estimates [estimates ...]
+
+   Measure cross-run agreement of an estimate across N independent runs.
+
+   Reads N estimate CSVs of the same quantity -- predicted features
+   (tfs-predict-theta, tfs-predict-growth, tfs-predict-epistasis) or fitted
+   parameters (tfs-extract-params) alike -- and reports, for every entity, how
+   much the runs disagree (reproducibility, in native units) and whether that
+   disagreement is explained by each run's reported uncertainty
+   (self-consistency, unit-free).
+
+   No thresholds are applied and no grades are assigned: every quantity is a
+   number, so filter downstream (e.g. `df["overdispersion"] > 2`) where the
+   cutline is recorded alongside the analysis that used it.
+
+   Writes {out_prefix}.csv (one row per entity, or per entity x group_by),
+   {out_prefix}_aggregate.csv (unless --no_aggregate), and
+   {out_prefix}_metadata.json (every resolved setting, so the run is
+   reconstructable).
+
+   positional arguments:
+     estimates             Either two or more estimate CSV paths (e.g. `rep1.csv
+                           rep2.csv`), or a single path to a manifest file listing one
+                           CSV path per line ('#' comments allowed). A single argument
+                           ending in '.csv' is treated as a direct path, not a manifest.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Prefix for the output files. Default 'tfs_compare_runs'.
+     --reference REFERENCE
+                           Path to a reference-run CSV. If given, switches to reference
+                           mode: each estimate run is scored by its deviation from this
+                           run (e.g. k-fold dropouts vs. a full-data fit). If omitted,
+                           runs are compared to their symmetric cross-run mean. (default:
+                           None)
+     --index_by INDEX_BY   The entity column being scored. Default None: auto-detect
+                           'genotype', else 'parameter', else the sole match-key column.
+     --group_by GROUP_BY [GROUP_BY ...]
+                           Extra columns to break each entity out by (e.g. `titrant_name
+                           titrant_conc`). Must be part of the match key. This is a
+                           statistical zoom: the finer the grouping, the fewer degrees of
+                           freedom behind every statistic. Check the n_rows / n_eff
+                           columns. (default: None)
+     --match_by MATCH_BY [MATCH_BY ...]
+                           The columns that make a row 'the same row' across runs.
+                           Default None: auto-detect every shared non-value, non-
+                           bookkeeping column.
+     --y_obs Y_OBS         Point-estimate column. Default None, which uses the
+                           --point_quantile column (q0.5).
+     --y_std Y_STD         Uncertainty column. Default None, which uses the symmetric
+                           quantile half-width (q0.841 - q0.159)/2. If neither is
+                           available the self-consistency axis is reported as NaN.
+     --point_quantile POINT_QUANTILE
+                           Quantile used as the point estimate. Default 0.5.
+     --sigma_quantiles SIGMA_QUANTILES SIGMA_QUANTILES
+                           Two ascending quantiles bracketing one sigma. Default (0.159,
+                           0.841).
+     --no_aggregate        Suppress {out_prefix}_aggregate.csv, the long-form table
+                           combining the N runs as an equal-weight mixture of their per-
+                           run posteriors (folding in both per-run posterior width and
+                           run-to-run spread; it does not shrink with N). Only the N
+                           estimate runs are mixed -- a reference run is never included.
+                           Aggregation needs at least two shared q<level> columns and is
+                           skipped with a warning when they are absent. It is also the
+                           slowest step on a large library, so this is the escape hatch.
+
+tfs-summarize-fit
+-----------------
+
+.. code-block:: text
+
+   usage: tfs-summarize-fit [-h] [--ref_theta_file REF_THETA_FILE]
+                            [--out_prefix OUT_PREFIX]
+                            run_dir
+
+   Evaluate theta prediction quality for a tfs-fit-model run directory.
+
+   Scans run_dir for a *_config.yaml, *_pred_theta.csv, *_pred_growth.csv
+   (optional), and *_losses.txt (optional).  Computes prediction statistics
+   and writes output files to the summary directory.
+
+   **CSV files are the authoritative outputs.**  Every plot written by this
+   function has a matched CSV containing the exact data used to generate it.
+   The PDFs are human-readable summaries; the CSVs are the record of truth
+   for downstream quantitative analysis.
+
+   Output files written:
+
+   - {out_prefix}_fit_summary.json — nested statistics with top-level
+     keys metadata, theta (training / test sub-keys), and growth
+     (training sub-key).
+   - {out_prefix}_theta_corr_training.csv — joined (ref, predicted)
+     theta pairs for training genotypes; ref column holds the training
+     observation used as ground truth.
+   - {out_prefix}_theta_corr_test.csv — same for test genotypes (only
+     written when a ref theta file is resolved and has matching rows).
+   - {out_prefix}_theta_corr.pdf — two-panel correlation plot for theta
+     (training left, test right).
+   - {out_prefix}_growth_corr.csv — a copy of the *_pred_growth.csv in
+     run_dir with ln_cfu/ln_cfu_std renamed ref/ref_std
+     (only written when that file is present).
+   - {out_prefix}_growth_corr.pdf — correlation plot for ln_cfu (only
+     written when *_pred_growth.csv is present).
+   - {out_prefix}_{genotype}_theta_fits.csv / .pdf — per-genotype
+     joined observation + prediction data and fit plot (one pair per
+     genotype present in both binding and prediction CSVs).
+   - {out_prefix}_{genotype}_trajectory.csv / .pdf — per-genotype
+     predicted growth trajectory data and plot (only when growth data is
+     configured and a posterior/params file is found).
+   - {out_prefix}_losses.pdf — training loss curve (only written when
+     *_losses.txt is present).
+   - {out_prefix}_tube_offsets.csv / _tube_offset_trends.csv /
+     _tube_offsets.pdf — the per-tube sample offsets in prior SDs and,
+     per condition, their Spearman trend with titrant and with time (only
+     when tfs-extract-params wrote *_sample_offset_offset.csv). A
+     condition whose trend has BH q < 0.05 is flagged structured: the
+     offsets are carrying growth (the offset mode) rather than tube noise.
+     The JSON's tube_offsets key holds the summary.
+
+   positional arguments:
+     run_dir               Directory containing model fit outputs.
+
+   options:
+     -h, --help            show this help message and exit
+     --ref_theta_file REF_THETA_FILE
+                           CSV with columns genotype, titrant_name, titrant_conc, and a
+                           theta column containing known theta values for evaluating out-
+                           of-sample prediction. When omitted the function looks for a
+                           *_sim_genotype_theta.csv file in *run_dir* automatically (the
+                           file produced by tfs-simulate). The theta column is resolved
+                           in order: theta_obs (preferred) → theta (fallback); it is
+                           renamed to ref in the output CSV. If neither column is present
+                           a warning is issued and test statistics are skipped. Pass this
+                           argument explicitly to use a theta reference from outside
+                           *run_dir* or with a non-standard name. (default: None)
+     --out_prefix OUT_PREFIX
+                           Prefix for output files. Defaults to
+                           {run_dir}/summary/tfs_summarize.
+
+tfs-fit-genotypes
+-----------------
+
+.. code-block:: text
+
+   usage: tfs-fit-genotypes [-h] [--out_prefix OUT_PREFIX]
+                            [--intercept_cols INTERCEPT_COLS]
+                            [--dk_geno_prior_sd DK_GENO_PRIOR_SD] [--min_obs MIN_OBS]
+                            [--num_workers NUM_WORKERS]
+                            growth_df growth_calibration_file
+
+   Fit the growth model to each genotype independently (frozen calibration).
+
+   positional arguments:
+     growth_df             Processed growth CSV (tfs-process-counts output).
+     growth_calibration_file
+                           Frozen per-condition growth calibration: a tfs-prefit-
+                           calibration priors CSV, or a wide
+                           condition_rep,growth_k,growth_m CSV.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Output prefix (see the module docstring for the files
+                           written). (default: 'tfs_fit_genotypes')
+     --intercept_cols INTERCEPT_COLS
+                           Comma-separated columns whose unique combinations each get a
+                           nuisance ln_cfu0 (default "replicate"; empty string -> single
+                           intercept).
+     --dk_geno_prior_sd DK_GENO_PRIOR_SD
+                           Std of the weak Normal prior on dk_geno (<=0 disables it).
+                           (default: 1.0)
+     --min_obs MIN_OBS     Skip genotypes with fewer than this many usable observations.
+                           (default: None)
+     --num_workers NUM_WORKERS
+                           Parallelize the per-genotype fits over a process pool: 1
+                           (default) serial; -1 uses os.cpu_count() - 1; N uses N.
+
+tfs-sample-prior
+----------------
+
+.. code-block:: text
+
+   usage: tfs-sample-prior [-h] [--out_prefix OUT_PREFIX] [--num_datasets NUM_DATASETS]
+                           [--no_noise] [--seed SEED]
+                           config_file
+
+   Draw synthetic datasets from the model prior.
+
+   For each dataset, two files are written:
+
+   <out_prefix>_NNN_growth.csv
+       Synthetic growth DataFrame with ln_cfu replaced by prior
+       predictions.  Observation noise drawn from Normal(0, ln_cfu_std)
+       is added unless --no_noise is given.  Pass it to
+       tfs-configure-model --growth_df to fit it.
+
+   <out_prefix>_NNN_ground_truth.h5
+       The latent parameters used to generate that dataset, in the same
+       HDF5 format as a tfs-sample-posterior output.  Pass this to
+       tfs-predict-growth or tfs-extract-params to inspect the
+       ground-truth values, or compare it to the fitted posterior for
+       parameter recovery checks.
+
+   When --num_datasets 1 (the default) the _NNN index is still
+   included in the filenames for consistency.
+
+   positional arguments:
+     config_file           Path to the YAML configuration file (same one used for tfs-
+                           fit-model).
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Prefix for all output files. Default 'tfs_prior'.
+     --num_datasets NUM_DATASETS
+                           Number of independent synthetic datasets to generate. Default
+                           1. Set to a larger value (e.g. 100) for Simulation-Based
+                           Calibration.
+     --no_noise            Turn off (on by default): If True (default), add Normal(0,
+                           ln_cfu_std) noise to each synthetic observation. Set to False
+                           to get the deterministic prior predictions.
+     --seed SEED           Base random seed. Dataset i uses seed + i to ensure
+                           independence. Default 0.
+
+tfs-simulate
+------------
+
+.. code-block:: text
+
+   usage: tfs-simulate [-h] [--out_prefix OUT_PREFIX] [--num_replicates NUM_REPLICATES]
+                       [--seed SEED] [--no_write_raw] [--no_write_growth]
+                       config_file
+
+   Simulate a TF selection experiment from a YAML configuration file.
+
+   Runs library_prediction once to establish ground-truth phenotypes, then
+   simulates num_replicates independent experimental replicates using
+   selection_experiment. Writes library, parameters, genotype_theta (long-form:
+   genotype/titrant_name/titrant_conc/theta), growth, growth_parameters
+   (per-condition condition_growth ground truth; see
+   tfscreen.simulate.growth_parameters_output.generate_growth_parameters_df),
+   and transformation_lam (single-row congression Poisson-rate ground truth;
+   see tfscreen.simulate.transformation_lam_output.generate_transformation_lam_df)
+   CSV files. A mutation-decomposed theta model drawn from its prior (the
+   thermodynamic models) also writes mutation_parameters (one row per
+   mutation: the true d_* deltas and, for PddG/PnnC, ddG_{structure}) and,
+   when the library has multi-mutants, pair_parameters (one row per pair:
+   the true epi_* terms); see
+   tfscreen.simulate.thermo_to_growth._mutation_truth_tables. PddG/PnnC
+   also write ddG_sigma (one row per structure: the true SD of ΔΔG about
+   the structural prior mean, set by the config's ddG_truth_sd, 1 kT by
+   default; see _ddG_sigma_truth_table). If the config
+   contains a 'binding_data' block, also writes a
+   simulated binding curve CSV (see
+   tfscreen.simulate.binding_data.generate_binding_df). If it contains a
+   'base_growth_data' block, also writes a simulated direct growth-rate
+   calibration CSV and a single-row k_ref ground-truth CSV (see
+   tfscreen.simulate.base_growth_data.generate_base_growth_df and
+   .generate_k_ref_df). If it contains a 'presplit_data' block, also writes
+   a simulated presplit CSV (see
+   tfscreen.simulate.presplit_data.generate_presplit_df). If it contains an
+   'od600' block, also writes one OD600 reading per tube for every
+   replicate, sequenced or not ('od600'; see tfscreen.simulate.od600), and
+   simulates od600.num_od_only_replicates extra replicates that get OD600
+   but no reads. With shared_transformation, every replicate (OD-only ones
+   included) draws its cells from one library assembly and transformation.
+
+   With library_genotypes and/or library_subsample the library is
+   restricted to a subset of the described genotypes before anything is
+   simulated ({out_prefix}_library.csv lists the kept ones); with the
+   default library_subsample_depth: full_library each kept sequence
+   keeps the pool share, transformants, reads and PCR templates it has in
+   the full library. See tfscreen.genetics.library_subsample.
+
+   positional arguments:
+     config_file           Path to the YAML run configuration file.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Output prefix: files are written as {out_prefix}_{name}.csv. A
+                           directory part (sim/tfs_sim) is created if absent. (default:
+                           'tfs_sim')
+     --num_replicates NUM_REPLICATES
+                           Number of independent experimental replicates to simulate.
+                           Default 2.
+     --seed SEED           Random seed. Overrides seed in the config file when provided.
+                           (default: None)
+     --no_write_raw        Turn off (on by default): Also write the experiment in the raw
+                           formats real data come in (one count file per tube in
+                           {out_prefix}_counts/, the tube table {out_prefix}_tubes.csv
+                           and, with an od600 block, the OD600 table and the
+                           calibration), so tfs-process-counts processes it exactly as it
+                           would a lab's data. The command is printed. See
+                           tfscreen.simulate.raw_output.
+     --no_write_growth     Turn off (on by default): Also write {out_prefix}_growth.csv,
+                           the growth table built directly from the simulated counts
+                           (with each row's true values). tfs-process-counts builds the
+                           same table from the raw files, so a full-size simulation can
+                           skip it: on a real library's size it is several GB. With an
+                           od600 block, tubes read below the calibration's detection
+                           threshold are left out of it, as they are out of the raw tube
+                           table (tfs-process-counts refuses them).
+
+tfs-build-empirical
+-------------------
+
+.. code-block:: text
+
+   usage: tfs-build-empirical [-h] [--binding_df BINDING_DF] [--out_prefix OUT_PREFIX]
+                              [--seed SEED]
+                              [--growth_calibration_file GROWTH_CALIBRATION_FILE]
+                              [--library_config LIBRARY_CONFIG]
+                              [--base_growth_df BASE_GROWTH_DF]
+                              [--thermo_data THERMO_DATA]
+                              [--intercept_cols INTERCEPT_COLS]
+                              [--dk_geno_prior_sd DK_GENO_PRIOR_SD] [--min_obs MIN_OBS]
+                              [--no_drop_railed] [--num_workers NUM_WORKERS]
+                              growth_df
+
+   Fit real screen data to an empirical phenotype generating distribution.
+
+   positional arguments:
+     growth_df             Processed growth CSV (tfs-process-counts output) for real
+                           data.
+
+   options:
+     -h, --help            show this help message and exit
+     --binding_df BINDING_DF
+                           Real binding CSV. Required unless growth_calibration_file is
+                           given; it identifies the growth slope m from data (the in-
+                           library anchors). (default: None)
+     --out_prefix OUT_PREFIX
+                           Output prefix. Writes <prefix>_phenotype_model.json (the
+                           generating distribution) and <prefix>_stage1_fits.csv (the
+                           per-genotype fits that feed Stage 2). Unless
+                           growth_calibration_file is given, also the
+                           <prefix>_configure_* / <prefix>_prefit_* intermediates.
+                           (default: 'tfs_empirical')
+     --seed SEED           Random seed for the pre-fit MAP calibration. Required unless
+                           growth_calibration_file is given. (default: None)
+     --growth_calibration_file GROWTH_CALIBRATION_FILE
+                           Skip the configure+prefit step and use this calibration
+                           directly — a prefit priors CSV or a wide
+                           condition_rep,growth_k,growth_m CSV. Use this to iterate on
+                           the Stage-1/2 knobs without re-running the MAP. (default:
+                           None)
+     --library_config LIBRARY_CONFIG
+                           Library YAML describing the screened library (the same file
+                           handed to tfs-process-fastq), forwarded to configure_model.
+                           Required unless growth_calibration_file is given. (default:
+                           None)
+     --base_growth_df BASE_GROWTH_DF
+                           Direct growth-rate calibration CSV forwarded to
+                           configure_model. (default: None)
+     --thermo_data THERMO_DATA
+                           Thermodynamic data path forwarded to configure_model (unused
+                           by the default hill_geno theta).
+     --intercept_cols INTERCEPT_COLS
+                           Comma-separated columns whose unique combinations each get a
+                           nuisance ln_cfu0 (default "replicate"; empty string -> single
+                           intercept).
+     --dk_geno_prior_sd DK_GENO_PRIOR_SD
+                           Std of the weak Normal prior on dk_geno (<=0 disables it).
+                           (default: 1.0)
+     --min_obs MIN_OBS     Skip genotypes with fewer than this many usable observations.
+                           (default: None)
+     --no_drop_railed      Turn off (on by default): Drop theta-logit-railed Stage-1 fits
+                           before the Stage-2 fit.
+     --num_workers NUM_WORKERS
+                           Parallelize the Stage-1 per-genotype fits over a process pool:
+                           1 (default) serial; -1 uses os.cpu_count() - 1; N uses N.
+                           Recommended for large libraries (the fits are embarrassingly
+                           parallel).
+
+tfs-setup-sim-grid
+------------------
+
+.. code-block:: text
+
+   usage: tfs-setup-sim-grid [-h] [--out_dir OUT_DIR] grid_yaml
+
+   Set up a directory grid for simulation runs.
+
+   Reads a grid YAML file, loads a base simulate config, expands all simulate
+   and template blocks into their Cartesian product, and for each combination:
+
+   1. Creates a subdirectory under *out_dir*.
+   2. Merges the simulate-block overrides into the base config and writes
+      tfs_sim_config.yaml inside that subdirectory.
+   3. Renders the Jinja2 template (output_file) with the template
+      variables and writes the result to the subdirectory.
+   4. Writes combo.json recording the variable assignments for that run.
+
+   A grid_summary.json is written to *out_dir* listing all created runs.
+   Input files named by the configs or template variables are copied into
+   <out_dir>/inputs/ and referenced as ../inputs/<name>, so the
+   *out_dir* directory can be moved as a unit. Every run is validated
+   before anything is written.
+
+   positional arguments:
+     grid_yaml          Path to the grid YAML file.
+
+   options:
+     -h, --help         show this help message and exit
+     --out_dir OUT_DIR  Root directory for the grid. Created if it does not exist.
+                        Default "sim_grid".
+
+tfs-setup-grid
+--------------
+
+.. code-block:: text
+
+   usage: tfs-setup-grid [-h] [--out_dir OUT_DIR] grid_yaml
+
+   Set up a directory grid for model runs.
+
+   Reads a grid YAML file, expands all configure_model and template blocks
+   into their Cartesian product, and for each combination:
+
+   1. Creates a subdirectory under *out_dir*.
+   2. Calls tfs-configure-model (via its Python API) to generate
+      tfs_configure_config.yaml, tfs_configure_priors.csv, and
+      tfs_configure_guesses.csv inside that subdirectory.  Incompatible
+      combinations are skipped and logged.
+   3. Renders the Jinja2 template (output_file) with the template
+      variables and writes the result to the subdirectory.
+   4. Writes combo.json recording the variable assignments for that run.
+
+   A grid_summary.json is written to *out_dir* listing all created
+   runs and any skipped combinations. Input files named by the
+   configure_model or template variables are copied into
+   <out_dir>/inputs/ and referenced as ../inputs/<name>, so the
+   *out_dir* directory can be moved as a unit. Every combination's inputs
+   and template are validated before anything is written.
+
+   positional arguments:
+     grid_yaml          Path to the grid YAML file.
+
+   options:
+     -h, --help         show this help message and exit
+     --out_dir OUT_DIR  Root directory for the grid. Created if it does not exist.
+                        Default "grid".
+
+tfs-summarize-grid
+------------------
+
+.. code-block:: text
+
+   usage: tfs-summarize-grid [-h] [--out_prefix OUT_PREFIX] grid_dir
+
+   Summarize a model grid created by tfs-setup-grid.
+
+   Scans every immediate subdirectory of *grid_dir* for combo.json and
+   collects the per-run variable assignments (configure_model, simulate and
+   template variables) into a summary CSV, with the statistics of the run's
+   *_fit_summary.json (in the run directory or its summary/, where
+   tfs-summarize-fit writes by default) when there is one.
+
+   positional arguments:
+     grid_dir              Path to the grid root directory created by tfs-setup-grid.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Prefix for the output CSV; {out_prefix}.csv is written.
+                           Defaults to {grid_dir}/grid_summary.
+
+tfs-summarize-calibration
+-------------------------
+
+.. code-block:: text
+
+   usage: tfs-summarize-calibration [-h] [--out_prefix OUT_PREFIX]
+                                    [--summary_subdir SUMMARY_SUBDIR]
+                                    [--replicate_keys REPLICATE_KEYS [REPLICATE_KEYS ...]]
+                                    [--baseline BASELINE [BASELINE ...]]
+                                    [--facet_by FACET_BY [FACET_BY ...]]
+                                    [--plot_quantity PLOT_QUANTITY]
+                                    [--regime_eps REGIME_EPS]
+                                    grid_dir
+
+   Summarize posterior calibration across a simulation grid.
+
+   positional arguments:
+     grid_dir              Directory of run subdirectories written by tfs-setup-sim-grid
+                           (each with combo.json and, when finished, summary/).
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Prefix for all outputs (default tfs_calibration).
+     --summary_subdir SUMMARY_SUBDIR
+                           Subdirectory holding the tfs-summarize-fit outputs (default
+                           summary).
+     --replicate_keys REPLICATE_KEYS [REPLICATE_KEYS ...]
+                           Grid variables that index replicates rather than arms (default
+                           seed and fit_seed). Keys absent from the grid are ignored.
+     --baseline BASELINE [BASELINE ...]
+                           key=value pairs selecting the baseline arm, e.g.
+                           guide_type=component guide_rank=None. When given, every other
+                           run is paired with the baseline run sharing all remaining grid
+                           variables (same simulated data, same fit seed). (default:
+                           None)
+     --facet_by FACET_BY [FACET_BY ...]
+                           Grid variables that split the calibration-curve plot into
+                           panels. (default: None)
+     --plot_quantity PLOT_QUANTITY
+                           Quantity plotted (default theta_test).
+     --regime_eps REGIME_EPS
+                           θ within [regime_eps, 1 - regime_eps] is resolvable (default
+                           0.01).
+
+tfs-check-tubes
+---------------
+
+.. code-block:: text
+
+   usage: tfs-check-tubes [-h] [--out_prefix OUT_PREFIX] [--min_counts MIN_COUNTS]
+                          [--min_genotypes MIN_GENOTYPES] [--flag_z FLAG_Z]
+                          growth_df
+
+   Score every tube's apparent titrant from its genotype composition and
+   flag tubes that are out of order within their row.
+
+   Within each library x condition_pre x condition_sel x titrant_name
+   panel, each tube's log-ratio composition has its row (replicate x t_pre
+   x t_sel) effect removed by a robust fit (medians, with a per-row scale
+   since the titrant's effect grows with time) and is projected on the
+   axis from the lowest concentration's composition (score 0) to the
+   highest's (score 1). Tube totals and read depth cancel, so OD600 and depth errors
+   do not enter. A tube is flagged when its score is out of order with the
+   rest of its row (leave-one-out isotonic fit) or far from its label's
+   expected score, by more than flag_z noise SDs; rows of fewer than
+   three tubes are not flagged. Rows missing some concentrations are
+   handled without the shift of centering a row on itself.
+
+   Writes {out_prefix}.csv, one row per tube (score, expected,
+   order_z, deviation_z, flagged and more), and prints each
+   panel's scores by row and concentration and every tube of the rows
+   with a flagged tube. Once a row is down to two unflagged tubes neither
+   is flagged, so read the whole row: a large order_z left there is
+   also suspect.
+
+   positional arguments:
+     growth_df             Growth table from tfs-process-counts (any long-form table with
+                           genotype, counts, replicate and titrant_conc).
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Output prefix. (default: 'tfs_tube_qc')
+     --min_counts MIN_COUNTS
+                           Use a genotype only if it has at least this many reads in
+                           every tube of its panel. (default: 1)
+     --min_genotypes MIN_GENOTYPES
+                           Fewest usable genotypes a panel may have. (default: 10)
+     --flag_z FLAG_Z       Flag a tube whose order violation or deviation from its
+                           expected score exceeds this many noise SDs. (default: 4.0)
+
+tfs-correct-tubes
+-----------------
+
+.. code-block:: text
+
+   usage: tfs-correct-tubes [-h] [--out_prefix OUT_PREFIX]
+                            tube_table_file corrections_file
+
+   Apply documented corrections to a tube table, leaving the original as is.
+
+   A correction is an entry in a YAML corrections file kept with the
+   experiment's data: an id, an action, the tubes it names (by
+   the tube table's sample), a reason, who decided it (by) and
+   when (date), and optional evidence such as a QC table. Actions:
+   swap (two tubes of one library exchange their design columns),
+   relabel (one tube gets the design values in set) and exclude
+   (tubes left out). The design columns are replicate,
+   condition_pre, t_pre, condition_sel, t_sel,
+   titrant_name and titrant_conc. Every other column (the sample
+   name and so its count file, OD600, tube totals, barcodes) stays with the
+   physical sample it was measured on, which is right whether the tubes
+   were swapped at the bench or the sample sheet was. library never
+   changes, and a swap across libraries is refused.
+
+   Writes {out_prefix}.csv (the corrected tube table, with a
+   correction_id column, for tfs-process-counts),
+   {out_prefix}_changes.csv (one row per changed value or excluded
+   tube, with its original and corrected value) and
+   {out_prefix}_provenance.json (the corrections and the SHA-256 of
+   the inputs, the evidence files and the output).
+
+   Fails on an unknown tube, a tube named in two corrections, a swap
+   across libraries or one that changes nothing, a relabel of a column that
+   is not a design column, a correction that leaves two tubes with the same
+   library and design, a missing or empty reason, by or date, or an output
+   that would overwrite an input.
+
+   positional arguments:
+     tube_table_file       The original tube table (CSV, TSV or Excel), in tfs-process-
+                           counts format.
+     corrections_file      YAML corrections file.
+
+   options:
+     -h, --help            show this help message and exit
+     --out_prefix OUT_PREFIX
+                           Output prefix. (default: 'tfs_tubes_corrected')
+
+tfs-fit-two-stage
+-----------------
+
+.. code-block:: text
+
+   usage: tfs-fit-two-stage [-h] [--out_dir OUT_DIR] [--step STEP]
+                            [--jackknife_task JACKKNIFE_TASK] [--no_consistency]
+                            config_file
+
+   Fit a growth-only relative model in two stages.
+
+   The full-library MAP makes the growth slope m too steep (each double
+   mutant pulls it a little and the pulls add), and congression flattens a
+   marker's m in any stage 1 large enough to pin X's scale
+   (planning/studies/m-bias-factorial/). This command runs, in out_dir:
+   stage1 (wt, spikes and singles with the congression mixture, max rule,
+   lambda fitted with two_stage.transformation_lambda as its prior: every
+   condition's k and m); stage2 (the full library with every condition's
+   k and m pinned at stage 1; MAP, Laplace, X); predict (stage 2's X, and
+   with two_stage.predict_epistasis its epistasis, predict/); consistency
+   (stage 2 on one library's tubes alone against the full stage 2, per
+   genotype z-scores, consistency/consistency.csv); summary
+   (out_dir/tfs_x.csv: per genotype and concentration, X's quantiles, the
+   genotype's reads in total and per library, its number of mutations,
+   in_training_data and the consistency z and flag; the table to read
+   results from).
+
+   X's intervals leave out k's uncertainty, shared by every genotype; on
+   simulations the deep genotypes' 95% intervals covered 0.88-0.90 up to
+   1e5 reads and 0.76 above (planning/studies/two-stage-validation/).
+   two_stage.kshift (experimental, off by default) adds it: jackknife
+   (two_stage.jackknife_subsamples refits of stage 1, each dropping
+   two_stage.jackknife_drop_frac of the tubes, stratified by condition and
+   IPTG: k's SE and main direction), then kshift (two stage 2 refits at k
+   plus and minus one jackknife SD, from stage 2's MAP at
+   two_stage.kshift_step_size with the dk_geno population held there:
+   kshift/shift.csv, added to predict's draws as one shared multiple; with
+   two_stage.kshift_control a k-unchanged control refit and
+   kshift/drift.csv). Validation run 2 found that shift a third or less of
+   the true one on simulations.
+
+   Every step but summary skips work whose outputs exist, so a run resumes
+   and a step reruns by deleting its directory; summary is rewritten each
+   time. --step runs one step, the k-shift steps included whatever
+   two_stage.kshift says.
+
+   positional arguments:
+     config_file           YAML whose top-level keys are tfs-configure-model's arguments
+                           (growth_df and library_config required; theta_model
+                           hill_relative, growth_shares_replicates true; file paths
+                           relative to the YAML), plus a two_stage block:
+                           transformation_lambda (mean, std; required), seed (1),
+                           num_posterior_samples (500), consistency (true),
+                           consistency_library (default the first library
+                           alphabetically), predict_epistasis (false), kshift (false;
+                           experimental, see above), jackknife_subsamples (30),
+                           jackknife_drop_frac (0.2), kshift_step_size (1e-5, the k-shift
+                           refits' starting step size), kshift_control (true, the
+                           k-unchanged control refit), fit (extra tfs-fit-model
+                           arguments) and posterior (extra tfs-sample-posterior
+                           arguments).
+
+   options:
+     -h, --help            show this help message and exit
+     --out_dir OUT_DIR     Directory for every step's outputs. Default 'two_stage'.
+     --step STEP           'all' (default) or one of stage1, jackknife, stage2, kshift,
+                           predict, consistency, summary.
+     --jackknife_task JACKKNIFE_TASK
+                           Run only this jackknife subsample (1 to jackknife_subsamples),
+                           for a cluster array; needs stage 1 done. The kshift step
+                           collects them. (default: None)
+     --no_consistency      Turn off (on by default): Run the consistency step with 'all'
+                           (default True; --no_consistency skips it, as does consistency:
+                           false in the YAML).
+
+tfs-process-binding
+-------------------
+
+.. code-block:: text
+
+   usage: tfs-process-binding [-h] [--protein_conc PROTEIN_CONC]
+                              [--protein_conc_units PROTEIN_CONC_UNITS]
+                              [--genotype_col GENOTYPE_COL] [--protein_col PROTEIN_COL]
+                              [--titrant_col TITRANT_COL] [--theta_col THETA_COL]
+                              [--conc_units CONC_UNITS] [--replicate_cols REPLICATE_COLS]
+                              [--titrant_name TITRANT_NAME] [--theta_std THETA_STD]
+                              [--sig_digits SIG_DIGITS] [--out_prefix OUT_PREFIX]
+                              binding_file
+
+   Average a lab binding table into a binding_df at one protein
+   concentration.
+
+   The table has one row per measurement (genotype, protein and titrant
+   concentrations, theta, and the columns naming its replicate). Rows
+   without theta are dropped; concentrations are rounded to sig_digits
+   significant digits so replicates from different days line up;
+   measurements within a replicate are averaged, then replicates per
+   genotype and titrant concentration (theta_obs). theta_std is the
+   standard error of that mean from one replicate SD pooled over every
+   replicated point at this protein concentration, or from --theta_std.
+   The titrant is written in mM.
+
+   Writes {out_prefix}.csv (genotype, titrant_name, titrant_conc,
+   theta_obs, theta_std; for tfs-configure-model --binding_df) and
+   {out_prefix}_summary.csv (replicate and measurement counts and each
+   point's own replicate SD).
+
+   positional arguments:
+     binding_file          The lab's binding table (CSV, TSV or Excel).
+
+   options:
+     -h, --help            show this help message and exit
+     --protein_conc PROTEIN_CONC
+                           Protein concentration to keep, in protein_conc_units.
+                           (default: 2.0)
+     --protein_conc_units PROTEIN_CONC_UNITS
+                           Unit of protein_conc: M, mM, uM or nM. (default: 'uM')
+     --genotype_col GENOTYPE_COL
+                           Genotype column. (default: 'genotype')
+     --protein_col PROTEIN_COL
+                           Protein concentration column. (default:
+                           'protein_dimer_conc_molar')
+     --titrant_col TITRANT_COL
+                           Titrant concentration column. (default: 'iptg_conc_molar')
+     --theta_col THETA_COL
+                           Measured theta column. (default: 'theta')
+     --conc_units CONC_UNITS
+                           Unit of the protein and titrant columns: M, mM, uM or nM.
+                           (default: 'M')
+     --replicate_cols REPLICATE_COLS
+                           Comma-separated columns that identify one replicate; empty for
+                           one replicate per row. (default: 'biorep,date')
+     --titrant_name TITRANT_NAME
+                           titrant_name for every row; must match the growth table's.
+                           (default: 'iptg')
+     --theta_std THETA_STD
+                           Fixed per-replicate SD instead of the pooled one. (default:
+                           None)
+     --sig_digits SIG_DIGITS
+                           Significant digits the concentrations are rounded to.
+                           (default: 6)
+     --out_prefix OUT_PREFIX
+                           Output prefix. (default: 'tfs_binding')
diff --git a/docs/source/downstream.rst b/docs/source/downstream.rst
new file mode 100644
index 00000000..cceb222d
--- /dev/null
+++ b/docs/source/downstream.rst
@@ -0,0 +1,413 @@
+===================
+Downstream analysis
+===================
+
+This page covers everything after a posterior exists: parameter tables,
+predicted occupancy and growth, epistasis, response-shape classification and
+cross-run comparison. Every flag and default is listed in :doc:`cli`. The
+steps that produce the posterior are in :doc:`fitting`, and the diagnostic
+summary of a finished run is in :doc:`summarize-fit`.
+
+The examples assume the default file names from the earlier steps:
+``tfs_configure_config.yaml`` from ``tfs-configure-model``,
+``tfs_fit_model_checkpoint.pkl`` from ``tfs-fit-model`` and
+``tfs_posterior.h5`` from ``tfs-sample-posterior``.
+
+Inputs and output columns
+-------------------------
+
+The model-based commands (``tfs-extract-params``, ``tfs-predict-theta``,
+``tfs-predict-growth`` and ``tfs-predict-epistasis``) take two positional
+arguments: the config YAML used for the fit and a parameter file. The
+parameter file is normally the ``.h5`` written by ``tfs-sample-posterior``.
+
+A MAP checkpoint (``.pkl``) is also accepted. It holds a single point, so the
+output has one ``q0.5`` column and no uncertainty. The predict commands
+convert the checkpoint to a one-draw posterior on the fly and leave it behind
+as ``{out_prefix}_map_posterior.h5``. SVI and NUTS checkpoints are refused;
+run ``tfs-sample-posterior`` on them first. For a MAP fit, run
+``tfs-sample-posterior`` too if you want intervals: it builds the Laplace
+approximation and writes an ``.h5`` like any other.
+
+Every posterior summary is written as bare ``q<level>`` columns with no
+feature-name prefix. A posterior file gives 17 levels:
+
+.. code-block:: text
+
+    q0.001  q0.005  q0.01  q0.025  q0.05  q0.1  q0.159  q0.25  q0.5
+    q0.75   q0.841  q0.9   q0.95   q0.975 q0.99 q0.995  q0.999
+
+``q0.5`` is the posterior median and the recommended point estimate.
+``q0.025`` and ``q0.975`` bound the 95% credible interval. ``q0.159`` and
+``q0.841`` bound the central 68% interval, so ``(q0.841 - q0.159) / 2`` is a
+robust one-sigma width. The table-based tools below (``tfs-extract-epistasis``,
+``tfs-cat-response``, ``tfs-compare-runs``) use ``q0.5`` and that half-width
+as their default value and uncertainty columns, so the outputs of one command
+feed the next without renaming.
+
+Parameter tables
+----------------
+
+``tfs-extract-params`` writes one CSV per parameter group.
+
+.. code-block:: bash
+
+    tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+The files are named ``{out_prefix}_{parameter}.csv`` with the default prefix
+``tfs_params``. Which files appear depends on the configured components. A
+``hill_geno`` fit gives ``tfs_params_log_hill_K.csv``,
+``tfs_params_hill_n.csv``, ``tfs_params_theta_low.csv`` and
+``tfs_params_theta_high.csv``, keyed by ``genotype`` and ``titrant_name``.
+``hill_mut`` adds per-mutation effects such as
+``tfs_params_d_log_hill_K.csv`` (keyed by ``mutation``) and, with pairs,
+epistasis terms such as ``tfs_params_epi_log_hill_K.csv`` (keyed by
+``pair``). ``hill_relative`` writes ``X_low`` and ``X_high`` in place of the
+theta baselines. Other components add their own files, for example
+``tfs_params_dk_geno.csv``, ``tfs_params_growth_k.csv`` and
+``tfs_params_growth_m.csv`` (keyed by ``condition_rep``, plus
+``replicate`` when replicates have their own growth parameters),
+``tfs_params_lam.csv`` for the ``mixture`` transformation,
+``tfs_params_k_ref.csv`` when base-growth data were supplied, and, with
+tube offsets, ``tfs_params_sample_offset_offset.csv`` (``level``) or
+``tfs_params_sample_offset_delta_k.csv`` (``normal``), one row per tube
+keyed by its design (``replicate``, ``library``, ``condition_pre``,
+``condition_sel``, ``titrant_name``, ``titrant_conc``, ``t_pre``,
+``t_sel``), with the offsets' SD in ``tfs_params_sample_offset_sigma.csv``.
+A held SD (``sigma_fixed``) is a deterministic site, so a checkpoint has no
+SD file.
+
+The thermodynamic PddG and PnnC models also write one table per structure
+of the per-mutation ΔΔG, ``tfs_params_ddG_{structure}.csv`` (for example
+``tfs_params_ddG_H.csv``). The models fit the constants, and the ΔΔG is
+computed from them: exactly in the models with an unfolded state, and as
+the most probable per-structure values given the prior in those without
+one, where the shift shared by all structures comes from the structural
+prior. They also write a table of its structural prior mean,
+``tfs_params_ddG_prior_mean_{structure}.csv`` (the ΔΔG CSV values for PddG,
+the network's prediction for PnnC). Both are keyed by ``mutation`` and named
+by the model's structures, whatever order the input file lists them in. The
+ΔΔG and the prior mean are deterministic sites, so they need a posterior
+file (a checkpoint has neither). The models
+also write ``tfs_params_ddG_sigma.csv``, one row per ``structure``: the SD
+σ_s of the ΔΔG about the structural prior mean, which the fit estimates per
+structure.
+
+``log_hill_K`` is the natural log of the Hill constant in the units of
+``titrant_conc``. It is not a base-10 log.
+
+Given a ``.pkl`` MAP checkpoint, ``tfs-extract-params`` writes a single
+``q0.5`` column. Deterministic sites such as ``ln_cfu0`` are absent from a
+checkpoint, so their files are skipped with a warning. Run
+``tfs-sample-posterior`` first to get them.
+
+Predicted occupancy
+-------------------
+
+``tfs-predict-theta`` predicts operator occupancy as a function of titrant
+concentration.
+
+.. code-block:: bash
+
+    tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+The output is ``tfs_pred_theta.csv``, one row per ``genotype``,
+``titrant_name`` and ``titrant_conc``, with the ``q<level>`` columns and an
+``in_training_data`` column. ``in_training_data`` is 1 when that genotype,
+titrant name and concentration triple was in the training data and 0
+otherwise.
+
+By default the command predicts every training genotype at every training
+titrant point. Three plain-text files add to that grid, one value per line
+with ``#`` comments allowed. ``--genotypes_file`` adds genotypes, written as
+slash-separated mutations such as ``M42I/K84L`` or as ``wt``.
+``--titrant_names_file`` and ``--titrant_concs_file`` add titrant points and
+must be given together. They pair line by line, and a single name is
+broadcast across every concentration. The added genotypes and points are
+unioned with the training set. Pass ``--only_files`` to predict only at the
+file inputs.
+
+A genotype that was not in the training data needs a theta component that can
+predict it. ``hill_mut`` assembles the genotype from its per-mutation effects.
+``hill_geno`` and ``hill_relative`` have no per-mutation structure and give
+every unseen genotype the population average for its titrant.
+``--genotype_batch_size`` (default 2000) caps the memory used for unseen
+genotypes. ``--num_samples N`` adds ``sample_0`` to ``sample_{N-1}`` columns
+holding joint posterior draws.
+
+A ``hill_relative`` fit predicts the wt-relative growth variable X rather
+than an occupancy. Its output carries an extra ``theta_scale`` column with the
+value ``X`` so that nothing downstream reads it as theta. X is 1 for wt at the
+low gauge concentration and 0 for wt at the high one. See :doc:`model` for the
+gauge.
+
+Predicted growth
+----------------
+
+``tfs-predict-growth`` predicts ln(CFU) from the fitted model.
+
+.. code-block:: bash
+
+    tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5
+
+The output is ``tfs_pred_growth.csv``, one row per genotype, replicate,
+condition, titrant and time point, with the ``q<level>`` columns, the observed
+``ln_cfu`` and ``ln_cfu_std`` where an observation exists, and
+``in_training_data``.
+
+``--genotypes_file`` and ``--titrant_concs_file`` extend the prediction grid
+as in ``tfs-predict-theta``, and ``--only_files`` restricts it to the file
+inputs. ``--titrant_names_file`` behaves differently here. It is a
+restrict-only filter applied after prediction: it narrows which titrant names
+appear in the output and never adds points.
+
+Growth prediction over a full library is the most memory-hungry step in this
+page. ``--genotype_batch_size`` sets how many genotypes go through the model
+at once. Left unset, it is estimated from the available device memory. Each
+batch costs one JAX recompilation. ``--num_marginal_samples`` limits how many
+posterior draws are run through the model when computing quantiles; by
+default all are used. ``--num_samples N`` adds ``sample_*`` columns as in
+``tfs-predict-theta``.
+
+For a quick check of the fit without a full sweep, ``--subset_genotypes``
+predicts a single memory-sized block. The block always holds the binding
+genotypes, the spiked genotypes and any genotypes from ``--genotypes_file``,
+and the rest is a random draw from the other genotypes. ``--seed`` makes the
+draw reproducible.
+
+.. code-block:: bash
+
+    tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+        --subset_genotypes --seed 1 --out_prefix tfs_pred_growth_subset
+
+Epistasis
+---------
+
+Two commands compute second-order epistasis on mutant cycles. Each cycle is a
+double mutant, its two single-mutant parents and wt. They differ in how they
+treat the posterior.
+
+``tfs-predict-epistasis`` works from the joint posterior. It draws theta for
+all four corners of each cycle from the same posterior sample, computes
+epistasis within that draw and then reports quantiles across draws. The
+interval therefore carries the posterior covariance between the corners.
+
+.. code-block:: bash
+
+    tfs-predict-epistasis tfs_configure_config.yaml tfs_posterior.h5
+
+The output is ``tfs_pred_epistasis.csv`` with ``genotype`` (the double
+mutant), ``titrant_name``, ``titrant_conc``, the ``q<level>`` columns and a
+trailing ``in_regime`` column. The default ``--scale logit`` takes additive
+epistasis of logit(theta). ``add`` takes ``(Y11 - Y10) - (Y01 - Y00)`` and
+``mult`` takes ``(Y11 / Y10) / (Y01 / Y00)``. ``--scale_constant`` multiplies
+the transformed value before the difference. Since logit(theta) equals
+-dG/RT, passing ``--scale_constant -0.6159`` reports logit epistasis as an
+interaction free energy in kcal/mol at 310.15 K. Only genotypes seen in
+training are supported, and an unseen genotype in ``--genotypes_file`` is an
+error.
+
+``in_regime`` is 1 only when the theta posterior of all four corners lies
+inside the resolvable band ``[regime_eps, 1 - regime_eps]``. The posterior is
+judged by its central ``--regime_ci`` interval, 95% by default, and
+``--regime_eps`` defaults to 0.01. Near 0 or 1, logit(theta) saturates and
+growth constrains it weakly, so epistasis there leans on the theta model's
+functional form and on cross-genotype covariance. Treat rows with
+``in_regime`` 0 as model-conditional, even when their interval is narrow. The
+flag checks posterior mass only. It does not test whether the growth signal
+exceeds the growth noise. With a MAP checkpoint there is one draw, so the
+check reduces to the point value.
+
+A ``hill_relative`` fit predicts X rather than theta. Pass ``--scale add``
+for it. The output then has no ``in_regime`` column.
+
+``tfs-extract-epistasis`` works from any long-form table with one row per
+genotype per condition. It computes epistasis from each corner's marginal
+estimate and propagates the errors as if the corners were independent. Use
+it for tables that do not come from the model, or for a quick look at a
+``tfs-predict-theta`` table.
+
+.. code-block:: bash
+
+    tfs-extract-epistasis tfs_pred_theta.csv \
+        --group_by titrant_name titrant_conc --scale logit
+
+``--y_obs`` defaults to ``q0.5`` when that column exists, and ``--y_std`` to
+``(q0.841 - q0.159) / 2``. ``--group_by`` names the columns that define a
+condition. Epistasis is computed within each condition, so for a predict-theta
+table pass both ``titrant_name`` and ``titrant_conc``. The default scale here
+is ``add``, unlike ``tfs-predict-epistasis``. ``logit`` requires values in
+(0, 1) and clamps them to ``[logit_eps, 1 - logit_eps]``. ``--scale_constant``
+works as above. On a ``hill_relative`` table only ``add`` is allowed. The
+output is ``tfs_epistasis.csv`` with one row per double mutant and
+condition, holding ``ep_obs`` and, when an uncertainty column is available,
+``ep_std``. ``--keep_extra`` keeps every input column.
+
+Prefer ``tfs-predict-epistasis`` for model output. Corners of a cycle share
+the fitted shared parameters, so their errors are correlated, and the
+marginal calculation can be off in either direction.
+
+Response shapes
+---------------
+
+``tfs-cat-response`` fits a family of empirical curve models to each group's
+``y_obs`` versus ``x_obs`` curve. It answers two separate questions: what
+shape the curve has, and whether the curve can be told apart from zero.
+
+.. code-block:: bash
+
+    tfs-cat-response tfs_pred_theta.csv titrant_conc --group_by titrant_name
+
+The positional arguments are the data file and ``x_obs``, the name of the
+independent-variable column. ``--y_obs`` is a flag. It defaults to ``q0.5``
+when that column exists. ``--y_std`` defaults to ``(q0.841 - q0.159) / 2``
+when both quantiles exist; otherwise the fit is unweighted. Groups are the
+``genotype`` column plus any ``--group_by`` columns. The same command works on
+epistasis tables, for example ``tfs-cat-response tfs_pred_epistasis.csv
+titrant_conc --group_by titrant_name``. The per-group fits run in parallel;
+``--num_workers`` defaults to -1, which uses all but one CPU.
+
+**Model x-scale.** The models fall into two camps, and they must be handed
+raw concentration in both cases. The Hill family (``repressor``,
+``inducer``, ``hill_repressor``, ``hill_inducer``) and the ``biphasic_*``
+models are written in raw concentration and take the log internally, so they
+are already sigmoids or peaks in log concentration. ``bell_peak_log``,
+``bell_dip_log`` and ``linear_log`` are shapes in log10 concentration and
+also apply the transform themselves. Concentrations of 0 are placed two
+decades below the lowest nonzero concentration before the log. The plain
+``bell_peak``, ``bell_dip`` and ``linear`` models are shapes in raw x.
+``flat`` does not depend on x. Never log-transform the x column yourself.
+
+**Shape.** ``--select_by`` picks the model. The default, ``shape``, is a
+liberal classifier meant for exploration. It first decides flat versus curvy
+from the residual autocorrelation of the flat fit: the curve is curvy when the
+autocorrelation p-value is below ``--curvy_cutoff`` (default 0.1). It then
+names the curvy shape by best weighted R², preferring the simpler model when
+two are close. In this mode the default model set is ``flat``, ``inducer``,
+``repressor``, ``bell_peak_log``, ``bell_dip_log``, ``biphasic_peak`` and
+``biphasic_dip``. ``curvy_cutoff`` is the knob to sweep; run a few values and
+inspect the curves. ``aicc`` picks the lowest small-sample AIC on weighted
+residuals. It is conservative and often calls a well-fit curve flat at small
+n. ``adequacy`` keeps the AICc pick unless a runs test finds clustered
+residuals, then moves to the best adequate model that is no simpler. It never
+demotes a curve to a simpler model. Outside ``shape`` mode the default model
+set is ``flat``, ``linear_log``, ``repressor``, ``inducer``,
+``bell_peak_log`` and ``bell_dip_log``. ``--models`` reaches any model in the
+library.
+
+The selected model is reported as ``best_model``, with ``shape`` giving its
+qualitative form: ``flat``, ``linear``, ``step``, ``peak``, ``dip`` or
+``biphasic``. ``aicc_best_model`` records the AICc pick for comparison.
+``shape_status`` reports the runs test on the selected model.
+
+**Magnitude.** Whether a curve is distinguishable from zero is judged on the
+observed data, not on the fitted curve. The test statistic is
+``sum((y_obs / y_std)**2)`` against a chi-squared distribution with n degrees
+of freedom. Its p-value, ``nonzero_p``, is corrected across curves by
+Benjamini-Hochberg to give ``nonzero_q``. ``fittable`` is True when
+``nonzero_q`` is below ``--alpha`` (default 0.05). ``all_equiv_zero`` is True
+when every point's interval lies inside the region of practical equivalence
+``[-rope_cutoff, rope_cutoff]``. By default ``rope_cutoff`` is
+``--rope_multiplier`` (default 2) times the median observed ``y_std``. That
+value scales with the noise, so whole intervals rarely fit inside it. Pass an
+explicit ``--rope_cutoff`` with biological meaning to make the call useful.
+The two flags together give three outcomes: ``fittable`` True is a real
+response; ``fittable`` False with ``all_equiv_zero`` True is confidently flat
+at zero; ``fittable`` False with ``all_equiv_zero`` False cannot be told.
+
+Shape and magnitude are independent. The intended read is to filter on
+``fittable`` and then look at ``shape``.
+
+The outputs, with the default prefix ``tfs_cat_response``:
+
+``tfs_cat_response.csv``
+    One row per group: ``best_model``, ``aicc_best_model``, ``shape``,
+    ``shape_status``, per-model fit statistics and parameters, and the
+    magnitude rollups (``nonzero_p``, ``nonzero_q``, ``n_nonzero``,
+    ``all_equiv_zero``, ``fittable``). ``omnibus_p`` and ``omnibus_q`` are a
+    model-based test reported for reference only. They gate nothing.
+
+``tfs_cat_response_assessment.csv``
+    One row per group and x for the best model: ``model``, ``fittable``,
+    ``x``, the observed ``y_obs`` and ``y_std``, the model's ``y_model`` and
+    its propagated error ``y_model_std``, then ``z`` (``y_obs / y_std``) and
+    the per-point ``sig_nonzero``.
+
+``tfs_cat_response_predictions.csv``
+    The best model's curve for each group. ``--write_all_predictions`` writes
+    every model's curve.
+
+``tfs_cat_response_{model}.csv``
+    One file per fitted model with its parameter table and per-group fit
+    statistics.
+
+Comparing runs
+--------------
+
+``tfs-compare-runs`` measures how much N independent estimates of the same
+quantity disagree, and whether each run's reported uncertainty explains the
+disagreement. The runs might differ by seed or by which data were held out.
+It accepts any long-form table with a point estimate: predict-theta, -growth
+and -epistasis output, and ``tfs-extract-params`` files alike.
+
+.. code-block:: bash
+
+    tfs-compare-runs seed1/tfs_pred_theta.csv seed2/tfs_pred_theta.csv \
+        seed3/tfs_pred_theta.csv
+
+Two or more arguments are read as CSV paths. A single argument is read as a
+manifest file listing one CSV path per line, unless it ends in ``.csv``.
+``--reference full/tfs_pred_theta.csv`` switches to reference mode, where each
+run is scored by its deviation from that run, as for k-fold dropouts against a
+full-data fit. Without it, runs are compared to their cross-run mean.
+
+Three sets of columns control the comparison, and all three are printed and
+recorded in the metadata. The **match key** is what makes a row the same row
+across runs. By default it is every column shared by all runs except the value
+columns (``q<level>``, ``y_obs``, ``y_std``) and bookkeeping columns such as
+``in_training_data`` and ``in_regime``. ``--match_by`` overrides it. The key
+must be unique within each run. **index_by** is the entity being scored. It is
+``genotype`` if present, else ``parameter``, else the only match-key column.
+A growth-parameter file keyed by both ``replicate`` and ``condition_rep``
+has no single entity column, so it needs ``--index_by condition_rep``. ``--group_by`` breaks each entity out
+further. The report key is ``index_by`` plus ``group_by``, with one output row
+per value. The match-key columns outside the report key are pooled over.
+
+``--group_by`` is a statistical zoom. At the finest grouping only N - 1
+degrees of freedom remain per row. Check ``n_rows`` and ``n_eff`` for the
+pooling depth.
+
+.. code-block:: bash
+
+    tfs-compare-runs seed1/tfs_params_growth_k.csv seed2/tfs_params_growth_k.csv \
+        --index_by condition_rep
+
+The command applies no thresholds and assigns no grades. Every quantity is a
+number, and any cutline belongs in the downstream analysis that uses it, such
+as ``df["overdispersion"] > 2``. The main output, ``tfs_compare_runs.csv``,
+has one row per report key with these columns: ``n_runs``, ``n_present``,
+``n_rows``, ``n_eff``, ``mode``, ``spread_estimator``, ``rms_sd``,
+``max_sd``, ``mean_value``, ``dynamic_range``, ``mean_reported_sigma``,
+``chi2``, ``dof``, ``overdispersion``, ``overdispersion_p`` and
+``overdispersion_q``.
+
+``rms_sd`` and ``max_sd`` measure run-to-run spread in the estimate's own
+units, so they cannot be compared across parameters. ``overdispersion`` is
+chi-squared over its degrees of freedom. It is unit-free and asks whether the
+spread exceeds what each run's sigma predicts; ``overdispersion_q`` is its
+Benjamini-Hochberg q-value. The default sigma is ``(q0.841 - q0.159) / 2``.
+That stays a good scale for spread but biases the overdispersion test for
+skewed posteriors, such as ``theta_low`` or ``theta_high`` near 0 or 1 or
+``hill_n`` near its bound. ``dynamic_range`` is the range of the target over
+the pooled axes. It is NaN when nothing is pooled.
+
+``tfs_compare_runs_aggregate.csv`` mixes the per-run posteriors as an
+equal-weight mixture, row by row on the match key. Its quantiles fold in both
+each run's width and the spread between runs, and they do not shrink with N.
+It needs at least two shared ``q<level>`` columns and never includes a
+reference run. It is the slow step on a large library, and ``--no_aggregate``
+skips it. ``tfs_compare_runs_metadata.json`` records every resolved setting.
+
+When the tables have a ``genotype`` column, rows come back in canonical
+genotype order: wt, then singles, then doubles, by site. Otherwise the main
+table is sorted by ``rms_sd``.
diff --git a/docs/source/fitting.rst b/docs/source/fitting.rst
new file mode 100644
index 00000000..84b3dfa8
--- /dev/null
+++ b/docs/source/fitting.rst
@@ -0,0 +1,593 @@
+=================
+Fitting the model
+=================
+
+A fit takes a processed growth table, binding data or both, and ends with
+posterior draws of every model parameter. It runs in up to four commands:
+
+1. ``tfs-configure-model`` checks the data and writes the model
+   configuration.
+2. ``tfs-prefit-calibration`` pins each condition's growth baseline and slope.
+   It runs only for models with binding data.
+3. ``tfs-fit-model`` finds the MAP point or fits a variational posterior.
+4. ``tfs-sample-posterior`` turns the fit into posterior draws.
+
+The model itself is described in :doc:`model` and its inputs in
+:doc:`model-inputs`. :doc:`cli` lists every flag. What to do with the
+posterior file is in :doc:`downstream`.
+
+Recommended routes
+==================
+
+**For a screen, use the two-stage fit,** ``tfs-fit-two-stage``, described
+in :doc:`screen-analysis`. It chains the commands on this page in the
+order and with the settings that gave calibrated intervals on simulations
+of the 2026 screen. The single fits below are its building blocks, and the
+place to look for what each setting does.
+
+Joint growth and binding model
+------------------------------
+
+*Experimental.* Binding and growth have not been reconciled on real data:
+no single protein concentration or monotone map carries the binding
+curves onto growth for every genotype, so binding cannot yet set the
+absolute scale of occupancy. Use this model for simulations and for
+method development, not for a screen's reported curves.
+
+With binding data for some genotypes, *θ* is an absolute occupancy. The
+configuration defaults are the current recommendations: the count likelihood,
+one level offset per tube and one starting abundance per replicate,
+pre-growth condition and genotype.
+
+.. code-block:: bash
+
+   tfs-configure-model --growth_df growth.csv --binding_df binding.csv \
+       --library_config library_config.yaml
+   tfs-prefit-calibration tfs_configure_config.yaml --seed 1
+   tfs-fit-model tfs_configure_config.yaml --seed 1 --analysis_method map
+   tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+       --skip_growth_observations
+
+Growth-only relative-X model
+----------------------------
+
+This is the model the two-stage fit uses. Fitted once on a full library,
+as below, its MAP makes the growth slope too steep and compresses *X*
+about 1.45-fold; the two-stage fit avoids that by taking the slope from
+wt, the spikes and the singles.
+
+Without binding data, growth fixes occupancy only up to an affine map. The
+``hill_relative`` occupancy component fits a wt-relative variable *X*
+instead (see :doc:`model`). The pre-fit refuses a growth-only model, so the
+per-condition growth priors come from ``--growth_priors`` or
+``--growth_priors_wt_rates`` at configuration, and the population SDs that
+ran away on real data are held with ``--set_priors``.
+
+On a full library the route is a MAP fit followed by the arrowhead Laplace.
+A MAP with level tube offsets runs in stages by default (see `Staged MAP`_
+below), which keeps the offsets from settling in a mode where they carry the
+population's growth.
+
+.. code-block:: bash
+
+   tfs-configure-model --growth_df growth.csv \
+       --library_config library_config.yaml \
+       --theta_model hill_relative \
+       --growth_shares_replicates \
+       --growth_priors growth_priors.csv \
+       --set_priors theta_log_hill_n_hyper_scale_fixed=0.5 sigma_fixed=0.17
+   tfs-fit-model tfs_configure_config.yaml --seed 1 --analysis_method map
+   tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+       --laplace arrowhead --skip_growth_observations
+
+On a small library the low-rank guide also
+recovers *X*:
+
+.. code-block:: bash
+
+   tfs-fit-model tfs_configure_config.yaml --seed 1 --analysis_method svi \
+       --guide_type auto_low_rank_multivariate_normal --guide_rank 20
+
+Configure
+=========
+
+``tfs-configure-model`` reads the data, checks it against the library,
+resolves the model components and writes four files:
+
+``{out_prefix}_config.yaml``
+   The configuration every later step reads: data paths, the component
+   choices and settings, the names of the priors and guesses files, the
+   library provenance and a ``provenance:`` block recording the tfscreen
+   version and command line.
+``{out_prefix}_priors.csv``
+   Every prior, one row per value.
+``{out_prefix}_guesses.csv``
+   Starting values for the fit.
+``{out_prefix}_library.csv``
+   The per-genotype library composition, for growth models.
+
+The default prefix is ``tfs_configure``. Like every command that takes
+``--out_prefix``, it also writes ``{out_prefix}_provenance.json``. A census of parameters and
+observations is printed and written to ``{out_prefix}_model_stats.csv`` and
+``{out_prefix}_model_stats.json`` unless ``--skip_model_stats`` is given. Do
+not edit these files by hand. The flags below set everything that needs
+setting.
+
+Data
+----
+
+``--growth_df`` and ``--binding_df`` are both flags, and at least one is
+required. With both, the joint model is configured. With only
+``--binding_df``, a binding-only model infers *θ* from the binding
+measurements alone. With only ``--growth_df``, a growth-only model infers
+*θ*, or *X* for ``hill_relative``, from growth alone. ``--presplit_df`` and
+``--base_growth_df`` add optional observations. :doc:`model-inputs`
+describes each table.
+
+``--library_config`` is required whenever ``--growth_df`` is given. It is the
+library YAML handed to ``tfs-process-fastq``. ``tfs-configure-model`` fails
+if any genotype in the data is missing from the library, which catches a
+residue-numbering or wt-sequence mismatch at once. It also fails if a spiked
+genotype named in the library has no growth data, since that usually means
+the counts were called against a different library file.
+``--allow_missing_spikes`` turns that error into a report, for an experiment
+in which a spike truly dropped out.
+
+Components
+----------
+
+Each ``--<axis>_model`` flag picks a component. :doc:`model` describes every
+option. The defaults are ``linear`` condition growth, ``instant``
+transition, ``hierarchical`` starting abundance, ``hierarchical_geno``
+pleiotropy, ``fixed`` activity, ``hill_geno`` occupancy, ``single``
+transformation, ``level`` tube offsets and the ``counts`` likelihood.
+``--batch_size`` sets the mini-batch of genotypes used by the optimizer,
+default 1024.
+
+Priors
+------
+
+``--set_priors name=value ...`` sets any scalar prior in the priors file. A
+name is a full row name, such as ``growth.sample_offset.sigma_fixed``, or any
+unique dotted suffix of one, such as ``sigma_fixed``. An unknown or
+ambiguous name is an error. The common uses hold a population SD that would
+otherwise run away:
+
+.. code-block:: bash
+
+   --set_priors sigma_fixed=0.17 theta_log_hill_n_hyper_scale_fixed=0.5
+
+``--growth_priors table.csv`` sets per-condition priors for ``linear``
+condition growth. It has a ``condition_rep`` column, plus ``replicate`` when
+replicates do not share conditions, and any of ``k_loc``, ``k_scale``,
+``m_loc`` and ``m_scale``. Conditions and values it leaves out keep their
+defaults.
+
+.. code-block:: text
+
+   condition_rep,k_loc,k_scale,m_loc,m_scale
+   kanR+kan,0.015,0.01,0.0,0.01
+   pheS+4CP,0.015,0.01,0.0,0.01
+
+``--growth_priors_wt_rates rates.csv`` derives the priors from wt
+monoculture growth rates, for ``hill_relative`` only. Its columns are
+``condition_sel``, ``titrant_conc``, ``rate_mean``, ``rate_sd`` and
+``num_replicates``. For each listed condition, *k* is wt's rate at the high
+gauge concentration and *m* is its rate at the low one minus *k*. Each SD is
+the standard error of the mean, floored at ``--growth_priors_sd_floor``,
+default 0.002 per minute. List only the conditions where the monoculture
+stands for the library's wt. These rows override ``--growth_priors`` for
+the conditions they list.
+
+Pre-fit calibration
+===================
+
+``tfs-prefit-calibration`` pins the per-condition growth parameters of a
+model with binding data. Condition baselines *k* and genotype effects
+*dk_g* trade off against each other, and without a pin the whole system can
+slide by a constant (see :doc:`model`). The pre-fit breaks the tie.
+
+It restricts the growth and binding data to the genotype and concentration
+cells present in both and builds a reduced model. Occupancy comes straight
+from the binding data, *dk_g* is 0, or pinned from ``--base_growth_df`` when
+given, and the other hierarchies are held at their prior locations. Only
+the ``condition_growth`` and ``growth_transition`` components are fit. After
+a MAP fit it computes Hessian SDs at the MAP point and rewrites the
+production priors and guesses files in place, after copying each to
+``.bak``. Each condition's *k* and *m* location becomes an indexed row in the
+priors file, and the scales are set from the Hessian between floors and
+ceilings:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 15 55
+
+   * - Flag
+     - Default
+     - Meaning
+   * - ``--k_scale_floor``
+     - 0.002
+     - Smallest prior SD on *k*, the day-to-day variation the Hessian
+       cannot see.
+   * - ``--m_scale_floor``
+     - 0.001
+     - Smallest prior SD on *m*.
+   * - ``--k_scale_ceiling``
+     - 0.1
+     - Largest prior SD on *k*, so a flat Hessian direction never loosens
+       the prior.
+   * - ``--m_scale_ceiling``
+     - 0.01
+     - Largest prior SD on *m*.
+   * - ``--pin_m``
+     - off
+     - Clamp *m* to the calibrated value instead of a soft prior.
+   * - ``--hessian_chunk_size``
+     - 64
+     - Hessian rows per device batch. Lower it if the device runs out of
+       memory.
+
+``--pin_m`` exists because the growth likelihood can pull *m* far from even
+a very tight soft prior. *k* always keeps a soft prior, since it carries real
+tube-to-tube variation.
+
+``--seed`` is required unless resuming with ``--checkpoint_file``. The
+optimizer and convergence flags are those of ``tfs-fit-model``. Its own
+outputs, with the default prefix ``tfs_prefit``, are diagnostics:
+``tfs_prefit_params.npz``, ``tfs_prefit_checkpoint.pkl``,
+``tfs_prefit_losses.txt`` and ``tfs_prefit_convergence.csv``.
+
+The pre-fit refuses a growth-only configuration, because it has no binding
+data to calibrate against. Set those priors at configuration instead.
+
+Fit
+===
+
+``tfs-fit-model config_file`` fits the model. ``--seed`` is required for a
+new fit. ``--analysis_method`` picks the method:
+
+``map``
+   Maximum a posteriori optimization. The result is a point.
+   ``tfs-sample-posterior`` builds a Laplace approximation around it.
+``svi`` (default)
+   Stochastic variational inference. A MAP warm-up runs first and the
+   variational guide starts at its point. ``tfs-sample-posterior`` samples
+   the guide.
+``nuts``
+   The No-U-Turn sampler, started at the MAP warm-up's point. It is exact
+   but only practical for small models. ``--nuts_num_warmup``,
+   ``--nuts_num_samples``, ``--nuts_num_chains``,
+   ``--nuts_target_accept_prob`` and ``--nuts_dense_mass`` control it.
+
+Choosing a method
+-----------------
+
+``svi`` with the component guide is the default, but the evidence so far
+favors the other options. On simulations of the growth-only relative model
+(``planning/studies/relative-fit/``, 30 runs), the component guide gave
+biased *X*, with 95% coverage of 0.06 under Poisson count noise and 0.38
+under realistic noise. The low-rank guide at rank 20 recovered *X*, with
+coverage of 0.81 and 0.76. MAP with the Laplace covered *X* and *k* and *m*
+best, with the widest intervals and the noisiest point. The arrowhead
+Laplace covered *X* at 0.97 and 0.94. On the full real library the low-rank
+guide slid along the ridge between *m* and *X* and ended far worse than the
+MAP, so MAP and the arrowhead Laplace is the full-library route. In every
+study, every variational guide collapsed the uncertainty in *k* and *m*:
+expect *X* intervals from a guide to undercover somewhat and *k* and *m*
+intervals to be too narrow (``planning/studies/svi-overconfidence/``). These
+are results on particular simulated designs and one real library, not
+general guarantees.
+
+Guides
+------
+
+For ``svi``, ``--guide_type`` picks the variational family:
+
+``component`` (default)
+   The mean-field guide assembled from the model components.
+``auto_normal``, ``auto_diagonal_normal``
+   NumPyro's mean-field normal autoguides.
+``auto_multivariate_normal``
+   A dense multivariate normal. Its memory grows with the square of the
+   number of parameters, and a warning is printed above 4 GB.
+``auto_low_rank_multivariate_normal``
+   A normal with a low-rank covariance. ``--guide_rank`` sets the rank,
+   numpyro's default when omitted. Rank 20 worked in the relative-fit
+   study.
+``delta``
+   A point mass, which is MAP through the SVI path.
+
+NumPyro class names such as ``AutoNormal`` also work. The guide flags are
+refused with ``map`` and ``nuts``. A resumed fit must use the guide its
+checkpoint was written with.
+
+``--guide_init_scale``, default 1e-4, sets the guide's starting width in
+each parameter's unconstrained units. For ``component`` it caps every guide
+scale at the start, and for an autoguide it is numpyro's ``init_scale``. It
+must be small. The same number applies to every parameter, and at 0.1 a
+growth rate per minute starts with an SD of many ln units over a selection.
+SVI then started about 1000 times above the warm-up's loss and descended
+into other optima. SVI widens the scales itself. ``--init_param_jitter``,
+default 0, multiplies the component guide's starting values by random noise.
+Leave it off when starting from a MAP point, because 0.1 moves a starting
+abundance near 15 by about 1.5 ln units.
+
+Starting points
+---------------
+
+A fresh MAP starts at the configured guesses. ``svi`` and ``nuts`` first run
+a MAP warm-up for at most ``--pre_map_num_epoch`` epochs, default 10000, which
+stops early when it converges. Its files carry the ``{out_prefix}_premap``
+prefix. ``--pre_map_num_epoch 0`` skips it.
+
+``--init_from params.npz`` starts the fit at the point saved by an earlier
+MAP fit, its ``{out_prefix}_params.npz``. Every site that file names starts
+there, and every other site starts at its guess. The earlier fit may be of a
+different model, such as a fit without tube offsets.
+
+Staged MAP
+^^^^^^^^^^
+
+The starting point matters because Adam moves every parameter by about one
+step size per step. On real data a cold MAP with level tube offsets carried
+the offsets and *k* and *m* by whole units in its first window, the
+step-size cuts then froze them there, and the offsets settled near ±2.8.
+Starting from a MAP without offsets plus each tube's best offset was 1.2e5
+nats better. ``tfs-fit-model`` builds that start itself. A fresh MAP of a
+model with ``sample_offset: level`` runs three MAPs:
+
+1. The tube offsets held at 0 (and a learned offset SD held at its prior
+   scale, since with every offset at 0 its MAP is 0). Files
+   ``{out_prefix}_stage1_*``.
+2. Every other site held at stage 1's MAP, so the offsets are the only
+   latents. Given the rest the tubes do not couple, so this is each tube's
+   best offset. Files ``{out_prefix}_stage2_*``.
+3. The joint MAP, started at stage 1's point plus stage 2's offsets, at
+   ``--staged_step_size`` (default 1e-4). This writes the run's own
+   ``{out_prefix}_*`` files.
+
+Each stage writes its own checkpoint and convergence record. Rerunning the
+same command reuses a stage whose ``_params.npz`` exists and resumes one
+that was interrupted, so to redo a stage delete its files. To resume the
+joint stage, pass its checkpoint with ``--checkpoint_file``.
+``--stage_offsets`` is ``auto`` by default: it stages a fresh level-offset
+MAP and nothing else (not SVI, not a resumed fit, not ``--init_from``).
+``off`` fits in one go; ``on`` insists on staging.
+
+On the dev data the offset mode is not only a trap for the optimizer. Run to
+convergence from the hand-built start, a fit moved into it and scored 7.9e4
+nats better than the staged MAP, with offsets that follow IPTG in the
+selection conditions and unphysical *k* and *m*. The staged MAP keeps the
+fit in the physical basin, but a longer or better fit can leave it.
+``tfs-summarize-fit`` checks every level-offset fit for this: it reports the
+tube offsets in prior SDs and flags a fit whose offsets trend with titrant or
+time within a condition (see :doc:`summarize-fit`, "Tube offsets"). The cause
+is filed for study (``planning/offset-mode-growth-transition.md``).
+
+``--checkpoint_file`` resumes a fit from its ``{out_prefix}_checkpoint.pkl``
+at the checkpoint's step size and convergence state. It cannot be combined
+with ``--init_from``. A new fit refuses to overwrite an existing checkpoint,
+so resume it, delete it or change ``--out_prefix``.
+
+Outputs
+-------
+
+With the default prefix ``tfs_fit_model``:
+
+``tfs_fit_model_checkpoint.pkl``
+   The optimizer state, written every ``--checkpoint_interval`` epochs,
+   default 10. It records the guide, step size, convergence state and
+   provenance. ``tfs-sample-posterior`` reads it.
+``tfs_fit_model_params.npz``
+   The fitted parameters. For a MAP fit these are the ``{site}_auto_loc``
+   arrays that ``--init_from`` reads.
+``tfs_fit_model_convergence.csv``, ``tfs_fit_model_losses.txt``
+   The convergence record, described below.
+``checkpoints/``
+   Numbered checkpoints every ``--epoch_checkpoint_interval`` epochs, default
+   1000. 0 disables them.
+
+A NUTS fit also writes its posterior file.
+
+Convergence
+===========
+
+``tfs-fit-model`` for MAP and SVI, the MAP warm-up and
+``tfs-prefit-calibration`` all stop the same way. Optimization runs in
+windows of ``--convergence_window_steps`` optimizer steps, default 2000 and
+never fewer than 10 epochs, so that with mini-batches every genotype is seen
+several times per window. At the end of each window the monitor asks three
+questions.
+
+**Is the loss still improving?** A line through the medians of 20 blocks of
+the window's losses gives the drop per window and its standard error. The
+loss is improving if the drop exceeds ``--convergence_z`` standard errors,
+default 3, and ``--loss_rtol`` of the loss, default 1e-6. The loss is judged
+against its own noise, so a noisy SVI loss neither stops early nor runs
+forever. A rising or oscillating loss is not improving. Before acting on
+``--patience`` stalled windows, default 3, the monitor fits one line through
+all of them. A slow descent hidden by noise in each window shows up there,
+and a significant drop resets the stall count.
+
+**Is any parameter still moving?** Each parameter's trend over the window is
+measured in units of its posterior SD: the paired guide scale for SVI, the
+prior SD for MAP, and log or logit units for positive or bounded
+parameters. The posterior SD is floored at 1% of the prior SD, because a
+mean-field guide can shrink a hierarchical scale's SD far below any honest
+width, and a negligible creep in those units would never settle. Movement
+explained by noise, or smaller than the optimizer can resolve at the current
+step size, does not count. A parameter moving more than
+``--param_tolerance``, default 0.05, is moving.
+
+**Does the median represent the mean?** The loss being minimized is a mean,
+but the test uses medians. If rare draws carry a huge penalty, the medians
+ignore them. The window's skew, how many robust SDs the mean sits above the
+median, exposes this. Benign noise stays below about 1, and the monitor
+treats a skew above 3 as a hidden penalty.
+
+The decisions follow from those answers. Above the final step size, after
+``--patience`` windows in a row without loss improvement, the step size is
+multiplied by ``--adam_step_size_cut``, default 0.1, down to
+``--adam_final_step_size``, default 1e-6, starting from ``--adam_step_size``,
+default 1e-3. Only the loss decides a cut. Some directions never settle, and
+holding the step size high for them wastes time, while a smaller step does
+not remove rare penalties. At the final step size the run converges after
+``--patience`` windows in which the loss has not improved, no parameter has
+moved and the skew is at most 3. A still-moving parameter or a skewed loss
+keeps the run going and is named in the log.
+
+**A MAP is judged by its exact loss.** A MAP's objective is a fixed
+function of the current point, so the only noise in its window losses comes
+from the mini-batches. On a full library that noise is large: at batch size
+4096 of 218,000 genotypes the trend's SE was about 5e4 nats per window, so a
+descent of 1e5 nats per window read as a stall and the step size was cut to
+its floor while the fit was still descending. A MAP fit (``tfs-fit-model
+--analysis_method map``, every stage of the staged MAP, the SVI pre-MAP and
+``tfs-prefit-calibration``) therefore computes the exact full-batch loss,
+``-log p``, at the end of each window: one forward pass over the library,
+in chunks of the batch size, a few percent of the run's time. A line
+through the last ``--patience`` + 1 exact losses gives the drop per window
+and its SE, which now measures only the optimizer's jitter about its path.
+The window is improving when the drop exceeds both ``--convergence_z``
+times the SE and ``--loss_rtol`` times the loss. A steady descent passes
+however slow it is; jitter about a level does not. The skew check is
+skipped, since the exact loss is the objective itself. SVI keeps the
+mini-batch test: its ELBO is noisy by nature.
+
+A window whose loss falls below −1000 times the magnitude of the run's first
+block ends the run as ``diverged``. That means the objective is unbounded,
+from a density singularity or from float32 error in a dense guide's own
+density. It is not convergence.
+
+``--max_num_epochs``, default 100000, is a cap only. A hierarchical MAP
+often has no finite optimum, because a hyperparameter scale can shrink
+forever, so MAP runs often end at the cap. A run that reaches it says so and
+names the largest remaining movement.
+
+The record
+----------
+
+``{out_prefix}_convergence.csv`` has one row per window:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 25 75
+
+   * - Column
+     - Meaning
+   * - ``step``, ``epoch``, ``step_size``
+     - Where the window ended and the step size it ran at.
+   * - ``loss``, ``loss_mean``
+     - Median and mean mini-batch loss over the window.
+   * - ``loss_exact``
+     - The exact full-batch loss at the end of the window (MAP only). The
+       loss test used it, and two MAP runs on the same model can be
+       compared by it.
+   * - ``loss_drop``, ``loss_drop_se``, ``loss_t``
+     - Drop per window, its standard error and their ratio.
+   * - ``loss_improving``
+     - Whether the loss test passed.
+   * - ``loss_skew``, ``loss_skewed``
+     - The skew and whether it exceeds 3.
+   * - ``worst_param``, ``worst_param_excess``, ``worst_param_drift``
+     - The parameter moving most, its movement beyond noise and its raw
+       drift.
+   * - ``params_moving``
+     - Whether any parameter exceeds ``--param_tolerance``.
+   * - ``plateau``, ``plateau_count``
+     - Whether this window stalled, and the run of stalled windows.
+   * - ``pooled_loss_t``
+     - The pooled trend's t statistic, when one was computed.
+   * - ``decision``
+     - ``continue``, ``cut``, ``converged`` or ``diverged``.
+
+``{out_prefix}_losses.txt`` has columns ``epoch``, ``loss``, ``step`` and
+``step_size``, one row per block of steps, where ``loss`` is the block's
+median.
+
+The optimizer
+-------------
+
+The optimizer is plain Adam. ``--adam_clip_norm`` opts into numpyro's
+``ClippedAdam``, which clips each gradient element to that value. Leave it
+off. These losses have gradients of 1e3 to 1e6 per element, so a clip
+applies to every element on every step, and Adam then follows the sign of
+each draw's gradient. Its fixed point is where those signs balance, not
+where the expected gradient is zero. With a rare large penalty, such as a
+steep, precisely measured binding curve that an occasional draw crosses,
+that point sat in violation for 5 to 90% of draws on every
+congression-calibration run. Without clipping the same fits converged.
+
+``--elbo_num_particles``, default 2, sets the draws per SVI step.
+
+Sample the posterior
+====================
+
+``tfs-sample-posterior config_file checkpoint_file`` writes
+``{out_prefix}.h5``, default ``tfs_posterior.h5``, which the prediction and
+extraction commands read. The checkpoint type decides what happens. An SVI
+checkpoint draws ``--num_posterior_samples`` samples from the guide, default
+10000. A NUTS checkpoint uses its saved samples. A MAP checkpoint, including
+SVI with the ``delta`` guide, becomes a Laplace approximation chosen by
+``--laplace``:
+
+``auto`` (default)
+   ``full`` up to ``--laplace_max_params`` MAP parameters, default 20000, and
+   ``arrowhead`` above.
+``full``
+   The Laplace from the dense Hessian. Its memory grows with the square of the
+   parameter count, which puts a full library out of reach.
+``arrowhead``
+   The same Gaussian computed one genotype at a time. Given the shared
+   parameters the genotypes do not couple, so the Hessian is per-genotype
+   blocks plus their coupling to the shared parameters: *k*, *m*,
+   hyperparameters and tube offsets. The shared parameters are drawn from
+   their marginal and each genotype from its conditional. It runs on a full
+   library.
+``blocks``
+   Per-genotype blocks with the shared parameters held at the MAP. It leaves
+   out the *k* and *m* uncertainty.
+``point``
+   The MAP point itself, one sample and no Hessian.
+
+Each Hessian eigenvalue is floored at the prior's curvature along its
+direction, so no direction of the Laplace is wider than the prior. A MAP that
+stopped at its epoch cap is not exactly at an optimum, and the arrowhead
+Laplace may find directions of the shared block with negative curvature.
+Those directions are held at the MAP and written to
+``{out_prefix}_held_directions.csv``, since they have no interval. Its
+columns are ``direction``, ``eigenvalue``, ``parameter`` and ``loading``,
+with the five largest loadings of each direction. On the relative-fit
+simulations the arrowhead Laplace covered *k* and *m* at 0.92 and 1.0 under
+Poisson noise and at 0.50 and 0.42 under realistic noise.
+
+``--skip_growth_observations`` leaves the per-observation growth sites out
+of the file. They are most of its size, about 100 GB per site at 500 draws on
+a 200,000-genotype library, and ``tfs-predict-growth`` recomputes growth from
+the parameters. ``--hessian_chunk_size`` and ``--genotype_chunk_size`` trade
+speed for memory if the device runs out. ``--sampling_batch_size`` and
+``--forward_batch_size`` do the same for sampling.
+
+Per-genotype fits
+=================
+
+``tfs-fit-genotypes`` is a non-Bayesian alternative. It fits the growth model
+to each genotype independently by maximum likelihood, holding each
+condition's *k* and *m* fixed at a calibration, and writes per-genotype Hill
+parameters and *θ* predictions. It applies no congression correction and no
+pooling across genotypes.
+
+.. code-block:: bash
+
+   tfs-fit-genotypes growth.csv tfs_configure_priors.csv --num_workers -1
+
+The calibration is a priors file written by ``tfs-prefit-calibration`` or a
+wide table with columns ``condition_rep``, ``growth_k`` and ``growth_m``. The
+default output prefix is ``tfs_fit_genotypes``.
+
+Running on a cluster
+====================
+
+``examples/tfmodel/run.srun`` is a Jinja template that ``tfs-setup-grid``
+renders into each run directory of a grid, not a standalone script. It shows
+the joint route as a Slurm job. See :doc:`grid`.
diff --git a/docs/source/grid.rst b/docs/source/grid.rst
index 1c7e8f23..2f30da9b 100644
--- a/docs/source/grid.rst
+++ b/docs/source/grid.rst
@@ -1,96 +1,101 @@
-====================================
-Grid Setup and Summarisation
-====================================
-
-``tfscreen`` provides two scripts for setting up parameter sweeps over model
-configurations or simulation settings, and one script for summarising the
-results once runs are complete.
-
-Overview
---------
-
-Both grid-setup scripts share the same concept:
-
-1. You write a **grid YAML** file describing a base configuration and a set of
-   parameter axes, each with a list of variants.
-2. The setup script takes the **Cartesian product** of all variant lists and
-   creates one subdirectory per combination.
-3. Inside each subdirectory the script writes a per-run configuration file and
-   optionally renders a **Jinja2 template** (e.g. a Slurm submission script).
-4. After all runs complete, ``tfs-summarize-grid`` collects results into a
-   summary CSV.
+=============
+Grids of runs
+=============
+
+A grid is a sweep: the same pipeline run over every combination of a few
+settings. ``tfs-setup-grid`` sets up a grid of model fits and
+``tfs-setup-sim-grid`` a grid of simulations. ``tfs-summarize-grid`` and
+``tfs-summarize-calibration`` collect the results once the runs finish.
+
+How a grid works
+----------------
+
+A **grid YAML** lists blocks of variants. The setup command takes the
+Cartesian product of every block and makes one run directory per
+combination. In each directory it writes the run's configuration, renders a
+Jinja2 template (a shell or Slurm script) with that run's template
+variables, and records the combination in ``combo.json``. It also writes
+``grid_summary.json`` at the top, listing every run.
+
+Run directories are named ``run_{index:04d}_<run_name>``, where
+``<run_name>`` is the rendered ``run_name`` template with unsafe characters
+replaced by underscores. Without a ``run_name`` the suffix is built from the
+variable values.
 
 .. code-block:: text
 
     my_grid/
-    ├── grid_summary.json          ← written by tfs-setup-grid
-    ├── linear__instant__seed0/
-    │   ├── combo.json             ← variable assignments for this run
+    ├── grid_summary.json
+    ├── inputs/                  copies of every input file
+    │   ├── binding.csv
+    │   ├── growth.csv
+    │   └── library_config.yaml
+    ├── run_0001_linear__instant__hill_geno__seed0/
+    │   ├── combo.json
     │   ├── tfs_configure_config.yaml
     │   ├── tfs_configure_priors.csv
     │   ├── tfs_configure_guesses.csv
-    │   └── run.srun               ← rendered Jinja2 template
-    ├── linear__instant__seed1/
-    │   └── ...
+    │   ├── tfs_configure_library.csv
+    │   └── run.srun
+    ├── run_0002_linear__instant__hill_geno__seed1/
     └── ...
 
-tfs-setup-grid
---------------
-
-Sets up a grid of model-fitting runs. For each combination the script calls
-``tfs-configure-model`` in the corresponding subdirectory, so all three
-configuration files (``_config.yaml``, ``_priors.csv``, ``_guesses.csv``) are
-already present when the grid is created.
+A grid directory is self-contained, so it can be moved as a unit on and off
+a cluster. Every input file a run needs is copied once into
+``<out_dir>/inputs/``, and each run's config and script refer to it as
+``../inputs/<name>``. Two different files with the same name are kept apart
+by a numeric suffix. Relative paths in the grid YAML resolve against the
+grid YAML's own directory. Setup checks every combination before writing
+anything and fails on a missing input file, an input that is a directory,
+a config value naming a file the grid does not know to copy, or a template
+that does not render. Launch each run from inside its own directory.
+
+Two kinds of variables go into the product. Variables in the
+``configure_model`` or ``simulate`` blocks go into the run's configuration.
+Variables in the ``template`` blocks go only into the rendered script. To
+use a value in both places, list it in both. ``run_name`` can use either
+kind. A variant with several keys keeps them together, so use one when
+settings only make sense in combination. The ``basename`` filter strips the
+directory from a file-valued variable:
+``run_name: "{{ growth_df | basename }}__{{ condition_growth }}"``.
+
+Model grids
+-----------
+
+``tfs-setup-grid`` calls ``tfs-configure-model`` in each run directory, so
+the configuration files exist as soon as the grid does:
 
 .. code-block:: bash
 
-    tfs-setup-grid grid.yaml --out_prefix my_grid
+    tfs-setup-grid grid.yaml --out_dir my_grid
 
-An :download:`annotated example <../../examples/tfmodel/grid.yaml>` is provided
-in ``examples/tfmodel/``. Once the grid is set up, submit all jobs with:
+The :download:`annotated example <../../examples/tfmodel/grid.yaml>` and
+its Slurm template :download:`run.srun <../../examples/tfmodel/run.srun>`
+are in ``examples/tfmodel/``. The template runs the pre-fit, a MAP fit, a
+Laplace posterior, parameter extraction, growth and *θ* prediction, and
+``tfs-cat-response``. Submit every run with:
 
 .. code-block:: bash
 
-    for d in my_grid/*/; do
-        cd "$d" && sbatch run.srun && cd -
-    done
+    for d in my_grid/run_*/; do (cd "$d" && sbatch run.srun); done
 
-Grid YAML format
-^^^^^^^^^^^^^^^^
+A shortened grid YAML:
 
 .. code-block:: yaml
 
-    # Directory name template (Jinja2); variables from both sections available.
-    run_name: "{{ condition_growth }}__{{ growth_transition }}__seed{{ seed }}"
-
-    # Jinja2 template rendered into each subdirectory (relative to this YAML).
+    run_name: "{{ condition_growth }}__{{ theta }}__seed{{ seed }}"
     output_file: run.srun
 
-    # --- configure_model blocks -----------------------------------------------
-    # Variables here are forwarded to tfs-configure-model.
-    # They are NOT injected into the Jinja2 template.
-
     configure_model:
-
-      # Fixed arguments — single variant = always selected.
       - name: data
         variants:
-          - binding_df: ../data/binding.csv
-            growth_df:  ../data/growth.csv
-            library_config: ../data/run_config.yaml
+          - binding_df: data/binding.csv
+            growth_df: data/growth.csv
+            library_config: data/library_config.yaml
 
-      # 'auto' enumerates every registered component for an axis.
-      # Incompatible combinations are skipped automatically.
       - name: condition_growth
         auto: condition_growth
 
-      # Manual enumeration — list only the components you want.
-      - name: growth_transition
-        variants:
-          - growth_transition: instant
-          - growth_transition: baranyi
-
-      # Joint (co-varying) block — keys in the same dict always move together.
       - name: theta_and_epistasis
         variants:
           - theta: hill_geno
@@ -98,67 +103,56 @@ Grid YAML format
           - theta: hill_mut
             epistasis: true
 
-    # --- template blocks ------------------------------------------------------
-    # Variables here are injected into the Jinja2 template only.
-    # They are NOT forwarded to tfs-configure-model.
-
     template:
       - name: seed
         variants:
           - seed: 0
           - seed: 1
 
-      - name: predict_genotypes
-        variants:
-          - predict_genotypes_file: /path/to/predict_genotypes.txt
-
-Key rules:
-
-* The Cartesian product is taken across **all** blocks (``configure_model`` +
-  ``template``).
-* ``configure_model`` variables accept any flag accepted by
-  ``tfs-configure-model`` (without the ``--`` prefix and ``_model`` suffix for
-  component axes; e.g. ``condition_growth`` rather than
-  ``--condition_growth_model``).
-* Relative paths in ``configure_model`` blocks (``binding_df``, ``growth_df``,
-  ``library_config``, ``thermo_data``) are resolved relative to the grid YAML
-  and re-expressed relative to each subdirectory in the written config.
-* The ``auto`` form enumerates every registered component for the given axis.
-  Incompatible combinations (e.g. ``power`` growth + ``logit`` theta_rescale)
-  are caught by ``tfs-configure-model``, skipped, and logged in
-  ``grid_summary.json``.
-* Use the ``basename`` Jinja2 filter to strip directory paths from
-  file-valued variables in ``run_name``:
-  ``"{{ binding_df | basename }}__{{ condition_growth }}"``.
-
-tfs-setup-sim-grid
-------------------
-
-Sets up a grid of simulation runs. Each subdirectory receives a
-``tfs_sim_config.yaml`` derived from a base config with per-run overrides
-applied.
+A ``configure_model`` variable is any ``tfs-configure-model`` argument
+without the leading ``--``. Component choices drop the ``_model`` suffix:
+``condition_growth`` rather than ``condition_growth_model``, and likewise
+``growth_transition``, ``ln_cfu0``, ``dk_geno``, ``activity``, ``theta``,
+``transformation``, ``theta_rescale``, ``theta_growth_noise``,
+``theta_binding_noise``, ``growth_noise`` and ``sample_offset``. The input
+files (``binding_df``, ``growth_df``, ``presplit_df``, ``base_growth_df``,
+``library_config``, ``thermo_data``) are copied into ``inputs/``.
+
+``auto: <axis>`` in place of ``variants`` enumerates every registered
+component on that axis. It is safe for small axes such as
+``condition_growth`` or ``growth_transition``. For ``theta`` it also yields
+``_simple``, a private component used only by the pre-fit, and every
+thermodynamic model, so list theta variants by hand. A combination that
+``tfs-configure-model`` refuses is skipped, and the reason is logged in
+``grid_summary.json``. For example, the ``mixture`` transformation with its
+default ``homodimer`` rule refuses any activity component but ``fixed``.
+
+Each run's configuration files are always ``tfs_configure_config.yaml``,
+``tfs_configure_priors.csv``, ``tfs_configure_guesses.csv`` and, for a
+growth model, ``tfs_configure_library.csv``. The template refers to them by
+those names.
+
+Simulation grids
+----------------
+
+``tfs-setup-sim-grid`` writes a ``tfs_sim_config.yaml`` in each run
+directory: a base simulate config with the run's overrides applied.
 
 .. code-block:: bash
 
-    tfs-setup-sim-grid simulate_grid.yaml --out_prefix my_sim_grid
-
-An :download:`annotated example <../../examples/simulate/simulate_grid.yaml>`
-is provided in ``examples/simulate/``.
+    tfs-setup-sim-grid simulate_grid.yaml --out_dir my_sim_grid
+    for d in my_sim_grid/run_*/; do (cd "$d" && bash run.sh); done
 
-Grid YAML format
-^^^^^^^^^^^^^^^^
-
-The simulate grid YAML follows the same structure as the model grid, with two
-differences:
-
-1. A ``base_config`` key is required, pointing to the base ``simulate_config.yaml``.
-2. The blocks are named ``simulate`` (not ``configure_model``).
+The :download:`example grid <../../examples/simulate/simulate_grid.yaml>`
+and its template :download:`run.sh <../../examples/simulate/run.sh>` are in
+``examples/simulate/``. The grid YAML has the same form as a model grid,
+with two differences. A ``base_config`` key names the base simulate config,
+and the config blocks are called ``simulate``:
 
 .. code-block:: yaml
 
-    base_config: ../simulate_config.yaml
-
-    run_name: "{{ theta_component }}__noise{{ tube_noise_sigma }}__seed{{ random_seed }}"
+    base_config: simulate_config.yaml
+    run_name: "{{ theta_component }}__noise{{ tube_noise_sigma }}__seed{{ seed }}"
     output_file: run.sh
 
     simulate:
@@ -174,52 +168,89 @@ differences:
 
       - name: seed
         variants:
-          - random_seed: 0
-          - random_seed: 42
+          - seed: 0
+          - seed: 42
 
     template:
       - name: num_replicates
         variants:
           - num_replicates: 3
 
-Key rules:
-
-* ``simulate`` variables override top-level keys in the base config. Nested
-  keys are not supported — override the entire top-level key if needed.
-* The ``auto`` form is not supported for simulate grids.
-
-Jinja2 template variables
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The rendered template receives all variables from the ``template`` blocks.
-``simulate`` / ``configure_model`` variables are **not** available in the
-template — add them to ``template`` as well if they are needed in both places.
-
-The ``run.srun`` and ``run.sh`` files in ``examples/tfmodel/`` and
-``examples/simulate/`` show realistic Slurm and shell templates. Inside a
-template, each run's configuration file is always named:
-
-* ``tfs_configure_config.yaml`` (model grid)
-* ``tfs_sim_config.yaml`` (simulate grid)
-
-tfs-summarize-grid
-------------------
-
-Scans a grid directory for completed runs and writes a flat summary CSV.
+A ``simulate`` variable replaces a top-level key of the base config. Nested
+keys cannot be overridden one at a time, so to change one key inside a
+block such as ``binding_data``, give the whole block as the variant. The
+keys must be valid simulate-config keys (see :doc:`simulation`); setup does
+not check them, but ``tfs-simulate`` refuses an unknown key when the run
+starts. The random seed is ``seed``. ``auto`` is not available in simulation
+grids. A library subsample (see :doc:`simulation`) is set the same way,
+e.g. a block whose variants are ``library_subsample: {double-1-2: 500}`` and
+``library_subsample: {double-1-2: 2000}``; keep ``library_subsample_seed``
+fixed so the runs compare nested libraries.
+
+File-valued simulate keys are copied into ``inputs/`` like model-grid
+inputs: ``thermo_data``, ``design``, ``library_genotypes``,
+``empirical.phenotype_model``, ``od600.calibration`` and any
+``binding_data`` ``choose_by`` that names a file. A relative path in the base config resolves against the base config's
+directory; one in a ``simulate`` override resolves against the grid YAML.
+
+A simulation grid becomes a calibration study when its template also
+configures and fits each simulated data set and runs ``tfs-summarize-fit``.
+Fit settings then go in the ``template`` blocks, since only the script
+sees them.
+
+Summarizing a model grid
+------------------------
+
+``tfs-summarize-grid`` collects a model grid into one table:
 
 .. code-block:: bash
 
     tfs-summarize-grid my_grid
 
-Output (default: ``my_grid/grid_summary.csv``):
+It writes ``my_grid/grid_summary.csv`` (``--out_prefix`` changes the path)
+with one row per run directory that has a ``combo.json``. The columns are
+the run name, the ``configure_model`` and ``template`` variables,
+``configure_complete`` (whether ``tfs_configure_config.yaml`` exists), and,
+when the run has a ``*_fit_summary.json`` from ``tfs-summarize-fit``, its
+statistics flattened into columns such as ``theta_training_rmse``,
+``theta_test_rmse``, ``growth_training_rmse`` and ``final_loss``.
+``tfs-summarize-fit`` writes that file to ``summary/`` by default, and
+``tfs-summarize-grid`` looks only in the run directory itself, so run it
+with ``--out_prefix tfs_summarize`` inside the run directory if the grid
+summary should pick it up.
+
+Summarizing calibration across a simulation grid
+------------------------------------------------
+
+``tfs-summarize-calibration`` asks whether the posterior intervals of a
+simulate-and-fit grid are honest. Each run directory needs its
+``combo.json`` and, once finished, the ``tfs-summarize-fit`` outputs in
+``summary/``, where the posterior quantiles are joined to the simulated
+truth for *θ* and for every fitted parameter with ground truth.
 
-* One row per subdirectory that contains a ``combo.json``.
-* Columns from the ``configure_model`` / ``template`` variable assignments.
-* ``configure_complete`` — whether ``tfs-configure-model`` finished (i.e.
-  ``tfs_configure_config.yaml`` is present).
-* Flattened fit-summary statistics from ``*_fit_summary.json`` if present
-  (e.g. ``theta_training_rmse``, ``growth_training_rmse``, ``final_loss``).
-* Calibration statistics from ``*_calib_stats.json`` if present (prefixed
-  with ``calib_``).
+.. code-block:: bash
 
-Use ``--out_prefix`` to write the CSV to a different location.
+    tfs-summarize-calibration my_sim_grid --baseline guide_type=component --facet_by guide_type
+
+For every run, quantity and stratum it computes the coverage of central
+intervals from 50% to 99%, the mean calibration error and bias (negative
+means intervals too narrow), PIT uniformity, mean interval widths, RMSE and
+Pearson r. Widths are there so a method cannot look calibrated just by being
+vague. Genotype-level quantities are also split by whether the genotype has
+binding data and by its purity (spike, bulk or mixed), and *θ* by whether
+the true value is resolvable (inside ``[regime_eps, 1 - regime_eps]``) or
+saturated. Runs that share every grid variable except the replicate keys
+(``--replicate_keys``, default ``seed`` and ``fit_seed``) form an arm and
+are averaged. With ``--baseline key=value ...`` every other run is paired
+with the baseline run fit to the same simulated data, and the paired
+differences are reported. As with ``tfs-compare-runs``, every value is a
+raw number with no thresholds or grades.
+
+Outputs, under ``--out_prefix`` (default ``tfs_calibration``):
+``_runs.csv`` (per run, quantity and stratum), ``_run_status.csv`` (which
+runs are finished and what is missing), ``_arms.csv`` (mean and SD over
+replicates), ``_paired.csv`` and ``_paired_summary.csv`` (with
+``--baseline``), a calibration-curve PDF for ``--plot_quantity`` (default
+``theta_test``), and ``_metadata.json`` with the resolved settings.
+
+See :doc:`cli` for every flag of these four commands.
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 18efbd57..32d351ac 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -9,13 +9,18 @@ tfscreen
    :caption: Contents:
 
    quickstart
-   simulation
+   screen-analysis
+   pipeline
    process-raw
    model-inputs
-   analysis
+   fitting
+   model
+   downstream
    summarize-fit
+   simulation
    grid
    ligandmpnn-features
+   cli
 
 Goal
 ----
@@ -33,32 +38,27 @@ The thermodynamic observable is operator occupancy (*θ*), inferred from *E. col
 
 The model in ``tfscreen.tfmodel`` is designed to jointly capture high-throughput growth data and low-throughput binding data, learning the quantitative relationship between operator occupancy and growth rate in the process.
 
-Quick Start
------------
-New to ``tfscreen``?  The :doc:`quickstart` page walks through a complete
-simulate-and-analyze run using the bundled example in
-``examples/simulate-and-analyze/``.
-
-Simulation
-----------
-`tfscreen` allows you to simulate high-throughput screens starting from thermodynamic models of transcription factor binding and activity. See the :doc:`simulation` page for more details.
-
-Raw Data Processing
--------------------
-`tfscreen` includes utilities to convert raw sequencing data into quantitative inputs for analysis. See the :doc:`process-raw` page for more details.
-
-Model Input Data
------------------
-The hierarchical model accepts binding, growth, base-growth, pre-split, and congression-calibration data. See the :doc:`model-inputs` page for what each contributes, how much is useful, and how to pass it in.
-
-Analysis
---------
-`tfscreen` provides robust statistical tools, including Bayesian hierarchical models, to extract biochemical parameters from screen data. See the :doc:`analysis` page for more details.
-
-Grid Setup
-----------
-Both the simulation and model-fitting workflows support parameter sweeps via a grid mechanism. See the :doc:`grid` page for the grid YAML format and the ``tfs-setup-grid``, ``tfs-setup-sim-grid``, and ``tfs-summarize-grid`` commands.
-
+Using tfscreen
+--------------
+
+:doc:`quickstart` runs the bundled simulate-and-fit example.
+:doc:`screen-analysis` is the route for analyzing a real screen, from read
+counts to per-genotype curves with intervals; start there with lab data.
+:doc:`pipeline` walks through every step from sequencing reads to a
+posterior, with the command for each. The pages after it cover each part
+in detail:
+
+- :doc:`process-raw`: reads to counts, OD600 to tube totals, counts to the
+  growth table.
+- :doc:`model-inputs`: what each data file and prior input gives the model.
+- :doc:`fitting`: configuring, fitting, convergence and posteriors.
+- :doc:`model`: the generative model and its components.
+- :doc:`downstream`: parameters, predictions, epistasis, curve classes and
+  cross-run comparison.
+- :doc:`summarize-fit`: diagnostics for one run.
+- :doc:`simulation` and :doc:`grid`: simulated experiments and parameter
+  sweeps.
+- :doc:`cli`: every command and flag, generated from the code.
 
 Indices and tables
 ==================
diff --git a/docs/source/ligandmpnn-features.rst b/docs/source/ligandmpnn-features.rst
index 1469cf82..20d35fcc 100644
--- a/docs/source/ligandmpnn-features.rst
+++ b/docs/source/ligandmpnn-features.rst
@@ -60,9 +60,9 @@ Create a YAML file with a top-level ``structures:`` mapping from short
 structure names to PDB file paths, plus ``n_chains_bearing_mut`` (the number
 of chains that carry each mutation: ``1`` for a monomer, ``2`` for a
 homodimer). The structure names must match what the downstream theta
-component expects. For the four-state (``O2_C4_*``) topologies the required
-keys are ``H``, ``HD``, ``L``, and ``LE2``, representing the four
-thermodynamic states:
+component expects. For the ``O2_C4_*`` models the required keys are ``H``,
+``HD``, ``L``, and ``LE2``, the four structures of their ΔΔG basis (HD is H
+bound to operator DNA, LE2 is L with both effector sites filled):
 
 .. code-block:: yaml
 
@@ -162,6 +162,13 @@ For each structure name ``{name}`` the file contains a group with:
 ``{name}/n_chains_bearing_mut``
     ``int32`` scalar: the ``n_chains_bearing_mut`` value from the input YAML.
 
+``{name}/wt_aa``
+    ``str`` (L,): the one-letter wild-type residue at each residue number,
+    read from the PDB (``X`` when unknown). Optional; files written before
+    it existed still load. When present, the loader checks it against the
+    library's wild-type residues and stops on a mismatch, which usually
+    means the PDB and the library number residues differently.
+
 Using the HDF5 File in a Run Config
 ------------------------------------
 
@@ -170,14 +177,19 @@ selecting a ``PnnC`` theta component:
 
 .. code-block:: bash
 
-    tfs-configure-model binding.csv --growth_df growth.csv \
+    tfs-configure-model --binding_df binding.csv --growth_df growth.csv \
         --theta_model thermo.O2_C4_K3_U0_a.PnnC \
-        --thermo_data ensemble.h5
+        --thermo_data ensemble.h5 \
+        --library_config library_config.yaml
 
-The path is saved into ``tfs_configure_config.yaml`` as the top-level
-``thermo_data`` key and read by ``tfscreen.tfmodel.generative.components.theta.thermo.io.load_struct_ensemble``
+The path is saved into ``tfs_configure_config.yaml`` under
+``components.thermo_data`` and read by ``tfscreen.tfmodel.generative.components.theta.thermo.io.load_struct_ensemble``
 at model-build time; you do not need to edit the config by hand. See
 ``configure_model_cli.py``'s ``thermo_data`` docstring for the full set of
 registry keys and their structure-name requirements (``PddG`` models instead
 take a hand-supplied ΔΔG CSV via the same flag — see
 ``load_ddG_prior_csv``).
+
+Every library residue must be in every structure. If the PDB numbering is
+offset from the library's, ``--thermo_residue_offset N`` adds N to the file's
+residue numbers.
diff --git a/docs/source/model-inputs.rst b/docs/source/model-inputs.rst
index f278ff6d..0828ada3 100644
--- a/docs/source/model-inputs.rst
+++ b/docs/source/model-inputs.rst
@@ -1,237 +1,467 @@
-=================
-Model Input Data
-=================
+============
+Model inputs
+============
 
-``tfs-configure-model`` (see :doc:`analysis`, Step 1) accepts up to five
-pieces of experimental input. Only ``binding_df`` is strictly required;
-everything else is optional and adds additional constraints to the
-model. This page describes what each input contributes to the fit, the
-rough amount of data that is useful in practice, and how it is passed in.
+``tfs-configure-model`` builds a model from data files and a few
+experimental numbers, and writes the configuration ``tfs-fit-model``
+reads. This page describes what each input contributes to the fit and how
+to pass it. The commands that produce the data files are in
+:doc:`process-raw`, every flag is in :doc:`cli`, and the model components
+are in :doc:`model`.
+
+At least one of ``--growth_df`` and ``--binding_df`` is required. Both are
+flags. With both, the joint model is configured; with growth only, a
+growth-only model; with binding only, a binding-only model.
 
 .. list-table::
    :header-rows: 1
-   :widths: 20 15 65
+   :widths: 26 24 50
 
-   * - Input
-     - Required?
+   * - Flag
+     - When
      - What it anchors
-   * - ``binding_df``
-     - Yes
-     - Absolute scale/shape of *θ*, independent of growth
-   * - ``growth_df``
-     - No (binding-only model if omitted)
-     - ln_cfu\ :sub:`0`, dk_geno, activity, and *θ* jointly, via growth rate
-   * - ``base_growth_df``
-     - No
-     - The condition-growth baseline / dk_geno identifiability confound
-   * - ``presplit_df``
-     - No
-     - ln_cfu\ :sub:`0` directly, from pre-split sequencing counts
-   * - ``transformation_lambda``
-     - No (required by some ``transformation_model`` choices)
-     - The congression-correction prior
-
-Binding Data (``binding_df``)
-==============================
-
-**Role**
-
-Direct, low-throughput measurements of operator occupancy (*θ*) as a
-function of titrant concentration, independent of any growth-rate
-observation. Because it measures *θ* directly rather than through the
-growth likelihood, binding data anchors the absolute scale and shape of
-the occupancy curve — resolving degeneracies (e.g. between *θ* and
-activity *A*) that growth data alone cannot separate. It is the only
-required input to ``tfs-configure-model``: with ``growth_df`` omitted, a
-binding-only model is configured that infers *θ* from ``binding_df``
-alone.
-
-**Scale**
-
-Binding curves are comparatively expensive to collect, so useful fits
-typically rely on **dozens** of measured genotype/titrant curves (e.g. a
-handful of calibration genotypes each measured across ~5-10 titrant
-concentrations) rather than a full library. Because growth data typically
-outnumber binding rows by several orders of magnitude, the binding
-log-likelihood is upweighted by ``binding_weight`` (auto-computed as
-``N_growth_rows / N_binding_rows`` unless overridden) so each binding
-observation carries a comparable gradient contribution to the average
-growth observation.
-
-**Format**
-
-A CSV (or ``pd.DataFrame``) with one row per (genotype, titrant_name,
-titrant_conc) measurement:
+   * - ``--growth_df``
+     - The screen itself
+     - ``ln_cfu0``, ``dk_geno``, activity and *θ* jointly, through growth
+   * - ``--library_config``
+     - Required with growth data
+     - Spiked genotypes, purity and pool shares for the congression model
+   * - ``--binding_df``
+     - Optional
+     - The absolute scale and shape of *θ*
+   * - ``--presplit_df``
+     - Optional
+     - ``ln_cfu0`` directly, from the presplit tube
+   * - ``--base_growth_df``
+     - Optional
+     - The growth baseline against ``dk_geno``
+   * - ``--transformation_lambda``
+     - Required by ``mixture``
+     - The congression rate
+   * - ``--theta_gauge_conc``
+     - ``hill_relative`` only
+     - Where wt's relative X is 1 and 0
+   * - ``--thermo_data``
+     - Thermodynamic *θ* models
+     - Structural features of each mutation
+   * - ``--thermo_ddG_units``, ``--thermo_temperature_K``,
+       ``--thermo_column_map``, ``--thermo_residue_offset``,
+       ``--thermo_max_missing_fraction``
+     - Optional
+     - How ``--thermo_data`` is read: ΔΔG units, column names, residue
+       numbering, coverage warning
+   * - ``--set_priors``, ``--growth_priors``, ``--growth_priors_wt_rates``
+     - Optional
+     - Prior values, without editing the priors CSV
+
+Growth data
+-----------
+
+``--growth_df`` is the table ``tfs-process-counts`` writes (or
+``tfs-simulate`` for a simulated experiment). It is the high-throughput
+part of the experiment: one sequencing run reports every genotype in a
+tube, so a screen has hundreds of thousands of rows. Growth identifies each
+genotype's starting abundance, its pleiotropic growth effect ``dk_geno``, its
+activity and its occupancy *θ* through
+
+``ln_cfu = ln_cfu0 + (k_pre + dk_geno + m_pre·A·θ)·t_pre + (k_sel + dk_geno + m_sel·A·θ)·t_sel``
+
+The model reads these columns:
 
 .. list-table::
    :header-rows: 1
-   :widths: 25 75
+   :widths: 26 74
 
    * - Column
      - Meaning
    * - ``genotype``
-     - Genotype string (``wt``, ``M42I``, ``M42I/K84L``, ...)
+     - Genotype name.
+   * - ``library``
+     - The transformed library. Two libraries may share condition names.
+   * - ``replicate``
+     - Biological replicate; 1 when absent.
+   * - ``condition_pre``, ``condition_sel``
+     - Pre-selection and selection conditions.
+   * - ``t_pre``, ``t_sel``
+     - Minutes of pre-selection and selection growth.
+   * - ``titrant_name``, ``titrant_conc``
+     - Titrant and its concentration.
+   * - ``ln_cfu``, ``ln_cfu_var``
+     - The genotype's cells in the tube, in log form, and its variance.
+       ``ln_cfu_std`` may be given instead of the variance, or ``cfu`` with
+       ``cfu_std`` or ``cfu_var``.
+   * - ``counts``
+     - Reads of the genotype in the tube. Count likelihood only.
+   * - ``sample_reads``
+     - The tube's total reads, ``__unknown__`` included. Count likelihood
+       only. Older files without it can supply ``adjusted_counts`` and
+       ``frequency``, from which it is derived about 1% high, by the same
+       factor for every genotype in a tube.
+   * - ``sample_ln_cfu``
+     - The tube's total cells, in log form. Count likelihood only.
+       ``sample_cfu`` with ``sample_cfu_std`` or ``sample_cfu_var`` works
+       too.
+
+**Likelihood.** The default, ``--growth_likelihood counts``, observes the
+reads directly. Each genotype's count in a tube is negative binomial with
+mean equal to the tube's reads times the genotype's predicted frequency,
+and a learned dispersion whose variance has a part proportional to the mean
+and a quadratic part. There is no pseudocount, so a genotype with zero
+reads is an observation rather than a floor. Real counts vary several times
+more than Poisson, and the dispersion describes that. The count likelihood
+needs ``--growth_noise_model zero``, the default. It pairs with the default
+``--sample_offset_model level``: one ``ln_cfu`` offset per tube, shared by
+every genotype in it, with a learned SD. A tube's total enters the count
+likelihood as given, so the level offset is what absorbs an error in it.
+The SD columns of the totals are not used. ``--set_priors sigma_fixed=0.17``
+holds that SD instead of learning it. On simulations the count likelihood
+cut the RMSE of *θ* by 35 to 60% against the alternative.
+
+The alternative, ``--growth_likelihood lncfu``, observes each row's
+``ln_cfu`` with a Student-t likelihood whose scale is the row's
+``ln_cfu`` SD. It needs none of the count columns.
+
+Binding data
+------------
+
+``--binding_df`` holds direct, low-throughput measurements of *θ* against
+titrant concentration. Because they measure *θ* without going through
+growth, they fix its absolute scale and shape and separate effects growth
+alone cannot, such as *θ* against activity *A*. Useful fits rely on dozens
+of curves, a handful of genotypes each measured at 5 to 10 concentrations.
+Growth rows outnumber binding rows by orders of magnitude. The binding
+log-likelihood is weighted by ``--binding_weight``, 1 by default, so each
+binding observation counts once. ``tfs-process-binding`` makes this table
+from a lab table of single measurements.
+
+.. list-table::
+   :header-rows: 1
+   :widths: 26 74
+
+   * - Column
+     - Meaning
+   * - ``genotype``
+     - Genotype name.
    * - ``titrant_name``
-     - Name of the titrant (matches ``growth_df`` naming when both are used)
+     - Titrant; it must match the growth table's naming.
    * - ``titrant_conc``
-     - Titrant concentration (float)
+     - Titrant concentration.
    * - ``theta_obs``
-     - Measured fractional occupancy, in [0, 1]
+     - Measured fractional occupancy.
    * - ``theta_std``
-     - Standard deviation / uncertainty of ``theta_obs``
+     - SD of ``theta_obs``.
 
-Passed as the required positional argument:
+In a joint model, binding rows whose genotype and titrant do not appear in
+the growth table are dropped with a message.
 
 .. code-block:: bash
 
-    tfs-configure-model binding.csv --growth_df growth.csv
-
-Growth Data (``growth_df``)
-==============================
-
-**Role**
-
-High-throughput growth-rate observations (``ln_cfu`` over time, per
-genotype/replicate/condition), produced by ``tfs-process-counts`` or
-``tfs-simulate``. This is the primary data source: it is what makes the
-approach *high-throughput*, and it jointly identifies ln_cfu\ :sub:`0`,
-``dk_geno`` (pleiotropic growth effect), activity, and *θ* through the
-growth likelihood
-``ln_cfu = ln_cfu0 + (k_pre + dk_geno + m_pre·A·θ)·t_pre + (k_sel + dk_geno + m_sel·A·θ)·t_sel``.
-Omitting it configures a binding-only model (see above).
-
-**Scale**
-
-Growth data is cheap per-genotype relative to binding data, since a
-single sequencing run reports abundances for the whole library
-simultaneously. Useful fits typically involve **hundreds of thousands**
-of rows — the product of library size (thousands of genotypes),
-replicates, conditions, titrant concentrations, and timepoints.
-
-**Format**
-
-A CSV with exactly the schema produced by ``tfs-process-counts`` /
-``tfs-simulate``; see :doc:`process-raw` for the full column
-specification (``genotype``, ``library``, ``replicate``,
-``condition_pre``, ``condition_sel``, ``titrant_name``, ``titrant_conc``,
-``t_pre``, ``t_sel``, ``ln_cfu``, ``ln_cfu_std``).
-
-Passed via the optional flag:
+    tfs-configure-model --binding_df binding.csv --growth_df growth.csv \
+        --library_config library_config.yaml
+
+Without binding data, growth fixes *θ* only up to an affine map: the growth
+slope ``m`` and baseline ``k`` absorb any rescaling of *θ*, so its absolute
+scale rests on the priors. A growth-only model has no binding likelihood,
+so ``--theta_binding_noise_model`` stays ``zero`` and ``--binding_weight``
+stays unset. ``tfs-prefit-calibration`` calibrates the growth link on the
+binding genotypes, so it refuses a growth-only model. The growth-only route
+is ``--theta_model hill_relative`` with its growth priors set from wt
+monoculture rates; see `Prior inputs`_ and :doc:`fitting`.
+
+Library description
+-------------------
+
+``--library_config`` is the library YAML, the same file given to
+``tfs-process-fastq``; its keys are described in :doc:`process-raw`. It is
+required with growth data and refused without it.
+
+``tfs-configure-model`` resolves it into ``{out_prefix}_library.csv``, one row
+per genotype with ``is_wt``, ``in_spiked_origin`` (encoded by a spiked
+sequence), ``pool_fraction`` (its expected share of the pool) and
+``bulk_fraction`` (the share of its cells that come from the bulk,
+congression-prone sub-libraries rather than from a monoclonal spike).
+``tfs-fit-model`` reads this snapshot, never the YAML, so the CSV is the
+place to override the design's numbers. ``in_spiked_origin`` sets which
+genotypes get the spiked ``ln_cfu0`` prior. ``bulk_fraction`` and
+``pool_fraction`` feed the ``mixture`` congression model.
+
+Two checks run against the data. Every genotype in the growth, presplit and
+base-growth tables must be in the library; a mismatch usually means the
+counts were called with a different config, and a residue-numbering
+difference mismatches nearly every mutant. Every spiked genotype must have
+growth data; a missing spike also usually means a different config.
+``--allow_missing_spikes`` turns the second check into a warning, for a
+spike that really dropped out.
+
+``library_mixture`` cannot be checked: no data file ever saw it. It is
+recorded as given in the configuration's ``library`` block, and that record
+is its only defense. Give the best estimate of what went into the pool.
+
+A simulation's library YAML can restrict the library to a subsample
+(``library_genotypes``, ``library_subsample``; see :doc:`simulation`). Given
+that same YAML, ``tfs-configure-model`` builds the snapshot from the same
+subset, with the composition the simulation used, and records the
+subsample keys under ``library.subsample``.
+
+Presplit data
+-------------
+
+``--presplit_df`` holds the abundances measured in the presplit tube, before
+the culture was divided into tubes (t = -t_pre). It constrains each covered
+genotype's starting abundance ``ln_cfu0`` directly instead of leaving it to
+the extrapolation of the growth fit. It is cheap, one tube per library,
+replicate and pre-selection condition, so it can cover the whole library.
+
+``tfs-process-counts --presplit`` writes it, with columns ``library``,
+``replicate``, ``condition_pre``, ``genotype``, ``ln_cfu`` and ``ln_cfu_std``.
+Rows for genotypes not in the growth table are dropped. Genotypes in the
+growth table but not in the presplit table are kept and simply have no
+presplit constraint.
 
 .. code-block:: bash
 
-    tfs-configure-model binding.csv --growth_df growth.csv
-
-base_growth Data (``base_growth_df``)
-========================================
-
-**Role**
-
-Direct, reference-condition growth-rate measurements for a small subset
-of genotypes (``wt`` at minimum). The ``condition_growth`` components'
-per-condition baseline (*k*) and the per-genotype pleiotropic effect
-(``dk_geno``) are only jointly identified up to an additive constant —
-``k += C, dk_geno -= C`` leaves the growth likelihood unchanged. Because
-``dk_geno`` is fixed to 0 for ``wt``, a direct measurement of wt's growth
-rate anchors the new ``k_ref`` scalar via
-``rate_obs ~ Normal(k_ref + dk_geno, rate_std)``, resolving this
-identifiability confound. See the **Per-condition growth priors** section
-of ``CLAUDE.md`` for the full mechanism (this is the complementary,
-insufficient-alone anchor; per-condition priors pinned during
-``tfs-prefit-calibration`` are the primary fix).
-
-**Scale**
+    tfs-configure-model --growth_df growth.csv --library_config library_config.yaml \
+        --presplit_df presplit.csv
 
-Small. ``wt`` is required after filtering against ``growth_df``; beyond
-that, ``base_growth_df`` may cover anywhere from just ``wt`` up to a
-handful of additional genotypes. Multiple rows for the same genotype are
-combined via inverse-variance weighting.
+Base growth data
+----------------
 
-**Format**
+``--base_growth_df`` holds direct growth-rate measurements in a reference
+condition for a few genotypes, wt at minimum. The condition baselines ``k``
+and the genotype effects ``dk_geno`` are identified only up to a shared
+constant: ``k + C`` with ``dk_geno - C`` leaves the growth likelihood
+unchanged. wt's ``dk_geno`` is fixed at 0, so a measurement of wt's rate
+pins a reference rate ``k_ref`` through
+``rate ~ Normal(k_ref + dk_geno, rate_std)``. This anchor is complementary;
+the per-condition priors set by ``tfs-prefit-calibration``, or by
+``--growth_priors``, are the main fix.
 
 .. list-table::
    :header-rows: 1
-   :widths: 25 75
+   :widths: 26 74
 
    * - Column
      - Meaning
    * - ``genotype``
-     - Genotype string; rows not present in ``growth_df`` are dropped
+     - Genotype name. Rows not in the growth table are dropped, and wt must
+       remain.
    * - ``rate``
-     - Measured reference-condition growth rate
+     - Measured growth rate in the reference condition, per minute.
    * - ``rate_std``
-     - Standard deviation of ``rate``. **Must be strictly positive** — a
-       value of 0 (e.g. from an unset ``noise`` in simulated data) causes
-       a division-by-zero that surfaces as a NaN-prior crash the first
-       time the model is traced (at ``tfs-prefit-calibration`` or
-       ``tfs-fit-model``, not at ``tfs-configure-model`` time).
+     - SD of ``rate``. It must be greater than 0.
 
-Passed via the optional flag:
+Several rows for one genotype are combined by inverse-variance weighting.
 
 .. code-block:: bash
 
-    tfs-configure-model binding.csv --growth_df growth.csv \
+    tfs-configure-model --growth_df growth.csv --library_config library_config.yaml \
         --base_growth_df base_growth.csv
 
-Pre-split Data (``presplit_df``)
-===================================
-
-**Role**
-
-Sequencing-derived abundance measurements taken *before* the library is
-divided into separate selection conditions (t = -t_pre). Genotypes it
-covers get a direct constraint on their initial population
-(ln_cfu\ :sub:`0`) instead of relying solely on the extrapolation implicit
-in the growth-rate fit.
+Congression rate
+----------------
 
-**Scale**
+``--transformation_lambda MEAN STD`` is not a data file. It is a measured
+congression rate, the tendency of a transformed cell to take up more than
+one plasmid, as a mean and SD in linear space. It sets a LogNormal prior on
+the ``mixture`` transformation's lambda. It is required when
+``--transformation_model`` is ``mixture`` and refused when it is ``single``,
+the default.
 
-Ideally the whole library, since it is collected from a single pooled
-sample at one timepoint (cheap relative to a full growth time-course).
-Genotypes present in ``growth_df`` but absent from ``presplit_df`` are
-not an error — they are kept and their ln_cfu\ :sub:`0` is masked out of
-this constraint.
-
-**Format**
-
-Produced by ``tfs-process-presplit``; see :doc:`process-raw` for the full
-column specification (``library``, ``replicate``, ``condition_pre``,
-``genotype``, ``ln_cfu``, ``ln_cfu_std``).
-
-Passed via the optional flag:
+Lambda is the rate of a zero-truncated Poisson. A transformant, a cell that
+took up at least one plasmid and survived selection, carries ``M`` plasmids
+with ``P(M = m) = Poisson(m; lambda) / (1 - exp(-lambda))``. A measured mean
+number of distinct plasmids per transformant,
+``E[M] = lambda / (1 - exp(-lambda))``, has to be converted to lambda first.
+This is the same lambda as the simulator's
+``transformation_poisson_lambda``.
 
 .. code-block:: bash
 
-    tfs-configure-model binding.csv --growth_df growth.csv \
-        --presplit_df presplit.csv
+    tfs-configure-model --binding_df binding.csv --growth_df growth.csv \
+        --library_config library_config.yaml \
+        --transformation_model mixture --transformation_lambda 0.36 0.05
+
+Relative-X gauge
+----------------
+
+``--theta_gauge_conc C_LO C_HI`` applies only to ``--theta_model
+hill_relative``, which fits growth alone on a wt-relative scale X: wt's X is
+1 at ``C_LO`` and 0 at ``C_HI``. The default is the lowest and highest
+titrant concentration in the growth table, and the resolved values are
+written to the configuration. Other theta models refuse the flag.
+
+Structural data
+---------------
+
+``--thermo_data`` is required by the ``PddG`` and ``PnnC`` thermodynamic
+theta models and ignored by the others. For ``PnnC`` models it is the HDF5
+file written by ``scripts/generate_struct_ensemble.py``; see
+:doc:`ligandmpnn-features`. For ``PddG`` models it is a CSV with a mutation
+column and one column of prior mean ΔΔG per structure: ``H``, ``HD``, ``L``
+and ``LE2`` for the ``O2_C4`` models, and ``H``, ``HO``, ``L``, ``LO``,
+``HE2`` and ``LE2`` for the ``O2_C12`` models.
+
+The mutation column may be called ``mut``, ``Mutation`` or ``mutation``, with
+labels like ``H29A``. An unnamed pandas index column is dropped. Columns the
+model does not use are ignored, and the loader prints their names. A missing
+structure column is an error that lists the columns found.
+``--thermo_column_map HD=HO`` reads a model structure from a column with
+another name. Rows that are not single mutations are skipped.
+
+**Units and sign.** ΔΔG\ :sub:`X` is the change a mutation makes to the free
+energy of state X, so a positive value destabilizes X. For an equilibrium
+constant K\ :sub:`A→B` = [B]/[A] the models use
+Δln K\ :sub:`A→B` = ΔΔG\ :sub:`A` − ΔΔG\ :sub:`B`, which needs ΔΔG in units of
+kT. By default the CSV values are used as they are, in kT.
+``--thermo_ddG_units kcal/mol`` (or ``kJ/mol``) divides them by RT once at
+load, at ``--thermo_temperature_K`` (default 298.15 K). At 298 K one kcal/mol
+is 1.69 kT, so kcal/mol values read as kT understate every effect by that
+factor. A predictor that reports folding ΔG, where negative means more
+stable, must be sign-flipped first.
+
+**Projection.** The effector constants are per site, and a doubly liganded
+structure XE2 has weight K\ :sub:`x_e`\ :sup:`2`\ ·e\ :sup:`2` relative to X,
+so ΔΔG\ :sub:`X` − ΔΔG\ :sub:`XE2` is twice Δln K\ :sub:`x_e`. The models
+map ΔΔG to constant shifts as follows:
 
-transformation_lambda
-========================
-
-**Role**
-
-Not a data file — a single experimentally measured ``(mean, std)`` pair
-describing plasmid congression (multiple plasmids entering one cell
-during transformation), in linear space (e.g. ``(0.36, 0.05)``). It is
-used to moment-match a LogNormal prior for the transformation model's
-lambda parameter, replacing the manual step of hand-editing the priors
-and guesses CSVs with rescaled log-space values. See
-:ref:`model-components`'s Transformation Correction section for what the
-``empirical``/``logit_norm``/``single`` models each do with it.
-
-**Scale**
-
-A single measurement (one mean, one uncertainty) from an independent
-congression-rate experiment — not a per-genotype or per-row dataset.
-
-**Format**
-
-Passed directly as two floats via ``--transformation_lambda``, required
-when ``--transformation_model`` is ``empirical`` or ``logit_norm``, and
-forbidden (must be omitted) when it is ``single``:
+.. list-table::
+   :header-rows: 1
+   :widths: 25 35 40
+
+   * - Constant
+     - ``O2_C12`` structures
+     - ``O2_C4`` structures
+   * - K\ :sub:`h_l` (C4: K\ :sub:`HL`)
+     - ΔΔG\ :sub:`H` − ΔΔG\ :sub:`L`
+     - ΔΔG\ :sub:`H` − ΔΔG\ :sub:`L`
+   * - K\ :sub:`h_o` (C4: K\ :sub:`op`)
+     - ΔΔG\ :sub:`H` − ΔΔG\ :sub:`HO`
+     - ΔΔG\ :sub:`H` − ΔΔG\ :sub:`HD`
+   * - K\ :sub:`h_e`
+     - (ΔΔG\ :sub:`H` − ΔΔG\ :sub:`HE2`)/2
+     - fixed at 0
+   * - K\ :sub:`l_o`
+     - ΔΔG\ :sub:`L` − ΔΔG\ :sub:`LO`
+     - fixed at 0
+   * - K\ :sub:`l_e` (C4: K\ :sub:`E`)
+     - (ΔΔG\ :sub:`L` − ΔΔG\ :sub:`LE2`)/2
+     - (ΔΔG\ :sub:`L` − ΔΔG\ :sub:`LE2`)/2
+   * - K\ :sub:`u` (``U1`` only)
+     - ΔΔG\ :sub:`H`
+     - ΔΔG\ :sub:`H`
+
+Before 2026-10-11 the ``O2_C4`` K\ :sub:`E` row was ΔΔG\ :sub:`L` −
+ΔΔG\ :sub:`LE2`, for a two-effector M\ :sup:`-2` constant.
+
+**Numbering and coverage.** The loader matches CSV labels to the library's
+mutations and prints how many it covers. It stops with an error when the
+CSV covers none of them, or when the CSV's wild-type residue at a library
+position differs from the library's. Both usually mean the two number
+residues differently. The error suggests the offset that lines them up, and
+``--thermo_residue_offset N`` adds N to every CSV residue number, for
+example 1 for a 0-based file. A library mutation absent from the CSV gets a
+prior mean of 0, no structural prediction. When more than
+``--thermo_max_missing_fraction`` of them (default 0.1) are absent, a
+warning lists them.
+
+The ``PnnC`` HDF5 path applies the same residue offset. Every library
+residue must be present in every structure, since there is no neutral
+feature value for a missing one. When the file stores ``wt_aa``, the wt
+residues are checked against the library too.
+
+The five ``--thermo_*`` flags are stored in the configuration as
+``components.thermo_options``. A configuration without it reads the file as
+before: kT, file numbering, model column names.
+
+A fit uses the CSV values as the prior means of each mutation's ΔΔG and
+estimates one SD σ_s per structure for how far the true ΔΔG stray from
+them, ΔΔG ~ Normal(CSV value, σ_s). The data see only the constants, so the
+fit puts this prior on the constant shifts it implies: each mutation's
+Δln K is multivariate normal around the projected CSV values, with the
+covariance that per-structure scatter produces. σ_s has a LogNormal prior
+whose median is 1 kT and whose 95% range is about 0.1 to 10 kT
+(``theta_ddG_sigma_loc`` = 0 and ``theta_ddG_sigma_scale`` = 1.2 in the
+priors file, in log kT). Every σ_s is identified by how the constants vary
+together, in every model. The fit starts at the projected CSV values and
+σ_s = 1 kT. Under MAP, σ_s settles near the scatter of the fitted constants
+about the CSV. A σ_s that runs toward 0 means the data cannot resolve the
+constants more finely than the CSV gives them, not that the CSV is exact.
+This can happen when the data are weak.
+
+To hold σ_s at a known value instead of estimating it, set
+``theta_ddG_sigma_fixed`` to that value in kT, for example
+``--set_priors theta_ddG_sigma_fixed=1.69``. The value is in kT whatever
+``--thermo_ddG_units`` says. Every structure gets the same value; there is
+no per-structure hold. The fit then has no σ_s parameters, the LogNormal
+prior is unused, and the extracted ``ddG_sigma`` table reports the held
+value. The default 0 estimates σ_s. A held σ_s suits a simulation study
+that compares a fit told the true noise with one that estimates it.
+
+The fit reports a per-structure ΔΔG for each mutation. In the models with
+an unfolded state (``U1``) it follows from the constants exactly. In the
+models without one (``U0``) every constant is a difference of structures, so
+a shift shared by all structures changes nothing the data see. There the
+reported ΔΔG is the most probable per-structure ΔΔG given the constants and
+the prior, and its shared shift comes from the CSV, not from the data.
+Compare U0 fits on their constants. To test whether the structural prior
+helps, simulate with the simulate-config key ``ddG_truth_sd`` (see
+:doc:`simulation`), which sets how far the simulated truth sits from the
+same CSV, and fit with the unchanged CSV.
+
+Prior inputs
+------------
+
+``tfs-configure-model`` writes every prior at its component default into
+``{out_prefix}_priors.csv``. Three flags change it at configure time, so the
+file is never edited by hand. Growth priors are applied first, then
+``--set_priors``.
+
+**--set_priors name=value ...** sets scalar priors. A name is a full row
+name of the priors CSV, such as
+``growth.sample_offset.sigma_fixed``, or a dotted suffix that matches
+exactly one row, such as ``sigma_fixed``. An unknown or ambiguous name is an
+error, and per-condition priors are set with ``--growth_priors`` instead.
+Common uses: ``sigma_fixed=0.17`` holds the SD of the level tube offsets;
+``theta_log_hill_n_hyper_scale_fixed=0.5`` holds the population SD of
+``log(hill_n)``; for ``hill_relative``, ``theta_X_low_hyper_scale_fixed``,
+``theta_X_delta_hyper_scale_fixed`` and
+``theta_log_hill_K_hyper_scale_fixed`` hold the population SDs of its other
+parameters; for the ``PddG`` and ``PnnC`` models,
+``theta_ddG_sigma_fixed`` holds the per-structure ΔΔG scale σ_s (full name
+``theta.theta_ddG_sigma_fixed``). A value of 0 leaves the SD learned.
+
+**--growth_priors table.csv** sets per-condition priors for ``linear``
+condition growth. The table has a ``condition_rep`` column, the condition
+name as it appears in ``condition_pre`` or ``condition_sel``, and any of
+``k_loc``, ``k_scale``, ``m_loc`` and ``m_scale``, in rates per minute. With
+a ``replicate`` column each row applies to one replicate; without it, to
+every replicate. Conditions and values the table leaves out keep the
+defaults, and a condition the model does not have is an error.
+``m_scale`` sets the scale for selective and non-selective conditions
+alike. Use it where ``tfs-prefit-calibration`` does not run, such as a
+growth-only model; the pre-fit overwrites ``k_loc`` and ``m_loc``.
+
+.. code-block:: text
+
+    condition_rep,k_loc,k_scale,m_loc,m_scale
+    kanR+kan,0.015,0.002,-0.010,0.003
+    kanR-kan,0.020,0.002,,
+
+**--growth_priors_wt_rates rates.csv** derives those priors for
+``hill_relative`` from wt monoculture growth rates. The columns are
+``condition_sel``, ``titrant_conc``, ``rate_mean``, ``rate_sd`` and
+``num_replicates``, with one row at each gauge concentration for every
+listed condition. In the X gauge wt grows at ``k`` at ``C_HI`` and at
+``k + m`` at ``C_LO``, so ``k`` is the rate at ``C_HI`` and ``m`` is the rate at
+``C_LO`` minus ``k``. Each SD is the standard error of the replicate mean,
+floored at ``--growth_priors_sd_floor`` (default 0.002 per minute). List only
+the conditions where the monoculture stands for the library's wt. These
+values override a ``--growth_priors`` table for the conditions they list,
+and they cannot be combined with a per-replicate table.
 
 .. code-block:: bash
 
-    tfs-configure-model binding.csv --growth_df growth.csv \
-        --transformation_model empirical \
-        --transformation_lambda 0.36 0.05
+    tfs-configure-model --growth_df growth.csv --library_config library_config.yaml \
+        --theta_model hill_relative \
+        --growth_priors_wt_rates wt_rates.csv \
+        --set_priors sigma_fixed=0.17 theta_log_hill_n_hyper_scale_fixed=0.5
diff --git a/docs/source/model.rst b/docs/source/model.rst
new file mode 100644
index 00000000..f80dfa57
--- /dev/null
+++ b/docs/source/model.rst
@@ -0,0 +1,609 @@
+=========
+The model
+=========
+
+``tfscreen.tfmodel`` is a hierarchical Bayesian model written in JAX and
+NumPyro. It infers each genotype's operator occupancy *θ* as a function of
+titrant concentration from growth data, binding data or both. This page
+describes the generative model and every component that can be swapped into
+it. :doc:`fitting` describes how to fit it, and :doc:`model-inputs` describes
+the data it reads.
+
+The growth equation
+===================
+
+Each genotype *g* grows in a tube at a rate set by its occupancy. With the
+default components, linear condition growth and an instant transition between
+the pre-growth and selection phases, the predicted abundance is
+
+.. math::
+
+   \ln \mathrm{cfu} = \ln \mathrm{cfu}_0
+       + (k_\mathrm{pre} + dk_g + A_g\, m_\mathrm{pre}\, \theta_g)\, t_\mathrm{pre}
+       + (k_\mathrm{sel} + dk_g + A_g\, m_\mathrm{sel}\, \theta_g)\, t_\mathrm{sel}
+       + \delta_\mathrm{tube}
+
+*k* and *m* are the baseline growth rate and the occupancy slope of each
+condition, shared by every genotype. *dk_g* is the genotype's pleiotropic
+growth effect, independent of the transcription factor. *A_g* is its TF
+activity. *θ_g* is its occupancy at the tube's titrant concentration.
+*δ_tube* is an offset shared by every genotype in one tube. Growth rates are
+per minute and times are in minutes.
+
+Each term comes from a component chosen at configuration time. The
+``condition_growth`` component sets how *k*, *m* and *θ* combine, the
+``growth_transition`` component sets how the two phases join, and so on down
+the list below. With the ``mixture`` transformation each genotype's cells are
+split into a clean class and several congressed classes, each grown through
+the same equation, and the classes are mixed as
+
+.. math::
+
+   \ln \mathrm{cfu} = \ln \mathrm{cfu}_0
+       + \log \sum_c w_c \exp(G_c) + \delta_\mathrm{tube}
+
+where *G_c* is the growth term of class *c* and *w_c* its weight.
+
+The predicted ``ln_cfu`` is observed through the growth likelihood. Binding
+data, when supplied, observe *θ* directly. Pre-split and base-growth data,
+when supplied, add their own observations of the starting abundance and of
+reference growth rates.
+
+.. _model-components:
+
+Components
+==========
+
+``tfs-configure-model`` selects one component per axis with a
+``--<axis>_model`` flag and writes the choice into the ``components:`` block
+of the configuration YAML. Every option, with its default, is in
+:doc:`cli`. The defaults are the current recommendations for a joint
+growth and binding model.
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 25 45
+
+   * - Flag
+     - Default
+     - Options
+   * - ``--condition_growth_model``
+     - ``linear``
+     - ``linear``, ``power``, ``saturation``
+   * - ``--growth_transition_model``
+     - ``instant``
+     - ``instant``, ``memory``, ``baranyi``, ``baranyi_k``, ``baranyi_tau``,
+       ``two_pop``
+   * - ``--ln_cfu0_model``
+     - ``hierarchical``
+     - ``hierarchical``, ``hierarchical_factored``
+   * - ``--dk_geno_model``
+     - ``hierarchical_geno``
+     - ``hierarchical_geno``, ``fixed``, ``pinned``
+   * - ``--activity_model``
+     - ``fixed``
+     - ``fixed``, ``hierarchical_geno``, ``horseshoe_geno``,
+       ``hierarchical_mut``, ``horseshoe_mut``
+   * - ``--theta_model``
+     - ``hill_geno``
+     - ``hill_geno``, ``hill_relative``, ``hill_mut``, ``categorical_geno``,
+       ``thermo.*``
+   * - ``--theta_rescale_model``
+     - ``passthrough``
+     - ``passthrough``, ``logit``
+   * - ``--transformation_model``
+     - ``single``
+     - ``single``, ``mixture``
+   * - ``--theta_growth_noise_model``
+     - ``zero``
+     - ``zero``, ``beta``, ``logit_normal``
+   * - ``--theta_binding_noise_model``
+     - ``zero``
+     - ``zero``, ``beta``, ``genotype_offset``
+   * - ``--growth_noise_model``
+     - ``zero``
+     - ``zero``, ``normal_kt``
+   * - ``--sample_offset_model``
+     - ``level``
+     - ``level``, ``zero``, ``normal``
+   * - ``--growth_likelihood``
+     - ``counts``
+     - ``counts``, ``lncfu``
+
+Each component's priors are written to ``{out_prefix}_priors.csv`` with rows
+named ``{group}.{component}.{field}``, such as
+``growth.sample_offset.sigma_fixed``. Change a scalar prior with
+``tfs-configure-model --set_priors name=value`` rather than by editing the
+file. A name can be the full row name or any unique dotted suffix of it.
+
+Condition growth
+----------------
+
+The ``condition_growth`` component maps occupancy to a growth rate in each
+condition. A condition is one marker and selection combination, such as
+``kanR+kan`` or ``pheS+4CP``.
+
+``linear`` (default)
+   *g = k + dk_g + A·m·θ*. One *k* and one *m* per condition. Control
+   conditions, those without selection, get a tight prior on *m* set by
+   ``m_scale_minus``, default 0.001. Selection conditions get a looser one
+   set by ``m_scale_plus``, default 0.01.
+``power``
+   *g = k + dk_g + A·m·θ*\ :sup:`n`, with a per-condition exponent *n*.
+``saturation``
+   *g = min + dk_g + A·(max − min)·θ/(1 + θ)*, with per-condition minimum and
+   maximum rates.
+
+``power`` and ``saturation`` assume *θ* lies in [0, 1], so pair them with the
+``passthrough`` rescale. ``--growth_shares_replicates`` gives every replicate
+the same condition parameters. Without it each replicate has its own.
+
+The baseline *k* is only identified jointly with *dk_g*: adding a constant to
+every *k* and subtracting it from every *dk_g* leaves the likelihood
+unchanged. Wt's *dk_g* is fixed at 0, but that single anchor is diluted by the
+size of the library, and the whole system can slide. The fix is a
+per-condition prior on the baselines. Every prior location and scale of these
+components accepts a scalar, applied to every condition, or one value per
+condition. ``tfs-prefit-calibration`` writes per-condition *k* and *m*
+locations into the priors file, as indexed rows labeled by ``condition_rep``,
+with a tight floored scale. Its ``--pin_m`` option clamps *m* to the
+calibrated value outright, because a soft prior on *m* can be overridden by the
+growth likelihood. *k* keeps a soft prior, since it carries real
+tube-to-tube variation. For a growth-only model, where the pre-fit does not
+run, set the per-condition priors at configuration with ``--growth_priors``
+or ``--growth_priors_wt_rates``. See :doc:`fitting`.
+
+Growth transition
+-----------------
+
+The ``growth_transition`` component joins the pre-growth and selection
+phases.
+
+``instant`` (default)
+   Each genotype switches to its selection rate at the start of selection.
+``memory``
+   Cells keep the pre-growth rate for a lag *τ = τ0 + k1/(θ + k2)*, then grow
+   at the selection rate. The lag depends on occupancy.
+``baranyi``
+   The rate moves from the pre-growth to the selection rate along a sigmoid in
+   time, with a per-condition midpoint *τ* and steepness.
+``baranyi_k``
+   As ``baranyi``, with the steepness reduced as the rate change grows.
+``baranyi_tau``
+   As ``baranyi``, with the midpoint delayed as the rate change grows.
+``two_pop``
+   Cells leave the pre-growth state at a constant per-cell rate, so the
+   culture is a mix of the two growth modes during the transition.
+
+Starting abundance
+------------------
+
+The ``ln_cfu0`` component sets each genotype's starting abundance.
+
+``hierarchical`` (default, recommended)
+   One starting abundance per replicate, pre-growth condition and genotype.
+   Each library class has its own pooled prior, and spiked genotypes and wt
+   have their own priors.
+``hierarchical_factored``
+   One genotype baseline per replicate, shared by every pre-growth
+   condition, plus one offset per tube. This is valid only when the
+   pre-growth conditions are split from one culture. In the screen, the kanR
+   and pheS libraries are transformed and grown separately, so each
+   genotype's starting abundance differs between them. On simulations of that
+   design the factored model pushed the difference into a confident
+   per-genotype error in *θ*, with 95% coverage of 0.09 above 1000 reads
+   against 0.80 for ``hierarchical``. ``tfs-configure-model`` refuses
+   ``hierarchical_factored`` when the growth table's ``library`` column shows
+   a replicate's pre-growth conditions come from different libraries.
+
+Pleiotropic growth effect
+-------------------------
+
+The ``dk_geno`` component sets *dk_g*, each genotype's effect on growth that
+does not go through the transcription factor.
+
+``hierarchical_geno`` (default)
+   A pooled, left-skewed prior: a shift minus a log-normal draw. Most
+   mutations are near neutral, a few help, and a long tail is deleterious.
+   Wt is fixed at 0.
+``fixed``
+   *dk_g* = 0 for every genotype.
+``pinned``
+   *dk_g* is fixed to supplied values for a subset of genotypes and 0 for the
+   rest. The values come from a CSV given as the orchestrator's
+   ``dk_geno_pins_file`` setting. ``tfs-configure-model`` has no flag for that
+   file, so this component is only reachable by building a
+   ``ModelOrchestrator`` in Python.
+
+Activity
+--------
+
+The ``activity`` component sets *A_g*, a factor on each genotype's occupancy
+term.
+
+``fixed`` (default)
+   *A* = 1 for every genotype.
+``hierarchical_geno``
+   One activity per genotype from a pooled log-normal prior.
+``horseshoe_geno``
+   One activity per genotype under a sparse horseshoe prior.
+``hierarchical_mut``
+   Log activity is a sum of per-mutation effects, plus pairwise epistasis with
+   ``--epistasis``, under hierarchical normal priors.
+``horseshoe_mut``
+   As ``hierarchical_mut``, under regularized horseshoe priors.
+
+A learned activity is refused with ``hill_relative`` and with the
+``mixture`` transformation's ``homodimer`` and ``heterodimer`` rules.
+
+Occupancy
+---------
+
+The ``theta`` component sets *θ* as a function of titrant concentration *c*.
+
+``hill_geno`` (default)
+   A Hill curve per genotype,
+   *θ = θ_low + (θ_high − θ_low)·sigmoid(n·(ln c − ln K))*. The parameters
+   are ``theta_low``, ``theta_high``, ``log_hill_K`` and ``hill_n``.
+   ``log_hill_K`` is the natural log of the midpoint concentration. Each
+   parameter is drawn from a population distribution whose location and
+   scale are learned.
+``hill_relative``
+   The Hill curve for a growth-only fit on a wt-relative scale *X* in place
+   of *θ*. See below.
+``hill_mut``
+   A Hill curve whose transformed parameters are wt's values plus a sum of
+   per-mutation effects, plus pairwise epistasis under a regularized
+   horseshoe prior when ``--epistasis`` is set. It can predict genotypes that
+   were never measured.
+``categorical_geno``
+   An independent *θ* at every titrant concentration, with no curve.
+``thermo.*``
+   Thermodynamic partition-function models. See
+   :ref:`thermo-naming` below. The ``PddG`` and ``PnnC`` variants need
+   ``--thermo_data``.
+
+The population SD of each Hill parameter is learned by default. On real
+data a learned SD can run away and stop shrinking poorly measured genotypes:
+a MAP on the development screen drove the SD of log(hill_n) to about 19, and
+*n* near 0.01 hid each genotype's response below the lowest nonzero
+concentration. ``theta_log_hill_n_hyper_scale_fixed`` holds that SD at a
+given value instead. A value of 0, the default, keeps it learned.
+
+.. code-block:: bash
+
+   tfs-configure-model ... --set_priors theta_log_hill_n_hyper_scale_fixed=0.5
+
+The relative-X fit
+~~~~~~~~~~~~~~~~~~
+
+Growth alone fixes an occupancy-like variable only up to an affine map,
+because *k* and *m* absorb any shift and scale. ``hill_relative`` therefore
+fits a wt-relative variable *X* with the Hill curve's form and real-valued
+baselines, ``X_low`` and ``X_delta``. Wt is gauged to *X* = 1 at a low
+concentration and *X* = 0 at a high one. ``--theta_gauge_conc c_lo c_hi``
+sets those two concentrations, by default the lowest and highest in the
+growth table. The resolved values are written to the configuration.
+
+``hill_relative`` is refused with binding data, with an activity other than
+``fixed``, a rescale other than ``passthrough``, condition growth other than
+``linear``, theta growth noise other than ``zero`` and, under the ``mixture``
+transformation, any congression *θ* rule but ``max``. Downstream tools know
+the output is *X*: ``tfs-predict-theta`` adds a ``theta_scale`` column set
+to ``X``, and epistasis on *X* is defined only on the additive scale.
+
+Besides ``theta_log_hill_n_hyper_scale_fixed``, ``hill_relative`` takes
+``theta_X_low_hyper_scale_fixed``, ``theta_X_delta_hyper_scale_fixed`` and
+``theta_log_hill_K_hyper_scale_fixed``. Learned, these ran to 3.6, 4.3 and 11
+on the development screen, against about 0.5, 0.5 and 0.7 among
+well-measured genotypes, so the fit shrank nothing.
+
+Occupancy rescale
+-----------------
+
+The ``theta_rescale`` component transforms *θ* before it enters condition
+growth.
+
+``passthrough`` (default)
+   *θ* is used as is.
+``logit``
+   *θ* is replaced by log(*θ*/(1 − *θ*)), which expands the range near 0
+   and 1.
+
+Congression
+-----------
+
+The ``transformation`` component models congression, more than one plasmid
+entering a cell during transformation.
+
+``single`` (default)
+   Every cell carries one plasmid.
+``mixture``
+   Each genotype's cells are a mixture of clean cells and congressed cells
+   that also carry plasmids drawn from the bulk library. Transformants carry
+   a zero-truncated Poisson(λ) number of plasmids. A genotype's congressed
+   fraction is its ``bulk_fraction`` from the library table times
+   1 − P(M = 1). Each class has its own cell-level *θ*, activity and
+   *dk_g*, grows through the full equation, and the classes are mixed at the
+   level of ``exp(ln_cfu)``.
+
+``mixture`` needs ``--transformation_lambda MEAN SD``, the measured
+congression rate in linear space, which becomes a log-normal prior on λ. It
+is refused with ``single``. Three settings fix how a congressed cell combines
+its plasmids:
+
+``--congression_theta_rule``
+   ``homodimer`` (default) combines the plasmids' occupancies through the
+   partition function of a homodimer with equal shares, ``heterodimer``
+   through that of a heterodimer, and ``max`` takes the highest *θ*. The
+   ``homodimer`` and ``heterodimer`` rules require ``activity`` ``fixed``.
+``--congression_dk_rule``
+   ``dilution`` (default) takes the share-weighted mean of *dk_g*, ``min``
+   the worst variant's, and ``softmin`` interpolates between them with
+   ``--congression_dk_alpha``.
+
+The congressed term is averaged over fixed co-resident sets drawn once per
+genotype from the bulk library. ``congression_sets`` sets how many sets of
+each co-resident count, default ``[12, 3, 1]`` for one, two and three
+co-residents, and ``congression_seed`` (default 0) makes the draw
+reproducible. ``tfs-configure-model`` writes both into the configuration's
+``components:`` block and has no flag for them.
+
+The transformations ``empirical`` and ``logit_norm`` were removed. A
+configuration that names them is refused with a message.
+
+Noise
+-----
+
+``--theta_growth_noise_model`` adds noise to the *θ* that enters growth, and
+``--theta_binding_noise_model`` to the *θ* that binding observes.
+
+``zero`` (default)
+   No noise.
+``beta``
+   *θ* is drawn from a beta distribution around the predicted value.
+``logit_normal``
+   Normal noise with a learned SD is added to logit(*θ*). Growth only.
+``genotype_offset``
+   Binding only. Each binding genotype's binding *θ* is its curve plus one
+   offset of its own, the same at every titrant concentration:
+   *δ_g = τ z_g*, *z_g ~ Normal(0, 1)*, *τ ~ HalfNormal(0.2)* (learned, or
+   held with ``offset_scale_fixed``). It absorbs an in vivo/in vitro
+   difference or a curve's day-normalization error for one genotype
+   without moving the growth side; *τ* says how far binding and growth
+   disagree across genotypes.
+
+``--growth_noise_model`` adds noise to growth itself.
+
+``zero`` (default)
+   No extra noise.
+``normal_kt``
+   A learned global SD on accumulated growth, added in quadrature to
+   ``ln_cfu_std``. It works only with the ``lncfu`` likelihood.
+
+Tube offsets
+------------
+
+The ``sample_offset`` component adds *δ_tube*, a shift shared by every
+genotype in one tube. A tube is one sequenced sample: one replicate, time,
+condition and titrant concentration. Anything that moves every genotype in a
+tube together lands here, such as PCR and genotype-calling efficiency or an
+error in the tube's total cell count.
+
+``level`` (default)
+   One ``ln_cfu`` offset per tube with a learned SD. ``sigma_fixed`` > 0
+   holds the SD at that value. Learned, the SD grew from its 0.2 prior scale
+   to 0.53 on the first real-data fit, and the offsets took over the
+   population's growth from *k* and *m*. Holding it near the per-tube OD600
+   scatter, about 0.17, keeps the tube totals binding:
+   ``--set_priors sigma_fixed=0.17``.
+``zero``
+   No offset.
+``normal``
+   One growth-rate offset per tube, multiplied by the tube's elapsed time.
+
+Observations
+============
+
+Growth likelihood
+-----------------
+
+``--growth_likelihood`` sets how growth data are observed.
+
+``counts`` (default)
+   Reads are observed directly as negative binomial,
+   *reads ~ NegBin(μ, var = μ(1 + φ) + μ²/r)*, where
+   *log μ = ln(sample reads) + ln_cfu_pred − ln(sample cfu)*. That is the
+   predicted frequency times the tube's total reads. φ and 1/r are learned
+   with log-normal priors. No pseudocount is used. It needs a ``counts``
+   column, each tube's total reads and each tube's total cells, which
+   ``tfs-process-counts`` writes. It refuses a growth noise other than
+   ``zero``. Pair it with ``level`` tube offsets, since an error in a tube's
+   total otherwise moves every genotype in it. On simulations it cut *θ*
+   RMSE by 35-60% against ``lncfu`` at similar coverage.
+``lncfu``
+   Student-t on ``ln_cfu`` with the table's ``ln_cfu_std``.
+
+``ModelOrchestrator`` itself still defaults to ``lncfu`` and no tube
+offset, so that configurations written before the count likelihood existed
+read back unchanged. ``tfs-configure-model`` writes the new defaults
+explicitly.
+
+Other observations
+------------------
+
+Binding data, from ``--binding_df``, observe *θ* with a normal likelihood.
+Each binding observation counts once: ``--binding_weight`` scales the
+binding likelihood and defaults to 1. Until 2026-10-16 the default was the
+ratio of growth rows to binding rows, which made the binding data weigh as
+much as the whole growth dataset. A model without binding data has no
+binding sites at all.
+
+Pre-split data, from ``--presplit_df``, observe ``ln_cfu`` at the start of
+pre-growth and constrain the starting abundance. Base-growth data, from
+``--base_growth_df``, observe reference-condition growth rates as
+*rate ~ Normal(k_ref + dk_g, rate_std)* and anchor *dk_g*. See
+:doc:`model-inputs` for both.
+
+.. _model-naming:
+
+Naming conventions
+==================
+
+Components that infer one value per genotype carry a ``_geno`` suffix, such
+as ``hill_geno``. Components that build each genotype from per-mutation
+effects carry ``_mut``, such as ``hill_mut``. Components with no natural
+per-mutation form, such as ``fixed``, have no suffix.
+
+.. _thermo-naming:
+
+Thermodynamic occupancy models
+------------------------------
+
+Occupancy models built from an explicit partition function are named
+``thermo.{MODEL}.{PRIOR}``. *MODEL* has four fields and a letter:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 10 60
+
+   * - Field
+     - Meaning
+   * - ``O``
+     - Oligomeric state; ``O2`` is a homodimer
+   * - ``C``
+     - Number of conformational states
+   * - ``K``
+     - Number of independent equilibrium constants
+   * - ``U``
+     - ``U0`` has folded states only; ``U1`` adds a folding equilibrium
+
+The trailing letter tells apart models with the same counts and implies no
+order. The implemented models are:
+
+* ``O2_C12_K5_U0_a`` (M3): the standard MWC lac repressor homodimer with
+  five constants.
+* ``O2_C12_K5_U1_a``: the same with a folding equilibrium.
+* ``O2_C4_K3_U0_a`` (M2): M3 restricted to three constants.
+* ``O2_C4_K3_U1_a``: the same with a folding equilibrium.
+
+The C4/K3 labels are historical. They named an earlier partition function
+for M2, which this one replaced on 2026-10-11, and no longer count M2's
+species. The registry keys keep them.
+
+**The MWC dimer.** The dimer is in a high operator affinity conformation H or
+a low affinity conformation L. K\ :sub:`h_l` = [L]/[H]. Conformation X binds
+operator with K\ :sub:`x_o` (M\ :sup:`-1`) and has two identical,
+independent effector sites, each with the microscopic (per-site) constant
+K\ :sub:`x_e` (M\ :sup:`-1`). The effector binding polynomial of
+conformation X is therefore (1 + K\ :sub:`x_e`\ ·e)\ :sup:`2` =
+1 + 2K\ :sub:`x_e`\ ·e + K\ :sub:`x_e`\ :sup:`2`\ ·e\ :sup:`2`. The 2
+counts the two ways to fill one site. Within a conformation the two sites do
+not interact, so the second macroscopic constant is one quarter of the first.
+Cooperativity comes only from the conformational equilibrium, when
+K\ :sub:`l_e` > K\ :sub:`h_e` and K\ :sub:`h_l` > 1. The ``U1`` models add an
+unfolded state U, K\ :sub:`u` = [U]/[H], that binds nothing.
+
+Until 2026-10-11 M3 used the polynomial of Sochor (2014),
+1 + K·e + K\ :sup:`2`\ ·e\ :sup:`2`, which lacks the factor of 2. That sets
+the ratio of the two macroscopic constants to 1 instead of 1/4, a fixed
+fourfold cooperativity inside each conformation that no constant can remove.
+M2 used a third form, one-step binding of two effectors to L with an
+M\ :sup:`-2` constant.
+
+M3 fits all five constants: K\ :sub:`h_l`, K\ :sub:`h_o`,
+K\ :sub:`h_e`, K\ :sub:`l_o` and K\ :sub:`l_e`. M2 is M3 with
+K\ :sub:`h_e` = 0 (H does not bind effector) and K\ :sub:`l_o` = 0 (L does
+not bind operator). Its three constants keep their older names:
+``ln_K_op`` is ln K\ :sub:`h_o`, ``ln_K_HL`` is ln K\ :sub:`h_l` and
+``ln_K_E`` is ln K\ :sub:`l_e`, a per-site constant in M\ :sup:`-1`. Both
+families compute θ with the same function, so M2 is nested in M3 exactly.
+
+TF is in large excess over operator: ``theta_tf_total_M`` defaults to
+1e-5 M of monomer, about 400 times a typical operator concentration. The
+models therefore leave operator-bound TF out of the TF mass balance, and the
+operator concentration does not enter them. With r = tf_total/2 the dimer
+concentration and F(e) the polynomial over all states not bound to operator,
+the free apo H dimer is h = r/F(e), the operator sees
+W = h·(K\ :sub:`h_o`\ ·P\ :sub:`H` + K\ :sub:`h_l`\ ·K\ :sub:`l_o`\ ·P\ :sub:`L`),
+and θ = W/(1 + W). Free effector e comes from the effector mass balance, a
+cubic in e, so effector bound to TF is subtracted from the total. At 1e-5 M
+TF this matters below about 10 µM effector. The equations are in
+``theta/thermo/mwc.py``.
+
+**Default wild type.** ``theta_tf_total_M`` is 1e-5 M monomer in both
+families, the PI's estimate of the in vivo TF level. The M3 defaults aim
+for strong repression and near-complete induction at that level. M3 takes
+K\ :sub:`h_l` = 6.3, K\ :sub:`h_o` = 4.2×10\ :sup:`8` M\ :sup:`-1` and
+K\ :sub:`l_o` = 0.1 M\ :sup:`-1` from Sochor (2014). At saturating IPTG only
+the doubly liganded HE2 and LE2 remain, so with
+ρ = K\ :sub:`l_e`/K\ :sub:`h_e` the operator sees
+
+    W\ :sub:`sat` = r·(K\ :sub:`h_o` + K\ :sub:`h_l`\ ·K\ :sub:`l_o`\ ·ρ\ :sup:`2`) / (1 + K\ :sub:`h_l`\ ·ρ\ :sup:`2`)
+
+and θ at saturation depends on ρ alone. We set θ(saturating) = 0.05,
+W\ :sub:`sat` = 1/19, and solved for ρ:
+
+    ρ\ :sup:`2` = (r·K\ :sub:`h_o` − W\ :sub:`sat`) / (K\ :sub:`h_l`\ ·(W\ :sub:`sat` − r·K\ :sub:`l_o`))
+
+which gives ρ ≈ 79.6. This departs from Sochor's fitted ρ ≈ 13.6. With
+Sochor's ρ the H conformation that remains with two effectors bound still
+occupies the operator at 1e-5 M TF, and θ falls only to 0.64. We then
+scaled K\ :sub:`h_e` and K\ :sub:`l_e` by one common factor, keeping ρ,
+so that the midpoint of the default wild-type θ(IPTG) curve is 80 µM IPTG.
+The midpoint is where θ is halfway between θ(0) and θ at saturating IPTG,
+with effector depletion at 1e-5 M TF. That gives K\ :sub:`h_e` ≈
+3.7×10\ :sup:`3` and K\ :sub:`l_e` ≈ 3.0×10\ :sup:`5` M\ :sup:`-1`, 0.39 times
+Sochor's K\ :sub:`l_e`. M2 takes the same K\ :sub:`h_l` and K\ :sub:`h_o`
+with K\ :sub:`l_e` = 2.5×10\ :sup:`5` M\ :sup:`-1`, also calibrated to an
+80 µM midpoint. Both start at θ(0) = 0.997. M3 falls to 0.05 at saturating
+IPTG and M2 to 0. The M3 curve has a Hill-like n of 1.7 (EC10 27 µM, EC90
+340 µM) and the M2 curve 2.1 (EC10 29 µM, EC90 230 µM).
+
+*PRIOR* sets how the equilibrium constants are parameterized:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 10 60
+
+   * - Name
+     - Description
+   * - ``PK``
+     - An independent normal prior on each log *K*, built from per-mutation
+       effects.
+   * - ``PddG``
+     - Priors centered on supplied ΔΔG estimates, a CSV given with
+       ``--thermo_data`` with one column per structure. Values are in kT
+       unless ``--thermo_ddG_units`` says otherwise; positive ΔΔG
+       destabilizes that structure. See :doc:`model-inputs`.
+   * - ``PnnC``
+     - A neural network that predicts per-conformation ΔΔG from structural
+       features, an HDF5 file given with ``--thermo_data``. See
+       :doc:`ligandmpnn-features`.
+
+In the ``U1`` models, ``PddG`` and ``PnnC`` take each mutation's effect on
+unfolding from the same ΔΔG values. A mutation shifts the free energy of each
+folded structure and leaves the unfolded state U unchanged. Because
+K\ :sub:`U` = [U]/[H], a mutation changes ln K\ :sub:`U` by its ΔΔG for H. A
+mutation that raises every folded structure by the same ΔΔG leaves the
+folded-state constants alone and raises K\ :sub:`U` by ΔΔG, a pure stability
+effect. The fit reports this shift per mutation as ``d_ln_K_U`` and per pair
+as ``epi_ln_K_U``, or ``d_ln_K_u`` and ``epi_ln_K_u`` for
+``O2_C12_K5_U1_a``. ``PK`` uses the same names for its free per-mutation
+unfolding shift. Before 2026-10-10 these models held K\ :sub:`U` at its wt
+value for every genotype, which moved U together with H and left every
+mutation's stability unchanged.
+
+Full names look like ``thermo.O2_C4_K3_U0_a.PK`` or
+``thermo.O2_C12_K5_U1_a.PnnC``.
+
+All the thermodynamic models read ``titrant_conc`` in mM, the units of the
+growth and binding tables, and multiply it by the prior
+``theta_conc_unit_scale`` (default 1e-3) so that the equations run in M.
+Every operator and effector constant is in M\ :sup:`-1`, and
+``theta_tf_total_M`` is in M of monomer. Priors files and checkpoints for
+these models written before 2026-10-11 hold constants with the older
+meanings. A priors file still reads, its ``theta_op_total_M`` row is
+ignored, and its old values would be taken with the new meanings, so
+regenerate it with ``tfs-configure-model``. Old checkpoints are not
+supported.
diff --git a/docs/source/pipeline.rst b/docs/source/pipeline.rst
new file mode 100644
index 00000000..acfaf9ff
--- /dev/null
+++ b/docs/source/pipeline.rst
@@ -0,0 +1,201 @@
+========
+Pipeline
+========
+
+This page lists every step from sequencing reads to a posterior, the command
+for each and the file each one hands to the next. The pages linked from each
+step explain the choices. :doc:`cli` lists every flag.
+
+To analyze a screen, follow :doc:`screen-analysis`: it runs steps 3-6 as one
+command, ``tfs-fit-two-stage``, with the settings that gave calibrated
+intervals. This page describes the commands that one chains.
+
+Overview
+--------
+
+.. code-block:: text
+
+   reads (one fastq pair per tube)
+     │  tfs-process-fastq            library_config.yaml
+     ▼
+   counts/counts_<tube>.csv
+     │  tfs-process-counts           tube table, OD600 table, OD600 calibration
+     ▼                               (tfs-calibrate-od600 makes the calibration)
+   growth.csv
+     │  tfs-configure-model          library_config.yaml, optional binding.csv,
+     ▼                               growth priors, held prior values
+   tfs_configure_config.yaml + _priors.csv + _guesses.csv + _library.csv
+     │  tfs-prefit-calibration       joint models only: pins growth k and m
+     │  tfs-fit-model                MAP or SVI
+     ▼
+   tfs_fit_model_checkpoint.pkl + _params.npz
+     │  tfs-sample-posterior         Laplace (MAP) or guide draws (SVI)
+     ▼
+   tfs_posterior.h5
+     │  tfs-extract-params, tfs-predict-theta, tfs-predict-growth,
+     │  tfs-predict-epistasis
+     ▼
+   per-parameter and per-prediction tables with q<level> columns
+
+Every command prints its provenance when it starts: the tfscreen version, the
+git commit with a flag for uncommitted changes, and the command line. A
+command that takes ``--out_prefix`` also writes it to
+``{out_prefix}_provenance.json``. The model configuration and every fit
+checkpoint carry the same record.
+
+1. Count reads
+--------------
+
+Run ``tfs-process-fastq`` once per tube. It calls each read pair against the
+library described by the library YAML and writes ``counts_<f1 name>.csv``
+with one row per library genotype, plus an ``__unknown__`` row for reads that
+matched none.
+
+.. code-block:: bash
+
+   tfs-process-fastq library_config.yaml tube01_R1.fastq.gz tube01_R2.fastq.gz --out_dir counts
+
+The same library YAML goes to ``tfs-configure-model`` later. Keep one per
+experiment. See :doc:`process-raw`.
+
+2. Tube totals and the growth table
+-----------------------------------
+
+The model needs the number of cells in each tube. The lab measures OD600, so
+the totals come from an OD600 calibration made once per instrument and strain
+with ``tfs-calibrate-od600``. ``tfs-process-counts`` reads the tube table, the
+OD600 readings, the calibration and the culture volume, computes each tube's
+total and its uncertainty, and turns the counts into the growth table. All
+libraries go through in one call; the tube table's ``library`` column keeps
+them apart.
+
+.. code-block:: bash
+
+   tfs-calibrate-od600 replicates.csv plate_counts.csv --out_prefix od600_calibration
+   tfs-process-counts tube_table.csv counts \
+       --od600_file od600.csv \
+       --od600_calibration_file od600_calibration.yaml \
+       --tube_volume_mL 5 \
+       --out_prefix growth
+
+Check the tube labels with ``tfs-check-tubes``, which flags tubes whose
+genotype composition is out of order with their IPTG label, and record
+swaps, relabels and exclusions in a corrections file that
+``tfs-correct-tubes`` applies to the tube table before
+``tfs-process-counts`` (see :doc:`screen-analysis`). Pooling resequenced
+files of one tube belongs in the lab's own script. ``examples/process_raw/`` has a synthetic tube table, OD600
+table and count files to run these commands on. See :doc:`process-raw`.
+
+3. Configure the model
+----------------------
+
+``tfs-configure-model`` reads the data, checks it against the library and
+writes the model configuration. The defaults are the current recommendations:
+the count likelihood, one level offset per tube and a separate starting
+abundance per library. Priors that need values other than the defaults are
+set here, never by editing the priors file.
+
+A joint model, with binding data:
+
+.. code-block:: bash
+
+   tfs-configure-model --growth_df growth.csv --binding_df binding.csv \
+       --library_config library_config.yaml
+
+A growth-only model on the wt-relative X scale, with per-condition growth
+priors and held population SDs:
+
+.. code-block:: bash
+
+   tfs-configure-model --growth_df growth.csv \
+       --library_config library_config.yaml \
+       --theta_model hill_relative \
+       --growth_shares_replicates \
+       --growth_priors growth_priors.csv \
+       --set_priors sigma_fixed=0.17 theta_log_hill_n_hyper_scale_fixed=0.5
+
+See :doc:`model-inputs` for the inputs and :doc:`model` for the components.
+
+4. Fit
+------
+
+For a joint model, ``tfs-prefit-calibration`` first fits a small model to the
+binding genotypes and pins each condition's growth baseline and slope in the
+priors file. It refuses growth-only models; their growth priors come from step
+3. Then ``tfs-fit-model`` runs MAP or SVI until the convergence monitor stops
+it.
+
+.. code-block:: bash
+
+   tfs-prefit-calibration tfs_configure_config.yaml --seed 1
+   tfs-fit-model tfs_configure_config.yaml --seed 1 --analysis_method map
+
+On a full library, MAP is the route that has worked; the SVI guides slid
+along the growth slope and X ridge of the growth-only model. A MAP with level
+tube offsets runs in three stages by default, so the offsets cannot settle in
+a mode where they carry the population's growth. See :doc:`fitting`.
+
+5. Posterior
+------------
+
+``tfs-sample-posterior`` turns a checkpoint into posterior draws. For a MAP
+checkpoint it builds a Laplace approximation. ``--laplace auto`` (the default)
+uses the full Laplace up to 20,000 parameters and the arrowhead Laplace above,
+which gives the same Gaussian computed one genotype at a time. Directions it
+had to hold at the MAP are written to ``{out_prefix}_held_directions.csv``,
+because they have no interval.
+
+.. code-block:: bash
+
+   tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+       --skip_growth_observations
+
+6. Results
+----------
+
+The posterior file feeds the extraction and prediction commands. Each writes
+bare quantile columns (``q0.025``, ``q0.5``, ``q0.975`` and so on).
+
+.. code-block:: bash
+
+   tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+   tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+   tfs-predict-epistasis tfs_configure_config.yaml tfs_posterior.h5
+
+A growth-only relative fit predicts X rather than an occupancy, so its
+epistasis is only defined on the additive scale: pass
+``tfs-predict-epistasis --scale add``. ``tfs-cat-response`` classifies the
+response curves, ``tfs-extract-epistasis``
+computes epistasis from any long-form table, and ``tfs-compare-runs``
+measures agreement between runs. See :doc:`downstream`.
+
+7. Check the fit
+----------------
+
+``tfs-summarize-fit`` gathers a run's diagnostics from its directory: the
+loss history, observed against predicted growth, and, for a fit with tube
+offsets, the offsets in prior SDs with a flag for offsets that trend with
+titrant or time. A flagged fit has offsets carrying growth the model leaves
+out, so read its curves with care. On a simulation it also compares every
+parameter and prediction with the truth. See :doc:`summarize-fit`.
+
+.. code-block:: bash
+
+   tfs-summarize-fit .
+
+Simulations
+-----------
+
+``tfs-simulate`` writes a simulated experiment and its ground truth from one
+YAML file. It writes the experiment in the raw formats a lab's data come in,
+one counts file per tube, the tube table and, with an ``od600`` block, the
+OD600 table and calibration, so a simulation enters the pipeline at step 2
+through the same ``tfs-process-counts`` command as real data; the command is
+printed. (Step 1 is replaced by the simulator's own sequencing model.) Its
+growth table, written directly, is the same table and can enter at step 3.
+A simulation can also follow a real experiment's tube table (the ``design``
+key), so the same chain runs on a simulated copy of the experiment with only
+the data paths changed. A full-size simulation of a real screen is large,
+tens of GB of memory and over an hour for one seed. ``tfs-setup-sim-grid``
+and ``tfs-setup-grid`` lay out grids of simulations and fits. See
+:doc:`simulation` and :doc:`grid`.
diff --git a/docs/source/process-raw.rst b/docs/source/process-raw.rst
index 768dfb19..e9225e61 100644
--- a/docs/source/process-raw.rst
+++ b/docs/source/process-raw.rst
@@ -1,205 +1,476 @@
 ===================
-Processing Raw Data
+Processing raw data
 ===================
 
-A typical TF screen involves growing bacteria transformed with a plasmid-encoded
-library of TF variants under one or more selection conditions (e.g. antibiotic resistance
-driven by a TF-regulated promoter). At each time-point an aliquot is
-plated to measure total colony-forming units (CFU) and deep-sequenced so
-that the absolute abundance of every genotype can be determined. The raw
-inputs to the pipeline are paired-end FASTQ files (one pair per sample) and
-a sample metadata table (``sample_df``) that links each sequenced tube to its
-biological context and its measured CFU count. The pipeline converts read
-counts into per-genotype log-CFU estimates (``ln_cfu``) that feed the
-hierarchical Bayesian growth model.
-
-There are three primary scripts for processing raw data:
-
-1. ``tfs-process-fastq``: Analyses paired-end FASTQ files to count the
-   occurrence of each genotype.
-2. ``tfs-process-counts``: Aggregates counts across multiple samples and
-   computes adjusted log-counts (``ln_cfu``) for downstream modelling.
-3. ``tfs-process-presplit``: Like ``tfs-process-counts``, but for the
-   pre-split time-point (before the library is divided into separate
-   selection conditions). The output anchors the initial genotype
-   abundances used by the growth model.
-
-Configuration File (run_config.yaml)
--------------------------------------
-
-``tfs-process-fastq`` requires a ``run_config.yaml`` file describing the
-library of expected sequences. You can view or download an
-:download:`example run_config.yaml <../../examples/process_raw/library_config.yaml>` file. Expected fields:
-
-* ``reading_frame``: Amino acid reading frame offset (0, 1, or 2).
-* ``first_amplicon_residue``: Amino acid residue number for the first in-frame
-  residue.
-* ``wt_seq``: The wildtype nucleic acid sequence.
-* ``degen_sites``: Degenerate codon pattern the same length as ``wt_seq``
-  (e.g. ``NNT``, ``NNK``, or ``.`` for wildtype).
-* ``tiles``: Contiguous blocks of library components cloned together.
-  ``.`` indicates wildtype; each unique character besides ``.`` defines a
-  tile (blocks must be contiguous).
-* ``expected_5p`` / ``expected_3p``: Flanking sequences immediately upstream
-  and downstream of the amplicon.
-* ``tile_combos``: List of strings such as ``single-x`` or ``double-x-y``,
-  where ``x`` and ``y`` match characters in ``tiles``. ``single-x``
-  specifies all single-mutation variants in tile ``x``; ``double-x-y``
-  specifies all pairwise combinations between tiles ``x`` and ``y``.
-* ``spiked_seqs``: Specific nucleic acid sequences (not part of the combinatorial
-  library) that should be identified as controls.
-
-tfs-process-fastq
------------------
-
-Reads paired-end FASTQ files and counts the protein genotype observed in each
-read pair. Each read is matched against the predefined library after quality
-filtering and flanking-sequence detection.
+A screen grows bacteria carrying a plasmid library of transcription factor
+variants under one or more selection conditions. Each time-point is its own
+tube. Every tube is deep-sequenced, and its total cell count is estimated,
+usually from OD600. Together these give the absolute abundance of every
+genotype in every tube.
+
+The raw-data half of the pipeline has four steps:
+
+1. ``tfs-process-fastq`` calls the genotype of every read pair in one
+   tube's FASTQ files and writes a count file for that tube.
+2. ``tfs-calibrate-od600`` fits the lab's OD600-to-CFU calibration. This is
+   done once per reader, plate type, volume and strain, not once per
+   experiment.
+3. You write a tube table that names every sequenced tube and gives its
+   design: library, replicate, conditions, titrant and times. When the
+   data show that a tube's label is wrong, ``tfs-correct-tubes`` applies a
+   documented correction and writes a corrected copy.
+4. ``tfs-process-counts`` combines the count files, the tube table and each
+   tube's OD600 into the growth table that ``tfs-configure-model`` reads.
+   With ``--presplit`` it writes the presplit table instead.
+
+Every flag and default is listed in :doc:`cli`. The model's side of these
+tables is described in :doc:`model-inputs`.
+
+To analyze a screen, :doc:`screen-analysis` runs steps 3 and 4 in order,
+with ``tfs-check-tubes`` to find mislabeled tubes, and then the fit.
+
+One tube per time-point
+-----------------------
+
+The protocol:
+
+1. Transform the library.
+2. Grow the transformed culture to a set OD.
+3. Take the ``presplit`` sample and read its OD600.
+4. Dilute the culture by the same factor into every tube, one per
+   condition, titrant concentration and planned time-point. Every tube of a
+   replicate therefore starts from the same known total: the presplit
+   OD600 estimate divided by the dilution factor. For a titration this is a
+   grid. Rows are time-points and columns are IPTG concentrations.
+5. At each time-point, pull every tube in that row. Read each pulled tube's
+   OD600, then spin the tubes down and freeze them at once. The spread in
+   stop time within a row is seconds, which is negligible on the time scale
+   of growth.
+6. After the experiment, process all samples together. Thaw and extract DNA
+   in batches of about 20, so three batches for 60 samples. Then run one
+   PCR block for every sample, then sequence. Record each sample's
+   extraction batch. For the current dataset it was not recorded; batches
+   generally, but not reliably, followed processing order.
+
+Pre-growth and selection both happen in the tubes, after the split. OD600
+is read on three bioreplicates of the experiment and two are sequenced.
+
+Each tube's total CFU comes from its OD600 through an OD-to-CFU
+calibration. The calibration depends on the plate reader, plate, volume and
+strain, so each lab makes its own with ``tfs-calibrate-od600``. A
+calibration gives CFU per mL. The pipeline works in cells in the whole
+tube, so ``tfs-process-counts`` multiplies by the tube's culture volume:
+``ln_cfu`` is ``ln(cfu/mL x tube volume)``. Under the strongest selection a
+tube may never reach the reader's detection threshold. Its OD600 then bounds
+its total from above rather than measuring it.
+
+Why tubes and not aliquots: pulling aliquots from a single tube over time
+meant stopping the shaker, pipetting and returning the tube across many
+IPTG conditions. That disturbed the growth trajectories more than the
+tube-to-tube differences of this design do.
+
+Consequences for the model:
+
+- A sequenced sample *is* a tube, and the time-points of one condition come
+  from different tubes. Anything that affects a whole tube is independent
+  from one time-point to the next, not carried along a trajectory. That
+  includes its growth environment over ``t_pre + t_sel``, its OD600 reading
+  and its PCR. The model's per-tube offset (``sample_offset``) is indexed
+  the same way, one offset per replicate x time x condition x titrant. The
+  default, ``level``, is a constant shift of the tube's ``ln_cfu`` shared by
+  every genotype in it, with a learned SD. It absorbs the tube's composition
+  offset and any error in its supplied total. ``normal`` is the older
+  alternative, a growth-rate offset scaled by ``t_pre + t_sel``, and
+  ``zero`` adds no offset.
+- A growth difference shared by every cell in a tube changes every
+  genotype's abundance by the same factor, so it does not change the
+  genotype *frequencies* the reads measure. It enters ``ln_cfu`` only
+  through the tube's total CFU. This is why a total smoothed across
+  bioreplicates gives much steadier ``ln_cfu`` trajectories than each tube's
+  own OD600 estimate. Earlier analyses used the pooled mean per condition
+  and time-point.
+- Everything before the split, such as transformation and outgrowth, is
+  shared by all tubes of a replicate. It sets each genotype's starting
+  abundance, ``ln_cfu0``, which the ``presplit`` sample measures directly.
+- No step is shared by the tubes of one row and not by the others. Rows
+  are stopped within seconds and processed with everything else. The one
+  batched step is DNA extraction. Extraction yield does not change a
+  sample's genotype frequencies, so it should not shift ``ln_cfu``. If it
+  matters at all, it is through the amount of template going into PCR,
+  which changes how noisy the counts are.
+
+The library config
+------------------
 
-**Outputs** (written to ``out_dir``):
+One YAML file describes the screened library. Pass the same file to
+``tfs-process-fastq`` and to ``tfs-configure-model --library_config``.
+Nothing checks that the two saw the same file, and a genetics mismatch
+shows up only later, when ``tfs-configure-model`` finds genotypes the
+library does not contain. An example is
+:download:`examples/process_raw/library_config.yaml
+<../../examples/process_raw/library_config.yaml>`.
+
+``reading_frame``
+    Reading frame of the amplicon: 0, 1 or 2.
+``first_amplicon_residue``
+    Residue number of the first in-frame codon. It sets the numbering of
+    every genotype name, so a wrong value mismatches every mutant.
+``wt_seq``
+    The wildtype DNA sequence of the amplicon.
+``degen_sites``
+    Degenerate codon pattern, the same length as ``wt_seq``: ``.`` for a
+    wildtype base, or a code such as ``nnt`` or ``nnk`` at library codons.
+``tiles``
+    Contiguous blocks of library sites cloned together. ``.`` is wildtype
+    and each other character names a tile. Blocks must be contiguous.
+``expected_5p``, ``expected_3p``
+    Flanking sequences immediately upstream and downstream of the amplicon.
+    Read only by ``tfs-process-fastq``.
+``tile_combos``
+    Which variants the library holds: ``single-x`` is every single mutant in
+    tile ``x`` and ``double-x-y`` every pairwise combination between tiles
+    ``x`` and ``y``.
+``spiked_seqs``
+    DNA sequences of monoclonal controls spiked into the pool. Their
+    genotypes may also occur in the bulk sub-libraries.
+``library_mixture``
+    Relative amount of each sub-library in the pool, keyed by the
+    ``tile_combos`` entries plus ``spiked``. Only ratios matter. Read only by
+    ``tfs-configure-model``, which uses it to compute each genotype's share
+    of the pool and how much of it comes from the bulk libraries. Nothing in
+    the data can check these numbers, so give the best estimate of what
+    went into the pool, not just the design.
+
+Any other key is ignored, so a ``tfs-simulate`` config can serve as the
+library config of a simulated experiment.
+
+Counting reads: tfs-process-fastq
+---------------------------------
+
+``tfs-process-fastq`` reads one tube's paired FASTQ files, trims each read
+to the amplicon between its flanks, masks low-quality bases and calls the
+protein genotype against every sequence the library config allows. The
+library config comes first:
 
-* ``stats_{filename}.csv`` — overall read success/failure statistics.
-* ``counts_{filename}.csv`` — raw counts for each expected genotype.
+.. code-block:: bash
 
-**Usage:**
+    tfs-process-fastq library_config.yaml \
+        kanR_kanRpkan_0mM_t0_R1.fastq.gz kanR_kanRpkan_0mM_t0_R2.fastq.gz \
+        --out_dir counts
+
+It writes two files into ``--out_dir`` (default ``counts``), named after the
+read-1 file:
+
+- ``counts_<f1 name>.csv``, with columns ``genotype`` and ``counts``. It has
+  one row for every genotype in the library, zeros included, plus one
+  ``__unknown__`` row. ``__unknown__`` counts read pairs that oriented and
+  trimmed cleanly but matched no library genotype, or matched more than
+  one. It is not a genotype. ``tfs-process-counts`` adds it to the tube's
+  read depth, the denominator of every frequency, and then drops it.
+- ``stats_<f1 name>.csv``, with columns ``success``, ``result``, ``counts``
+  and ``fraction``: how many read pairs ended in each outcome, such as a
+  missing flank, a read too short after quality trimming, or a disagreement
+  between the forward and reverse reads. Check it when a tube has few
+  reads or a large ``__unknown__`` share.
+
+``tfs-process-counts`` finds a tube's count file by globbing
+``counts*<sample>*.csv``, so name the FASTQ files after the tubes. Each
+tube's name must match exactly one file. A tube named ``t1`` next to one
+named ``t10`` matches two files and is an error.
+
+The main tuning flags are ``--phred_cutoff`` (default 10),
+``--min_read_length`` (default 50), ``--allowed_num_flank_diffs`` (default 1)
+and ``--allowed_diff_from_expected`` (default 2). ``--num_workers`` defaults to
+-1, which uses every CPU but one, and ``--chunk_size`` (default 10000) is the
+number of reads sent to a worker at a time.
+
+The tube table
+--------------
+
+The tube table has one row per sequenced tube. It is a CSV, TSV or Excel
+file.
+
+.. list-table::
+   :header-rows: 1
+   :widths: 22 78
+
+   * - Column
+     - Meaning
+   * - ``sample``
+     - Unique tube name, matched to its count file.
+   * - ``library``
+     - The transformed library the tube came from. Optional; ``default``
+       when absent. kanR and pheS libraries are transformed and grown up
+       separately, so they are separate libraries.
+   * - ``replicate``
+     - Biological replicate.
+   * - ``condition_pre``
+     - Growth condition before selection, such as ``kanR-kan``.
+   * - ``t_pre``
+     - Minutes of pre-selection growth.
+   * - ``condition_sel``
+     - Selection condition, such as ``kanR+kan``.
+   * - ``t_sel``
+     - Minutes of selection growth.
+   * - ``titrant_name``
+     - The titrant, such as ``iptg``.
+   * - ``titrant_conc``
+     - Titrant concentration; 0 for none.
+   * - ``od600``
+     - The tube's OD600 reading. Optional; it can come from a separate
+       ``--od600_file`` instead.
+
+``tfs-process-counts`` itself needs only ``sample``, the tube totals and,
+with ``--presplit``, ``replicate`` and ``condition_pre``. Every other column
+is carried through to the output, and ``tfs-configure-model`` needs the
+design columns above. A selection condition is one that never appears as a
+``condition_pre`` within its library; the control arm repeats the
+pre-selection condition as its ``condition_sel``.
+
+Lab-specific cleanup belongs in the lab's own script, before the tube table
+is written: tubes resequenced and pooled, sample sheets merged. tfscreen
+takes the tube table as the record of what each tube is. When the data
+later show that a record is wrong, correct it with ``tfs-correct-tubes``
+(below) rather than editing the table by hand.
+
+Correcting the tube table: tfs-correct-tubes
+--------------------------------------------
+
+Sometimes a tube's contents disagree with its label. In one screen the
+genotype composition of the tube labeled 0.1 uM IPTG looked like a 1 mM
+tube, and the tube labeled 1 mM looked like a low-IPTG tube. A correction
+like that is data: it should say which tubes, what changes, why, who
+decided and when, and it should be applied by a tool so the original
+table stays as it was. ``tfs-correct-tubes`` does that:
 
 .. code-block:: bash
 
-    tfs-process-fastq <f1_fastq> <f2_fastq> <out_dir> <run_config> [options]
+    tfs-correct-tubes tube_table.csv corrections.yaml \
+        --out_prefix tube_table_corrected
+
+The corrections file is YAML and lives with the experiment's data, under
+version control, next to the tube table it corrects:
+
+.. code-block:: yaml
+
+    description: Corrections to the screen's tube table.
+    corrections:
+      - id: pheS-rep2-t82-swap
+        action: swap
+        tubes: [tube_a, tube_b]
+        reason: >
+          Composition QC: the tube labeled 0.1 uM looks like a 1 mM tube
+          and the one labeled 1 mM like a low-IPTG tube.
+        by: A. Researcher
+        date: 2026-10-15
+        evidence: qc/apparent_iptg.csv
+
+``id``, ``action``, ``tubes`` (the tube table's ``sample`` names),
+``reason``, ``by`` and ``date`` are required. ``evidence`` is optional, a
+string or a list; one that names a file, relative to the corrections file,
+is hashed into the provenance. The table ``tfs-check-tubes`` writes, which
+flags tubes whose composition disagrees with their titrant label, is the
+usual evidence for a swap. There are three actions:
+
+- ``swap``: two tubes of one library exchange their design columns.
+- ``relabel``: one tube takes the design values given in ``set``, for
+  example ``set: {t_sel: 125}``. Three relabels express a rotation of three
+  tubes.
+- ``exclude``: one or more tubes are left out.
+
+**What a correction moves.** Only the design columns: ``replicate``,
+``condition_pre``, ``t_pre``, ``condition_sel``, ``t_sel``,
+``titrant_name`` and ``titrant_conc``. Every other column stays with the
+physical sample: its ``sample`` name and so its count file, its OD600 and
+tube totals, its barcodes and sequencing run. That is right however the
+swap happened. If the tubes were swapped at the bench, the reads and the
+OD600 were measured on the culture now known to have the other design. If
+the sample sheet mapped files to the wrong tubes, the count file came from
+the culture whose design is the other label, and that culture's OD600 was
+read when it was pulled. Either way a sample's measurements stay together
+and only its label changes. ``library`` never changes: libraries are
+transformed and grown separately, so a swap between them is refused.
+
+No tube may appear in two corrections, so every correction applies to the
+original table and their order does not matter. The command also refuses
+an unknown tube, a swap that changes nothing, a relabel of a column that
+is not a design column, a result in which two tubes share a library and
+design, an empty ``reason``, ``by`` or ``date``, a table that was already
+corrected, and an output that would overwrite an input.
+
+**Output.** ``{out_prefix}.csv`` is the corrected tube table, in the
+original row order without excluded tubes, plus a ``correction_id`` column
+(empty for tubes no correction touched). Pass it to
+``tfs-process-counts`` in place of the original; ``correction_id`` is
+carried into the growth table. ``{out_prefix}_changes.csv`` has one row per
+changed value or excluded tube, with its original and corrected value.
+``{out_prefix}_provenance.json`` records the run, the SHA-256 of the tube
+table, the corrections file, every evidence file and the corrected table,
+and the corrections themselves.
+
+OD600 calibration
+-----------------
 
-**Positional arguments:**
+``tfs-calibrate-od600`` fits the calibration from two small experiments, both
+done with the same handling as production samples: same reader, plate type,
+volume and strain.
+
+**Repeated readings of a dilution series.** Read each dilution several
+times, repeating the whole step each time: swirl the culture, pipette into
+the plate, read. The file has one row per reading, with columns
+``dilution`` (relative to the undiluted culture) and ``od600``. The largest
+relative SD across dilutions is the reading noise. The detection threshold
+is midway between the mean readings of the two most dilute samples, where
+the series has flattened onto the reader's floor. ``--detection_threshold``
+overrides it.
+
+**Plate counts of cultures whose OD600 was read.** The file has one row per
+plate, with columns ``od600``, ``colonies``, ``dilution`` (the total dilution
+factor before plating), ``plated_volume_mL``, ``num_dilutions`` and
+``plating_steps``. CFU/mL is ``colonies * dilution / plated_volume_mL``. Its
+relative variance is ``1 / colonies`` for counting plus
+``pipette_rel_error^2`` for each dilution and plating step
+(``--pipette_rel_error``, default 0.02).
+
+A polynomial of CFU/mL in OD600 is fit by weighted least squares
+(``--degree``, default 2):
 
-* ``f1_fastq``: Path to the read-1 FASTQ file (gzip-compressed accepted).
-* ``f2_fastq``: Path to the read-2 FASTQ file.
-* ``out_dir``: Directory to write output CSV files (created if absent).
-* ``run_config``: Path to the library configuration YAML file.
+.. code-block:: bash
 
-**Optional arguments:**
+    cd examples/od600
+    python make_example_data.py
+    tfs-calibrate-od600 replicates.csv plate_counts.csv --out_prefix od600_calibration
 
-* ``--phred_cutoff``: Minimum Phred quality score; bases below this threshold
-  are replaced with ``N`` (default: 10).
-* ``--min_read_length``: Discard reads shorter than this length (default: 50).
-* ``--allowed_num_flank_diffs``: Allowed mismatches when locating 5′ and 3′
-  flanks (default: 1).
-* ``--allowed_diff_from_expected``: Allowed mismatches from library genotypes
-  (default: 2).
-* ``--print_raw_seq``: If set, prints sequence matches to stdout for debugging.
-* ``--max_num_reads``: Stop after this many reads.
-* ``--chunk_size``: Block size for multiprocessing batches.
-* ``--num_workers``: Number of parallel workers (default: available CPUs − 1).
+``od600_calibration.yaml`` holds the coefficients and their full covariance,
+the reading noise, the detection threshold and the calibrated OD600 range.
+This is the file ``tfs-process-counts`` reads. ``od600_calibration.pdf``
+shows the dilution series, the fit and the calibrated CFU's relative error.
+The two CSVs hold the per-dilution noise and the per-plate fit with its
+standardized residuals.
 
-tfs-process-counts
-------------------
+The covariance matters. The curve's error is one error shared by every tube
+calibrated with it. It does not average out across tubes and must not be
+treated as independent per-tube noise, so the pipeline keeps it apart from
+the reading noise: ``tfscreen.process_raw.od600.cfu_per_mL_error_components``
+returns the two parts separately, and ``tfs-process-counts`` writes them as
+separate columns.
 
-Takes the per-sample count CSVs produced by ``tfs-process-fastq``, aggregates
-them according to a sample metadata file, and converts raw counts into
-ln(CFU) values using per-sample CFU estimates.
+The files in ``examples/od600/`` are synthetic and show the formats. They
+are not any instrument's calibration; do not use them for real data.
 
-**Output:** A single CSV file (``output_file``) containing ``ln_cfu`` per
-genotype across all samples, ready for hierarchical modelling.
+Counts to the growth table: tfs-process-counts
+----------------------------------------------
 
-**Usage:**
+``tfs-process-counts`` takes the tube table and the directory of count
+files:
 
 .. code-block:: bash
 
-    tfs-process-counts <sample_df> <counts_csv_path> <output_file> [options]
-
-**Positional arguments:**
-
-* ``sample_df``: Path to a CSV file describing samples. Must contain a unique
-  ``sample`` column (used as the row index). Required columns:
-
-  * ``sample`` *(index)* — unique identifier for each sequenced tube; used
-    to match this row to a counts CSV file.
-  * ``library`` — name of the physical library this sample belongs to. Genotypes
-    are filtered and frequency-normalised within each library.
-  * ``sample_ln_cfu`` — natural log of the total colony-forming units (CFU)
-    measured for this tube.
-  * ``sample_ln_cfu_std`` — standard deviation of ``sample_ln_cfu``.
-
-  Instead of ``sample_ln_cfu``/``sample_ln_cfu_std``, you may supply
-  ``sample_ln_cfu_var``, or the linear-space ``sample_cfu`` with
-  ``sample_cfu_std`` (or ``sample_cfu_var``); the log-space columns are then
-  inferred. Log-space columns are used when both are present.
-
-  The following columns are not used by ``tfs-process-counts`` itself but are
-  carried through into the ``ln_cfu`` output and are **required by the growth
-  model** (``tfs-fit-model``):
-
-  * ``replicate`` — integer replicate index distinguishing biological
-    replicates that share the same condition.
-  * ``condition_pre`` — name of the pre-selection growth condition (e.g.
-    ``-kan``). Identifies the baseline growth arm.
-  * ``condition_sel`` — name of the selection condition (e.g. ``+kan``).
-  * ``titrant_name`` — name of the chemical titrant applied (e.g.
-    ``IPTG``). Use a constant placeholder (e.g. ``none``) when no titrant
-    is present.
-  * ``titrant_conc`` — concentration of the titrant (float; 0 for no titrant).
-  * ``t_pre`` — duration (minutes) of pre-selection growth.
-  * ``t_sel`` — duration (minutes) of selection growth.
-
-* ``counts_csv_path``: Directory containing the per-sample count CSV files.
-  Each file is found by globbing
-  ``{counts_glob_prefix}*{sample}*.csv`` within this directory.
-* ``output_file``: Path for the output ln_cfu CSV.
-
-**Optional arguments:**
-
-* ``--counts_glob_prefix``: File prefix used when globbing for count files
-  (default: ``counts``).
-* ``--min_genotype_obs``: Minimum total counts across all samples for a
-  genotype to be retained (default: 10).
-* ``--pseudocount``: Pseudocount added to zero counts before log transformation
-  (default: 1).
-* ``--verbose``: If set, prints a summary of matched samples and file paths.
-
-tfs-process-presplit
---------------------
-
-Processes count files from the pre-split time-point — the single pooled
-sample collected **before** the library is divided into separate selection
-arms. These abundances anchor the initial per-genotype CFU values (``ln_cfu0``)
-in the growth model. The output CSV is passed to ``tfs-fit-model`` via the
-``presplit_df`` argument.
-
-The interface is identical to ``tfs-process-counts``, but ``sample_df`` must
-also include ``replicate`` and ``condition_pre`` columns so that the output
-can be matched to the correct growth conditions.
-
-**Output:** A single CSV file (``output_file``) with columns ``replicate``,
-``condition_pre``, ``genotype``, ``ln_cfu``, and ``ln_cfu_std``.
-
-**Usage:**
+    tfs-process-counts tube_table.csv counts \
+        --od600_file od600.csv \
+        --od600_calibration_file od600_calibration.yaml \
+        --tube_volume_mL 5 \
+        --out_prefix growth
+
+It writes ``growth.csv`` (default ``tfs_growth.csv``) and prints which count
+file it matched to each tube. ``--no_verbose`` turns that listing off.
+
+**Tube totals.** Every tube needs its total cells, given one of two ways.
+The first is OD600. Give one reading per tube, either as ``--od600_file``
+(columns ``sample`` and ``od600``) or as an ``od600`` column in the tube
+table, together with ``--od600_calibration_file`` and ``--tube_volume_mL``.
+The calibration gives CFU per mL and the volume turns that into cells in the
+tube. These columns are then added:
+
+- ``sample_cfu``: cells in the tube.
+- ``sample_cfu_std``: its SD, with the curve and reading errors combined.
+- ``sample_cfu_curve_std``: the calibration curve's part, shared by every
+  tube read through the same calibration.
+- ``sample_cfu_reading_std``: the reading's own part, independent per tube.
+- ``od600_in_calibrated_range``: False for a reading outside the OD600 range
+  the plate counts covered, where the curve is extrapolated.
+
+A reading below the calibration's detection threshold is an error, since
+its total is only an upper bound. Drop those tubes from the tube table.
+
+The second way is to supply the totals yourself, as ``sample_cfu`` with
+``sample_cfu_std`` (or ``sample_cfu_var``) in the tube table, or the log
+forms ``sample_ln_cfu`` with ``sample_ln_cfu_std`` (or
+``sample_ln_cfu_var``). Totals must be cells in the whole tube. Use one
+route for the whole table. A tube table that supplies totals and OD600
+together is an error.
+
+**Several libraries.** All libraries go through one call. Genotypes are
+filtered and filled in within each library: a genotype with fewer than
+``--min_genotype_obs`` reads (default 10) summed over a library's tubes is
+dropped from that library, and a genotype kept in a library gets a row with
+zero counts in every tube of that library where it was not seen.
+
+**Output.** One row per genotype and tube. It carries the tube table's
+columns and the tube totals, plus:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 22 78
+
+   * - Column
+     - Meaning
+   * - ``genotype``
+     - Genotype name: ``wt``, ``M42I``, ``M42I/K84L``.
+   * - ``counts``
+     - Reads of the genotype in the tube, without pseudocount.
+   * - ``sample_reads``
+     - The tube's total reads, ``__unknown__`` included, without
+       pseudocounts. The count likelihood uses it as the tube's depth.
+   * - ``adjusted_counts``
+     - ``counts + --pseudocount`` (default 1).
+   * - ``frequency``
+     - ``adjusted_counts`` over the tube's read depth plus one pseudocount
+       per genotype.
+   * - ``sample_ln_cfu``, ``sample_ln_cfu_std``
+     - The tube's total in log form.
+   * - ``ln_cfu``, ``ln_cfu_var``
+     - ``ln(frequency) + sample_ln_cfu``, the genotype's cells in the tube,
+       and its variance: binomial sampling of the frequency plus
+       ``sample_ln_cfu_std^2``.
+   * - ``cfu``, ``cfu_var``
+     - The same in linear form.
+
+The model takes this file as it is (``tfs-configure-model --growth_df``). By
+default it observes ``counts`` against ``sample_reads`` and ``sample_ln_cfu``
+rather than ``ln_cfu``; see :doc:`model-inputs`.
+
+**Presplit.** With ``--presplit``, the tube table lists the presplit tubes,
+one per library, replicate and ``condition_pre``, and must have
+``replicate`` and ``condition_pre`` columns. The output, ``tfs_presplit.csv``
+by default, has columns ``library``, ``replicate``, ``condition_pre``,
+``genotype``, ``ln_cfu`` and ``ln_cfu_std``. Pass it to
+``tfs-configure-model --presplit_df``.
+
+A worked example
+----------------
+
+``examples/process_raw/`` holds a small synthetic experiment: two libraries
+(kanR and pheS), each with a selection and a control condition, two IPTG
+concentrations and three time-points, for 24 tubes. ``make_example_data.py``
+writes ``tube_table.csv``, ``od600.csv`` and ``counts/``, the count files in
+the format ``tfs-process-fastq`` writes. It uses the calibration in
+``examples/od600/``:
 
 .. code-block:: bash
 
-    tfs-process-presplit <sample_df> <counts_csv_path> <output_file> [options]
-
-**Positional arguments:**
-
-* ``sample_df``: Path to a CSV file describing the pre-split samples. Must
-  contain a unique ``sample`` column plus:
+    cd examples/process_raw
+    python make_example_data.py
+    tfs-process-counts tube_table.csv counts \
+        --od600_file od600.csv \
+        --od600_calibration_file ../od600/od600_calibration.yaml \
+        --tube_volume_mL 5 \
+        --out_prefix growth
 
-  * ``library`` — physical library name (optional; defaults to ``default`` when
-    absent, but must be consistent with the growth ``sample_df``).
-  * ``replicate`` — replicate index; matched to the growth data.
-  * ``condition_pre`` — pre-selection condition name; matched to the growth
-    data.
-  * ``sample_ln_cfu`` — natural log of the total CFU for this tube.
-  * ``sample_ln_cfu_std`` — standard deviation of ``sample_ln_cfu``.
+``growth.csv`` is a growth table ready for the next step:
 
-  As for ``tfs-process-counts``, these may instead be inferred from
-  ``sample_ln_cfu_var`` or from ``sample_cfu`` with ``sample_cfu_std`` (or
-  ``sample_cfu_var``).
-
-* ``counts_csv_path``: Directory containing the per-sample count CSV files.
-* ``output_file``: Path for the output presplit CSV.
+.. code-block:: bash
 
-**Optional arguments:**
+    tfs-configure-model --growth_df growth.csv --library_config library_config.yaml
 
-* ``--counts_glob_prefix``: File prefix for globbing count files (default:
-  ``counts``).
-* ``--min_genotype_obs``: Minimum total counts for a genotype to be retained
-  (default: 10).
-* ``--pseudocount``: Pseudocount added before log transformation (default: 1).
-* ``--verbose``: If set, prints a summary of matched samples and file paths.
+The numbers are made up. They show the formats, not a realistic screen.
diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst
index d1fa59ae..e60d1ca8 100644
--- a/docs/source/quickstart.rst
+++ b/docs/source/quickstart.rst
@@ -1,33 +1,44 @@
 ===========
-Quick Start
+Quick start
 ===========
 
-This page walks through a complete simulate-and-analyze run using the
-bundled example in ``examples/simulate-and-analyze/``.  By the end you
-will have simulated a synthetic high-throughput TF-library screen, fitted
-the hierarchical Bayesian model, and produced a full set of diagnostic
-plots and statistics.
+This page runs the bundled example in ``examples/simulate-and-analyze/``
+from start to finish. It simulates a small TF-library screen, fits the
+hierarchical Bayesian model to the simulated data, and compares the fit with
+the known truth.
 
-The example uses a small library (~600 genotypes across three tiles)
-so the full pipeline finishes in roughly 20–60 minutes on a laptop CPU or a
-few minutes on a GPU.
+The example library has 483 genotypes: wild type, the single mutants at
+three NNT sites (M42 in tile 1, H74 and K84 in tile 2), the double mutants
+between the two tiles, and nine spiked control sequences, three of which
+are not in the bulk library.
+It is grown under kanR and pheS selection at eight IPTG concentrations in
+two replicates. The fit is the slow step. Expect tens of minutes on a
+laptop CPU and a few minutes on a GPU.
+
+To analyze a real screen, follow :doc:`screen-analysis` instead.
 
 Prerequisites
 -------------
 
-Install ``tfscreen`` and verify the entry points are on your PATH:
+Install ``tfscreen`` and check that the commands are on your PATH:
 
 .. code-block:: bash
 
     git clone https://github.com/harmslab/tfscreen
     cd tfscreen
     pip install -e .
-    tfs-simulate --help   # should print usage
+    tfs-simulate --help
+
+JAX and Numpyro are installed as dependencies. On Linux, pip's plain
+``jax`` runs on the CPU only; for a GPU with CUDA 12, install the
+``cuda12`` extra instead and check that JAX sees the GPU:
+
+.. code-block:: bash
 
-The example requires JAX and Numpyro, which are installed as dependencies.
-On Apple Silicon you may need to install the Metal-accelerated ``jax-metal``
-package separately; on a Linux cluster with a GPU, install the appropriate
-``jaxlib`` wheel for your CUDA version.
+    pip install -e ".[cuda12]"
+    python -c "import jax; print(jax.devices())"
+
+tfscreen needs Python 3.12 or newer.
 
 Getting the example
 -------------------
@@ -39,118 +50,160 @@ Copy the example directory to a working location:
     cp -r examples/simulate-and-analyze/ ~/tfscreen-example
     cd ~/tfscreen-example
 
-The directory contains:
-
-* ``simulate_config.yaml`` — simulation parameters (library genetics,
-  growth conditions, binding data).
-* ``hill_params.csv`` — per-genotype Hill binding parameters that set the
-  ground-truth θ values used during simulation.
-* ``run.sh`` — the pipeline script.
+It holds three files. ``simulate_config.yaml`` sets up the simulation:
+library genetics, conditions, growth parameters, binding and presplit data.
+It also serves as the library description for ``tfs-configure-model``.
+``hill_params.csv`` holds the Hill parameters of wild type and three spiked
+single mutants, which become their true *θ* curves and their binding data.
+``run.sh`` is the pipeline.
 
 Running the pipeline
 --------------------
 
-.. code-block:: bash
-
-    bash run.sh simulate_config.yaml out/ 1
+Run it from the example directory, since the config names
+``hill_params.csv`` by a relative path:
 
-The three positional arguments are:
-
-1. The simulation config file.
-2. The output directory (created automatically).
-3. The random seed (``1`` reproduces the documented example outputs).
-
-On a local machine the script uses JAX's CPU parallelism
-(``XLA_FLAGS="--xla_force_host_platform_device_count=8"``).  On a
-cluster, comment that line out and uncomment ``module load cuda/...``
-instead.
-
-The script runs nine steps in sequence, printing a ``>>>`` header before
-each one:
+.. code-block:: bash
 
-.. list-table::
-   :header-rows: 1
-   :widths: 5 30 65
-
-   * - Step
-     - Command
-     - What it does
-   * - 1
-     - ``tfs-simulate``
-     - Builds the genotype library, draws per-genotype θ from the Hill
-       model, converts θ to growth rates, and writes simulated sequencing
-       data.
-   * - 2
-     - ``tfs-configure-model``
-     - Validates inputs, selects model components, writes
-       ``tfs_configure_config.yaml``.
-   * - 3
-     - ``tfs-prefit-calibration``
-     - Fast MAP fit to calibrate the growth-linking-function priors.
-   * - 4
-     - ``tfs-fit-model``
-     - Main SVI inference; writes ``tfs_fit_model_checkpoint.pkl``.
-   * - 5
-     - ``tfs-sample-posterior``
-     - Draws posterior samples; writes ``tfs_posterior.h5`` (~5 GB).
-   * - 6
-     - ``tfs-extract-params``
-     - Summarises the posterior into per-parameter CSV files.
-   * - 7
-     - ``tfs-predict-theta``
-     - Predicts θ at every (genotype, titrant concentration) in the
-       training data.
-   * - 8
-     - ``tfs-predict-growth``
-     - Predicts ln(CFU) with posterior quantiles for all training
-       observations.
-   * - 9
-     - ``tfs-summarize-fit``
-     - Computes fit statistics and writes diagnostic plots and CSVs to
-       ``out/summary/``.
+    bash run.sh simulate_config.yaml out 1
+
+The arguments are the simulate config, the output directory (created if
+needed) and the random seed (default 1). The seed goes to the simulation,
+the pre-fit and the fit.
+
+On a laptop the script has JAX spread work over eight CPU devices
+(``XLA_FLAGS="--xla_force_host_platform_device_count=8"``). On a cluster
+with a GPU, comment that line out and uncomment ``module load cuda/...``.
+The ``#SBATCH`` lines at the top let you submit the same script with
+``sbatch``.
+
+The script runs nine steps and prints a ``>>>`` header before each one.
+
+1. ``tfs-simulate simulate_config.yaml --out_prefix out/tfs_sim --seed 1``
+   builds the library, draws every genotype's *θ* curve from the
+   ``hill_mut`` model with sparse epistasis, grows two replicates and
+   sequences them. It writes the growth, binding and presplit tables plus
+   the ground truth, and the experiment in raw form: one counts file per
+   tube in ``tfs_sim_counts/`` and the tube table ``tfs_sim_tubes.csv``. The
+   script then moves into ``out/`` and runs ``tfs-process-counts`` on the
+   raw files, exactly as on a lab's data, to make
+   ``tfs_sim_processed_growth.csv``.
+
+2. ``tfs-configure-model`` picks the model. The script passes the binding,
+   processed growth and presplit tables and the simulate config as
+   ``--library_config``, and chooses components that match how the data
+   were simulated: ``linear`` condition growth, an ``instant`` growth
+   transition, ``hierarchical`` ln_cfu0, ``hierarchical_geno`` dk_geno,
+   ``fixed`` activity, ``hill_mut`` theta with ``--epistasis``, the
+   ``single`` transformation (the simulation puts one plasmid in each
+   cell), ``passthrough`` theta rescaling, ``logit_normal`` growth-side
+   theta noise and ``zero`` binding-side noise. Growth is observed through
+   the ``counts`` likelihood with a ``level`` offset per tube and ``zero``
+   growth noise, the recommended default. ``--growth_shares_replicates``
+   gives both replicates the same growth parameters.
+
+3. ``tfs-prefit-calibration`` runs a MAP fit on a simplified model of the
+   genotypes with binding data and writes each condition's growth baseline
+   *k* and slope *m* into the priors and guesses CSVs.
+
+4. ``tfs-fit-model --analysis_method svi`` runs a MAP warm-up, then fits the
+   variational guide. This is the long step.
+
+5. ``tfs-sample-posterior --skip_growth_observations`` draws 10,000
+   posterior samples from the guide. The flag leaves the stored
+   per-observation growth sites out of the file. They would be several GB
+   here, and the later steps recompute growth from the parameter samples.
+
+6. ``tfs-extract-params`` writes posterior quantiles for each parameter
+   group.
+
+7. ``tfs-predict-theta`` predicts *θ* at every genotype and concentration in
+   the training data.
+
+8. ``tfs-predict-growth --num_marginal_samples 500`` predicts every
+   training ln_cfu from 500 posterior samples.
+
+9. ``tfs-summarize-fit .`` compares the predictions with the simulated
+   truth and writes plots and statistics to ``out/summary/``.
+
+:doc:`pipeline` explains each step and :doc:`cli` lists every flag.
 
 Expected outputs
 ----------------
 
-After the run completes, ``out/`` will contain:
+Every command writes a ``*_provenance.json`` next to its outputs with the
+tfscreen version, git commit and command line. After the run, ``out/``
+holds:
 
 .. list-table::
    :header-rows: 1
-   :widths: 38 62
+   :widths: 40 60
 
-   * - File / directory
+   * - File
      - Contents
-   * - ``tfs_sim_library.csv``
-     - All genotypes in the simulated library.
+   * - ``tfs_sim_counts/``, ``tfs_sim_tubes.csv``
+     - The raw experiment: one counts file per tube and the tube table.
+   * - ``tfs_sim_processed_growth.csv``
+     - The growth table ``tfs-process-counts`` made from them, which the
+       model reads.
    * - ``tfs_sim_growth.csv``
-     - Simulated ln(CFU) data (same format as ``tfs-process-counts`` output).
+     - The same table written directly by the simulator, with each row's
+       true values.
    * - ``tfs_sim_binding.csv``
-     - Simulated binding curve observations.
+     - Simulated binding curves for the four genotypes in
+       ``hill_params.csv``.
+   * - ``tfs_sim_presplit.csv``
+     - Simulated presplit abundances.
+   * - ``tfs_sim_library.csv``
+     - Every genotype in the library and its origin.
    * - ``tfs_sim_parameters.csv``
-     - Ground-truth per-genotype parameters (θ, dk_geno, etc.).
+     - True per-genotype parameters (Hill parameters, ``dk_geno``,
+       activity).
+   * - ``tfs_sim_genotype_theta.csv``
+     - True *θ* for every genotype and concentration.
+   * - ``tfs_sim_growth_parameters.csv``
+     - True growth *k* and *m* for each condition.
+   * - ``tfs_sim_transformation_lam.csv``
+     - The simulated congression rate.
+   * - ``tfs_sim_input-config.yaml``
+     - The simulate config as run.
    * - ``tfs_configure_config.yaml``
-     - Model configuration read by all downstream steps.
+     - Model configuration read by every later step.
+   * - ``tfs_configure_priors.csv``, ``tfs_configure_guesses.csv``
+     - Priors and starting values, updated by the pre-fit (the originals
+       are kept as ``.bak``).
+   * - ``tfs_configure_library.csv``
+     - Library composition resolved from the simulate config.
+   * - ``tfs_configure_model_stats.csv``, ``tfs_configure_model_stats.json``
+     - Parameter and observation census of the configured model.
+   * - ``tfs_prefit_*``
+     - Pre-fit diagnostics: checkpoint, parameters, losses, convergence.
    * - ``tfs_fit_model_checkpoint.pkl``
      - Fitted model checkpoint.
+   * - ``tfs_fit_model_params.npz``
+     - Fitted guide parameters.
+   * - ``tfs_fit_model_losses.txt``, ``tfs_fit_model_convergence.csv``
+     - Loss trace and one row per convergence window.
+   * - ``tfs_fit_model_premap_*``
+     - The MAP warm-up's checkpoint, parameters, losses and convergence.
+   * - ``checkpoints/``
+     - Numbered checkpoints written during the fit.
    * - ``tfs_posterior.h5``
-     - Posterior samples (~5 GB; not committed to the repo).
+     - Posterior samples.
    * - ``tfs_params_*.csv``
-     - Posterior summaries (quantiles) for each parameter group.
+     - Posterior quantiles for each parameter group.
    * - ``tfs_pred_theta.csv``
-     - Predicted θ with posterior quantiles.
+     - Predicted *θ* with posterior quantiles.
    * - ``tfs_pred_growth.csv``
-     - Predicted ln(CFU) with posterior quantiles.
+     - Predicted ln_cfu with posterior quantiles.
    * - ``summary/``
-     - Diagnostic plots and statistics from ``tfs-summarize-fit``.
+     - Plots and statistics from ``tfs-summarize-fit``.
 
 Next steps
 ----------
 
-* **Understand each analysis step** — see :doc:`analysis` for the full CLI
-  reference covering every ``tfs-*`` command in the pipeline.
-* **Interpret the diagnostic outputs** — see :doc:`summarize-fit` for a
-  guided walkthrough of every plot and statistic in ``out/summary/``.
-* **Simulate a parameter sweep** — see :doc:`grid` for how to run a
-  Cartesian grid of configurations.
-* **Use real data** — see :doc:`process-raw` for converting FASTQ reads
-  into the ``growth.csv`` format that ``tfs-configure-model`` accepts.
+:doc:`summarize-fit` walks through every plot and statistic in
+``out/summary/``. :doc:`simulation` documents the simulate config, so you
+can change the library, the conditions or the noise. :doc:`grid` runs a
+sweep of settings. :doc:`process-raw` turns real FASTQ files into the growth
+table that ``tfs-configure-model`` reads.
diff --git a/docs/source/screen-analysis.rst b/docs/source/screen-analysis.rst
new file mode 100644
index 00000000..3fe0c773
--- /dev/null
+++ b/docs/source/screen-analysis.rst
@@ -0,0 +1,347 @@
+==================
+Analyzing a screen
+==================
+
+This page takes a real screen from per-tube read counts to one table of
+per-genotype response curves with 95% intervals. It is the route used for
+the current manuscript. :doc:`pipeline` lists every command the package
+has; this page is the subset that a screen analysis needs, in order, with
+the reasons for each choice.
+
+The result is a curve of *X* against IPTG for every genotype. *X* is an
+occupancy-like variable gauged on wild type: wt's *X* is 1 at the lowest
+IPTG concentration and 0 at the highest. Read `What X is`_ before using
+the numbers.
+
+.. contents:: On this page
+   :local:
+   :depth: 1
+
+What you need
+=============
+
+From the experiment:
+
+- **One counts file per tube**, from ``tfs-process-fastq`` (see
+  :doc:`process-raw`).
+- **The tube table**: one row per sequenced tube with its library,
+  replicate, pre-growth and selection conditions, times and IPTG
+  concentration.
+- **OD600 readings** for every tube and the **OD600 calibration** for the
+  instrument and strain (``tfs-calibrate-od600``), plus the culture volume.
+- **The library YAML**, the same file given to ``tfs-process-fastq``.
+- **The measured co-transformation rate** lambda, as a mean and SD. The
+  fit requires it. For the 2026 screen it was 0.357 (likelihood-ratio 95%
+  CI 0.156-0.674), entered as 0.38 +- 0.15.
+
+For the fit:
+
+- **Loose growth priors**: one row per condition with a growth baseline
+  ``k`` and slope ``m``. The values only need to be the right order of
+  magnitude; ``examples/tfmodel/growth_priors_loose.csv`` is the file used
+  for the 2026 screen.
+- **A GPU**, and tfscreen installed with GPU JAX: on a Linux cluster,
+  ``pip install -e ".[cuda12]"`` in a fresh environment with Python 3.12
+  or newer, then check that ``python -c "import jax;
+  print(jax.devices())"`` lists a GPU (see :doc:`quickstart`). Save the
+  environment's ``pip freeze`` with the analysis, so its versions are on
+  record. The whole analysis takes about half a day of GPU time (see
+  `Cost`_).
+
+The next section turns the counts, tube table and OD600 readings into the
+growth table the fit reads, checking the tube labels on the way. If a
+tube turns out to be mislabeled, you will also write a corrections file
+there.
+
+Make the growth table
+=====================
+
+The fit reads one **growth table**, ``growth.csv``: every genotype's
+reads and cell numbers in every tube, made by ``tfs-process-counts``. It
+is the ``growth_df`` in the fit's YAML (`Configure the fit`_). Before
+trusting it, check that each tube's label matches its contents.
+
+If you have already made and checked the growth table, for example
+following :doc:`process-raw`, skip to `Configure the fit`_.
+
+These commands read every tube's counts for the whole library, which is
+too much for a cluster's login node. Run them on a compute node with
+plenty of memory, for example in an interactive session:
+
+.. code-block:: bash
+
+   srun --account=YOUR_ACCOUNT --mem=32G --cpus-per-task=4 --time=02:00:00 --pty bash
+
+``tfs-process-counts`` used 4-6 GB per library and a few minutes on the
+2026 screen; ``tfs-check-tubes`` holds the whole growth table in memory,
+so ask for more than that.
+
+**1. Make a first growth table and check the tubes.**
+
+.. code-block:: bash
+
+   tfs-process-counts tube_table.csv counts \
+       --od600_file od600.csv \
+       --od600_calibration_file od600_calibration.yaml \
+       --tube_volume_mL 5 \
+       --out_prefix growth_unchecked
+   tfs-check-tubes growth_unchecked.csv --out_prefix tube_qc
+
+``tfs-check-tubes`` scores each tube's apparent IPTG concentration from
+its genotype composition and flags tubes that are out of order in their
+row of the design (one replicate at one time). Tube totals and read depth
+cancel out of the score, so it tests the labels, not the OD600.
+
+**2. If no tube is flagged**, the first table is the one to use: rename
+``growth_unchecked.csv`` to ``growth.csv`` and go on to
+`Configure the fit`_.
+
+**3. If tubes are flagged**, read the whole row of each flagged tube in
+``tube_qc.csv`` and decide what happened. A single swap is common and is
+fixed with a ``swap``; a row whose order cannot be recovered is left out
+with ``exclude``. Write each decision into a corrections file, a YAML
+file kept with the experiment's data, recording who decided it, when,
+why and the evidence (format: :doc:`process-raw`, "Correcting the tube
+table: tfs-correct-tubes"; example:
+``examples/process_raw/corrections.yaml``).
+
+**4. Apply the corrections and make the growth table again** from the
+corrected tube table:
+
+.. code-block:: bash
+
+   tfs-correct-tubes tube_table.csv corrections.yaml --out_prefix tube_table_corrected
+   tfs-process-counts tube_table_corrected.csv counts \
+       --od600_file od600.csv \
+       --od600_calibration_file od600_calibration.yaml \
+       --tube_volume_mL 5 \
+       --out_prefix growth
+
+This writes ``growth.csv``, the table the fit uses. The original tube
+table is never edited; ``tube_table_corrected_changes.csv`` lists every
+change.
+
+For the 2026 screen one row is excluded: pheS+4CP, replicate 2, t_sel 82.
+Its IPTG tubes were out of order in a way no single swap explains.
+
+Configure the fit
+=================
+
+The fit is one command, ``tfs-fit-two-stage``, driven by one YAML file.
+Its top-level keys are ``tfs-configure-model``'s arguments; the
+``two_stage`` block holds the fit's own settings. Paths are relative to
+the YAML. Start from ``examples/tfmodel/two_stage.yaml``:
+
+.. code-block:: yaml
+
+   growth_df: growth.csv
+   library_config: library_config.yaml
+   theta_model: hill_relative
+   growth_shares_replicates: true
+   batch_size: 4096
+   allow_missing_spikes: true
+   growth_priors: growth_priors_loose.csv
+   set_priors:
+     - sigma_fixed=0.17
+     - theta_log_hill_n_hyper_scale_fixed=0.5
+   skip_model_stats: true
+
+   two_stage:
+     transformation_lambda: [0.38, 0.15]   # the measured lambda, required
+     seed: 1
+     num_posterior_samples: 500
+     consistency: true
+     consistency_library: kanR
+     predict_epistasis: true
+
+Keep everything except the data paths and ``transformation_lambda`` as
+shown. These are the settings the validation used.
+``consistency_library`` only names which library's tubes the consistency
+check refits alone (see `What the fit does`_); it is a convention, not a
+modeling choice, and defaults to the first library alphabetically
+(``kanR`` here). ``sigma_fixed`` holds the per-tube level offsets at an SD of 0.17
+(left free, they absorbed the population's growth on real data), and the
+held log Hill-n spread keeps genotypes from hiding their response below
+the lowest IPTG concentration. Set ``predict_epistasis: true`` if you want
+epistasis on *X*.
+
+The command refuses settings it does not support: binding data, theta
+noise models, the experimental joint-fit priors and a marker warp
+(``m_warp``) in the growth priors.
+
+What the fit does
+=================
+
+Each step writes its own directory under ``--out_dir`` and is skipped
+when its outputs exist, so a stopped run resumes where it stopped.
+
+1. **stage1**: wt, the spiked controls and the single mutants (about 950
+   genotypes), fitted with the congression mixture and the measured
+   lambda. This gives every condition's growth baseline *k* and slope *m*.
+   The full library cannot be used for this: each double mutant pulls *m*
+   a little steeper, and over 200,000 doubles the pulls compress *X* about
+   1.45-fold.
+2. **stage2**: the full library with every condition's *k* and *m* pinned
+   at stage 1's values, then the Laplace approximation of the posterior.
+3. **predict**: stage 2's *X* from the posterior draws, and its epistasis
+   with ``predict_epistasis: true``.
+4. **consistency**: stage 2 refitted on one library's tubes alone
+   (``consistency_library``, kanR here), compared genotype by genotype
+   with the fit on both markers.
+5. **summary**: the results table, ``tfs_x.csv``.
+
+Two more steps, **jackknife** and **kshift**, try to add *k*'s
+uncertainty to the intervals. They are experimental and off by default
+(``kshift: false``): on simulations the shift they measured was a third
+or less of the true one (``planning/studies/two-stage-validation/``,
+run 2).
+
+Run it
+======
+
+Run it one of two ways, not both.
+
+**On a SLURM cluster** (the usual case), copy
+``examples/tfmodel/two_stage_submit.sh`` and
+``examples/tfmodel/two_stage_step.srun`` next to your YAML, edit the
+account, partition and time in ``two_stage_step.srun`` for your cluster,
+and run the submit script from the login node:
+
+.. code-block:: bash
+
+   bash two_stage_submit.sh two_stage.yaml two_stage
+
+That is the only command to run. It submits one job per step, each
+waiting on the steps it needs, and prints the job ids. Logs go to
+``two_stage/logs/``. Every path the jobs use is made absolute, so it
+works from any directory. If a job fails, fix the cause and run the
+submit script again: finished steps are skipped.
+
+**On one machine with a GPU**, run everything in order with one command:
+
+.. code-block:: bash
+
+   tfs-fit-two-stage two_stage.yaml --out_dir two_stage
+
+This is also what each cluster job calls, one step at a time
+(``tfs-fit-two-stage two_stage.yaml --out_dir two_stage --step stage2``
+and so on).
+
+Cost
+----
+
+On one GPU, for a library of about 200,000 genotypes: stage 1 takes 15-30
+minutes, stage 2 two to five hours with its Laplace, and the consistency
+refit, which runs beside prediction, about five hours. About half a day
+in all.
+
+Read the results
+================
+
+``{out_dir}/tfs_x.csv`` has one row per genotype and IPTG concentration:
+
+``genotype``, ``num_mutations``, ``titrant_name``, ``titrant_conc``
+   The row.
+``q0.001`` ... ``q0.999``
+   Quantiles of *X* (``q0.5`` the median, ``q0.025`` and ``q0.975`` the
+   95% interval).
+``k_shift``
+   Blank unless the experimental ``kshift`` is on: then *X*'s shift per
+   SD of *k* at this row, already included in the quantiles.
+``in_training_data``
+   1 if the genotype was measured at this concentration.
+``theta_scale``
+   ``X``.
+``reads``, ``reads_kanR``, ``reads_pheS``
+   The genotype's reads over all tubes, in total and per library.
+``consistency_z``, ``inconsistent``
+   How far the genotype's kanR-only *X* sits from its both-marker *X*,
+   in SDs after the overall map between the two fits, and whether that
+   exceeds 2 SD. Blank for wt and when the check did not run.
+
+Which genotypes to use
+----------------------
+
+Use genotypes with at least 1,000 reads. Below that, *X* is mostly set by
+the population prior: the intervals still cover the truth, but the point
+estimates carry little information about the genotype. On a simulation
+of the 2026 screen, the double mutants' *X* against the truth, by read
+depth:
+
+=========== =========== ====== ====== ============ ============
+reads       genotypes   slope  RMSE   95% coverage median width
+=========== =========== ====== ====== ============ ============
+<= 100      37,707      0.04   2.60   0.98         5.9
+100-1e3     89,022      0.39   0.54   0.90         1.18
+1e3-1e4     64,362      0.88   0.17   0.88         0.48
+1e4-1e5     15,283      1.02   0.07   0.90         0.20
+> 1e5       1,225       1.02   0.05   0.76         0.07
+=========== =========== ====== ====== ============ ============
+
+The slope is the truth regressed on the fit; 1 means the fitted *X* is on
+the true scale. The 2026 screen has about 77,000 double mutants above
+1,000 reads.
+
+Report genotypes flagged ``inconsistent`` separately rather than dropping
+them silently. On the 2026 screen about 10% of deep genotypes are flagged,
+against about 2% on simulations with no marker problem, so most flags are
+a real disagreement between the markers whose cause is not known.
+
+What X is
+=========
+
+Growth alone fixes occupancy only up to an affine map: any change of
+scale and offset in occupancy is absorbed by each condition's *k* and
+*m*. So the fit does not estimate occupancy. It estimates *X*, which is
+the in vivo occupancy put on a scale where wt reads 1 at the lowest IPTG
+and 0 at the highest. If wt is not fully repressed or fully induced in the
+cell, *X* is still 1 and 0 there.
+
+*X* is not in vitro binding. Compare the two after the fit, and expect
+them to differ:
+
+- Growth curves are broader than binding curves (Hill n 0.7-0.95 in *X*
+  against 1.9-3.2 in binding at 20 uM protein).
+- wt's growth midpoint (about 21 uM IPTG) is about 8 times its midpoint
+  in binding at 2 uM protein, and matches its midpoint at 20 uM.
+- Order can invert: M42I switches at a fifth of wt's IPTG in growth and
+  needs 4-5 times wt's in binding.
+
+No single protein concentration or monotone map carries binding onto
+growth for every genotype, so binding is not used to set *X*'s scale.
+
+Epistasis on *X* is defined only on the additive scale
+(``tfs-predict-epistasis --scale add``), since *X* has no fixed bounds.
+
+What the intervals carry
+========================
+
+Each genotype's interval comes from the Laplace approximation of stage 2,
+with every condition's *k* and *m* held at stage 1's values. On
+simulations of this design, the deep genotypes' 95% intervals covered the
+truth 0.88-0.91 of the time up to 1e5 reads, and 0.76-0.81 above 1e5
+(table above; the second simulation seed gave 0.91 and 0.81). Treat them
+as somewhat narrow, most of all for the deepest genotypes.
+
+What they leave out:
+
+- **k's uncertainty.** Stage 1 estimates each condition's growth
+  baseline with an error that shifts every non-wt genotype's *X* by about
+  the same amount (one SD of *k* moved *X* by 0.03-0.04 on simulations).
+  It matters most where the per-genotype interval is narrowest, which is
+  why the deepest genotypes undercover. It cancels from differences
+  between genotypes, rankings and epistasis, which are better calibrated
+  than single curves.
+- **Stage 1's small bias in m**, 2-6% steep on simulations. It stretches
+  *X* by about the same factor. Resampling cannot cover a bias.
+- **The pheS gap.** In the pool, pheS selection is about half as strong
+  as wt's monoculture rate predicts. *X* is set mostly through kanR, so
+  this need not distort it, but it could if the cause depends on
+  genotype. The consistency flag is the check.
+- **kanR's absolute slope.** The fit takes *m* from the tube totals.
+  Within-tube comparisons, which do not use the totals, give a kanR slope
+  about 2.5 times smaller. Binding at 2 uM protein supports the totals'
+  value. If the within-tube value were right, *X*'s scale would change.
+- **Congression's per-genotype error.** Stage 2 does not fit the
+  congression mixture; for most genotypes the error is small next to the
+  interval.
diff --git a/docs/source/simulation.rst b/docs/source/simulation.rst
index 5ab2b0a2..ca2a2dd1 100644
--- a/docs/source/simulation.rst
+++ b/docs/source/simulation.rst
@@ -2,110 +2,523 @@
 Simulation
 ==========
 
-The ``tfscreen.simulate`` module generates synthetic high-throughput TF-library
-selection experiments. Starting from a thermodynamic model of TF binding, it
-builds a genotype library, samples per-genotype occupancy (*θ*) values, applies
-the growth model to convert occupancy to fitness, and then simulates the
-sequencing step. The result is a ``growth.csv`` that has exactly the same
-format as the output of ``tfs-process-counts``, so simulated data can be fed
-directly into ``tfs-configure-model`` for end-to-end benchmarking.
-
-Configuration File (simulate_config.yaml)
-------------------------------------------
-
-All simulation parameters are collected in a single YAML file. An
+The ``tfscreen.simulate`` module generates synthetic high-throughput screens
+of TF libraries. It builds a genotype library, draws each genotype's
+operator occupancy (*θ*) curve, converts *θ* to growth rates, grows the
+library through the selection experiment and sequences it. The simulated
+growth table has the same format as the output of ``tfs-process-counts``, so
+it goes straight into ``tfs-configure-model``. Because the ground truth is
+known, a simulation is how we benchmark a model choice or an inference
+method. :doc:`quickstart` runs one end to end.
+
+The simulation config
+---------------------
+
+One YAML file holds every simulation setting. The
 :download:`annotated example <../../examples/simulate/simulate_config.yaml>`
-is provided in ``examples/simulate/``. The top-level sections are:
-
-**Library genetics** — same keys as the ``run_config.yaml`` used by
-``tfs-process-fastq`` (``reading_frame``, ``wt_seq``, ``degen_sites``,
-``tiles``, ``expected_5p``, ``expected_3p``, ``tile_combos``,
-``spiked_seqs``). This means a single config file can drive both simulation
-and raw-data processing.
-
-**Phenotype** — controls how genotype *θ* values are drawn:
-
-* ``theta_component`` — registered theta-model name (e.g. ``hill_geno``,
-  ``thermo.O2_C12_K5_U0_a.PK``).
-* ``theta_rng_seed`` — JAX RNG seed for reproducible *θ* sampling.
-* ``theta_priors`` (optional) — dict of hyperparameter overrides for the
-  chosen component.
-* ``thermo_data`` (optional) — path to structural/thermodynamic data required
-  by ``PnnC`` and ``PddG`` theta components.
-
-**Conditions** — ``condition_blocks`` list; each entry specifies:
-``library``, ``titrant_name``, ``titrant_conc`` list, ``condition_pre``,
-``t_pre``, ``condition_sel``, and ``t_sel`` list.
-
-**Growth** — per-condition ``{m, b}`` parameters mapping *θ* to growth rate
-(*k = b + A·m·θ*). ``dk_geno_hyper_*`` sets the pleiotropic growth-cost
-distribution. ``activity_wt`` and ``activity_mut_scale`` control per-genotype
-TF activity.
-
-**Experimental parameters** — ``transform_sizes``, ``library_mixture``,
-``lib_assembly_skew_sigma``, ``transformation_poisson_lambda``,
-``multi_plasmid_combine_fcn``, ``cfu0``, ``tube_noise_sigma``,
-``total_num_reads``, ``prob_index_hop``, ``random_seed``.
-
-**Growth transition** (optional) — ``growth_transition`` list; one entry per
-``condition_pre`` that has a detectable lag phase. Supported models: ``instant``,
-``memory``.
-
-**Binding data** (optional) — ``binding_data`` block; if present, a simulated
-binding CSV is also written. Required sub-keys: ``genotypes``,
-``titrant_name``, ``titrant_conc``, ``noise``.
+in ``examples/simulate/`` comments every key. ``tfs-simulate`` refuses a key
+it does not know, so a misspelled key fails at once rather than being
+ignored. Times are in minutes and growth rates are per minute throughout.
+
+Library genetics
+^^^^^^^^^^^^^^^^
+
+The keys ``reading_frame``, ``first_amplicon_residue``, ``wt_seq``,
+``degen_sites``, ``tiles``, ``tile_combos``, ``spiked_seqs``,
+``expected_5p`` and ``expected_3p`` describe the library. They are the same
+keys ``tfs-process-fastq`` reads, and together with ``library_mixture`` they
+are what ``tfs-configure-model --library_config`` reads. Keep one file per
+experiment and hand that same file to ``tfs-simulate``, ``tfs-process-fastq``
+and ``tfs-configure-model --library_config``. Each tool ignores the keys it
+does not use. Nothing checks that ``library_mixture`` is the same in all
+three places, so a single file is the only guard against drift.
+
+Subsampling the library
+^^^^^^^^^^^^^^^^^^^^^^^
+
+A real double-mutant library has hundreds of thousands of genotypes, far
+too many for some studies (a full Laplace posterior needs a dense Hessian
+over every genotype's parameters). Four optional keys restrict the described
+library to a subset. They belong with the library keys, because
+``tfs-configure-model --library_config`` reads them too and builds its
+library snapshot from the same subset. ``tfs-process-fastq`` ignores them.
+
+.. code-block:: yaml
+
+    # keep every single and 1,000 random cross-tile doubles
+    library_subsample:
+      double-1-2: 1000
+    library_subsample_seed: 20261010
+
+    # or keep exactly the genotypes listed in a file
+    library_genotypes: sampled_genotypes.txt
+
+``library_genotypes`` names a text file with one genotype per line (``#``
+comments allowed), relative to the YAML's own directory. Every genotype not
+listed is dropped from every sub-library that encodes it, so the file lists
+everything to keep, singles included. A listed genotype the library does
+not encode is an error. ``library_subsample`` keeps ``N`` genotypes of a
+sub-library, drawn at random, and leaves the sub-libraries it does not name
+whole. It needs ``library_subsample_seed``, an integer. That seed is
+separate from ``seed`` on purpose: ``tfs-configure-model`` never sees
+``tfs-simulate --seed``, and both must draw the same genotypes. Each
+sub-library draws from its own stream, and a smaller ``N`` keeps a subset
+of what a larger one keeps, so a grid over ``N`` compares nested libraries.
+Both keys can be given; the random draw is then made from the listed
+genotypes. wt and every genotype a spiked sequence encodes are always kept,
+in every sub-library that encodes them, and do not count toward ``N``. A
+``double-x-y`` sub-library also encodes singles (and wt) wherever a site's
+codon set includes the wt residue; those are drawn like any other member.
+
+``library_subsample_depth`` says what the experiment settings describe.
+With ``full_library`` (the default) ``library_mixture``, ``transform_sizes``,
+``total_num_reads`` and ``pcr_template_molecules`` describe the full
+library, and every kept sequence keeps the pool share, transformants, reads
+and PCR templates it has there: each sub-library's mixture weight and
+transformants are multiplied by its kept fraction of sequences, and the
+reads and templates by the kept share of the pool. ``cfu0`` is not changed,
+so tube totals and OD600 stay those of the full experiment and each kept
+genotype has 1 / (kept share) times the cells. A grid over ``N`` then varies
+only the number of genotypes, not the depth per genotype. With
+``as_written`` the settings apply to the subsampled library as given: the
+subsample is the library. ``tfs-simulate`` prints a table of genotypes and
+sequences kept per sub-library and the settings it used.
+
+``tfs_sim_library.csv``, the raw count files and every other output list
+only the kept genotypes. Pass the same YAML to ``tfs-configure-model
+--library_config``; its snapshot (``*_library.csv``) then holds the same
+genotypes with the composition the simulation used, and its config records
+the subsample keys under ``library.subsample``. ``tfs-setup-sim-grid``
+copies a ``library_genotypes`` file into the grid, and ``library_subsample``
+is an ordinary grid variable.
+
+Phenotypes
+^^^^^^^^^^
+
+``theta_component`` names the theta model that draws each genotype's *θ*
+curve. ``hill_geno`` draws an independent Hill curve per genotype around a
+wild-type reference. ``hill_mut`` draws per-mutation effects, adds them and,
+when the library has double mutants, adds sparse pairwise epistasis from a
+regularized horseshoe. The ``thermo.*`` keys are thermodynamic
+partition-function models; ``PnnC`` and ``PddG`` variants also need
+``thermo_data``, the structural ensemble (HDF5) or the per-mutation ΔΔG CSV.
+An optional ``thermo_options`` block says how to read it: ``ddG_units``
+(``kT``, the default, ``kcal/mol`` or ``kJ/mol``), ``temperature_K``,
+``column_map``, ``residue_offset`` and ``max_missing_fraction``, with the
+same meaning as the ``--thermo_*`` flags of ``tfs-configure-model`` (see
+:doc:`model-inputs`). Without it the CSV is read as kT, so the simulated
+truth is unchanged.
+
+In a ``PddG`` or ``PnnC`` simulation the truth is drawn per structure: each
+mutation's ΔΔG in each structure is the structural value plus Normal noise
+with SD σ_s, one σ_s per structure, and the true constants are its
+projection. That is exactly the distribution the models' prior on the
+constants assumes. The simulator fixes σ_s at ``ddG_truth_sd`` instead of
+drawing it from its prior, so the key is the dial for how far the truth
+sits from the structural prior. It is in the
+unit of ``thermo_options`` ``ddG_units`` and converted once at load, like
+the CSV: with ``ddG_units: kcal/mol``, ``ddG_truth_sd: 0.5`` means
+0.5 kcal/mol. Give one number for every structure or a mapping that names
+each structure of the model, such as ``{H: 0.5, HO: 0.5, L: 1.0, LO: 1.0,
+HE2: 0.5, LE2: 0.5}``. With 0 the truth is the CSV exactly. Without the key
+σ_s is 1 kT in every structure. The noise is independent across mutations
+and structures. The true σ_s goes to ``tfs_sim_ddG_sigma.csv`` (one row per
+structure, in kT), which ``tfs-summarize-fit`` compares with the fit's
+``ddG_sigma`` table. The setting changes only the simulated draw. A fit of
+the result still centers the prior on the unperturbed CSV, which its
+``ddG_prior_mean_{structure}`` tables report, and estimates σ_s under its
+own prior. For ``PnnC`` the structural value
+in a simulation is the untrained network's output, 0. Other theta
+components refuse the key, as does ``phenotype_source: empirical``. In a
+``tfs-setup-sim-grid`` grid it is an ordinary top-level variable, and a
+mapping is given whole as one variant.
+
+``theta_sim_priors`` overrides the simulation priors of ``hill_geno`` and
+``hill_mut`` (wild-type curve, perturbation widths, epistasis scale), and
+``theta_priors`` overrides the hyperparameters of the other components. The
+example config lists every key. ``theta_rescale`` (``passthrough``, the
+default, or ``logit``) transforms *θ* before it enters the growth model, as
+the fit-side component of the same name does.
+
+The ``growth`` block maps *θ* to a growth rate for every condition named in
+``condition_blocks``. Each entry is a dict whose optional ``model`` key picks
+the form. The default ``linear`` takes ``b`` and ``m`` and gives
+*k = b + A·m·θ + dk_geno*. ``power`` takes ``b``, ``a`` and ``n``, and
+``saturation`` takes ``kmin`` and ``kmax``. These match the fit's
+``condition_growth`` components, and ``tfs-simulate`` writes the values as
+ground truth under the fit's names (``growth_k``, ``growth_m`` and so on).
+
+Each genotype's pleiotropic growth effect is drawn as
+``dk_geno = dk_geno_hyper_shift - exp(Normal(dk_geno_hyper_loc,
+dk_geno_hyper_scale))``, with wild type at 0. The three ``dk_geno_hyper_*``
+keys are required unless ``dk_geno_zero: true``, which sets every
+genotype's ``dk_geno`` to 0.
+
+TF activity *A* defaults to 1 for every genotype. With the default
+``activity_component: fixed``, ``activity_wt`` sets wild type and
+``activity_mut_scale`` > 0 draws each mutant's log *A* around it.
+``activity_component`` can instead be ``hierarchical_geno`` or
+``horseshoe_geno``, numpy versions of the fit's priors, tuned through
+``activity_priors``.
+
+``phenotype_source: empirical`` replaces all of this with phenotypes
+resampled from a distribution fit to real data. See
+`Empirical phenotypes`_ below.
+
+Conditions
+^^^^^^^^^^
+
+``condition_blocks`` is a list. Each entry gives a ``library``, a
+``titrant_name``, a ``titrant_conc`` list, a ``condition_pre`` and its
+``t_pre``, and a ``condition_sel`` with a ``t_sel`` list. Every combination
+of concentration and selection time is one tube. Every ``condition_pre`` and
+``condition_sel`` must appear in ``growth``.
+
+To follow a real experiment's layout exactly, give ``design`` instead: the
+path to a tube table, the format ``tfs-process-counts`` reads (``sample``,
+``library``, ``replicate``, ``condition_pre``, ``t_pre``, ``condition_sel``,
+``t_sel``, ``titrant_name``, ``titrant_conc``; other columns are ignored).
+Each replicate gets exactly its own tubes, so irregular time points and
+replicates sampled at different times carry over, and the replicates are
+the design's (``--num_replicates`` is ignored). The raw experiment keeps the
+design's tube names, so the simulated tube table has the real layout. A
+design cannot be combined with ``condition_blocks`` or with
+``od600.num_od_only_replicates``. Like every path in the config, it is read
+relative to the directory ``tfs-simulate`` runs in.
+
+``growth_transition`` is an optional list with one entry per
+``condition_pre``, describing the switch from pre-growth to selection.
+The simulator supports ``instant`` (no lag), ``memory`` (``tau0``, ``k1``,
+``k2``; a lag that depends on *θ*), ``baranyi`` (``tau_lag``, ``k_sharp``)
+and ``two_pop`` (``k_trans``). Omit the block for an instant transition
+everywhere. The fit has two more transition components, ``baranyi_k`` and
+``baranyi_tau``, that the simulator does not have.
+
+Transformation and congression
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+``transform_sizes`` gives the number of transformants for each entry in
+``tile_combos`` plus ``spiked``. ``library_mixture`` gives the ratio in
+which those sub-libraries are pooled. ``lib_assembly_skew_sigma`` spreads
+the starting genotype frequencies log-normally; 0 makes them even. It is one
+number, or one per sub-library (``{double-1-2: 2.0, single-1: 0.8, ...}``):
+a pool of doubles is usually far less even than one of singles.
+
+Transformants are counted, not listed one by one. Cells carrying one plasmid
+are drawn as a number of clones per genotype, and each genotype becomes one
+weighted row, so a library can carry a realistic number of transformants
+(hundreds per genotype, tens of millions in all) at the cost of one row per
+genotype. Nothing downstream changes: founder sampling and demographic noise
+add up exactly over identical clones. Cells with several plasmids stay one
+row each; ``max_congressed_cells`` caps how many are kept, representing the
+rest by a weighted sample of that many cells (an approximation that lumps
+their noise).
+``cfu0`` is the number of cells in each tube at the start of pre-growth: one
+number, or one per library (``cfu0: {kanR: 2.6e7, pheS: 5.3e7}``) when the
+libraries were grown up separately. A tube's simulated total scales with
+it, which makes it easy to match a real experiment's densities.
+
+``transformation_poisson_lambda`` gives each cell a zero-truncated Poisson
+number of plasmids; 0 or null gives exactly one. A cell with several
+plasmids splits its abundance among them and has one growth rate, built from
+cell-level physics. ``congression_theta_rule`` sets the cell's *θ*:
+``homodimer`` (the default) combines the plasmids' *θ* odds weighted by
+share, ``heterodimer`` sits closer to the mean logit, and ``max`` lets the
+highest-*θ* plasmid set *θ* and activity. ``homodimer`` and ``heterodimer``
+need activity 1. ``congression_dk_rule`` sets the cell's ``dk_geno``:
+``dilution`` (the default, the share-weighted mean), ``softmin`` with a
+finite ``congression_dk_alpha``, or ``min``, where the worst plasmid sets
+the cost. ``congression_dk_alpha`` is required by ``softmin`` and refused by
+the other rules. The fit's ``mixture`` transformation uses the same rules.
+
+``tube_noise_sigma`` adds one growth-rate offset per tube, shared by every
+genotype in it.
+
+Sequencing and sampling noise
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+``total_num_reads`` is the total number of reads across all tubes of all
+replicates, split evenly over the sequenced tubes. ``prob_index_hop`` is the
+fraction of reads reassigned to a random genotype.
+``unassigned_read_fraction`` (one number, or a map from library to a
+number) sends that share of each tube's reads to ``__unknown__``, as reads
+that call no library genotype; they count toward the tube's reads but carry
+no genotype. Real libraries can lose a large share of their reads this way.
+``seed`` makes the run reproducible; ``tfs-simulate --seed`` overrides it.
+
+Without further settings, read counts are Poisson given each tube's
+composition. Real counts vary 5 to 18 times more than that at 100 to 3,000
+reads (``planning/studies/noise-anatomy/``). Five optional keys, all off by
+default, add the physical sources one at a time. ``founder_sampling`` seeds
+each tube with a Poisson number of cells from each transformant clone.
+``demographic_growth`` then grows each clone from its founders with
+birth-death noise and needs ``founder_sampling``. ``shared_transformation``
+draws every replicate from one library assembly and transformation, as from
+one glycerol stock, instead of redrawing them per replicate.
+``pcr_template_molecules`` draws each tube's reads from that many template
+molecules, and ``pcr_amplification_cv`` gives each template's amplification
+that coefficient of variation. The count variance grows by about
+(reads per tube / templates)(1 + cv²), so matching real data needs templates
+on the order of a tenth of the reads per tube.
+
+``condition_selector`` and ``library_selector`` name the columns that define
+a growth condition and a library. The defaults are right for every config we
+know of; leave them out.
+
+OD600
+^^^^^
+
+An optional ``od600`` block gives every tube one OD600 reading.
+``calibration`` is a calibration YAML from ``tfs-calibrate-od600`` (the one
+in ``examples/od600/`` is synthetic). The simulator inverts it to get each
+tube's true OD600 from its true total, then adds reading noise and applies
+the detection threshold. ``tube_volume_mL`` (default 5.0) converts the
+calibration's per-mL units to the tube. ``num_od_only_replicates`` adds
+replicates that are read but not sequenced. With
+``sample_cfu_from_od600: true`` the growth table gets each tube's total as
+estimated from its reading, as in the lab, instead of the true total.
+
+A tube read below the detection threshold has only an upper bound on its
+total, so the lab drops it, and ``tfs-process-counts`` refuses such a
+reading rather than guess. ``tfs-simulate`` drops these tubes from every
+route the same way: from the raw tube table and count files, and from the
+direct growth table ``tfs_sim_growth.csv`` (also with ``--no_write_raw``).
+It prints the dropped tubes. ``tfs_sim_od600.csv`` still lists every
+reading with its ``od600_detectable`` flag, and the presplit table, which
+samples the library before the split, is not affected.
+
+Binding data
+^^^^^^^^^^^^
+
+An optional ``binding_data`` block writes a simulated binding table for
+``tfs-configure-model --binding_df``. ``titrant_name``, ``titrant_conc`` and
+``noise`` describe the assay. Noise is Gaussian on ``theta_obs`` and is not
+clipped to [0, 1], since the fit's binding likelihood is an unclipped
+Normal. ``clip_theta_obs: true`` restores the old clipping.
+
+Two sub-blocks pick the measured genotypes. ``spiked_binding`` draws from
+``spiked_seqs``, the clean monoclonal controls. ``library_binding`` draws
+from the rest of the library, so its genotypes grow under congression like
+any bulk genotype, which is what lets the fit learn the growth slope *m*
+from data. Each sub-block takes ``choose_by``: ``stratified`` (curves spread
+across the prior), ``random``, or the path to a CSV of measured Hill
+parameters (``genotype``, ``theta_low``, ``theta_high``, ``log_hill_K``,
+``hill_n``) that become those genotypes' true phenotypes. ``num`` sets how
+many genotypes to pick and is forbidden with a file. It is optional for
+``spiked_binding`` (default all spikes) and required for a non-file
+``library_binding``. ``library_binding`` picks its genotypes after the
+growth simulation, from those that survived with growth data. A file of
+measured parameters needs a Hill theta component (``hill_geno`` or
+``hill_mut``).
+
+Binding theta is the true theta at the binding concentrations, which need
+not match the growth concentrations: the simulator evaluates each genotype's
+drawn parameters there, so any theta component works. For the Hill
+components, ``stratified`` and ``random`` give the chosen spikes new curves
+drawn from the prior. For the thermodynamic components (``thermo.*``) every
+genotype's theta follows from per-mutation equilibrium constants, so the
+spikes keep their own curves and K truth, and ``stratified`` or ``random``
+only chooses which ``num`` spikes are measured: ``stratified`` takes the
+spikes whose binding curves are most spread out.
+
+Presplit and base-growth data
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+An optional ``presplit_data`` block writes a presplit table for
+``tfs-configure-model --presplit_df``: one ``ln_cfu`` per replicate,
+``condition_pre`` and genotype, sequenced before the culture is split, with
+the true ``ln_cfu_0`` alongside. Its one optional key, ``noise``, adds
+Gaussian noise on the ln scale. ``presplit_data:`` with nothing under it
+turns it on with no extra noise.
+
+An optional ``base_growth_data`` block writes direct growth-rate
+measurements for ``tfs-configure-model --base_growth_df``. ``k_ref`` (the
+wild-type rate) is required. ``genotypes`` (default ``[wt]``) lists genotypes
+already in the library, each measured at ``k_ref`` plus its own
+``dk_geno``. ``rates`` overrides the true rate for listed genotypes, and
+``noise`` is the measurement SD, also reported as ``rate_std``. Set
+``noise`` above 0 if the table will be fit: ``rate_std`` is a likelihood
+scale, and a value of 0 crashes the pre-fit and the fit the first time the
+model is traced.
 
 tfs-simulate
 ------------
 
-Runs a full simulation from a config file and writes output CSVs.
+``tfs-simulate`` runs a simulation from a config file:
+
+.. code-block:: bash
+
+    tfs-simulate simulate_config.yaml --out_prefix sim/tfs_sim --num_replicates 2 --seed 1
 
-**Usage:**
+It draws the ground-truth library and phenotypes once, then simulates
+``--num_replicates`` independent replicates (default 2). Files are written as
+``{out_prefix}_{name}.csv``; the default prefix is ``tfs_sim``, and a
+directory part of the prefix is created if needed. ``tfs-simulate`` refuses
+to overwrite existing outputs. See :doc:`cli` for every flag.
+
+Every run writes these files:
+
+``tfs_sim_growth.csv``
+    The analysis-ready growth table, the same format as
+    ``tfs-process-counts`` output, with the read counts and tube totals the
+    count likelihood needs. It also carries each row's true ``theta``,
+    ``dk_geno``, ``activity``, ``k_pre``, ``k_sel`` and ``ln_cfu_0``.
+``tfs_sim_library.csv``
+    Every genotype in the library and its origin.
+``tfs_sim_parameters.csv``
+    Ground-truth per-genotype parameters (the theta model's parameters,
+    ``dk_geno``, activity). For the thermodynamic models these are the
+    per-genotype equilibrium constants, such as ``ln_K_op``, ``ln_K_HL``
+    and ``ln_K_E``.
+``tfs_sim_genotype_theta.csv``
+    Ground-truth *θ* per genotype and titrant concentration.
+    ``tfs-summarize-fit`` finds this file and uses it as the reference for
+    out-of-sample *θ*.
+``tfs_sim_growth_parameters.csv``
+    Ground truth for each condition's growth parameters, keyed by
+    ``condition_rep``.
+``tfs_sim_transformation_lam.csv``
+    The configured congression rate, the ground truth for the fit's ``lam``.
+``tfs_sim_input-config.yaml``
+    The config as run, with ``--seed`` applied.
+``tfs_sim_provenance.json``
+    Version, git commit and command line of the run.
+
+The thermodynamic theta models also write ``tfs_sim_mutation_parameters.csv``,
+one row per mutation with its true ``d_*`` effects and, for PddG and PnnC,
+its true ``ddG_{structure}`` values. When the library has multi-mutants they
+write ``tfs_sim_pair_parameters.csv`` as well, one row per mutation pair with
+its true ``epi_*`` terms. The column names match the tables
+``tfs-extract-params`` writes, so ``tfs-summarize-fit`` can score them. The
+``ddG_{structure}`` columns follow the model's structure names, whatever order
+the ΔΔG CSV or structure ensemble lists them in. PddG and PnnC also write
+``tfs_sim_ddG_sigma.csv``, the true σ_s of each structure (``structure``,
+``ddG_sigma``; see ``ddG_truth_sd`` above).
+
+The optional blocks add ``tfs_sim_binding.csv`` (and, with
+``library_binding``, ``tfs_sim_library_binding.csv`` listing the in-library
+genotypes chosen), ``tfs_sim_presplit.csv``, ``tfs_sim_base_growth.csv``
+with ``tfs_sim_k_ref.csv``, and ``tfs_sim_od600.csv``.
+
+The raw experiment
+^^^^^^^^^^^^^^^^^^
+
+``tfs-simulate`` also writes the experiment the way a lab hands it to the
+pipeline, so a simulation is processed by exactly the commands real data go
+through (see :doc:`process-raw`):
+
+``tfs_sim_counts/counts_tube0001.csv``, ...
+    One file per sequenced tube in ``tfs-process-fastq``'s format: every
+    library genotype with its read count, zeros included, and the
+    ``__unknown__`` row (0; the simulator assigns every read). Tubes are
+    named with fixed-width ids, so each name matches exactly one file.
+``tfs_sim_tubes.csv``
+    The tube table: ``sample``, ``library`` and the design columns, with no
+    ground truth. Without an ``od600`` block it also carries each tube's
+    total (``sample_cfu``, ``sample_cfu_std``).
+``tfs_sim_tube_od600.csv``, ``tfs_sim_od600_calibration.yaml``
+    With an ``od600`` block: one OD600 reading per tube and a copy of the
+    calibration. A tube read below the detection threshold is left out of
+    the tube table, as a lab would drop it, and the dropped tubes are
+    printed. ``tfs_sim_growth.csv`` leaves out the same tubes.
+
+``tfs-simulate`` prints the ``tfs-process-counts`` command that processes
+these files. Its output matches ``tfs_sim_growth.csv`` row for row: the same
+counts, tube totals and *ln_cfu*. The ln_cfu values differ slightly only
+where a library genotype never appears in any tube, because a real counts
+file lists it and its pseudocount enters the denominator. ``--no_write_raw``
+skips the raw files, which on a full-size library are one file per tube
+with every genotype. ``--no_write_growth`` skips ``tfs_sim_growth.csv``,
+which ``tfs-process-counts`` rebuilds from the raw files anyway. The
+presplit table is still written directly.
+
+A simulation the size of a real screen (a few hundred thousand genotypes
+in about a hundred tubes) is large: tens of GB of memory at its peak, an
+hour or more of computing, and several GB of output, most of it the growth
+table and the count files.
+
+Empirical phenotypes
+--------------------
+
+Prior-predictive phenotypes test whether a fit recovers the truth when the
+truth looks like the model's prior. To test it on phenotypes shaped like a
+real library, fit the real data to a phenotype-generating distribution and
+resample the simulated library from it. The ground truth stays known.
+
+``tfs-build-empirical`` builds that distribution from a real growth table.
+It configures a ``hill_geno`` model with linear growth, runs the pre-fit to
+calibrate each condition's *k* and *m*, fits each genotype's growth curves
+separately by maximum likelihood (Stage 1), then fits a multivariate normal
+to those estimates with their estimation noise deconvolved (Stage 2):
 
 .. code-block:: bash
 
-    tfs-simulate <config_file> <output_dir> [options]
+    tfs-build-empirical growth.csv --binding_df binding.csv \
+        --library_config library_config.yaml --seed 42 --out_prefix emp
 
-**Positional arguments:**
+It writes ``emp_phenotype_model.json``, the distribution, and
+``emp_stage1_fits.csv``, the per-genotype fits, plus the configure and
+pre-fit files under ``emp_configure_*`` and ``emp_prefit_*``. The pre-fit is
+the slow step. Once a calibration exists, pass it with
+``--growth_calibration_file`` (the pre-fit priors CSV, or a CSV with
+``condition_rep``, ``growth_k`` and ``growth_m``) to skip configuring and
+pre-fitting. ``--binding_df``, ``--library_config`` and ``--seed`` are then
+not needed. ``--num_workers -1`` runs the Stage 1 fits in parallel.
 
-* ``config_file``: Path to the simulate YAML configuration file.
-* ``output_dir``: Directory to write output files (created if absent).
+The Stage 1 fits do not correct for congression, so the distribution carries
+its small bias (about 0.05 ln units in the measured regime).
 
-**Optional arguments:**
+To simulate from the distribution, set two keys in an ordinary simulate
+config:
 
-* ``--output_prefix``: Prefix for all output filenames (default: ``tfscreen_``).
-* ``--num_replicates``: Number of independent experimental replicates to
-  simulate (default: 2).
+.. code-block:: yaml
 
-**Outputs** (all written to ``output_dir``):
+    phenotype_source: empirical
+    empirical:
+      phenotype_model: /abs/path/to/emp_phenotype_model.json
 
-* ``{prefix}library.csv`` — all genotypes in the library.
-* ``{prefix}phenotype.csv`` — ground-truth growth rates per genotype.
-* ``{prefix}genotype_theta.csv`` — ground-truth *θ* per genotype and
-  titrant concentration.
-* ``{prefix}growth.csv`` — analysis-ready ln(CFU) data (same format as
-  ``tfs-process-counts`` output).
-* ``{prefix}binding.csv`` — simulated binding curve data (only written when
-  ``binding_data`` is present in the config).
+Every genotype then gets a resampled Hill curve and ``dk_geno``; wild type
+keeps its own Stage 1 fit with ``dk_geno`` 0. ``theta_component`` is forced
+to ``hill_geno``, activity is forced to 1, and ``theta_priors`` and
+``theta_sim_priors`` are ignored. The ``growth`` block must hold the same
+calibrated *k* and *m* the build used, or the simulated growth will not
+match the distribution. ``binding_data`` still chooses which genotypes are
+measured, and their curves come from the resampled phenotypes. The
+:download:`empirical example config <../../examples/simulate-empirical/simulate_config.yaml>`
+shows a complete file.
 
-tfs-setup-sim-grid
-------------------
+Prior-predictive datasets
+-------------------------
 
-Creates a directory grid for sweeping simulate parameters (see
-:doc:`grid` for a full description of the grid format). Each subdirectory
-receives its own ``tfs_sim_config.yaml`` derived from a base config, plus
-an optional rendered shell script.
+``tfs-sample-prior`` draws synthetic datasets from a configured model's own
+prior rather than from the simulator. It takes the YAML written by
+``tfs-configure-model``:
 
 .. code-block:: bash
 
-    tfs-setup-sim-grid simulate_grid.yaml --out_prefix my_sim_grid
+    tfs-sample-prior tfs_configure_config.yaml --num_datasets 10 --seed 0 --out_prefix tfs_prior
 
-An :download:`annotated example grid YAML <../../examples/simulate/simulate_grid.yaml>`
-is provided in ``examples/simulate/``. After running the command, launch all
-simulations with:
+Each dataset gives ``tfs_prior_NNN_growth.csv``, the configured growth table
+with ``ln_cfu`` replaced by a prior draw, and
+``tfs_prior_NNN_ground_truth.h5``, the latent values that generated it in
+the format of a ``tfs-sample-posterior`` file. Observation noise from
+``ln_cfu_std`` is added unless ``--no_noise`` is given. Fitting these
+datasets checks the inference against the model it assumes, the basis of
+simulation-based calibration. ``tfs-simulate`` is the stronger test, since
+its physics is not the fit's model.
+
+Simulation grids
+----------------
+
+``tfs-setup-sim-grid`` sets up a sweep of simulate settings, one directory
+per combination, each with its own ``tfs_sim_config.yaml`` and a rendered
+run script:
 
 .. code-block:: bash
 
-    for d in my_sim_grid/*/; do
-        cd "$d" && bash run.sh && cd -
-    done
+    tfs-setup-sim-grid simulate_grid.yaml --out_dir my_sim_grid
+
+The :download:`example grid YAML <../../examples/simulate/simulate_grid.yaml>`
+and its ``run.sh`` template are in ``examples/simulate/``. :doc:`grid`
+describes the grid format, and ``tfs-summarize-calibration`` pools the
+posterior calibration across a finished simulate-and-fit grid.
diff --git a/docs/source/summarize-fit.rst b/docs/source/summarize-fit.rst
index 25315f70..7cafdd86 100644
--- a/docs/source/summarize-fit.rst
+++ b/docs/source/summarize-fit.rst
@@ -2,58 +2,84 @@
 tfs-summarize-fit
 =================
 
-``tfs-summarize-fit`` collects the outputs of a completed model-fitting run,
-computes prediction quality statistics, and writes a set of diagnostic plots
-and tables to a ``summary/`` subdirectory.
+``tfs-summarize-fit`` collects the outputs of a finished fit, computes
+prediction statistics and writes diagnostic plots and tables. It is the quick
+look at a run, and the place where simulated runs are scored against their
+ground truth.
 
-.. note::
+.. code-block:: bash
 
-   **CSV files are the authoritative outputs.**  Every PDF written by this
-   command has a matching CSV containing the exact data used to generate it.
-   Use the CSVs for downstream quantitative analysis; treat the PDFs as
-   human-readable summaries.
+    tfs-summarize-fit out/
 
-Usage
------
+The one positional argument is the run directory. Outputs go to
+``{run_dir}/summary/`` with the prefix ``tfs_summarize`` unless
+``--out_prefix`` says otherwise. ``--ref_theta_file`` names a theta reference
+table; see below. The full flag list is in :doc:`cli`.
 
-.. code-block:: bash
+Every PDF has a matching CSV holding the exact data behind it. Use the CSVs
+for any quantitative analysis and treat the PDFs as summaries.
+
+What the command reads
+----------------------
+
+The command finds its inputs by file-name suffix inside ``run_dir``. When
+several files match, it warns and uses the first in alphabetical order. Each
+missing input switches off only the outputs that need it.
+
+``*_config.yaml``
+    The model config from ``tfs-configure-model``. Other YAML files in the
+    directory, such as a simulate config, are ignored. The config also points
+    to the binding data, used as training theta, and to the guesses CSV.
+
+``*_pred_theta.csv``
+    Theta predictions from ``tfs-predict-theta``.
 
-    tfs-summarize-fit <run_dir> [options]
+``*_pred_growth.csv``
+    Growth predictions from ``tfs-predict-growth``. Optional.
 
-**Positional arguments:**
+``*_losses.txt``
+    The loss history from ``tfs-fit-model``. The pre-MAP and pre-fit loss
+    files (``*_premap_losses.txt``, ``*_prefit_losses.txt``) are skipped.
+    Optional.
 
-* ``run_dir`` — directory containing the completed model-fit outputs (i.e.
-  the ``out/`` directory produced by ``run.sh``).
+``*_posterior.h5`` or ``*_params.npz``
+    The source for the growth trajectories, with the posterior preferred.
+    ``tfs-sample-posterior`` writes ``{out_prefix}.h5``, so the default name
+    ``tfs_posterior.h5`` matches but a custom prefix such as ``run1.h5``
+    does not. The MAP ``*_params.npz`` from ``tfs-fit-model`` is the
+    fallback. Optional.
 
-**Optional arguments:**
+``*_params_*.csv``
+    Parameter tables from ``tfs-extract-params``. Compared to truth on
+    simulated runs. Optional.
 
-* ``--ref_theta_file`` — CSV with columns ``genotype``, ``titrant_name``,
-  ``titrant_conc``, and a ``theta_obs`` or ``theta`` column containing
-  known θ values for evaluating out-of-sample predictions.  When omitted,
-  the script automatically looks for ``*_sim_genotype_theta.csv`` in
-  ``run_dir`` (the file written by ``tfs-simulate``).
-* ``--out_prefix`` — prefix for all output files.  Defaults to
-  ``{run_dir}/summary/tfs_summarize``.
+Simulated runs also have ground truth from ``tfs-simulate``:
 
-What the script needs
----------------------
+``*_sim_genotype_theta.csv``
+    True theta for every genotype and titrant point. Used as the theta test
+    reference unless ``--ref_theta_file`` names another table. A reference
+    table needs ``genotype``, ``titrant_name``, ``titrant_conc`` and a
+    ``theta_obs`` or ``theta`` column.
 
-The script scans ``run_dir`` for:
+``*_sim_parameters.csv``
+    True per-genotype parameters.
 
-* ``*_config.yaml`` — model configuration (required).
-* ``*_pred_theta.csv`` — theta predictions from ``tfs-predict-theta`` (required).
-* ``*_sim_genotype_theta.csv`` — ground-truth θ from ``tfs-simulate``
-  (used for test statistics; absent for real-data runs).
-* ``*_pred_growth.csv`` — growth predictions from ``tfs-predict-growth``
-  (optional; enables growth statistics and plots).
-* ``*_losses.txt`` — training loss history from ``tfs-fit-model``
-  (optional; enables the loss-curve plot).
-* ``*_params_*.csv`` — parameter summaries from ``tfs-extract-params``
-  (optional; enables parameter-recovery plots when ``*_sim_parameters.csv``
-  is also present).
+``*_sim_mutation_parameters.csv`` and ``*_sim_pair_parameters.csv``
+    True per-mutation and per-pair parameters of the thermodynamic models.
 
-Output file reference
----------------------
+``*_sim_growth_parameters.csv``
+    True per-condition growth parameters.
+
+``*_sim_k_ref.csv``
+    The true base growth rate ``k_ref``.
+
+``*_sim_transformation_lam.csv``
+    The true congression rate ``lam`` of the ``mixture`` transformation.
+
+Output files
+------------
+
+All names below use the default prefix ``tfs_summarize``.
 
 .. list-table::
    :header-rows: 1
@@ -62,273 +88,382 @@ Output file reference
    * - File
      - Contents
    * - ``tfs_summarize_fit_summary.json``
-     - Nested statistics dict (see :ref:`fit-summary-json`).
-   * - ``tfs_summarize_theta_corr.pdf`` / ``.csv``
-     - Two-panel θ correlation: training (left) and test (right).
+     - Statistics and run metadata (see :ref:`fit-summary-json`).
+   * - ``tfs_summarize_theta_corr.pdf``
+     - Two-panel theta correlation: training on the left, test on the right.
    * - ``tfs_summarize_theta_corr_training.csv``
-     - Joined (ref, predicted) θ pairs for training genotypes.
+     - Training theta predictions joined to the binding observations, which
+       are in the ``ref`` column.
    * - ``tfs_summarize_theta_corr_test.csv``
-     - Same for test genotypes.
+     - Theta predictions joined to the reference table, which is in the
+       ``ref`` column.
    * - ``tfs_summarize_growth_corr.pdf`` / ``.csv``
-     - Observed vs predicted ln(CFU) across all training points.
+     - Observed against predicted ln(CFU). The CSV is a copy of
+       ``*_pred_growth.csv`` with ``ln_cfu`` and ``ln_cfu_std`` renamed to
+       ``ref`` and ``ref_std``.
    * - ``tfs_summarize_{genotype}_theta_fits.pdf`` / ``.csv``
-     - Per-genotype θ curve overlaid with binding observations.
+     - Predicted theta curve over the binding observations, one pair per
+       binding genotype.
    * - ``tfs_summarize_{genotype}_trajectory.pdf`` / ``.csv``
-     - Per-genotype predicted ln(CFU) vs time across all conditions.
+     - Predicted ln(CFU) against time in every condition.
    * - ``tfs_summarize_losses.pdf``
-     - Training loss curve.
-   * - ``tfs_summarize_theta_training_calibration.pdf``
-     - Calibration plots for training θ (PIT histogram + coverage curve).
-   * - ``tfs_summarize_theta_test_calibration.pdf``
-     - Calibration plots for test θ.
-   * - ``tfs_summarize_growth_calibration.pdf``
-     - Calibration plots for growth predictions.
+     - The loss history.
+   * - ``tfs_summarize_{name}_calibration.pdf``, ``_pit.csv``,
+       ``_calibration_curve.csv``
+     - Interval calibration for ``theta_training``, ``theta_test``,
+       ``growth`` and each ``params_*`` table with a reference.
    * - ``tfs_summarize_params_{name}.pdf`` / ``.csv``
-     - Per-parameter scatter of simulated vs inferred values (simulated
-       data only).
-   * - ``tfs_summarize_params_{name}_calibration.pdf``
-     - Calibration plots for each inferred parameter (simulated data only).
+     - A parameter table with the true value added as ``ref``. Simulated
+       runs only.
+   * - ``tfs_summarize_tube_offsets.pdf`` / ``.csv``,
+       ``tfs_summarize_tube_offset_trends.csv``
+     - The per-tube sample offsets in prior SDs and their trends with titrant
+       and time, one row per condition (see `Tube offsets`_). Fits with tube
+       offsets only.
+
+Slashes and spaces in genotype names become underscores in file names, so
+``M42I/K84L`` gives ``tfs_summarize_M42I_K84L_trajectory.pdf``.
+
+The theta fit plots need binding data with a ``theta_std`` column. The
+trajectory plots need growth data and a posterior or params file. With binding
+data, trajectories are drawn for the binding genotypes. Without it, they are
+drawn for wt, the spiked genotypes and 10 other genotypes picked at random
+with a fixed seed, so a rerun draws the same ones.
+
+Calibration needs at least two ``q<level>`` columns. A MAP checkpoint passed
+to the predict commands gives only ``q0.5``, so its predictions get
+correlation statistics but no calibration outputs.
+
+Relative-X fits
+---------------
+
+A ``hill_relative`` fit predicts the wt-relative growth variable X rather
+than theta (see :doc:`model`). For such a fit, ``metadata.theta_scale`` in the
+JSON is ``X``; it is ``theta`` otherwise. Simulated truth is mapped onto the
+X scale before comparison. The mapping uses the gauge concentrations recorded
+in the config, wt's true theta at those concentrations from the reference
+table, and each genotype's simulated activity from ``*_sim_parameters.csv``.
+The true ``growth_k`` and ``growth_m`` are mapped the same way: k becomes
+wt's growth at the high gauge concentration and m its change between the two.
+That mapping needs a single titrant. With several, the growth truth is left
+blank. When the truth cannot be mapped, for example because the reference has
+no wt value at a gauge concentration, the theta test comparison is skipped
+with a warning rather than made on the wrong scale.
 
 Reading the outputs
 -------------------
 
 .. _fit-summary-json:
 
-fit_summary.json — at a glance
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The summary JSON
+~~~~~~~~~~~~~~~~
 
-The JSON file gives a rapid numerical overview of the run.  Open it in any
-text editor or load it with ``json.load``.
+``tfs_summarize_fit_summary.json`` has four top-level keys. ``metadata``
+describes the run. ``theta`` holds ``training`` and ``test`` statistics, and
+``growth`` holds ``training`` statistics. ``tube_offsets`` holds the
+tube-offset summary (see `Tube offsets`_). A block that could not be
+computed, or a fit without tube offsets, is ``null``. The example below, with illustrative values, is a simulated
+growth-only run: there is no binding data, so ``theta.training`` is null.
 
 .. code-block:: json
 
    {
      "metadata": {
-       "n_parameters": 5005,
-       "n_theta_training_points": 32,
+       "run_dir": "/home/user/runs/run_0001/out",
+       "ref_theta_file": "/home/user/runs/run_0001/out/tfs_sim_genotype_theta.csv",
+       "timestamp": "2026-10-01T14:02:11.512340",
+       "n_parameters": 412,
+       "n_theta_training_points": null,
        "n_theta_test_points": 3752,
        "n_growth_training_points": 54180,
-       "final_loss": 7281255.0
+       "final_loss": 251873.4,
+       "theta_scale": "X"
      },
      "theta": {
-       "training": { "pearson_r": 0.999, "r_squared": 0.998, "rmse": 0.016 },
-       "test":     { "pearson_r": 0.936, "r_squared": 0.876, "rmse": 0.140 }
+       "training": null,
+       "test": {
+         "pct_success": 1.0,
+         "rmse": 0.071,
+         "normalized_rmse": 0.058,
+         "pearson_r": 0.968,
+         "spearman_r": 0.951,
+         "r_squared": 0.937,
+         "mean_error": -0.004,
+         "residual_corr": -0.12,
+         "residual_corr_p_value": 1.3e-13,
+         "bp_p_value": 0.002
+       }
      },
      "growth": {
-       "training": { "pearson_r": 0.981, "r_squared": 0.963, "rmse": 0.577 }
-     }
+       "training": {
+         "pct_success": 1.0,
+         "rmse": 0.41,
+         "normalized_rmse": 0.031,
+         "pearson_r": 0.991,
+         "spearman_r": 0.987,
+         "r_squared": 0.982,
+         "mean_error": 0.002,
+         "residual_corr": -0.05,
+         "residual_corr_p_value": 1.1e-31,
+         "bp_p_value": 0.0
+       }
+     },
+     "tube_offsets": null
    }
 
-Key fields:
-
-* ``n_parameters`` — total number of free parameters (from the guesses CSV).
-  Compare to ``n_growth_training_points`` to check for over-parameterisation.
-* ``final_loss`` — the converged SVI ELBO (negative; more negative = better).
-  Use this to compare runs with identical data but different components or seeds.
-* ``theta.training`` — statistics comparing the model's θ predictions against
-  the direct **binding observations** used as training data.  These will
-  always be near-perfect because the model is conditioned on these values.
-* ``theta.test`` — statistics comparing θ predictions against the full
-  ground-truth θ grid from ``tfs-simulate``.  This is the real generalization
-  diagnostic: the vast majority of test points were never directly observed.
-* ``growth.training`` — statistics comparing predicted ln(CFU) against the
-  observed growth data.
-
-Useful derived checks:
-
-* ``theta.test.r_squared > 0.85`` is a reasonable threshold for a well-fitting
-  model on a library of this size and noise level.
-* A large gap between ``theta.training.rmse`` and ``theta.test.rmse`` signals
-  that the model fits the spiked genotypes well but struggles to generalize
-  to the broader library.
-* ``residual_corr`` and its p-value test for autocorrelation in the residuals.
-  A significant value indicates systematic structure the model is not capturing.
-
-Training-loss curve (tfs_summarize_losses.pdf)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The loss curve shows the SVI ELBO at each recorded epoch.  A healthy run
-shows a smooth decrease that levels off to a plateau; a run that has not
-converged will still be declining at the final epoch.  If the loss spikes
-upward mid-run, the learning rate or the prior scales may need adjustment.
-
-Theta correlation (tfs_summarize_theta_corr.pdf)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The metadata keys:
+
+* ``run_dir`` and ``ref_theta_file`` are the resolved absolute paths.
+  ``ref_theta_file`` is null when no reference was found.
+* ``timestamp`` is when the summary ran.
+* ``n_parameters`` is the number of rows in the config's guesses CSV, a rough
+  size of the model.
+* ``n_theta_training_points``, ``n_theta_test_points`` and
+  ``n_growth_training_points`` count the points behind each statistics block.
+* ``final_loss`` is the last entry of ``*_losses.txt``. Each entry is the
+  median loss over one convergence window, not a single step. The loss is the
+  negative ELBO for SVI and the negative log joint density for MAP. Lower is
+  better, and it is usually positive. Compare it only between runs on the same
+  data and model.
+* ``theta_scale`` is ``X`` for a relative fit and ``theta`` otherwise.
+
+Each statistics block compares the ``q0.5`` prediction with its reference:
+
+* ``pearson_r``, ``spearman_r`` and ``r_squared`` measure agreement.
+  Spearman tests whether genotypes are ranked correctly.
+* ``rmse`` is the root mean squared error. ``normalized_rmse`` divides it by
+  the 2.5 to 97.5 percentile range of the reference, so it reads as error
+  relative to the signal.
+* ``mean_error`` is the average of prediction minus reference, the bias.
+* ``residual_corr`` and ``residual_corr_p_value`` test whether the error
+  depends on the true value. A significant correlation means systematic
+  structure, often shrinkage of the extremes toward the middle.
+* ``bp_p_value`` is the Breusch-Pagan test. A small value means the error
+  variance changes with the true value.
+* ``pct_success`` is the fraction of predictions that are not NaN.
+* Coverage of the reference by the posterior intervals is in the
+  calibration outputs below, not in these blocks.
+
+The three blocks mean different things. ``theta.training`` compares
+predictions with the binding observations the model was fit to, so it should
+be close to perfect. A poor value points to a data-loading or configuration
+problem. ``theta.test`` compares predictions with the full reference grid,
+which on a simulated run covers every genotype at every concentration, nearly
+all of them never measured directly. That is the real test of the fit. A large
+gap between training and test error means the model fits the anchors but does
+not carry that accuracy to the library. ``growth.training`` compares
+predicted with observed ln(CFU) for every observed point.
+
+Tube offsets
+~~~~~~~~~~~~
+
+A ``level`` tube offset is meant to absorb tube noise: an error in a tube's
+total or its composition, independent from tube to tube and about the size of
+the OD600 scatter. On the dev data the MAP's better optimum used the offsets
+for something else. Within each selection condition they followed IPTG, from
++2 to -4 ln units across 0 to 1 mM, and stayed constant over time, carrying
+growth the model could not express (see :doc:`fitting`, "Staged MAP"). This
+check makes that visible.
+
+It reads ``*_sample_offset_offset.csv`` (``*_sample_offset_delta_k.csv`` for
+``normal`` offsets) from ``tfs-extract-params``. The prior SD comes from
+``*_sample_offset_sigma.csv`` when the SD was learned, or from
+``sigma_fixed`` in the priors file when it was held.
+
+* ``tfs_summarize_tube_offsets.csv`` is the offsets table with ``offset``
+  (the ``q0.5``) and ``z``, the offset in prior SDs.
+* ``tfs_summarize_tube_offset_trends.csv`` has one row per condition
+  (``library``, ``condition_pre``, ``condition_sel``, ``titrant_name``):
+  the mean and SD of the offsets, in ln units and in prior SDs; the Spearman
+  correlation of the offset with titrant concentration (``rho_titrant``,
+  ``p_titrant``) and with selection time (``rho_time``, ``p_time``);
+  ``r2_titrant``, the share of the offsets' variance explained by the mean
+  at each concentration, which catches a non-monotone pattern; and
+  Benjamini-Hochberg q values over every condition and both trends.
+  ``structured`` is true when either q is below 0.05.
+* ``tfs_summarize_tube_offsets.pdf`` plots the offsets against titrant, one
+  panel per selection condition, colored by time.
+* The JSON's ``tube_offsets`` block holds ``n_tubes``, ``sigma`` and its
+  source, the offsets' ``sd``, ``sd_over_sigma``, the median, 95th
+  percentile and maximum of ``|z|``, the ``range``, and ``structured`` with
+  the list of structured conditions.
+
+Noise offsets have ``sd_over_sigma`` near 1 and no structured condition. A
+structured fit is not wrong by construction, since a real tube effect can
+trend with IPTG, but its offsets are carrying signal the growth model
+leaves out, and its *k*, *m* and curves should not be read until that is
+understood.
+
+Loss history
+~~~~~~~~~~~~
+
+``tfs_summarize_losses.pdf`` plots each window's median loss against epoch.
+A healthy run falls and levels off. A run still falling at the end did not
+converge. ``*_convergence.csv`` from ``tfs-fit-model`` records why the run stopped; see
+:doc:`fitting`.
+
+Theta correlation
+~~~~~~~~~~~~~~~~~
 
 .. figure:: _static/saa_theta_corr.png
    :alt: Two-panel theta correlation plot
    :width: 100%
 
-   **Left panel** — training θ: the model's posterior-median prediction
-   vs the direct binding observations used as input.  Near-perfect agreement
-   is expected and is not by itself a measure of model quality.  **Right
-   panel** — test θ: predictions vs the full ground-truth θ grid from
-   simulation, covering all library genotypes at all concentrations.  Points
-   far from the diagonal indicate genotypes or concentrations where the
-   mutation-additivity assumption breaks down.
-
-The two panels serve different purposes:
+   Left: training theta, the posterior median against the binding
+   observations used as input. Right: test theta, predictions against the
+   simulated ground truth for every library genotype at every concentration.
 
-* **Training panel** confirms that the model correctly ingest the binding
-  data — any large deviation here points to a data-loading or configuration
-  error.
-* **Test panel** is the generalization diagnostic.  The model must predict θ
-  for thousands of genotypes that were never directly measured; it does so by
-  combining per-mutation effects inferred from the spiked controls and the
-  growth data.  Scatter around the diagonal reflects the irreducible noise in
-  that extrapolation.
+The training panel confirms that the model took in the binding data. The test
+panel is the generalization check. The model must predict theta for thousands
+of genotypes never measured directly, so scatter around the diagonal reflects
+how well growth alone pins each genotype down. Points far from the diagonal
+mark genotypes or concentrations the model cannot resolve. On a relative fit
+both axes are on the X scale.
 
-Per-genotype theta fits (tfs_summarize_{genotype}_theta_fits.pdf)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Per-genotype theta fits
+~~~~~~~~~~~~~~~~~~~~~~~
 
 .. figure:: _static/saa_wt_theta_fits.png
    :alt: Wild-type theta fit
    :width: 60%
 
-   Wild-type θ curve: posterior-median prediction (line) overlaid with the
-   binding observations (circles).  The x-axis is on a log scale.  A sigmoid
-   transition from high θ at low [IPTG] to low θ at high [IPTG] is the
-   expected shape for a repressor inactivated by its inducer.
+   Wild-type theta: the posterior median prediction as a line over the binding
+   observations as points, on a log concentration axis. A repressor released
+   by its inducer goes from high theta at low IPTG to low theta at high IPTG.
 
-One plot is produced per genotype that appears in both the binding data and
-the theta predictions.  For spiked genotypes with measured binding curves
-these plots confirm that the Hill model adequately describes the shape of the
-induction curve.
+One plot is drawn per genotype found in both the binding data and the theta
+predictions. These plots check that the theta model describes the shape of
+each measured induction curve.
 
-Growth trajectories (tfs_summarize_{genotype}_trajectory.pdf)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Growth trajectories
+~~~~~~~~~~~~~~~~~~~
 
-The trajectory plots show predicted ln(CFU) versus time for every growth
-condition, with the observed data points (two replicates) overlaid on the
-posterior-median prediction and its 95% credible interval.
+The trajectory plots show predicted ln(CFU) against time for every condition,
+with the posterior median, its 95% interval and the observed points. Each
+genotype gets one page with one panel per condition. A dashed vertical line
+marks the switch from pre-growth to selection.
 
-One page is produced per genotype; each page contains one panel per
-condition (library × selection × titrant concentration).  The dashed
-vertical line marks the transition from pre-growth to selection medium.
+Things to look for:
 
-Typical patterns to look for:
-
-* **Pre-growth panels** (``kanR-kan``, ``pheS-4CP`` without selection):
-  all genotypes should grow at similar rates; large deviations suggest a
-  problematic ``dk_geno`` estimate.
-* **Selection panels at extreme IPTG**: the two markers should respond in
-  opposite directions as θ moves from ~1 to ~0 with increasing [IPTG].
-* **Replicate agreement**: the two colored lines should be close together;
-  large replicate-to-replicate variation indicates high tube noise or a
+* In pre-growth, genotypes should grow at similar rates. A large deviation
+  suggests a poor ``dk_geno`` estimate.
+* At the extremes of IPTG the two markers should move in opposite directions
+  as theta goes from about 1 to about 0.
+* Replicates should agree. Large disagreement points to tube noise or a
   confounded sample.
 
-Growth correlation (tfs_summarize_growth_corr.pdf)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Growth correlation
+~~~~~~~~~~~~~~~~~~
 
 .. figure:: _static/saa_growth_corr.png
    :alt: Growth prediction correlation
    :width: 65%
 
-   Observed vs predicted ln(CFU) across all genotypes, conditions, and
-   time-points in the training data (54,180 points in the example run).
-   Points cluster tightly along the diagonal except at low ln(CFU) values
-   (~3–7), where sequencing noise dominates and the model's predictions
-   spread somewhat.
+   Observed against predicted ln(CFU) for every genotype, condition and time
+   point in the training data. Points spread more at low ln(CFU), where
+   sequencing noise dominates.
 
-A tight cluster along the diagonal confirms that the growth model
-accurately reproduces the data.  Systematic vertical or horizontal bands
-point to conditions or time-points where the model consistently over- or
-under-predicts; these are usually caused by incorrect ``{m, b}`` priors for
-those conditions or by tube-noise events in specific samples.
+A tight cluster along the diagonal means the growth model reproduces the data.
+Vertical or horizontal bands mark conditions or time points the model over- or
+under-predicts throughout. Those usually come from poor per-condition growth
+priors or from bad tubes.
 
 .. _calibration-plots:
 
-Calibration plots (tfs_summarize_*_calibration.pdf)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Calibration
+~~~~~~~~~~~
 
 .. figure:: _static/saa_growth_calibration.png
    :alt: Growth calibration plots
    :width: 100%
 
-   Growth-prediction calibration.  **Left** — PIT histogram; the dashed
-   red line shows the ideal uniform distribution.  **Right** — calibration
-   curve; the dashed black line is perfect calibration.
-
-Calibration plots assess whether the model's **posterior uncertainty
-intervals** are appropriately sized — neither too narrow (overconfident)
-nor too wide (underconfident).
-
-**PIT histogram** (left panel)
-
-The Probability Integral Transform (PIT) value for each observation is the
-fraction of the posterior predictive distribution that falls below the
-observed value.  For a perfectly calibrated model, PIT values are
-uniformly distributed on [0, 1] and the histogram should be flat.
-
-* **U-shaped histogram** (spikes near 0 and 1, flat middle): many
-  observations fall outside the predicted intervals → posterior is
-  **overconfident** (intervals too narrow).
-* **Hump-shaped histogram** (peak in the middle, low edges): observations
-  cluster inside the predicted intervals → posterior is **underconfident**
-  (intervals too wide).
-
-**Calibration curve** (right panel)
-
-For each nominal coverage level *α* (x-axis), the calibration curve shows
-the empirical fraction of observations that fall within the model's *α*
-credible interval (y-axis).
-
-* **Below the diagonal**: empirical coverage < nominal → **overconfident**.
-* **Above the diagonal**: empirical coverage > nominal → **underconfident**.
-* **On the diagonal**: perfectly calibrated.
-
-The example growth-calibration plot shows U-shaped PIT and a curve below
-the diagonal.  This slight overconfidence in growth predictions is typical
-of SVI: the mean-field variational approximation tends to underestimate
-posterior variance.  It does not indicate a model misspecification; the
-point predictions (correlation, R²) remain accurate.
-
-Similar calibration plots are produced for theta (training and test) and for
-each parameter group when ground-truth values are available.
-
-Parameter recovery (tfs_summarize_params_{name}.pdf)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+   Growth-prediction calibration. Left: PIT histogram, with the dashed red
+   line at the ideal uniform. Right: calibration curve, with the dashed black
+   line at perfect calibration.
+
+Calibration asks whether the posterior intervals have the right width. The PIT
+value of an observation is the fraction of its predictive distribution below
+the observed value, interpolated from the ``q<level>`` columns. For a
+calibrated model the PIT values are uniform on [0, 1] and the histogram is
+flat. A U shape, with spikes near 0 and 1, means many observations fall
+outside their intervals: the posterior is overconfident. A hump in the middle
+means the intervals are too wide.
+
+The calibration curve plots, for each nominal coverage level, the fraction of
+observations inside that interval. Below the diagonal is overconfident and
+above is underconfident. ``_pit.csv`` holds the PIT values (``true_val``,
+``pit``) and ``_calibration_curve.csv`` the curve (``nominal``,
+``empirical``).
+
+The example shows a U-shaped PIT and a curve below the diagonal: the growth
+intervals are somewhat too narrow, while the point predictions stay accurate.
+Variational guides tend to understate posterior variance, so check calibration
+before trusting interval widths. Theta calibration on a simulated run is the
+more important check, since theta is what downstream analysis uses.
+
+Parameter recovery
+~~~~~~~~~~~~~~~~~~
 
 .. figure:: _static/saa_params_log_hill_K.png
    :alt: log_hill_K parameter recovery
    :width: 65%
 
-   Simulated vs inferred ``log_hill_K`` (log₁₀ of the Hill dissociation
-   constant) for all library genotypes.  Each point is one genotype; the
-   dashed line is perfect recovery.  Good agreement across the dynamic range
-   confirms that the model correctly identifies which genotypes have shifted
-   binding affinities.
-
-Parameter-recovery plots are produced only when the run directory contains
-a ``*_sim_parameters.csv`` file (i.e. when running on simulated data).  They
-compare the posterior-median inferred value for each parameter against the
-ground truth used during simulation.
-
-One plot is produced per parameter group.  Versions are produced for:
-
-* **Direct parameters** — per-genotype values (e.g. ``log_hill_K``,
-  ``theta_high``, ``theta_low``, ``hill_n``).
-* **Diff parameters** (``_d_`` infix) — per-mutation effect sizes relative to
-  wild-type (e.g. ``d_log_hill_K``, ``d_logit_low``).
-* **Epistasis parameters** (``_epi_`` infix) — pairwise epistatic deviations
-  from additivity for double-mutant genotypes.
-
-Each parameter group also has a companion calibration plot
-(``*_calibration.pdf``) assessing whether the inferred uncertainty correctly
-covers the true value.
-
-Interpreting parameter recovery
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-* Tight scatter around the diagonal for all parameters → model recovers the
-  ground truth; the experimental design has sufficient signal.
-* Tight recovery for direct parameters but wide scatter for diff/epi
-  parameters → the model identifies which genotypes differ from wild-type
-  but cannot resolve the per-mutation contributions as precisely.
-* Systematic bias (all points shifted to one side of the diagonal) →
-  mis-specified priors, an incorrect model component, or a calibration issue
-  in the pre-fit step.
-* Good recovery for ``log_hill_K`` but poor for ``hill_n`` or ``theta_high``
-  → the mid-range of the induction curve is well-constrained but the shape
-  of the saturation plateaux is not (common when the growth data covers only
-  a restricted θ range for most genotypes).
+   Simulated against inferred ``log_hill_K``, the natural log of the Hill
+   constant, for every library genotype. Each point is one genotype and the
+   dashed line is perfect recovery.
+
+Parameter recovery runs only on simulated runs. Each ``*_params_*.csv`` table
+from ``tfs-extract-params`` is matched to its truth, written with a ``ref``
+column as ``tfs_summarize_params_{name}.csv``, plotted against ``q0.5`` and
+checked for calibration.
+
+* Per-genotype tables such as ``log_hill_K``, ``hill_n``, ``theta_low``,
+  ``theta_high`` and ``dk_geno`` take their truth from
+  ``*_sim_parameters.csv``. Tables on a log or logit scale are matched by
+  transforming the truth.
+* Per-mutation tables (``d_`` prefix) and pair tables (``epi_`` prefix) are
+  joined to ``*_sim_mutation_parameters.csv`` and
+  ``*_sim_pair_parameters.csv`` when those files have the column, as they do
+  for the thermodynamic models. Otherwise, as for ``hill_mut``, a ``d_`` table
+  is compared with the true value minus wt's and an ``epi_`` table with the
+  additive epistasis of the true values.
+* The per-structure ``ddG_{structure}`` tables of the PddG and PnnC models
+  are joined to the ``ddG_{structure}`` columns of
+  ``*_sim_mutation_parameters.csv`` on ``mutation``. Each
+  ``ddG_prior_mean_{structure}`` table, the structural prior mean, is
+  compared with the same truth to show how well the prior alone predicts it.
+  It is a point, so it gets no calibration; read its ``rmse`` and
+  ``pearson_r`` in ``tfs-summarize-calibration``. A fit without ΔΔG, such as
+  PK on a PddG simulation, writes no ddG tables. Then one note is printed in
+  place of a warning per structure, and its ``d_`` tables are scored against
+  the same truth. In a model without an unfolded state (``U0``) the
+  per-structure ΔΔG shares one shift across structures that the data do not
+  determine; the fit takes it from the prior. For those fits the
+  ``ddG_{structure}`` summary is still written, but with the truth in a
+  ``ref_unidentified`` column and ``per_state_identified`` set to False, and
+  it gets no plot or calibration. A note says so. Score U0 fits on their
+  ``d_`` tables.
+* ``params_ddG_sigma``, the per-structure SD of the PddG and PnnC ΔΔG about
+  their prior means, is joined to ``*_sim_ddG_sigma.csv`` on ``structure``.
+  It gets a CSV and a calibration check but no plot.
+* ``params_growth_k``, ``params_growth_m`` and the other growth-model tables
+  are joined to ``*_sim_growth_parameters.csv`` on ``condition_rep``.
+* ``params_k_ref`` and ``params_lam`` are single values compared with
+  ``*_sim_k_ref.csv`` and ``*_sim_transformation_lam.csv``. They get a CSV
+  and a calibration check but no plot.
+
+A table whose truth cannot be resolved is skipped. A truth column with no
+matching parameter table produces a warning.
+
+How to read recovery:
+
+* Tight scatter around the diagonal means the design has the signal to
+  recover the parameter.
+* Good recovery of per-genotype values with wide scatter in ``d_`` or
+  ``epi_`` values means the model sees which genotypes differ but cannot split
+  the difference into per-mutation parts as precisely.
+* A shift of all points to one side points to a misspecified prior, a wrong
+  component or a problem in the pre-fit calibration.
+* Good ``log_hill_K`` with poor ``hill_n`` or ``theta_high`` means the
+  midpoint of the curve is well constrained and its plateaus are not. That is
+  common when most genotypes span only part of the theta range.
diff --git a/examples/od600/make_example_data.py b/examples/od600/make_example_data.py
new file mode 100644
index 00000000..96e43bc1
--- /dev/null
+++ b/examples/od600/make_example_data.py
@@ -0,0 +1,46 @@
+"""
+Write synthetic inputs for tfs-calibrate-od600 (replicates.csv and
+plate_counts.csv), to show the formats and exercise the tool.
+
+The numbers are made up, not any instrument's: a culture with
+cfu/mL = 8e8 OD + 2e8 OD^2, a reader whose readings sit on a 0.088 floor
+with 1.5% noise, 1:3 dilution series read ten times, and 16 cultures
+plated at 1e5 dilution (five 1:10 steps, 0.1 mL spread). Each lab
+measures its own calibration; do not use these for real data.
+
+    python make_example_data.py
+    tfs-calibrate-od600 replicates.csv plate_counts.csv --out_prefix od600_calibration
+"""
+
+import numpy as np
+import pandas as pd
+
+rng = np.random.default_rng(20260927)
+
+
+def cfu_per_mL(od):
+    return 8e8 * od + 2e8 * od ** 2
+
+
+# Ten readings of each of six 1:3 dilutions of an OD600 ~0.6 culture.
+floor, rel_sd = 0.088, 0.015
+dilution = 1.0 / 3.0 ** np.arange(6)
+rows = []
+for d in dilution:
+    true = floor + 0.52 * d
+    for _ in range(10):
+        rows.append({"dilution": d,
+                     "od600": round(true * (1 + rel_sd * rng.normal()), 4)})
+pd.DataFrame(rows).to_csv("replicates.csv", index=False)
+
+# Sixteen cultures across OD600 0.12-0.6, each read once and plated.
+od_true = np.linspace(0.12, 0.6, 16)
+od_read = od_true * (1 + rel_sd * rng.normal(size=od_true.size))
+fold, volume = 1e5, 0.1
+colonies = rng.poisson(cfu_per_mL(od_true) * volume / fold)
+pd.DataFrame({"od600": np.round(od_read, 4),
+              "colonies": colonies,
+              "dilution": fold,
+              "plated_volume_mL": volume,
+              "num_dilutions": 5,
+              "plating_steps": 1}).to_csv("plate_counts.csv", index=False)
diff --git a/examples/od600/od600_calibration.yaml b/examples/od600/od600_calibration.yaml
new file mode 100644
index 00000000..b3d74b98
--- /dev/null
+++ b/examples/od600/od600_calibration.yaml
@@ -0,0 +1,33 @@
+# OD600-to-CFU calibration, written by tfs-calibrate-od600.
+# cfu/mL = sum_i coefficients[i] * OD600^i. covariance is the
+# coefficients' (shared by every tube calibrated with this file);
+# reading_rel_sd is one reading's SD as a fraction of the reading.
+# Specific to one plate reader, plate, volume and strain.
+kind: od600_to_cfu_per_mL
+degree: 2
+coefficients:
+- -3232113.9094636133
+- 854172076.6876991
+- 41938427.08096051
+covariance:
+- - 227467162769893.9
+  - -1599191946265586.5
+  - 2296356870091127.0
+- - -1599191946265586.5
+  - 1.2197351004649222e+16
+  - -1.828231333112995e+16
+- - 2296356870091127.0
+  - -1.828231333112995e+16
+  - 2.857868475371398e+16
+chi2_reduced: 0.39237605605574044
+reading_rel_sd: 0.01886048606352703
+detection_threshold: 0.09292
+calibrated_od600_range:
+- 0.1192
+- 0.5997
+num_plate_counts: 16
+pipette_rel_error: 0.02
+source:
+  replicate_file: replicates.csv
+  plate_count_file: plate_counts.csv
+written: '2026-09-27'
diff --git a/examples/od600/plate_counts.csv b/examples/od600/plate_counts.csv
new file mode 100644
index 00000000..62835a3f
--- /dev/null
+++ b/examples/od600/plate_counts.csv
@@ -0,0 +1,17 @@
+od600,colonies,dilution,plated_volume_mL,num_dilutions,plating_steps
+0.1192,97,100000.0,0.1,5,1
+0.1477,131,100000.0,0.1,5,1
+0.1842,151,100000.0,0.1,5,1
+0.2146,186,100000.0,0.1,5,1
+0.2448,207,100000.0,0.1,5,1
+0.2795,240,100000.0,0.1,5,1
+0.3126,239,100000.0,0.1,5,1
+0.3424,317,100000.0,0.1,5,1
+0.3782,344,100000.0,0.1,5,1
+0.4067,347,100000.0,0.1,5,1
+0.4452,379,100000.0,0.1,5,1
+0.4669,411,100000.0,0.1,5,1
+0.504,438,100000.0,0.1,5,1
+0.5335,483,100000.0,0.1,5,1
+0.5735,479,100000.0,0.1,5,1
+0.5997,527,100000.0,0.1,5,1
diff --git a/examples/od600/replicates.csv b/examples/od600/replicates.csv
new file mode 100644
index 00000000..0c9f558f
--- /dev/null
+++ b/examples/od600/replicates.csv
@@ -0,0 +1,61 @@
+dilution,od600
+1.0,0.5893
+1.0,0.6039
+1.0,0.6042
+1.0,0.6119
+1.0,0.6056
+1.0,0.604
+1.0,0.588
+1.0,0.6141
+1.0,0.6065
+1.0,0.6303
+0.3333333333333333,0.2582
+0.3333333333333333,0.2655
+0.3333333333333333,0.2713
+0.3333333333333333,0.2583
+0.3333333333333333,0.2624
+0.3333333333333333,0.2627
+0.3333333333333333,0.2592
+0.3333333333333333,0.2647
+0.3333333333333333,0.2646
+0.3333333333333333,0.2723
+0.1111111111111111,0.1426
+0.1111111111111111,0.1474
+0.1111111111111111,0.1491
+0.1111111111111111,0.1465
+0.1111111111111111,0.1456
+0.1111111111111111,0.1504
+0.1111111111111111,0.144
+0.1111111111111111,0.1456
+0.1111111111111111,0.1467
+0.1111111111111111,0.1442
+0.037037037037037035,0.1074
+0.037037037037037035,0.1042
+0.037037037037037035,0.1085
+0.037037037037037035,0.1075
+0.037037037037037035,0.1044
+0.037037037037037035,0.1078
+0.037037037037037035,0.1085
+0.037037037037037035,0.1063
+0.037037037037037035,0.1064
+0.037037037037037035,0.1086
+0.012345679012345678,0.0931
+0.012345679012345678,0.0972
+0.012345679012345678,0.0975
+0.012345679012345678,0.0942
+0.012345679012345678,0.093
+0.012345679012345678,0.0967
+0.012345679012345678,0.0946
+0.012345679012345678,0.0947
+0.012345679012345678,0.0948
+0.012345679012345678,0.0953
+0.00411522633744856,0.0895
+0.00411522633744856,0.0941
+0.00411522633744856,0.0889
+0.00411522633744856,0.0899
+0.00411522633744856,0.0897
+0.00411522633744856,0.0925
+0.00411522633744856,0.0907
+0.00411522633744856,0.0904
+0.00411522633744856,0.0903
+0.00411522633744856,0.0913
diff --git a/examples/process_raw/README.md b/examples/process_raw/README.md
new file mode 100644
index 00000000..01695707
--- /dev/null
+++ b/examples/process_raw/README.md
@@ -0,0 +1,45 @@
+# Raw-data example
+
+A small synthetic experiment in the formats `tfs-process-counts` reads. The
+numbers are made up. They show the formats, not a realistic screen.
+
+- `library_config.yaml`: the library description. The same file goes to
+  `tfs-process-fastq` and to `tfs-configure-model --library_config`.
+- `make_example_data.py`: writes the three inputs below from a fixed seed.
+- `tube_table.csv`: one row per sequenced tube. Two libraries (kanR and
+  pheS), each with a selection and a control condition, two IPTG
+  concentrations and three time points, for 24 tubes.
+- `od600.csv`: one OD600 reading per tube.
+- `counts/`: one count file per tube, in the format `tfs-process-fastq`
+  writes, including the `__unknown__` row.
+
+- `corrections.yaml`: example corrections for `tfs-correct-tubes` (one
+  swap, one relabel, one exclusion). The tubes and reasons are made up.
+
+The OD600 calibration is in `../od600/`. To build the growth table:
+
+```bash
+python make_example_data.py
+tfs-process-counts tube_table.csv counts \
+    --od600_file od600.csv \
+    --od600_calibration_file ../od600/od600_calibration.yaml \
+    --tube_volume_mL 5 \
+    --out_prefix growth
+```
+
+To apply the corrections first and build the growth table from the
+corrected tube table:
+
+```bash
+tfs-correct-tubes tube_table.csv corrections.yaml --out_prefix tubes_corrected
+tfs-process-counts tubes_corrected.csv counts \
+    --od600_file od600.csv \
+    --od600_calibration_file ../od600/od600_calibration.yaml \
+    --tube_volume_mL 5 \
+    --out_prefix growth
+```
+
+`growth.csv` is ready for
+`tfs-configure-model --growth_df growth.csv --library_config library_config.yaml`.
+
+See `docs/source/process-raw.rst` for the formats and the steps.
diff --git a/examples/process_raw/corrections.yaml b/examples/process_raw/corrections.yaml
new file mode 100644
index 00000000..cb17af2c
--- /dev/null
+++ b/examples/process_raw/corrections.yaml
@@ -0,0 +1,34 @@
+# Corrections to tube_table.csv, applied by tfs-correct-tubes.
+#
+# The tubes and reasons are made up: they show the format. A real
+# corrections file lives with the experiment's data, under version control,
+# next to the tube table it corrects.
+
+description: Example corrections to the synthetic tube table.
+
+corrections:
+  - id: pheS-t60-iptg-swap
+    action: swap
+    tubes: [pheS_pheSp4CP_0mM_t60, pheS_pheSp4CP_1mM_t60]
+    reason: >
+      Composition QC: the tube labeled 0 mM IPTG looks like a 1 mM tube and
+      the one labeled 1 mM like a 0 mM tube. The person who ran the screen
+      says a swap is probable.
+    by: A. Researcher
+    date: 2026-10-15
+    evidence: Apparent-IPTG score from a composition QC table.
+
+  - id: kanR-t120-time
+    action: relabel
+    tubes: [kanR_kanRpkan_0mM_t120]
+    set: {t_sel: 125}
+    reason: The bench log says this tube was pulled at 125 minutes.
+    by: A. Researcher
+    date: 2026-10-15
+
+  - id: kanR-control-t0-failed
+    action: exclude
+    tubes: [kanR_kanRmkan_1mM_t0]
+    reason: Library prep failed; the tube has almost no reads.
+    by: A. Researcher
+    date: 2026-10-15
diff --git a/examples/process_raw/counts/counts_kanR_kanRmkan_0mM_t0.csv b/examples/process_raw/counts/counts_kanR_kanRmkan_0mM_t0.csv
new file mode 100644
index 00000000..8a16ec82
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRmkan_0mM_t0.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2028
+K84L,700
+M42I/K84L,578
+H29T/L63V,1668
+V30L/L63R,758
+V30Y/L63S,1094
+V30R/L63N,1995
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+V30T/L63R,886
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+L63F,1041
+V30R/L62Y,2698
+H29L/L63V,2607
+H29N/L62F,628
+H29S/L62A,748
+H29Y/L63Y,1403
+H29D/L62C,1242
+H29Y/L63T,4043
+V30H/L63N,838
+H29A/L63S,1811
+V30C/L63R,2020
+V30D/L63S,1628
+L62D,966
+V30D/L62I,1420
+H29P/L62V,3146
+__unknown__,2566
diff --git a/examples/process_raw/counts/counts_kanR_kanRmkan_0mM_t120.csv b/examples/process_raw/counts/counts_kanR_kanRmkan_0mM_t120.csv
new file mode 100644
index 00000000..ce3bcf5e
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRmkan_0mM_t120.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,1924
+K84L,424
+M42I/K84L,389
+H29T/L63V,1099
+V30L/L63R,659
+V30Y/L63S,242
+V30R/L63N,2668
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+L63F,660
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+H29L/L63V,2056
+H29N/L62F,405
+H29S/L62A,515
+H29Y/L63Y,1157
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+H29Y/L63T,4680
+V30H/L63N,729
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+V30D/L63S,1849
+L62D,1098
+V30D/L62I,2137
+H29P/L62V,2659
+__unknown__,2467
diff --git a/examples/process_raw/counts/counts_kanR_kanRmkan_0mM_t60.csv b/examples/process_raw/counts/counts_kanR_kanRmkan_0mM_t60.csv
new file mode 100644
index 00000000..f2e78244
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRmkan_0mM_t60.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,1969
+K84L,548
+M42I/K84L,518
+H29T/L63V,1356
+V30L/L63R,689
+V30Y/L63S,510
+V30R/L63N,2322
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+H29V/L62F,1836
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+H29N/L63D,681
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+H29G/L63D,447
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+H29L/L63V,2394
+H29N/L62F,536
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+H29Y/L63Y,1277
+H29D/L62C,1008
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+V30H/L63N,782
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+V30D/L63S,1759
+L62D,1009
+V30D/L62I,1801
+H29P/L62V,2931
+__unknown__,2466
diff --git a/examples/process_raw/counts/counts_kanR_kanRmkan_1mM_t0.csv b/examples/process_raw/counts/counts_kanR_kanRmkan_1mM_t0.csv
new file mode 100644
index 00000000..40a21020
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRmkan_1mM_t0.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2091
+K84L,738
+M42I/K84L,518
+H29T/L63V,1670
+V30L/L63R,751
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+H29N/L62F,653
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+L62D,919
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+H29P/L62V,2998
+__unknown__,2518
diff --git a/examples/process_raw/counts/counts_kanR_kanRmkan_1mM_t120.csv b/examples/process_raw/counts/counts_kanR_kanRmkan_1mM_t120.csv
new file mode 100644
index 00000000..6c58e939
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRmkan_1mM_t120.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2017
+K84L,417
+M42I/K84L,390
+H29T/L63V,1130
+V30L/L63R,668
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+V30F/L62F,1664
+V30T/L63R,560
+H29G/L63D,373
+H29C/L62A,1177
+H29L/L62C,805
+V30P/L62S,2758
+L63F,602
+V30R/L62Y,2415
+H29L/L63V,2106
+H29N/L62F,391
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+H29Y/L63Y,1163
+H29D/L62C,727
+H29Y/L63T,4572
+V30H/L63N,677
+H29A/L63S,2218
+V30C/L63R,3912
+V30D/L63S,1874
+L62D,1089
+V30D/L62I,2182
+H29P/L62V,2579
+__unknown__,2368
diff --git a/examples/process_raw/counts/counts_kanR_kanRmkan_1mM_t60.csv b/examples/process_raw/counts/counts_kanR_kanRmkan_1mM_t60.csv
new file mode 100644
index 00000000..c560c5ba
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRmkan_1mM_t60.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2099
+K84L,521
+M42I/K84L,465
+H29T/L63V,1336
+V30L/L63R,758
+V30Y/L63S,567
+V30R/L63N,2249
+V30T/L62S,1592
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+V30D/L63S,1732
+L62D,1015
+V30D/L62I,1821
+H29P/L62V,2981
+__unknown__,2464
diff --git a/examples/process_raw/counts/counts_kanR_kanRpkan_0mM_t0.csv b/examples/process_raw/counts/counts_kanR_kanRpkan_0mM_t0.csv
new file mode 100644
index 00000000..20f2bbf1
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRpkan_0mM_t0.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2004
+K84L,734
+M42I/K84L,560
+H29T/L63V,1661
+V30L/L63R,753
+V30Y/L63S,1073
+V30R/L63N,2076
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+H29N/L63D,1006
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+H29G/L63D,487
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+L63F,1149
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+L62D,940
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+H29P/L62V,2979
+__unknown__,2506
diff --git a/examples/process_raw/counts/counts_kanR_kanRpkan_0mM_t120.csv b/examples/process_raw/counts/counts_kanR_kanRpkan_0mM_t120.csv
new file mode 100644
index 00000000..902407c4
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRpkan_0mM_t120.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,1974
+K84L,409
+M42I/K84L,372
+H29T/L63V,1136
+V30L/L63R,694
+V30Y/L63S,223
+V30R/L63N,2574
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+L62D,1091
+V30D/L62I,2175
+H29P/L62V,2623
+__unknown__,2566
diff --git a/examples/process_raw/counts/counts_kanR_kanRpkan_0mM_t60.csv b/examples/process_raw/counts/counts_kanR_kanRpkan_0mM_t60.csv
new file mode 100644
index 00000000..d1f45488
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRpkan_0mM_t60.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2035
+K84L,528
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+__unknown__,2502
diff --git a/examples/process_raw/counts/counts_kanR_kanRpkan_1mM_t0.csv b/examples/process_raw/counts/counts_kanR_kanRpkan_1mM_t0.csv
new file mode 100644
index 00000000..7bc56422
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRpkan_1mM_t0.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2003
+K84L,729
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+__unknown__,2488
diff --git a/examples/process_raw/counts/counts_kanR_kanRpkan_1mM_t120.csv b/examples/process_raw/counts/counts_kanR_kanRpkan_1mM_t120.csv
new file mode 100644
index 00000000..2edda5a6
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRpkan_1mM_t120.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,1883
+K84L,448
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+__unknown__,2567
diff --git a/examples/process_raw/counts/counts_kanR_kanRpkan_1mM_t60.csv b/examples/process_raw/counts/counts_kanR_kanRpkan_1mM_t60.csv
new file mode 100644
index 00000000..64068367
--- /dev/null
+++ b/examples/process_raw/counts/counts_kanR_kanRpkan_1mM_t60.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2053
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diff --git a/examples/process_raw/counts/counts_pheS_pheSm4CP_0mM_t0.csv b/examples/process_raw/counts/counts_pheS_pheSm4CP_0mM_t0.csv
new file mode 100644
index 00000000..bafcca5f
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSm4CP_0mM_t0.csv
@@ -0,0 +1,35 @@
+genotype,counts
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diff --git a/examples/process_raw/counts/counts_pheS_pheSm4CP_0mM_t120.csv b/examples/process_raw/counts/counts_pheS_pheSm4CP_0mM_t120.csv
new file mode 100644
index 00000000..57b7fdbf
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSm4CP_0mM_t120.csv
@@ -0,0 +1,35 @@
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diff --git a/examples/process_raw/counts/counts_pheS_pheSm4CP_0mM_t60.csv b/examples/process_raw/counts/counts_pheS_pheSm4CP_0mM_t60.csv
new file mode 100644
index 00000000..4351b36d
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSm4CP_0mM_t60.csv
@@ -0,0 +1,35 @@
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+H29D/L62C,972
+H29Y/L63T,4392
+V30H/L63N,775
+H29A/L63S,2011
+V30C/L63R,2833
+V30D/L63S,1824
+L62D,1003
+V30D/L62I,1814
+H29P/L62V,2902
+__unknown__,2441
diff --git a/examples/process_raw/counts/counts_pheS_pheSm4CP_1mM_t0.csv b/examples/process_raw/counts/counts_pheS_pheSm4CP_1mM_t0.csv
new file mode 100644
index 00000000..95c75095
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSm4CP_1mM_t0.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2033
+K84L,721
+M42I/K84L,564
+H29T/L63V,1604
+V30L/L63R,735
+V30Y/L63S,1092
+V30R/L63N,2046
+V30T/L62S,1207
+V30P/L62D,744
+H29V/L62F,1385
+H29N/L63G,1002
+H29N/L63D,993
+V30F/L62F,1752
+V30T/L63R,880
+H29G/L63D,531
+H29C/L62A,961
+H29L/L62C,1277
+V30P/L62S,1739
+L63F,1061
+V30R/L62Y,2602
+H29L/L63V,2570
+H29N/L62F,657
+H29S/L62A,745
+H29Y/L63Y,1325
+H29D/L62C,1270
+H29Y/L63T,4157
+V30H/L63N,864
+H29A/L63S,1762
+V30C/L63R,2053
+V30D/L63S,1745
+L62D,905
+V30D/L62I,1434
+H29P/L62V,3066
+__unknown__,2518
diff --git a/examples/process_raw/counts/counts_pheS_pheSm4CP_1mM_t120.csv b/examples/process_raw/counts/counts_pheS_pheSm4CP_1mM_t120.csv
new file mode 100644
index 00000000..149b21c9
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSm4CP_1mM_t120.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,1903
+K84L,416
+M42I/K84L,360
+H29T/L63V,1145
+V30L/L63R,633
+V30Y/L63S,236
+V30R/L63N,2619
+V30T/L62S,1884
+V30P/L62D,464
+H29V/L62F,2354
+H29N/L63G,627
+H29N/L63D,426
+V30F/L62F,1714
+V30T/L63R,522
+H29G/L63D,367
+H29C/L62A,1151
+H29L/L62C,779
+V30P/L62S,2786
+L63F,661
+V30R/L62Y,2439
+H29L/L63V,2132
+H29N/L62F,358
+H29S/L62A,488
+H29Y/L63Y,1244
+H29D/L62C,737
+H29Y/L63T,4509
+V30H/L63N,656
+H29A/L63S,2116
+V30C/L63R,3930
+V30D/L63S,1860
+L62D,1098
+V30D/L62I,2166
+H29P/L62V,2673
+__unknown__,2547
diff --git a/examples/process_raw/counts/counts_pheS_pheSm4CP_1mM_t60.csv b/examples/process_raw/counts/counts_pheS_pheSm4CP_1mM_t60.csv
new file mode 100644
index 00000000..fc08a4ee
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSm4CP_1mM_t60.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2047
+K84L,508
+M42I/K84L,514
+H29T/L63V,1399
+V30L/L63R,780
+V30Y/L63S,498
+V30R/L63N,2360
+V30T/L62S,1548
+V30P/L62D,631
+H29V/L62F,1810
+H29N/L63G,786
+H29N/L63D,690
+V30F/L62F,1771
+V30T/L63R,726
+H29G/L63D,464
+H29C/L62A,1001
+H29L/L62C,985
+V30P/L62S,2267
+L63F,861
+V30R/L62Y,2658
+H29L/L63V,2349
+H29N/L62F,520
+H29S/L62A,636
+H29Y/L63Y,1241
+H29D/L62C,979
+H29Y/L63T,4330
+V30H/L63N,739
+H29A/L63S,2051
+V30C/L63R,2932
+V30D/L63S,1810
+L62D,1055
+V30D/L62I,1768
+H29P/L62V,2864
+__unknown__,2422
diff --git a/examples/process_raw/counts/counts_pheS_pheSp4CP_0mM_t0.csv b/examples/process_raw/counts/counts_pheS_pheSp4CP_0mM_t0.csv
new file mode 100644
index 00000000..2fe19a41
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSp4CP_0mM_t0.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2041
+K84L,733
+M42I/K84L,521
+H29T/L63V,1612
+V30L/L63R,794
+V30Y/L63S,1057
+V30R/L63N,2112
+V30T/L62S,1217
+V30P/L62D,749
+H29V/L62F,1522
+H29N/L63G,1018
+H29N/L63D,933
+V30F/L62F,1701
+V30T/L63R,929
+H29G/L63D,498
+H29C/L62A,888
+H29L/L62C,1262
+V30P/L62S,1728
+L63F,1131
+V30R/L62Y,2618
+H29L/L63V,2571
+H29N/L62F,667
+H29S/L62A,736
+H29Y/L63Y,1376
+H29D/L62C,1242
+H29Y/L63T,4131
+V30H/L63N,766
+H29A/L63S,1803
+V30C/L63R,2085
+V30D/L63S,1646
+L62D,931
+V30D/L62I,1432
+H29P/L62V,3152
+__unknown__,2398
diff --git a/examples/process_raw/counts/counts_pheS_pheSp4CP_0mM_t120.csv b/examples/process_raw/counts/counts_pheS_pheSp4CP_0mM_t120.csv
new file mode 100644
index 00000000..e50e05d7
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSp4CP_0mM_t120.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,1997
+K84L,405
+M42I/K84L,428
+H29T/L63V,1156
+V30L/L63R,653
+V30Y/L63S,246
+V30R/L63N,2680
+V30T/L62S,1924
+V30P/L62D,437
+H29V/L62F,2238
+H29N/L63G,657
+H29N/L63D,425
+V30F/L62F,1656
+V30T/L63R,578
+H29G/L63D,328
+H29C/L62A,1152
+H29L/L62C,734
+V30P/L62S,2794
+L63F,624
+V30R/L62Y,2504
+H29L/L63V,2128
+H29N/L62F,400
+H29S/L62A,485
+H29Y/L63Y,1219
+H29D/L62C,677
+H29Y/L63T,4497
+V30H/L63N,673
+H29A/L63S,2158
+V30C/L63R,3887
+V30D/L63S,1867
+L62D,1051
+V30D/L62I,2192
+H29P/L62V,2722
+__unknown__,2428
diff --git a/examples/process_raw/counts/counts_pheS_pheSp4CP_0mM_t60.csv b/examples/process_raw/counts/counts_pheS_pheSp4CP_0mM_t60.csv
new file mode 100644
index 00000000..18a37077
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSp4CP_0mM_t60.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2016
+K84L,540
+M42I/K84L,471
+H29T/L63V,1427
+V30L/L63R,736
+V30Y/L63S,497
+V30R/L63N,2400
+V30T/L62S,1535
+V30P/L62D,568
+H29V/L62F,1832
+H29N/L63G,763
+H29N/L63D,667
+V30F/L62F,1682
+V30T/L63R,701
+H29G/L63D,442
+H29C/L62A,1024
+H29L/L62C,978
+V30P/L62S,2269
+L63F,816
+V30R/L62Y,2593
+H29L/L63V,2398
+H29N/L62F,526
+H29S/L62A,655
+H29Y/L63Y,1298
+H29D/L62C,955
+H29Y/L63T,4427
+V30H/L63N,794
+H29A/L63S,2085
+V30C/L63R,2923
+V30D/L63S,1820
+L62D,908
+V30D/L62I,1812
+H29P/L62V,2890
+__unknown__,2552
diff --git a/examples/process_raw/counts/counts_pheS_pheSp4CP_1mM_t0.csv b/examples/process_raw/counts/counts_pheS_pheSp4CP_1mM_t0.csv
new file mode 100644
index 00000000..103cf1dc
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSp4CP_1mM_t0.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2008
+K84L,695
+M42I/K84L,562
+H29T/L63V,1590
+V30L/L63R,780
+V30Y/L63S,1040
+V30R/L63N,2081
+V30T/L62S,1212
+V30P/L62D,731
+H29V/L62F,1459
+H29N/L63G,954
+H29N/L63D,957
+V30F/L62F,1772
+V30T/L63R,975
+H29G/L63D,465
+H29C/L62A,883
+H29L/L62C,1196
+V30P/L62S,1759
+L63F,1099
+V30R/L62Y,2578
+H29L/L63V,2607
+H29N/L62F,656
+H29S/L62A,748
+H29Y/L63Y,1344
+H29D/L62C,1324
+H29Y/L63T,4117
+V30H/L63N,846
+H29A/L63S,1848
+V30C/L63R,2079
+V30D/L63S,1620
+L62D,917
+V30D/L62I,1473
+H29P/L62V,3064
+__unknown__,2561
diff --git a/examples/process_raw/counts/counts_pheS_pheSp4CP_1mM_t120.csv b/examples/process_raw/counts/counts_pheS_pheSp4CP_1mM_t120.csv
new file mode 100644
index 00000000..a64e0e43
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSp4CP_1mM_t120.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,1968
+K84L,406
+M42I/K84L,400
+H29T/L63V,1131
+V30L/L63R,691
+V30Y/L63S,284
+V30R/L63N,2723
+V30T/L62S,1946
+V30P/L62D,466
+H29V/L62F,2261
+H29N/L63G,614
+H29N/L63D,489
+V30F/L62F,1589
+V30T/L63R,518
+H29G/L63D,400
+H29C/L62A,1199
+H29L/L62C,749
+V30P/L62S,2676
+L63F,635
+V30R/L62Y,2387
+H29L/L63V,2076
+H29N/L62F,391
+H29S/L62A,475
+H29Y/L63Y,1186
+H29D/L62C,744
+H29Y/L63T,4578
+V30H/L63N,677
+H29A/L63S,2196
+V30C/L63R,3855
+V30D/L63S,1890
+L62D,1077
+V30D/L62I,2170
+H29P/L62V,2706
+__unknown__,2447
diff --git a/examples/process_raw/counts/counts_pheS_pheSp4CP_1mM_t60.csv b/examples/process_raw/counts/counts_pheS_pheSp4CP_1mM_t60.csv
new file mode 100644
index 00000000..8f1e9c97
--- /dev/null
+++ b/examples/process_raw/counts/counts_pheS_pheSp4CP_1mM_t60.csv
@@ -0,0 +1,35 @@
+genotype,counts
+wt,2054
+K84L,576
+M42I/K84L,524
+H29T/L63V,1314
+V30L/L63R,729
+V30Y/L63S,508
+V30R/L63N,2327
+V30T/L62S,1482
+V30P/L62D,580
+H29V/L62F,1839
+H29N/L63G,782
+H29N/L63D,668
+V30F/L62F,1753
+V30T/L63R,771
+H29G/L63D,450
+H29C/L62A,1026
+H29L/L62C,971
+V30P/L62S,2290
+L63F,855
+V30R/L62Y,2646
+H29L/L63V,2359
+H29N/L62F,513
+H29S/L62A,612
+H29Y/L63Y,1308
+H29D/L62C,1020
+H29Y/L63T,4410
+V30H/L63N,763
+H29A/L63S,2050
+V30C/L63R,2816
+V30D/L63S,1745
+L62D,999
+V30D/L62I,1801
+H29P/L62V,2904
+__unknown__,2555
diff --git a/examples/process_raw/library_config.yaml b/examples/process_raw/library_config.yaml
index 00e38435..b85c2c89 100644
--- a/examples/process_raw/library_config.yaml
+++ b/examples/process_raw/library_config.yaml
@@ -1,9 +1,10 @@
 
 # ---------------------------------------------------------------------------
-# Library configuration for tfs-process-fastq
+# Library configuration for tfs-process-fastq and tfs-configure-model
 #
-# Pass this file as the run_config argument:
-#   tfs-process-fastq R1.fastq.gz R2.fastq.gz out/ library_config.yaml
+# Pass this one file to both:
+#   tfs-process-fastq library_config.yaml R1.fastq.gz R2.fastq.gz --out_dir counts
+#   tfs-configure-model --growth_df growth.csv --library_config library_config.yaml
 #
 # If you are running both tfs-simulate and tfs-process-fastq on the same
 # experiment, you can pass the simulate config directly to tfs-process-fastq
@@ -40,7 +41,7 @@ tile_combos: ["single-1", "single-2", "double-1-2"]
 
 # Control sequences added as spikes.  Must be the same length as wt_seq.
 spiked_seqs:
-- gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctggcggcgccgtcgcaaattgtcgcggcgattctgtctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg  # k84l
+- gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctgcacgcgccgtcgcaaattgtcgcggcgattctgtctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg  # k84l
 - gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgattgcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctgcacgcgccgtcgcaaattgtcgcggcgattctgtctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg  # m42i/k84l
 
 # ---------------------------------------------------------------------------
diff --git a/examples/process_raw/make_example_data.py b/examples/process_raw/make_example_data.py
new file mode 100644
index 00000000..e73d9964
--- /dev/null
+++ b/examples/process_raw/make_example_data.py
@@ -0,0 +1,82 @@
+"""
+Write a synthetic experiment in the formats tfs-process-counts reads.
+
+    python make_example_data.py
+    tfs-process-counts tube_table.csv counts \
+        --od600_file od600.csv \
+        --od600_calibration_file ../od600/od600_calibration.yaml \
+        --tube_volume_mL 5 --out_prefix growth
+
+It writes:
+
+- ``tube_table.csv``: one row per sequenced tube. ``sample`` names the tube
+  (and its count file); ``library`` is the transformed library it came from
+  (kanR and pheS are transformed and grown up separately); the rest is the
+  design: ``replicate``, ``condition_pre``, ``t_pre``, ``condition_sel``,
+  ``t_sel`` (minutes), ``titrant_name``, ``titrant_conc`` (mM).
+- ``od600.csv``: one OD600 reading per tube (``sample``, ``od600``).
+- ``counts/counts_<sample>.csv``: ``genotype``, ``counts``, the format
+  tfs-process-fastq writes, including the ``__unknown__`` row of reads that
+  matched no library genotype.
+
+The numbers are made up: a few dozen genotypes of the library in
+``library_config.yaml`` growing at random rates, read at about 50,000 reads
+per tube. They show the formats; they are not a realistic screen.
+"""
+
+import os
+
+import numpy as np
+import pandas as pd
+
+from tfscreen.genetics import library_composition_table
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+rng = np.random.default_rng(20261003)
+
+# Genotypes: wt, the spikes and a random handful of library members.
+lib = library_composition_table(os.path.join(HERE, "library_config.yaml"))
+spikes = list(lib.loc[lib["in_spiked_origin"], "genotype"])
+others = [g for g in lib["genotype"] if g != "wt" and g not in spikes]
+genotypes = ["wt"] + spikes + list(rng.choice(others, size=30, replace=False))
+rate_shift = dict(zip(genotypes, rng.normal(0.0, 0.004, len(genotypes))))
+rate_shift["wt"] = 0.0
+
+# Design: two libraries, each with a selection and a control condition, two
+# IPTG concentrations and three time points, one replicate.
+rows = []
+for library, marker, agent in (("kanR", "kanR", "kan"), ("pheS", "pheS", "4CP")):
+    for sel in (f"{marker}+{agent}", f"{marker}-{agent}"):
+        for conc in (0.0, 1.0):
+            for t_sel in (0, 60, 120):
+                name = f"{library}_{sel.replace('+', 'p').replace('-', 'm')}" \
+                       f"_{conc:g}mM_t{t_sel}"
+                rows.append({"sample": name, "library": library,
+                             "replicate": 1,
+                             "condition_pre": f"{marker}-{agent}", "t_pre": 30,
+                             "condition_sel": sel, "t_sel": t_sel,
+                             "titrant_name": "iptg", "titrant_conc": conc})
+tubes = pd.DataFrame(rows)
+tubes.to_csv(os.path.join(HERE, "tube_table.csv"), index=False)
+
+# OD600 grows with time; every reading inside the example calibration's range.
+od = 0.15 * np.exp(0.006 * tubes["t_sel"].to_numpy()) \
+    * rng.lognormal(0.0, 0.03, len(tubes))
+pd.DataFrame({"sample": tubes["sample"], "od600": np.round(od, 4)}).to_csv(
+    os.path.join(HERE, "od600.csv"), index=False)
+
+# Counts: each genotype's share grows by its own rate offset; reads are a
+# multinomial draw with 5% of reads matching no genotype.
+start = rng.dirichlet(np.full(len(genotypes), 5.0))
+counts_dir = os.path.join(HERE, "counts")
+os.makedirs(counts_dir, exist_ok=True)
+shift = np.array([rate_shift[g] for g in genotypes])
+for _, tube in tubes.iterrows():
+    w = start * np.exp(shift * (tube["t_pre"] + tube["t_sel"]))
+    w = 0.95 * w / w.sum()
+    reads = rng.multinomial(50_000, np.append(w, 0.05))
+    pd.DataFrame({"genotype": genotypes + ["__unknown__"],
+                  "counts": reads}).to_csv(
+        os.path.join(counts_dir, f"counts_{tube['sample']}.csv"), index=False)
+
+print(f"Wrote tube_table.csv, od600.csv and {len(tubes)} count files.")
diff --git a/examples/process_raw/od600.csv b/examples/process_raw/od600.csv
new file mode 100644
index 00000000..13c677c6
--- /dev/null
+++ b/examples/process_raw/od600.csv
@@ -0,0 +1,25 @@
+sample,od600
+kanR_kanRpkan_0mM_t0,0.1506
+kanR_kanRpkan_0mM_t60,0.2014
+kanR_kanRpkan_0mM_t120,0.3086
+kanR_kanRpkan_1mM_t0,0.1469
+kanR_kanRpkan_1mM_t60,0.2177
+kanR_kanRpkan_1mM_t120,0.2987
+kanR_kanRmkan_0mM_t0,0.1571
+kanR_kanRmkan_0mM_t60,0.2153
+kanR_kanRmkan_0mM_t120,0.3007
+kanR_kanRmkan_1mM_t0,0.1508
+kanR_kanRmkan_1mM_t60,0.221
+kanR_kanRmkan_1mM_t120,0.3062
+pheS_pheSp4CP_0mM_t0,0.1561
+pheS_pheSp4CP_0mM_t60,0.2153
+pheS_pheSp4CP_0mM_t120,0.3113
+pheS_pheSp4CP_1mM_t0,0.1494
+pheS_pheSp4CP_1mM_t60,0.2108
+pheS_pheSp4CP_1mM_t120,0.3081
+pheS_pheSm4CP_0mM_t0,0.1524
+pheS_pheSm4CP_0mM_t60,0.2115
+pheS_pheSm4CP_0mM_t120,0.2986
+pheS_pheSm4CP_1mM_t0,0.1587
+pheS_pheSm4CP_1mM_t60,0.2158
+pheS_pheSm4CP_1mM_t120,0.3085
diff --git a/examples/process_raw/tube_table.csv b/examples/process_raw/tube_table.csv
new file mode 100644
index 00000000..f92bdb23
--- /dev/null
+++ b/examples/process_raw/tube_table.csv
@@ -0,0 +1,25 @@
+sample,library,replicate,condition_pre,t_pre,condition_sel,t_sel,titrant_name,titrant_conc
+kanR_kanRpkan_0mM_t0,kanR,1,kanR-kan,30,kanR+kan,0,iptg,0.0
+kanR_kanRpkan_0mM_t60,kanR,1,kanR-kan,30,kanR+kan,60,iptg,0.0
+kanR_kanRpkan_0mM_t120,kanR,1,kanR-kan,30,kanR+kan,120,iptg,0.0
+kanR_kanRpkan_1mM_t0,kanR,1,kanR-kan,30,kanR+kan,0,iptg,1.0
+kanR_kanRpkan_1mM_t60,kanR,1,kanR-kan,30,kanR+kan,60,iptg,1.0
+kanR_kanRpkan_1mM_t120,kanR,1,kanR-kan,30,kanR+kan,120,iptg,1.0
+kanR_kanRmkan_0mM_t0,kanR,1,kanR-kan,30,kanR-kan,0,iptg,0.0
+kanR_kanRmkan_0mM_t60,kanR,1,kanR-kan,30,kanR-kan,60,iptg,0.0
+kanR_kanRmkan_0mM_t120,kanR,1,kanR-kan,30,kanR-kan,120,iptg,0.0
+kanR_kanRmkan_1mM_t0,kanR,1,kanR-kan,30,kanR-kan,0,iptg,1.0
+kanR_kanRmkan_1mM_t60,kanR,1,kanR-kan,30,kanR-kan,60,iptg,1.0
+kanR_kanRmkan_1mM_t120,kanR,1,kanR-kan,30,kanR-kan,120,iptg,1.0
+pheS_pheSp4CP_0mM_t0,pheS,1,pheS-4CP,30,pheS+4CP,0,iptg,0.0
+pheS_pheSp4CP_0mM_t60,pheS,1,pheS-4CP,30,pheS+4CP,60,iptg,0.0
+pheS_pheSp4CP_0mM_t120,pheS,1,pheS-4CP,30,pheS+4CP,120,iptg,0.0
+pheS_pheSp4CP_1mM_t0,pheS,1,pheS-4CP,30,pheS+4CP,0,iptg,1.0
+pheS_pheSp4CP_1mM_t60,pheS,1,pheS-4CP,30,pheS+4CP,60,iptg,1.0
+pheS_pheSp4CP_1mM_t120,pheS,1,pheS-4CP,30,pheS+4CP,120,iptg,1.0
+pheS_pheSm4CP_0mM_t0,pheS,1,pheS-4CP,30,pheS-4CP,0,iptg,0.0
+pheS_pheSm4CP_0mM_t60,pheS,1,pheS-4CP,30,pheS-4CP,60,iptg,0.0
+pheS_pheSm4CP_0mM_t120,pheS,1,pheS-4CP,30,pheS-4CP,120,iptg,0.0
+pheS_pheSm4CP_1mM_t0,pheS,1,pheS-4CP,30,pheS-4CP,0,iptg,1.0
+pheS_pheSm4CP_1mM_t60,pheS,1,pheS-4CP,30,pheS-4CP,60,iptg,1.0
+pheS_pheSm4CP_1mM_t120,pheS,1,pheS-4CP,30,pheS-4CP,120,iptg,1.0
diff --git a/examples/simulate-and-analyze/README.md b/examples/simulate-and-analyze/README.md
index c3765eb3..d89fb772 100644
--- a/examples/simulate-and-analyze/README.md
+++ b/examples/simulate-and-analyze/README.md
@@ -1,33 +1,37 @@
 # simulate-and-analyze
 
-A complete end-to-end example: simulate a synthetic TF-library screen and fit
-the hierarchical Bayesian model to the simulated data.
+A complete example: simulate a small TF-library screen, then fit the
+hierarchical Bayesian model to the simulated data and compare the fit with
+the known truth.
 
 ## Contents
 
 | File | Purpose |
 |------|---------|
-| `simulate_config.yaml` | Simulation parameters (library genetics, conditions, binding data) |
-| `hill_params.csv` | Per-genotype Hill binding parameters used to generate ground-truth θ |
-| `run.sh` | Pipeline script (simulate → configure → calibrate → fit → predict → summarise) |
+| `simulate_config.yaml` | Simulation settings (library genetics, conditions, growth, binding and presplit data); also the library description for `tfs-configure-model --library_config` |
+| `hill_params.csv` | Hill parameters for wt and three spiked single mutants, used as their true θ curves and binding data |
+| `run.sh` | The pipeline: simulate, configure, pre-fit, fit, sample the posterior, extract, predict, summarize |
 
 ## Running
 
+Run from this directory, since the config names `hill_params.csv` by a
+relative path:
+
 ```bash
-bash run.sh simulate_config.yaml out/ 1
+bash run.sh simulate_config.yaml out 1
 ```
 
-The three arguments are the config file, the output directory (created
-automatically), and the random seed.  Seed `1` reproduces the documented
-example outputs.
+The arguments are the simulate config, the output directory (created if
+needed) and the random seed (default 1).
 
-Running time is roughly 20–60 minutes on a laptop CPU or a few minutes on a
-GPU.  On a cluster, comment out the `XLA_FLAGS` line in `run.sh` and
-uncomment `module load cuda/...` instead.
+The fit is the slow step: expect tens of minutes on a laptop CPU and a few
+minutes on a GPU. On a cluster, comment out the `XLA_FLAGS` line in `run.sh`
+and uncomment `module load cuda/...`; the `#SBATCH` lines let you submit it
+with `sbatch`.
 
 ## Documentation
 
-See the [quickstart](https://tfscreen.readthedocs.io/en/latest/quickstart.html)
-for a guided walkthrough and the
-[tfs-summarize-fit reference](https://tfscreen.readthedocs.io/en/latest/summarize-fit.html)
-for a detailed guide to interpreting the diagnostic outputs in `out/summary/`.
+The [quickstart](https://tfscreen.readthedocs.io/en/latest/quickstart.html)
+walks through every step and every output file. The
+[tfs-summarize-fit guide](https://tfscreen.readthedocs.io/en/latest/summarize-fit.html)
+explains the diagnostic outputs in `out/summary/`.
diff --git a/examples/simulate-and-analyze/run.sh b/examples/simulate-and-analyze/run.sh
index 09ff0d9b..8fa59d64 100644
--- a/examples/simulate-and-analyze/run.sh
+++ b/examples/simulate-and-analyze/run.sh
@@ -38,9 +38,12 @@ fi
 # 1. Simulate library
 # ---------------------------------------------------------------------------
 # tfs-simulate reads the config YAML and writes simulated growth data,
-# binding curves, and ground-truth parameter CSVs to run_dir.
+# binding curves, and ground-truth parameter CSVs as run_dir/tfs_sim_*. It
+# also writes the experiment in the raw formats a lab's data come in: one
+# counts file per tube (tfs_sim_counts/) and the tube table
+# (tfs_sim_tubes.csv).
 echo ">>> Simulate library"
-tfs-simulate "${config_file}" "${run_dir}" --seed "${seed}"
+tfs-simulate "${config_file}" --out_prefix "${run_dir}/tfs_sim" --seed "${seed}"
 
 # The simulate config also describes the library (genetics keys +
 # library_mixture), so tfs-configure-model reads the same file.  Resolve it to
@@ -49,20 +52,38 @@ library_config="$(cd "$(dirname "${config_file}")" && pwd)/$(basename "${config_
 
 cd "${run_dir}"
 
+# ---------------------------------------------------------------------------
+# 1b. Process the raw counts, exactly as for real data
+# ---------------------------------------------------------------------------
+# The growth table the model reads comes from the per-tube count files and the
+# tube table, through the same command a lab runs. (This config has no od600
+# block, so the tube table carries each tube's total; with one, add the
+# --od600_file, --od600_calibration_file and --tube_volume_mL that
+# tfs-simulate prints.) The presplit table is still written directly by
+# tfs-simulate.
+echo ">>> Process counts"
+tfs-process-counts tfs_sim_tubes.csv tfs_sim_counts \
+    --out_prefix tfs_sim_processed_growth --no_verbose
+
 # ---------------------------------------------------------------------------
 # 2. Configure model
 # ---------------------------------------------------------------------------
-# tfs-configure-model validates the data, maps labels to indices, selects
-# model components, and writes tfs_configure_config.yaml + priors/guesses CSVs.
-# Edit the flags here to change which model components are used.
+# tfs-configure-model validates the data, selects model components, and
+# writes tfs_configure_config.yaml, the priors/guesses/library CSVs and a
+# parameter census (tfs_configure_model_stats.*). Edit the flags here to
+# change which model components are used. The data were simulated with
+# hill_mut theta and epistasis, one plasmid per cell
+# (transformation_poisson_lambda: 0) and an instant growth transition, so the
+# model below matches the simulation; growth_likelihood counts with a level
+# tube offset is the recommended default.
 echo ">>> Configure model"
 tfs-configure-model \
-    tfs_sim_binding.csv \
-    --growth_df tfs_sim_growth.csv \
+    --binding_df tfs_sim_binding.csv \
+    --growth_df tfs_sim_processed_growth.csv \
     --presplit_df tfs_sim_presplit.csv \
     --condition_growth_model linear \
     --growth_transition_model instant \
-    --ln_cfu0_model hierarchical_factored \
+    --ln_cfu0_model hierarchical \
     --dk_geno_model hierarchical_geno \
     --activity_model fixed \
     --theta_model hill_mut \
@@ -70,7 +91,9 @@ tfs-configure-model \
     --theta_rescale_model passthrough \
     --theta_growth_noise_model logit_normal \
     --theta_binding_noise_model zero \
-    --growth_noise_model normal_kt \
+    --growth_likelihood counts \
+    --sample_offset_model level \
+    --growth_noise_model zero \
     --library_config "${library_config}" \
     --growth_shares_replicates \
     --epistasis
@@ -78,60 +101,68 @@ tfs-configure-model \
 # ---------------------------------------------------------------------------
 # 3. Pre-fit calibration
 # ---------------------------------------------------------------------------
-# A fast MAP fit on a simplified model calibrates the growth-linking-function
-# priors (m and b) before the full inference run.  Updates priors.csv and
-# guesses.csv in place.
+# A MAP fit on a simplified model, using the genotypes that have binding
+# data, calibrates each condition's growth baseline k and slope m. It writes
+# them into tfs_configure_priors.csv and tfs_configure_guesses.csv in place
+# (keeping .bak copies) and leaves its own diagnostics as tfs_prefit_*.
 echo ">>> Pre-fit calibration"
 tfs-prefit-calibration tfs_configure_config.yaml --seed "${seed}"
 
 # ---------------------------------------------------------------------------
 # 4. Fit model (SVI)
 # ---------------------------------------------------------------------------
-# Main hierarchical Bayesian inference.  SVI produces a full approximate
-# posterior; for a point estimate only, use --analysis_method map instead.
+# Main hierarchical Bayesian inference. SVI starts from a MAP warm-up
+# (tfs_fit_model_premap_*) and fits the component guide, an approximate
+# posterior. Writes tfs_fit_model_checkpoint.pkl, _params.npz, _losses.txt
+# and _convergence.csv. --analysis_method map gives the MAP point instead;
+# tfs-sample-posterior then builds a Laplace posterior from it.
 echo ">>> Fit model"
 tfs-fit-model \
     tfs_configure_config.yaml \
     --seed "${seed}" \
-    --analysis_method svi \
-    --convergence_tolerance 0.0001
+    --analysis_method svi
 
 # ---------------------------------------------------------------------------
 # 5. Sample posterior
 # ---------------------------------------------------------------------------
-# Draw posterior samples from the SVI variational distribution and write
-# them to an HDF5 file used by the prediction steps below.
+# Draw posterior samples from the fitted guide and write them to
+# tfs_posterior.h5 for the steps below. --skip_growth_observations leaves out
+# the stored per-observation growth sites (growth_pred, growth_obs). They are
+# most of the file (several GB here at the default 10000 samples), and
+# nothing below reads them: tfs-predict-growth and tfs-summarize-fit
+# recompute growth from the parameter samples.
 echo ">>> Sample posterior"
-tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    --skip_growth_observations
 
 # ---------------------------------------------------------------------------
 # 6. Extract parameter estimates
 # ---------------------------------------------------------------------------
-# Summarise the posterior into per-parameter CSV files (quantiles, means).
+# Summarize the posterior into per-parameter CSV files (tfs_params_*.csv,
+# one column per quantile).
 echo ">>> Extract parameter estimates"
 tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
 
 # ---------------------------------------------------------------------------
 # 7. Predict theta
 # ---------------------------------------------------------------------------
-# Predict operator occupancy θ as a function of titrant concentration for
-# every genotype in the training data.
+# Predict operator occupancy θ at every (genotype, titrant concentration) in
+# the training data. Writes tfs_pred_theta.csv.
 echo ">>> Predict theta"
 tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
 
 # ---------------------------------------------------------------------------
 # 8. Predict growth
 # ---------------------------------------------------------------------------
-# Predict ln(CFU) with posterior uncertainty for every training observation.
+# Predict ln(CFU) with posterior uncertainty for every training observation,
+# from 500 of the posterior samples. Writes tfs_pred_growth.csv.
 echo ">>> Predict growth"
-tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 --num_marginal_samples=500
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 --num_marginal_samples 500
 
 # ---------------------------------------------------------------------------
-# 9. Summarise fit
+# 9. Summarize fit
 # ---------------------------------------------------------------------------
-# Collects all outputs, computes statistics, and writes diagnostic PDFs and
-# CSVs to the summary/ subdirectory.
-echo ">>> Summarise fit"
+# Compares the predictions with the simulated truth (tfs_sim_genotype_theta.csv)
+# and writes diagnostic PDFs and CSVs to the summary/ subdirectory.
+echo ">>> Summarize fit"
 tfs-summarize-fit .
-
-cd ..
diff --git a/examples/simulate-and-analyze/simulate_config.yaml b/examples/simulate-and-analyze/simulate_config.yaml
index 895c2024..72539c3d 100644
--- a/examples/simulate-and-analyze/simulate_config.yaml
+++ b/examples/simulate-and-analyze/simulate_config.yaml
@@ -179,12 +179,20 @@ lib_assembly_skew_sigma: 1.25
 # Poisson with this lambda.  Set to 0 or null for exactly one plasmid per cell.
 transformation_poisson_lambda: 0
 
-# When a cell carries multiple plasmids, growth rates are combined via this
-# function.  Recommended: 'min' for repressors of antibiotic resistance (tightest
-# binder dominates); 'max' for inducers.  Default: 'mean' for all libraries.
-multi_plasmid_combine_fcn:
-  kanR: "min"
-  pheS: "max"
+# How a cell carrying several plasmids gets its growth rate. The cell's theta
+# comes from congression_theta_rule, at each condition: 'homodimer'
+# (logit theta_cell = log of the share-weighted mean of the plasmids'
+# theta odds), 'heterodimer' (closer to the mean logit) or 'max' (the
+# highest-theta plasmid sets theta and TF activity). 'homodimer' and
+# 'heterodimer' need activity 1 (activity_mut_scale 0). Its dk_geno comes
+# from congression_dk_rule, the soft-min family
+# dk_cell = -(1/alpha) log sum_g x_g exp(-alpha dk_g): 'dilution'
+# (alpha -> 0, the share-weighted mean), 'softmin' (finite alpha, set by
+# congression_dk_alpha, in units of 1/dk) or 'min' (alpha -> inf, the worst
+# variant sets the cost). Growth is then computed once from these cell-level
+# values. All three keys are optional; these are the defaults (no alpha).
+congression_theta_rule: homodimer
+congression_dk_rule: dilution
 
 # Colony-forming units per mL at the start of pre-growth.
 cfu0: 6.5e7
diff --git a/examples/simulate-empirical/simulate_config.yaml b/examples/simulate-empirical/simulate_config.yaml
index 8b8afb02..f7b0ea62 100644
--- a/examples/simulate-empirical/simulate_config.yaml
+++ b/examples/simulate-empirical/simulate_config.yaml
@@ -10,13 +10,13 @@
 #
 # Workflow:
 #   # 1. real data -> empirical generating distribution (writes emp_phenotype_model.json)
-#   tfs-build-empirical growth.csv 42 \
-#       --binding_file binding.csv --library_config library_config.yaml \
+#   tfs-build-empirical growth.csv --seed 42 \
+#       --binding_df binding.csv --library_config library_config.yaml \
 #       --out_prefix emp
-#   # 2. simulate from it
-#   tfs-simulate simulate_config.yaml out_dir/
+#   # 2. simulate from it (writes out/tfs_sim_*.csv)
+#   tfs-simulate simulate_config.yaml --out_prefix out/tfs_sim
 #
-# See the "Empirical phenotype pipeline" section of CLAUDE.md for the full story.
+# See docs/source/simulation.rst ("Empirical phenotypes") for the full story.
 
 # ---------------------------------------------------------------------------
 # Empirical phenotype source   (the only empirical-specific keys)
@@ -59,7 +59,7 @@ expected_5p: cgcgtggtgaaccag
 expected_3p: gtagaacgaagcggc
 tile_combos: ["single-1", "single-2", "double-1-2"]
 spiked_seqs:
-- gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctggcggcgccgtcgcaaattgtcgcggcgattctgtctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg  # k84l
+- gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctgcacgcgccgtcgcaaattgtcgcggcgattctgtctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg  # k84l
 - gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgattgcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctgcacgcgccgtcgcaaattgtcgcggcgattctgtctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg  # m42i/k84l
 
 # ---------------------------------------------------------------------------
@@ -103,11 +103,13 @@ condition_blocks:
 # Growth rate parameters
 # ---------------------------------------------------------------------------
 
-# Linear growth linkage per condition:  k = m * theta + b.
+# Linear growth linkage per condition:  k = b + A * m * theta + dk_geno
+# (A = 1 here).
 #
 # IMPORTANT: for a self-consistent empirical simulation these MUST be the same
-# calibrated values the phenotype fit used -- i.e. the per-condition values the
-# pre-fit wrote into <emp>_configure_priors.csv.  For each condition_rep:
+# calibrated values the phenotype fit used, i.e. the per-condition values the
+# pre-fit wrote into <emp>_configure_priors.csv (or the values in the file you
+# passed as --growth_calibration_file).  For each condition_rep:
 #     b  =  value of parameter  growth.condition_growth.k_loc   (baseline)
 #     m  =  value of parameter  growth.condition_growth.m_loc   (slope)
 # (The numbers below are placeholders -- replace them with your calibration.)
@@ -144,9 +146,8 @@ lib_assembly_skew_sigma: 1.25
 # attenuation your empirical distribution is (currently) uncorrected for, so
 # keep it matched to the real library.
 transformation_poisson_lambda: 1.5
-multi_plasmid_combine_fcn:
-  kanR: "min"
-  pheS: "max"
+congression_theta_rule: homodimer   # needs activity 1 (the empirical path forces it)
+congression_dk_rule: dilution
 
 cfu0: 65_000_000.0
 tube_noise_sigma: 0.002
diff --git a/examples/simulate/run.sh b/examples/simulate/run.sh
index 7271fb41..b67e9092 100644
--- a/examples/simulate/run.sh
+++ b/examples/simulate/run.sh
@@ -8,7 +8,7 @@ set -e
 #
 # NOTE: tfs-setup-sim-grid already applied the simulate: overrides and wrote
 # tfs_sim_config.yaml in this directory.  Per-run config choices (noise level,
-# thermodynamic model, random seed, etc.) live there; they do not appear here.
+# thermodynamic model, seed, etc.) live there; they do not appear here.
 # ---------------------------------------------------------------------------
 
 NUM_REPLICATES={{ num_replicates }}
@@ -18,6 +18,9 @@ NUM_REPLICATES={{ num_replicates }}
 # ---------------------------------------------------------------------------
 
 echo ">>> Running simulation"
-tfs-simulate tfs_sim_config.yaml . \
-    --output_prefix sim_ \
+# Writes tfs_sim_growth.csv, tfs_sim_genotype_theta.csv, ... in this
+# directory. Keep the tfs_sim prefix: tfs-summarize-fit finds the ground
+# truth as *_sim_genotype_theta.csv.
+tfs-simulate tfs_sim_config.yaml \
+    --out_prefix tfs_sim \
     --num_replicates ${NUM_REPLICATES}
diff --git a/examples/simulate/simulate_config.yaml b/examples/simulate/simulate_config.yaml
index 78c193de..0defe041 100644
--- a/examples/simulate/simulate_config.yaml
+++ b/examples/simulate/simulate_config.yaml
@@ -3,6 +3,10 @@
 # Library genetic information
 # ---------------------------------------------------------------------------
 
+# These keys, plus library_mixture below, describe the library. Hand this same
+# file to tfs-process-fastq and to tfs-configure-model --library_config; each
+# tool ignores the keys it does not use.
+
 # Reading frame 0, 1, 2. No reverse (negative) strand allowed.
 reading_frame: 0
 
@@ -35,15 +39,29 @@ tile_combos: ["single-1", "single-2", "double-1-2"]
 # Control sequences (e.g. known variants) added as spikes.  Must be the same
 # length as wt_seq.
 spiked_seqs:
-- gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctggcggcgccgtcgcaaattgtcgcggcgattctgtctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg  # k84l
+- gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctgcacgcgccgtcgcaaattgtcgcggcgattctgtctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg  # k84l
 - gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgattgcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctgcacgcgccgtcgcaaattgtcgcggcgattctgtctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg  # m42i/k84l
 
+# Optional: simulate (and fit) only a subset of the described genotypes.
+# library_subsample keeps N random genotypes of a sub-library (others whole)
+# and needs its own integer seed; library_genotypes names a file listing
+# every genotype to keep, relative to this file. wt and spiked genotypes are
+# always kept. With library_subsample_depth: full_library (the default) the
+# mixture, transform sizes, reads and PCR templates below describe the full
+# library and each kept sequence keeps its share; as_written applies them to
+# the subsample as given. tfs-configure-model --library_config reads these
+# keys too. See docs/source/simulation.rst, "Subsampling the library".
+# library_subsample:
+#   double-1-2: 200
+# library_subsample_seed: 1
+# library_genotypes: genotypes.txt
+# library_subsample_depth: full_library
+
 # ---------------------------------------------------------------------------
 # Phenotype calculation
 # ---------------------------------------------------------------------------
 
-# Registered theta component name.  Any key in model_registry["theta"] is
-# valid except "_simple" (calibration-only).
+# Theta component: hill_geno, hill_mut or a thermo.* partition-function key.
 #
 # Thermodynamic components (physics-based operator-occupancy model):
 #   "thermo.O2_C12_K5_U0_a.PK"   — dimer with operator architecture O2, 12-mer
@@ -63,9 +81,11 @@ spiked_seqs:
 #                   exact reference curve.
 #
 # Perturbation-based components ignore theta_priors and instead use
-# theta_sim_priors (see below).  They do not call define_model; tfs-simulate
-# will fall back to prior-predictive sampling only for components that lack a
-# simulate() function.
+# theta_sim_priors (see below). Components without their own simulation
+# routine draw theta from the component's prior instead.
+#
+# theta_rescale (optional): 'passthrough' (default) or 'logit', applied to
+# theta before it enters the growth model, as the fit-side component.
 theta_component: "thermo.O2_C12_K5_U0_a.PK"
 
 # Optional: path to structural/thermodynamic data file required by PnnC and
@@ -74,6 +94,17 @@ theta_component: "thermo.O2_C12_K5_U0_a.PK"
 #   .csv         → per-mutation per-structure ddG prior means (PddG)
 # thermo_data: null
 
+# Optional: how to read thermo_data (see docs/source/model-inputs.rst).
+# thermo_options:
+#   ddG_units: kcal/mol      # kT (default), kcal/mol or kJ/mol
+#   residue_offset: 0
+
+# Optional (PddG/PnnC only): SD of the simulated per-structure ddG truth around
+# the thermo_data values, in ddG_units. A number for every structure, or
+# {structure: sd} naming each one. 0 makes the truth the table exactly.
+# Omit for 1 kT per structure. The true SDs go to tfs_sim_ddG_sigma.csv.
+# ddG_truth_sd: 0.5
+
 # Optional: overrides for the theta component's default model hyperparameters.
 # Any key returned by the component's get_hyperparameters() can be overridden.
 # Used by thermodynamic/hierarchical components; ignored when theta_component
@@ -136,6 +167,12 @@ theta_component: "thermo.O2_C12_K5_U0_a.PK"
 # Each block defines one set of growth conditions.  The Cartesian product of
 # titrant_conc × t_sel gives one sample per entry.  condition_pre and
 # condition_sel must also appear as keys in the 'growth' section below.
+#
+# To copy a real experiment's layout instead (irregular times, replicates
+# sampled differently), replace condition_blocks with
+#   design: tube_table.csv
+# a tube table in the format tfs-process-counts reads; each replicate then
+# gets exactly its own tubes, under the table's names.
 condition_blocks:
   - library: "kanR"
     titrant_name: "iptg"
@@ -165,9 +202,23 @@ condition_blocks:
 # Growth rate parameters
 # ---------------------------------------------------------------------------
 
-# Linear mapping from operator occupancy (theta) to growth rate k for each
-# condition:  k = m * theta + b.  Every condition_pre and condition_sel that
-# appears in condition_blocks must have an entry here, and vice versa.
+# Mapping from operator occupancy (theta) to growth rate for each condition,
+# in units of 1/minute (times are in minutes). The default linear model is
+#   k = b + A * m * theta + dk_geno
+# An entry can instead set model: power (keys b, a, n) or
+# model: saturation (keys kmin, kmax). Every condition_pre and condition_sel
+# that appears in condition_blocks must have an entry here, and vice versa.
+# A linear entry may add warp_a (> 0) and warp_x0 (default 0.5): growth then
+# follows a logistic warp f(theta), f(0) = 0 and f(1) = 1, for a marker whose
+# growth is a switch-like (large warp_a) or graded function of occupancy; the
+# fit's m_warp, truth written as warp_log_a = log(warp_a) and warp_x0.
+#   kanR+kan: {m: -0.009933, b: 0.0, warp_a: 8.0, warp_x0: 0.5}
+#
+# growth_titrant_scale (s, default 1): growth sees each genotype's curve at
+# s times the nominal titrant concentration, binding at the nominal one (the
+# fit's theta_growth_log_conc_shift = log s, hill_geno). Truth in
+# {out_prefix}_growth_titrant_scale.csv.
+# growth_titrant_scale: 0.5
 growth:
   kanR+kan: {m: -0.009933, b: 0.000000}
   kanR-kan: {m:  0.000808, b: 0.004670}
@@ -190,7 +241,10 @@ dk_geno_hyper_shift: 0.02
 # by inference convention.  Set activity_mut_scale > 0 to draw each mutant
 # genotype's log(A) independently from Normal(log(activity_wt), activity_mut_scale).
 # activity_mut_scale=0 (default) gives all genotypes identical activity, matching
-# the 'fixed' activity inference component.
+# the 'fixed' activity inference component. activity_component (default
+# 'fixed') can instead be 'hierarchical_geno' or 'horseshoe_geno', numpy
+# versions of the fit's priors, tuned by an activity_priors dict; both ignore
+# activity_wt and activity_mut_scale.
 activity_wt: 1.0
 activity_mut_scale: 0.0
 
@@ -214,29 +268,45 @@ library_mixture:
   spiked: 4
 
 # Sigma of the log-normal used to draw initial genotype frequencies.  0 = flat
-# distribution; 1–2 is realistic for a typical transformation.
+# distribution; 1–2 is realistic for a typical transformation.  One number, or
+# one per tile combo (e.g. {double-1-2: 2.0, single-1: 0.8, single-2: 0.8,
+# spiked: 0.5}; a real pool of doubles was much less even than its singles).
 lib_assembly_skew_sigma: 1.25
 
+# Optional: cap on the number of multi-plasmid (congressed) cells simulated one
+# by one; more are represented by a weighted sample of this many. Single-
+# plasmid cells are always merged into one row per genotype, so transform_sizes
+# can be realistic (tens of millions) without this cap only when congression is
+# off. Omit for no cap.
+# max_congressed_cells: 2_000_000
+
 
 # Each bacterium receives a number of plasmids drawn from a zero-truncated
 # Poisson with this lambda.  Set to 0 or null for exactly one plasmid per cell.
 transformation_poisson_lambda: 1.5
 
-# When a cell carries multiple plasmids, growth rates are combined via this
-# function.  Recommended: 'min' for repressors of antibiotic resistance (tightest
-# binder dominates); 'max' for inducers.  Default: 'mean' for all libraries.
-multi_plasmid_combine_fcn:
-  kanR: "min"
-  pheS: "max"
-
-# Colony-forming units per mL at the start of pre-growth.
+# How a cell carrying several plasmids gets its growth rate. The cell's theta
+# comes from congression_theta_rule, at each condition: 'homodimer'
+# (logit theta_cell = log of the share-weighted mean of the plasmids'
+# theta odds), 'heterodimer' (closer to the mean logit) or 'max' (the
+# highest-theta plasmid sets theta and TF activity). 'homodimer' and
+# 'heterodimer' need activity 1 (activity_mut_scale 0). Its dk_geno comes
+# from congression_dk_rule, the soft-min family
+# dk_cell = -(1/alpha) log sum_g x_g exp(-alpha dk_g): 'dilution'
+# (alpha -> 0, the share-weighted mean), 'softmin' (finite alpha, set by
+# congression_dk_alpha, in units of 1/dk) or 'min' (alpha -> inf, the worst
+# variant sets the cost). Growth is then computed once from these cell-level
+# values. All three keys are optional; these are the defaults (no alpha).
+congression_theta_rule: homodimer
+congression_dk_rule: dilution
+
+# Cells in each tube at the start of pre-growth.
 cfu0: 65_000_000.0
 
-# Per-tube environmental growth-rate noise (hr⁻¹).  Each physical tube gets its
-# own delta_k ~ Normal(0, tube_noise_sigma); the kt contribution per tube is
-# delta_k * (t_pre + t_sel), shared by all genotypes in that tube.  A value of
-# ~0.001–0.002 hr⁻¹ is typical for well-controlled liquid cultures.
-# Set to 0 or null to disable.
+# Per-tube environmental growth-rate noise, in growth-rate units (1/minute
+# here).  Each physical tube gets its own delta_k ~ Normal(0, tube_noise_sigma);
+# the kt contribution per tube is delta_k * (t_pre + t_sel), shared by all
+# genotypes in that tube.  Set to 0 or null to disable.
 tube_noise_sigma: 0.002
 
 # ---------------------------------------------------------------------------
@@ -251,6 +321,9 @@ tube_noise_sigma: 0.002
 #   tau0 = baseline lag (minutes)
 #   k1   = occupancy-dependence of lag (higher = more effect)
 #   k2   = curvature constant (> 0; prevents division by zero)
+# "baranyi" model: rate switches smoothly from k_pre to k_sel around
+#   tau_lag (minutes) with sharpness k_sharp (> 0)
+# "two_pop" model: cells switch irreversibly to k_sel at rate k_trans (> 0)
 growth_transition:
   - condition_pre: "pheS-4CP"
     model: "memory"
@@ -268,27 +341,88 @@ growth_transition:
 # Data collection parameters
 # ---------------------------------------------------------------------------
 
-# Total sequencing reads across all samples.
+# Total sequencing reads across all samples (all replicates together), split
+# evenly over the sequenced tubes.
 total_num_reads: 25_000_000
 
+# Optional: share of each tube's reads that call no library genotype and are
+# reported as __unknown__ (one number, or one per library). Real libraries
+# can lose a large share this way, e.g. to structural variants.
+# unassigned_read_fraction: {kanR: 0.66, pheS: 0.2}
+
 # Fraction of reads re-assigned to a randomly chosen genotype (index hopping).
 # Set to 0 or null to disable.
 prob_index_hop: 0.05
 
-# Random seed for reproducibility.  Set to null for a random seed each run.
+# Random seed for reproducibility.  Set to null for a random seed each run;
+# tfs-simulate --seed overrides it.
 seed: null
 
+# ---------------------------------------------------------------------------
+# Sampling noise (optional; all off when omitted)
+# ---------------------------------------------------------------------------
+
+# Real read counts vary 5-18x more than Poisson at 100-3,000 reads
+# (planning/studies/noise-anatomy/). These switches add the physical sources;
+# each can be turned on alone to measure what it does to a fit.
+#
+# founder_sampling: each tube is seeded with a Poisson number of cells from
+#   each transformant clone (mean cfu0 * frequency), drawn anew for every
+#   tube, so a rare genotype's starting abundance differs between tubes.
+# demographic_growth: given its founders, a clone's cells at harvest are
+#   Gamma(founders, exp(kt)) when it grows and Binomial(founders, exp(kt))
+#   when it shrinks. Needs founder_sampling.
+# shared_transformation: every replicate draws its cells from one library
+#   assembly and transformation (one glycerol stock), as in the real
+#   protocol, instead of redrawing them per replicate.
+# pcr_template_molecules: reads are drawn from this many template molecules
+#   per tube. The count variance grows by about
+#   (reads per tube / templates) * (1 + pcr_amplification_cv^2).
+# pcr_amplification_cv: CV of each template's amplification (PCR
+#   jackpotting). Needs pcr_template_molecules.
+#
+# founder_sampling: true
+# demographic_growth: true
+# shared_transformation: true
+# pcr_template_molecules: 2_000_000
+# pcr_amplification_cv: 1.0
+
+# One OD600 reading per tube, from its true total through an OD600-to-CFU
+# calibration (the one in ../od600/ is synthetic; a lab makes its own with
+# tfs-calibrate-od600). Writes tfs_sim_od600.csv with every tube of every
+# replicate.
+#   calibration:            calibration YAML written by tfs-calibrate-od600
+#   tube_volume_mL:         tube volume; the calibration is per mL (5.0)
+#   num_od_only_replicates: extra replicates with OD600 but no reads (0)
+#   sample_cfu_from_od600:  give the pipeline each tube's total as estimated
+#                           from its OD600, as the lab does, instead of the
+#                           true total (false)
+#
+# od600:
+#   calibration: ../od600/od600_calibration.yaml
+#   tube_volume_mL: 5.0
+#   num_od_only_replicates: 1
+#   sample_cfu_from_od600: true
+
 # ---------------------------------------------------------------------------
 # Binding data simulation (optional)
 # ---------------------------------------------------------------------------
 
-# If present, tfs-simulate also writes a binding CSV with per-genotype theta
-# curves.  titrant_name must match e_name in observable_calc_kwargs.
+# If present, tfs-simulate also writes a binding CSV (tfs_sim_binding.csv)
+# for tfs-configure-model --binding_df.  noise is the Gaussian SD on
+# theta_obs, not clipped to [0, 1] (clip_theta_obs: true restores clipping).
+# choose_by can also be the path to a CSV of Hill parameters (genotype,
+# theta_low, theta_high, log_hill_K, hill_n) that become those genotypes'
+# true phenotypes; it needs a Hill theta component.
 #
 # binding_data:
 #   titrant_name: iptg
 #   titrant_conc: [0, 0.0001, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1.0]
 #   noise: 0.02
+#   # One offset per binding genotype added to its whole curve,
+#   # Normal(0, genotype_offset_sd) (the fit's genotype_offset); truth in
+#   # {out_prefix}_binding_offsets.csv. Default 0.
+#   genotype_offset_sd: 0.1
 #   # Clean, monoclonal controls (pool = spiked_seqs).
 #   #   choose_by: <params file> | stratified | random ; num optional (no file)
 #   spiked_binding:
@@ -308,8 +442,7 @@ seed: null
 # reference-condition growth-rate "measurements" for a subset of genotypes,
 # tied to the per-genotype dk_geno already assigned above and a shared
 # reference rate k_ref. Feeds tfs-configure-model's --base_growth_df, which
-# anchors the hierarchical model's dk_geno/condition_growth identifiability
-# (see CLAUDE.md).
+# anchors the hierarchical model's dk_geno/condition_growth identifiability.
 #
 # k_ref (required): the reference (wt) growth rate, hr^-1.
 # genotypes (optional, default ["wt"]): genotypes to include. Every genotype
@@ -353,8 +486,8 @@ seed: null
 #   presplit_data:
 #
 # ---- What the presplit CSV represents -------------------------------------
-# This simulates the sequencing aliquot taken at t = -t_pre, just before
-# the culture is split into titrant-concentration conditions.  Because it is
+# This simulates the sequencing sample taken at t = -t_pre, just before
+# the culture is split into tubes.  Because it is
 # taken before any pre-selection growth, each ln_cfu value directly
 # constrains ln_cfu0 in the hierarchical model.
 #
diff --git a/examples/simulate/simulate_grid.yaml b/examples/simulate/simulate_grid.yaml
index a842023b..c025c1d2 100644
--- a/examples/simulate/simulate_grid.yaml
+++ b/examples/simulate/simulate_grid.yaml
@@ -2,12 +2,12 @@
 # Example grid YAML for tfs-setup-sim-grid
 #
 # Usage:
-#   tfs-setup-sim-grid simulate_grid.yaml --out_prefix my_sim_grid
+#   tfs-setup-sim-grid simulate_grid.yaml --out_dir my_sim_grid
 #
-# This creates one subdirectory per combination, writes a tfs_sim_config.yaml
-# in each (base config + overrides), and renders run.sh with the template
-# variables.  Run all simulations with:
-#   for d in my_sim_grid/run_*/; do cd "$d" && bash run.sh && cd -; done
+# This creates one subdirectory per combination (run_<index>_<run_name>),
+# writes a tfs_sim_config.yaml in each (base config + overrides), and renders
+# run.sh with the template variables.  Run all simulations with:
+#   for d in my_sim_grid/run_*/; do (cd "$d" && bash run.sh); done
 # ---------------------------------------------------------------------------
 
 # Base simulate config.  Each run inherits all settings from this file; the
@@ -27,8 +27,9 @@ output_file: run.sh    # in the same examples/simulate/ directory
 # simulate blocks
 #
 # Variables here override top-level keys in the base config.  They are NOT
-# injected into the Jinja2 template.  Paths are resolved relative to this
-# YAML and re-expressed relative to each subdirectory in the written config.
+# injected into the Jinja2 template.  Relative file paths are resolved against
+# this YAML; every input file is copied into <out_dir>/inputs/ and each run
+# refers to it as ../inputs/<name>, so the grid directory can be moved.
 #
 # Multi-key variants always move together and are never split by the Cartesian
 # product — use this to keep parameters that are only meaningful in combination.
@@ -44,7 +45,8 @@ simulate:
 
   # --- Theta component (thermodynamic model) --------------------------------
   #
-  # Any key in model_registry["theta"] except "_simple" is valid.
+  # hill_geno, hill_mut or a thermo.* partition-function key (PnnC and PddG
+  # variants also need thermo_data).
   # Uncomment additional variants to compare thermodynamic models.
 
   - name: thermodynamic_model
@@ -61,7 +63,8 @@ simulate:
 
   # --- Random seed ---------------------------------------------------------
   #
-  # Varying the seed with the same other settings tests reproducibility.
+  # 'seed' is the simulate config's seed key. Varying it with the other
+  # settings fixed gives independent simulated data sets.
 
   - name: seed
     variants:
diff --git a/examples/tfmodel/grid.yaml b/examples/tfmodel/grid.yaml
index 21775e41..d1fb7a0c 100644
--- a/examples/tfmodel/grid.yaml
+++ b/examples/tfmodel/grid.yaml
@@ -2,12 +2,16 @@
 # Example grid YAML for tfs-setup-grid
 #
 # Usage:
-#   tfs-setup-grid grid.yaml --out_prefix my_grid
+#   tfs-setup-grid grid.yaml --out_dir my_grid
 #
-# This creates one subdirectory per combination, calls tfs-configure-model
-# inside each, and renders run.srun with the template variables.
+# This creates one subdirectory per combination, named
+# run_<index>_<run_name> (run_0001_linear__instant__hill_geno__seed0, ...),
+# calls tfs-configure-model inside each, and renders run.srun with the
+# template variables. The data paths below are placeholders: point them at
+# your own tfs-process-counts / binding tables and library YAML first, since
+# setup fails on a missing input file.
 # Run all jobs with:
-#   for d in my_grid/run_*/; do cd "$d" && sbatch run.srun && cd -; done
+#   for d in my_grid/run_*/; do (cd "$d" && sbatch run.srun); done
 # ---------------------------------------------------------------------------
 
 # Directory name for each run.  Variables from both configure_model and
@@ -24,7 +28,8 @@ output_file: run.srun
 #
 # Variables here are forwarded to tfs-configure-model.  They are NOT
 # injected into the Jinja2 template.  Paths are resolved relative to this
-# YAML and re-expressed relative to each subdirectory in the written config.
+# YAML; each input file is copied into <out_dir>/inputs/ and the written
+# config refers to the copy, so the grid directory can be moved as a unit.
 # ---------------------------------------------------------------------------
 
 configure_model:
@@ -37,16 +42,20 @@ configure_model:
         growth_df:  ../data/growth.csv
         # The library description -- the same YAML used for tfs-process-fastq.
         # Required whenever growth_df is given.
-        library_config: ../data/run_config.yaml
+        library_config: ../data/library_config.yaml
 
   - name: fixed_components
     variants:
-      - activity: horseshoe_geno
+      # The mixture's default congression_theta_rule, homodimer, needs
+      # activity 'fixed' (activity 1 for every genotype); a learned activity
+      # (hierarchical_geno, horseshoe_geno, ...) needs
+      # congression_theta_rule: max.
+      - activity: fixed
         dk_geno: hierarchical_geno
-        transformation: empirical
+        transformation: mixture
         # (mean, std) of the experimentally measured congression lambda, in
-        # linear space -- required whenever transformation is 'empirical'
-        # or 'logit_norm' (transformation and transformation_lambda always travel
+        # linear space -- required whenever transformation is 'mixture'
+        # (transformation and transformation_lambda always travel
         # together, hence the joint variant here).
         transformation_lambda: [0.3572, 0.13]
         theta_rescale: passthrough
@@ -55,9 +64,10 @@ configure_model:
   #
   # 'auto: condition_growth' expands to one variant per entry in the
   # model_registry["condition_growth"] registry (currently: linear,
-  # power, saturation).  Incompatible combinations (e.g.
-  # power + logit theta_rescale) are caught by tfs-configure-model,
-  # skipped automatically, and logged in grid_summary.json.
+  # power, saturation).  Incompatible combinations are caught by
+  # tfs-configure-model, skipped automatically, and logged in
+  # grid_summary.json.  'auto: theta' would include the private '_simple'
+  # component used only by the pre-fit; list theta variants by hand.
 
   - name: condition_growth
     auto: condition_growth
@@ -104,8 +114,8 @@ template:
       - seed: 0
       - seed: 1
 
-  # Path to the genotypes file for tfs-predict-theta.
-  # Use an absolute path, or a path relative to each run subdirectory.
+  # Path to the genotypes file for tfs-predict-theta (absolute, or relative
+  # to this YAML).  It is copied into <out_dir>/inputs/ like any input.
   - name: predict_genotypes
     variants:
       - predict_genotypes_file: /path/to/predict_genotypes.txt
diff --git a/examples/tfmodel/growth_priors_loose.csv b/examples/tfmodel/growth_priors_loose.csv
new file mode 100644
index 00000000..4cfb4e48
--- /dev/null
+++ b/examples/tfmodel/growth_priors_loose.csv
@@ -0,0 +1,5 @@
+condition_rep,k_loc,k_scale,m_loc,m_scale
+kanR+kan,0.015,0.01,0.0,0.01
+kanR-kan,0.015,0.01,0.0,0.01
+pheS+4CP,0.015,0.01,0.0,0.01
+pheS-4CP,0.015,0.01,0.0,0.01
diff --git a/examples/tfmodel/run.srun b/examples/tfmodel/run.srun
index ea86e6e5..04ebfbeb 100644
--- a/examples/tfmodel/run.srun
+++ b/examples/tfmodel/run.srun
@@ -33,16 +33,17 @@ RUN_SEED={{ seed }}
 PREDICT_GENOTYPES_FILE="{{ predict_genotypes_file }}"
 
 # ---------------------------------------------------------------------------
-# Run prefit calibration
+# Pre-fit calibration: pins each condition's growth k and m in the priors CSV
 # ---------------------------------------------------------------------------
 
 echo ">>> Prefit calibration"
 tfs-prefit-calibration tfs_configure_config.yaml \
-    --seed ${RUN_SEED} \
-    --convergence_tolerance 0.00001
+    --seed ${RUN_SEED}
 
 # ---------------------------------------------------------------------------
-# MAP fit
+# MAP fit. Writes tfs_fit_model_checkpoint.pkl, _params.npz, _losses.txt and
+# _convergence.csv. Step-size cuts and the stop rule use the defaults
+# (tfs-fit-model --help lists them).
 # ---------------------------------------------------------------------------
 
 echo ">>> MAP fit"
@@ -50,23 +51,22 @@ tfs-fit-model \
     tfs_configure_config.yaml \
     --seed ${RUN_SEED} \
     --analysis_method map \
-    --adam_step_size 1e-6 \
-    --convergence_check_interval 100 \
-    --convergence_window 50 \
-    --checkpoint_interval 100 \
-    --max_num_epochs 100000000 \
-    --pre_map_num_epoch 100000 \
-    --convergence_tolerance 0.0005 \
-    --patience 5
+    --checkpoint_interval 100
 
 # ---------------------------------------------------------------------------
-# Sample posterior
+# Sample posterior. A MAP checkpoint becomes a Laplace posterior: 'auto'
+# takes the full Laplace up to --laplace_max_params MAP parameters and the
+# arrowhead Laplace above. --skip_growth_observations leaves the stored
+# per-observation growth sites out of the file (most of its size);
+# tfs-predict-growth recomputes growth from the parameter samples.
 # ---------------------------------------------------------------------------
 
 echo ">>> Sample posterior"
 tfs-sample-posterior \
     tfs_configure_config.yaml \
     tfs_fit_model_checkpoint.pkl \
+    --laplace auto \
+    --skip_growth_observations \
     --sampling_batch_size 10 \
     --num_posterior_samples 1000 \
     --seed ${RUN_SEED}
@@ -93,5 +93,7 @@ tfs-predict-theta \
 
 echo ">>> Categorize response"
 tfs-cat-response \
-    tfs_theta_pred.csv \
+    tfs_pred_theta.csv \
+    titrant_conc \
+    --group_by titrant_name \
     --num_workers -1
diff --git a/examples/tfmodel/two_stage.yaml b/examples/tfmodel/two_stage.yaml
new file mode 100644
index 00000000..0427ac77
--- /dev/null
+++ b/examples/tfmodel/two_stage.yaml
@@ -0,0 +1,47 @@
+# Example configuration for tfs-fit-two-stage (planning/two-stage-fit.md).
+#
+#   tfs-fit-two-stage two_stage.yaml --out_dir two_stage
+#
+# Top-level keys are tfs-configure-model's arguments, under the same names;
+# file paths are relative to this file. The two-stage fit needs the
+# growth-only relative model (theta_model: hill_relative, linear growth)
+# with one k and m per condition (growth_shares_replicates: true). It sets
+# the transformation itself: stage 1 fits the congression mixture, stage 2
+# does not.
+
+# --- Data ---
+growth_df: tfs_growth.csv              # tfs-process-counts output
+library_config: library_config.yaml    # the library YAML used to call the counts
+
+# --- Model (as for tfs-configure-model) ---
+theta_model: hill_relative
+growth_shares_replicates: true
+batch_size: 4096
+allow_missing_spikes: true
+growth_priors: growth_priors_loose.csv # loose per-condition k/m priors for stage 1
+set_priors:
+  - sigma_fixed=0.17
+  - theta_log_hill_n_hyper_scale_fixed=0.5
+skip_model_stats: true
+
+# --- Two-stage settings ---
+two_stage:
+  # Congression lambda prior (mean, std), required: the measured value. For
+  # the dev-data screen, 0.357 with a likelihood-ratio 95% CI of 0.156-0.674,
+  # about 0.38 +- 0.15 as a mean and SD.
+  transformation_lambda: [0.38, 0.15]
+  seed: 1
+  num_posterior_samples: 500
+  consistency: true            # stage 2 on one library alone; --no_consistency skips
+  consistency_library: kanR    # default: the first library alphabetically
+  predict_epistasis: false
+  # Experimental: k's uncertainty in X (jackknife + k-shift refits). Off:
+  # on simulations the shift came out a third or less of the true one
+  # (planning/studies/two-stage-validation/, run 2).
+  kshift: false
+  jackknife_subsamples: 30     # stage 1 refits for k's uncertainty
+  jackknife_drop_frac: 0.2     # share of tubes dropped per refit
+  kshift_step_size: 1.0e-5     # the k-shift refits' starting step size
+  kshift_control: true         # a third refit with k unchanged; kshift/drift.csv
+  fit: {}                      # extra tfs-fit-model arguments
+  posterior: {}                # extra tfs-sample-posterior arguments
diff --git a/examples/tfmodel/two_stage_step.srun b/examples/tfmodel/two_stage_step.srun
new file mode 100644
index 00000000..ab65487e
--- /dev/null
+++ b/examples/tfmodel/two_stage_step.srun
@@ -0,0 +1,38 @@
+#!/bin/bash -l
+#SBATCH --account=YOUR_ACCOUNT
+#SBATCH --partition=gpu
+#SBATCH --time=00-06:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# One step of tfs-fit-two-stage, submitted by two_stage_submit.sh, which
+# sets the job name, log files, dependencies and (for the jackknife) the
+# array. Edit the account, partition and time above for your cluster, and
+# the module line below for its CUDA.
+#
+#   CFG   the two-stage YAML (absolute path)
+#   OUT   the output directory (absolute path)
+#   STEP  stage1 | stage2 | predict | consistency | summary, and with
+#         two_stage.kshift jackknife | kshift_predict
+#         (jackknife runs task SLURM_ARRAY_TASK_ID)
+
+module load cuda/12.4.1
+set -e
+CFG="${CFG:?set CFG}"
+OUT="${OUT:?set OUT}"
+STEP="${STEP:?set STEP}"
+echo ">>> ${STEP} ${SLURM_ARRAY_TASK_ID:-} $(date)"
+case "${STEP}" in
+    jackknife)
+        tfs-fit-two-stage "${CFG}" --out_dir "${OUT}" \
+            --jackknife_task "${SLURM_ARRAY_TASK_ID:?run as an array}" ;;
+    kshift_predict)
+        tfs-fit-two-stage "${CFG}" --out_dir "${OUT}" --step kshift
+        tfs-fit-two-stage "${CFG}" --out_dir "${OUT}" --step predict ;;
+    *)
+        tfs-fit-two-stage "${CFG}" --out_dir "${OUT}" --step "${STEP}" ;;
+esac
+echo ">>> Done ${STEP} ${SLURM_ARRAY_TASK_ID:-} $(date)"
diff --git a/examples/tfmodel/two_stage_submit.sh b/examples/tfmodel/two_stage_submit.sh
new file mode 100644
index 00000000..cba9ba8f
--- /dev/null
+++ b/examples/tfmodel/two_stage_submit.sh
@@ -0,0 +1,37 @@
+#!/bin/bash
+# Submit tfs-fit-two-stage as chained SLURM jobs:
+#
+#   bash two_stage_submit.sh two_stage.yaml out_dir
+#
+# stage1; then stage2; then predict and consistency (after stage2); then
+# the summary table (after both). With two_stage.kshift true (experimental)
+# the jackknife array runs beside stage2 and kshift runs before predict. Logs go to out_dir/logs/. Every path is made
+# absolute, so it runs from any directory. Each step skips finished work,
+# so submitting the chain again resumes it. Set JACKKNIFE_PARALLEL to run
+# more or fewer jackknife refits at once (default 10).
+set -e
+CFG="$(cd "$(dirname "$1")" && pwd)/$(basename "$1")"
+OUT="$(mkdir -p "$2" && cd "$2" && pwd)"
+HERE="$(cd "$(dirname "$0")" && pwd)"
+SRUN="${HERE}/two_stage_step.srun"
+N="$(python -c "import yaml,sys; c=yaml.safe_load(open(sys.argv[1])); print((c.get('two_stage') or {}).get('jackknife_subsamples', 30))" "${CFG}")"
+KSHIFT="$(python -c "import yaml,sys; c=yaml.safe_load(open(sys.argv[1])); print(bool((c.get('two_stage') or {}).get('kshift', False)))" "${CFG}")"
+mkdir -p "${OUT}/logs"
+sub() {  # name dependency extra-args...
+    local name=$1 dep=$2; shift 2
+    sbatch --parsable --job-name="ts_${name}" \
+        --output="${OUT}/logs/${name}_%A_%a.out" --error="${OUT}/logs/${name}_%A_%a.err" \
+        ${dep:+--dependency=afterok:${dep}} \
+        --export=ALL,CFG="${CFG}",OUT="${OUT}",STEP="${name}" "$@" "${SRUN}"
+}
+a=$(sub stage1 "")
+c=$(sub stage2 "${a}")
+if [ "${KSHIFT}" = "True" ]; then
+    b=$(sub jackknife "${a}" --array=1-${N}%${JACKKNIFE_PARALLEL:-10})
+    d=$(sub kshift_predict "${b}:${c}")
+else
+    d=$(sub predict "${c}")
+fi
+e=$(sub consistency "${c}")
+f=$(sub summary "${d}:${e}")
+echo "stage1 ${a}; stage2 ${c}${b:+; jackknife ${b} (1-${N})}; predict ${d}; consistency ${e}; summary ${f}"
diff --git a/planning/.gitignore b/planning/.gitignore
new file mode 100644
index 00000000..ca57e91d
--- /dev/null
+++ b/planning/.gitignore
@@ -0,0 +1,2 @@
+history
+studies/fetch.sh
diff --git a/planning/README.md b/planning/README.md
new file mode 100644
index 00000000..4ed71505
--- /dev/null
+++ b/planning/README.md
@@ -0,0 +1,92 @@
+# planning/
+
+Three kinds of things live here, kept together so they can link to each
+other:
+
+- **Ideas** (`status: idea`): tasks that matter but are deliberately out of
+  scope for current work. Filing an idea here means "we decided not to do
+  this now," not "we will do this."
+- **Plans** (`status: active`): the few agreed multi-step plans being worked
+  now. Keeping them next to the ideas they spin off means an idea's `related`
+  link and a plan's step list can point at each other.
+- **Studies** (`studies/`): the numerical or simulation work that answers a
+  question a plan or idea raised. See "Studies" below.
+
+Ideas and plans are one Markdown file each at the top level; the status in the
+header tells them apart. (This directory was called `future/` until
+2026-09-23.)
+
+## Why a directory and not issues
+
+GitHub issues are the usual home for a backlog, but they live outside the
+repository: they aren't versioned with the code they describe, can't be read
+offline, and can't be read by tools working in a checkout. These files are
+plain Markdown with a small YAML header, so both people and scripts can list
+and filter them.
+
+## File format
+
+Name files with short kebab-case slugs (`estimate-dk-alpha-by-varying-lambda.md`).
+Start each file with this header:
+
+```yaml
+---
+title: One-line name of the idea
+status: idea        # idea | active | promoted | dropped | done
+filed: 2026-09-13   # date filed (YYYY-MM-DD)
+area: tfmodel       # tfmodel | simulate | analysis | process_raw | experiment | docs
+revisit_when: >-
+  The condition under which this becomes worth doing.
+related:            # repo paths or other planning/ files; may be empty
+  - src/tfscreen/simulate/selection_experiment.py
+---
+```
+
+Then the body, with these sections (an `active` plan may use its own
+structure, such as assumptions, decisions and ordered steps):
+
+- **Context:** what we were doing when the idea came up.
+- **Idea:** what to do.
+- **Why not now:** the reason it was deferred.
+- **What it would take:** rough scope, dependencies, open questions.
+
+## Lifecycle
+
+- `idea`: filed and waiting.
+- `active`: an agreed plan being worked now. Keep its step list current.
+- `done`: an active plan that is finished. Add a line saying where the result
+  lives.
+- `promoted`: work started. Add a line saying where it went (branch, changelog
+  entry, plan). Keep the file as a record.
+- `dropped`: decided against. Add a line saying why. Keep the file so the idea
+  isn't re-proposed without that context.
+
+To list open ideas:
+
+```bash
+grep -l "^status: idea" planning/*.md
+```
+
+## Studies
+
+A study answers one question that a plan or idea raised, and its result feeds
+a decision. Each study gets its own directory, `studies/<slug>/`, holding at
+least:
+
+- `README.md` with:
+  - **Question** and the **decision it fed**, linking the plan step.
+  - **How to run** it, from a scratch directory.
+  - **Inputs**: repository paths only, with fixed seeds.
+  - **Commit** the recorded results were produced on.
+  - **Results**: the numbers the decision rests on.
+- the script(s) that produce those results.
+
+Rules:
+
+- The plan records the decision and a short summary; the study README keeps
+  the full numbers. Link both ways.
+- Generated outputs (CSVs, figures) are not committed unless small and cited;
+  the README's results are the record.
+- Once a plan cites a study, freeze it. If the question changes, write a new
+  study rather than rewriting the old one, so the citation stays valid.
+- `dev/` stays untracked scratch space. Anything a plan cites moves here.
diff --git a/planning/absolute-abundance-measurements.md b/planning/absolute-abundance-measurements.md
new file mode 100644
index 00000000..61d3a681
--- /dev/null
+++ b/planning/absolute-abundance-measurements.md
@@ -0,0 +1,116 @@
+---
+title: Measurements that fix each tube's total population, especially under strong selection
+status: idea
+filed: 2026-09-26
+area: experiment
+revisit_when: >-
+  Now for the few bench experiments the current dataset can still use
+  (they feed roadmap steps 5-6); again when the next screen is designed
+  (earlier timepoints, longer time base).
+related:
+  - planning/analysis-roadmap.md
+  - docs/source/process-raw.rst
+  - src/tfscreen/tfmodel/generative/observe/base_growth.py
+---
+
+**Context:** The analysis roadmap (`planning/analysis-roadmap.md`, "Reads and
+totals") builds each tube's total population `N_s` into the fit as a smooth
+curve observed through OD600. Reads measure only frequencies, so the total is
+the sole source of the absolute scale, and with it the condition growth rates
+`k_c` and, in the relative fit, `m_c`. Two gaps (user, 2026-09-26):
+
+- **OD600 has no signal under the strongest selection.** Tubes that barely
+  grow (particularly kan) stay below the reader's detection threshold
+  (OD 0.096) at the timepoints we have. Censoring turns such a reading into
+  an upper bound, not a measurement.
+- **The next screen should sample earlier**, to resolve growth over a longer
+  time base, and early tubes are below the threshold too.
+
+What already constrains a blind tube:
+
+- **The start.** The culture is grown to a presplit OD600, then diluted by
+  the same factor into every tube, so every tube of a replicate starts from
+  a known total (calibration plus pipetting error), shared by all of them.
+- **Later, visible tubes of the same curve.** A smooth curve through a known
+  start and later detectable points interpolates the early blind ones. This
+  covers the future design if late points stay visible.
+- **Other columns of the same condition.** `k_c` and `m_c` are shared by all
+  IPTG columns of a condition and each genotype's X is a smooth Hill curve
+  across columns, so a total that is visible in some columns constrains the
+  others through the reads. This fails when every column of a condition is
+  blind at a timepoint (then `k_c` trades freely with the curves), and it
+  leans on the Hill shape exactly where the gauge needs it most (wt at 0 and
+  1 mM).
+
+**Idea:** add measurements of the absolute scale that do not depend on
+OD600, chosen by what each one pins.
+
+*For the current dataset (a few targeted experiments):*
+
+1. **wt monoculture growth rates in each selection condition at the gauge
+   concentrations** (0 and 1 mM IPTG), in the screen's tube format and
+   volume, started at the screen's split density (known from presplit OD
+   and dilution; aminoglycoside killing depends on inoculum density, so
+   matching it matters), counted by plating over the screen's time window.
+   Three replicates. In the relative fit this measures `k_c` (wt at 1 mM)
+   and `m_c` (wt at 0 minus wt at 1 mM) directly: the gauge stops depending
+   on the blind tubes. Add one or two controls: a non-binding repressor
+   (IPTG-independent X, the internal standard of roadmap C4) and, if one
+   exists, a super-repressor. Needs a new observer (roadmap step 6): the
+   clean growth rate `k_c + dk_g + m_c X_g(c)` of a named genotype in a
+   named condition and concentration. Today's `base_growth` observes only
+   `k_ref + dk_geno` in one reference condition. Caveat: a monoculture
+   lacks the library's other cells (density, any cross-protection), which
+   the matched starting density only partly addresses.
+   **Extended after study 0a (2026-09-26):** run the five genotypes with
+   in vitro binding data (wt, M42I, H74A, K84L, M42I/H74A) across all eight
+   IPTG concentrations in kan and 4CP. That measures the growth-binding
+   map directly, with no library and no totals problem. 0a found the
+   library data inconsistent with one linear map at the in vitro
+   concentration scale (kan responds at roughly 30-100x lower IPTG; 4CP
+   does not track binding), but could not rule out errors in the
+   per-column totals.
+2. **OD600 against plate counts under selection**, a few screen-like tubes
+   in the strongest conditions at densities the reader can see. Tests
+   whether the unstressed calibration transfers to stressed cells: dead
+   cells and filaments scatter light without forming colonies (roadmap
+   D11). Roughly ten to fifteen plates.
+3. **qPCR of total plasmid in the extracted DNA, if any remains.** Relative
+   plasmid amount per sample, calibrated against the samples whose OD600 was
+   visible, gives relative totals for the blind ones. Limited by extraction
+   yield and copy number varying between samples, so it is a weak
+   constraint, but it reaches where OD600 cannot. Only if the DNA exists.
+
+*For the next screen (built into the design):*
+
+4. **A cell spike-in counting standard.** At each pull, before spin-down,
+   pipette a fixed volume of a quantified frozen stock of cells carrying a
+   plasmid with the same amplicon and a unique sequence absent from the
+   library (for example a synonymous-codon wt, the same trick planned for
+   telling spikes from bulk). Then `N_s = N_std * reads_library /
+   reads_std`, at any density sequencing can see, with no plates and one
+   pipetting step per tube. It is co-extracted and co-amplified with the
+   library, so extraction and PCR differences cancel; the stock is
+   quantified once, carefully, by plating. Choose `N_std` so the standard
+   takes about 1-5% of reads. It also cross-checks OD600 wherever both
+   exist. In the model it is one more "genotype" with a known, non-growing
+   abundance per tube, and it fixes `P_s` directly.
+5. **Flow cytometry with counting beads** (and a live/dead stain), as an
+   alternative or check: absolute counts well below OD600's floor, and it
+   separates live from dead cells. Costs instrument time per tube.
+6. Keep OD600 for the visible tubes and the presplit; it is free and the
+   calibration exists.
+
+**Why not now:** Filed while revising the roadmap; the user will choose which
+bench experiments are feasible for the current analysis (a few, not many).
+
+**What it would take:**
+
+- Bench time for 1-3 (1 is the most valuable for the relative fit).
+- Model: the monoculture growth observer (roadmap step 6); a spike-in
+  standard is a small addition to the count likelihood (roadmap step 7).
+- Simulator: monoculture rates and a spike-in standard as optional outputs,
+  to size how much each helps before running it (roadmap step 4).
+- Open question for 1: which conditions are blind at which timepoints in the
+  current data (roadmap study 0c answers it), so the experiment covers
+  exactly those.
diff --git a/planning/analysis-roadmap-summary.md b/planning/analysis-roadmap-summary.md
new file mode 100644
index 00000000..d138632d
--- /dev/null
+++ b/planning/analysis-roadmap-summary.md
@@ -0,0 +1,477 @@
+---
+title: What the analysis roadmap branch did and found
+status: done
+filed: 2026-10-02
+area: tfmodel
+revisit_when: >-
+  A record. Revisit when picking up any item under "Where to go from here",
+  or before a methods section is written from it.
+related:
+  - planning/analysis-roadmap.md
+  - planning/studies/count-likelihood/README.md
+  - planning/studies/relative-fit/README.md
+  - planning/studies/svi-overconfidence/README.md
+  - planning/studies/noise-anatomy/README.md
+  - planning/studies/od600-population/README.md
+  - planning/studies/growth-binding-map/README.md
+  - planning/studies/real-data-fit/README.md
+  - planning/experiment-pipeline.md
+---
+
+Branch `claude/physics-improvements-roadmap-03a273`, 2026-09-26 to
+2026-10-02. The plan is `planning/analysis-roadmap.md`; this file is the
+outcome. Part 1 is written as a methods narrative, with the reasons and the
+decisive numbers. Part 2 lists the bugs caught on the way. Part 3 is where
+to go next, and part 4 is what it takes to reproduce the work and what goes
+into the merge.
+
+Numbers about the real screen are summary statistics from
+`planning/dev-data/` (gitignored). The data themselves never enter the
+repository.
+
+# 1. Methods
+
+## Measurement model
+
+**Reads and totals.** Each tube is one timepoint: it is pulled, read for
+OD600, spun down and frozen, and sequenced later. Reads measure
+frequencies, and OD600 measures the tube's total. A tube that grew a little
+faster than its neighbors scales every genotype and the total by the same
+factor, so frequencies do not see it. We therefore modeled reads against
+the tube total instead of attaching per-tube noise to every genotype.
+
+**Count noise.** We measured it before choosing a likelihood
+(`planning/studies/noise-anatomy/`). Doubles are 97% of rows, with a median
+of 3-5 reads per tube and 23-33% zeros. Per-tube variance was 5-18 times
+Poisson at 100-3,000 reads, which points to a bottleneck upstream of the
+reads (founder cells or PCR templates) rather than a constant-CV floor.
+Today's `ln_cfu_var` understated the noise 5-10 fold. Low counts were
+mostly low abundance, not crashes: 88% of double rows under 5 reads in
+selective tubes were already under 5 reads without the drug. This study ran
+on an older processing snapshot and has not been rerun on the new one.
+
+**Count likelihood.** We chose to observe reads directly, as
+`reads ~ NegBin(mu, mu (1 + phi) + mu^2 inv_r)`, with no pseudocount
+(`planning/studies/count-likelihood/`):
+- The linear `phi` term matches the bottleneck signature.
+- numpyro's own negative binomial lost several nats per observation to
+  float32 cancellation at 1e4-1e6 reads, so we implemented Loader's
+  algorithm and tested it against scipy at high depth.
+- We paired it with one constant-prior level offset per tube, so that an
+  error in a tube's supplied total does not move every genotype in it.
+
+We tested it against the `ln_cfu` Student-t likelihood on 24 simulations:
+2 noise models x 3 seeds x 4 fits. On the final run, theta 95% coverage
+was 0.83 / 0.84 for counts (Poisson / realistic noise) against 0.72 / 0.83
+for `ln_cfu`. Theta RMSE was 0.035 / 0.072 against 0.100 / 0.109, and
+dk_geno coverage was 0.71-0.86 against 0.53-0.55. At a true congression
+lambda of 0, counts found 0.02-0.06, where `ln_cfu` found about 0.12, so
+most of the spurious congression came from the read floor. Counts with
+level offsets became the `tfs-configure-model` default.
+
+**OD600 calibration.** We ported the lab's OD600-to-CFU notebook into a
+generic tool, `tfs-calibrate-od600`. It fits a weighted polynomial and
+keeps the full coefficient covariance, because the curve error is shared
+by every tube and must not be treated as per-tube noise. The lab's data and
+constants stay out of the repository. The port reproduced the notebook's
+coefficients to 2e-9 and found that the notebook never divided by the
+plated volume, so its totals were 10x low. Growth rates and frequencies are
+unaffected; absolute levels are.
+
+**Simulator realism.** We added the noise sources the data showed, each
+switchable so its effect on a fit can be measured: founder sampling,
+demographic growth, a shared transformation across replicates, a PCR
+template bottleneck and OD600 through an inverse calibration. Matching the
+real 5-18x over-Poisson noise needs templates on the order of reads per
+tube / 10.
+
+## The growth-only relative fit
+
+**Why a relative variable.** A first, model-free look at five spiked
+genotypes rejected a single linear map from in vitro binding theta to
+growth (reduced chi-square 6.7 in kan; `planning/studies/growth-binding-map/`,
+provisional). Growth alone fixes an occupancy-like variable only up to an
+affine map, so we fit a wt-relative growth variable X instead of theta
+(`theta: hill_relative`). It has Hill shapes with real-valued baselines,
+and wt is gauged to X = 1 at 0 IPTG and X = 0 at 1 mM. One X per genotype
+and IPTG concentration is shared by kan and 4CP, and each condition's k and
+m map it to growth. Then k is wt's rate at 1 mM and m is wt's change
+between 0 and 1 mM. The orchestrator refuses combinations the gauge cannot
+support: binding data, learned activity, logit rescaling, non-linear growth
+and partition-function congression rules.
+
+**ln_cfu0.** We fit one starting abundance per replicate x pre-condition x
+genotype (`ln_cfu0: hierarchical`), because the kanR and pheS libraries are
+transformed and grown separately. The factored model, which shares a
+genotype's abundance across libraries, pushed the real between-library
+difference (SD 0.17-0.29 in simulation) into a confident X error. At more
+than 1000 reads, 95% coverage was 0.09 against 0.80, and RMSE was 0.101
+against 0.027. The orchestrator now refuses the factored model across
+libraries.
+
+## Inference: what we tested and chose
+
+**The m·X ridge.** Growth constrains mostly `m (X_g - X_wt)`. The posterior
+is therefore a curved ridge along m·X that only wt's absolute growth change
+pins, through the tube totals. Every inference choice below follows from
+that.
+
+**Guides on simulations.** We compared five inference arms on 30
+simulations, after fixing the start, scale and Laplace bugs in Part 2
+(`planning/studies/relative-fit/`, run 4).
+
+| Arm | X 95% coverage, Poisson / realistic | X RMSE, Poisson / realistic |
+|---|---|---|
+| Mean-field component guide | 0.06 / 0.38 | 0.30 / 0.29 |
+| Low-rank guide (any rank from 20 to numpyro's default) | 0.81 / 0.76 | 0.07 / 0.12 |
+| MAP + full Laplace | 0.98 / 0.94 | 0.11 / 0.29 |
+| Two-stage fit (refit at Laplace draws of k and m) | 0.91 / 0.78 | 0.08 / 0.11 |
+
+- The component guide slides along the ridge.
+- MAP + full Laplace covered best, with the noisiest point and the widest
+  intervals.
+- Every guide collapsed k and m: k coverage was 0, and m coverage 0-0.25.
+- A dense full-covariance guide ran its loss to -1e23 through float32 error
+  in its own log density.
+- NUTS stayed at the maximum tree depth with R-hat 3.5 under every mass
+  matrix and in float64, so we used calibration over many seeds as the
+  reference instead.
+
+**Real data rejected the guides.** On the full library (217,945
+genotypes), both the component and the low-rank guide fit the counts
+3.3-3.4e5 nats worse than the MAP, again by sliding along the ridge.
+A full Laplace is out of reach there, with about 2.4M parameters.
+
+**Per-genotype Laplace.** Given the shared parameters (k, m,
+hyperparameters, tube offsets), genotypes do not couple. On a simulation's
+full Hessian every cross-genotype entry was exactly 0, wt included. So the
+Hessian splits into one 9-11 parameter block per genotype, and we get all
+the blocks from B Hessian-vector products per genotype chunk. The code
+refuses a model whose blocks leak. With the shared parameters held at the
+MAP, this block Laplace covered X 0.93 / 0.84 on simulations, with k/m
+coverage 0.
+
+**Arrowhead Laplace.** We then added each genotype's coupling to the shared
+parameters and the shared block, at one more Hessian-vector product per
+shared parameter. We draw the shared parameters from their marginal, the
+Schur complement, and each genotype from its conditional given them. That
+is the full Laplace, and on a toy model it matched the dense inverse
+Hessian exactly, chunked or not.
+
+A MAP stopped short of an optimum needed two rules:
+- A floored direction of a genotype block carries no coupling. Kept, the
+  couplings of floored blocks subtracted 6e10 from m's curvature of 1e10.
+- A negative direction of the Schur complement is held at the MAP. Floored
+  at the prior instead, it gave k its prior SD and X widths of 1-3.
+
+On the simulations it covered X 0.97 / 0.94 (0.95-0.98 above 1000 reads)
+and k/m 0.92 / 1.0 (Poisson) and 0.50 / 0.42 (realistic). That is close to
+the full Laplace's 0.92 / 0.92 and 0.58 / 0.50, so the realistic-noise
+shortfall is not the approximation's. **MAP + arrowhead Laplace is the
+full-library route.** On the real library it ran in 20 minutes.
+
+**Laplace floor.** Each Hessian eigenvalue is floored at the prior's
+curvature along its eigenvector, so no direction is wider than the prior.
+The old flat 1e-3 floor turned negative eigenvalues into variances of 1000.
+
+## Fitting the real screen
+
+**Data.** The screen had 118 tubes after dropping three failed ones, and
+22.9M count rows over 217,945 genotypes.
+- Tube totals came from OD600 through the calibration. Every screen
+  reading lay inside the calibrated range.
+- Binding curves came from one joint fit of anisotropy to theta and Hill
+  curves, with per-day levels and robust residuals, interpolated to 10 µM
+  protein.
+- The library has nine spikes. The processing config listed three, and its
+  M42I codon differed from the spikes'; we corrected both.
+- The initial kan and phe samples were swapped, and we swapped them back.
+
+**Starting point.** A level-offset MAP from a cold start landed in a ±2.8
+offset mode with unphysical k and m. Adam moves every parameter about one
+step size per step, so the fast parameters were carried by whole units in
+the first window, and the step-size cuts froze them there. We started
+instead from the no-offset MAP plus small per-tube offsets
+(`tfs-fit-model --init_from`), which was 8.6e5 nats better. The offsets'
+SD is held at 0.17, the per-tube OD600 scatter. Learned, it grew until each
+tube's offset absorbed the population's growth.
+
+**Population SDs.** The learned log(hill_n) population SD ran to about 19,
+giving n from 0.01 to 13. In 42% of genotypes the curve then changed more
+between 0 and 0.1 µM IPTG than across the whole measured range, and the
+counts show no such step. We hold that SD at 0.5. Holding X_low, X_delta
+and log K as well, at 0.5 / 0.5 / 1.0 against learned 3.6 / 4.3 / 11, is a
+sensitivity check (below).
+
+**Growth priors.** k ~ N(0.015, 0.01) and m ~ N(0, 0.01) per condition,
+written by hand, since the pre-fit refuses growth-only models. The wt
+monoculture rates as kan priors changed m in the fifth digit.
+
+**Final fit.** The final fit is `rel_off_n05`. It is growth-only, with the
+count likelihood, level offsets at SD 0.17, the n population SD held at
+0.5, loose priors and nine spikes, followed by the arrowhead Laplace. It fits
+the counts 2.0e4 nats better than the best joint MAP, which used the same
+offsets. Without offsets, the growth-only fit was likewise 2.2e4 nats ahead
+of the joint one. Its
+median X 95% width by genotype read depth is:
+
+| Reads | 95% width |
+|---|---|
+| ≤ 100 | 2.6 |
+| 1e2-1e3 | 1.2 |
+| 1e3-1e4 | 0.69 |
+| 1e4-1e5 | 0.42 |
+| > 1e5 | 0.33 |
+
+## Assumptions we checked
+
+- **Genotype blocks are uncoupled given the shared parameters,** checked on
+  a full Hessian and guarded by a leak check on every run.
+- **The simulator matches the fit model:** growth k + dk + m·A·θ to 2e-16,
+  θ to the Hill truth to 1.4e-5.
+- **Every per-genotype latent is mini-batch safe,** by shape and by order,
+  registry-wide.
+- **The level offsets are not the unassigned-read share** (correlation
+  -0.21 / -0.41). Closure of the frequencies improved with them (SD 0.16
+  to 0.11).
+- **The kanR and pheS libraries differ in composition** (Spearman about
+  0.1), which supports per-library ln_cfu0.
+- **Unselected kan shows no IPTG effect** (wt 1 mM / 0 mM log-ratio about
+  0).
+- **The split density from the protocol agrees** with extrapolation of the
+  unselected tubes within 1.5-fold.
+- **wt's within-series growth in the screen matches its monoculture** in
+  kan (IPTG effect 0.013 vs 0.014). In pheS+4CP it does not: library wt
+  keeps growing at high IPTG where the monoculture stops.
+
+## What the results are insensitive to
+
+- **The spike curve shapes, K and n, stay inside their arrowhead Laplace
+  intervals across:**
+  - holding all four Hill population SDs (`rel_off_hs`);
+  - adding the congression mixture (`rel_off_hs_mix`, `max` rule, lambda
+    prior matched to the measured 0.357 with likelihood-ratio 95% CI
+    0.16-0.67);
+  - the k/m uncertainty.
+- **The k and m priors** (loose against monoculture).
+- **The SVI guide's rank** (20 against numpyro's default).
+
+**Sensitive, and under-constrained: m and the absolute X scale.** Holding
+the population SDs made every |m| 38% larger and scaled X by 0.77, and the
+mixture moved |m| another 21%. These shifts are far outside m's Laplace
+interval of ±4-5%. Each step fit the counts about 2.2e4 nats better and
+grew the tube offsets. The offsets follow IPTG, in opposite directions in
+kan and 4CP. A per-tube offset moves every genotype's predicted frequency
+in that tube, so a library-wide IPTG trend can sit in the offsets instead
+of in m·X. The Laplace intervals on k and m are therefore conditional on
+where the MAP sits on the ridge. We report curve shapes and baselines
+relative to wt, not rates.
+
+## What the fit says about growth and binding
+
+- **Growth curves are broader than binding curves.** Spike growth n is
+  0.6-1.2 (every 95% upper bound ≤ 1.17), against binding n 1.6-2.5. This
+  holds for clean spikes and bulk-sourced genotypes alike and survived the
+  congression mixture, so it is not congression.
+- **M42I induces below wt in growth,** with K 6.4 µM (95% 4.2-9.6) against
+  wt 12 µM (8.8-16.7). In binding its K is about 10x wt's, at both 2 and
+  20 µM protein.
+- **Every mutant falls to a common floor near X -0.5 by 1 mM,** including
+  weak binders whose binding plateaus sit above it. Taking the floor as
+  zero occupancy, growth says wt is about 70% induced at 1 mM, where
+  binding says 93%.
+- **No single apparent protein concentration and no shared monotone map
+  carries binding onto growth.** The best shared concentration left an RMS
+  of 0.285 in X, and isotonic maps 0.21-0.25 at every concentration. The
+  departures, 0.3-1.0 in X, are well outside the 0.15-0.35 interval
+  widths.
+
+# 2. Bugs caught and fixed
+
+## Inference and optimization
+
+- **SVI started far from the pre-MAP point.** The guide's initial scale was
+  0.1 in each site's units, plus a 0.1 multiplicative jitter. SVI began
+  about 1000x above the pre-MAP loss and fell into wrong optima: mirror
+  modes, halved m. This invalidated the absolute coverage of every earlier
+  SVI grid. Fix: scale 1e-4, jitter 0 (c57d9ee5).
+- **That fix froze the hyperparameter scales.** It put them on their
+  `greater_than(1e-4)` bound, which is -inf unconstrained, so they never
+  moved. This affected every component-guide fit from 2026-09-27 to
+  2026-09-29, which we reran as relative-fit run 4 and the
+  svi-overconfidence fix grid. Fix: start above the bound (9db28286).
+- **Step-size cuts came mid-descent.** The cut rule judged each window
+  alone. 20 of 23 first cuts on the count-likelihood v2 grid came during a
+  descent that pooled to t = 4-14, and one cut led into a wrong optimum.
+  Fix: pool the stalled windows before any cut or stop (740ebc19).
+- **A runaway loss (-8e23) was reported as converged.** Fix: a `diverged`
+  stop (32a7ec12).
+- **NUTS chains started at random,** because no site matched the start
+  values. Fix (8e244cf0).
+
+## Laplace and posteriors
+
+- **The pre-fit took its Hessian at exp(value) of every positive site.**
+  Constrained and unconstrained values were mixed. phi overflowed and 8
+  count fits crashed, and on `ln_cfu` fits the written k scale was 4-9x too
+  loose. We reran the whole first count-likelihood grid. Fix (c65390aa).
+- **The flat 1e-3 Laplace floor** turned negative eigenvalues into
+  variances of 1000. It caused 5 of 6 Laplace blowups in relative-fit runs
+  2-3 and spread m 50x on the two-stage grid. Fix: the prior-curvature
+  floor (9db28286).
+- **The Laplace kept the mini-batch likelihood scale at full batch,** 53x
+  on the real screen at batch 4096. No result was affected. Fix (cd208d15).
+
+## Model components and numerics
+
+- **The factored ln_cfu0 assumed one library.** It moved the kanR/pheS
+  difference into confident theta errors in every study grid. Fix: refuse
+  it across libraries and rerun the grids with `hierarchical` (722393cf).
+- **The factored ln_cfu0's centered tube offset had an unbounded density,**
+  so no MAP existed. Fix: non-centered offsets (32a7ec12).
+- **The negative binomial concentration underflowed** for near-extinct
+  genotypes, giving NaN gradients. Fix: a floor (c65390aa).
+- **A horseshoe local scale squared to infinity** for low-read doubles.
+  Fix: overflow-safe helpers (740ebc19).
+
+## Simulator, processing and I/O
+
+- **Simulated binding observations were clipped to [0, 1]** against an
+  unclipped likelihood. That affected 10-17% of anchors. Fix (6e2903d5).
+- **`tfs-extract-params` mislabeled ln_cfu0 rows** after any missing
+  genotype cell. Correlation with presplit was 0.09-0.25 instead of 0.999.
+  The fits themselves were fine. Fix (079680bd).
+- **Prediction failed with per-tube offsets.** Prediction now zeroes them,
+  giving the typical tube (775f8b79).
+- **Absent prior groups reloaded from the priors CSV as NaN.** Fix
+  (98fb6db0).
+- **`read_configuration` silently dropped guesses for sites outside
+  `init_params`.** This is how the cold-start offset mode arose. Fix:
+  `--init_from` (faa5b116).
+
+## Data problems found
+
+- The OD600 notebook omitted the plated-volume division.
+- The initial kan and phe samples were swapped.
+- The processing config listed 3 of 9 spikes, with a wrong M42I codon.
+- Sample-name forms differed between files.
+- One tube was missing from `sample_df`.
+- The old snapshot's `presplit.csv` had residue numbering one lower than
+  `growth.csv`.
+- That snapshot's FASTQ processing sent all unknown reads to wt, which is
+  why the step 0 studies are provisional.
+
+## Fit pathologies that needed new options
+
+These were model behaviors, not code bugs:
+- runaway Hill population SDs, now held with
+  `theta_*_hyper_scale_fixed`;
+- a learned tube-offset SD that absorbed growth, now held with
+  `sigma_fixed`;
+- a single m prior width for every condition; `m_scale_plus` and
+  `m_scale_minus` now accept per-condition arrays.
+
+# 3. Where to go from here
+
+In rough order of payoff for the science:
+
+1. **The binding/growth relationship, as its own study.** The facts to
+   explain are firm: broader growth curves, a common floor, wt only about
+   70% induced in vivo, and M42I's K inverted. Candidates:
+   - cell-to-cell variation in IPTG uptake or repressor level, which
+     flattens a steep θ(IPTG), testable first on wt's binding titration;
+   - a full thermodynamic binding model with protein concentration.
+
+   The direct bench test is monoculture growth of the spikes across the
+   titration (`planning/absolute-abundance-measurements.md`).
+2. **The IPTG-patterned tube offsets.** Candidates:
+   - an OD600-to-CFU relation that shifts under selection (roadmap D11);
+   - a library-wide growth effect the linear growth model lacks.
+
+   A bench check of OD against plate counts under selection would separate
+   them, and the population model (roadmap step 6) would carry it.
+3. **Pinning the X scale and m, if rates are ever needed.**
+   - wt monoculture rates as an observer, not just a prior;
+   - a presplit measurement for this screen;
+   - a per-tube spike-in counting standard for future screens.
+4. **A MAP that reaches its optimum.** Every hierarchical MAP stopped at
+   its epoch cap, and on real data it is a saddle along the m·X ridge.
+   - The Laplace needs floor and hold rules for that reason.
+   - Low-depth genotypes are noisy at the MAP: on simulations, X RMSE was
+     1.4 at ≤ 100 reads against 0.22 for the low-rank point.
+   - Newton steps with the arrowhead Hessian are one route.
+   - Holding the population SDs shrinks the low-depth genotypes, but only
+     together with the ridge slide.
+5. **Run the pipeline, then simulate the real design at full size**
+   (`planning/experiment-pipeline.md`; the reviewer simulations in
+   `planning/studies/real-data-fit/README.md`).
+6. **Rerun the provisional step 0 studies** on the new processing:
+   0a growth-binding map, 0b noise anatomy, 0c OD600 population. Finish
+   0c's curve-form decision (roadmap D9).
+7. **Remaining roadmap steps:**
+   - 2: counts and OD600 through processing;
+   - 6: the population model;
+   - 7b: growth transitions, test then purge, with 4CP the likely case;
+   - 8 and 9: the learned map and the joint fit;
+   - the kan-vs-4CP consistency check (D5), which a shared X cannot test
+     alone.
+8. **Known calibration gaps on simulations:**
+   - k/m undercover under realistic noise even with the full Laplace;
+   - the joint fit's saturated high plateau undercovers (0.31);
+   - the pre-fit under the count likelihood wrote k priors far from truth.
+
+# 4. Reproducing this work, and what goes into the merge
+
+**What reproduces now.** Every package change is committed with tests, and
+the CHANGELOG and CLAUDE.md describe it. These studies have tracked scripts
+and grids with fixed seeds:
+- `count-likelihood`
+- `relative-fit`
+- `svi-overconfidence`
+- `noise-anatomy`
+- `od600-population`
+- `growth-binding-map`
+- `step0-data`
+
+Their grid outputs are gitignored and live only on the cluster and local
+disk.
+
+**Gaps in the tracked studies, closed 2026-10-03:**
+- Each study README's "Commit" section now names the commit each grid ran
+  on. Where the README had not recorded it, the commit is inferred from the
+  file times of the downloaded `run.out` files, and the README says so.
+- `relative-fit/laplace_blocks.sh block|arrowhead` reproduces the block and
+  arrowhead Laplace tables from a finished `relative_fit_v4/`. Rerun on
+  2026-10-03, the arrowhead mode matched the README's 95% table exactly
+  (50% coverage within 0.01, from new draws).
+- The roadmap's step 5 and 7 entries and the relative-fit decision text are
+  current, and the relative-fit README opens with the current answer.
+- The one-off diagnoses in `svi-overconfidence` (NUTS variants, the plateau
+  tests, the truth-pinned fit) are marked in its README as records, not
+  reproducible results. Scripting each one is not worth it.
+
+**The real-data fit.** Its scripts are now tracked in
+`planning/studies/real-data-fit/`, in `processing/`, `fit/` and `analysis/`
+(Mike, 2026-10-03). The data, the run outputs and the full lab notebook stay
+in the gitignored `planning/dev-data/`, where the scripts are symlinks to
+the tracked copies. The study README gives the final fit's start chain as
+commands, with the commit behind each run, and lists the dead ends.
+
+The final fit's start depends on a chain of six earlier runs. We will not
+shorten it by rerunning it here. The pipeline plan
+(`planning/experiment-pipeline.md`) builds the staged start into
+`tfs-fit-model`, and its done criterion is this fit rerun from one
+experiment file. That rerun becomes the official, shortened recipe. The
+same plan makes the simulator write the raw formats, so full-size
+simulations of this design can answer reviewers.
+
+**The merge itself.** `main` has not moved since 2026-09-26, so the merge
+is mechanical. The full suite, slow and smoke tests
+included, ran on 2026-10-03: 5,325 passed and 8 smoke tests failed. The 8
+built mock data with `ln_cfu` only and had not been run since the count
+likelihood became the configure default (2620a5b3). They now ask for the
+`ln_cfu` likelihood their data imply, and all 44 smoke tests pass; the
+count path keeps its smoke coverage through the fixture-based tests. The
+stale entries are fixed (above).
+The roadmap stays `active`: steps 2, 6, 7b, 8 and 9 are not done (Mike,
+2026-10-03).
diff --git a/planning/analysis-roadmap.md b/planning/analysis-roadmap.md
new file mode 100644
index 00000000..5da16549
--- /dev/null
+++ b/planning/analysis-roadmap.md
@@ -0,0 +1,712 @@
+---
+title: Roadmap for analysis improvements alongside the congression plan
+status: active
+filed: 2026-09-26
+area: tfmodel
+revisit_when: >-
+  Active now, on a branch parallel to main. Update the step list as steps
+  finish, and re-check the constraints each time a congression step lands on
+  main.
+related:
+  - planning/congression-physics-plan.md
+  - planning/analysis-roadmap-summary.md
+  - planning/experiment-pipeline.md
+  - planning/growth-only-relative-fit.md
+  - planning/count-likelihood.md
+  - planning/per-sample-level-offset.md
+  - planning/empirical-mixture-refit.md
+  - planning/estimate-spike-fraction-from-growth-curvature.md
+  - planning/estimate-dk-alpha-by-varying-lambda.md
+  - planning/plasmid-segregation-low-copy.md
+  - planning/combination-specific-expression-effects.md
+  - planning/absolute-abundance-measurements.md
+  - src/tfscreen/process_raw/counts_to_lncfu.py
+  - src/tfscreen/tfmodel/generative/components/sample_offset/normal.py
+---
+
+**Outcome (2026-10-02):** `planning/analysis-roadmap-summary.md` records
+what this branch built, tested and found, the bugs fixed, where to go next
+and how to reproduce it. Steps 2, 6, 7b, 8 and 9 are not done.
+
+## Why
+
+The congression plan (`planning/congression-physics-plan.md`) is close to
+done on `main` (step 4 is implemented and its cross-rule grid is set up; step
+5, the dk rule, is left). Its remaining work is mostly waiting for
+simulations. This plan orders the other analysis changes, to be built in
+parallel on a second branch, so that they fit together and fit with what
+congression still changes.
+
+The user's priorities (2026-09-26):
+
+1. **Growth-only, wt-relative fit**, then measure the growth-binding map
+   empirically (`planning/growth-only-relative-fit.md`).
+2. **Low-count genotypes handled rigorously** instead of by pseudocount
+   (`planning/count-likelihood.md`, with `planning/per-sample-level-offset.md`
+   as its first half).
+
+A third item joined on 2026-09-26: **model the total population in each
+tube**, from OD600 through a lab-specific calibration, instead of ingesting a
+per-tube CFU estimate. Once the fit works from reads, the total is the only
+source of the absolute scale, so it has to be modeled at the same level.
+
+## Reads and totals: where each number comes from
+
+The experiment (`docs/source/process-raw.rst`, "One tube per time-point"):
+after the `presplit` sample, a replicate's culture is split into one tube per
+condition x IPTG concentration x timepoint; a timepoint pulls a whole row of
+tubes. Each pulled tube gets an OD600 reading, is spun down and frozen, and
+is later extracted and sequenced. OD600 is read for three bioreplicates;
+two are sequenced.
+
+For tube s, with true total population `N_s` and genotype populations
+`n_{g,s}`:
+
+- **Reads measure frequencies.** `reads_{g,s} ~ depth_s * n_{g,s} / N_s`,
+  plus a small per-tube composition offset `u_s` (PCR and genotype-calling
+  efficiency: the `__unknown__` fraction differs between tubes; the
+  denominator review measured it at about 0.026 ln units).
+- **OD600 measures the total.** `N_s = cal(OD_s) * volume`, where `cal` is
+  the lab's OD-to-CFU calibration.
+- **Tube growth noise cancels from frequencies.** A tube that grew a little
+  faster or slower than its neighbors (a shared rate shift `eps_s`) scales
+  every `n_{g,s}` and `N_s` by the same `exp(eps_s)`, so `n_{g,s} / N_s` does
+  not see it. It shows up only in the OD600 value.
+
+So the genotype model can predict smooth `n_{g,s}` (no tube term), the
+population is a smooth curve `P_s = ln N_s` fitted across tubes, and
+`ln_cfu_{g,s} = ln(freq_{g,s}) + P_s` is free of tube noise. This is why the
+earlier practice (every tube assigned the pooled CFU of its condition and
+timepoint across the three bioreplicates) worked so much better than per-tube
+CFU: it removed the tube noise along with the OD noise. The replacement is a
+smooth population curve per replicate x condition x concentration, with a
+shared shape and per-replicate levels, observed through every OD600 reading,
+including the bioreplicate that was not sequenced.
+
+The curve and the genotype model describe the same cells:
+`P_s = logsumexp_g ln n_{g,s}`. That coupling is not enforced directly (it
+would need every genotype in every step, which breaks mini-batching). It is
+enforced by the data: fitting every genotype's frequency makes
+`sum_g exp(ln n_{g,s} - P_s) = 1` up to the `__unknown__` share.
+
+What stays per tube and genotype is only what does not cancel: counting
+noise, PCR jackpotting, and genotype-by-tube differences (a tube whose
+selection was slightly stronger treats genotypes differently).
+
+**Where the total is known and where it is not** (user, 2026-09-26):
+
+- **Start: known.** The culture grows to a presplit OD600 and is diluted by
+  the same factor into every tube, so every tube of a replicate starts at
+  `cal(OD_presplit) * volume / dilution`, one value shared by all of them.
+  Units: `ln_cfu = ln(cfu/mL * 5 mL)`, the calibration giving cfu/mL.
+- **Visible tubes: measured.** OD600 on every pulled tube, same reader, plate
+  and 200 uL as the calibration.
+- **Blind tubes: not measured.** Under the strongest selection (particularly
+  kan) the population barely grows and OD600 stays below the detection
+  threshold at the timepoints we have; the next screen wants earlier
+  timepoints, also below it. Censoring gives only an upper bound. A blind
+  tube is constrained by its curve's known start and any later visible
+  points, and through the reads by other columns of the same condition
+  (`k_c`, `m_c` shared; each genotype's X smooth across columns). When every
+  column of a condition is blind at a timepoint, `k_c` trades freely with
+  the curves. Measurements that close this are filed separately:
+  `planning/absolute-abundance-measurements.md` (a few for the current
+  data, notably wt monoculture growth rates at the gauge concentrations; a
+  cell spike-in counting standard for the next screen).
+
+## Disposition of every idea in `planning/`
+
+| Idea | Disposition | Where |
+|---|---|---|
+| `growth-only-relative-fit` | **Track G**, priority 1 | steps 0a, 1, 5, 8, 9 |
+| `count-likelihood` | **Track N**, priority 2 | steps 0b, 2, 4, 7 |
+| `per-sample-level-offset` | Track N; becomes the population model plus the composition offset `u_s` | step 6 |
+| OD-to-CFU at the experimental level (new, 2026-09-26) | Track N | steps 0c, 2, 3, 4, 6 |
+| `absolute-abundance-measurements` (new, 2026-09-26) | Bench work, user's choice; the model side is in steps 4, 6, 7 | now (1-3), next screen (4-5) |
+| censoring floor rows (congression 3.5 open item) | **Superseded by step 7**; not built (D1) | - |
+| lowering the default `binding_weight` (congression open item) | Stays on main; this plan asks it to default to 1 (C7) | main |
+| setting lambda from the measurement (congression open item) | Stays on main; independent of this plan | main |
+| `empirical-mixture-refit` | Deferred until main's step 5 and this plan's step 5 | after step 9 |
+| `estimate-spike-fraction-from-growth-curvature` | Park: future runs tag spikes by codon | - |
+| `estimate-dk-alpha-by-varying-lambda` | Experiment; needs main's step 5 | - |
+| `plasmid-segregation-low-copy` | Independent; simulator-only; pick up when the low-copy design is planned | any time |
+| `combination-specific-expression-effects` | Park: needs bench data | - |
+
+## Compatibility constraints
+
+These hold across all steps. Each one names the collision it prevents.
+
+**C1. Congression rule vs relative X.** Step 4 on main made `homodimer` the
+default theta rule. It takes `logit(theta)`, so it needs absolute theta in
+(0, 1). The relative growth variable X is real-valued and defined only up to
+an affine map, so the partition-function rules do not apply to it (the idea
+file's invariance argument holds only for `max`: `max f(X) = f(max X)` for
+increasing f). Rule: a relative-X theta component runs with `single` or with
+`mixture` under `congression_theta_rule: max`, and the orchestrator refuses
+`homodimer`/`heterodimer` with it (the same pattern as the refusal of a
+learned activity). The dk rules (dilution now, soft-min after main's step 5)
+act on dk in rate units and are unaffected. X must be oriented to increase
+with repression for `max` to mean "tightest binder wins". The `max` branch of
+`mixture.cell_classes` must not clip X to [THETA_EPS, 1 - THETA_EPS]; check
+this in step 5. Once step 9 learns the map f, the partition-function rules
+act on `f^-1(X)` and are available again.
+
+**C2. Observers become selectable.** Track N replaces the growth and presplit
+likelihoods. Make the observation model an orchestrator setting written to
+the config and carried as static data (`growth_likelihood: lncfu | counts`),
+the same pattern as `congression_theta_rule`. Do not use a YAML `components:`
+key: observers are not registry components, and every `components:` key
+becomes an orchestrator kwarg. `tfs-prefit-calibration`, `tfs-sample-prior`
+and prediction then follow the setting with no change of their own.
+
+**C3. Tube noise lives in the OD600 likelihood, not on genotypes.** A tube is
+a timepoint (one sequenced sample, independent of the other timepoints). Its
+shared growth deviation cancels from frequencies (see "Reads and totals"), so
+no per-tube latent is added to every genotype for it. It appears as scatter
+of OD600 about the population curve, with variance `(sigma_tube * t)^2` plus
+the OD reading noise, over `t_pre + t_sel` (pre-growth and selection both
+happen in the tube, D7). The only per-tube latent on genotypes is the
+composition offset `u_s`, with a time-independent prior. Today's
+`sample_offset/normal` (a per-tube rate offset on every genotype) becomes
+that `u_s` (D2). The simulator's tube noise is already per timepoint (its
+`condition_selector` includes `t_sel`).
+
+**C4. The absolute scale, and with it `k_c` and `m_c`, comes from OD600.**
+From reads alone, any per-tube constant can be added to every genotype, which
+absorbs `k_condition * t` entirely: reads fix relative growth (dk_geno and
+`m * theta` differences), not absolute rates. The population curves fix the
+absolute rates. In Track G this matters twice: `k_c` is wt's absolute growth
+at 1 mM IPTG, and `m_c` is wt's growth difference between 0 and 1 mM, which
+are different tubes (different grid columns), so it rests on the columns'
+population curves. Genotypes whose X does not depend on IPTG (broken
+repressors) are internal standards across columns and should be reported as a
+check. Steps 5 and 6 must report `k_c` and `m_c` recovery, not only theta
+coverage.
+
+**C5. Everything stays mini-batch safe.** The population curve parameters
+and `u_s` are global sites (per replicate x condition x concentration, per
+tube), like the condition parameters. The count likelihood is per genotype
+and scaled by `scale_vector`. Index hopping into tube s comes from the same
+genotype in the lane's other tubes, which are in the same batch row.
+Sequencing errors that turn one genotype into another would couple
+genotypes; leave them in `__unknown__`. The OD600 likelihood does not involve
+genotypes and is never scaled. The new theta component follows the registry
+rules (library-sized plates, `batch_idx` slicing, `return_population`,
+`{site}_loc(s)`/`{site}_scale(s)`), and `test_batch_safety.py`,
+`test_component_population.py` and `test_initialization.py` must pass
+registry-wide.
+
+**C6. Process the raw data once.** Before any Track N model work, the
+processed files carry: counts, per-tube depth (including `__unknown__`), and
+a per-tube OD600 table covering every bioreplicate, sequenced or not, with
+the reader's detection flag. The real data is reprocessed once (step 2) and
+every later step reads the same files. `ln_cfu`/`ln_cfu_var` stay, for plots
+and for the `lncfu` likelihood.
+
+**C7. Binding at weight 1 when it returns.** Track G removes binding from the
+fit; step 9 brings it back as an explicit measurement of the map at weight 1
+with realistic SDs. The binding-weight change on main should therefore make
+1 the default, not a smaller ratio.
+
+**C8. Downstream tools on the X scale.** Logit epistasis, `in_regime` and
+anything that assumes theta in (0, 1) do not apply to X. `tfs-predict-theta`
+must label its output as X for a relative fit (a column or metadata flag, so
+nothing downstream mistakes one for the other); `tfs-extract-epistasis`
+allows only `--scale add` on it until f is known. `tfs-summarize-calibration`
+needs the simulated truth on the X scale: with growth linear in theta and
+the gauge of D4 (`X_wt(c_lo) = 1`, `X_wt(c_hi) = 0`), true
+`X_g(c) = (theta_g(c) - theta_wt(c_hi)) / (theta_wt(c_lo) - theta_wt(c_hi))`.
+
+**C9. Shared files, small diffs.** Main will still touch
+`transformation/mixture.py`, `simulate/cell_rules.py`,
+`simulate/selection_experiment.py` (cell rules), `model_orchestrator.py` and
+`configure_model_cli.py` (a `congression_dk_rule` setting). This branch stays
+out of `mixture.py` and `cell_rules.py` except for C1's check, keeps its
+orchestrator and CLI changes additive, and syncs with main after every
+congression step lands.
+
+**C10. The method is generic; the calibration is the lab's.** An OD-to-CFU
+calibration depends on the plate reader, plate, volume and strain, so no
+lab's calibration data or constants go into this repository. The repository
+holds the method: a CLI that fits a calibration from a documented input
+format and writes a calibration file, the functions that read and apply it,
+and an example built from synthetic data. The existing lab calibration
+(`tfscreen-notebooks/od600-to-cfu/`, outside the repo) is the first user and
+the test that the port reproduces it. A lab that measures totals another
+way (plating, flow) supplies per-tube totals with their own error model;
+that is a scientific alternative, not a compatibility path (D12).
+
+**C11. The calibration's own uncertainty is shared by every tube.** The
+current `od600_to_cfu` adds the calibration-curve error (`J C J^T`,
+approximated by a quadratic in OD) to each tube's CFU variance as if it were
+independent per tube. It is one error, shared by every tube in every
+experiment calibrated with that curve: it cannot average out across tubes,
+and it mostly does not matter for growth rates (a multiplicative error
+cancels from slopes; only curvature error does not). The calibration file
+therefore keeps the full parameter covariance, and the fit treats the
+calibration parameters as global quantities (D10), never as per-tube noise.
+
+**C12. Count noise scales with the mean, and rare genotypes start from few
+cells.** Study 0b found per-tube variance 5-18 times Poisson at 100-3,000
+reads, the signature of a bottleneck upstream of the reads, not a
+constant-CV floor. The count likelihood's dispersion must allow
+`var = phi * mu` (NB1 / quasi-Poisson), with at most a small NB2 floor. A
+typical double has a few founder cells per tube at the split, drawn anew for
+every tube, so its starting abundance differs between tubes; the shared
+`ln_cfu0` is a mean, and founder noise is part of the per-tube dispersion.
+The simulator must produce both (step 4), or its calibration grids
+understate real noise.
+
+## Steps
+
+Keep this list current. Two tracks, G (growth-only) and N (noise, counts and
+totals), interleaved in this order. G comes first by priority; step 8 (the
+map) waits for Track N because a real map can look like "no map" when the
+posteriors are overconfident (bulk theta 95% coverage was 0.66 in the
+congression calibration grid, 0.78-0.87 after convergence fixes).
+Breaking changes are allowed throughout (D12); each modeling step ends with
+a simulation checked by `tfs-summarize-calibration`.
+
+**Implementation order (D14, 2026-09-26).** While the refined data are
+gathered, build what does not hang on it: 1 -> 4 -> 7 (with supplied
+totals) -> 5 -> 3, then 2's OD600 part, 6, 7b, 8, 9 as data arrive. Step 7
+comes before step 6: until the population model exists, each tube's total
+is the supplied `sample_ln_cfu` (as today) and the tube offset is
+`ln depth_s - sample_ln_cfu_s + u_s`; step 6 later replaces the supplied
+totals. The count and depth columns of step 2 come with step 7. The
+congression plan's step 6 (re-validate on the count likelihood) waits on
+step 7.
+
+0. **Studies on real data** (no package code; run in parallel; all can start
+   now).
+   Data: the 2026-07-23 snapshot, extracted by
+   `planning/studies/step0-data/` (113 sequenced tubes, 215,402 genotypes).
+   **The first results are provisional** (user, 2026-09-26): that
+   snapshot's FASTQ processing assigned every unknown read to wt and used a
+   very stringent Q cutoff, and its binding data were five genotypes at the
+   original protein concentration. A new processing batch is running, and
+   new binding data exist (10 genotypes, two lower repressor
+   concentrations whose curves match the in vivo concentration range).
+   Re-run 0a and 0b on those before acting on them.
+   - [x] **0a. Growth-binding map, model-free** (G's step 0). Per genotype with
+     binding data: slope of `ln_cfu` on `t_sel` per replicate x condition x
+     concentration; subtract wt; plot against binding theta (Hill fits if
+     concentrations differ), spiked and bulk separately. Decides the base
+     case (one collapsing curve: linear or monotone; genotype-specific scatter:
+     no map). Also answers the idea file's open question on whether growth
+     and binding concentrations coincide. Study:
+     `planning/studies/growth-binding-map/`.
+     Result (2026-09-26): concentrations coincide (eight IPTG points). Only
+     five genotypes (all spiked, all near wt in vitro) have binding data.
+     wt-relative, total-free slopes have no leverage. Absolute slopes (with
+     the smoothed totals) reject one linear map at s = 1 in kan (reduced
+     chi2 6.7; genotype-specific slopes p = 2e-5): growth rises between
+     0.001 and 0.01 mM IPTG where in vitro theta is still 1. An in vivo
+     concentration scale s = 30-100 brings reduced chi2 to about 3 (kan m
+     about -0.025 per min). 4CP growth does not track binding. **The base
+     case is not supported**; the map needs s (step 8), and the direct test
+     is monoculture growth of these genotypes across the titration
+     (`planning/absolute-abundance-measurements.md`).
+   - [x] **0b. Read noise anatomy.** From the real counts: fraction of rows
+     below 20 and below 5 reads, by genotype class and condition; the
+     per-tube composition offset `u_s` (shared residual of frequencies of
+     well-measured genotypes, wt and spikes, within a tube); a look for
+     block structure in residual level or dispersion along the likely
+     processing order (the extraction batches were not recorded, D8);
+     replicate
+     spread of high-count spike ratios against the binomial expectation,
+     which sizes the count dispersion. Sets priors for steps 6 and 7 and the
+     simulator parameters of step 4. Study: `planning/studies/noise-anatomy/`.
+     Result (2026-09-26): doubles (97% of rows) have a median of 3-5 reads
+     per tube (23-33% zero, 72-85% under 20); singles about 5,300; spikes
+     29,000-56,000. Composition offset `u_s` SD 0.018. Per-tube count
+     variance is 5-18 times Poisson at 100-3,000 reads (pooled: variance
+     = 0.0055 + 8.4 x Poisson): a bottleneck upstream of the reads
+     (template molecules and/or founder cells), so the dispersion scales
+     with the mean (C12). Today's `ln_cfu_var` understates the noise 5-10
+     fold. No extraction-batch analysis was possible from the recorded
+     order. Low counts are mostly low abundance, not crashes: about 88% of
+     double rows under 5 reads in selective tubes belong to genotypes
+     already under 5 reads without the drug; only 0.1% of doubles with >= 20
+     reads without the drug fall below 5 in half their selective tubes.
+   - [ ] **0c. OD600 anatomy.** (First pass done; waiting on data.) All OD600 readings for the three
+     bioreplicates, through the lab calibration: the shape of `ln N(t)` per
+     condition x concentration (pre-growth and selection, lag, any
+     saturation), how much of the between-bioreplicate difference is a level
+     (shared split density) and how much a slope; scatter about a smooth
+     curve against the 2% reading noise (the excess is tube growth noise);
+     readings below the detection threshold (0.096) or above the calibrated
+     range (0.59), listed by condition, concentration and timepoint (which
+     tubes are blind decides which bench experiments are worth running);
+     whether kan and 4CP tubes differ in ways that suggest the
+     calibration does not transfer to stressed cells (D11). Decides the
+     curve form (D9). Study: `planning/studies/od600-population/`.
+     First pass (2026-09-26, the two sequenced replicates only): replicate
+     2 reads +0.24 ln units above replicate 1 (a level; per-condition
+     levels add nothing), but replicate-specific slopes are also
+     significant (p = 7e-5). Tube scatter 0.17 ln units against 0.03
+     reading noise, not growing with time. No tube below threshold (lowest
+     1.5x, kan+ 0 mM, which barely grows). Per-condition population slopes
+     have SE 0.003-0.007 per min, comparable to `m`: these tubes alone do
+     not pin `k_c`. Still needed: the third bioreplicate's OD600, the
+     repeated OD600 runs behind the smoothed totals, and the presplit
+     OD600 and dilution.
+1. [x] **Binding-optional plumbing** (G).
+   The list in the idea file: `binding_df` becomes `--binding_df` in
+   `tfs-configure-model` (at least one of growth/binding required; no phantom
+   `data.binding`); the orchestrator gates binding tensors, `BindingData`, the
+   auto `binding_weight`, `theta_binding_noise` and `observe_binding` on
+   `binding_df is None`; `model.py` gates the binding path statically;
+   `analysis/prediction.py` and `summarize_fit_cli.py` cope (default
+   trajectory subset: wt, spikes, and a fixed-seed sample); fail fast on
+   `binding_weight` or non-`zero` binding noise without binding;
+   `tfs-prefit-calibration` refuses a growth-only config with a clear
+   message. Update positional callers (`examples/simulate-and-analyze/run.sh`,
+   the grid templates) and the tests named in the idea file; a growth-only
+   smoke test on `growth-smoke.csv` + `library-smoke.yaml`. Check that a
+   joint fit is unchanged (fixed-seed MAP loss against main): not for
+   compatibility, but to show the plumbing changed nothing it should not.
+   Done 2026-09-26. Joint model log density at a fixed seed identical to
+   before (`single` and `mixture`). Also fixed: absent prior groups
+   (`priors.binding` here, `priors.growth` for binding-only) reloaded from
+   the priors CSV as NaN; they are now left out and stay `None`. Tests:
+   `tests/tfscreen/tfmodel/test_growth_only.py`, a growth-only smoke test
+   in `tests/smoke-tests/test_configure_run_smoke.py`.
+2. **Counts and OD600 through processing** (N; breaking).
+   - `counts_to_lncfu` writes `counts` and per-tube depth (with
+     `__unknown__`) next to today's columns. The same for
+     `tfs-process-presplit`.
+   - A per-tube OD600 input (all bioreplicates, sequenced or not; a
+     `sequenced` flag or the absence of reads marks the third) and the
+     calibration file of step 3. Per-tube CFU columns are no longer an input;
+     totals come from OD600 (or another lab's total measurement, C10).
+   - An interim smoothed total: `tfs-process-counts` can compute
+     `sample_ln_cfu` from a population curve fitted to all bioreplicates' OD
+     (the form from 0c), in place of per-tube or pooled CFU. It generalizes
+     the pooled practice and gives step 5 a sound total on real data before
+     step 6 exists. Its per-tube SDs are correlated (one curve), which the
+     `lncfu` likelihood ignores; step 6 fixes that.
+   - The orchestrator requires counts, depth and the OD table. Old
+     processed files are not supported; reprocess the real data (C6) and
+     regenerate simulations.
+3. [x] **Generic OD600 calibration** (N; new CLI; C10, C11).
+   `tfs-calibrate-od600`: port the notebook's method (technical-replicate
+   reading noise, detection threshold, errors-in-variables polynomial of CFU
+   on OD, counting plus pipetting error on the plate counts) with a
+   documented CSV input format and a configurable polynomial degree. It
+   writes a calibration YAML with coefficients, the **full** parameter
+   covariance, reading noise, detection threshold, calibrated OD range and
+   the volume convention (per mL; the model's `ln_cfu` is per 5 mL sample,
+   so the tube volume is explicit). Shared read/apply functions in
+   `process_raw/`. Test: reproduces the lab notebook's constants and CFU
+   estimates from its spreadsheets (run locally; the data stay out of the
+   repo), and a synthetic example in `examples/`.
+   Done 2026-09-27: `tfs-calibrate-od600` (`process_raw/od600.py`,
+   `scripts/calibrate_od600_cli.py`), documented CSV inputs, a
+   `kind: od600_to_cfu_per_mL` YAML with the full covariance, and
+   `examples/od600/`. The simulator's `od600` block reads the new format
+   (the old one is refused). Run locally on the lab's spreadsheets, it
+   reproduces the notebook's coefficients to 2e-9, the detection threshold
+   (0.0957) and the reading noise (1.96%) exactly, given the notebook's
+   CFU/mL. **But the notebook's CFU/mL omits the division by the plated
+   volume** (0.1 mL): the sheet's own `CFU_per_mL` column is 10x the value
+   fitted, so the lab's constants, and every CFU total and `ln_cfu` made
+   with them, are 10x (ln 10 = 2.3) low. Frequencies, growth rates, theta
+   and dk_geno are unaffected (a constant factor cancels); `ln_cfu0`, the
+   presplit level, simulated `cfu0` tuned against real `ln_cfu`, and the
+   absolute totals the population model (step 6) will use are affected.
+   The notebook's quadratic approximation of the curve error (`P/Q/R`) is
+   replaced by the exact `J C J^T`; they differ by up to 1.8x at low OD.
+4. [x] **Simulator realism** (N; simulator only).
+   The simulator has index hopping (`prob_index_hop`) and per-tube growth
+   noise (per timepoint, matching the design) but writes
+   `sample_cfu_std = 0.0` and no OD. Add: OD600 per tube through an inverse
+   calibration (from a calibration YAML) with reading noise, the detection
+   threshold and the calibrated range; bioreplicates that get OD only (no
+   reads); founder sampling per tube (each tube seeded with a Poisson
+   draw of cells per genotype at the split, not `total_cfu0 * freq`; 0b);
+   a template bottleneck before sequencing (reads drawn from a limited
+   number of template molecules; 0b); PCR jackpotting (Gamma-Poisson counts with a configured
+   dispersion); a per-tube composition offset; optional monoculture
+   growth-rate measurements and a spike-in counting standard, to size how
+   much each would help before it is run. Each source can be switched off,
+   so its effect on the fit can be measured (a scientific switch, D12).
+   Values from 0b and 0c. Needed to validate steps 6 and 7, and step 5
+   under realistic noise.
+   Done 2026-09-26: `founder_sampling`, `demographic_growth`,
+   `pcr_template_molecules`, `pcr_amplification_cv`, an `od600` block
+   (`simulate/od600.py`; OD-only replicates, `tfs_sim_od600.csv`,
+   `sample_cfu_from_od600`), plus `shared_transformation`: every replicate
+   used to redraw the library assembly and transformation, unlike the real
+   protocol's single glycerol stock. With all options off the simulator's
+   output is byte-identical to before. Checked on the example config: the
+   template bottleneck adds `(reads / templates)(1 + cv^2)` to the
+   variance/Poisson ratio, as designed; the example config's 260k reads
+   per tube against 2M templates gives only 1.2-1.8x, so matching the real
+   5-18x needs templates on the order of reads per tube / 10 (about 1-2M
+   at the real ~18M reads per tube). The example config's tubes also reach
+   OD600 2.9, above a realistic calibrated range; tune `cfu0` and timing
+   before using its OD600. Deferred: monoculture growth-rate outputs and a
+   spike-in counting standard (waiting on the bench decisions).
+5. [x] **Relative-X fit** (G; new component, opt-in). (Done 2026-09-28;
+   `planning/studies/relative-fit/`. Point estimates match the anchored
+   joint fit without binding data when fit with
+   `--guide_type auto_low_rank_multivariate_normal`. The mean-field
+   component guide is biased along the m·X ridge. Intervals from every
+   SVI arm, joint included, are too narrow; that is a separate problem.
+   Updated 2026-10-02: run 4, after the start, scale and Laplace fixes,
+   confirmed the guide verdict (component X 95% coverage 0.06 / 0.38,
+   low-rank 0.81 / 0.76, Poisson / realistic). On the real library the
+   low-rank guide slid along the ridge (3.3e5 nats worse than the MAP), so
+   the full-library route is the MAP plus the arrowhead Laplace
+   (`tfs-sample-posterior --laplace_blocks --laplace_shared`): X 0.97 /
+   0.94 and k/m close to the full Laplace on run 4. Real-data results:
+   `planning/dev-data/real_fit/README.md` (gitignored) and
+   `planning/analysis-roadmap-summary.md`.)
+   - A relative Hill theta component: hill_geno's curve with real-valued
+     baselines, population priors centered on wt, registry rules (C5). Gauge
+     (D4): wt's curve is pinned to `X = 1` at `c_lo` and `X = 0` at `c_hi`
+     (per titrant), so wt keeps its K and n free and its baselines follow
+     from them. `c_lo`/`c_hi` are an orchestrator setting written to the
+     config, defaulting to the lowest and highest measured concentration
+     (0 and 1 mM IPTG in the current data).
+   - One X per genotype x titrant concentration, shared by every condition
+     (D5); each condition's `k_c` and `m_c` (sign included) map it to growth.
+     Report the check: per genotype, kan and 4CP growth should be affine in
+     each other. Genotypes that break it are the first evidence against the
+     design hypothesis.
+   - A growth-only configuration forces `activity: fixed`,
+     `theta_rescale: passthrough`, `condition_growth: linear`, and refuses
+     `hill_mut`/thermo theta components and partition-function congression
+     rules (C1).
+   - Outputs labelled as X; downstream restrictions (C8).
+   - `tfs-summarize-calibration` computes the X-scale truth (C8).
+   - Validation on one simulation: joint vs relative fit, the relative fit
+     compared with X-scale truth; genotypes with simulated binding reported
+     separately (the calibration tool already stratifies on the simulated
+     binding file, so this is a held-out comparison); `k_c`/`m_c` recovery
+     and the IPTG-independent genotypes as internal standards (C4). Then
+     under step 4's noise. Real data runs use step 2's smoothed total.
+   Implemented 2026-09-26: `theta: hill_relative`
+   (`generative/components/theta/hill_relative.py`; baselines `X_low` and
+   `X_delta` real-valued and hierarchical, priors centered on wt's 1 and
+   -1; wt's baselines computed from its K and n by `gauge_baselines`, with
+   the occupancy span floored at 1e-3). The gauge is the orchestrator
+   setting `theta_gauge_conc` (`--theta_gauge_conc`; default the measured
+   min and max, written to the config). The orchestrator refuses binding
+   data, learned activity, `logit` rescaling, non-linear growth, theta
+   noise, and non-`max` rules under `mixture` (C1; `mixture`'s `max`
+   branch does not clip, checked). C8: `THETA_SCALE = "X"`;
+   `tfs-predict-theta` writes `theta_scale = X`; epistasis tools allow only
+   `add` (no `in_regime`); `tfs-summarize-fit` maps theta and
+   `growth_k`/`growth_m` truth onto the gauge (with simulated activity);
+   `tfs-summarize-calibration` labels those rows `theta_regime = X`.
+   Prediction on a genotype subset keeps wt for the gauge. Registry-wide
+   batch-safety, batch-order and guide-naming tests pass for it. Not built:
+   the kan-vs-4CP consistency check (X is shared by construction, so a fit
+   cannot test it alone; needs a per-condition-family X variant or a
+   model-free comparison, to be decided with the real-data run). Validation
+   grid: `planning/studies/relative-fit/` (24 runs: noise x likelihood x
+   joint/relative x 3 seeds; not yet run).
+6. **Population model in the fit** (N; replaces the per-tube offset).
+   - Population curves: `P = ln N` per replicate x condition x concentration
+     over `t_pre + t_sel`, starting at the replicate's split density (from
+     the presplit OD600 and the dilution, with calibration and pipetting
+     error; one value per replicate), with a shape shared across replicates
+     and per-replicate deviations (form from 0c, D9). Global sites, a few
+     per curve.
+   - OD600 likelihood: every reading of every bioreplicate, through the
+     calibration (global, D10), with reading noise plus tube growth noise
+     `(sigma_tube * t)^2` (C3); readings below the threshold are censored,
+     readings above the calibrated range are refused or flagged.
+   - Optional monoculture growth observer, for bench experiment 1 of
+     `planning/absolute-abundance-measurements.md`: a measured growth rate
+     of a named genotype in a named condition and concentration observes
+     the clean-class rate `k_c + dk_g + m_c X_g(c)`. Unlike `base_growth`
+     (`k_ref + dk_geno` in one reference condition), it pins `k_c`/`m_c`
+     where OD600 is blind.
+   - The `lncfu` likelihood observes frequencies against the model:
+     `ln freq_{g,s} ~ ln n_{g,s} - P_s + u_s`, counting variance only. The
+     per-row `ln_cfu` stays an output, now built from `P`.
+   - `sample_offset` becomes the composition offset `u_s`: per tube, constant
+     prior (0b), no time scaling (D2). Exposed in `tfs-configure-model`.
+   - Validation: the calibration grid with step 4's noise on, against the
+     step 2 interim total: theta coverage, `k_c`/`m_c` recovery (C4), in the
+     joint and the relative fit.
+7. [x] **Count likelihood** (N). (Validated 2026-09-27. The fourth run,
+   2026-09-28, with `ln_cfu0: hierarchical`: theta 95% coverage 0.83 / 0.84
+   against 0.72 / 0.83 for `lncfu`, RMSE 0.035 / 0.072 against 0.100 /
+   0.109. `counts` with `sample_offset: level` is the
+   `tfs-configure-model` default since 2620a5b3, and the real-data fits use
+   it.)
+   `growth_likelihood: counts` (C2): growth and presplit observers with
+   `reads_{g,s} ~ NegBin(depth_s * exp(ln n_{g,s} - P_s + u_s), dispersion)`,
+   `P` from step 6 (until then, the supplied `sample_ln_cfu`, D14), `u`
+   from step 6's `sample_offset`, and a learned dispersion with both a term
+   proportional to the mean and a quadratic term,
+   `var = mu (1 + phi) + mu^2 / r`, so the refined data only set priors
+   (C12; one per experiment; no
+   per-batch term, D8), optional index hopping (`E[reads] = N (p_g + h q_g)`),
+   and, for future screens, a spike-in counting standard (a non-growing
+   pseudo-genotype of known abundance per tube, which fixes `P_s`).
+   No pseudocount in the fit. This supersedes censoring (D1). Validation: the
+   calibration grid against step 6's `lncfu` model, including the
+   true-lambda-0 arm where the mixture currently finds lambda ~0.12, a
+   leading suspect for which is the floor. Low-count genotypes are the
+   stratum to watch.
+   Implemented 2026-09-26 (with supplied totals, D14):
+   `generative/observe/growth_counts.py` (`reads ~ NegBin(mu, mu (1 + phi)
+   + mu^2 inv_r)`, `log mu = ln reads + ln_cfu_pred - ln total`), a
+   float32-accurate log-pmf (Loader's algorithm; numpyro's negative
+   binomial lost several nats per observation at 1e4-1e6 reads),
+   `sample_offset: level` (one constant-prior offset per tube, D2), and
+   `sample_reads` from `counts_to_lncfu`; the orchestrator derives tube
+   reads from `adjusted_counts / frequency` for files processed before, so
+   the new processing batch needs no rerun. Refuses `growth_noise` other
+   than `zero`. Presplit stays `ln_cfu` (its data have no counts yet);
+   index hopping is not modeled yet. Validation grid:
+   `planning/studies/count-likelihood/` (24 runs). First run (2026-09-27):
+   11 of 12 `counts` runs failed, on two bugs now fixed (the pre-fit's
+   Hessian was taken at `exp` of every positive site's MAP, overflowing on
+   `phi`; the concentration underflowed for near-extinct genotypes, giving
+   NaN gradients). Rerun needed, `lncfu` arms included (the Hessian bug
+   also loosened their `k_scale`). The one finished `counts` mixture run
+   found lambda 0.027 at a true 0 (the `lncfu` mixture found ~0.12).
+   Second run (2026-09-27, 23 of 24 finished): counts cuts theta RMSE by a
+   third to two thirds against `lncfu`, at coverage close to `lncfu`/`zero`
+   (all arms still under-cover, 0.66-0.81 at 95%); lambda at a true 0 is
+   0.02-0.06 (not ~0.12), with intervals that exclude 0. Two failures
+   outside the likelihood: hill_mut's horseshoe local scales widen for
+   low-read doubles until a draw overflows (NaN), and a premature step-size
+   cut during a noisy descent (one wrong fit). Both fixed (overflow-safe
+   horseshoe helpers; pooled check of stalled windows before a cut, which
+   would have kept 20 of 23 v2 runs from cutting mid-descent). A third run
+   is needed. Third run (2026-09-27, all 24 fine): counts cuts theta
+   RMSE 35-60% and dk_geno RMSE 40-85% against `lncfu`, at about the same
+   coverage as `lncfu`/`zero` (0.79 against 0.82 realistic; 0.65 against
+   0.76 poisson), under-covering most for mixed-purity and near-saturated
+   genotypes; lambda at a true 0 is 0.02-0.06. The remaining
+   overconfidence is the fit's, not the likelihood's. Details:
+   `planning/studies/count-likelihood/README.md`.
+   - [ ] **7b. Growth regime: test, choose, purge** (N). The
+     `growth_transition` components (`instant`, `memory`, `baranyi`,
+     `baranyi_k`, `baranyi_tau`, `two_pop`) were unconstrained by the data
+     under the old noise model (user, 2026-09-26). Re-test them once steps
+     6-7 are in: on simulations with known transitions (does the fit recover
+     a lag it was given, and stay at `instant` when there is none?), then on
+     real data. Keep `instant` (the null) and at most one physically
+     motivated model, most likely `two_pop` (cells switch from pre-selection
+     to selection growth at a first-order rate `k_trans`, so each genotype's
+     trajectory is a two-exponential, the per-genotype counterpart of D9's
+     population form); fix its fallback for `g_pre - g_sel - k_trans <= 0`
+     if it is kept. **Purge the rest** from the fit
+     (`generative/components/growth_transition/`, `_baranyi.py`), the
+     simulator (`simulate/growth/transition_linkage.py`,
+     `TRANSITION_REGISTRY`), examples, grid `auto` enumeration, tests, docs
+     and CLAUDE.md (D12, D13).
+8. **The growth-binding map** (G's stage 2; study first).
+   On real data, relative-X fits (step 5) with the step 7 likelihood; errors in
+   both variables, genotype as the unit; the nested family identity -> affine
+   f -> monotone f, with a global concentration scale s; spiked and bulk
+   separately (bulk on a compressed spiked curve = congression). A study in
+   `planning/studies/`; a CLI only if it earns one. On simulation first with a
+   known nonlinear f and scale s, to check recovery.
+9. **Joint fit with the learned map** (G; scope set by step 8).
+   A learnable `theta_rescale` (f) and concentration scale s; binding back at
+   weight 1 (C7); absolute theta and the partition-function congression rules
+   return through `f^-1` (C1). If step 8 finds no map, this step becomes a
+   report of the disagreement, not a model.
+
+After step 9: revisit `planning/empirical-mixture-refit.md` (Stage 1 could
+then use the count likelihood and needs no binding-based prefit).
+
+## Decisions
+
+User, 2026-09-26.
+
+- **D1. No censoring.** Step 7 replaces it (and the masked 3.5 grid showed
+  the floor does not drive the mixture's behavior). If main needs a floor fix
+  before step 7 lands, dropping rows below a read threshold is available now.
+- **D2. `sample_offset` becomes the composition offset `u_s`.** Revised
+  2026-09-26 after the OD discussion: tube growth noise moves to the OD600
+  likelihood (C3), so the per-tube latent on genotypes keeps only the
+  time-independent composition part (PCR, calling). No new component.
+- **D3. One `u_s` per tube, independent across timepoints.** No random walk.
+- **D4. Gauge: pin wt at two reference concentrations**, `X_wt = 1` at 0 and
+  `X_wt = 0` at 1 mM IPTG, whether or not those are asymptotes. It uses the
+  full accessible dynamic range of growth.
+- **D5. X is shared between kan and 4CP** (the core design hypothesis), with
+  a consistency check (step 5).
+- **D6. One long-lived branch.**
+- **D7. Tube growth noise scales with `t_pre + t_sel`.** Protocol: transform,
+  grow to a set OD, take `presplit`, split into one tube per condition x
+  concentration x timepoint (a grid: rows timepoints, columns IPTG), pull a
+  whole row per timepoint. Pre-growth and selection both happen in the
+  tube. Everything before the split is shared by a replicate's tubes and
+  lands in `ln_cfu0`, which `presplit` measures.
+- **D8. No per-row term.** A row's tubes are read, spun down and frozen
+  immediately (stop-time spread: seconds); all samples are then thawed and
+  extracted in batches of about 20 (three for 60 samples) and amplified in
+  one PCR block. Extraction batch is the only shared step; yield does not
+  change genotype frequencies, but it could change PCR template input and so
+  count dispersion. The batch assignment was not recorded (user,
+  2026-09-26): batches of 20 generally followed processing order, but not
+  reliably. So the model has **no batch term**: an assignment that is only
+  probably right would put guesses into the likelihood. If 0b sees block
+  structure along the likely order, it is reported as unexplained
+  dispersion and absorbed by the shared dispersion, not modeled. Future
+  screens should record each sample's extraction batch (a `sample_df`
+  column), so the question can be answered then.
+
+- **D10. Calibration parameters are latent** (user, 2026-09-26; may be
+  revised): the calibration file's mean and covariance are a
+  multivariate-normal prior on the polynomial coefficients (three for a
+  quadratic), so their shared uncertainty is carried once (C11). Fixed at
+  the best fit stays available as a check.
+- **D12. Breaking changes are fine** (user, 2026-09-26). No compatibility
+  with old files, configs or checkpoints is kept for its own sake. A
+  switch or an old path survives only for a scientific reason: to measure
+  whether something matters (the `lncfu` vs `counts` likelihood, the noise
+  sources in the simulator, `instant` as the null growth transition).
+- **D13. One framing for growth regimes** (user, 2026-09-26). Once a
+  physically motivated model is chosen (7b; D9 for the population curve),
+  models outside that framing are removed, not kept as options.
+- **D14. Build order while data are gathered** (user, 2026-09-26): steps
+  1, 4, 7 (supplied totals), 5, 3; see "Steps".
+
+## Open
+
+- **D9. Population curve form.** Per replicate x condition x concentration,
+  a known start (presplit), a shared shape plus per-replicate deviations
+  (the user's observation: slopes agree across bioreplicates, levels do
+  not). Under selection the total can fall before it rises (susceptible
+  cells stall or die while resistant genotypes take over), so a monotone
+  form is wrong there. Candidates: a sum of two exponentials,
+  `log(a e^{r1 t} + b e^{r2 t})` (physically motivated: two
+  subpopulations); piecewise linear over pre-growth and selection; a
+  low-order polynomial or spline. 0c decides. Deriving the curve from the
+  genotype model (`logsumexp_g ln n_g`) is exact in principle but not
+  closable: `__unknown__` reads (about two thirds of kan reads) are cells
+  of unmodeled genotypes that still count toward OD600.
+  Decide together with 7b, but keep the two questions apart: the
+  population curve can bend with no per-genotype lag at all, because
+  selection changes the library's composition (fast growers take over the
+  total). Curvature in the totals is not evidence for `two_pop` or a
+  Baranyi lag; only the per-genotype trajectories in the reads are. If
+  genotypes do follow `two_pop`, the population is a sum of their
+  two-exponentials, and a two-exponential population curve is a
+  reduction of that, not the same model.
+- **D11. Does the calibration transfer to selected cells?** It was measured
+  on unstressed Keio cells in LB. Kanamycin- or 4CP-stressed cells can change
+  size and shape, and dead cells still scatter light (and still carry
+  plasmid DNA into the reads). 0c looks for signs of it; a bench check
+  (OD vs plate counts under selection) would settle it.
+- **Where the extra count noise comes from** (C12). A technical replicate
+  (re-amplify and re-sequence the same extracted DNA for a few tubes)
+  separates the template/PCR part from founder sampling and biology. Cheap
+  if DNA remains.
+
+Answered (user, 2026-09-26): OD600 is read with the calibration's setup
+(same reader, plate, 200 uL); the presplit is read and then diluted
+identically into every tube (above); `ln_cfu` is `ln(cfu/mL * 5 mL)`; each
+row of a bioreplicate is read as a block within 5 minutes of its freeze.
diff --git a/planning/combination-specific-expression-effects.md b/planning/combination-specific-expression-effects.md
new file mode 100644
index 00000000..48f93cd6
--- /dev/null
+++ b/planning/combination-specific-expression-effects.md
@@ -0,0 +1,29 @@
+---
+title: Combination-specific effects of co-expressed variants on protein level
+status: idea
+filed: 2026-09-13
+area: tfmodel
+revisit_when: >-
+  If double-transformation or native mass spec experiments show effects that
+  depend on the specific pair of variants rather than on each variant alone.
+related:
+  - planning/congression-physics-plan.md
+---
+
+**Context:** The congression plan assumes total repressor expression per cell
+does not depend on how many plasmids a cell carries, and that each variant
+contributes independently (a share-weighted theta and a soft-min dk).
+
+**Idea:** Specific combinations could break this. For example, a very
+unstable heterodimer might trigger a strong unfolded-protein response that
+lowers the functional protein concentration or adds a growth cost beyond
+either variant alone. That is a genotype-by-genotype interaction that feeds
+back onto protein concentration, and so onto both theta and dk.
+
+**Why not now:** Not resolvable with a single-lambda screen, where any given
+pair of genotypes almost never shares a cell, and there is no evidence yet
+that it matters.
+
+**What it would take:** Bench data first (pairs co-expressed, protein levels
+and growth measured). A model would need pair-specific terms, which is only
+plausible for a small, targeted set of pairs.
diff --git a/planning/congression-physics-plan.md b/planning/congression-physics-plan.md
new file mode 100644
index 00000000..82982911
--- /dev/null
+++ b/planning/congression-physics-plan.md
@@ -0,0 +1,788 @@
+---
+title: Match simulator and fit on the physics of congression
+status: active
+filed: 2026-09-13
+area: tfmodel
+revisit_when: >-
+  Active now. Update the step list as steps finish.
+related:
+  - src/tfscreen/simulate/selection_experiment.py
+  - src/tfscreen/tfmodel/generative/components/transformation/mixture.py
+  - src/tfscreen/tfmodel/generative/model.py
+  - src/tfscreen/tfmodel/model_orchestrator.py
+  - src/tfscreen/tfmodel/analysis/prediction.py
+  - planning/empirical-mixture-refit.md
+  - src/tfscreen/simulate/transformation_lam_output.py
+  - src/tfscreen/genetics/library_design.py
+  - planning/estimate-dk-alpha-by-varying-lambda.md
+  - planning/plasmid-segregation-low-copy.md
+  - planning/combination-specific-expression-effects.md
+  - planning/estimate-spike-fraction-from-growth-curvature.md
+  - planning/per-sample-level-offset.md
+  - planning/count-likelihood.md
+---
+
+## Why
+
+A code analysis revealed the simulator and the fit describe congression
+(cells carrying more than one plasmid) differently:
+
+1. **The simulator combines whole growth rates.** `_sim_growth` in
+   `simulate/selection_experiment.py` takes the min (kanR) or max (pheS) of each
+   co-resident plasmid's full `k*t`, including its `dk_geno` and the condition
+   baseline. The min/max is meant to encode "the tightest binder sets theta,"
+   but `dk_geno` varies more across genotypes (SD about 0.018 per minute in the
+   sim prior) than the whole theta signal (|m| = 0.0099 kanR, 0.0062 pheS). So
+   the selection is driven by `dk_geno`: in cells with a co-resident, the chosen
+   plasmid is the highest-theta one only about 52% of the time (chance level).
+   Monte Carlo over the sim prior (theta uniform as a stand-in) gives a growth
+   error with SD of about 0.01 per minute, 1 to 1.5 theta units. The fit
+   corrects theta only, so the error lands on lambda, `m`, the growth
+   baselines and theta.
+2. **"Spiked" genotypes are not congression-free.** Their codons also occur in
+   the bulk sub-libraries. In the old sim config only 0.3% of wt cells and 14 to
+   20% of M42I/H74A/K84L cells came from the monoclonal spike, yet
+   `--spiked` masks all of them as clean. This is also true of the real data,
+   because spiked and library copies can't be told apart by sequence.
+
+Checked and ruled out: the definition of lambda (sim cell sizes are
+zero-truncated Poisson(lambda), so a focal plasmid's co-residents are exactly
+Poisson(lambda), as the fit assumes; true only while every plasmid got its
+whole cell's abundance. Since step 1 a cell's abundance is shared among its
+plasmids, and the co-resident weights are zero-truncated; corrected in 3.4),
+and the fit's unweighted background
+distribution (the lambda that reproduces the sim's true theta inflation is 0.30
+to 0.35 against a true 0.357).
+
+## Where we start (code on `main`, 2026-09-23)
+
+**Fit.** Congression is a correction to theta alone, applied in
+`generative/model.py` through the `transformation` component:
+
+- A congressed genotype's growth theta is replaced by the expected dominant
+  maximum `E[max(theta_g, theta_background)]` over Poisson(lambda)
+  co-residents (`_congression.update_thetas`). The background distribution is
+  logit-normal (`logit_norm`) or the empirical theta of the whole population
+  (`empirical`); `single` turns the correction off.
+- Which genotypes are corrected is a yes/no `congression_mask`, built in
+  `model_orchestrator.py` from the spiked genotypes (from the library
+  composition table's `in_spiked_origin`, or the legacy `spiked_genotypes`
+  list). `ln_cfu0_spiked_mask` is exactly `~congression_mask`, so one flag sets
+  both purity and the `ln_cfu0` prior class.
+- dk_geno is never touched by congression.
+- The corrected theta then passes through `theta_growth_noise` and
+  `theta_rescale` before growth is assembled.
+- Under mini-batching the empirical background is computed over the full
+  population (`population_theta`), and prediction code that subsets genotypes
+  must supply `external_theta_population` (`analysis/prediction.py`).
+- The same `update_thetas` operator is reused by Stage 1.5 of
+  `tfs-fit-genotypes` / `tfs-build-empirical`
+  (`tfmodel/genotype_fit/congression.py`).
+
+**Library composition.** `tfs-configure-model --library_config` writes
+`{out_prefix}_library.csv` (`genetics.library_composition_table`), with a
+per-genotype `bulk_fraction` (the `f_g` below) and `pool_fraction`. The model
+reads the table but does not yet use either fraction; it only derives the
+spiked set from `in_spiked_origin`.
+
+**Simulator.** `_sim_growth` (`simulate/selection_experiment.py`) combined each
+co-resident plasmid's whole `k*t` with a per-marker-library
+`multi_plasmid_combine_fcn` (`gmean`/`mean`/`min`/`max`/`sum`; default `mean`;
+the example configs used `min` for kanR and `max` for pheS). Replaced in
+step 2 (2026-09-23) by cell-level theta and dk rules.
+
+**Known simulator/design mismatch.** `_sim_sequencing` and
+`_calc_genotype_cfu0` credit every plasmid in a cell with that cell's whole
+abundance, so a co-transformed cell counts once per plasmid.
+`expected_library_composition` assumes the opposite: a cell's plasmid copies
+are shared among the variants it carries, so `bulk_fraction` does not depend
+on lambda. Fixed in step 1 (2026-09-23): the simulator now splits a cell's
+abundance among its plasmids.
+
+## Biology and assumptions
+
+Provenance: user, 2026-09-13, unless noted.
+
+- **The repressor is a dimeric lac repressor, not tetrameric.**
+- **Protein is in large excess over DNA:** about 10 uM dimer against about
+  10 nM operator. No depletion, so each variant's statistical weight on the
+  operator is linear in its share of the protein.
+- **Heterodimers: unknown.** Working assumption is mostly homodimers, which is
+  intuition, not data. Native mass spectrometry (collaborator) should resolve
+  it.
+- **Total repressor expression per cell does not depend on plasmid number.**
+  Each variant is diluted by its share. Combination-specific effects on
+  expression are out of scope (`planning/combination-specific-expression-effects.md`).
+- **Plasmids are high copy and assumed to persist without segregating** over
+  the experiment (roughly 3 to 7 doublings). The next design moves to a
+  low-copy plasmid, where segregation matters
+  (`planning/plasmid-segregation-low-copy.md`).
+- **The dataset has a single lambda** (0.3572) and is headed for publication.
+  Parameters that only a lambda series could identify are set from biology
+  and checked by sensitivity, not fit.
+- **Spiked and library copies are indistinguishable** in this dataset. Future
+  runs will give spikes distinct codons.
+- **dk_geno spread is tight in this library** (bench, 2026-09-23): monoculture
+  growth rates of eight genotypes, chosen to be biased toward expected poor
+  growers, were 0.024 +/- 0.001. Not guaranteed for other libraries.
+- **Overnight outgrowth does not shift the clean/congressed class weights**
+  (user, 2026-09-23). The mixture weights `w_clean`/`w_congressed` are the
+  split at transformation (`f_g`, lambda), but the mixture needs the split at
+  `t = -t_pre`, after an overnight of non-selective growth. Congressed cells
+  of genotype g would be reweighted by roughly
+  `exp((dk_cell - dk_g) * t_overnight)`; theta plays little role without
+  selection. Presplit sees only the total, so it cannot correct this. We
+  assume the shift is negligible and leave it unmodeled. Revisit if step 3's
+  calibration shows bias concentrated in bulk genotypes with large |dk_geno|.
+
+## Decisions
+
+### Combine each physical quantity at its own level
+
+Build the cell's theta and dk from its plasmids, then assemble growth once:
+
+```
+k_cell = k_condition + dk_cell + m * A * theta_cell
+```
+
+Never combine whole growth rates across plasmids.
+
+### Theta: partition function over the protein mixture
+
+Write `z_g = theta_g / (1 - theta_g)` (odds at single-variant concentration)
+and `x_g` for variant g's share of subunits in the cell. Two limits:
+
+| Assembly | Cell odds | Logit form |
+|---|---|---|
+| Homodimers only | `sum_g x_g * z_g` | `log sum_g x_g exp(l_g)` |
+| Random heterodimers, additive half-site energies | `(sum_g x_g * sqrt(z_g))^2` | `2 log sum_g x_g exp(l_g / 2)` |
+
+Both are soft maxima between the share-weighted mean logit and the max; the
+heterodimer form sits closer to the mean. A strong binder in a 1:1 cell loses
+log 2 (homodimer) or log 4 (heterodimer) logit units compared with a max rule.
+The heterodimer rule is exact per titrant concentration only if subunits bind
+ligand independently; allosteric coupling between subunits makes it
+approximate. Default: homodimers, until native mass spec says otherwise.
+The choice is made in step 4 from the bench experiments below.
+
+### dk_geno: soft-min family
+
+```
+dk_cell = -(1/alpha) * log sum_g x_g exp(-alpha * dk_g)
+```
+
+`alpha -> 0` is dilution (share-weighted mean burden); `alpha -> inf` is
+dominance (the worst variant sets the cost). Alpha is set from biology with a
+sensitivity check, not fit (`planning/estimate-dk-alpha-by-varying-lambda.md`).
+Under dilution the average effect is a uniform shrinkage of each genotype's
+dk toward the population mean, by `E[1/M]` over the abundance-weighted cell
+size (`M` zero-truncated Poisson, see 3.4) = 0.913 at lambda = 0.357 (0.841
+under the old size-biased weighting), which the hierarchical dk_geno prior
+mostly absorbs.
+
+The fit's current behavior (each plasmid keeps its own dk) has no cell-level
+counterpart: a cell carrying two plasmids has one growth rate. It is wrong,
+not just approximate, so we accept a breaking change here (user, 2026-09-23).
+Both simulator and fit start from **dilution** (alpha -> 0); step 5 adds the
+rest of the family and the sensitivity check.
+
+Like theta's background, dk's co-resident background must come from the full
+genotype population, not the current mini-batch, and prediction paths that
+subset genotypes need an external dk population reference.
+
+### Mix at the observable level
+
+A genotype's cells are a mixture of classes growing at different rates, so
+mix `exp(ln_cfu)`, not rates or theta. With `f_g` the fraction of genotype g's
+cells from the bulk library (computed from the library design), a focal plasmid
+has no co-residents with probability `exp(-lambda)` (superseded in 3.4: with
+a cell's abundance shared among its plasmids, the clean fraction of a bulk
+genotype's abundance is the zero-truncated `P(M = 1) = lambda exp(-lambda) /
+(1 - exp(-lambda))`, so `w_congressed = f_g (1 - P(M = 1))`):
+
+```
+w_congressed = f_g * (1 - exp(-lambda))
+w_clean      = 1 - w_congressed
+ln_cfu_g(t)  = ln_cfu0_g + log[ w_clean * exp(k_clean t)
+                              + w_congressed * E_S exp(k_{g,S} t) ]
+```
+
+`S` is the random set of co-resident genotypes. This form covers every
+genotype: a pure spike has `f = 0`, a pure bulk genotype has `f = 1` (and is
+still 70% clean cells). Whether `E_S exp(k t)` can be approximated by
+`exp(E_S[k] t)` must be checked numerically on the real design and timepoints.
+
+`k t` above is shorthand. The model's growth is pre-growth plus selection,
+routed through the `growth_transition` component, and both `t_pre` and `t_sel`
+growth can carry congression artifacts (user, 2026-09-23). So the mixture is
+over each class's whole `ln_cfu` trajectory: each class gets its own `k_pre`
+and `k_sel`, the `growth_transition` model is applied per class, and the
+classes are mixed last.
+
+Other observations:
+
+- **base_growth** measures a constant rate (`k_ref + dk_geno`) and is not
+  mixed (user, 2026-09-23).
+- **presplit** observes `ln_cfu0` directly at `t = -t_pre`. The pool there is
+  a mixture of clean and congressed cells, but no growth has accrued yet in
+  the model's time frame, so the mixture term is `log(w_clean + w_cong) = 0`
+  and the presplit likelihood stays `ln_cfu0_g ~ presplit`. Presplit is a
+  direct estimate of `ln_cfu0_g` for every genotype. Congression still enters
+  it in two ways:
+  - *Counting.* For presplit and growth to estimate the same `ln_cfu0_g`,
+    both must count a co-transformed cell with its abundance split among its
+    variants (simulator: step 1; fit: same definition of `ln_cfu0`).
+  - *Overnight growth.* Differential growth of clean and congressed cells
+    during the overnight outgrowth before `t = -t_pre` is absorbed into the
+    free per-genotype `ln_cfu0_g`. It also shifts the class weights away from
+    the design values; that shift is not modeled (see assumptions).
+- **binding** stays clean (below).
+
+Open: `theta_growth_noise` and `theta_rescale` currently act on the corrected
+theta. In the mixture they must act on each class's theta before growth is
+assembled. Decide in step 3 whether the noise term is drawn once per genotype
+and shared across classes (the natural reading) or per class.
+
+### Split `--spiked` into two concepts
+
+`--spiked` currently sets both congression purity and the high `ln_cfu0` prior
+class. Keep one `ln_cfu0` latent per genotype for total abundance, with the
+existing spiked/bulk prior on it; carry purity in `f_g`. Genotypes can't simply
+be removed from `--spiked`, because their abundance really is far higher than
+the bulk's.
+
+### Fit design (step 3)
+
+Agreed 2026-09-23 (user). Today's fit replaces a bulk genotype's theta by
+`E[max]` over co-residents and grows one trajectory with the genotype's own
+dk, i.e. it averages theta over the genotype's cells where the reads average
+`exp(ln_cfu)`. The replacement:
+
+```
+ln_cfu_g(t) = ln_cfu0_g + logsumexp_c [ log w_{g,c} + G_{g,c}(t) ] + delta_sample
+```
+
+- **Classes c.** *Clean*: the genotype's own theta, activity and dk.
+  *Congressed*: the genotype plus co-residents; cell theta and activity from
+  the theta rule (max), dk from the dk rule (dilution), matching the
+  simulator (`simulate/cell_rules.py`).
+- **Weights.** `w_cong = f_g * (1 - exp(-lambda))`, `w_clean = 1 - w_cong`
+  (zero-truncated form since 3.4: `w_cong = f_g (1 - P(M = 1))`);
+  time-independent. `f_g` is `bulk_fraction` from the library table.
+- **G** is each class's whole trajectory through the existing pipeline
+  (`theta_rescale` -> `calculate_growth` -> `growth_transition`) over pre and
+  selection growth. Classes go on a new leading axis; the growth and
+  transition components are elementwise and should broadcast (verify).
+- **Unchanged:** `ln_cfu0`, `delta_sample`, growth noise, presplit,
+  base_growth, binding.
+- **`congression_mask` is split.** Purity -> `f_g`; the `ln_cfu0` prior
+  class -> `in_spiked_origin` (spikes really are far more abundant). Legacy
+  `spiked_genotypes` -> `f_g = 0` for spiked, 1 otherwise.
+- **Co-resident background** needs the full-population theta (exists) and
+  dk (new), mini-batch safe, with external references for prediction.
+  Pooled vs per-origin background is decided by 3.0 (each sub-library is
+  transformed separately, so a single's co-residents come from the single
+  library and a double's from the double library).
+- **`theta_growth_noise`** acts on the genotype's own theta before classes
+  are built; co-residents use noiseless population values.
+- **lambda** stays a latent with the measured prior; it now enters the
+  weights as well as the co-resident draws.
+- **Estimator (from 3.0; user, 2026-09-23): fixed Monte Carlo quadrature.**
+  Before fitting, draw once, per genotype, K co-resident sets from the pooled
+  bulk composition (`pool_fraction * bulk_fraction`), stratified by count:
+  K_1 sets with N = 1, K_2 with N = 2, K_3 with N = 3 (e.g. 12 / 3 / 1; N >= 4
+  is about 0.3% of congressed cells at lambda = 0.357 and is dropped). The sets
+  are integer indices stored with the data and never redrawn. Each step builds
+  1 + K cells per genotype from the *current* parameters (focal and
+  co-resident theta/activity/dk looked up by index), weights set k by
+  `w_cong * P(N = n_k | N >= 1; lambda) / K_{n_k}` so lambda stays
+  differentiable, and evaluates the likelihood once. Per-genotype sets
+  (rather than one shared set) keep the quadrature errors independent across
+  genotypes, so they do not push shared parameters (k, m, lambda) in a common
+  direction. Redrawing every step is rejected: the log of a fresh K-sample
+  mean is biased low and adds gradient noise. K is configurable (default 16).
+- **Components.** Keep `single` and `empirical`; retire `logit_norm` (a
+  parametric theta background with no dk counterpart).
+
+### Binding data stays uncongressed
+
+`congression_mask` already acts only on the growth theta path
+(`generative/model.py`), so binding measurements on monoclonal stocks stay
+clean even when a genotype's growth data is mixed. Keep it that way.
+
+## Real library design
+
+Tune simulations to the design used for the dataset:
+
+```yaml
+transform_sizes:
+  single-1: 100_000
+  single-2: 100_000
+  double-1-2: 300_000
+  spiked: 1_000
+
+library_mixture:
+  single-1: 100
+  single-2: 100
+  double-1-2: 1_000
+  spiked: 1
+```
+
+## Experiments (bench)
+
+Underway (user, 2026-09-23); first result 2026-09-24 (below). They decide the
+theta rule in step 4.
+
+- **Double transformation, tight + dead binder:** relative to the tight binder
+  alone, a max rule predicts no shift, homodimer mixing about 2x weaker
+  apparent K, heterodimer mixing about 4x, dominant negative more. The sign
+  (shift or not) is expected to be easy to resolve; magnitude is hard. Control:
+  the same variant on two distinguishable plasmids should reproduce the single
+  transformant under every rule.
+- **Native mass spectrometry** of co-expressed variants (collaborator) to
+  measure heterodimer formation.
+
+Record results here when they arrive.
+
+**Result, 2026-09-24 (user; student's experiment).** Growth in the pheS
+selection (4CP) at three IPTG concentrations; high IPTG induces the toxin and
+slows growth. Transformations at high DNA concentration of wt, D88A (super
+repressor, tight binder), K84L (barely binds), and the pairs wt:D88A and
+wt:K84L. At the intermediate IPTG: D88A fastest, wt intermediate, K84L no
+growth. wt:D88A grew faster than wt, slightly slower than D88A; wt:K84L grew
+faster than K84L, slightly slower than wt. So each pair grows between its
+parents, close to the tighter binder.
+
+- User's reading: not consistent with weaker-binder dominance; does not
+  separate stronger-binder dominance (max) from a partition-function rule.
+  Encouraging that the sign of the correction is right.
+- Limits (user): no direct lambda, so the mix of double transformants and
+  single transformants of each parent in the paired cultures is unknown
+  (high DNA concentration should give many doubles); pheS arm only.
+- Caveat (Claude): single transformants of both parents are present in a
+  paired culture, and the faster parent's singles take over under
+  exponential growth. So "between the parents, close to the faster" is
+  partly expected under any rule, including weak-binder dominance, if singles
+  make up much of the culture. What separates the rules is how close to the
+  faster parent the pair is given the fraction of doubles. It could be
+  firmed up by estimating that fraction (genotyping single colonies from the
+  paired transformation), by the shape of the growth curve (a mixture of
+  subpopulations speeds up over time as the fast one takes over; a clonal
+  double grows at a constant rate), or by growing verified clonal doubles,
+  though those segregate over generations
+  (`planning/plasmid-segregation-low-copy.md`).
+- Native MS: not available.
+
+## Steps
+
+Keep this list current. Work the steps in order and keep this conversation's
+scope to them; anything else goes into its own `planning/` file or task.
+Each step from 1 on ends with a simulation and a fit that use the same rules,
+checked with `tfs-summarize-calibration`.
+
+-2. **Bring in the mini-batch scrambling fix.**
+   - [x] Port the MAP/autoguide genotype-aliasing fix and its tests
+     (`inference/batch_safety.py`, `test_batch_safety.py`) from
+     `guide-selection` as a separate commit, code only. Every fit used to
+     check the steps below depends on it.
+-1. **Update this plan** with the review of 2026-09-23.
+   - [x] Starting-point description, simulator/design mismatch, dk and
+     mixture decisions, bench pointer, step list. 2026-09-23.
+0. **Groundwork.**
+   - [x] Per-genotype `f_g` from the library definition:
+     `genetics.library_composition_table` → `bulk_fraction` in
+     `{out_prefix}_library.csv`, via `tfs-configure-model --library_config`
+     (commit 77befcb).
+   - [x] Bring in `tfs-summarize-calibration`
+     (`analysis/calibration_grid.py`, `analysis/scripts/summarize_calibration_cli.py`)
+     from `guide-selection` (commit e925b90), code only, no grids or results.
+     2026-09-23.
+1. **Simulator: share a cell's plasmid copies.**
+   - [x] `_sim_sequencing` and `_calc_genotype_cfu0` split a cell's abundance
+     among the variants it carries, so simulated composition matches
+     `bulk_fraction` and no longer depends on lambda. 2026-09-23
+     (`_plasmid_shares`; a test checks simulated abundance against the
+     design `pool_fraction` at lambda = 0, 0.357 and 1.5).
+2. **Simulator restructure.**
+   - [x] Build each cell's theta and dk from its plasmids with pluggable rules,
+     then assemble growth once
+     (`k_cell = k_condition + dk_cell + m * A * theta_cell`). Replaces
+     `multi_plasmid_combine_fcn` on whole `k*t`. 2026-09-23:
+     `simulate/cell_rules.py`, `selection_experiment._cell_kt`; config keys
+     `congression_theta_rule`, `congression_dk_rule`.
+   - [x] First rules: max theta (the fit's current theta rule) and dilution dk.
+     TF activity follows the theta rule: it comes from the plasmid that sets
+     theta (user, 2026-09-23). Revisit in step 4, where a partition-function
+     theta rule would put activity into the plasmid weights.
+3. **Fit: observable-level mixture.** Design agreed 2026-09-23 (see "Fit
+   design (step 3)"). 3.0 runs alongside 3.1/3.2; only 3.3's estimator
+   waits on it. 3.1, 3.2 and 3.4 are behavior-preserving commits; 3.3 is the
+   breaking one.
+   - [x] **3.0 Numerical study** (no fitting), 2026-09-23. Ground truth from
+     `library_prediction` on `examples/simulate/simulate_config.yaml` (843
+     genotypes, thermo theta prior, memory growth transition, 48 growth
+     conditions, t_sel 95 to 200 min) with the real design's
+     `library_mixture` and lambda = 0.357, and the step 2 cell rules. "Exact"
+     `E_S exp(G)`: N = 1 enumerated over the co-resident library, N >= 2 by
+     Monte Carlo. dk_geno deviations scaled by s (sim prior at s = 1: SD
+     0.068/min, 5th to 95th percentile -0.13 to 0.014/min). Error = approx -
+     exact `ln_cfu`, RMS over genotype x condition cells within 7 ln units of
+     wt (detectable):
+
+     | estimator | s = 0 | s = 0.015 | s = 0.03 | s = 0.1 | s = 0.3 | s = 1 |
+     |---|---|---|---|---|---|---|
+     | no congression | 0.041 | 0.053 | 0.097 | 0.287 | 0.758 | 1.507 |
+     | today's fit rule | 0.019 | 0.041 | 0.093 | 0.293 | 0.770 | 1.524 |
+     | `exp(E_S[G])` | 0.006 | 0.006 | 0.008 | 0.036 | 0.206 | 1.086 |
+     | `E[G] + Var[G]/2` | 0.000 | 0.000 | 0.001 | 0.025 | 0.509 | 12.6 |
+     | N = 1 only (exact) | 0.003 | 0.004 | 0.010 | 0.030 | 0.080 | 0.127 |
+     | MC, K = 4 | 0.016 | 0.016 | 0.032 | 0.072 | 0.181 | 0.379 |
+     | MC, K = 16 | 0.008 | 0.011 | 0.010 | 0.028 | 0.072 | 0.187 |
+     | MC, K = 32 | 0.005 | 0.006 | 0.010 | 0.019 | 0.064 | 0.142 |
+     | pooled background | 0.002 | 0.002 | 0.002 | 0.004 | 0.008 | 0.024 |
+
+     s = 0.015 is the measured regime for the current library (dk SD about
+     0.001/min; see "Biology and assumptions").
+
+     Findings:
+     - The congression effect is large once dk varies (0.29 to 1.5 ln units
+       RMS), and **today's fit rule is no better than no correction** at any
+       s > 0: averaging theta cannot represent a mixture of growth rates.
+     - Averaging the rate first is adequate only for a narrow dk spread
+       (s <= 0.1) and worsens with time; the second-order cumulant fails
+       (heavy dk tails).
+     - Monte Carlo with K fixed co-resident sets (common random numbers,
+       shared by all genotypes) has near-zero mean error at every s, and its
+       RMS falls roughly as 1/sqrt(K). This is the only estimator that holds
+       across the dk range.
+     - **Pooled background is fine** (<= 0.024): no per-origin backgrounds
+       needed. Caveat: in the sim prior, singles and doubles have similar
+       theta/dk distributions; real doubles may be more often broken.
+     - N >= 2 matters little but not nothing (N = 1 only: 0.13 at s = 1);
+       Monte Carlo covers it at no extra cost.
+     - **In the measured regime (s ~ 0.015) the whole effect is modest**:
+       0.053 RMS, 0.105 p95 (comparable to per-point `ln_cfu` noise, but
+       systematic), and today's rule removes only about a quarter of it. Every
+       estimator except small-K MC is accurate there. We still build MC
+       (decision 2026-09-23): tight dk is not guaranteed for other libraries,
+       and K is a knob.
+     Study: `planning/studies/congression-estimator/`.
+   - [x] **3.1 Plumbing.** Done 2026-09-23: `GrowthData.bulk_fraction`,
+     `ModelOrchestrator._build_bulk_fraction`; `congression_mask` still
+     drives today's correction until 3.3. `f_g` (from `bulk_fraction`) and the `ln_cfu0`
+     prior class (from `in_spiked_origin`) as separate `GrowthData` fields,
+     built by the orchestrator from the library table or the legacy
+     `spiked_genotypes` list (spiked -> `f_g = 0`, others 1). No behavior
+     change: fits identical.
+   - [x] **3.2 Full-population dk_geno and activity access** (API only).
+     Done 2026-09-23. Activity was added to the step: the cell's activity
+     follows the theta rule, so co-residents' activity is needed too. Every
+     `dk_geno` and `activity` component takes `return_population=False`;
+     when True it also returns the library-ordered per-genotype values from
+     the same draw (`generative/components/_population.py::per_genotype`), or
+     `None` when the latents arrived as a batch-sized substitution.
+     `GrowthData.external_dk_population` / `external_activity_population`
+     are the fallback. Nothing requests the population yet, so fits are
+     identical. Building the external arrays in `analysis/prediction.py`
+     moves to 3.4.
+     Found for 3.3: `theta/categorical_geno` slices its latent to the batch
+     at sample time, so `model.py`'s full-population `calc_theta` (arange
+     `batch_idx`/`geno_theta_idx`) returns batch-ordered theta at full batch
+     and indexes past the batch under mini-batching. Fixed in 3.3a.
+   - 3.3 lands as three commits: 3.3a library-ordered population theta
+     (bug fix), 3.3b co-resident sets drawn into `GrowthData` (no behavior
+     change), 3.3c the mixture itself (breaking; includes the
+     `analysis/prediction.py` change, since prediction runs the model and
+     must supply full-library populations and the fit's co-resident sets).
+   - [x] **3.3a Library-ordered population theta.** Done 2026-09-23:
+     `categorical_geno` keeps theta library-sized and indexes through
+     `batch_idx[geno_theta_idx]`; `test_population_theta.py` checks every
+     theta component.
+   - [x] **3.3b Co-resident sets.** Done 2026-09-23:
+     `ModelOrchestrator._draw_coresident_sets` / `_coresident_pool`;
+     `GrowthData.coresident_idx` (num_genotype, K, N_max; -1 = empty) and
+     `coresident_n`; settings `congression_sets` (default [12, 3, 1]) and
+     `congression_seed` (default 0), recorded in the config. Pool: bulk share
+     (`pool_fraction * bulk_fraction`) of genotypes with growth data,
+     `__unknown__` excluded; legacy path uniform over non-spiked (user,
+     2026-09-23). An empty pool leaves every slot -1; 3.3c must refuse a
+     congression model with `bulk_fraction > 0` and an empty pool.
+   - [x] **3.3c Mixture.** Done 2026-09-23. Code plan and decisions agreed 2026-09-23 (user):
+     - *Transformation interface.* `define_model`/`guide` sample lambda only
+       (no `anchors`). New `cell_classes(focal, population, params, data)`
+       returns `(theta, activity, dk, log_w)` with a leading class axis.
+       `NEEDS_POPULATION` replaces `NEEDS_FULL_POPULATION_THETA`. `single`:
+       one class, `log_w = 0`, identical to today. The mixture component is
+       named **`mixture`**; `transformation: empirical` is refused with a
+       message explaining the change, so old configs do not silently switch
+       physics. `logit_norm` is removed. `_congression.update_thetas` stays as
+       a plain function for Stage 1.5 until 3.4.
+     - *`model.py`.* theta -> noise on the genotype's own theta -> population
+       theta/dk/activity (external if supplied, else the full-population
+       `calc_theta` and `return_population=True`) -> gather
+       `coresident_idx[batch_idx]` -> classes (max theta and its activity per
+       titrant point; dilution dk; -1 slots masked) on a class axis ahead of
+       `(rep, time, cp, cs, tn, tc, batch)` -> per class `theta_rescale`,
+       `calculate_growth`, `growth_transition` (each called once; they
+       broadcast) -> `ln_cfu_pred = ln_cfu0 + logsumexp(log_w + G, 0) +
+       delta_sample`. `theta_growth_pred` stays the genotype's own
+       uncorrected theta. The guide skips class construction.
+     - *Weights* (superseded in 3.4 by the zero-truncated form). `w_cong = f_g (1 - exp(-lambda))`; `log w_clean =
+       log1p(-w_cong)`; `log w_k = log f_g + log(1 - exp(-lambda)) + log P(N
+       = n_k | N in strata; lambda) - log K_{n_k}` (strata renormalized over
+       the counts that have sets), so the lambda gradient stays finite for
+       `f_g = 0`.
+     - *Refusals.* A `mixture` model with some `bulk_fraction > 0` and an
+       empty co-resident pool is refused at build time. The
+       `categorical_geno`/`empirical` incompatibility check is deleted (fixed
+       in 3.3a).
+     - *`congression_mask` is removed* from `GrowthData`, `batch.py`, the
+       orchestrator and tests.
+     - *Prediction.* `predict()` passes full-library
+       `external_{theta,dk,activity}_population` (dk/activity: posterior
+       medians of their deterministic sites; theta: the theta component at
+       posterior-median parameters, evaluated on the prediction's
+       concentration grid) and the subset's rows of
+       the original `coresident_idx`. A raw MAP checkpoint (no stored
+       deterministic sites) with a `mixture` model raises, pointing to
+       `tfs-sample-posterior`.
+     - *Cost.* Growth tensors grow by (1 + K); default K stays 16
+       (`[12, 3, 1]`). Main runs mini-batch. A memory-flat `lax.scan` over
+       classes would need growth_transition components split into sample and
+       compute; deferred unless memory bites.
+     - *Tests.* `single` regression; mixture vs an independent numpy
+       implementation; lambda -> 0 and `f_g = 0` reduce to `single`; finite
+       gradients; registry-wide batch shape/order checks; subset prediction
+       equals full prediction rows; refusals; smoke tests.
+   - [x] **3.4 Other consumers.** Done 2026-09-24, three commits:
+     - *Weights (a 3.3c fix found while checking the lambda echo).* Since
+       step 1 the simulator shares a cell's abundance among its plasmids, so
+       a genotype's abundance by co-resident count n is the zero-truncated
+       `P(M = n + 1)`, not Poisson(n; lambda) (which assumed each plasmid got
+       the whole cell). At lambda = 0.357 a bulk genotype is 83% clean, not
+       70%; the old weights would have recovered lambda ~ 0.18 against a
+       simulated 0.357. `mixture._log_class_weights` now uses
+       `w_cong = f_g (1 - P(M = 1))`, `P(M = 1) = lambda e^-lambda /
+       (1 - e^-lambda)`, and strata by `P(M = n + 1)`; a test checks them
+       against simulated cell shares. Zero-truncation is physical: the zero
+       class (no plasmid, marker dead, ...) never grows under selection
+       (user, 2026-09-24). The 3.0 study used the old weights for its
+       "exact" reference as well as the estimators, so its comparisons
+       stand.
+     - *Lambda echo.* With the weights fixed, the fit's `lam` is the
+       simulator's `transformation_poisson_lambda`; docstrings and
+       `docs/source/model-inputs.rst` define it.
+     - *Stage 1.5 retired* (user, 2026-09-24): `--congression_lambda` removed
+       from `tfs-fit-genotypes`/`tfs-build-empirical`; `genotype_fit/
+       congression.py` and `transformation/_congression.py` deleted. The
+       whole empirical pipeline is to be reviewed before it is used again;
+       the replacement is filed as `planning/empirical-mixture-refit.md`.
+     - *`tfs-summarize-calibration`* strata: `origin` (spiked/bulk) became
+       `purity` (`spike`/`mixed`/`bulk`, from the sub-libraries encoding each
+       genotype, i.e. `bulk_fraction` 0, between, or 1).
+   - [x] **3.5 Calibration.** Step 2 simulator vs the new fit, with
+     `tfs-summarize-calibration`; watch bulk genotypes without binding data.
+     Grid set up 2026-09-24 (user: cluster, both extra arms, `mixed` left
+     unstratified): `planning/studies/congression-calibration/`, 54 runs =
+     simulated lambda {0, 0.357, 1.0} x dk spread {wide, tight} x 3 seeds x
+     fit {single, mixture with matched lambda prior, mixture with the
+     measured 0.357 prior}, paired against `single`. Both sides use the max
+     theta rule, so this checks the machinery and also serves as the
+     baseline for step 4. Whether to run it before the bench result on the
+     dominance rule is still open (2026-09-24).
+     Ran 2026-09-24 (all 54 converged); not yet usable. The k/dk_geno slide
+     is unanchored in this pipeline (no wt pin, no `base_growth`, prefit
+     `k_scale` at its ceiling); congression sits below the design's noise
+     floor (`single`'s errors do not change with lambda); and lambda is
+     pulled up whatever the truth. Diagnosis: the mixture uses its
+     congressed classes to fit the read-count detection floor (pseudocount
+     1 puts 0-read rows +1.3 ln above the truth), which real data share.
+     Next: `grid_masked.yaml` (rows with < 5 reads dropped) to test the
+     diagnosis; then a censored likelihood for floor observations (user's
+     preferred fix), then the full grid re-run with a `base_growth` anchor.
+     Details: `planning/studies/congression-calibration/README.md`.
+     Filed 2026-09-24 as follow-ups: `planning/per-sample-level-offset.md`
+     and `planning/count-likelihood.md`. Censoring waits on the masked
+     results (user).
+     Masked re-run (2026-09-24): dropping rows with < 5 reads changed almost
+     nothing, so the floor was not the driver. Revised diagnosis: the
+     default `binding_weight` (growth rows / binding rows, ~251) with
+     simulated binding SD 0.001 dominates the objective; the mixture's
+     congressed classes let bulk genotypes hand their growth away (dk
+     dilution) so theta, and shared hill_mut effects, can follow binding.
+     Next: `grid_bw1.yaml` (binding_weight 1). The weight and the real
+     binding SDs (SE of a fitted Hill curve) are open questions for real
+     data (user, 2026-09-24).
+     Weight 1 (2026-09-24) did not fix it either (it did improve theta
+     coverage). Third diagnosis: the k/dk_geno slide is held only by wt's
+     dk_geno pin, the Student-t growth likelihood makes abandoning wt cheap
+     (the mixture fit misses wt by ~13.6 ln units), and the prefit k prior
+     never engaged (k_scale at its 0.1 ceiling). No gradient bug: the
+     mixture's SVI gradient matches `single`'s. Next: `grid_anchor.yaml`
+     (prefit `--k_scale_ceiling 0.005`, weight 1).
+     Review (2026-09-25): none of the 3.5 fits converged. The stop rule is
+     relative to the loss at the start of SVI (~500 times the final loss).
+     The anchored grid's epoch checkpoints show that the mixture's k
+     overshoot is an early SVI excursion, still unwinding at the stop,
+     while `single` holds k at the truth. The simulated co-resident pool is
+     also 39% wt. Earlier diagnoses need re-judging on converged fits.
+     Proposed routes forward, not yet decided:
+     `planning/studies/congression-calibration-convergence/`.
+     Integrated 2026-09-25: the review's claims check out, and the
+     controls agree (under MAP the lambda-0 mixture matches `single`, k
+     offset +0.0003). The endpoint diagnoses in the calibration README are
+     superseded. Next (user): resume anchored runs 0001/0002/0005 to
+     convergence (`resume.srun`); fixing the SVI stop rule and warm-up goes
+     to its own session, since it affects every `tfs-fit-model` fit.
+     Resumed to convergence (2026-09-25): the lambda-0 mixture matches
+     `single` (k at the truth, theta RMSE 0.131 vs 0.129), confirming the
+     transient; converging halves theta RMSE (0.23 to 0.13) but leaves
+     coverage low (0.55-0.66). Still open on converged fits: lambda pulled
+     up (0.043 at truth 0; 1.30 at truth 1.0 under a 1.0 +/- 0.05 prior).
+     The library enumeration bug behind the 39% wt pool is fixed
+     (bff2c51); new grids simulate the corrected library. Converged at
+     lambda 1, the mixture beats `single` on fit (loss 5.40e4 vs 5.59e4)
+     and dk_geno (RMSE 0.0049 vs 0.0105) but not theta (0.173 vs 0.155),
+     with lambda at 1.30. The convergence overhaul (5bf5b78) is in; next:
+     a lambda profile and the coverage question, on new grids.
+     Baseline and profile grids (2026-09-25, new convergence code and
+     library): the stop rule never fired (13/13 hit the cap), and the
+     lambda-1 seed-1 simulation blew up at full step size in `single` and
+     the mixture alike, making the profile grid uninterpretable; both went
+     to the convergence work. On the stable pairs, the lambda-1 mixture
+     recovers lambda (0.98) and cuts dk_geno error ~3x, theta slightly
+     worse than `single`; at lambda 0 the mixture still finds lambda
+     ~0.15. Next: re-run after the convergence fixes.
+     Before the re-run (2026-09-26): study binding noise 0.025 (was 0.001)
+     and a simulated Hill-coefficient limit of 4 (new
+     `theta_sim_priors.max_hill_n`); the optimizer was updated. Lowering the
+     model's default binding weight is planned as its own step (user).
+     Re-run (2026-09-26): all 13 runs converged, no blow-ups. At lambda 1
+     the mixture beats `single` (bulk theta RMSE 0.104 vs 0.125 and 0.074
+     vs 0.084; dk_geno error ~3x lower; coverage 0.81-0.87 vs 0.58-0.77),
+     with the gain in bulk genotypes without binding data. Lambda is
+     recovered roughly (0.94, 0.80 at a true 1.0) with overconfident
+     intervals; at a true 0 the mixture finds ~0.12 yet still fits theta
+     better than `single`. The lambda profile is flat within loss noise
+     over 0.6-1.4, and theta error changes little across it.
+     Closing (user, 2026-09-26): a confirming round (`grid_measured.yaml`:
+     lambda 0.357 x seeds 1-3; `grid_seed3.yaml`: a third seed at lambda 0
+     and 1), then step 4. Open items, recorded rather than pursued now:
+     - Lambda's intervals are overconfident and its profile is flat over
+       0.6-1.4 in these data; this supports setting lambda from the
+       measurement (a tight prior or a constant) rather than learning it.
+     - At a true lambda 0 the mixture finds ~0.12 and still fits theta
+       better than `single`: its congressed classes absorb something in the
+       data (the read-count floor is the leading candidate; censoring is
+       the planned fix).
+     - Theta 95% coverage is 0.78-0.87 for both models.
+     - The model's default binding weight (growth rows / binding rows) is
+       to be lowered, and the binding SDs rethought (raw anisotropy points,
+       an assay-to-cell term); its own step.
+     Confirming round (2026-09-26, all 10 converged): the mixture beats
+     `single` in all 9 pairs (3 seeds x lambda 0, 0.357, 1) on loss, bulk
+     theta RMSE (-0.018, -0.034, -0.017), coverage (+0.04, +0.13, +0.18)
+     and dk_geno. Lambda is right on average at 0.357 (0.27, 0.44, 0.35)
+     but low at 1.0 (0.94, 0.80, 0.66) and high at 0 (~0.12); the
+     seed-to-seed spread is 30-50 times the reported posterior SD, which
+     strengthens the case for setting lambda from the measurement. Step 3.5
+     closed.
+4. **Theta rule.** Homodimer vs heterodimer soft max, chosen from the bench
+   results, in both simulator and fit.
+   Design agreed 2026-09-26 (user):
+   - Rules `max` (kept), `homodimer` (cell logit `log sum_g x_g exp(l_g)`)
+     and `heterodimer` (`2 log sum_g x_g exp(l_g / 2)`), with `l_g` = logit
+     theta_g per titrant concentration. The bench result rules out
+     weak-binder dominance but cannot separate `max` from the
+     partition-function rules; **default `homodimer`**, as the plan said,
+     until native mass spec says otherwise.
+   - **Shares are equal:** `x_g = 1/M` for a cell with M plasmids. Library
+     frequency sets which plasmids share a cell (the co-resident draw), not
+     their shares once together.
+   - **Activity is held at 1.** The partition-function rules require it, on
+     both sides (the simulator and the fit refuse otherwise), and the
+     default activity component becomes `fixed` (was `horseshoe_geno`).
+     Physically, activity would scale a variant's share of active repressor
+     (cell odds `sum_g x_g A_g z_g`), which disagrees with the clean class's
+     `A * theta` whenever A != 1. The first paper holds A = 1 for the lac
+     repressor; the real treatment waits for activators.
+   - Fit: `mixture.cell_classes` takes a log-sum-exp over the focal plasmid
+     and its valid co-resident slots in place of the argmax. The rule is an
+     orchestrator setting (`congression_theta_rule`, default `homodimer`)
+     written to the config and carried as a static `GrowthData` field;
+     `tfs-configure-model --congression_theta_rule`. Simulator: new rules in
+     `simulate/cell_rules.py`; `congression_theta_rule` defaults to
+     `homodimer`.
+   - Validation: a cross-rule grid (`max` vs `homodimer`, simulated with one
+     and fit with the other) at lambda 0.357 and 1.
+     Implemented 2026-09-26. Cross-rule grid set up:
+     `planning/studies/congression-calibration/grid_crossrule.yaml`
+     (simulate {max, homodimer} x fit {single, mixture-max,
+     mixture-homodimer} x lambda {0.357, 1} x seeds 1-2; 24 runs).
+     Cross-rule results (2026-09-26, all 24 converged): the rule is not
+     identifiable from screen data at this design. Homodimer vs max fits
+     of the same simulation differ by -0.006 to +0.005 in bulk theta RMSE
+     and ~0 in dk_geno, whichever rule simulated the data, and both beat
+     `single` in all 16 pairs. The homodimer fit has the lower loss in 7
+     of 8 pairs and puts lambda 0.01-0.07 higher. `homodimer` stays the
+     default; the physical choice waits on native mass spec. Step 4 done.
+5. **dk rule.** Soft-min family with an alpha sensitivity check, in both
+   simulator and fit.
+   Design agreed 2026-09-26 (user):
+   - Rules `dilution` (alpha -> 0, the share-weighted mean; **stays the
+     default**), `softmin` (finite alpha from `congression_dk_alpha`, in
+     units of 1/dk) and `min` (alpha -> inf, the worst variant sets the
+     cost). The limits are named rules, so neither end is approximated by
+     an extreme alpha; alpha is required by `softmin` and refused by the
+     others. Equal shares `x_g = 1/M`, as for theta.
+   - Scale: alpha matters once alpha times the spread of a cell's dk values
+     reaches ~1. For a 1:1 cell with dk 0 and -0.03: dilution -0.015,
+     alpha 100 -0.024, min -0.030.
+   - Fit: `mixture.cell_classes` takes a log-sum-exp (softmin) or a min
+     over the focal plasmid and its valid co-resident slots; orchestrator
+     settings `congression_dk_rule`/`congression_dk_alpha`, written to the
+     config and carried as static `GrowthData` fields;
+     `tfs-configure-model --congression_dk_rule/--congression_dk_alpha`.
+     Simulator: `simulate/cell_rules.py` (`dk_softmin`, `dk_min`), config
+     keys of the same names. In float32 the fit's softmin has absolute
+     error ~1e-7 / alpha, so tiny alpha is a job for `dilution`.
+   - Sensitivity check: simulate {dilution, min} x fit {single,
+     mixture-dilution, mixture-min}, homodimer theta rule on both sides,
+     x seeds 1-2, on the study's wide dk spread (a deliberate worst case,
+     user) at lambda 0.357 and 1, plus the realistic tight spread at 0.357
+     (36 runs, `planning/studies/congression-calibration/grid_dkrule.yaml`).
+     No intermediate alpha: if the two ends agree, everything between does.
+   - Side effect to watch: under `min` a slow genotype's congressed cells
+     are at least as slow as it is, closing the route (an earlier,
+     unconverged diagnosis) by which a genotype pushes its own dk down and
+     lets its congressed cells carry its growth.
+     Results (2026-09-26, all 36 converged): at the realistic tight dk
+     spread the rule does not matter (theta, dk_geno and loss agree within
+     0.001 RMSE and 30 loss units), and the mixture does not beat `single`
+     on theta there. At the wide spread the dilution fit gives the best
+     theta whichever rule simulated the data, while the min fit gains
+     nothing over `single` even when min is true; dk_geno and lambda need
+     the right rule (min data at lambda 1: min fit lambda 1.02/0.89 and best
+     dk_geno; dilution fit lambda 0.47/0.38). The dilution mixture's theta
+     gain does not track congression (same size on min data, at lambda 0
+     and 1, absent at tight spread): it needs slow genotypes that a faster
+     congressed class can rescue, which points at the read-count floor
+     (untested). At the tight spread lambda is pulled to 0.54-0.97 at a
+     true 0.357. `dilution` stays the default. Step 5 done.
+6. **Re-validate on the count likelihood.** Open (user, 2026-09-26): the
+   congression arc (steps 1-5) is closed; the read-count floor is to be
+   handled by fitting read counts directly (`planning/count-likelihood.md`,
+   a separate piece of work), not by the masked test proposed after step 5.
+   Once that model exists, re-run `grid_baseline.yaml` (lambda 0 and 1) and
+   `grid_dkrule.yaml` on it (`planning/studies/congression-calibration/`):
+   - The theta gain of the mixture over `single` that remains is what
+     congression costs. Today's gain (about -0.018 bulk theta RMSE) is the
+     same at lambda 0 and 1 and on min-simulated data, so it is probably
+     mostly the floor.
+   - The dk_geno gain should persist: it grows with lambda and follows the
+     true dk rule (about 3x lower error at lambda 1), so it is congression.
+   - Check the fitted lambda at the tight dk spread, pulled to 0.54-0.97 at
+     a true 0.357 against a 0.357 +/- 0.05 prior, before trusting a fitted
+     lambda on real data (whose dk spread is tight).
+   The other open items under 3.5 (lambda's overconfident intervals,
+   coverage below nominal, the binding weight and SDs) stand.
diff --git a/planning/count-likelihood.md b/planning/count-likelihood.md
new file mode 100644
index 00000000..740484a8
--- /dev/null
+++ b/planning/count-likelihood.md
@@ -0,0 +1,80 @@
+---
+title: Fit read counts directly instead of ln_cfu
+status: promoted
+filed: 2026-09-24
+area: tfmodel
+revisit_when: >-
+  After censoring and the per-sample offset are in and the congression
+  calibration grid has been re-run. The user expects to get here (2026-09-24):
+  the data side is mostly propagating information we drop today.
+related:
+  - planning/analysis-roadmap.md
+  - planning/congression-physics-plan.md
+  - planning/per-sample-level-offset.md
+  - planning/studies/congression-calibration/README.md
+  - src/tfscreen/process_raw/counts_to_lncfu.py
+  - src/tfscreen/tfmodel/generative/observe/growth.py
+  - src/tfscreen/tfmodel/generative/observe/presplit.py
+---
+
+**Promoted 2026-09-26** into `planning/analysis-roadmap.md` (Track N, step 7,
+implemented with supplied tube totals; validation grid in
+`planning/studies/count-likelihood/`).
+
+**Context:** The congression calibration grid (2026-09-24) found that the
+mixture used its congressed classes to fit the read-count detection floor.
+`ln_cfu` is computed from counts with a pseudocount of 1, so rows with 0
+reads sit +1.26 ln units above the truth and 1-2 reads +0.62. Censoring those
+rows is the next step. It keeps "below the floor" but loses the difference
+between 0 and 4 reads, and it leaves the rest of the approximation in place.
+
+**Idea:** Make read counts the observation. Per sample s and genotype g:
+
+```
+reads[g, s] ~ NegBin(mean = exp(ln_cfu_pred[g, s] + o_s), dispersion)
+```
+
+with `o_s` a per-sample intercept whose prior is centered on plating,
+`o_s ~ ln N_s - sample_ln_cfu_s` with the plating uncertainty as its width.
+With `o_s` free this matches a multinomial over the tube (the Poisson trick),
+but each genotype's term stands alone, so genotype mini-batching still works.
+The reads carry how genotypes compare within a tube; plating carries the
+absolute scale. (Revised 2026-09-26: the total comes from OD600, not
+plating, and `o_s` is built from a smooth population curve fitted to every
+bioreplicate's OD600; see `planning/analysis-roadmap.md`, "Reads and
+totals".) Predictions and all downstream outputs stay in `ln_cfu`.
+
+What it gives that `ln_cfu` with censoring cannot:
+
+- **Exact low-count information:** 0 vs 4 reads pins down when a genotype
+  crashed, which carries theta information at strong selection.
+- **The right noise model everywhere:** no first-order binomial approximation
+  (poor below ~20 reads), no pseudocount, and a learned overdispersion for
+  PCR jackpotting, which is probably why high-count rows look overconfident.
+- **Misassignment as additive physics:** index hopping, cross-contamination
+  and barcode-calling efficiency add reads in count space,
+  `E[reads] = N (p_g + h q_g + ...)`, which is what produces a floor. Index
+  hopping into sample s comes from genotype g in the other samples of the
+  lane, so it only couples a genotype to itself across samples (batch safe).
+  Sequencing errors that turn one genotype into another would couple
+  genotypes; leave them to the `__unknown__` bucket.
+
+**Why not now:** The user wants the masked grid results and censoring first
+(2026-09-24). The core noise model needs design: today tube growth noise is
+per-row variance (`sigma_k`). In a count model, the per-tube shared part goes
+into `o_s` (see `planning/per-sample-level-offset.md`) and the per-row part
+into the NegBin dispersion; a per-(tube, genotype) latent would be far too
+many parameters.
+
+**What it would take:**
+
+- **Data:** keep raw counts, per-sample depth `N_s` (including the
+  `__unknown__` reads) and plating through `tfs-process-counts` and
+  `tfs-process-presplit`. Most of it is already in the processed CSVs.
+- **Model:** new growth and presplit observers; per-sample `o_s` latents
+  (built by the per-sample offset idea); a dispersion model; optionally index
+  hopping and calling efficiency.
+- **Interactions:** `o_s` vs `ln_cfu0` and the k/dk_geno slide; the congression
+  mixture is unchanged (it acts on `ln_cfu_pred`).
+- **Checks:** the calibration grid with index hopping and PCR overdispersion
+  turned on in the simulator, against the censored `ln_cfu` model.
diff --git a/planning/deep-coverage.md b/planning/deep-coverage.md
new file mode 100644
index 00000000..f0e4b0a1
--- /dev/null
+++ b/planning/deep-coverage.md
@@ -0,0 +1,230 @@
+---
+title: Honest intervals for the well-measured genotypes
+status: active
+filed: 2026-10-09
+area: tfmodel
+revisit_when: >-
+  Active. Goal: what this experiment's data support, without new
+  sequencing (user, 2026-10-09).
+related:
+  - planning/two-stage-fit.md
+  - planning/studies/m-bias-factorial/README.md
+  - planning/studies/km-anchor/README.md
+  - planning/studies/full-size-sim/README.md
+  - planning/experiment-pipeline.md
+  - planning/offset-mode-growth-transition.md
+  - src/tfscreen/tfmodel/priors_edit.py
+  - src/tfscreen/tfmodel/inference/posteriors.py
+---
+
+## Why
+
+On the full-size simulation calibrated to the real screen's depth
+(`planning/studies/full-size-sim/`, 2026-10-09), genotypes above 1e3 reads
+rank well (X r 0.93-0.99) but their 95% intervals cover 0.56 down to 0.08
+with depth (realistic noise), and worse under Poisson counts. The error is
+shared: X is compressed (|m| 30-45% too large) and shifted (k slid up
+against dk_geno), and the arrowhead Laplace holds exactly those directions
+at the MAP, so k and m carry no interval. About 77,000 real doubles have
+more than 1e3 reads; these are the curves the manuscript can use, and
+their intervals have to be honest.
+
+The real fit is off the same way in kanR+kan (k 0.030 and m -0.020 against
+the wt monoculture's 0.015 and -0.014, as in the simulation, whose truth
+is the monoculture), so the real kanR curves are likely compressed too.
+
+## Assumptions
+
+- Ranking is not the problem; the absolute X scale (k and m) is. Counts
+  pin only relative frequencies; the tube totals pin the scale weakly
+  through free per-tube offsets.
+- The wt monoculture rates are the experiment's independent measurement of
+  that scale, with their own error (SE of the replicate mean, at least
+  0.002 per minute day to day).
+- No new sequencing. New fits, and new simulations only to test.
+
+## Steps
+
+1. [x] **Anchor k and m with the monoculture rates**
+   (`planning/studies/km-anchor/`). Three fit-only arms: the realistic
+   simulation with the monoculture priors, the same with the rates
+   perturbed by their own error, and the real data with the monoculture
+   priors. Read: deep X coverage and slope against the baseline, k/m
+   against truth, held directions, and on the real data whether k and m
+   move to the monoculture with the offsets staying clean (or whether the
+   data fight the anchor, which would point at a real in-pool difference
+   such as the selection-onset transient).
+   Done 2026-10-09: the data reject the anchor in the simulation and the
+   real data alike. k moves only along the k/dk_geno slide; m stays about
+   40% too large in kanR (sim truth -0.0138, fit -0.018 to -0.020 even with
+   the truth as a 0.002-SD prior; real fit -0.020 against the monoculture's
+   -0.014). The bias is in the fitted likelihood, a misspecification
+   (`planning/studies/km-anchor/`).
+1b. [x] **Find the misspecification behind |m|.** A reduced-library
+   factorial on the simulation, local and fast: the same design and
+   conditions, switching off one of congression, per-tube rate noise and
+   realistic count noise at a time (and the dk_geno prior's shape), and
+   measuring the m bias. Whatever removes it is what the fit must model or
+   correct before step 2 is worth doing. Set up 2026-10-09 as
+   `planning/studies/m-bias-factorial/` (5 arms x 2 seeds, 5,256 doubles,
+   cluster: the staged MAP was too slow on a laptop).
+   Round 1 (2026-10-09) was inconclusive. The reduced `base` arm gave an m
+   ratio of 1.03-1.14 against 1.47 at full size, because shrinking the
+   library left wt at 81% of each tube's reads (31% at full size) and
+   dropped 25 tubes below OD600 detection. Every arm fell within seed
+   noise. Round 2 refits subsets of the full-size simulation's genotypes
+   instead: no doubles, plus deep doubles, plus as many shallow doubles.
+   Round 2 (2026-10-10) found it: m is right without the doubles (kanR
+   1.03, pheS 0.89), about 1.2 with 81,000 doubles of either depth, and
+   1.47 with all 208,000. Each double pulls m a little, and the pulls add.
+   That fits the joint MAP's Neyman-Scott bias, or a per-genotype
+   misspecification of the doubles. Next: the same no-doubles fit on the
+   real data (does kanR m reach the monoculture's -0.014?), and on the
+   simulation the full library with k and m held at the no-doubles values
+   (round 3, `run_held.srun`).
+   Real data without doubles (2026-10-10): kanR+kan k 0.0160, m -0.0141,
+   on the wt monoculture (0.0150, -0.0141) with loose priors; the real
+   kanR curves fitted with all genotypes are compressed by about 1.45.
+   pheS+4CP moves away from the monoculture (m 0.0061 against 0.0144):
+   step 3.
+   Round 3 (2026-10-10), the simulation's full library with k and m held
+   at its no-doubles MAP: above 1e4 reads X's slope 0.95 (was 1.36),
+   RMSE 0.05-0.07 (0.16), 95% coverage 0.90 / 0.65 at 1e4-1e5 / >1e5
+   (0.29 / 0.10); the k/dk_geno slide is gone. The remaining gap is k and
+   m's own error, which the held fit leaves out: step 2.
+   Round 4 (set up 2026-10-10): the real fit holds kanR only (tight prior
+   at the real no-doubles MAP; pheS loose until step 3), checked first on
+   the simulation, since X is shared and pheS's m stays free.
+   Round 4 result: holding kanR alone is enough (the free pheS m follows
+   to 1.04 of the truth), but the tight prior leaked about 11% of the
+   doubles' pull (deep coverage 0.82 / 0.42 against the pins' 0.90 /
+   0.65). Real: kanR m -0.0133, pheS m 0.0064, half the monoculture's.
+   Round 5 repeats both arms with `linear`'s new per-condition pins.
+   Round 5 result (2026-10-11): the pins hold exactly and pheS's free m is
+   right, but pheS's free k reopens the k/dk_geno slide: deep X shifts up
+   by 0.04-0.09, coverage 0.67 / 0.20. Pinning every condition at the
+   no-doubles fit (round 3) closes it, and the real pheS no-doubles m is
+   what the data give with X set by kanR, so the real fit should pin all
+   four conditions.
+   Round 6 (2026-10-11), the real fit with all four pinned: every
+   genotype's X stretches by a uniform 1.47 against the recipe fit (r
+   0.99-1.00 above 100 reads), the factor the simulation predicted; no k,
+   m or slide direction is held. The two-stage fit (k and m from wt,
+   spikes and singles; the full library at those values) is the route.
+   Still open: k/m uncertainty in X (step 2), a second simulation seed for
+   stage 1 (pheS m 0.89 of the truth on seed 1; `sim_realistic_s2` is the second calibration, seed 1), pheS (step 3), and
+   moving the two stages into the pipeline once step 2 settles how.
+2. [ ] **Carry k and m's error into X.** The held fit (step 1b, round 3)
+   gives X intervals conditional on k and m. Take k and m's uncertainty
+   from the no-doubles fit (its Laplace), refit the full library held at a
+   handful of draws, and pool the draws' posteriors, as the two-stage fit
+   of `planning/studies/svi-overconfidence/two_stage.py` did. A soft prior
+   at the no-doubles SD will not do: the doubles pulled m through a 0.002
+   prior (`planning/studies/km-anchor/`). Check coverage by depth against
+   round 3 (0.90 / 0.65 above 1e4 reads).
+   Set up 2026-10-11 on the seed-1 simulation (`km_draws.py`,
+   `run_km_draws.srun`, `run_pool.srun`; 10 draws), with a second
+   simulation seed run through both stages (`run_two_stage.srun`) to test
+   stage 1's own error, which a stage-1 Laplace may understate
+   (`planning/studies/m-bias-factorial/`, "Step 2", "Second seed").
+   Found 2026-10-11: the stage-1 Laplace is far too narrow (m SD about
+   0.6%), and stage 1's m error is a bias that repeats across seeds
+   (kanR 2% steep, pheS 10% shallow; 12% under Poisson counts), so the
+   draws were cancelled. k's error is noise several times its Laplace SD.
+   Congression is the pheS bias's cause: without it stage 1's pheS m is
+   1.035 of the truth (0.895 with it); kanR does not move. Next: a stage
+   1 of wt and the spikes (little congression, few genotypes), on both
+   seeds and the real data. Step 2 now needs (a) the stage-1 m bias
+   fixed or corrected and (b) k's uncertainty from resampling stage 1,
+   not its Laplace.
+   (a) done 2026-10-12 in the simulation. Spikes alone worked on the
+   simulation but not the real data (nine genotypes cannot pin X's
+   scale), and adding the 300 deepest singles brought the pheS bias back.
+   Congression is not the doubles' pull, which persists without it. A
+   stage 1 with the congression mixture (max rule, lambda prior 0.357 +-
+   0.15) removes the pheS bias (pheS m 1.02-1.04 of the truth). The recipe
+   is a mixture stage 1 and a `single` stage 2: deepest-genotype coverage
+   0.76 / 0.67 on seeds 1 / 2 (0.65 / 0.51 before); a mixture stage 2
+   adds nothing for the deep genotypes. Left: a small steep m bias
+   (2-6%), k's uncertainty (b), and, for the real data, wt's purity (the
+   X gauge), which the mixture needs.
+   Real mixture stage 1 (2026-10-12): congression barely moves the real
+   pheS m (0.0061 to 0.0068), so the real pheS gap (about 2x the
+   monoculture) is mostly not congression (step 3). The data lean toward
+   wt mostly bulk (the real config as written: 25 nats better, fitted
+   lambda 0.365 on the measured 0.357). kanR within 4% of the monoculture.
+   The real stage 2 at that stage 1 (`held_mix_real`, 2026-10-12)
+   converged with no k, m or slide direction held; its X is round 6's
+   compressed about 6% (r >= 0.998), 1.39 times the original recipe's.
+   Next (decided 2026-10-12, user): (i) k and m's uncertainty from a
+   delete-d jackknife of the mixture stage 1 (drop 20% of tubes,
+   stratified by condition and IPTG; tubes cannot be duplicated, so no
+   classic bootstrap), checked on the simulation against stage 1's known
+   errors on both seeds (`run_jackknife.srun`, `jackknife_summary.py`);
+   (ii) then carry it into X. Refitting stage 2 at every draw costs
+   2-5 GPU hours each; k and m mostly shift and stretch X, so refit
+   stage 2 at two or three draws, measure that map per genotype, and
+   apply it to all the jackknife draws. Check coverage by depth on the
+   simulation.
+   Jackknife result (seed 1, 2026-10-12): k's jackknife SE is about five
+   times its Laplace SD and covers k's errors (0.5-0.8 SE); m's agrees
+   with its Laplace and its error (6-9 SE) is the accepted bias. 94% of
+   k's variance is one direction, so two stage 2 refits along it (plus
+   and minus one SD) measure X's k sensitivity; `k_band.py` widens X's
+   intervals with it. Scored on seed 1 (2026-10-13): 95% coverage 0.94 at
+   1e4-1e5 reads and 0.91 above 1e5 (0.90 and 0.76 without), the rest
+   being the accepted m bias. k's uncertainty is a shift shared by every
+   non-wt genotype (one SD moves X by -0.032 to -0.038, genotype-to-
+   genotype SD 0.005), so the pipeline should add it as one shared
+   offset per posterior draw, not as independent width.
+   Accepted as a limitation (user, 2026-10-12): stage 1's small steep m
+   bias (2-6% in the simulation, an X slope of 1.03-1.05). Resampling
+   does not cover a bias; quantify it from the simulation for the paper,
+   and correct it from the simulation only if the methods need it.
+3. [ ] **pheS+4CP.** Accepted as a limitation for now, possibly for the
+   paper (user, 2026-10-12): the real pool's pheS selection is about half
+   the wt monoculture's (m 0.0068 against 0.0144) and congression explains
+   little of it. The two-stage fit takes pheS's m from the pool data with X
+   set through kanR, so the gap need not distort X's scale. It could
+   distort the curves' shapes if its cause depends on genotype (a
+   selection-onset transient, `planning/offset-mode-growth-transition.md`).
+   Check: stage 2 on the kanR data alone, against the both-marker fit, on
+   the real data and on the seed-1 simulation (which shows what agreement
+   looks like when pheS is fine). If they agree, the gap is an observation
+   about the pool, not a problem with the curves.
+   Result (2026-10-12): the median real genotype agrees across markers as
+   in the simulation, but 8% of the deep doubles disagree beyond 2 SD
+   (2.5% in the simulation). Keep the gap as a limitation and flag those
+   genotypes (marker-consistency z between the kanR-only and both-marker
+   fits). Candidate cause: dk_geno shared across markers while real
+   fitness effects may differ in kan and 4CP; a marker-specific dk_geno
+   would test it. Tested 2026-10-12 without a refit (`control_dk_check.py`):
+   the tail's X offsets track the difference in control-condition growth
+   between libraries no more than on the simulation, where dk is shared
+   (r 0.40 in both; slopes about 6, against about 34 for a real effect),
+   and the real controls barely agree on any genotype's dk (r 0.04). Not
+   supported; the tail stays unexplained and flagged.
+4. [ ] **Then the shallow genotypes' points.** Hold the X population SDs
+   (`theta_{X_low,X_delta,log_hill_K}_hyper_scale_fixed`) so genotypes
+   below 1e3 reads shrink toward the population instead of scattering;
+   their intervals already cover.
+5. [ ] **The pipeline.** Make the two-stage fit one documented recipe (or
+   one command): the stage-1 subset (wt, spikes, singles) fitted with the
+   congression mixture, stage 2 on the full library with every
+   condition's k and m pinned at it, and the resampled intervals of step
+   2. The students' design simulations and the NSF preliminary data use
+   it. Step 2's method is settled (2026-10-13): a delete-d jackknife of
+   stage 1 (20% of tubes, about 30 refits of about 950 genotypes), two
+   stage 2 refits at plus and minus one SD along k's main direction
+   (started from the stage 2 MAP), and the X shift between them added to
+   the posterior draws as one shared offset per draw. Pieces to build:
+   the stage-1 subset and its mixture fit, the jackknife, the two
+   refits, and the shared-offset draws in `tfs-sample-posterior` or a
+   post-processing step; plus the marker-consistency flag (step 3).
+   Planned in `planning/two-stage-fit.md` (active, 2026-10-13).
+
+## Out of scope
+
+New sequencing or a new screen design (`planning/design-noise-sweep.md`),
+and the simulator's remaining depth mismatches (dropout tail, wt's share of
+reads), which do not touch the deep genotypes.
diff --git a/planning/design-noise-sweep.md b/planning/design-noise-sweep.md
new file mode 100644
index 00000000..d7650b45
--- /dev/null
+++ b/planning/design-noise-sweep.md
@@ -0,0 +1,47 @@
+---
+title: Which noise source to buy down in the next screen (PCR templates, cells per tube)
+status: idea
+filed: 2026-10-09
+area: experiment
+revisit_when: >-
+  When the next experiment is being designed (students' simulations for the
+  NSF preliminary data, due 2026-11-20), and after the deep genotypes'
+  coverage is fixed on this experiment's data, so the sweep's coverage
+  numbers mean something.
+related:
+  - planning/studies/full-size-sim/README.md
+  - planning/studies/full-size-sim/make_sim_config.py
+  - planning/studies/noise-anatomy/
+---
+
+**Context.** The calibrated full-size simulation of the dev-data screen
+(2026-10-09, `planning/studies/full-size-sim/`, arms `sim_realistic_s2` and
+`sim_poisson_s2`) matches the real screen's read depth. The two arms differ
+only in count noise, and the difference is large where most real doubles
+sit: at 100-1,000 total reads (about 90,000 doubles) X correlates with the
+truth at 0.96 under Poisson counts and 0.62 under the realistic noise; at
+100 reads or fewer, 0.72 against 0.26. The realistic noise is founder
+sampling plus a PCR template bottleneck, calibrated to the real counts'
+5-18x overdispersion (`planning/studies/noise-anatomy/`): templates at a
+tenth of the reads, which by itself multiplies count variance about
+13-fold.
+
+**Idea.** Sweep the noise sources on this simulation to say what each buys
+before the next screen: `pcr_template_molecules` (more template DNA into
+PCR), `cfu0` (more cells per tube, so less founder noise), and
+`total_num_reads` for comparison. Score X recovery and coverage by depth
+bin, especially at 100-1,000 reads, against the cost of each change at the
+bench.
+
+**Why not now.** The immediate goal is what this experiment's data support
+without new sequencing: first the deep genotypes' coverage, which is lost
+to shared k/m errors with no interval. The sweep's coverage numbers inherit
+that problem until it is fixed.
+
+**What it would take.** `make_sim_config.py` already writes the realistic
+config; add arms with templates at 1x, 3x and 10x the current value, `cfu0`
+at 3x and 10x, and reads at 2x. Each arm is one simulation (about 15
+minutes locally) plus the cluster fit. Students can run the grid once the
+fit's k/m handling is settled. Open: whether the realistic noise model's
+split between founder and PCR noise matches the real one (the noise-anatomy
+study fit their sum to the counts, not each part).
diff --git a/planning/empirical-mixture-refit.md b/planning/empirical-mixture-refit.md
new file mode 100644
index 00000000..ccf420d0
--- /dev/null
+++ b/planning/empirical-mixture-refit.md
@@ -0,0 +1,63 @@
+---
+title: Correct the empirical pipeline's per-genotype fits for congression
+status: idea
+filed: 2026-09-24
+area: simulate
+revisit_when: >-
+  When the empirical-phenotype pipeline (tfs-build-empirical,
+  tfs-fit-genotypes) is reviewed as a whole, and after steps 4-5 of the
+  congression plan settle the theta and dk cell rules.
+related:
+  - planning/congression-physics-plan.md
+  - src/tfscreen/tfmodel/genotype_fit/fit.py
+  - src/tfscreen/simulate/scripts/build_empirical_cli.py
+  - src/tfscreen/tfmodel/scripts/fit_genotypes_cli.py
+  - src/tfscreen/simulate/cell_rules.py
+  - src/tfscreen/tfmodel/generative/components/transformation/mixture.py
+---
+
+**Context:** Step 3.4 of the congression plan. `tfs-build-empirical` builds
+simulation ground truth from real data: per-genotype maximum-likelihood fits
+(Stage 1), a population distribution (Stage 2), resampling (Stage 3). Real
+bulk genotypes are measured with congression built in, so resampling their
+fits and simulating congression again counts it twice. The optional Stage 1.5
+(`--congression_lambda`) removed that bias from the theta curves with the
+theta-only `E[max(theta_g, co-residents)]` operator. Step 3.3c dropped that
+operator from the main fit (the 3.0 study showed averaging theta cannot
+represent a mixture of growth rates), and Stage 1.5 also used Poisson rather
+than zero-truncated co-resident weights, treated purity as spiked/bulk, and
+ignored dk dilution. It was retired on 2026-09-24 (user); the pipeline now
+builds its distribution from uncorrected fits.
+
+**Idea:** Replace Stage 1.5 with an iterated Stage 1 refit under the same
+observable-level mixture as `tfs-fit-model`:
+
+- Pass 1 is plain Stage 1.
+- In each later pass, refit every genotype with
+  `ln_cfu = ln_cfu0 + logsumexp_c(log w_c + G_c)`. The genotype's own
+  parameters are free; its congressed classes combine them with fixed
+  co-resident sets whose theta, activity and dk come from the previous pass,
+  through the simulator's cell rules (`simulate/cell_rules.py`) and the
+  zero-truncated weights with `bulk_fraction`.
+- Iterate until the fits stop moving (expect 2-3 passes).
+
+This corrects dk as well as theta, follows any change to the cell rules, and
+gives each genotype a covariance under the corrected model.
+
+**Why not now:** The user wants the whole empirical pipeline reviewed before
+it is used again, and the theta and dk rules are about to change (steps 4-5).
+In the measured regime the bias is modest (about 0.05 ln units RMS; see
+`planning/studies/congression-estimator/`).
+
+**What it would take:**
+
+- A mixture likelihood in `genotype_fit/fit.py`, sharing the class
+  construction with `transformation/mixture.py` or `cell_rules.py` rather
+  than a third copy.
+- Co-resident sets as in `ModelOrchestrator._draw_coresident_sets`.
+- Cost: about (1 + K) times the work per fit, times the number of passes. The
+  fits within a pass stay independent and parallel.
+- Tests: reduction to plain Stage 1 as lambda -> 0; recovery of simulated
+  phenotypes from a congressed simulation.
+- Open question: whether the refit should share lambda with, or estimate it
+  alongside, the main fit.
diff --git a/planning/estimate-dk-alpha-by-varying-lambda.md b/planning/estimate-dk-alpha-by-varying-lambda.md
new file mode 100644
index 00000000..d2634d4e
--- /dev/null
+++ b/planning/estimate-dk-alpha-by-varying-lambda.md
@@ -0,0 +1,44 @@
+---
+title: Estimate the dk_geno combining exponent (alpha) by varying congression
+status: idea
+filed: 2026-09-13
+area: experiment
+revisit_when: >-
+  After the congression physics work settles on the soft-min dk_geno rule and
+  the current single-lambda dataset is published.
+related:
+  - planning/congression-physics-plan.md
+  - src/tfscreen/simulate/selection_experiment.py
+  - src/tfscreen/tfmodel/generative/components/transformation/mixture.py
+---
+
+**Context:** While fixing the simulator/fit mismatch in how co-resident
+plasmids combine growth effects, we chose to combine each physical quantity at
+its own level. For the pleiotropic growth cost, a cell carrying variants with
+dimer shares `x_g` gets
+
+```
+dk_cell = -(1/alpha) * log( sum_g x_g * exp(-alpha * dk_g) )
+```
+
+`alpha -> 0` is dilution (share-weighted mean burden); `alpha -> inf` is
+dominance (the worst variant sets the cost). Burden that scales with the amount
+of misfolded protein points toward small alpha; aggregation or mixed-oligomer
+poisoning points toward large alpha.
+
+**Idea:** Estimate alpha empirically with a screen run at several deliberately
+different congression levels (lambda). Alpha only acts through co-resident
+cells, so a single low lambda (0.36 in our dataset, where 70% of a genotype's
+cells have no co-resident) leaves it weakly identified. Varying lambda changes
+the co-resident fraction and the burden mixture it sees while the per-genotype
+dk_geno values stay fixed.
+
+**Why not now:** Our dataset has a single lambda, so alpha is set from biology
+and checked by sensitivity (alpha = 0 vs. large), not fit. The experiment is a
+separate project.
+
+**What it would take:**
+- Wet lab: the same library transformed at 2 to 3 lambda values (for example
+  0.1 / 0.4 / 1.0), with lambda measured independently for each.
+- Model: alpha as a shared latent with lambda-indexed transformation parameters.
+- Simulation first, to find the lambda spacing that identifies alpha.
diff --git a/planning/estimate-spike-fraction-from-growth-curvature.md b/planning/estimate-spike-fraction-from-growth-curvature.md
new file mode 100644
index 00000000..77e8b901
--- /dev/null
+++ b/planning/estimate-spike-fraction-from-growth-curvature.md
@@ -0,0 +1,31 @@
+---
+title: Estimate a genotype's spike/bulk fraction from ln_cfu curvature
+status: idea
+filed: 2026-09-13
+area: tfmodel
+revisit_when: >-
+  If design-computed spike/bulk fractions (f_g) look wrong for real data, or
+  after the observable-level mixture (congression plan step 3) is in place.
+related:
+  - planning/congression-physics-plan.md
+---
+
+**Context:** In the congression plan, each genotype's cells mix clean and
+congressed classes, weighted by `f_g`, the fraction of its cells from the bulk
+library. We compute `f_g` from the library design (`transform_sizes`,
+`library_mixture`, codon degeneracy).
+
+**Idea:** Two subpopulations growing at different rates give a
+`ln_cfu(t)` that is not linear in time. A genotype that is genuinely clean
+grows along a straight line; a mixed one curves. With enough timepoints that
+curvature carries information about `f_g`, and could check or refine the
+design-based value for real data where transformation efficiencies are
+uncertain.
+
+**Why not now:** `f_g` from the design is good enough to start, the curvature
+signal is weak over 3 timepoints per condition, and future runs will
+distinguish spikes by codon, which measures purity directly.
+
+**What it would take:** The observable-level mixture from the plan, then a
+`f_g` prior centered on the design value (for example a Beta) instead of a
+fixed value, plus a simulation to see whether it is identifiable.
diff --git a/planning/experiment-pipeline.md b/planning/experiment-pipeline.md
new file mode 100644
index 00000000..6fb35845
--- /dev/null
+++ b/planning/experiment-pipeline.md
@@ -0,0 +1,293 @@
+---
+title: One experiment file from raw data to posterior
+status: active
+filed: 2026-10-03
+area: tfmodel
+revisit_when: >-
+  Agreed 2026-10-03 for the next branch, after
+  claude/physics-improvements-roadmap-03a273 merges. Keep the step list
+  current while it is worked. Re-ordered 2026-10-05: steps 6 and 7 next,
+  step 5 (the orchestrator) deferred, then a science prioritization.
+related:
+  - planning/analysis-roadmap.md
+  - planning/analysis-roadmap-summary.md
+  - planning/studies/real-data-fit/README.md
+  - src/tfscreen/process_raw/scripts/process_counts_cli.py
+  - src/tfscreen/tfmodel/scripts/configure_model_cli.py
+  - src/tfscreen/tfmodel/scripts/prefit_calibration_cli.py
+  - src/tfscreen/tfmodel/scripts/fit_model_cli.py
+  - src/tfscreen/simulate/empirical/
+---
+
+## Why
+
+The CLI chain was built for the old flow: `tfs-process-fastq`, then
+`tfs-process-counts`, then `tfs-configure-model`, then
+`tfs-prefit-calibration`, then `tfs-fit-model`. The analysis-roadmap
+branch changed what goes in and how a fit has to be run. It added the
+count likelihood, OD600 tube totals, the growth-only relative fit, held
+population SDs, staged MAP starts and the arrowhead Laplace. Each gap
+between the old chain and the new needs was patched by hand. The real fit
+(`planning/studies/real-data-fit/`) took three hand steps:
+
+1. **Processing.** `prep_dev_data.py` turned OD600 into tube totals through
+   the calibration, fixed the library config and cleaned up lab problems.
+   `tfs-process-counts` then ran once per library, and the outputs were
+   joined by hand.
+2. **Priors.** `set_growth_priors.py` wrote k/m priors, because
+   `tfs-prefit-calibration` refuses growth-only models, and `sed` set the
+   held population SDs and the tube-offset SD in the generated priors CSV.
+3. **The staged MAP.** A no-offset MAP, then `make_init_npz.py` and
+   `check_warm.py`, then `tfs-fit-model --init_from`, over a chain of six
+   runs.
+
+Nobody else can run that, and it cannot be rerun on a full-size simulation
+without redoing it by hand.
+
+## Approach
+
+Fix the three seams first, each as a small, tested change to an existing
+CLI. Then add a thin orchestrator that only calls the CLIs in order. An
+orchestrator built first would wrap the hacks and freeze them.
+`tfs-build-empirical` is the precedent for a CLI that runs others.
+
+## Done when
+
+Revised 2026-10-05 (user), with step 5 deferred and the offset mode found
+to be a model problem (`planning/offset-mode-growth-transition.md`):
+
+- The real fit reruns through the documented CLI chain (process, configure,
+  fit, posterior, extract, predict) with no hand-edited intermediate file
+  and no hand-built start. The chain's commands are the official recipe
+  for the real fit, replacing the `real-data-fit` study's six-run chain.
+- A full-size simulation of the same design, written by `tfs-simulate` in
+  the raw formats (step 6), runs through the same chain with only the data
+  paths changed. That is the start of the reviewer simulations in
+  `planning/studies/real-data-fit/README.md`, "Next", and the footing for
+  every model study after this plan: a user can reproduce one end to end.
+- Both report the tube-offset diagnostic (step 7), so a fit in the offset
+  mode is flagged, not accepted silently.
+
+The original criterion, every spike's K and n inside the `rel_off_n05_alap`
+intervals, was dropped: run to convergence, the start behind
+`rel_off_n05` moves into the offset mode, so its intervals are not a
+reference.
+
+## Constraints
+
+- **P1. Lab data and calibrations stay out of the repository** (roadmap
+  C10). The experiment YAML names files; the repository holds formats,
+  methods and synthetic examples.
+- **P2. Breaking changes are fine** (roadmap D12). Old processed files and
+  configs need not load.
+- **P3. Leave room for the population model** (roadmap step 6). Processing
+  supplies per-tube totals from OD600 today. Step 6 will instead fit a
+  population curve to every OD600 reading inside the model. Keep the OD600
+  table a first-class input, so that change replaces the totals step
+  without changing the experiment file's shape.
+- **P4. Lab-specific cleanup stays the lab's.** Swapped samples, pooled
+  resequencing and failed tubes are the lab's business. The pipeline takes
+  a clean tube table and documents its format. It may offer a hook for a
+  per-experiment cleanup script, but it encodes none of that cleanup.
+- **P5. Every run records its provenance.** Each CLI writes the tfscreen
+  version and git commit into its outputs, and the orchestrator also
+  records input checksums. The study READMEs had to infer commits from
+  file times (2026-10-03).
+
+## Steps
+
+0. [x] **Provenance in every CLI** (P5). `generalized_main` prints, and
+   each writer records, the version, the commit (with a dirty flag) and the
+   command line. Do this first and small; it pays off at once.
+   Done in 0.5.0 (`util/provenance.py`): every run prints it and writes
+   `{out_prefix}_provenance.json`; the configure YAML and fit checkpoints
+   embed it. Posterior `.h5` files do not yet; their run's JSON sits
+   beside them.
+1. [x] **Processing takes OD600 directly** (seam 1).
+   - `tfs-process-counts` takes the per-tube OD600 table and a
+     `tfs-calibrate-od600` file. It computes `sample_cfu` and its SD
+     itself, keeping the shared curve error apart from the per-reading
+     noise (C11). Supplied `sample_cfu` stays available.
+   - It processes several libraries in one call, from one tube table with
+     a `library` column. That replaces the per-library runs and the join.
+   - Document the tube-table and OD600 formats with a synthetic example in
+     `examples/`.
+   - The spike list and the M42I codon problem came from a processing
+     config that disagreed with the real spikes. Check at configure time
+     that every spike named in the library YAML is present in the counts.
+   Done in 0.5.0: `--od600_file`, `--od600_calibration_file`,
+   `--tube_volume_mL` (`od600.tube_totals_from_od600`); several libraries
+   already worked in one call (per-library filter and fill), now tested;
+   `examples/process_raw/`; `tfs-configure-model` refuses a missing spike
+   unless `--allow_missing_spikes`. `tfs-process-presplit` became
+   `tfs-process-counts --presplit`.
+2. [x] **Configure exposes everything that was hand-edited** (seam 2).
+   - Flags for `sigma_fixed` on level offsets and for every
+     `theta_*_hyper_scale_fixed`.
+   - A growth-prior step for growth-only models, replacing
+     `set_growth_priors.py`. Per-condition k/m loc and scale come from
+     defaults, a table, or wt monoculture rates (the `monokan` rule:
+     k = rate at the high gauge concentration, m = the difference). The
+     pre-fit stays for joint models.
+   - Decide whether the growth-only defaults change: n held at 0.5, X_low,
+     X_delta and log K held, offsets at 0.17. The real fit and the
+     sensitivity runs say how much each matters
+     (`planning/analysis-roadmap-summary.md`).
+   Flags done in 0.5.0: `--set_priors name=value ...` (any scalar prior,
+   by full name or unique suffix), `--growth_priors` (per-condition table)
+   and `--growth_priors_wt_rates` (the monokan rule, hill_relative only),
+   in `tfmodel/priors_edit.py`. Closed 2026-10-05. The defaults did not
+   change (user, 2026-10-04); the decision moved to
+   `planning/offset-mode-growth-transition.md`, since the held SDs and the
+   offset SD are likely to change with the growth-transition model.
+3. [x] **The staged MAP inside `tfs-fit-model`** (seam 3).
+   - With level offsets on, the fit stages itself. First a MAP with the
+     offsets held at 0. Then each tube's offset alone, with everything else
+     held (what `small_offsets.py` did, cheap). Then the joint MAP from
+     there at a small step size.
+   - Each stage writes its own checkpoint and convergence record, so a
+     stage can be inspected or resumed.
+   - This retires `make_init_npz.py`, `make_warm_start.py` and
+     `check_warm.py`. Keep `--init_from` for experiments.
+   - Test on a simulation: does a cold level-offset MAP reproduce the ±2.8
+     offset trap, and does the staged one avoid it? Then on the real data.
+   Built after 0.5.0: `tfs-fit-model --stage_offsets` (default `auto`),
+   `inference/staged_map.py`, with unit and smoke tests. The trap was only
+   ever seen on the full dev data, so the test runs there first
+   (`planning/studies/staged-map/`); a full-size simulation waits on step
+   6. Retire the hand scripts once the study passes.
+   Study result (2026-10-05): the staged MAP avoids the offset mode, but run
+   to convergence on the exact full-batch loss (also added) the offset mode
+   scores best: it is the model's preferred optimum, not a trap, and its
+   IPTG-structured offsets point at a missing selection-onset transient
+   (roadmap 7b).
+   Closed 2026-10-05 as machinery (user): the staged MAP and exact-loss
+   convergence work, and the staged fit is the physical one (3.7e5 nats
+   better than `rel_off_n05` as it was used). The open problem is the
+   model, carried by `planning/offset-mode-growth-transition.md`: under
+   the current model the offset mode is the better optimum, so a longer
+   or better fit can still leave the physical basin. Step 7's offset
+   diagnostic guards against that until the model is fixed. The hand
+   scripts retire in step 7.
+4. [x] **Posterior defaults for large libraries.** Choose the Laplace
+   automatically from the library size: the full Laplace below a
+   parameter threshold, the arrowhead (`--laplace_blocks --laplace_shared`)
+   above it. Report the held Schur directions in a file as well as the
+   log, since they say which k/m directions have no interval.
+   Done in 0.5.0: `tfs-sample-posterior --laplace auto|full|arrowhead|blocks|point`,
+   auto switching at `--laplace_max_params` (20,000 MAP parameters, about
+   1.6 GB of float32 Hessian); `{out_prefix}_held_directions.csv`. A model
+   whose genotypes couple (hill_mut) cannot use the arrowhead, so auto
+   fails on a large one of those; there is no full-library route for it.
+5. [ ] **The orchestrator.** `tfs-run-experiment experiment.yaml`.
+   Deferred 2026-10-05 (user): a convenience over the CLI chain, which
+   steps 6 and 7 document and test first.
+   - The YAML names the inputs (counts directory, tube table, OD600 table,
+     calibration, library YAML, optional binding and monoculture rates)
+     and the choices (model, priors, staging, posterior, predictions).
+   - It runs processing, configure, priors, fit, posterior, extract and
+     predict in order, calling the existing CLI functions.
+   - Each stage writes into its own subdirectory and is skipped if its
+     outputs are already there, so a run is resumable and a stage can be
+     rerun by deleting it.
+   - It writes a manifest: commit, input checksums, the resolved YAML.
+   - It can emit a SLURM script for the fit and posterior stages.
+     Cluster-specific settings (account, partition, memory) come from the
+     YAML, never from code.
+   - Scoring (`score_counts.py`) becomes a CLI if the orchestrator reports
+     a fit's count likelihood; otherwise it stays a study script.
+6. [x] **The simulator writes the same raw formats**.
+   - `tfs-simulate` writes per-tube count files in `tfs-process-fastq`'s
+     format (`counts_<sample>.csv`: `genotype`, `counts`, with the
+     `__unknown__` row), the tube table and the OD600 table in step 1's
+     formats, so a simulated experiment enters at `tfs-process-counts`,
+     exactly where real data do. Ground truth stays in its own files.
+   - Keep the current direct outputs (`tfs_sim_growth.csv` and the rest)
+     until every study grid and example has moved to the raw path, then
+     decide whether to drop them.
+   - Test: `tfs-process-counts` on the simulator's raw output reproduces
+     the simulator's own growth table (same counts, tube totals from the
+     simulated OD600 through the same calibration).
+   - Then the full-size simulation of the real design (library, tube grid,
+     depth, OD600), as a study.
+   Raw formats done 2026-10-05 (`simulate/raw_output.py`, written by
+   `tfs-simulate` by default): the round trip matched the direct table
+   exactly on the simulate-and-analyze example (51,000 rows) and, through
+   OD600, on the simulate example; `examples/simulate-and-analyze/run.sh`
+   uses it. Raw presplit is not written yet (the presplit table is still
+   direct). Left: the full-size simulation of the real design.
+   Full-size simulation set up 2026-10-05: `planning/studies/full-size-sim/`
+   (config derived from the dev data by `make_sim_config.py`; the
+   simulator's new `design` key copies the real 118-tube layout), to run on
+   the cluster. A local pilot ran (85 min, 68 GB peak); `cfu0` is now
+   calibrated per library from a pilot. The calibrated simulation still
+   misses the real tube totals in a structured way (IPTG, time), which is
+   recorded for step 8.
+7. [x] **Validate and document** (after step 6). Without the orchestrator:
+   - Run the real data and the full-size simulation through the CLI chain
+     with no hand step, and write the commands down as the recipe
+     (`planning/studies/real-data-fit/`, and a page in `docs/`).
+   - Add the tube-offset diagnostic: `tfs-summarize-fit` reports each
+     level-offset fit's offsets in prior SDs and their correlation with
+     titrant within each selection condition, and flags a fit whose
+     offsets carry structure (the offset mode). Pass test for this step:
+     both runs reproduce, and the diagnostic is clean or flagged.
+   - Update `docs/` and CLAUDE.md, and retire `make_init_npz.py`,
+     `make_warm_start.py` and `check_warm.py` (frozen in the study; no
+     longer part of any recipe).
+   Code and docs done 2026-10-05: `tfs-extract-params` writes the tube
+   offsets labeled by tube (`sample_offset/_tubes.py`), and
+   `tfs-summarize-fit` reports them in prior SDs with a Spearman trend
+   against titrant and time per condition, flagging BH q < 0.05
+   (`tfmodel/analysis/tube_offsets.py`; JSON `tube_offsets`). The recipe is
+   `planning/studies/real-data-fit/fit/run_recipe.srun` and
+   `docs/source/pipeline.rst` (step 7, "Check the fit", added). One seam
+   found and closed: `prep_dev_data.py`'s tube table carried OD600 and
+   totals both, which `tfs-process-counts` refuses, so it now also writes
+   `tube_table.csv` and `tube_od600.csv`. The hand scripts are marked
+   retired in the study README. Left: the two cluster runs (recipe, and
+   `full-size-sim`), each checked for the diagnostic's verdict.
+   Recipe run 2026-10-06: end to end, offsets clean, reproduces
+   `staged_auto2` (log K r 0.992; README "Recipe"). The full-size
+   simulation ran end to end and its offsets are clean, but it found a
+   simulator bug (hill_geno drew wt like any genotype; seed 1's wt was
+   flat), fixed the same day. Corrected arms 2026-10-07: chain clean,
+   offsets clean; X undercovers, worse with depth (95% coverage 0.66
+   realistic, 0.29 Poisson), from shared k/m/dk_geno errors the arrowhead
+   Laplace holds at the MAP (`planning/studies/full-size-sim/` results).
+   Step 7 done 2026-10-07: both runs reproduce through the documented
+   chain and the diagnostic gives a verdict; the recovery numbers are step
+   8's baseline. Also found: the low overall X r is depth, not k/m (r
+   0.96-0.99 above 1e4 reads), and the simulated depth distribution does
+   not match the real screen (real median double 480 total reads against
+   ~1e4 simulated; 64% of real doubles below 1e3). Both in the study
+   README.
+8. [ ] **Then: prioritize the science** (user, 2026-10-05). With steps 6
+   and 7 in, any model change can be tested end to end on simulations a
+   user can reproduce. Step back and list the model work (the growth
+   transition, the offsets' freedom, the population model, the
+   growth-binding map, and others), how the changes interact, and what
+   order to do them in. From step 7's simulation, on the list: match the
+   simulated library's depth distribution to the real screen first (every
+   simulated recovery number depends on it); anchor k against dk_geno and
+   give the held shared directions an interval; hold the X population SDs
+   (shrinkage for shallow genotypes); pooling across doubles (hill_mut)
+   for the 64% of real doubles below 1e3 reads; a level-like simulated
+   tube noise matched to the real offsets. Not to be decided before steps 6 and 7.
+
+## Out of scope
+
+- The population model and the OD600 likelihood (roadmap step 6), and the
+  growth-transition decision (7b, now
+  `planning/offset-mode-growth-transition.md`). This plan only keeps room
+  for them (P3).
+- The growth-binding relationship study (summary, Part 3, item 1).
+- Workflow engines (Snakemake, Nextflow). A Python orchestrator over the
+  CLI functions is enough at this size, and adds no dependency.
+
+## Open
+
+- Whether the orchestrator replaces `tfs-build-empirical`'s internal
+  configure and pre-fit, or calls it.
+- How binding data enter a growth-only experiment file. Today they are only
+  compared after the fit; roadmap step 9 brings them back into the model.
diff --git a/planning/findings-ledger.md b/planning/findings-ledger.md
new file mode 100644
index 00000000..2dd12b61
--- /dev/null
+++ b/planning/findings-ledger.md
@@ -0,0 +1,210 @@
+---
+title: Findings ledger for the screen's model (established, refuted, open)
+status: active
+filed: 2026-10-18
+area: tfmodel
+revisit_when: >-
+  Read before filing a new plan or study on the growth/binding model,
+  lambda, the k/dk_geno slide or the slope m; add a line whenever a study
+  establishes, refutes or reopens something.
+related:
+  - planning/joint-fit-return.md
+  - planning/analysis-roadmap-summary.md
+  - planning/growth-only-relative-fit.md
+  - planning/congression-physics-plan.md
+  - planning/two-stage-fit.md
+  - planning/absolute-abundance-measurements.md
+  - planning/studies/joint-fit-sim/README.md
+  - planning/studies/joint-fit-real/README.md
+---
+
+## Why this file
+
+On 2026-10-18 a review of the planning files, the studies and the git log
+found that a week of joint-fit work had re-found several results from July
+and early October (listed under "Re-found" below). Plans had cited history
+only back to their own start. This page collects what is known, so a new
+plan starts from it. Each entry names its source; `planning/dev-data/` is
+gitignored and is cited by file. Dates are when a result was found.
+
+## Established
+
+**Growth identifies occupancy only up to an affine map.** k and m absorb any
+affine change of theta (`g = k + dk + m theta`), so growth alone cannot fix
+theta's scale (CLAUDE.md, Relative-X fit; roadmap step 5). Every "squeeze"
+below is this direction: the doubles' pull on m, the singles' squeeze in
+an absolute-theta stage 1 (joint-fit-sim run 6: theta_fit = 0.048 + 0.90
+theta_true), and the slide of X against k.
+
+**Binding genotypes are what resist the squeeze, as about 1/sqrt(n).**
+July: about 20 in-library binding anchors with m free gave a theta slope of
+0.91 and m ratios 0.95/1.14, robust to a wrong lambda (memory
+`project_theta_recovery_congression_confound`, `out11/libexp`, 2026-07-08).
+Quantified 2026-10-18 (joint-fit-sim run 10): without congression the
+growth-only singles' theta RMSE is 0.010 + 0.053 / sqrt(n + 2) for n = 0-50
+binding singles, insensitive to which singles; coverage 0.11 to 0.51.
+This holds only if binding and in vivo occupancy share one scale (see Open).
+
+**The full-library MAP steepens m about 1.45x through the doubles** (not
+congression: still 1.37 without it); the two-stage fit (k and m from wt,
+spikes and singles, pinned for the full library) removes most of it
+(m-bias-factorial rounds 2-5, 7fcef80c, b8be0d2b, 2026-10-10/12). Stage 1
+itself keeps a smaller bias (2-6% relative; 14% absolute without binding
+singles, joint-fit-sim run 5).
+
+**The markers read occupancy through differently shaped maps:** kanR
+switch-like (Hill n at the bound of 4 on X), pheS graded (n about 1); one
+shared linear X blends them; a logistic warp of kanR gains about 8,000 nats
+(relative-m runs 2-4, 6aa9ae4b, d540edfc). In an absolute-theta model the
+warp, s and per-genotype offsets are recoverable on simulations when the
+full model starts from the plain one (joint-fit-sim runs 1-3).
+
+**lambda absorbs misfit.** Converged fits recover it on simulations
+(0.004 at 0, 0.98 at 1; count likelihood 0.02-0.06 at 0), but its profile
+is flat over 0.6-1.4, seeds spread it 30-50 posterior SDs, and it runs away
+when the model conflicts with the data: 0.6-2.4 in July (2.41 with m
+clamped against binding anchors), 0.82-2.10 in joint-fit-real run 1. Real
+fits that did not conflict gave 0.17-0.37 (congression-calibration README;
+congression-physics-plan step 3.5; m-bias-factorial lines ~1226-1236;
+dev-data real_fit 2026-10-02).
+
+**The congression theta rule is not identifiable from the screen**
+(cross-rule grid, 3d60dfd2): homodimer is the physical default;
+`hill_relative` needs `max` (the only rule invariant to an increasing map).
+The fit's fixed co-resident draws leave about 3 points of m bias however
+many there are (joint-fit-sim run 9); more draws (75) are cheap except in
+Laplace memory.
+
+**The k/dk_geno/k_ref slide is a symmetry, not a bug** (`k += C, dk -= C`).
+Per-condition k priors (128cd477), base_growth/k_ref (7e083271) and the wt
+monoculture anchor (34b8143a) each failed to close it on its own; pinning
+k and m from stage 1 nearly closes it (median dk error -0.0057 to -0.0008);
+what remains is the dk_geno population location against wt, which the
+stage 2 Laplace holds (two-stage-validation, 7d01721a).
+
+**dk_geno is not rankable from the screen:** the two control conditions
+disagree (r = 0.03-0.04) and the noise exceeds the spread (memory
+`project_dk_geno_not_rankable`; deep-coverage step 3).
+
+**The binding weight was not the driver of the joint fit's distortion.**
+At the old default (about 251 x SD 0.001) binding dominated the objective;
+at weight 1 theta coverage rose but the mixture's drift stayed
+(congression-calibration second diagnosis, grid_bw1, 2026-09-24). The
+default is 1 since 45ffb7fe.
+
+**Theta intervals undercover because of the shared parameters' bias, not
+the per-genotype uncertainty:** at the true k and m without congression
+they cover 0.91; with congression 0.70 (the mixture adds per-genotype error
+the model does not carry); fitted, 0.1-0.5 (joint-fit-sim runs 5, 10).
+
+## The binding-to-growth link (the central open problem)
+
+Facts, each found more than once:
+
+- **wt's IPTG midpoint in growth is about 10x its 2 uM binding midpoint:**
+  growth near 30 uM IPTG, 2 uM binding near 3 uM; 20 uM binding matches
+  wt's midpoint (x-vs-binding, 2026-10-15; joint-fit-real run 1). An
+  earlier form with 10 uM binding: dev-data real_fit, 2026-09-29.
+- **wt is not fully induced in vivo:** about 70% at 1 mM where binding says
+  93% (2026-10-01; roadmap summary), a floor of 0.15-0.24 in run 1.
+- **Growth curves are broader than binding curves:** spike growth n
+  0.6-1.2 against binding n 1.6-2.5 (2026-10-01). Rechecked on the
+  kanR-only two-stage X: n 0.7-0.95 for most genotypes against 1.9-3.2 at
+  20 uM (x-vs-binding run 2).
+- **M42I's order inverts:** binding K about 10x wt's at both 2 and 20 uM;
+  growth K about half of wt's (6.4 against 12 uM IPTG). Protein
+  concentration does not move that ratio, so no single concentration
+  removes it; per genotype, wt prefers about 12 uM protein and M42I about
+  1.9 (dev-data real_fit 2026-10-01, `scan_protein.py`). Rechecked on
+  the two-stage, kanR-only X (x-vs-binding run 2, 2026-10-18): M42I's K is
+  0.18x wt's in growth against 3.6x (2 uM) and 5.3x (20 uM) in binding, a
+  factor of 20-30; M42I/H74A keeps the order but sits 10x lower in growth;
+  D88A and V95N agree within 2x.
+- **No single protein concentration or shared monotone map carries binding
+  onto growth** (best shared concentration 4.5 uM, rms 0.285; isotonic
+  0.21-0.25; departures 0.3-1.0 against interval widths 0.15-0.35;
+  2026-10-01, "no map").
+- **The 2 uM agreement that motivated rejoining (kanR X against binding,
+  rms 0.12) was in a gauge built from wt's own binding endpoints**, which
+  absorbs any affine level and scale difference; in an absolute model the
+  difference reappears (joint-fit-real run 1: every binding offset negative,
+  -0.07 to -0.28).
+- **Westerns put total repressor in the 100s of uM,** so an "apparent
+  protein concentration" is a lumped mapping, not a concentration
+  (dev-data README, binding plan).
+
+So binding cannot yet set theta's absolute scale: every route that uses
+it that way (the 2026-09 joint fit, roadmap step 8's maps, the 10 uM
+interpolation, joint-fit-real run 1) has met these facts.
+
+## Refuted or retired
+
+- Up-weighting binding (N_growth/N_binding) as needed for binding to
+  matter; and as the cause of the joint fit's distortion (see above).
+- One shared X for both markers (D5's test failed; relative-m).
+- OD600 tube totals as the cause of the within-tube/fit m gap (freeing the
+  totals left the ratio at -0.49; relative-m run 1).
+- A free per-concentration IPTG k (unidentified; within-tube-m run 2).
+- Tighter or moved theta population priors as a fix for the squeeze
+  (joint-fit-sim run 6: moving the location changes nothing; holding the
+  SDs at the truth makes it worse).
+- The k-shift refits for k's uncertainty in X (they leave the MAP and
+  slide; two-stage-validation).
+- "The prefit zeros dk_geno, so k is low" (July; disconfirmed: wt anchors
+  k).
+
+## Open
+
+1. **The binding-to-growth link** (above). Candidates on record: cell-to-cell
+   variation in IPTG uptake or repressor level (broadens and shifts in vivo
+   curves; testable on wt first); a binding model in protein concentration
+   with a fitted in vivo concentration; an affine calibration between the
+   assays. The bench tests on record: monoculture growth of the spikes
+   across the titration (`planning/absolute-abundance-measurements.md`);
+   binding on more singles helps only once the link holds.
+2. ~~Whether M42I's inversion and the broad growth curves survive on the
+   two-stage, kanR-only X.~~ Both survive (x-vs-binding run 2, 2026-10-18).
+   Both genotypes carrying M42I respond at lower IPTG in vivo than in vitro
+   relative to wt; a genotype-specific in vivo factor (repressor level?)
+   is a hypothesis, testable with expression data.
+3. **lambda's prior:** fixing it from the measurement was proposed
+   (2026-09-26, congression-physics-plan step 3.5) and never done; later
+   decisions loosened it ("a readout, not a measurement", 2026-10-02;
+   "keep it fitted", 2026-10-11). Unreconciled. wt purity (excess wt, about
+   11-16% of the pool) moves fitted lambda 2x (0.365 cfg against 0.174
+   wtspiked) and is still open at the bench.
+4. **The dk_geno population location in stage 2:** holding it at stage 1's
+   value is proposed, not run.
+5. **Honest theta intervals:** the shared-scale error (carry its uncertainty
+   into each genotype, as `--shared_shift` does for k) and congression's
+   per-genotype error (the mixture's form). The k shift from stage 2
+   refits fails both ways tried: at step size 1e-3 the refits slide along
+   the dk_geno population (real X +0.32 with k unchanged; two-stage
+   validation run 1), and with that population held they under-respond
+   (a third or less of the true shift on simulations; run 2). The 0.5.0
+   release ships without it: deep coverage 0.88-0.91 up to 1e5 reads,
+   0.76-0.81 above (2026-10-20).
+6. **The theta_low/dk_geno ridge at 0 IPTG** (July): proposed fixes (drop 0
+   IPTG from epistasis inputs; base_growth on genotypes other than the
+   binding ones) never tested.
+
+## Contradictions to keep in mind
+
+- Study 0a (2026-09-26, buggy data, 22 uM protein) put growth's response at
+  30-100x lower IPTG than binding; every later comparison puts wt's at
+  about 10x higher IPTG than its 2 uM binding.
+- Within-tube pheS m 0.018 (within-tube-m run 3) predates the swapped-row
+  fix; without the row it is 0.0125.
+- July "wt alone anchors k" against September/October "wt's pin is too
+  weak against the slide" (different models and scales).
+
+## Re-found on 2026-10-17/18 (joint-fit-sim, joint-fit-real)
+
+- Binding singles pin m (July's in-library anchors).
+- The singles' affine squeeze (the documented gauge symmetry).
+- lambda running away under a model conflict (July).
+- wt's 10x midpoint offset and its in vivo floor (2026-10-01, 2026-10-15).
+- New in that work: the joint model's pieces recover on simulations; the
+  full model must start from the plain one; the 1/sqrt(n + 2) scaling; the
+  mixture's fixed-draw floor; intervals honest at the true k and m without
+  congression.
diff --git a/planning/growth-only-relative-fit.md b/planning/growth-only-relative-fit.md
new file mode 100644
index 00000000..e13b4ac7
--- /dev/null
+++ b/planning/growth-only-relative-fit.md
@@ -0,0 +1,183 @@
+---
+title: Fit growth alone on a wt-relative scale, then measure the growth-binding map
+status: promoted
+filed: 2026-09-26
+area: tfmodel
+revisit_when: >-
+  When we want to test, rather than assume, that in vitro binding and in vivo
+  growth report the same occupancy. Step 0 (model-free look) can be run any
+  time on the existing real data.
+related:
+  - planning/analysis-roadmap.md
+  - src/tfscreen/tfmodel/model_orchestrator.py
+  - src/tfscreen/tfmodel/generative/model.py
+  - src/tfscreen/tfmodel/scripts/configure_model_cli.py
+  - src/tfscreen/tfmodel/scripts/prefit_calibration_cli.py
+  - src/tfscreen/tfmodel/analysis/prediction.py
+  - src/tfscreen/tfmodel/generative/components/theta/hill_geno.py
+  - src/tfscreen/tfmodel/generative/components/theta_rescale/
+  - planning/congression-physics-plan.md
+  - planning/studies/congression-calibration/README.md
+---
+
+**Promoted 2026-09-26** into `planning/analysis-roadmap.md` (Track G: steps
+0a, 1, 5, 8, 9). Step 1, the growth-only plumbing listed below, is done;
+step 5, the relative-X fit, is implemented as `theta: hill_relative` (gauge:
+wt pinned at the measured concentration range's ends), with its validation
+grid in `planning/studies/relative-fit/`.
+
+**Context:** While calibrating fits for congression, it became clear we want
+to fit growth data without binding data, at least to check whether the two
+are consistent. An investigation (2026-09-25) found the plumbing is small but
+that growth data alone do not fix an absolute theta scale. Discussion then
+reframed the goal: don't ask for absolute theta yet.
+
+The joint fit currently asserts, at minimum:
+
+1. In vitro binding equals in vivo occupancy (same effective [IPTG], same
+   [protein], no genomic-site competition, ...).
+2. Growth is linear in that occupancy: `g = k_c + dk_g + m_c·A_g·θ_g`.
+
+It also overweights binding: each binding row is scaled by
+`N_growth_rows / N_binding_rows` (`model_orchestrator.py`, `binding_weight`
+auto-computation), so the binding set as a whole counts as much as the entire
+growth dataset. Any binding/growth mismatch is resolved by distorting growth.
+
+**Idea:** Infer a hidden, IPTG-dependent growth variable X from growth alone,
+on a scale defined relative to wt. Then ask empirically what transfer function
+maps X to the theta measured in vitro. Base case: linear. Likely case:
+monotone but nonlinear. Worst case: no map. Only after that, build whatever
+map we find into a joint fit.
+
+*Why a relative scale is identifiable.* Growth data fix X only up to a global
+affine map `X -> a·X + b` (m and k compensate); this is exactly the
+scale/sign non-identifiability of theta without binding (m -> c·m,
+θ -> θ/c; θ -> 1-θ, m -> -m, k -> k+m). Hill K and n are invariant under it,
+as are ratios to wt (e.g. a genotype's dynamic range relative to wt's). Fix
+the gauge by convention: `X_wt = 1` at no IPTG, `X_wt = 0` at saturating
+IPTG. Then `k_c` = wt's growth at saturating IPTG and `m_c` = wt's growth
+change across IPTG, read straight from abundant wt data; the sign of m falls
+out per condition. No binding data and no binding-based prefit needed.
+(Design choice: pin wt's Hill asymptotes, simplest, or its values at the
+measured end concentrations, if wt does not saturate in range.)
+
+*What stays assumed.*
+- One X per genotype × [IPTG], shared across conditions and pre/sel phases.
+  Within a condition "growth linear in X" is a definition; across conditions
+  it is testable (kan and 4CP responses should be affine in each other per
+  genotype).
+- X is Hill-shaped in IPTG. A strongly nonlinear true map would show as lack
+  of fit.
+- dk_geno is independent of IPTG and condition. The baselines are the weakest
+  quantity: a genotype's X at no IPTG trades against dk_geno and is separated
+  only by selective-vs-control contrasts (control conditions have not ranked
+  dk_geno well so far).
+- Congression: the mixture's `max` theta rule is invariant under any
+  increasing map (`max f(θ) = f(max θ)`), so it carries over if X is oriented
+  to increase with repression. The `homodimer` default (congression step 4)
+  and `heterodimer` take `logit(θ)` and are not invariant; a relative fit must
+  use `max` or `single` (`planning/analysis-roadmap.md`, C1).
+
+**Step 0 (model-free, do first).** For the genotypes with binding data:
+regress ln_cfu on t_sel per genotype × replicate × condition × concentration
+(slope = selection-phase growth rate, since t_pre is fixed within a sample);
+subtract wt (cancels per-tube artifacts, leaving `dk_g + m_c·(X_g - X_wt)`);
+plot against binding theta, interpolating binding through its Hill fits if
+concentrations differ. Spiked genotypes are deep and congression-free. One
+collapsing curve → linear/monotone case; genotype-specific scatter → the
+worse case. This answers which case we are in before any component is built.
+
+**Stage 2 design (the map).**
+- The map is between curves, not points: `X_g(c) = f(θ_g(s·c))` with a global
+  concentration scale s (in vivo effective [IPTG], assumption 1) and a value
+  map f (assumption 2). Nested family: identity → affine f → monotone f, plus
+  per-genotype deviations (activity, in-cell effects).
+- Stratify spiked vs bulk: congression should show as bulk genotypes on a
+  compressed version of the spiked curve.
+- Errors in both variables; genotype is the unit, concentrations are
+  within-genotype points.
+- Risk: converged mean-field SVI is overconfident (bulk theta 95% coverage
+  ~0.66 in the congression-calibration study), which would make a real
+  monotone map look like "no map". Needs an extra dispersion term or better
+  calibrated posteriors.
+- Eventual formalization: `theta_rescale` is already a fixed transfer
+  function slot (`passthrough`/`logit`); the joint model would make it
+  learnable and add s, with binding at weight 1.
+
+**Why not now:** Current work is the congression physics plan; this is a
+change of strategy for what the fit asserts, and step 0 should decide whether
+it is worth building.
+
+**What it would take:**
+
+*Growth-only plumbing* (found 2026-09-25; no model component reads
+`data.binding`, and batching, `get_batch`, `batch_safety`, `RunInference`,
+extraction, `predict-theta`/`-epistasis`, `model_stats` counts and config data
+paths already cope with no binding):
+- `configure_model_cli.py`: `binding_df` is a required positional that raises
+  when None → make it `--binding_df`, require at least one of growth/binding
+  in the body (like `--library_config`). Line ~389 writes a phantom
+  `data.binding: binding.csv` when binding is absent. Breaks positional
+  callers: `examples/simulate-and-analyze/run.sh`, `test_base_growth.py`,
+  `test_configure_and_run.py` (which asserts `configure_model(None)` raises).
+- `ModelOrchestrator`: joint `_initialize_data` builds binding tensors,
+  `BindingData` and auto `binding_weight` unconditionally (first failure at
+  `_read_binding_df`); gate them. `_setup_batching` with an empty binding set
+  already gives `num_binding=0`. In `_initialize_classes`, drop
+  `theta_binding_noise`, skip `observe_binding`, set `priors.binding=None`
+  explicitly (`populate_dataclass(BindingPriors, {})` raises). Infer the mode
+  from `binding_df is None` rather than adding a `growth_only` config key
+  (every `components:` key becomes an orchestrator kwarg).
+- `generative/model.py`: gate the binding prediction, noise, deterministics
+  and observer on `data.binding is not None` (static structure, jit-safe).
+- `analysis/prediction.py`: `orchestrator.binding_df.copy()` crashes, breaking
+  `tfs-predict-growth`, `tfs-sample-prior` and summarize-fit trajectories.
+- `summarize_fit_cli.py`: with no binding genotypes the trajectory plots fan
+  out to every genotype (a CSV + PDF each); needs a default subset.
+- Fail fast on `binding_weight` or non-`zero` `theta_binding_noise_model`
+  without binding. Minor: `predict_growth_cli.py` survives only via a
+  try/except; `model_stats` warning wording; setup-grid `auto` enumerates
+  `theta_binding_noise`; `None` prior leaves round-trip through the priors CSV
+  as NaN (pre-existing, binding-only has it too).
+- Tests: none cover growth-only. `growth-smoke.csv` + `library-smoke.yaml`
+  without binding is a ready fixture. Needs analogues in `test_model.py`,
+  `test_model_orchestrator.py`, `test_batch_safety.py`, `test_model_stats.py`,
+  `test_configure_and_run.py`, and a smoke test.
+
+*Relative-X fit:*
+- New theta component (e.g. relative Hill): hill_geno's curve form but with
+  real-valued baselines (not logit-bounded to [0,1], since mutants can fall
+  outside wt's range), wt baselines pinned to (1, 0), population priors
+  re-centered on wt. Must follow the registry rules (library-sized plates,
+  `return_population`, `{site}_loc(s)`/`{site}_scale(s)` naming).
+- Force `activity=fixed` (redundant with X's amplitude), `passthrough`,
+  `linear` growth; per-genotype curves only (hill_mut's logit additivity and
+  the thermo components presuppose absolute theta).
+- `tfs-prefit-calibration` (needs binding by construction) becomes
+  unnecessary or a wt-only fit.
+- Downstream: logit epistasis, `in_regime` and anything assuming θ in (0,1)
+  do not apply to X; only additive-on-X epistasis until f is known.
+
+*Stage 2 analysis:* a study script first (`planning/studies/`), a CLI only if
+it earns one.
+
+*Validation:* on one sim, compare joint vs relative-X fits;
+`tfs-summarize-calibration` stratifies by `has_binding` from the simulated
+binding file (not what the fit saw), so held-out comparison is free. A sim
+with a known nonlinear f and an [IPTG] scale s would test whether stage 2
+recovers them.
+
+*Alternative considered (absolute theta without binding in the fit):* keep
+absolute theta but take m from binding only through the prefit
+(`tfs-prefit-calibration --binding_df` for a growth-only config, with
+`--pin_m`), or copy a calibration from another run, or rely on priors. Set
+aside because it still asserts assumptions 1 and 2; the out11 ablation also
+found free m with ~20 in-library binding anchors beat the prefit clamp
+(theta slope 0.91 vs 0.85; older machinery).
+
+**Open questions:**
+- Gauge: decided (roadmap D4): pin wt at 0 and 1 mM IPTG.
+- Shared X across conditions: decided (roadmap D5): shared, with a kan-vs-4CP
+  consistency check.
+- Do growth and binding concentrations coincide in the real data?
+- Default genotype subset for summarize-fit trajectories without binding.
diff --git a/planning/joint-fit-return.md b/planning/joint-fit-return.md
new file mode 100644
index 00000000..778e9232
--- /dev/null
+++ b/planning/joint-fit-return.md
@@ -0,0 +1,289 @@
+---
+title: Return to a joint fit of growth and binding, with per-marker maps
+status: active
+filed: 2026-10-16
+area: tfmodel
+revisit_when: >-
+  Agreed 2026-10-16. Supersedes the open steps of planning/relative-m.md and is roadmap
+  step 9 (planning/analysis-roadmap.md) with step 8's map measured.
+related:
+  - planning/growth-only-relative-fit.md
+  - planning/analysis-roadmap.md
+  - planning/analysis-roadmap-summary.md
+  - planning/deep-coverage.md
+  - planning/two-stage-fit.md
+  - planning/relative-m.md
+  - planning/studies/real-data-fit/README.md
+  - planning/studies/m-bias-factorial/README.md
+  - planning/studies/two-stage-validation/README.md
+  - planning/studies/x-vs-binding/README.md
+  - planning/studies/within-tube-m/README.md
+  - planning/studies/relative-m/README.md
+  - src/tfscreen/tfmodel/generative/components/growth/linear.py
+  - src/tfscreen/tfmodel/generative/components/theta/hill_geno.py
+  - src/tfscreen/tfmodel/model_orchestrator.py
+---
+
+## Review, 2026-10-18
+
+Step 6's first run and a review of the planning files and git log
+(`planning/findings-ledger.md`) changed the footing of this plan:
+
+- The 2 uM agreement in "The arc" item 5 was in a gauge built from wt's own
+  binding endpoints, which absorbs any affine level and scale difference
+  between binding and growth. In the absolute model the difference
+  reappears (step 6, run 1: every binding offset negative).
+- The arc starts on 2026-09-26 and misses July's work: about 20 in-library
+  binding anchors already pinned m then, and lambda already ran away under
+  a model conflict.
+- The binding-to-growth link has failed three times (the 2026-09 joint fit,
+  the 2026-10-01 "no map" with M42I's inverted K, and step 6 run 1), so
+  binding cannot yet set theta's absolute scale.
+
+Steps 6-8 are paused until the link is decided (the ledger's Open item 1).
+The simulation results (step 5) stand as a test of the model under its own
+assumptions.
+
+## Why
+
+We split binding from growth on 2026-09-26 because the joint fit asserted
+more than the data could support, and step 8 of the roadmap then found "no
+map" between growth and binding. Three weeks of work since have removed
+most of the reasons for that verdict and found the pieces the original plan
+said would be needed. Absolute occupancy is also what the congression
+correction (the partition-function rules) and the long-term goal (learning
+thermodynamic ensembles from these data) need. This plan brings binding
+back, at its proper weight, with the map we have now measured.
+
+## The arc
+
+**1. The split (2026-09-26).** The joint fit asserted that in vitro binding
+equals in vivo occupancy and that growth is linear in it, and its default
+binding weight (`N_growth_rows / N_binding_rows`) made binding count as much
+as the whole growth dataset, so any mismatch distorted growth
+(`planning/growth-only-relative-fit.md`). The plan: fit a wt-relative X from
+growth alone (3f628bf7 the idea, 98fb6db0 growth-only models, 5820c19f
+`hill_relative`), then measure the map from X to binding (roadmap step 8:
+identity, affine, monotone f, with a global IPTG scale s; spikes and bulk
+apart), then rejoin (step 9: learnable f and s, binding at weight 1). It
+kept one X shared by both markers (decision D5) and named the test: "across
+conditions it is testable (kan and 4CP responses should be affine in each
+other per genotype)".
+
+**2. "No map" (to 2026-10-05).** The first real relative fits
+(`planning/studies/real-data-fit/`, c5de9f24) found growth curves broader
+than binding (n 0.6-1.2 against 1.6-2.5), M42I's K on the wrong side of
+wt's, a common mutant floor near X -0.5, and no shared protein
+concentration or monotone map carrying binding onto growth. Step 8 was
+recorded as "no map". The tube-offset mode (27109e26) and the full-size
+simulation (bf4a1821: X undercovers at depth, |m| 20-40% too large, k off
+along the k/dk_geno slide) followed.
+
+**3. The m bias (2026-10-09 to 10-12).** Anchoring k and m on the wt
+monoculture failed (34b8143a): the fit preferred an m about 40% too steep.
+The m-bias study (7fcef80c) found the cause: each double pulls m a little
+and the pulls add, so the full-library MAP compresses X about 1.47-fold.
+The fix is the two-stage fit: k and m from wt, spikes and singles (with the
+congression mixture), pinned for the full library (58b83b28, b8be0d2b; the
+real X stretches a uniform 1.47). So the "no map" fits had a compressed X.
+
+**4. The two-stage command and its validation (2026-10-13 to 10-15).**
+`tfs-fit-two-stage` (3ce04cc5) reproduced the study, but its k-shift
+refits failed: they leave the MAP and slide along the dk_geno population
+(7d01721a). The marker-consistency flag fired on 10.6% of deep real
+genotypes against 1.9% on simulations, and pheS alone compressed X against
+kanR, even with wt's curve held (b6655671).
+
+**5. Binding again (2026-10-15).** With the new binding data (nine
+genotypes, 2 and 20 uM repressor dimer, normalized to day-of controls) and
+the two-stage X: **kanR-alone X matched 2 uM binding with no correction**
+(rms 0.12 against 0.30 on 2026-10-01), pheS did not
+(`planning/studies/x-vs-binding/`).
+
+**6. Why pheS disagreed (2026-10-15 to 10-16).** In order
+(`planning/studies/within-tube-m/`, `planning/studies/relative-m/`):
+- within-tube contrasts (tube totals cancel) disagreed with stage 1's m in
+  both markers, but freeing the totals did not move m (e5501aba): not OD600;
+- a log-linear IPTG term shared by every genotype helped kanR a little;
+  a free one was unidentified (aea7f6f4, d8060513);
+- the markers read occupancy through differently shaped maps: kanR
+  switch-like (n at 4), pheS graded (n about 1), wt alike (6aa9ae4b). This
+  is D5's test, and it fails: one shared linear X blends a steep readout
+  with a broad one, which alone can make "growth broader than binding";
+- one row of pheS tubes was swapped (3d5fbc43; the lab agrees it is
+  probable; `tfs-check-tubes` 76ce30d3, `tfs-correct-tubes` f15f5b4c);
+- a logistic warp of kanR's growth in occupancy lowers the loss by 8,000
+  nats and is nearly linear on a simulation (d540edfc, a1670702);
+- relative information alone cannot fix the scale of X: each estimate of m
+  rests on a different anchor (tube totals, priors and Hill shape, or weak
+  binders being flat), and they differ 2.5x in kanR (ba1752ba, 9a0a2f80,
+  b86c0ecb). Binding at 2 uM supports the larger kanR m.
+
+## What we know now
+
+- kanR growth is close to linear in 2 uM binding theta for the spikes,
+  once X is not compressed.
+- pheS growth is a graded, differently shaped function of the same
+  occupancy; one shared linear map cannot serve both markers.
+- The scale of the growth variable needs an external anchor; binding is
+  the natural one.
+- Still open: M42I grows 0.3-0.4 below its 2 uM binding at low IPTG; wt's
+  IPTG midpoint matches 20 uM binding while the mutants' levels match 2 uM
+  (an in vivo IPTG scale s, or an effective protein concentration).
+
+## Carry over
+
+- The count likelihood and the staged MAP.
+- The two-stage fit for the doubles' m bias (stage 1: wt, spikes and
+  singles; stage 2: full library with stage 1's growth parameters pinned).
+- Per-marker maps from occupancy to growth instead of one shared linear X
+  (`linear`'s `m_warp`).
+- The corrected tube table (`tfs-correct-tubes`) and the tube QC
+  (`tfs-check-tubes`).
+- Binding at weight 1: each binding observation counts as one observation.
+  The default `N_growth_rows / N_binding_rows` goes.
+
+## Design
+
+**theta.** Absolute occupancy per genotype from a Hill curve in IPTG
+(`hill_geno`), shared by both markers and observed by binding.
+
+**Growth.** g = k_c + dk_g + m_c f_c(theta) per condition, with f_c the
+per-marker map: start from `m_warp`'s logistic warp (f(0) = 0, f(1) = 1),
+for both selection markers, now identifiable because binding fixes theta.
+Whether kanR needs a warp at all is a result, not an assumption.
+
+**Binding.** theta_obs at 2 uM against the genotype's theta at an IPTG
+scaled by a global s (in vivo IPTG = s x nominal), weight 1. A per-genotype
+deviation between in vivo and in vitro (a hierarchical offset on the
+binding genotypes' logit theta or log K, with a learned scale) lets a
+genotype like M42I disagree without moving the rest. Binding sets the
+scale of theta and the maps; the 200,000 growth genotypes set their own
+curves.
+
+**Congression.** With absolute theta, the partition-function rules
+(`homodimer`, the physical default) return to stage 1's mixture.
+
+**Two stages.** Stage 1 (wt, spikes, singles, binding) fits k, m, the
+maps, s and the deviation scale; stage 2 (full library) pins them. The
+k/dk_geno slide and the intervals for k and m are revisited once m is
+anchored by binding (the k-shift refits failed and are not reused).
+
+## Steps
+
+1. [x] **Agree this plan** (user, 2026-10-16).
+2. [x] **Binding input.** `tfs-process-binding` (2026-10-16): the lab's
+   table to `binding_df` at one protein concentration (default 2 uM),
+   replicate means, `theta_std` from a pooled replicate SD. On the
+   2026-10-15 table, 2 uM gives 110 points over 7 genotypes (D88A and
+   V95N were measured only at 20 uM). Only wt is replicated there (five
+   days), so the pooled SD (0.13, 31 dof) is wt's day-to-day scatter, about
+   three times the point-to-point scatter within one curve. A day effect
+   is shared by a whole curve, so treating the points as independent
+   overstates how well a curve's level is known; step 4(b)'s per-genotype
+   deviation is where that shared error should go. At 20 uM the pooled SD
+   is 0.059 (150 dof).
+3. [x] **Weight 1** (2026-10-16). `binding_weight` None now resolves to 1;
+   an explicit weight is kept. Stored configs carry their resolved weight,
+   so none change; the count-likelihood, congression-calibration,
+   svi-overconfidence and real-data-fit studies already passed 1.
+4. [ ] **Model pieces**, each with tests.
+   - (a) [x] The global IPTG scale s between binding and growth
+     concentrations (2026-10-16): `hill_geno`'s
+     `theta_growth_log_conc_shift_scale` > 0 samples log s; growth sees the
+     curve at s times nominal IPTG, binding at nominal. Tested on the smoke
+     data through MAP, Laplace, extraction and growth prediction, single
+     and mixture. `tfs-predict-theta` reports the curve in binding units;
+     an in vivo option can follow if step 6 wants it.
+   - (b) [x] The per-genotype in vivo/in vitro deviation for binding
+     genotypes (2026-10-16): `theta_binding_noise: genotype_offset`, one
+     additive theta offset per binding genotype, constant over IPTG,
+     tau * z_g with a learned tau. Additive in theta because a curve's
+     day-normalization error and M42I's disagreement are level shifts; a
+     log K deviation can follow if step 6's residuals show midpoint shifts.
+   - (c) [x] `m_warp` and the mixture's partition-function rules with
+     `hill_geno` (2026-10-16): nothing refused them; the warp acts on each
+     cell class's theta after the mixture combines plasmids.
+     `tests/tfscreen/tfmodel/generative/test_joint_warp.py` traces every
+     piece together and checks a near-linear warp reproduces the unwarped
+     model through the mixture; `tfs-extract-params` now writes
+     `warp_log_a`/`warp_x0` per warped condition. End to end on the smoke
+     data (`--growth_priors` with an `m_warp` column, homodimer mixture,
+     s, offsets): MAP, Laplace, extraction, chunked prediction.
+5. [ ] **Simulation first.** Add a per-condition occupancy warp to the
+   simulator's growth linkage (it has linear, power and saturation, no
+   warp), simulate kanR switch-like and pheS graded with binding for the
+   spikes at known s and deviations, and check stage 1 recovers m, the
+   maps, s and theta, and that binding at weight 1 does not distort growth.
+   - [x] Simulator keys (2026-10-16): `warp_a`/`warp_x0` on a linear
+     growth entry, `growth_titrant_scale`, `binding_data.genotype_offset_sd`,
+     with truth files.
+   - [ ] The study (`planning/studies/joint-fit-sim/`): joint and null
+     simulations from the full-size realistic config, fitted with the full
+     and plain joint stage 1 (three runs).
+     Run 1 (2026-10-17): on the joint simulation the full model recovers
+     kanR's m (2%) and warp and s (16%) and halves the singles' theta error
+     of the plain model; on the null simulation it finds a wrong mode (the
+     same steep warp on both markers, s 0.25, large offsets, theta off by
+     0.3). A warp shared by both markers is pinned only by the binding
+     genotypes, which the offsets loosen. Next: is that a trap or a
+     degeneracy (null fitted with the plain model; compare losses).
+     Runs 2-3 (2026-10-17): it was a trap; the full model started from
+     the plain fit's MAP recovers the null (near-linear warps, s covering
+     1, offsets covering 0) and the joint truth (kanR's switch, s, the
+     offsets). Open before step 6: m 15-25% steep even with the true model,
+     and theta intervals covering 14-17%.
+     Runs 4-7 (2026-10-17): the m bias is an affine squeeze of the
+     singles' theta (k absorbs the constant, m the slope) that only binding
+     genotypes resist, pushed by the logit population prior and by the
+     congression mixture's approximation. Twenty binding singles cut it from
+     14% to 6% on the no-congression simulation (weak pooling, 14% to 11%;
+     both, 5%). Experimental implication: binding on about twenty singles
+     spanning theta. Still open: the remaining 5%, the congression part
+     with binding singles, and theta coverage (0.38).
+     Run 8: with congression, twenty binding singles halve the bias (21% to
+     12%), leaving the mixture's approximation as the largest piece; fifty
+     singles take the no-congression bias only to 5%; the full recipe on
+     the joint truth with twenty gets kanR's m within 1%, pheS's within 7%,
+     s covered. Next: the mixture's co-resident draws (`congression_sets`).
+     Run 9: 75 co-resident sets instead of 16 take the congression null's
+     bias from 12% to 9% with twenty binding singles; 430 sets only to 8.6%,
+     against 6% without congression. The remaining 3 points are the
+     mixture's form, not its draws. Many sets run the Laplace Hessian out of
+     GPU memory.
+6. [ ] **Real stage 1.** wt, spikes and singles with binding at 2 uM, the
+   swapped row corrected; report m against the within-tube values, the
+   maps, s, the deviations (M42I), and binding residuals per genotype;
+   compare likelihoods with and without each piece.
+   Set up 2026-10-18 (`planning/studies/joint-fit-real/`): plain, then
+   full from plain, 75 co-resident sets, the current 2 uM binding (seven
+   genotypes), the swapped row dropped. Expect m 17-21% steep and theta
+   intervals too narrow (joint-fit-sim runs 8-10); rerun when the updated
+   binding (and any binding singles) arrive.
+   Run 1 (2026-10-18): the 2 uM binding sits below growth's theta for every
+   genotype (offsets -0.07 to -0.28, all negative) and wt's binding
+   midpoint is about 10x lower in IPTG than growth's; s (0.53) does not
+   absorb it, lambda runs to 0.8-2.1, and m and the warps are not
+   interpretable. The binding-to-growth link at 2 uM needs more than s and
+   per-genotype offsets.
+7. [ ] **Real stage 2 and intervals.** Full library pinned; theta
+   intervals; the marker-consistency flag rate against the simulations'.
+8. [ ] **Into `tfs-fit-two-stage`.** A joint mode replacing the relative
+   one's k shift; docs.
+
+## Open questions
+
+- Binding at 2 uM only, or both concentrations through a binding model in
+  protein concentration (which the thermodynamic-ensemble goal will want)?
+  Start with 2 uM.
+- s alone, or s plus an effective protein concentration? Start with s.
+- The binding data are still being replicated; when do they freeze for
+  step 6?
+- Bulk-only binding genotypes (G65H, V95N) carry congression; fit them
+  through the mixture like any bulk genotype.
+
+## Out of scope
+
+- The selection-onset transient (`planning/offset-mode-growth-transition.md`).
+- Thermodynamic ensembles (long term; this plan supplies the absolute
+  theta they need).
diff --git a/planning/offset-mode-growth-transition.md b/planning/offset-mode-growth-transition.md
new file mode 100644
index 00000000..23da28bb
--- /dev/null
+++ b/planning/offset-mode-growth-transition.md
@@ -0,0 +1,121 @@
+---
+title: The tube-offset mode and the growth transition at the switch to selection
+status: idea
+filed: 2026-10-05
+area: tfmodel
+revisit_when: >-
+  After pipeline plan steps 6 and 7 (the simulator's raw formats, the
+  documented chain and the offset diagnostic), as part of the science
+  prioritization in pipeline step 8 (user, 2026-10-05). Its simulation
+  steps should use the raw-format path. Keep the step list current while
+  it is worked.
+related:
+  - planning/studies/staged-map/README.md
+  - planning/experiment-pipeline.md
+  - planning/analysis-roadmap.md
+  - src/tfscreen/tfmodel/inference/staged_map.py
+  - src/tfscreen/tfmodel/generative/components/growth_transition/
+  - src/tfscreen/tfmodel/generative/components/sample_offset/level.py
+  - src/tfscreen/simulate/growth/transition_linkage.py
+---
+
+## Why
+
+The staged MAP (`tfs-fit-model --stage_offsets`, pipeline plan step 3) was
+built to keep a level-offset fit out of what we called the offset trap, a
+mode where the tube offsets carry the population's growth. Run 2 of
+`planning/studies/staged-map/`, with every MAP run to convergence on the
+exact full-batch loss, showed that the trap is not an optimizer artifact.
+It is the best point found so far of the model's objective.
+
+- `ref_refit`, the hand-chained reference fit run to convergence, ended
+  7.9e4 nats (log joint) above the staged MAP and 4.5e5 above where it
+  started, and it got there by moving into the offset mode: offsets up to
+  -4.2, 10-25 prior SDs each at `sigma_fixed` 0.17, with growth k and m
+  unphysical (kanR+kan k 0.036, m -0.050; about 0.01 elsewhere).
+- The offsets are not noise. In both selection conditions they are a smooth
+  function of IPTG, with opposite signs (kanR selection about +2 at 0 mM to
+  -3 to -4 at 1 mM; pheS selection -1 to +1.9), nearly the same at all three
+  time points, and near 0 in the no-selection conditions.
+- The staged MAP's offsets are small and patternless (SD 0.21) and its k
+  uniform (about 0.027). It reaches that point only because it stops in a
+  local optimum. A longer run, a better optimizer or a multi-start would
+  leave it.
+
+An offset that is constant in time and structured by IPTG and selection is
+a population-level shift set before the first sampled time point. IPTG is
+added at `-t_pre` (user, 2026-10-05): the pre-growth exists so the cells
+express their markers before selection starts, so the shift is not
+"marker not yet expressed". It points at the selection onset instead: a
+transient when the selection agent arrives (a kanamycin lag or die-off of
+low-resistance cells, a delay before 4CP takes effect) whose size depends on
+the marker level, hence on IPTG. The `instant` growth transition cannot
+produce such a transient, so the offsets supply it. This is roadmap step 7b
+arriving from the real data.
+
+Pipeline plan step 3 is closed as machinery: the staged MAP and
+exact-loss convergence work, and the staged fit is the physical one. This
+plan carries the model problem. Until it is solved, a longer or better fit
+can leave the physical basin; pipeline step 7's tube-offset diagnostic
+flags a fit that does.
+
+## Done when
+
+- One growth-transition model (or `instant` with an explicit reason) makes
+  the physical mode the best-scoring one on the dev data: a cold fit, the
+  staged fit and a refit from the offset mode all end at the same optimum,
+  with tube offsets small and without IPTG structure.
+- The same holds on a simulation with a known selection-onset transient,
+  and `instant` is kept where the simulation has none.
+- Roadmap step 7b's purge is done for the models not kept.
+
+## Steps
+
+1. [ ] **Where the offset mode's likelihood comes from** (local, existing
+   runs). Extend `planning/studies/staged-map/score_map.py` to report the
+   count log-likelihood by tube and by genotype class, and compare
+   `ref_refit` with `staged_auto2`. If the 9e4-nat gain sits in the
+   selection tubes, spread across the library and concentrated at the
+   first sampled time point, the transient explanation stands; if it sits
+   in a few genotypes or tubes, look there instead.
+2. [ ] **Simulation with a known transient.** Simulate a small library with
+   a selection-onset transient (`two_pop` or `baranyi` in
+   `simulate/growth/transition_linkage.py`) whose size depends on theta.
+   Fit `instant` with level offsets: does the IPTG-structured offset mode
+   appear and score best? Fit the matching transition: does it vanish? A
+   control simulation with no transient should keep `instant`.
+3. [ ] **Dev-data grid.** Transition `instant`, `two_pop` and one more if
+   step 2 argues for it, each fit three ways: staged, cold, and refit from
+   `ref_refit`'s point (`--init_from`). Score every end point with
+   `score_map.py`; report offsets by tube design and k/m. About 9 runs of
+   about 4 h. Fix `two_pop`'s fallback for `g_pre - g_sel - k_trans <= 0`
+   first if it is a candidate (roadmap 7b).
+4. [ ] **Decide and purge** (roadmap 7b, D12, D13). Keep `instant` and at
+   most one transition model; remove the rest from the fit, the simulator,
+   examples, grid `auto` enumeration, tests, docs and CLAUDE.md.
+5. [ ] **Confirm the staged MAP under the chosen model.** Rerun the
+   staged-map comparison: the staged MAP should reach the best-scoring
+   mode, and cold and refit starts should not beat it. Then compare spike
+   K and n between converged fits.
+6. [ ] **Revisit the growth-only defaults** (moved from pipeline step 2):
+   the held log(n), X_low, X_delta and log K population SDs and the offset
+   SD 0.17, under the chosen transition model.
+
+## Also open
+
+- **The slow tail.** Every converged MAP stopped on the `loss_rtol` floor
+  (61 nats per window on the dev data) while still descending about 40
+  nats per window at step size 1e-6, after 1-1.2 M steps (4 h). The floor
+  sets where they stop. If that matters for the decision above, a final
+  full-batch second-order polish (L-BFGS on `full_batch_loss`) is the
+  proper fix; decide after step 3 shows whether end points move.
+- **The offsets' own freedom.** An i.i.d. N(0, 0.17) prior cannot stop
+  offsets from absorbing any pattern shared by every genotype in a tube,
+  because each tube's likelihood outweighs its one prior term by orders of
+  magnitude. If no transition model removes the pattern, constrain the
+  offsets structurally instead (no IPTG or time structure) or let the
+  population model (roadmap step 6) carry the tube totals.
+
+## Out of scope
+
+- The orchestrator (pipeline plan step 5), deferred.
diff --git a/planning/per-sample-level-offset.md b/planning/per-sample-level-offset.md
new file mode 100644
index 00000000..b97bb0b8
--- /dev/null
+++ b/planning/per-sample-level-offset.md
@@ -0,0 +1,64 @@
+---
+title: Per-sample (tube-level) ln_cfu offset in the growth likelihood
+status: idea
+filed: 2026-09-24
+area: tfmodel
+revisit_when: >-
+  After censored floor observations are in (step 3.5 of the congression
+  plan), or sooner if theta coverage stays well below nominal once the
+  floor bias is fixed.
+related:
+  - planning/analysis-roadmap.md
+  - planning/congression-physics-plan.md
+  - planning/count-likelihood.md
+  - planning/studies/congression-calibration/README.md
+  - src/tfscreen/tfmodel/generative/observe/growth.py
+  - src/tfscreen/tfmodel/generative/components/growth_noise/normal_kt.py
+  - src/tfscreen/tfmodel/generative/components/sample_offset/normal.py
+  - src/tfscreen/process_raw/counts_to_lncfu.py
+---
+
+**Context:** Diagnosing the congression calibration grid (2026-09-24). On
+well-measured simulated rows (more than 100 reads) the error in `ln_cfu` was
+0.38 ln units, almost all of it shared by every genotype in a sample:
+per-sample SD 0.374, within-sample 0.054. It is the simulator's per-tube
+growth noise (`tube_noise_sigma` x t). Real data add a second shared term:
+every genotype's `ln_cfu` in a tube carries the same plating (`sample_ln_cfu`)
+error, and the per-tube denominator review found a PCR component too.
+
+The model treats these as independent per-row noise: `growth_noise`
+(`normal_kt`) adds `sigma_k` in quadrature to each row's `ln_cfu_std`, and
+`sample_ln_cfu_std` is folded into each row's `ln_cfu_var` in
+`counts_to_lncfu`. A shared offset seen as N independent errors gives N-fold
+too much information about everything that differs between tubes, and too
+little about what is shared. Bulk theta 95% coverage was about 0.65 in every
+arm of the grid, `single` included; this is one candidate cause.
+
+**Idea:** Add one latent level offset per sample, `o_s`, shared by all
+genotypes in the tube: `ln_cfu_obs[g, s] ~ ln_cfu_pred[g, s] + o_s + e[g, s]`.
+Its prior width comes from the known shared sources (plating SD, tube growth
+noise over the elapsed time, a PCR term); the per-row noise keeps only the
+counting part. `sigma_k` then shrinks to what is genuinely per-row.
+
+**Why not now:** The detection-floor bias (censoring) comes first; it is a
+concrete bias the mixture exploits. This one is a calibration improvement
+whose size in real data is not yet measured.
+
+**What it would take:**
+
+- Superseded in design (2026-09-26, `planning/analysis-roadmap.md`, "Reads
+  and totals" and step 6): tube growth noise cancels from read frequencies,
+  so it belongs in an OD600 likelihood around a smooth population curve, not
+  on every genotype. `sample_offset` keeps only a per-tube composition
+  offset (PCR, calling) with a time-independent prior (roadmap D2). The
+  "plating" SD below is really the OD-to-CFU error; CFU comes from OD600.
+- `counts_to_lncfu` to write the counting variance and the plating variance
+  separately instead of summing them into `ln_cfu_var`.
+- `o_s` is a per-sample latent: mini-batch safe as a global site, like the
+  condition parameters.
+- Interaction with `ln_cfu0` and the k/dk_geno slide: `o_s` at the first
+  timepoint trades against `ln_cfu0`; the prior has to keep it identified.
+- The sim to test it: the calibration grid, comparing theta coverage with and
+  without the offset.
+- It is the `o_s` of `planning/count-likelihood.md`; building it first makes
+  that project smaller.
diff --git a/planning/plasmid-segregation-low-copy.md b/planning/plasmid-segregation-low-copy.md
new file mode 100644
index 00000000..63fc668c
--- /dev/null
+++ b/planning/plasmid-segregation-low-copy.md
@@ -0,0 +1,42 @@
+---
+title: Plasmid segregation ("curing") of multiple transformants in low-copy designs
+status: idea
+filed: 2026-09-13
+area: experiment
+revisit_when: >-
+  When designing or analyzing the next experiment, which moves to a low-copy
+  plasmid.
+related:
+  - planning/congression-physics-plan.md
+  - src/tfscreen/simulate/selection_experiment.py
+---
+
+**Context:** The congression model assumes co-transformed plasmids persist in
+a cell for the whole experiment. That is reasonable for the current high-copy
+plasmids over roughly 3 to 7 doublings. The next experimental design switches
+to a low-copy plasmid.
+
+**Idea:** Two plasmids with the same origin are incompatible: under no
+selection that distinguishes them, a lineage drifts toward carrying only one
+type, faster at low copy number. Two consequences:
+
+1. **Modeling:** congression effects would fade over time, so the congressed
+   fraction and each cell's variant shares `x_g` become functions of the
+   number of generations. The observable-level mixture in the congression plan
+   would need time-dependent class weights.
+2. **Design tradeoff:** extra rounds of replication before selection would
+   "cure" multiple transformants toward single-genotype cells, but outgrowth
+   also costs library diversity (bottlenecks, drift, growth-rate bias against
+   slow genotypes). There should be an optimal number of pre-selection
+   doublings that balances curing against diversity.
+
+**Why not now:** The current dataset uses high-copy plasmids, so the
+persistence assumption holds.
+
+**What it would take:**
+- A segregation model for plasmid copy number n per cell (for example random
+  partitioning at division), giving the probability a co-transformed lineage is
+  still mixed after g doublings.
+- Simulation: extend `_sim_transform`/`_sim_growth` with per-cell share drift.
+- Design calculation: congression remaining vs. library diversity retained, as
+  a function of pre-selection doublings, for candidate copy numbers.
diff --git a/planning/relative-m.md b/planning/relative-m.md
new file mode 100644
index 00000000..d71af725
--- /dev/null
+++ b/planning/relative-m.md
@@ -0,0 +1,144 @@
+---
+title: Take m from relative information only (no tube totals in X)
+status: idea
+filed: 2026-10-15
+area: tfmodel
+revisit_when: >-
+  Proposed 2026-10-15 for the user's review. If agreed it becomes the
+  plan for replacing the two-stage fit's k shift
+  (planning/two-stage-fit.md step 4).
+related:
+  - planning/studies/within-tube-m/README.md
+  - planning/studies/two-stage-validation/README.md
+  - planning/studies/x-vs-binding/README.md
+  - planning/two-stage-fit.md
+  - planning/offset-mode-growth-transition.md
+  - src/tfscreen/tfmodel/generative/components/sample_offset/level.py
+  - src/tfscreen/tfmodel/generative/observe/growth_counts.py
+  - src/tfscreen/tfmodel/analysis/shared_shift.py
+---
+
+## Why
+
+The within-tube study (`planning/studies/within-tube-m/`, runs 1-3) found
+that the fit's growth slopes disagree with the part of the data that does
+not depend on tube totals:
+
+- Against flat pure spikes in the same tube, wt's swing from 0 to 1 mM
+  IPTG is -0.005 per minute in kanR+kan and +0.018 in pheS+4CP. Stage 1
+  pins -0.0148 and +0.0070.
+- Across 903 deep singles the pheS swing over the kanR swing is -2.2 to
+  -2.5 (wt -3.9, wide); stage 1's ratio is -0.47. wt sits with the
+  library, so the problem is the fit's m, not wt.
+- Stage 1 does not reproduce the within-tube contrast (predicted 0.004 in
+  pheS against 0.018 observed); on a simulation it does.
+
+The fit's absolute scale comes from the OD600-derived tube totals, which
+tie each condition's k and m to the population's growth. A conversion
+from OD600 to cells that drifts with IPTG or cell state under selection
+would bias m, and the monoculture (also OD-based) with it. Even with that
+map measured, OD600 is a noisy input. X does not need it.
+
+## The idea
+
+The count likelihood is `log mu = ln_sample_reads + ln_cfu_pred -
+sample_ln_cfu + offset_tube`. With the per-tube level offset free (a flat
+prior), it absorbs each tube's total, and the likelihood keeps only the
+relative abundances of genotypes within each tube. Then:
+
+- **k drops out.** Every tube is one time point, so `k (t_pre + t_sel)` is
+  common to every genotype in it and the tube's offset absorbs it. k can be
+  pinned anywhere; X does not depend on it.
+- **m is still identified** through the gauge: X_wt goes from 1 at 0 IPTG
+  to 0 at 1 mM, so wt's contrast with the rest of the library sets m. This
+  is the within-tube statistic, used by the fit itself.
+- **dk_geno stays identified** relative to wt (dk_wt = 0) through the
+  no-selection tubes.
+- **A growth shift shared by every genotype in a tube** (the IPTG term of
+  run 2) is absorbed by the offsets, so the log-linear titrant k is not
+  needed.
+- **k's uncertainty no longer reaches X**, so the shared k shift and its
+  refits (which failed, `planning/studies/two-stage-validation/`) go.
+  m's uncertainty replaces it, and needs no refits: with the data's
+  contrasts fixed, scaling m by (1 + e) scales every X_g - X_wt by
+  1 / (1 + e), so the shared shift per draw is -e (X_g - X_wt) at each
+  concentration, through the existing `--shared_shift`.
+- **The OD600 tables become optional** for X. They still give absolute
+  growth rates.
+
+Everything needed for the first test exists: `sample_offset: level` with
+`sigma_fixed` large holds the offsets' prior nearly flat, and
+`--growth_priors` with `k_pinned` = 1, `m_pinned` = 0 pins k and leaves m
+free.
+
+## What could go wrong
+
+- **Precision.** m now rests on contrasts against wt. wt's own within-tube
+  swing has an SE of about 0.002-0.004 per minute (10-40% of m), though the
+  fit also uses every other genotype's contrast with wt. The jackknife will
+  say what m's SE is. If it is too wide, binding-anchored spikes could add
+  scale information later.
+- **The offset mode.** Freed offsets went badly before
+  (`planning/offset-mode-growth-transition.md`: IPTG-structured offsets,
+  k and m unphysical). There k was free and the full library's doubles
+  pulled m (the 1.45x bias). Here k is pinned and stage 1 has no doubles,
+  so the offsets are meant to carry the totals. If m still runs away (the
+  old fit reached -0.05 in kanR), the relative information is not what we
+  think, and step 1 shows it.
+- **The dk_geno population direction** that stage 2's Laplace held (the
+  population location against wt) may stay soft. Watch the held
+  directions.
+- **Simulations have consistent totals,** so relative and absolute fits
+  should agree there; they test that relative-only is unbiased and say
+  what it costs in width, not that it fixes an OD bias. A simulation with
+  an IPTG-dependent OD bias would test that directly (optional, step 5).
+
+## Steps
+
+1. [x] **Relative stage 1** (no code). Run 1 (2026-10-15,
+   `planning/studies/relative-m/`): the seeds pass, but on the real data
+   m does not move (pheS/kanR -0.49 against -0.47 absolute and -2.2
+   within tube); the fit discards pheS's IPTG response because the X
+   shared by both markers cannot follow their different IPTG curves.
+   Steps 2-6 wait on a per-marker midpoint test. Setup as planned: The two-stage fit's stage 1 with
+   `sigma_fixed=10` and k pinned at the current stage 1's values, m free,
+   on the real data, seed 1 and seed 2 (3 runs of about 30 minutes).
+   Pass:
+   - seeds: m within about 5% of the truth, as the absolute stage 1 is;
+   - real: m near the within-tube values (kanR+kan about -0.005, pheS+4CP
+     about +0.012 to +0.018, ratio about -2.3);
+   - the within-tube posterior-predictive check (`ppc_within_tube.py`)
+     reproduces the observed contrast;
+   - no held direction in the Laplace beyond what stage 1 has now.
+   Also report the offsets by tube design (they now carry the totals and
+   any shared IPTG term).
+2. [ ] **m's precision.** The stage 1 jackknife (30 subsamples) on the
+   relative stage 1, real data: m's SE and its direction across the two
+   markers.
+3. [ ] **Relative stage 2** on seed 1, seed 2 and the real data, k and m
+   pinned at the relative stage 1, offsets free. Compare with run 1 of the
+   two-stage validation:
+   - seeds: deep-double X coverage (run 1, absolute: 0.90 / 0.76 without
+     the k shift);
+   - real: X against binding at 2 uM for kanR alone and pheS alone (both
+     should now match if m was the problem), and the marker-consistency
+     flag rate (10.6% deep in run 1; it should fall toward the
+     simulations' 1.9%).
+4. [ ] **m's uncertainty in X.** The analytic scale shift from step 2's
+   jackknife through `--shared_shift`; coverage on the seeds.
+5. [ ] *(Optional)* **A simulation with an IPTG-dependent OD bias**, if
+   the simulator's `od600` block can be given one: the absolute fit should
+   fail and the relative one should not.
+6. [ ] **Fold into `tfs-fit-two-stage`** if steps 1-4 pass: a relative
+   mode (free offsets, k pinned, m jackknife, scale shift) that replaces
+   the k shift; retire `kshift`; update `planning/two-stage-fit.md`.
+
+Independent of these, the bench test: OD600 against plate counts under
+each selection across IPTG. It would explain the absolute fits' m and
+give absolute growth rates, but X would not depend on it.
+
+## Out of scope
+
+- The full-library m bias from the doubles (stage 2 keeps m pinned).
+- The selection-onset transient (`planning/offset-mode-growth-transition.md`),
+  unless step 1's offsets point at it.
diff --git a/planning/release-0.5.0.md b/planning/release-0.5.0.md
new file mode 100644
index 00000000..c5f99820
--- /dev/null
+++ b/planning/release-0.5.0.md
@@ -0,0 +1,244 @@
+---
+title: Close out the 0.5.0 release (X with honest intervals, runnable by the lab)
+status: active
+filed: 2026-10-19
+area: release
+revisit_when: >-
+  Decisions settled by the user, 2026-10-19. Keep the checklist current;
+  0.5.0 and its 0.5.x bugfix releases are what Natalie's manuscript uses.
+related:
+  - planning/two-stage-fit.md
+  - planning/deep-coverage.md
+  - planning/relative-m.md
+  - planning/findings-ledger.md
+  - planning/studies/two-stage-validation/README.md
+  - planning/studies/relative-m/README.md
+  - CHANGELOG.md
+---
+
+## Goal
+
+Someone in the lab other than Mike (Natalie first) takes a screen from
+per-tube counts to per-genotype X curves with reasonable 95% intervals,
+using one documented command and no study scripts. The release is that
+route, validated, documented and tagged.
+
+**The deliverable is X, not theta.** X is the wt-relative occupancy-like
+variable of `hill_relative` (wt X = 1 at the lowest IPTG, 0 at the
+highest). Comparing X with binding happens after the fit and is not part
+of the release.
+
+## Where things stand (2026-10-19)
+
+- **The route.** `tfs-fit-two-stage` (`planning/two-stage-fit.md`).
+  Stage 1 fits wt, the spikes and the singles with the congression
+  mixture (`max` rule). Stage 2 fits the full library with every
+  condition's k and m pinned. A jackknife of stage 1 gives k's error, and
+  a marker-consistency flag runs at the end. Validation run 1 reproduced
+  the study's stage 1, jackknife, stage 2 and flag
+  (`planning/studies/two-stage-validation/`).
+- **Coverage.** On the simulations, deep doubles' 95% X coverage without
+  the k shift is 0.90 / 0.76 (seed 1, 1e4-1e5 / >1e5 reads) and
+  0.91 / 0.81 (seed 2). The study's hand-built shift reached 0.94 / 0.91.
+- **The k-shift step does not work as built.** Its refits leave stage 2's
+  MAP and slide along the dk_geno population against wt (real X +0.32,
+  the same with k unchanged). So the command's shift measures where the
+  optimizer stopped. It got 0.915 / 0.858.
+- **Congression in X** needs the `max` rule. The partition-function rules
+  cannot be used with X, and stage 2 is `single` anyway.
+- **The kanR warp** (`m_warp`) is real on the real stage 1: about 8,000
+  nats for two parameters, and nearly linear on the simulation. It closes
+  little of the pheS gap. A pheS warp collapsed to a step. Warping both
+  markers was not tested. The warp is not carried through
+  `tfs-fit-two-stage`: its pin table holds only k and m.
+- **Monoculture rates.** The only monoculture data are wt's k and m
+  (`2025-10-02_control-experiments.xlsx`). As k/m priors the data
+  rejected them (`planning/deep-coverage.md` step 1). They are now a
+  check only: the real stage 1's kanR is within 4% of them, and pheS's m
+  is half. No mutant monoculture dk_geno data are in the dev data. The
+  `base_growth_df` input exists and the two-stage YAML accepts it, but it
+  has never been run with `hill_relative`.
+- **Docs.** `docs/source/fitting.rst` (last edited 2026-10-05) describes a
+  single `hill_relative` fit. The two-stage route appears only in
+  `cli.rst`. The quickstart is about simulations.
+- **Tests.** The full suite passed on 2026-10-18 (6,109 passed).
+  `__version__` already reads 0.5.0. The last tag is v0.4.4.
+
+## Decisions (user, 2026-10-19)
+
+1. [x] **k's uncertainty in X.** Repair the shift first: start the two
+   refits at the final step size with the dk_geno population location
+   held at stage 1's (ledger open item 4), then one validation round. If
+   that fails, ship without the shift and report the no-shift coverage
+   (0.90 / 0.76-0.81). Replacing it with k and m free under the jackknife
+   covariance is out of scope.
+   Outcome (2026-10-20): the repair failed validation round 2 (the shift a
+   third or less of the true one on simulations;
+   `planning/studies/two-stage-validation/`, run 2), so the release ships
+   without it (user: 0.76-0.90 coverage is fine for now).
+   `two_stage.kshift` defaults to false and skips the jackknife too.
+2. [x] **The kanR warp is out of 0.5.0.** Its gain (about 8,000 nats on
+   the real stage 1) is recorded for 0.6. Stage 2's X stays validated
+   without it.
+3. [x] **Drop pheS+4CP replicate 2, t_sel 82.** Data: the IPTG tubes look
+   reversed with the 0 tube in place (the tube labeled 0.1 uM is a 1 mM
+   tube, the one labeled 1 uM a 30 uM tube), but the 10 uM slot does not
+   fit and the low tubes' order cannot be recovered
+   (`planning/studies/relative-m/`, run 7). The lab found no mechanism
+   for a three-way swap, and Natalie rules out a flip of the IPTG tubes
+   with the 0 tube in place. The row goes in a corrections file as an
+   exclusion, with that evidence.
+4. [x] **The lambda prior** stays at the measured value (0.38 +- 0.15).
+   The docs say it is required and where it comes from.
+5. [x] **Usable genotypes.** The docs state one default read-depth
+   threshold and one policy for the consistency flag. Set both from
+   validation round 2's coverage by depth and the flag's no-signal rate.
+   Set 2026-10-20: 1,000 reads (seed 1: slope 0.88 and RMSE 0.17 at
+   1e3-1e4 reads, against 0.39 and 0.54 below); flagged genotypes are
+   reported separately, not dropped.
+6. [x] **kanR's m scale** (`planning/studies/relative-m/`, run 6: the
+   absolute m against the within-tube m, about 2.5x apart). The two-stage
+   fit takes m from the tube totals, which binding at 2 uM supports and
+   the weak binders' flat kanR response does not. Decided: ship the
+   tube-totals m and state the range as a limitation in the
+   interpretation section.
+
+## Code
+
+6. [x] Implement decision 1 in `tfs-fit-two-stage` and its tests.
+   Done 2026-10-19: `kshift_step_size` (1e-5), the dk_geno population
+   held at stage 2's MAP (not stage 1's: holding stage 1's would change
+   the validated stage 2), and a k-unchanged control refit with
+   `kshift/drift.csv`. Validation round 2 (item 12) is set up in
+   `planning/studies/two-stage-validation/`, run 2.
+7. [x] The dev data's corrections file: exclude pheS+4CP replicate 2,
+   t_sel 82 (decision 3). Document
+   `tfs-correct-tubes` -> `tfs-process-counts` as the route, since the
+   two-stage fit reads the processed growth table.
+   Done 2026-10-19: `planning/dev-data/tube_corrections.yaml` (gitignored,
+   C10) excludes the row's six pheS+4CP tubes (the pheS-4CP control tubes
+   of that replicate and time stay), checked with `tfs-correct-tubes` (118
+   tubes to 112). `planning/studies/real-data-fit/fit/run_recipe.srun` runs
+   `tfs-correct-tubes` before `tfs-process-counts` (`CORRECTIONS`, default
+   `../../tube_corrections.yaml`). The recipe page (item 15) documents the
+   route.
+8. [x] Refuse or warn on the experimental joint-fit pieces in a two-stage
+   config: `theta_growth_log_conc_shift_scale`, `genotype_offset` and a
+   binding table. Mark them experimental in their docstrings.
+   Done 2026-10-19: `read_two_stage_config` refuses `binding_df`, theta
+   noise other than `zero`, the joint-fit and warp priors in `set_priors`,
+   and an `m_warp` column with any 1 in `growth_priors` (stage 1 would fit
+   the warp and stage 2's pins drop it). `genotype_offset`, hill_geno's
+   titrant shift and linear's `m_warp` say they are experimental.
+9. [x] Make the outputs easy to read: one top-level table per genotype
+   and concentration with X quantiles, read depth, `in_training_data` and
+   the consistency flag, so users never open the step directories.
+   Done 2026-10-19: the `summary` step writes `{out_dir}/tfs_x.csv`
+   (also `k_shift`, `num_mutations` and reads per library). On run 1's
+   real outputs: 217,945 genotypes, 1.7 M rows, about 40 s to count the
+   reads. A depth flag waits on decision 5's threshold.
+10. [x] Turn epistasis on X on in the recipe (`predict_epistasis`), or
+    document how to run `tfs-predict-epistasis --shared_shift` afterward.
+    Natalie's paper needs it.
+    Done: the screen page's YAML sets `predict_epistasis: true` and says
+    when to (the command's default stays false). With the k shift off
+    there is no `--shared_shift` to document.
+11. [x] Pin or floor the dependency versions that broke CI (pandas 3).
+    Check a fresh install on the cluster.
+    Done locally 2026-10-20 (`planning/studies/fresh-install/`): floors in
+    `pyproject.toml` at the maintainer's passing environment (no caps; CI
+    tests the newest) and a `cuda12` extra for GPU jax on Linux; a fresh
+    venv on the newest releases (pandas 3.0.6, numpy 2.5.4, jax 0.12.0)
+    passed the full suite. Install docs (README, quickstart, screen page)
+    updated. The cluster install works (user, by hand, 2026-10-20). No
+    constraints file in the repo (user): the manuscript records its own
+    versions (item 23). CI's 3.11 jobs then failed (jax 0.11 has no 3.11
+    wheel), so tfscreen needs Python 3.12+ and CI tests 3.12-3.14.
+
+## Validation
+
+12. [x] Validation round 2 with decision 1 in: seed 1, seed 2 and the
+    real data through the command. Done 2026-10-20: the repair failed
+    (shift too small on the simulations), so decision 1's fallback
+    applies; seed 1's coverage by depth without the shift is recorded in
+    `planning/studies/two-stage-validation/`, run 2.
+13. [x] A dry run by Natalie on the dev data from a clean install,
+    following only the docs. Every question she asks becomes a docs fix.
+    Done 2026-10-22 to 2026-10-24: the user and Natalie both ran it. Docs
+    fixes: run processing on a compute node, `consistency_library` is a
+    convention, run the submit script or the command (not both), and the
+    growth-table section rewritten as numbered steps (the inputs list no
+    longer implies a corrections file already exists).
+14. [x] `two-stage-fit.md` step 4 marked done, and the plan's status set.
+    Done 2026-10-24: steps 4 and 8 done; the plan stays active for steps
+    5 and 7 (the design-sweep tools, after the release).
+
+## Docs
+
+15. [x] A real-data recipe page: counts, `tfs-process-counts`,
+    `tfs-check-tubes` and `tfs-correct-tubes`, `tfs-configure-model`'s
+    inputs, `tfs-fit-two-stage`, the outputs. Include cost and wall time,
+    and an example SLURM script with the jackknife as an array.
+16. [x] An interpretation section:
+    - what X is, and why it is not binding (the affine map; the M42I
+      case);
+    - what the intervals include: k's shift, if decision 1 lands;
+    - what they leave out: stage 1's 2-6% m bias, the pheS gap,
+      congression error per genotype, and kanR's m scale (decision 6);
+    - that shallow genotypes are prior-dominated;
+    - what the consistency flag means.
+17. [x] `fitting.rst`: make the two-stage route the default for
+    growth-only screens. Mark the single-MAP `hill_relative` fit, the
+    joint absolute fit and the thermodynamic components as advanced or
+    experimental.
+18. [x] `README.rst` and `quickstart.rst`: point to the recipe page.
+   Drafted 2026-10-19: `docs/source/screen-analysis.rst` ("Analyzing a
+   screen") covers 15 and 16, with `examples/tfmodel/two_stage_submit.sh`,
+   `two_stage_step.srun` and `growth_priors_loose.csv`; `fitting.rst`
+   points to it and marks the joint model experimental (17); the index,
+   quickstart and README point to it (18). Updated 2026-10-20 for the
+   fallback: no k shift, the 1,000-read threshold, the coverage-by-depth
+   table, and k's uncertainty listed among what the intervals leave out.
+
+19. [x] Regenerate `cli.rst`, and update CLAUDE.md.
+    Done 2026-10-19: `cli.rst` was current (its test passes). CLAUDE.md:
+    the relative-X section names the two-stage fit as the route (it said
+    the choice was open), and the planning section leads with this plan.
+    `pipeline.rst` points to the screen page and to `tfs-check-tubes` /
+    `tfs-correct-tubes` (it sent tube cleanup to the lab's own script).
+
+## Release mechanics
+
+20. [x] CHANGELOG: fold `[Unreleased]` into the existing `[0.5.0]`
+    section and change its date (2026-10-03) to the release date. 0.5.0
+    was never tagged or shared in the lab (user, 2026-10-19): the bump
+    came before the two-stage work, so the release stays 0.5.0 and
+    `__version__` is already right. Add an upgrade note: the removed
+    commands and the configs that no longer load.
+    Done 2026-10-24: `[0.5.0] - 2026-10-24` opens with a new summary,
+    then the entries since 2026-10-03, then "Changes up to 2026-10-03"
+    (the earlier summary and entries); `[Unreleased]` is empty. The
+    upgrade notes are in the release notes draft
+    (`.claude/2026-10-24_release-notes-v0.5.0.md`). If the tag date
+    differs, change the section's date.
+21. [x] Full test suite, with `--runslow` and the smoke tests. Run lint.
+    Done 2026-10-24: 6,145 passed with `--runslow` (fresh venv, newest
+    releases, `-n 6`); smoke tests 45 passed; fatal lint clean.
+22. [ ] Tag v0.5.0 and write GitHub release notes. Install that tag on
+    the cluster.
+23. [ ] Record the commit and version Natalie's analysis uses in her
+    manuscript folder, with the environment's `pip freeze`.
+
+## Out of scope (after 0.5.0)
+
+- The official analysis for the paper, including X against binding
+  (`planning/studies/x-vs-binding/`) and the M42I re-prep.
+- Thermodynamic models with a floating protein concentration (NSF).
+- Experimental design: `tfs-build-sim-config` and the scoring CLI
+  (`two-stage-fit.md` steps 5 and 7), and
+  `planning/design-noise-sweep.md`.
+  - The kanR warp (decision 2), the relative-m plan,
+    the k/m-under-jackknife-prior alternative (decision 1), the joint fit
+    (`planning/joint-fit-return.md`, paused), the X population SDs held for
+    shallow genotypes (`deep-coverage.md` step 4), and mutant monoculture
+    dk_geno data as `base_growth_df`.
diff --git a/planning/studies/congression-calibration-convergence/README.md b/planning/studies/congression-calibration-convergence/README.md
new file mode 100644
index 00000000..6a25b8d7
--- /dev/null
+++ b/planning/studies/congression-calibration-convergence/README.md
@@ -0,0 +1,287 @@
+# Congression calibration: SVI convergence review
+
+**Question.** Four rounds of diagnosis on the step 3.5 calibration grid
+(detection floor, binding weight, unanchored k/dk_geno slide, ELBO/Jensen)
+each explained the mixture fits' endpoints, and none of the fixes changed
+them. Are those endpoints converged fits at all? If they are not, what does
+the mixture actually do during training?
+
+**Decision it feeds.** What to do next in step 3.5 of
+[`planning/congression-physics-plan.md`](../../congression-physics-plan.md):
+whether to keep diagnosing the mixture model, or fix the optimization first.
+The routes forward below are proposals, not decisions.
+
+## Summary
+
+- **None of the 3.5 fits converged**, `single` included. The stop rule
+  measures loss change relative to the loss at the start of SVI, which is
+  ~500 times the final loss, so runs stop while the loss is still falling
+  20 to 30% per 1000 epochs and the guide scales are still shrinking.
+- **The mixture's "k drift" is an early excursion that was still unwinding
+  at the stop.** In the high-variance start of SVI (guide scales at prior
+  width, nu collapsing to ~1.3), the mixture's `growth_k` overshoots the
+  truth by +0.12 at lambda 0 and +0.05 at lambda 1, then comes back
+  slowly. `single`, on the same data, holds `growth_k` at the truth
+  throughout.
+- **The simulated library is dominated by wt**, which is 39% of the
+  co-resident pool, so most congressed classes are "genotype + wt". wt is
+  also the only genotype with a pinned dk_geno.
+- Nothing here points at the congression physics or the mixture's design.
+  The earlier diagnoses should be re-judged on converged fits.
+
+## How to run
+
+The scripts read a grid pulled from the cluster (`fetch.sh` in
+`planning/studies/congression-calibration/`), by default the anchored grid
+(`grid_anchor.yaml`) inside that study directory. From this directory, with
+tfscreen importable:
+
+```bash
+python trace_checkpoints.py                  # runs 0001, 0002, 0005
+python coresident_pool.py                    # run 0002
+GRID=/path/to/grid python trace_checkpoints.py run_0002 run_0003
+```
+
+Both print to stdout and write nothing. `trace_checkpoints.py` takes about
+20 s on a laptop CPU.
+
+## Inputs
+
+- Anchored grid (`planning/studies/congression-calibration/grid_anchor.yaml`,
+  set up on c7b228d, run 2026-09-24): binding weight 1, prefit
+  `--k_scale_ceiling 0.005`, lambda 0 and 1.0, wide dk_geno spread, seed 1.
+  Run 0001 is `single`, 0002 the mixture with the matched lambda prior at
+  simulated lambda 0 (fitted lambda ~0.004), 0005 the mixture with the matched
+  prior at lambda 1.0. Runs 0001 and 0002 were fit to the same simulated data.
+- Each run's `checkpoints/*_checkpoint.pkl` (every 1000 epochs),
+  `tfs_fit_model_checkpoint.pkl`, `tfs_fit_model_losses.txt`,
+  `tfs_sim_growth_parameters.csv`, `tfs_configure_library.csv`,
+  `tfs_sim_library.csv` and `tfs_sim_presplit.csv`.
+
+## Commit
+
+Scripts written and run on 2d86049 (2026-09-25).
+
+## Results
+
+### 1. The stop rule fires on the start-up transient
+
+`RunInference._update_loss_deque`
+(`src/tfscreen/tfmodel/inference/run_inference.py`) defines the metric as
+
+```
+|mean(old window) - mean(new window)| / (loss_start - loss_best)
+```
+
+with `loss_start` taken once, when the first two 10-epoch windows of SVI
+fill. At that point the component guide's per-genotype scales are still at
+their initial 1.0 and the initial values carry 0.1 jitter, so `loss_start` is
+~8e7. The runs end near 1.5e5 to 2.3e5:
+
+| run | `loss_start` | `loss_best` | stop threshold on the 10-epoch change (tolerance 1e-4) |
+|---|---|---|---|
+| 0001 `single` | 7.64e7 | 2.29e5 | 7,620 |
+| 0002 mixture, lambda 0 | 8.93e7 | 1.68e5 | 8,910 |
+| 0005 mixture, lambda 1 | 8.91e7 | 1.34e5 | 8,900 |
+
+The threshold is effectively "the loss has fallen by 99.99% of its initial
+value," not "the loss has stopped changing." At the stop the smoothed loss is
+still falling 20 to 30% per 1000 epochs (run 0002: 3.6e5 at epoch 7000,
+2.5e5 at 8000, 1.9e5 at 9000).
+
+The original 54-run grid (default binding weight) ended at losses of 9e6 to
+1.2e7, even further from converged.
+
+### 2. The pre-MAP warm-up is close to a no-op
+
+`_run_map` (`src/tfscreen/tfmodel/scripts/fit_model_cli.py`) sets the
+learning-rate schedule to decay from `adam_step_size` to
+`adam_final_step_size` (1e-3 to 1e-6) over `max_num_epochs`, and the pre-MAP
+passes `max_num_epochs=pre_map_num_epoch` (1000). The step size therefore
+collapses within the warm-up. Adam moves a parameter by roughly the step
+size per step, so about 0.15 in total over the warm-up. The pre-MAP loss
+goes from 6.5e7 to 5.3e7, against ~1.5e5 at the end of SVI. SVI starts far
+from the mode, with prior-width guide scales (`dk_geno_offset_scales` and the
+other per-genotype scales initialize to 1.0).
+
+### 3. Parameter trajectories
+
+`trace_checkpoints.py`. `growth_k` for kanR+kan (truth 0.0107) in the three
+runs; guide scale, nu, sigma_k and loss from run 0002 (runs 0001 and 0005
+follow the same pattern). nu and sigma_k are guide medians; "dk scale" is
+the median per-genotype `dk_geno_offset_scales`.
+
+| epoch | `single` | mixture, λ 0 | mixture, λ 1 | dk scale | nu | sigma_k | loss |
+|---|---|---|---|---|---|---|---|
+| 1000 | 0.007 | 0.012 | -0.003 | 1.00 | 19.6 | 0.04 | 6.0e7 |
+| 2000 | 0.003 | 0.096 | 0.051 | 0.93 | 7.5 | 0.10 | 1.9e7 |
+| 3000 | 0.013 | 0.125 | 0.057 | 0.85 | 3.0 | 0.25 | 5.0e6 |
+| 4000 | 0.016 | 0.135 | 0.046 | 0.78 | 1.3 | 0.57 | 2.0e6 |
+| 5000 | 0.011 | 0.128 | 0.042 | 0.65 | 1.3 | 1.08 | 9.7e5 |
+| 6000 | 0.010 | 0.128 | 0.039 | 0.50 | 2.1 | 1.15 | 5.5e5 |
+| 7000 | 0.011 | 0.118 | 0.039 | 0.36 | 3.5 | 1.06 | 3.6e5 |
+| 8000 | | 0.107 | 0.039 | 0.26 | 5.2 | 0.91 | 2.5e5 |
+| 9000 | | 0.094 | 0.034 | 0.20 | 6.4 | 0.77 | 1.9e5 |
+| stop | 0.009 (7982) | 0.092 (9234) | 0.030 (9876) | 0.18 | 6.6 | 0.74 | 1.8e5 |
+
+The other three conditions move the same way (run 0002 at the stop: 0.090,
+0.109, 0.115 against truths 0.015, 0.021, 0.029), and `dk_geno`'s hyper-shift
+mirrors them (0.024 at epoch 1000, -0.169 at 4000, -0.079 at the stop).
+Lambda in run 0005 also overshoots and returns: 1.02, 1.27 (epoch 2000), 0.95
+(8000), 0.98 at the stop.
+
+What the table shows:
+
+- Between epochs 1000 and 5000 the fit escapes the high guide variance by
+  inflating the noise: nu falls from ~20 to ~1.3 (Cauchy-like tails) and
+  sigma_k rises from 0.04 to ~1.1 ln units. `single` does the same (nu 1.1 at
+  epoch 5000).
+- In that phase nothing holds the k/dk_geno slide. Under near-Cauchy tails,
+  wt's pinned dk_geno costs only logarithmically to abandon, and the prefit's
+  tight k prior (scale 0.005; ~1,400 log units at the run 0002 peak) is small
+  next to a loss of 1e6 to 1e7.
+- `single` wanders by about ±0.01 and ends at the truth. The mixture's
+  `growth_k` jumps within the first 1000 epochs of SVI and peaks near epoch
+  4000. It then returns at about 0.01 per 1000 epochs as the guide scales
+  shrink and nu recovers, and was still falling when the run stopped.
+
+This also accounts for the earlier findings from the endpoints. Under the
+mixture's own joint density, `single`'s solution beat the mixture's endpoint
+by ~18,000, and the mean gradient at that endpoint pointed k back down
+(`diag_basin.py`, `diag_gradient.py`). That is what a fit stopped
+mid-recovery looks like, not a real mode or an overshoot.
+
+### 4. Why the mixture's excursion is larger (hypothesis, untested)
+
+In the high-variance phase, a guide draw often puts a genotype's dk_geno deep
+in the lognormal's negative tail, so its clean class dies in that draw. Its
+congressed classes rescue the draw, because dilution halves the focal dk.
+Most congressed classes contain wt (result 5), whose dk_geno is pinned at 0.
+Sliding k up by C and every other dk_geno down by C leaves clean classes
+unchanged but speeds up every wt-containing class by C/2, which makes the
+rescue more effective and improves the expected log-likelihood under the
+guide.
+
+The excursion sizes fit this: rescue classes weighted ~0.2% (lambda 0.004)
+need a larger boost than classes weighted 42% (lambda 1), and the excursion
+is +0.12 against +0.05. This is the Jensen/ELBO idea in a concrete form. It
+acts in the start-up phase, when guide scales are 0.8 to 1.0. `diag_elbo.py`
+evaluates at the endpoints, where the scales are 0.15 to 0.26 and the effect is
+much weaker.
+
+### 5. The simulated library is dominated by wt
+
+`coresident_pool.py`:
+
+| quantity | value |
+|---|---|
+| wt share of the co-resident pool | 0.392 (next largest: 0.023) |
+| wt slots per genotype, `congression_sets` [12, 3, 1] | 8.2 of 21 |
+| wt entries in `single-1` / `single-2` / `double-1-2` | 30 of 46 / 36 of 67 / 1080 of 3082 |
+| wt share of presplit cells (true ln_cfu0), replicate 1 / 2 | 0.61 / 0.22 |
+
+`LibraryManager._prepare_indiv_lib_blocks`
+(`src/tfscreen/genetics/library_manager.py`) treats every codon of a tile as
+a site, and a non-degenerate codon's only "mutant" is its wt codon. The study
+config's `degen_sites` has one degenerate codon in tile 1's 31 codons and two
+in tile 2's 37, so each single sub-library gets one wt entry per
+non-degenerate codon. For example, `single-1` has 30 wt entries plus the 16
+NNT codons at site 42. The simulator and the fit share the enumerator, so
+this is not a sim/fit mismatch. It does make this simulation unrepresentative
+of a library whose tiles are fully degenerate.
+
+**Resolved 2026-09-25 (bff2c51):** it was a bug. A site is now a codon whose
+designed sequence differs from wt. Fully degenerate designs (the real
+library, the examples) enumerate as before; this study config goes from
+46/67/3082 sequences (30/36/1080 wt) to 16/32/512 (0/1/0 wt), and wt's
+co-resident share from 0.39 to 0.003. Every grid in the calibration study
+was simulated and fit under the old enumeration (consistently on both
+sides); new grids from this config simulate a different library.
+
+### 6. Status of the earlier diagnoses
+
+| diagnosis (study README) | status |
+|---|---|
+| Detection floor | The floor bias itself (0 reads: +1.26 ln) is a data-side fact and stands. The "congressed classes are worth 36,700" measurement was taken at an unconverged endpoint. |
+| Binding weight | Weight 251 times a binding SD of 0.001 dominating the objective is a property of the objective and stands. Its role in the mixture's drift is undetermined; the weight-1 fits drifted the same way, as the transient predicts. |
+| Unanchored slide and Student-t | Partly right: the slide is loose during the transient because nu collapses. "The mixture abandons wt by 13.6 ln units" is a mid-recovery snapshot. |
+| ELBO/Jensen (`diag_elbo.py`) | Plausible as the driver of the early excursion (result 4), but testing it at the endpoints measures a much weaker effect. |
+| Grid-level numbers (lambda pulled up, bulk theta coverage 0.65, mixture theta RMSE above `single`'s, `single`'s -0.02 k drift) | All from unconverged fits, `single`'s baseline included. None are yet properties of the models. |
+
+## Is the congression architecture at fault?
+
+Probably not:
+
+- The forward model is verified: lambda -> 0 reproduces `single` to 1e-6.
+- The estimator is accurate at the true parameters (step 3.0,
+  `planning/studies/congression-estimator/`).
+- The matched-prior lambda 1 fit recovered lambda (0.98).
+
+Two structural points to keep in view:
+
+- **Growth data barely inform lambda in the measured regime.** Congression
+  there is ~0.05 ln RMS, and `single`'s errors did not change with the
+  simulated lambda. The plan already sets such parameters from biology.
+- **A mixture of exponentials is dominated at late times by its fastest
+  class.** That mimics the detection-floor bias in dying genotypes and makes
+  a genotype's likelihood bimodal (clean-dominated vs congressed-dominated).
+  Inference is most fragile there. Look at it once fits converge.
+
+## Routes forward (proposals)
+
+In rough order of cost:
+
+1. **Resume the anchored runs to real convergence.** No new simulations. A
+   resumed fit re-bases the stop rule: `run_optimization` resets
+   `loss_start`, so the denominator becomes the resumed run's own
+   improvement. From a *copy* of a run directory (the resume overwrites
+   `tfs_fit_model_checkpoint.pkl` and `tfs_fit_model_losses.txt`):
+
+   ```bash
+   tfs-fit-model tfs_configure_config.yaml --seed 1 \
+       --checkpoint_file tfs_fit_model_checkpoint.pkl \
+       --convergence_tolerance 0.0001
+   ```
+
+   Keep `--max_num_epochs` at its default. It also sets the step-size decay
+   horizon, and the optimizer's step count carries over from the checkpoint,
+   so shortening it drops the step size abruptly. Against the resumed run's
+   smaller improvement, the loss noise will probably keep the stop rule from
+   firing, so expect the full 100,000 epochs: about 20 min on the cluster
+   GPU (these runs took ~12 s per 1000 epochs). Then run
+   `trace_checkpoints.py` on the copy. This settles "unconverged" vs "a real
+   pathology" for runs 0001, 0002 and 0005.
+2. **Fix the start and the stop** (code changes):
+   - run the pre-MAP to convergence, with a step-size schedule that does not
+     collapse within the warm-up;
+   - start the component guide's per-genotype scales small (for example 0.01
+     to 0.1, like AutoNormal's `init_scale`) rather than at prior width;
+   - measure convergence against the current loss or parameter movement,
+     not the start-up transient.
+3. **Profile lambda with MAP.** Fix lambda on a grid, optimize everything
+   else to convergence, on the lambda 0 and lambda 1 data. This answers "does
+   the data pull lambda up?" without SVI.
+4. **Test self-consistency before realism.** Fit data generated by the
+   numpyro model itself (fixed known parameters), `single` vs mixture at
+   lambda 0. This separates inference from sim/fit mismatch. Then add the
+   realism back one piece at a time: per-tube noise, pseudocount floor,
+   binding.
+5. **Consider pinning lambda** as a constant by default, with lambda as a
+   latent an option. This follows the plan's rule for parameters that only a
+   lambda series could identify.
+6. **Re-judge the earlier diagnoses on converged fits** (result 6).
+7. **Library.** Decide whether the wt enumeration is intended, and use a
+   simulation library with a realistic wt share. Check the real library's wt
+   share of the co-resident pool.
+
+`grid_controls.yaml` (2d86049) is worth holding until item 2 is done:
+
+- Its MAP arms use the same stop rule (MAP `loss_start` ~6.5e7) and will
+  likely stop early too.
+- Its `fixed0` arm (lambda ~1e-6) still has congressed classes, at weight
+  ~5e-7, which a large enough early excursion could use. Drift there would
+  not by itself mean a bug in the mixture code.
+
+**Practice.** Before interpreting any fit, run `trace_checkpoints.py` (or
+equivalent) on its `checkpoints/` and confirm that the key parameters have
+stopped moving.
diff --git a/planning/studies/congression-calibration-convergence/coresident_pool.py b/planning/studies/congression-calibration-convergence/coresident_pool.py
new file mode 100644
index 00000000..c975a2e4
--- /dev/null
+++ b/planning/studies/congression-calibration-convergence/coresident_pool.py
@@ -0,0 +1,73 @@
+"""
+Composition of the co-resident pool the mixture draws congressed classes
+from, and wt's share of simulated cells.
+
+The pool is the bulk share (pool_fraction * bulk_fraction) of genotypes with
+growth data, __unknown__ excluded, as in
+ModelOrchestrator._coresident_pool. Also reports how many wt entries each
+sub-library enumerates (tfs_sim_library.csv) and wt's share of presplit
+cells per replicate.
+
+Run from this directory. GRID names a pulled grid directory (default: the
+anchored grid inside the congression-calibration study); the argument is a
+run-number prefix (default: run_0002). The pool depends only on the library,
+so any run of the grid gives the same answer.
+"""
+
+import glob
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+import yaml
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+GRID = os.environ.get("GRID", os.path.join(
+    HERE, "..", "congression-calibration", "congression_calibration_anchor"))
+RUN = sys.argv[1] if len(sys.argv) > 1 else "run_0002"
+
+run_dir = glob.glob(os.path.join(GRID, RUN + "_*"))[0]
+
+
+def path(name):
+    return os.path.join(run_dir, name)
+
+
+lib = pd.read_csv(path("tfs_configure_library.csv"))
+lib["genotype"] = lib.genotype.astype(str)
+grown = set(pd.read_csv(path("tfs_sim_growth.csv"),
+                        usecols=["genotype"]).genotype.astype(str))
+lib = lib[lib.genotype.isin(grown) & (lib.genotype != "__unknown__")].copy()
+lib["share"] = lib.pool_fraction * lib.bulk_fraction
+lib["share"] /= lib.share.sum()
+
+print(f"=== {os.path.basename(run_dir)}")
+print("Co-resident pool, largest shares:")
+print(lib.sort_values("share", ascending=False).head(6)[
+    ["genotype", "pool_fraction", "bulk_fraction", "share"]].to_string(index=False))
+
+sets = yaml.safe_load(open(path("tfs_configure_config.yaml")))
+sets = sets["components"]["congression_sets"]
+slots = sum(num_sets * (n + 1) for n, num_sets in enumerate(sets))
+wt = lib.set_index("genotype").share["wt"]
+print(f"\nwt share {wt:.3f}: {slots * wt:.1f} of {slots} co-resident slots per "
+      f"genotype are wt (congression_sets {sets})")
+
+sim_lib = pd.read_csv(path("tfs_sim_library.csv"))
+sim_lib["genotype"] = sim_lib.genotype.astype(str)
+totals = sim_lib.groupby("library_origin").degeneracy.sum()
+wt_rows = sim_lib[sim_lib.genotype == "wt"].set_index("library_origin")
+print("\nwt entries per sub-library (enumerated codon combinations):")
+for origin, row in wt_rows.iterrows():
+    print(f"  {origin:12s} {row.degeneracy:6d} of {totals[origin]:6d} "
+          f"({row.degeneracy / totals[origin]:.2f})")
+
+presplit = pd.read_csv(path("tfs_sim_presplit.csv"))
+presplit["genotype"] = presplit.genotype.astype(str)
+presplit["cfu"] = np.exp(presplit.ln_cfu_0_true)
+keys = ["replicate", "condition_pre"]
+share = (presplit[presplit.genotype == "wt"].groupby(keys).cfu.sum()
+         / presplit.groupby(keys).cfu.sum())
+print("\nwt share of presplit cells (true ln_cfu0):")
+print(share.round(3).to_string())
diff --git a/planning/studies/congression-calibration-convergence/trace_checkpoints.py b/planning/studies/congression-calibration-convergence/trace_checkpoints.py
new file mode 100644
index 00000000..e4a89f7a
--- /dev/null
+++ b/planning/studies/congression-calibration-convergence/trace_checkpoints.py
@@ -0,0 +1,114 @@
+"""
+Trace a fit's guide parameters across its epoch checkpoints, to see whether
+they had stopped moving when SVI declared convergence.
+
+For each run, one row per checkpoint (checkpoints/*_checkpoint.pkl every 1000
+epochs, then the final tfs_fit_model_checkpoint.pkl): the per-condition
+growth_k guide loc, dk_geno's hyper-shift loc and median per-genotype offset
+guide scale, the guide medians of nu, sigma_k and lambda, and the smoothed
+loss. A truth row comes from tfs_sim_growth_parameters.csv. The stop line
+gives the convergence bookkeeping stored in the final checkpoint and the
+10-epoch loss change below which a tolerance of 1e-4 (run.srun) stops.
+
+Run from this directory. GRID names a pulled grid directory (default: the
+anchored grid inside the congression-calibration study); the arguments are
+run-number prefixes (default: run_0001 run_0002 run_0005). The default runs
+take about 20 s on a laptop CPU.
+"""
+
+import glob
+import os
+import sys
+import warnings
+
+warnings.filterwarnings("ignore")
+
+import dill
+import numpy as np
+import pandas as pd
+
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.inference.run_inference import RunInference
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+GRID = os.environ.get("GRID", os.path.join(
+    HERE, "..", "congression-calibration", "congression_calibration_anchor"))
+RUNS = sys.argv[1:] or ["run_0001", "run_0002", "run_0005"]
+TOLERANCE = 1e-4
+
+
+def smoothed_loss(run_dir):
+    losses = pd.read_csv(os.path.join(run_dir, "tfs_fit_model_losses.txt"),
+                         header=None, names=["epoch", "loss", "change"])
+    losses = losses[pd.to_numeric(losses.epoch, errors="coerce").notna()]
+    losses = losses.astype(float).set_index("epoch").loss
+    return losses.rolling(50, center=True, min_periods=1).median()
+
+
+def condition_order(run_dir):
+    priors = pd.read_csv(os.path.join(run_dir, "tfs_configure_priors.csv"))
+    k = priors[priors.parameter == "growth.condition_growth.k_loc"]
+    return list(k.sort_values("flat_index").condition_rep)
+
+
+def guide_median(params, name):
+    """Median of a LogNormal guide site with loc parameter ``name``."""
+    if name not in params:
+        return np.nan
+    return float(np.exp(np.ravel(np.asarray(params[name]))[0]))
+
+
+def trace(run_dir):
+    cwd = os.getcwd()
+    os.chdir(run_dir)
+    try:
+        orch, init_params = read_configuration("tfs_configure_config.yaml")
+        ri = RunInference(orch, 0)
+        files = sorted(glob.glob("checkpoints/*_checkpoint.pkl"))
+        files.append("tfs_fit_model_checkpoint.pkl")
+        loss = smoothed_loss(".")
+        conditions = condition_order(".")
+
+        rows = []
+        for f in files:
+            svi, state = ri.restore_svi_from_checkpoint(
+                f, init_params=init_params)
+            p = svi.get_params(state)
+            with open(f, "rb") as handle:
+                stored = dill.load(handle)
+            epoch = stored["current_step"] // ri._iterations_per_epoch
+            row = {"epoch": epoch}
+            k = np.ravel(np.asarray(p["condition_growth_k_locs"]))
+            row.update({f"k[{c}]": v for c, v in zip(conditions, k)})
+            row["dk_shift"] = float(np.ravel(p["dk_geno_hyper_shift_loc"])[0])
+            row["dk_scale_med"] = float(np.median(p["dk_geno_offset_scales"]))
+            row["nu"] = guide_median(p, "growth_nu_loc")
+            row["sigma_k"] = guide_median(p, "growth_noise_sigma_k_loc")
+            row["lam"] = guide_median(p, "transformation_lam_loc")
+            row["loss"] = float(loss.iloc[np.argmin(np.abs(loss.index - epoch))])
+            rows.append(row)
+
+        truth = pd.read_csv("tfs_sim_growth_parameters.csv")
+        truth = truth.set_index("condition_rep").growth_k
+        truth_row = {"epoch": "truth"}
+        truth_row.update({f"k[{c}]": truth[c] for c in conditions})
+        rows.append(truth_row)
+
+        denom = stored["loss_start"] - stored["loss_best"]
+        stop = (f"stop: loss_start {stored['loss_start']:.3g}, loss_best "
+                f"{stored['loss_best']:.3g}; tolerance {TOLERANCE:g} stops once "
+                f"the 10-epoch change in mean loss is below "
+                f"{TOLERANCE * denom:,.0f}")
+    finally:
+        os.chdir(cwd)
+    return pd.DataFrame(rows), stop
+
+
+if __name__ == "__main__":
+    pd.set_option("display.width", 250)
+    for run in RUNS:
+        run_dir = glob.glob(os.path.join(GRID, run + "_*"))[0]
+        df, stop = trace(run_dir)
+        print(f"\n=== {os.path.basename(run_dir)}")
+        print(df.to_string(index=False, float_format=lambda v: f"{v:.4g}"))
+        print(stop)
diff --git a/planning/studies/congression-calibration/.gitignore b/planning/studies/congression-calibration/.gitignore
new file mode 100644
index 00000000..7419ba81
--- /dev/null
+++ b/planning/studies/congression-calibration/.gitignore
@@ -0,0 +1,11 @@
+# Grid outputs (tens of GB) and the calibration summary; see README.md.
+# Every grid is set up as congression_calibration_<name>/.
+congression_calibration*/
+calib/
+diagnosis/*.csv
+fetch.sh
+# Written by tfs-setup-sim-grid next to a grid set up here; not inputs.
+grid_summary.json
+inputs/
+# Renamed copies of old grid runs
+*.old/
diff --git a/planning/studies/congression-calibration/README.md b/planning/studies/congression-calibration/README.md
new file mode 100644
index 00000000..f009a984
--- /dev/null
+++ b/planning/studies/congression-calibration/README.md
@@ -0,0 +1,773 @@
+# Congression calibration study
+
+**Question.** Fit to data from the step 2 simulator, is the observable-level
+congression mixture (`transformation: mixture`) calibrated, and does it beat
+`single` where congression matters? In particular:
+
+- Does the fit recover lambda?
+- Are theta and the per-genotype parameters calibrated for bulk genotypes
+  *without* binding data, whose theta is informed only by their growth?
+- What does fitting the mixture cost when there is no congression
+  (lambda = 0), and how much does a misspecified lambda prior hurt?
+
+**Decision it feeds.** Step 3.5 of
+[`planning/congression-physics-plan.md`](../../congression-physics-plan.md):
+whether the mixture is ready to use on real data. The simulator and the fit
+use the same cell rules (max theta, dilution dk), so this checks the fit's
+machinery given matched physics, not whether the rules are biologically
+right. The mixture-vs-single effect sizes are specific to the max rule;
+re-run the grid when step 4 changes the rule.
+
+## Design
+
+Base config: [`simulate_config.yaml`](simulate_config.yaml), from
+`examples/simulate-and-analyze/` (483 genotypes, hill_mut theta, instant
+growth transitions, kanR and pheS selections) with the real design's
+`library_mixture` and 20 in-library binding anchors (`library_binding`).
+Purity: 474 bulk genotypes, 3 pure spikes (D88A, H74A/K84L,
+M42I/H74A/K84L), and 6 mixed (wt and the spiked singles, `bulk_fraction`
+0.99 to 1.0; M42I/H74A and M42I/K84L, 0.74).
+
+[`grid.yaml`](grid.yaml): 3 x 2 x 3 x 3 = 54 runs.
+
+| axis | levels |
+|---|---|
+| simulated lambda | 0 (null), 0.357 (measured), 1.0 (stress) |
+| dk_geno spread (`dk_geno_hyper_loc`) | -3.5 (wide, SD 0.018/min), -7.0 (tight, SD 0.0006/min, the measured regime) |
+| seed (simulation and fit) | 1, 2, 3 |
+| fit (`fit`, `lam_prior`) | `single`/`off`; `mixture`/`matched` (prior at the simulated lambda, 0.01 when it is 0, SD 0.05); `mixture`/`measured` (prior 0.357 +/- 0.05 whatever the truth) |
+
+At simulated lambda 0.357 the matched and measured fits are the same fit;
+their results should agree up to GPU nondeterminism (a reproducibility
+check).
+
+Each run ([`run.srun`](run.srun)): `tfs-simulate` -> `tfs-configure-model`
+(the simulate-and-analyze components; `--transformation_model` and
+`--transformation_lambda` from the fit arm) -> `tfs-prefit-calibration` ->
+`tfs-fit-model` (SVI) -> `tfs-sample-posterior` -> `tfs-extract-params` ->
+`tfs-predict-theta` -> `tfs-predict-growth` -> `tfs-summarize-fit`.
+
+## How to run
+
+On the cluster, from a scratch directory, with this repository checked out.
+`tfs-setup-sim-grid` copies `hill_params.csv` into the grid's `inputs/`
+directory and each run refers to it as `../inputs/hill_params.csv`, so the grid
+directory can be moved as a unit after setup:
+
+```bash
+tfs-setup-sim-grid /path/to/tfscreen/planning/studies/congression-calibration/grid.yaml \
+    --out_prefix congression_calibration
+for d in congression_calibration/run_*/; do (cd "$d" && sbatch run.srun); done
+```
+
+When the runs finish:
+
+```bash
+tfs-summarize-calibration congression_calibration --out_prefix calib/congression \
+    --baseline fit=single lam_prior=off \
+    --facet_by transformation_poisson_lambda dk_geno_hyper_loc
+```
+
+Arms average over `seed` (the default `--replicate_keys`).
+
+## What to look at
+
+- `lam`: posterior against the simulated value, by lambda and prior.
+- theta (`theta_test`) and per-genotype parameters by `purity` x
+  `has_binding`: coverage, calibration bias, width, RMSE; paired deltas
+  against `single`.
+- The lambda = 0 arm: the mixture's cost when nothing is congressed.
+- The `measured` prior at lambda 0 and 1.0: robustness to a wrong prior.
+
+## Commit
+
+Grid set up and run on `6e4735e` (2026-09-24).
+
+## Results (grid.yaml, 2026-09-24)
+
+All 54 runs finished and SVI converged. `tfs-summarize-calibration` outputs
+are in `calib/` (not committed). The grid does not yet answer its question,
+for three reasons:
+
+1. **The k/dk_geno slide is unanchored.** In every run the `growth_k` error
+   mirrors the `dk_geno` error, and the spread of the `dk_geno` errors is
+   small (0.001 to 0.01/min): each genotype's dk is recovered up to one
+   global constant. Nothing pins it here: `hierarchical_geno` does not pin
+   wt, there is no `base_growth` data, and the prefit's Hessian-derived
+   `k_scale` hit its 0.1 ceiling. `single` fits drift -0.02 to 0 in k,
+   mixture fits +0.02 to +0.07 (true baselines 0.01 to 0.03). `dk_geno` and
+   `growth_k` calibration are therefore meaningless in this grid.
+2. **Congression is below the noise floor of this design.** `single`'s
+   errors do not change with the simulated lambda (growth RMSE 0.469 at
+   lambda 0 vs 0.471 at 1.0, tight dk; bulk theta without binding 0.180 vs
+   0.181), although 42% of a bulk genotype's cells are congressed at
+   lambda 1.0.
+3. **Lambda is pulled up whatever the truth.** With the 0.357 +/- 0.05
+   prior, posterior medians were 0.46 to 0.66 at a true lambda of 0,
+   0.46 to 0.60 at 0.357, and 0.44 to 0.63 at 1.0. With the matched prior at
+   1.0: 1.00 to 1.28. Seed 3 is highest in every arm.
+
+Also: the mixture's theta RMSE is slightly worse than `single`'s in every
+arm (0.20 to 0.22 vs 0.18, bulk genotypes), including lambda 0 with the
+matched prior, where the fitted lambda is ~0.004. Bulk theta 95% coverage is
+~0.65 in every arm, `single` included (a baseline overconfidence, separate
+from congression). Binding genotypes are recovered well everywhere (theta
+RMSE 0.015).
+
+## First diagnosis: the detection floor (2026-09-24; superseded, see below)
+
+Scripts: [`diagnosis/`](diagnosis/) (run from that directory against the
+pulled grid). Runs 0001 (`single`) and 0002 (mixture, matched prior, true
+lambda 0, fitted lambda 0.004) were fit to the same data.
+
+- The mixture's forward model is right: at `single`'s posterior-median
+  latents with lambda = 1e-8 it reproduces `single`'s `growth_pred` to 1e-6.
+- The mixture fit leans on its congressed classes anyway: switching them off
+  at its own latents costs 36,700 in growth log-likelihood. A class weighted
+  0.2% can only matter that much by outgrowing the clean class by ~6 ln
+  units. The fit gets there through dk dilution: slow genotypes (true
+  dk_geno -0.02 to -0.05) are fit at ~-0.12, and their congressed cells,
+  with dk averaged against a co-resident, carry the late timepoints.
+- What those classes fit is the read-count floor. `ln_cfu` uses a
+  pseudocount of 1 (`process_raw/counts_to_lncfu.py`, the same path as real
+  data), so a dying genotype's late timepoints stall above the truth:
+
+  | reads | mean error vs truth (ln) | reported SD |
+  |---|---|---|
+  | 0 | +1.26 | 1.00 |
+  | 1-2 | +0.62 | 0.71 |
+  | 3-5 | +0.22 | 0.45 |
+  | 6-20 | +0.07 | 0.29 |
+
+  A single exponential cannot bend upward; a dying clean class plus a slower
+  congressed class can. Genotypes relying on congressed classes have zero
+  reads in 29% of their rows, against 6% for the rest.
+- This accounts for the grid results: more congressed weight fits the floor
+  better (lambda pulled up everywhere), the dk shifts move the fits along
+  the unanchored slide, and the mixture spends flexibility on the artifact.
+  `single` is biased by the same points, less visibly.
+- Real data share the pseudocount path, so a mixture fit to real data would
+  do the same.
+
+The 0.4 ln-unit error on well-measured rows is the simulator's per-tube
+growth noise (`tube_noise_sigma` x t; per-sample SD 0.374, within-sample
+0.054), which `growth_noise` is meant to absorb. Not a data problem.
+
+## Masked re-run (grid_masked.yaml, 2026-09-24): floor not the driver
+
+`grid_masked.yaml` dropped growth rows with fewer than 5 reads (`min_counts`
+in `run.srun`) and refit grid runs 0001-0003 and 0037-0039. The cluster
+reproduced the original simulations exactly. Comparison
+([`diagnosis/compare_masked.py`](diagnosis/compare_masked.py)), true lambda 0:
+
+| | full data | masked |
+|---|---|---|
+| lambda, measured prior (0.357 +/- 0.05) | 0.55 | 0.53 |
+| k offset, mixture (matched prior) | +0.062 | +0.065 |
+| bulk theta RMSE, mixture vs `single` | 0.267 vs 0.219 | 0.270 vs 0.220 |
+
+Lambda 1.0 behaved the same. The mixture still leans on its congressed
+classes (switching them off costs 21,100 in growth log-likelihood, against
+36,700 unmasked). The floor bias is real and censoring is still worth doing,
+but it does not drive the mixture's behavior.
+
+## Second diagnosis: the binding weight (2026-09-24; superseded, see below)
+
+Scripts: [`diagnosis/diag_basin.py`](diagnosis/diag_basin.py) and
+[`diagnosis/diag_sites.py`](diagnosis/diag_sites.py) (runs 0001/0002). The
+joint log density of the two lambda-0 fits differs almost entirely in the
+binding likelihood:
+
+| term | mixture fit - `single` fit |
+|---|---|
+| binding likelihood, as weighted in the fit | +1,044,800 |
+| growth likelihood | -37,600 |
+| everything else | about -1,100 |
+
+`ModelOrchestrator` scales the binding likelihood by `binding_weight`,
+which defaults to growth rows / binding rows (about 54,300 / 216 = 251
+here), and the simulated binding data have `theta_std` 0.001. Together they
+dominate the objective; `single` cannot fit them (residuals ~15 sigma,
+theta ~0.015). The mixture gives the fit a way out: pushing a genotype's own
+dk_geno far down removes its clean cells from its trajectory, its congressed
+classes (dk averaged with a co-resident) carry its growth, and its theta is
+free to follow binding. The 20 in-library binding genotypes gain 1,430; the
+4 spiked ones gain 2,730, mostly H74A (2,020), through hill_mut mutation
+effects shared with bulk genotypes carrying H74 changes. This accounts for
+lambda being pulled up, the slide, and the worse bulk theta and growth.
+Not a mixture bug: the weight also bends `single` fits, less visibly.
+
+Background (user, 2026-09-24): the weight was added when, with ~200,000
+growth genotypes and ~10 binding genotypes, fits ignoring the binding data
+won. That points to a conflict between the modalities over shared
+parameters (m, k, shared mutation effects) rather than a volume problem;
+the binding assay has since moved closer to cellular conditions. The
+binding SDs used on real data are the SE of a Hill curve fitted to the
+anisotropy points, which counts 9 correlated values as independent and
+leaves no room for differences between assay and cell.
+
+## Binding weight 1 (grid_bw1.yaml, 2026-09-24): weight not the driver
+
+The same 6 simulations refit with `--binding_weight 1`
+([`diagnosis/compare_masked.py`](diagnosis/compare_masked.py), which now
+takes the follow-up grids as arguments). True lambda 0:
+
+| | original | masked | weight 1 |
+|---|---|---|---|
+| lambda, measured prior (0.357 +/- 0.05) | 0.55 | 0.53 | 0.47 |
+| k offset, mixture (matched prior) | +0.062 | +0.065 | +0.080 |
+| bulk theta RMSE, mixture | 0.267 | 0.270 | 0.247 |
+| bulk theta RMSE, `single` | 0.219 | 0.220 | 0.227 |
+
+Theta 95% coverage improved in every arm at weight 1 (0.71 to 0.75, from
+0.61 to 0.70), so the weight matters for calibration, but the mixture's
+behavior survived. At weight 1 the mixture fit is worse than `single` on
+both binding and growth, so no trade-off explains it.
+
+## Third diagnosis: the unanchored slide (2026-09-24; superseded, see below)
+
+- **Optimization, not the objective.** Under the mixture model's own joint
+  density, `single`'s solution at lambda 0.004 scores ~18,000 log units
+  better than where the mixture's SVI stopped
+  ([`diagnosis/diag_basin.py`](diagnosis/diag_basin.py), bw1 grid).
+- **The drift happens in SVI, after the MAP warm-up.** After the warm-up all
+  fits have sensible `growth_k` (truth 0.011 to 0.029); the matched-prior
+  mixture then climbs to 0.089 to 0.112, with the between-condition
+  differences collapsing, and every `dk_geno` falls by ~0.09 (`ln_cfu0`
+  barely moves, so it is not a per-genotype trap).
+- **No gradient bug.** The mean SVI gradient (64 draws) at `single`'s
+  solution is the same for both models (kanR+kan `growth_k`: -1.90e6 vs
+  -1.86e6), as is the exact joint-density gradient
+  ([`diagnosis/diag_gradient.py`](diagnosis/diag_gradient.py)). Both pull k
+  up from `single`'s point and back down from the mixture's: `single`
+  stopped short, the mixture overshot.
+- **The only anchor is weak.** `hierarchical_geno` pins wt's `dk_geno` to 0,
+  and it stays 0 in both fits. The mixture fit abandons wt instead: it
+  predicts wt's `ln_cfu` ~13.6 ln units too high (`single`: +1.3). The
+  growth likelihood is Student-t (fitted nu ~9 `single`, ~6.4 mixture),
+  whose cost grows only logarithmically: that miss costs ~2,000 log units
+  here, against ~44,000 under a Normal.
+- **The intended anchor never engaged.** The prefit's per-condition k prior
+  is meant to pin the slide, but its Hessian-based `k_scale` hit the 0.1
+  ceiling in every run.
+
+## Anchored grid (grid_anchor.yaml, 2026-09-25)
+
+Binding weight 1 plus a tight prefit k prior (`--k_scale_ceiling 0.005`,
+prefit `k_scale` 0.005). True lambda 0:
+
+| | original | weight 1 | weight 1 + tight k prior |
+|---|---|---|---|
+| k offset, `single` | -0.015 | -0.007 | -0.0004 |
+| k offset, mixture (matched prior) | +0.062 | +0.080 | +0.083 |
+| lambda, measured prior (0.357 +/- 0.05) | 0.55 | 0.47 | 0.45 |
+| bulk theta RMSE, mixture vs `single` | 0.267 vs 0.219 | 0.247 vs 0.227 | 0.261 vs 0.230 |
+
+The prior fixed `single`; the mixture drifted ~17 prior SDs past it. Two
+further endpoint checks were inconclusive or negative: the ELBO at the two
+solutions ([`diagnosis/diag_elbo.py`](diagnosis/diag_elbo.py)) and the
+population arrays the mixture looks co-residents up in, which match each
+genotype's own values exactly
+([`diagnosis/diag_population.py`](diagnosis/diag_population.py)).
+
+## Controls (grid_controls.yaml, 2026-09-25)
+
+Lambda-0 simulation, anchored settings. All three were still improving at
+the stop.
+
+| run | k offset | fitted lambda | final loss |
+|---|---|---|---|
+| mixture, MAP | +0.0003 | 0.0047 | 6.5e4 |
+| `single`, MAP | -0.0013 | | 6.2e4 |
+| mixture, lambda pinned ~1e-6, SVI | +0.016 | 1e-6 | 1.7e5 |
+| mixture, matched prior, SVI (anchored grid) | +0.083 | 0.004 | 1.8e5 |
+
+Without SVI's guide draws the mixture lands where `single` does; MAP also
+reaches much lower loss. The pinned-lambda SVI fit drifts less but still
+drifts.
+
+## Review: the fits never converged (2026-09-25)
+
+An independent review
+([`../congression-calibration-convergence/`](../congression-calibration-convergence/README.md))
+found that none of the 3.5 fits converged, `single` included, and it
+supersedes the endpoint diagnoses above. Verified here:
+
+- The stop rule (`RunInference._update_loss_deque`) divides the loss change
+  by the improvement since the start of SVI, whose loss is ~500 times the
+  final loss. Runs stop while the loss is still falling: run 0002 of the
+  anchored grid went 5.5e5, 3.6e5, 2.5e5, 1.9e5 over epochs 6000-9000 and
+  stopped at 9234. "SVI run converged" in the logs did not mean converged;
+  the diagnoses above took it at face value.
+- The pre-MAP warm-up's step size decays to 1e-6 within its own 1000
+  epochs, so SVI starts far from the mode with prior-width guide scales.
+- Epoch checkpoints show `single` holding k at the truth while the
+  mixture's k jumps early (to +0.12 near epoch 4000, with nu ~1.3) and
+  returns slowly; it was cut off mid-return. The controls agree: under MAP
+  the mixture matches `single`.
+- The simulated co-resident pool is 39% wt (the library enumerator adds a
+  wt entry per non-degenerate codon); being checked separately.
+
+What stands from the earlier sections: the read-count floor bias (0 reads
++1.26 ln) and the binding weight's dominance of the objective, as facts about
+the data and the objective. Their effect on converged fits is open.
+
+## Resumed to convergence (resume.srun, 2026-09-25)
+
+Anchored runs 0001 (`single`, lambda 0), 0002 (mixture, matched prior,
+lambda 0) and 0005 (mixture, matched prior, lambda 1.0), resumed from their
+checkpoints for the full default 100,000 epochs. Traced with
+`../congression-calibration-convergence/trace_checkpoints.py`: all three
+stop moving after ~70,000 epochs, at losses of 5.2e4 to 5.4e4 (1.7e5 to
+1.9e5 at the old stops). The resumed stop rule never fired, as expected.
+
+| growth_k | kanR+kan | kanR-kan | pheS+4CP | pheS-4CP |
+|---|---|---|---|---|
+| truth | 0.0107 | 0.0154 | 0.0214 | 0.0286 |
+| `single`, lambda 0 | 0.0102 | 0.0139 | 0.0212 | 0.0286 |
+| mixture, lambda 0 | 0.0109 | 0.0145 | 0.0220 | 0.0294 |
+| mixture, lambda 1 | 0.0120 | 0.0170 | 0.0226 | 0.0295 |
+
+| run | fit | lambda (95%) | k offset | dk offset | bulk theta RMSE | bulk theta 95% coverage |
+|---|---|---|---|---|---|---|
+| 0001 `single`, lambda 0 | stopped | | -0.0004 | +0.0020 | 0.230 | 0.75 |
+| | converged | | -0.0005 | +0.0014 | 0.129 | 0.66 |
+| 0002 mixture, lambda 0 | stopped | 0.004 (0.003-0.005) | +0.083 | -0.095 | 0.261 | 0.70 |
+| | converged | 0.043 (0.039-0.048) | +0.0002 | +0.0003 | 0.131 | 0.66 |
+| 0005 mixture, lambda 1 | stopped | 0.98 (0.92-1.04) | +0.021 | -0.031 | 0.227 | 0.73 |
+| | converged | 1.30 (1.29-1.32) | +0.0013 | -0.0029 | 0.173 | 0.55 |
+
+- **The transient explanation is confirmed.** Converged, the lambda-0
+  mixture matches `single` (k, dk_geno, theta RMSE 0.131 vs 0.129). The k
+  drift, the abandoned wt and the worse theta were artifacts of stopping
+  early.
+- **Converging halves the theta error** (bulk, no binding: 0.23 to 0.13).
+- **Coverage got worse** as the guide scales shrank (0.66, and 0.55 for the
+  lambda-1 mixture): converged mean-field SVI is overconfident here.
+- **Lambda is still pulled up, now with tight intervals that exclude the
+  truth:** 0.043 at a true 0, and 1.30 at a true 1.0 under a 1.0 +/- 0.05
+  prior (six prior SDs). Open.
+- Growth-noise nu at convergence: 61 (`single`), 28 (mixture lambda 0),
+  6.5 (mixture lambda 1). The lambda-1 mixture needs much heavier tails.
+
+`single` on the lambda-1 data (run 0004), resumed the same way, also
+converged (parameters flat after ~70,000 epochs). Against the lambda-1
+mixture (run 0005), same data:
+
+| lambda 1, converged | `single` (0004) | mixture (0005) |
+|---|---|---|
+| final loss | 5.59e4 | 5.40e4 |
+| dk_geno RMSE | 0.0105 | 0.0049 |
+| growth_m RMSE | 0.0012 | 0.0009 |
+| bulk theta RMSE | 0.155 | 0.173 |
+| log_hill_K RMSE | 1.03 | 1.17 |
+| bulk theta 95% coverage | 0.58 | 0.55 |
+| lambda | | 1.30 (truth 1.0) |
+
+The mixture fits the data better and halves the dk_geno error (the 3.0
+study's main congression channel, dk dilution) but does not recover theta:
+`single`'s theta RMSE rises from 0.129 at lambda 0 to 0.155 at lambda 1,
+and the mixture's is 0.173. The overestimated lambda may be
+over-correcting theta; the lambda profile tests that.
+
+These grids used the library enumeration from before bff2c51 (wt 39% of the
+co-resident pool; see the convergence study, result 5). Simulation and fit
+shared it, so these comparisons stand; new grids from `simulate_config.yaml`
+simulate a different library.
+
+## Baseline and profile grids on 5bf5b78: the gradient clip (2026-09-25)
+
+All 13 runs of `grid_baseline.yaml` and `grid_profile.yaml` ran to 100,000
+steps without converging, and the lambda-1 seed-1 fits (baseline 0005/0006,
+profile 0.8-1.4) jumped to 4-5 times their best loss and stayed there
+through three step-size cuts. Neither is a convergence-rule problem at
+root; both come from `ClippedAdam`'s elementwise gradient clip at 1.0
+(`adam_clip_norm`, older than 5bf5b78).
+
+- **The per-step loss is quantized**: 41k + n x 243k (n = 0-5) on run 0005.
+  Each quantum is one binding point of M42C/K84N (from `hill_params.csv`, so
+  in every simulation): true `hill_n` 24.6, K between the 0.001 and 0.003
+  grid points, measured with SD 0.001. A guide draw that shifts the step
+  across a grid point flips theta 0.99 <-> 0.007 there (`diagnosis/diag_clip.py`,
+  part 1).
+- **Every run carries these penalties at a steady share of steps**, old code
+  and new: ~5% (the resumed anchored runs), 5-15% (seed 2), 27-32% (lambda-0
+  seed 1), 46-89% (lambda-1 seed 1). The "blow-ups" are the 250-step block
+  medians flipping to the penalized level once the share passes 50%; guide
+  parameters moved by at most ~0.4 across the jump.
+- **The share is set by the clip.** Gradients here are 1e3-1e6 per element,
+  so every element is clipped every step and Adam follows each draw's
+  gradient sign; it settles where the signs balance, not where the mean
+  gradient vanishes. At checkpoint 40000 of run 0005, mutation 1's
+  `theta_d_logit_delta_offset_locs` has mean gradient -7.9e3 +/- 0.9e3 (away
+  from the penalty) and mean clipped gradient -0.04; for
+  `theta_d_logit_low_offset_locs` the clip reverses the sign (-1.2e3 raw,
+  +0.14 clipped). The balance point does not depend on the step size (48%
+  at 1e-3 and at 1e-6), so the cuts locked it in.
+- **Resumed from run 0005's checkpoints** (`diag_clip.py` part 3, local CPU):
+
+| from | clip | step size | penalized share per 2000 steps | mean loss |
+|---|---|---|---|---|
+| step 8000 (before the jump) | 1.0 | 1e-3 | 0.17, 0.42, 0.57, 0.77, 0.97 | 96k -> 485k |
+| step 8000 | none | 1e-3 | 0.01, 0.00 | 47k -> 38k |
+| step 40000 (after) | none | 1e-3 | 0.21, 0.07, 0.04, 0.02, 0.00 | 102k -> 40k |
+| step 40000 | 1.0 | 1e-4 | 0.48 throughout | ~200k |
+
+- **Fresh fits of run 0005's data** (`tfs-fit-model`, CPU): unclipped, the
+  penalties vanish by step 8000 and SVI **converges at step 46,000** (3 cuts,
+  loss 38.5k, below the clipped run's best of 41k). With the clip and the old
+  start (`--pre_map_num_epoch 0`) it settles at 8-11% penalized (mean loss
+  68k), not 48%: 5bf5b78's pre-MAP start only picked a worse clipped
+  equilibrium on this simulation.
+- **The non-stop (failure 1) is the same clip, plus a yardstick problem.**
+  At 1e-6 the flagged locations crept in one direction at ~10% of Adam's top
+  speed for 60k steps (clipped dynamics), measured against guide SDs that had
+  collapsed to ~4e-4 (hill_mut `theta_sigma_d_*`, `theta_epi_tau`). The
+  collapse itself is the mean-field ELBO (it happens unclipped too; the raw
+  and clipped gradients agree on shrinking), so those reported widths are
+  not honest posterior SDs. Unclipped, the worst movement at 1e-6 was
+  0.02-0.03 SDs and the rule stopped the run.
+
+Fixed in the optimizer (CHANGELOG, "Gradient clipping is off by default"):
+plain Adam by default (`--adam_clip_norm` opts back in); at the final step
+size a window whose mean sits more than 3 robust SDs above its median (rare
+penalties the median hides) is not a plateau; posterior SDs used to measure
+parameter movement are floored at 1% of the prior SD. Every result on these
+two grids, and any clipped fit with this binding set, is biased and should
+be rerun. Separately, binding noise 0.001 on step-like curves is much tighter
+than real assays; to be loosened in the simulation config.
+
+## Next
+
+The convergence work has landed (5bf5b78: noise-referenced stop rule,
+step-size cuts on stalls, SVI started from a converged pre-MAP with guide
+scales capped at 0.1; `--convergence_tolerance` is gone and `run.srun` /
+`resume.srun` no longer pass it). New grids use it and simulate the
+corrected library (bff2c51).
+
+1. Why lambda is pulled up on converged fits: profile lambda (fixed lambda
+   on a grid, everything else converged) on the lambda-0 and lambda-1 data,
+   then test candidate causes one at a time (the read-count floor, which
+   curves dying trajectories the way a mixture does; realized vs design
+   library composition in the simulator's transformation, including
+   `lib_assembly_skew_sigma`; the fixed co-resident sets).
+2. Coverage: why converged fits are overconfident (guide family, the
+   binding weight).
+
+Set up (2026-09-25), anchored settings (binding weight 1, prefit
+`--k_scale_ceiling 0.005`), corrected library (483 genotypes as before;
+wt's co-resident share 0.0026, from 0.39):
+
+- `grid_baseline.yaml`: lambda 0 and 1.0 x {`single`, mixture with the
+  matched prior} x seeds 1 and 2 (8 runs). Does the new stop rule end runs
+  cleanly, and do the converged results above hold on the corrected
+  library?
+- `grid_profile.yaml`: the mixture with lambda held at 0.6, 0.8, 1.0, 1.2,
+  1.4 (`lam_prior: fixed`, prior SD 0.1% of the value) on the lambda-1,
+  seed-1 simulation (5 runs). Loss against lambda says whether the data
+  prefer lambda > 1; theta error against lambda says whether theta is
+  recovered at the true lambda.
+
+## Baseline and profile grids (2026-09-25)
+
+Both on the noise-referenced convergence code (5bf5b78) and the corrected
+library (bff2c51). **The stop rule never fired: all 13 runs ran to the
+100,000-step cap** ("SVI run has not yet converged"). After three 10x
+step-size cuts (to 1e-6) the parameter criterion still reads hill_mut's
+hierarchical scales as moving by 0.3-0.5 posterior SDs; their guide SDs
+have collapsed to 0.001-0.005, so noise-level movement counts.
+
+**Blow-ups on one simulation.** On the lambda-1, seed-1 simulation, both
+`single` (baseline 0005) and the mixture (0006), and four of the five
+profile runs (lambda fixed at 0.8-1.4), reach a good loss (4-6e4) and then
+jump ~5x at full step size between steps 8,000 and 14,000 (profile run
+0003: one step at epoch 8,750 took it from 7.2e4 to 2.8e5 with guide
+parameters moving at most ~0.4). The step-size cuts then lock the damage in.
+Flagged parameters: hill_mut's `theta_sigma_d_*` scales. Not
+congression-specific (`single` does it too) and not seen before 5bf5b78 on
+the analogous runs. The profile grid is therefore uninterpretable: only the
+lambda-0.6 run avoided the blow-up. Both failures went to the convergence
+work as a separate task.
+
+**The stable pairs** (losses ended within ~2% of their best, so usable with
+that caveat):
+
+| | lambda (95%) | dk_geno RMSE | bulk theta RMSE | bulk theta 95% coverage |
+|---|---|---|---|---|
+| lambda 0, seed 1: `single` / mixture | - / 0.155 (0.151-0.160) | 0.0023 / 0.0017 | 0.133 / 0.121 | 0.82 / 0.83 |
+| lambda 0, seed 2: `single` / mixture | - / 0.149 (0.145-0.154) | 0.0021 / 0.0017 | 0.116 / 0.101 | 0.79 / 0.82 |
+| lambda 1, seed 2: `single` / mixture | - / 0.98 (0.97-0.99) | 0.0077 / 0.0023 | 0.106 / 0.121 | 0.76 / 0.81 |
+
+- At lambda 1 the mixture recovers lambda (0.98 at a true 1.0; 1.30 on the
+  old library) and cuts the dk_geno error ~3x; bulk theta is still slightly
+  worse than `single`'s.
+- At lambda 0 the mixture still finds lambda ~0.15 (matched prior centered
+  at 0.01), yet its theta and dk_geno are slightly better than `single`'s.
+  What the congressed classes absorb there is open (the read-count floor is
+  one candidate).
+- Coverage is better than on the old code (0.76-0.83, from 0.55-0.66).
+
+Next: wait for the convergence fixes, then re-run the profile grid and more
+seeds of the baseline.
+
+## Realism fixes before the re-run (2026-09-26)
+
+The blow-ups traced in part to unrealistic binding data: SD 0.001 on
+theta, and near-step curves (the simulator drew Hill coefficients up to 270;
+50 of 483 genotypes above 4, including 5 of the 20 in-library binding
+anchors). From here on the study config uses binding noise 0.025 (the
+experimental theta error, user) and `theta_sim_priors.max_hill_n: 4.0`
+(new; larger draws are set to 4, so ~10% of genotypes sit exactly at 4).
+The optimizer was also updated. Grids after this date are not comparable
+with earlier ones.
+
+The model's default binding weight (growth rows / binding rows) is also to
+be lowered, but that is a change to the model for real fits; it is left
+for its own step. These grids run at `--binding_weight 1` regardless.
+
+## Re-run with realistic data and the fixed optimizer (2026-09-26)
+
+`grid_baseline.yaml` and `grid_profile.yaml` again, with binding noise
+0.025, `max_hill_n` 4 and the updated optimizer. **All 13 runs converged**
+under the stop rule (40,000-48,000 steps), each ending at its best loss; no
+blow-ups. `tfs-summarize-calibration` outputs: `calib/baseline_*`.
+
+Baseline (bulk theta and parameters; 95% intervals for lambda):
+
+| lambda, seed | fit | lambda | dk_geno RMSE | log_hill_K RMSE | bulk theta RMSE | bulk theta coverage | loss |
+|---|---|---|---|---|---|---|---|
+| 0, 1 | `single` | | 0.0023 | 0.72 | 0.108 | 0.79 | 35,874 |
+| 0, 1 | mixture | 0.131 (0.128-0.134) | 0.0015 | 0.67 | 0.089 | 0.82 | 35,153 |
+| 0, 2 | `single` | | 0.0024 | 0.72 | 0.097 | 0.80 | 32,480 |
+| 0, 2 | mixture | 0.113 (0.110-0.116) | 0.0013 | 0.71 | 0.079 | 0.86 | 32,116 |
+| 1, 1 | `single` | | 0.0092 | 0.94 | 0.125 | 0.58 | 37,837 |
+| 1, 1 | mixture | 0.942 (0.936-0.948) | 0.0032 | 0.75 | 0.104 | 0.81 | 36,285 |
+| 1, 2 | `single` | | 0.0080 | 0.65 | 0.084 | 0.77 | 29,437 |
+| 1, 2 | mixture | 0.803 (0.796-0.810) | 0.0024 | 0.61 | 0.074 | 0.87 | 28,182 |
+
+Paired against `single` (`calib/baseline_paired_summary.csv`, theta, mean
+of 2 seeds), the gain is in bulk genotypes without binding data:
+
+| lambda | stratum | delta theta RMSE | delta 95% coverage |
+|---|---|---|---|
+| 0 | bulk, no binding | -0.018 | +0.05 |
+| 0 | with binding / spike / mixed | ~0 | small |
+| 1 | bulk, no binding | -0.015 | +0.16 |
+| 1 | with binding / spike / mixed | ~0 | small |
+
+Profile (mixture, lambda fixed, lambda-1 seed-1 data):
+
+| lambda held at | 0.6 | 0.8 | 1.0 | 1.2 | 1.4 |
+|---|---|---|---|---|---|
+| loss | 36,319 | 35,812 | 36,294 | 35,870 | 36,013 |
+| bulk theta RMSE | 0.111 | 0.107 | 0.105 | 0.102 | 0.102 |
+| dk_geno RMSE | 0.0044 | 0.0034 | 0.0032 | 0.0041 | 0.0052 |
+
+- **The mixture now beats `single` where congression is real** (lambda 1):
+  bulk theta RMSE 0.104 vs 0.125 and 0.074 vs 0.084, dk_geno error cut
+  ~3x, coverage 0.81-0.87 vs 0.58-0.77, and a better fit. The earlier
+  "mixture worse on theta" results came from unconverged or unstable fits.
+- **Lambda is recovered roughly, with overconfident intervals:** 0.94 and
+  0.80 at a true 1.0; intervals ~+/-0.007 exclude the truth. At a true 0
+  the mixture still finds 0.11-0.13, yet fits better than `single` on
+  theta, dk_geno and coverage there too, so its congressed classes absorb
+  something real in the data (the read-count floor is the leading
+  candidate).
+- **The profile is flat within the loss noise** (differences of a few
+  hundred, not monotone): these data barely pin lambda between 0.6 and 1.4.
+  Theta error changes little across that range (0.111 to 0.102), and
+  dk_geno error is lowest near the true lambda. So a misestimated lambda
+  costs little in theta.
+- Coverage remains below nominal for both fits (0.78-0.87), the mixture's
+  less so.
+
+## Confirming round (grid_measured.yaml, grid_seed3.yaml, 2026-09-26)
+
+Same settings as the re-run above (`max` rule on both sides). **All 10 runs
+converged** (38,000-48,000 steps). Outputs: `calib/measured_*`, and
+`calib/pooled_*` for the baseline grid plus seed 3 (runs linked into
+`calib/pooled_all/`).
+
+Measured regime (lambda 0.357, prior 0.357 +/- 0.05):
+
+| seed | fit | lambda (95%) | dk_geno RMSE | bulk theta RMSE | bulk theta coverage | loss |
+|---|---|---|---|---|---|---|
+| 1 | `single` | | 0.0057 | 0.112 | 0.69 | 32,654 |
+| 1 | mixture | 0.266 (0.262-0.269) | 0.0025 | 0.083 | 0.83 | 31,733 |
+| 2 | `single` | | 0.0056 | 0.145 | 0.53 | 31,228 |
+| 2 | mixture | 0.439 (0.434-0.444) | 0.0017 | 0.110 | 0.64 | 29,604 |
+| 3 | `single` | | 0.0064 | 0.106 | 0.72 | 34,176 |
+| 3 | mixture | 0.347 (0.341-0.353) | 0.0026 | 0.068 | 0.85 | 32,850 |
+
+Seed 3 at lambda 0 and 1:
+
+| lambda | fit | lambda (95%) | dk_geno RMSE | bulk theta RMSE | bulk theta coverage | loss |
+|---|---|---|---|---|---|---|
+| 0 | `single` | | 0.0023 | 0.103 | 0.69 | 33,228 |
+| 0 | mixture | 0.119 (0.116-0.123) | 0.0018 | 0.086 | 0.71 | 32,675 |
+| 1 | `single` | | 0.0088 | 0.099 | 0.56 | 25,878 |
+| 1 | mixture | 0.656 (0.650-0.662) | 0.0037 | 0.078 | 0.78 | 24,694 |
+
+Paired against `single` (bulk genotypes without binding data; mean +/- SE
+over 3 seeds):
+
+| lambda | delta theta RMSE | delta 95% coverage | delta dk_geno RMSE |
+|---|---|---|---|
+| 0 | -0.018 +/- 0.000 | +0.04 +/- 0.01 | -0.0008 |
+| 0.357 | -0.034 +/- 0.003 | +0.13 +/- 0.01 | -0.0036 |
+| 1 | -0.017 +/- 0.004 | +0.18 +/- 0.04 | -0.0056 |
+
+Genotypes with binding data are unchanged (delta theta RMSE ~0).
+
+- **The mixture beats `single` in all 9 pairs** on fit (loss), bulk theta
+  RMSE, coverage and dk_geno. The gain is largest in the measured regime.
+- **Lambda: roughly right on average, badly overconfident per run.** At
+  0.357 the three seeds give 0.27, 0.44 and 0.35 (mean 0.35); at 1.0 they
+  give 0.94, 0.80 and 0.66; at 0 they give 0.13, 0.11 and 0.12. The
+  seed-to-seed spread (SD ~0.09 at 0.357, ~0.14 at 1.0) is 30-50 times
+  the reported posterior SD (~0.003), and seed 3 at 1.0 sits 7 prior SDs
+  below its matched prior. Theta is recovered well regardless, as the flat
+  profile predicted.
+- Coverage is still below nominal (mixture 0.64-0.86).
+
+Step 3.5 is closed on these results. Open items are recorded in the plan.
+
+## Step 4 changes that affect this study (2026-09-26)
+
+The fit's default congression theta rule is now `homodimer` and the default
+activity `fixed`. `simulate_config.yaml` keeps `congression_theta_rule: max`
+so the recorded grids stay reproducible, and `run.srun` now passes the fit
+the rule the simulation used (`fit_theta_rule` overrides it, for cross-rule
+grids). The confirming round (`grid_measured.yaml`, `grid_seed3.yaml`) was
+set up before this change and runs `max` on both sides.
+
+`grid_crossrule.yaml` (set up 2026-09-26) is the step 4 validation: simulate
+with `max` or `homodimer`, then fit each simulation with `single` and with
+the mixture under each rule, at lambda 0.357 and 1.0 x seeds 1-2 (24 runs).
+Does the right rule recover theta, and what does the wrong one cost? The
+summary commands are in the grid file's header.
+
+## Cross-rule grid (grid_crossrule.yaml, 2026-09-26)
+
+**All 24 runs converged**, and each fit used the rule it was set up with
+(`max` and `homodimer` fits of the same data stop at the same step only
+because the windows are 2,000 steps). Outputs: `calib/crossrule_*`
+(against `single`), `calib/crossrule_vs_max_*` (homodimer against max on
+the same simulation).
+
+Bulk genotypes without binding data (theta RMSE, 95% coverage, loss):
+
+| lambda | simulated | seed | `single` | mixture, `max` | mixture, `homodimer` |
+|---|---|---|---|---|---|
+| 0.357 | `max` | 1 | 0.112, 0.69, 32,610 | 0.083, 0.83, 31,727 | 0.076, 0.85, 31,675 |
+| 0.357 | `max` | 2 | 0.144, 0.53, 31,240 | 0.108, 0.65, 29,468 | 0.104, 0.69, 29,411 |
+| 0.357 | `homodimer` | 1 | 0.122, 0.69, 32,739 | 0.091, 0.81, 31,824 | 0.083, 0.82, 31,770 |
+| 0.357 | `homodimer` | 2 | 0.147, 0.53, 31,169 | 0.108, 0.67, 29,694 | 0.105, 0.63, 29,581 |
+| 1 | `max` | 1 | 0.125, 0.58, 37,829 | 0.104, 0.81, 36,292 | 0.117, 0.79, 35,861 |
+| 1 | `max` | 2 | 0.084, 0.77, 29,421 | 0.074, 0.87, 28,173 | 0.071, 0.86, 28,159 |
+| 1 | `homodimer` | 1 | 0.125, 0.55, 37,729 | 0.095, 0.83, 36,202 | 0.101, 0.82, 36,241 |
+| 1 | `homodimer` | 2 | 0.087, 0.76, 29,479 | 0.086, 0.84, 28,251 | 0.075, 0.85, 28,171 |
+
+Homodimer fit minus max fit, same simulation (mean +/- SE over 2 seeds):
+
+| lambda | simulated | delta theta RMSE | delta coverage | delta dk_geno RMSE | fitted lambda (max / homodimer) |
+|---|---|---|---|---|---|
+| 0.357 | `max` | -0.006 +/- 0.001 | +0.03 | 0.0000 | 0.27, 0.44 / 0.27, 0.47 |
+| 0.357 | `homodimer` | -0.006 +/- 0.003 | -0.01 | 0.0000 | 0.26, 0.43 / 0.27, 0.45 |
+| 1 | `max` | +0.005 +/- 0.008 | -0.01 | 0.0000 | 0.94, 0.80 / 0.98, 0.85 |
+| 1 | `homodimer` | -0.003 +/- 0.008 | +0.00 | -0.0001 | 0.88, 0.77 / 0.91, 0.84 |
+
+- **The rule is not identifiable from these data, and the wrong one costs
+  nothing measurable.** Theta, dk_geno and coverage differ between the two
+  mixture fits by less than the seed-to-seed spread whichever rule
+  simulated the data. The homodimer fit has the lower loss in 7 of 8
+  pairs, including all 4 simulated with `max`, by 14-430, within the
+  loss noise seen in the lambda profile.
+- **Both mixture fits beat `single` in all 16 pairs** (bulk theta RMSE
+  -0.010 to -0.041, coverage +0.12 to +0.18, dk_geno error ~2.5x lower).
+- The homodimer fit puts lambda slightly higher (by 0.01-0.07), closer to
+  the truth at 1.0. Partition-function mixing pulls a strong binder's
+  theta down in a congressed cell, so matching the same data takes a
+  little more congression than `max` needs.
+- The simulated data do differ by rule (`ln_cfu` differs by 0.1 on average
+  between the `max` and `homodimer` simulations of the same seed; this
+  includes resampled read counts), but not in a way the fit can pin.
+
+`homodimer` stays the default (the physics, the user's call in step 4); the
+choice between the rules waits on native mass spec, not on screen data.
+
+## Step 5: dk rule sensitivity (grid_dkrule.yaml, set up 2026-09-26)
+
+The fit and simulator now offer the soft-min dk family (`dilution`,
+`softmin` with `congression_dk_alpha`, `min`). `run.srun` fits with the
+simulation's dk rule and alpha unless `fit_dk_rule`/`fit_dk_alpha` are
+set. `grid_dkrule.yaml` simulates with `dilution` or `min` and fits each
+simulation with `single` and the mixture under each rule (homodimer theta
+rule on both sides), at the wide dk spread (worst case) at lambda 0.357 and
+1.0 and the tight, realistic spread at 0.357, x seeds 1-2 (36 runs). Does
+the dk rule matter, and does it matter at a realistic dk spread? Summary
+commands are in the grid file's header.
+
+### Results (2026-09-26)
+
+**All 36 runs converged**, each fit with its intended dk rule. Outputs:
+`calib/dkrule_*` (against `single`), `calib/dkrule_vs_dilution_*` (min
+against dilution on the same simulation). Means over seeds 1-2 (bulk theta
+without binding data; lambda per seed):
+
+| design | simulated | fit | bulk theta RMSE | coverage | dk_geno RMSE | lambda |
+|---|---|---|---|---|---|---|
+| wide dk, lambda 0.357 | dilution | `single` | 0.135 | 0.60 | 0.0057 | |
+| | | mixture, dilution | 0.094 | 0.75 | 0.0023 | 0.27, 0.47 |
+| | | mixture, min | 0.140 | 0.61 | 0.0055 | 0.13, 0.34 |
+| | min | `single` | 0.115 | 0.71 | 0.0023 | |
+| | | mixture, dilution | 0.090 | 0.75 | 0.0018 | 0.11, 0.19 |
+| | | mixture, min | 0.121 | 0.70 | 0.0021 | 0.15, 0.44 |
+| wide dk, lambda 1 | dilution | `single` | 0.103 | 0.68 | 0.0086 | |
+| | | mixture, dilution | 0.088 | 0.83 | 0.0028 | 0.91, 0.84 |
+| | | mixture, min | 0.107 | 0.68 | 0.0080 | 0.83, 0.76 |
+| | min | `single` | 0.103 | 0.76 | 0.0024 | |
+| | | mixture, dilution | 0.084 | 0.82 | 0.0050 | 0.47, 0.38 |
+| | | mixture, min | 0.116 | 0.78 | 0.0017 | 1.02, 0.89 |
+| tight dk, lambda 0.357 | dilution | `single` | 0.065 | 0.70 | 0.00025 | |
+| | | mixture, dilution | 0.064 | 0.78 | 0.00032 | 0.55, 0.90 |
+| | | mixture, min | 0.065 | 0.76 | 0.00028 | 0.56, 0.97 |
+| | min | `single` | 0.065 | 0.71 | 0.00026 | |
+| | | mixture, dilution | 0.064 | 0.79 | 0.00034 | 0.54, 0.89 |
+| | | mixture, min | 0.065 | 0.76 | 0.00029 | 0.55, 0.96 |
+
+- **At a realistic (tight) dk spread the dk rule does not matter:** theta,
+  dk_geno and loss agree between the two rules to within 0.001 RMSE and
+  30 loss units, whichever rule simulated the data. The mixture does not
+  beat `single` on theta there either (-0.001); it does improve coverage
+  (+0.06 to +0.08).
+- **At the wide spread, the dilution fit gives the best theta whichever
+  rule simulated the data** (0.084-0.094 vs 0.103-0.135 for `single`),
+  and it also has the lower loss on min-simulated data at lambda 0.357
+  (by 360-480). The min fit gains nothing on theta over `single`, even
+  when min is the truth (0.121 vs 0.115; 0.116 vs 0.103 at lambda 1).
+- **dk_geno and lambda need the right rule.** On min-simulated data at
+  lambda 1, the min fit recovers lambda (1.02, 0.89) and gives the best
+  dk_geno (0.0017); the dilution fit puts lambda at 0.47 and 0.38 and its
+  dk_geno error is twice `single`'s. On dilution-simulated data the
+  dilution fit cuts dk_geno error ~3x and the min fit does not. So the
+  min fit is consistent with its own data: no sign of a fitting bug.
+- **What the dilution mixture's theta gain comes from.** It does not
+  track congression: it is as large on min-simulated data (where
+  congressed cells cannot outgrow a slow genotype) as on dilution data,
+  about the same at lambda 0 in the baseline grid (-0.018) as at lambda 1
+  (-0.017), and absent at the tight dk spread. It needs slow genotypes and
+  a rule that lets congressed cells outgrow them. The read-count floor fits
+  that description (a dying genotype's late timepoints stall above the
+  truth, and only a faster-growing class can bend its trajectory up), but
+  this is an inference from the pattern, not yet a test. The dk_geno gain,
+  by contrast, grows with lambda and follows the right rule: that part is
+  congression.
+- **Lambda at the tight spread is pulled well above the truth** (0.54 to
+  0.97 at a true 0.357, against a 0.357 +/- 0.05 prior), identically under
+  both rules. With little dk spread only theta mixing carries congression,
+  and the fit leans on it; worth understanding before trusting a fitted
+  lambda on real data (where the spread is tight).
+
+`dilution` stays the default: at a realistic spread the choice does not
+matter, and at a wide one no rule does better on theta.
diff --git a/planning/studies/congression-calibration/diagnosis/compare_masked.py b/planning/studies/congression-calibration/diagnosis/compare_masked.py
new file mode 100644
index 00000000..8f6bac85
--- /dev/null
+++ b/planning/studies/congression-calibration/diagnosis/compare_masked.py
@@ -0,0 +1,60 @@
+"""
+Compare follow-up runs (grid_masked.yaml: rows with < 5 reads dropped;
+grid_bw1.yaml: binding_weight 1) with their twins in grid.yaml (same
+simulated data). Run from the study directory; arguments name the follow-up
+grid directories (default: congression_calibration_masked).
+"""
+import glob
+import json
+import sys
+
+import numpy as np
+import pandas as pd
+
+PAIRS = {"0001": "0001", "0002": "0002", "0003": "0003",
+         "0004": "0037", "0005": "0038", "0006": "0039"}
+
+
+def summarize(d):
+    c = json.load(open(f"{d}/combo.json"))
+    s, t = c["simulate"], c["template"]
+    out = dict(lam_true=s["transformation_poisson_lambda"],
+               arm=f"{t['fit']}/{t['lam_prior']}")
+    lam = glob.glob(f"{d}/summary/*_params_lam.csv")
+    if lam:
+        r = pd.read_csv(lam[0]).iloc[0]
+        out["lam_q50"], out["lam_q025"], out["lam_q975"] = r["q0.5"], r["q0.025"], r["q0.975"]
+    k = pd.read_csv(glob.glob(f"{d}/summary/*_params_growth_k.csv")[0])
+    dk = pd.read_csv(glob.glob(f"{d}/summary/*_params_dk_geno.csv")[0])
+    out["k_off"] = (k["q0.5"] - k["ref"]).mean()
+    out["dk_off"] = (dk["q0.5"] - dk["ref"]).mean()
+    out["dk_rmse_centered"] = ((dk["q0.5"] - dk["ref"]) - out["dk_off"]).pow(2).mean() ** 0.5
+
+    th = pd.read_csv(glob.glob(f"{d}/summary/*_theta_corr_test.csv")[0])
+    lib = pd.read_csv(glob.glob(f"{d}/*_sim_library.csv")[0])
+    binding = set(pd.read_csv(glob.glob(f"{d}/*_sim_binding.csv")[0]).genotype.astype(str))
+    spiked = set(lib.loc[lib.library_origin == "spiked", "genotype"].astype(str))
+    th["genotype"] = th.genotype.astype(str)
+    bulk_nobind = th[~th.genotype.isin(spiked | binding)]
+    lo, hi = bulk_nobind["q0.025"], bulk_nobind["q0.975"]
+    out["theta_rmse"] = ((bulk_nobind["q0.5"] - bulk_nobind["ref"]) ** 2).mean() ** 0.5
+    out["theta_cov95"] = ((lo <= bulk_nobind.ref) & (bulk_nobind.ref <= hi)).mean()
+    return out
+
+
+GRIDS = sys.argv[1:] or ["congression_calibration_masked"]
+rows = []
+for m, o in PAIRS.items():
+    runs = [("original", glob.glob(f"congression_calibration/run_{o}_*")[0])]
+    runs += [(g.replace("congression_calibration_", ""), glob.glob(f"{g}/run_{m}_*")[0])
+             for g in GRIDS]
+    for tag, d in runs:
+        r = summarize(d)
+        r["data"] = tag
+        rows.append(r)
+
+df = pd.DataFrame(rows)
+cols = ["lam_true", "arm", "data", "lam_q025", "lam_q50", "lam_q975",
+        "k_off", "dk_off", "dk_rmse_centered", "theta_rmse", "theta_cov95"]
+pd.set_option("display.width", 220)
+print(df[cols].sort_values(["lam_true", "arm", "data"]).round(4).to_string(index=False))
diff --git a/planning/studies/congression-calibration/diagnosis/diag_basin.py b/planning/studies/congression-calibration/diagnosis/diag_basin.py
new file mode 100644
index 00000000..b4ffc70d
--- /dev/null
+++ b/planning/studies/congression-calibration/diagnosis/diag_basin.py
@@ -0,0 +1,57 @@
+"""
+Is the mixture fit's solution better than single's solution under the
+mixture model (joint log density, priors included)? And where does the
+mixture's better binding fit come from? Run from this directory; GRID
+selects the grid (default: the unmasked one).
+"""
+import sys
+sys.argv = [sys.argv[0]]
+from diag_lam0 import *
+import pandas as pd
+from numpyro.infer.util import log_density
+
+(d1, o1, s1, st1), (d2, o2, s2, st2) = load("run_0001"), load("run_0002")
+lat1, _ = median_latents(o1, s1, st1)
+lat2, _ = median_latents(o2, s2, st2)
+data = full_batch(o2)
+
+
+def joint(orch, lat):
+    lp, _ = log_density(seed(orch.jax_model, 0), (), dict(data=full_batch(orch), priors=orch.priors), lat)
+    return float(lp)
+
+
+x = dict(lat1); x["transformation_lam"] = lat2["transformation_lam"]
+print("mixture model joint log density:")
+print("  at mixture fit          %.1f" % joint(o2, lat2))
+print("  at single fit (same lam) %.1f" % joint(o2, x))
+y = dict(lat2); y.pop("transformation_lam")
+print("single model joint log density:")
+print("  at single fit           %.1f" % joint(o1, lat1))
+print("  at mixture fit          %.1f" % joint(o1, y))
+
+# Binding log-lik per genotype at each fit
+def binding_by_geno(orch, lat):
+    tr = trace(substitute(seed(orch.jax_model, 0), data=lat)).get_trace(
+        data=full_batch(orch), priors=orch.priors)
+    site = tr["binding_obs"]
+    lp = site["fn"].log_prob(site["value"])
+    if site.get("mask") is not None:
+        lp = lp * site["mask"]
+    return np.asarray(lp)
+
+b1 = binding_by_geno(o1, lat1); b2 = binding_by_geno(o2, lat2)
+bt = o2.binding_tm
+names = bt.tensor_dim_names
+labels = list(bt.tensor_dim_labels[names.index("genotype")])
+gain = (b2 - b1).reshape(-1, b1.shape[-1]).sum(axis=0) if b1.shape[-1] == len(labels) else None
+if gain is None:
+    print("binding tensor layout", names, b1.shape)
+else:
+    lib = pd.read_csv(glob.glob(d2 + "/*_sim_library.csv")[0])
+    spiked = set(lib.loc[lib.library_origin == "spiked", "genotype"].astype(str))
+    df = pd.DataFrame({"genotype": [str(g) for g in labels], "binding_gain": gain})
+    df["spiked"] = df.genotype.isin(spiked)
+    print("\nbinding log-lik gain (mixture fit - single fit), by origin:")
+    print(df.groupby("spiked").binding_gain.agg(["count", "sum"]).round(1))
+    print(df.sort_values("binding_gain", ascending=False).head(8).round(1).to_string(index=False))
diff --git a/planning/studies/congression-calibration/diagnosis/diag_clip.py b/planning/studies/congression-calibration/diagnosis/diag_clip.py
new file mode 100644
index 00000000..3e6d8e92
--- /dev/null
+++ b/planning/studies/congression-calibration/diagnosis/diag_clip.py
@@ -0,0 +1,148 @@
+"""
+Why the step-3.5 baseline/profile fits never converged and "blew up": the
+per-step loss is 41k + n * 243k (n = draws crossing one binding step), and
+ClippedAdam's elementwise clip makes Adam settle where gradient *signs*
+balance, holding a large share of draws across the step.
+
+Three checks at one checkpoint, from inside a run directory:
+
+    cd congression_calibration_baseline/run_0005_lam1_0_seed1_single_off
+    python ../../diagnosis/diag_clip.py checkpoints/0040000_checkpoint.pkl
+
+1. events: per-draw binding log-likelihood; which observations carry the
+   penalty, and the predicted theta there.
+2. gradients: mean raw vs mean clipped (+/-1) gradient of the loss for the
+   parameters that differ most between penalized and clean draws.
+3. resume: --steps optimizer steps from the checkpoint with and without the
+   clip (--lr, default 1e-3); share of penalized steps and mean loss per
+   2000 steps.
+
+Needs this worktree's src on PYTHONPATH (the editable install may point at
+another checkout).
+"""
+
+import argparse
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+from numpyro import handlers
+
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.inference.run_inference import (
+    RunInference,
+    adam_optimizer,
+)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("checkpoint")
+parser.add_argument("--draws", type=int, default=40)
+parser.add_argument("--steps", type=int, default=8000)
+parser.add_argument("--lr", type=float, default=1e-3)
+args = parser.parse_args()
+
+orch, init_params = read_configuration("tfs_configure_config.yaml")
+ri = RunInference(orch, seed=1)
+svi, state = ri.restore_svi_from_checkpoint(args.checkpoint,
+                                            init_params=init_params)
+params = svi.get_params(state)
+data = jax.device_put(orch.data)
+# The training index (binding genotypes first, the rest shuffled); the
+# library-ordered data.batch_idx pairs genotypes with the wrong rows.
+batch = orch.get_batch(data, jnp.asarray(ri.model.get_random_idx()))
+
+
+def penalized(losses):
+    """Steps carrying at least one step-crossing penalty (~2.4e5)."""
+    return losses > np.percentile(losses, 1) + 1.2e5
+
+
+# --- 1. events -------------------------------------------------------------
+
+lp, loc = [], []
+for i in range(args.draws):
+    key = jax.random.PRNGKey(i)
+    g = handlers.trace(handlers.seed(
+        handlers.substitute(orch.jax_model_guide, data=params), key)
+    ).get_trace(priors=orch.priors, data=batch)
+    m = handlers.trace(handlers.replay(handlers.seed(orch.jax_model, key), g)
+                       ).get_trace(priors=orch.priors, data=batch)
+    site = m["binding_obs"]
+    fn = site["fn"]
+    lp.append(np.asarray(fn.log_prob(site["value"])).ravel())  # masked
+    loc.append(np.broadcast_to(np.asarray(fn.base_dist.loc),
+                               site["value"].shape).ravel())
+lp, loc = np.array(lp), np.array(loc)
+obs = np.asarray(batch.binding.theta_obs).ravel()
+shape = batch.binding.theta_obs.shape
+print("\n1. binding log-likelihood per draw:",
+      np.round(lp.sum(1)).astype(int).tolist())
+for j in np.where(lp.min(0) < -1e4)[0]:
+    idx = tuple(int(i) for i in np.unravel_index(j, shape))
+    hit = lp[:, j] < -1e4
+    print(f"   obs {idx} theta_obs {obs[j]:.4f}: penalized in {hit.mean():.0%} "
+          f"of draws, predicted theta {np.round(loc[hit, j], 3)[:4]} "
+          f"(clean draws ~{np.median(loc[~hit, j]):.4f})")
+
+# --- 2. gradients ----------------------------------------------------------
+
+u = svi.optim.get_params(state.optim_state)
+
+
+def loss(p, key):
+    return svi.loss.loss(key, svi.constrain_fn(p), orch.jax_model,
+                         orch.jax_model_guide, priors=orch.priors, data=batch)
+
+
+vg = jax.jit(jax.value_and_grad(loss))
+L, G = [], []
+for i in range(4 * args.draws):
+    v, g = vg(u, jax.random.PRNGKey(1000 + i))
+    L.append(float(v))
+    G.append(g)
+L = np.array(L)
+ev = penalized(L)
+print(f"\n2. {ev.mean():.0%} of {L.size} draws penalized; mean loss "
+      f"{L.mean():.4g}, median {np.median(L):.4g}")
+rows = []
+for k in u:
+    g = np.stack([np.asarray(x[k]).ravel() for x in G])
+    if ev.all() or not ev.any():
+        continue
+    diff = np.abs(g[ev].mean(0) - g[~ev].mean(0))
+    j = int(np.argmax(diff))
+    rows.append((diff[j], k, j, g[:, j].mean(),
+                 g[:, j].std() / np.sqrt(len(g)),
+                 np.clip(g[:, j], -1, 1).mean(),
+                 np.mean(np.abs(g) > 1)))
+rows.sort(reverse=True)
+print(f"   {'parameter[element]':44s} {'raw mean':>10s} {'+/- se':>8s} "
+      f"{'clipped':>8s}   array's |grad| > 1 (clipped)")
+for _, k, j, raw, se, clipped, frac in rows[:10]:
+    print(f"   {k + f'[{j}]':44s} {raw:10.3g} {se:8.2g} {clipped:8.3f}   "
+          f"{frac:.0%} of elements x draws")
+
+# --- 3. resume with and without the clip -------------------------------------
+
+for clip in (1.0, None):
+    _, st = ri.restore_svi_from_checkpoint(args.checkpoint,
+                                           init_params=init_params)
+    svi.optim = adam_optimizer(args.lr, clip)
+
+    def step(s, idx):
+        return svi.update(s, priors=orch.priors,
+                          data=orch.get_batch(data, idx))
+
+    scan = jax.jit(lambda s, idx: jax.lax.scan(step, s, idx))
+    losses = []
+    for _ in range(args.steps // 250):
+        st, lb = scan(st, jnp.asarray(ri.model.get_random_idx(num_batches=250)))
+        losses.append(np.asarray(lb))
+    losses = np.concatenate(losses)
+    ev = penalized(losses)
+    w = 2000
+    print(f"\n3. resume, clip {clip}, step size {args.lr:g}:")
+    print("   penalized share per 2000:",
+          [round(float(ev[a:a + w].mean()), 2) for a in range(0, ev.size, w)])
+    print("   mean loss per 2000:      ",
+          [int(losses[a:a + w].mean()) for a in range(0, ev.size, w)])
diff --git a/planning/studies/congression-calibration/diagnosis/diag_elbo.py b/planning/studies/congression-calibration/diagnosis/diag_elbo.py
new file mode 100644
index 00000000..e0503a0e
--- /dev/null
+++ b/planning/studies/congression-calibration/diagnosis/diag_elbo.py
@@ -0,0 +1,42 @@
+"""
+Does the ELBO (averaged over guide draws) prefer the mixture fit's endpoint
+while the joint density at median latents prefers single's solution? That
+would mean guide noise, passed through the mixture's convex logsumexp and
+max-theta, is what drives the SVI drift. Run from this directory; GRID
+selects the grid.
+"""
+import sys
+sys.argv = [sys.argv[0]]
+from diag_lam0 import *
+from numpyro.infer import Trace_ELBO
+
+N_DRAWS = 64
+(d1, o1, s1, st1), (d2, o2, s2, st2) = load("run_0001"), load("run_0002")
+p1, p2 = s1.get_params(st1), s2.get_params(st2)
+start = {k: (p1[k] if k in p1 else p2[k]) for k in p2}
+elbo = Trace_ELBO(num_particles=1)
+
+
+def mean_loss(params, orch, svi, n=N_DRAWS):
+    f = jax.jit(lambda p, key: elbo.loss(key, p, orch.jax_model, svi.guide,
+                                          data=full_batch(orch), priors=orch.priors))
+    v = np.array([float(f(params, jax.random.PRNGKey(i))) for i in range(n)])
+    return v.mean(), v.std() / np.sqrt(n)
+
+
+for tag, params in (("single's solution (+ mixture lambda)", start),
+                    ("mixture's own endpoint", p2)):
+    m, se = mean_loss(params, o2, s2)
+    print(f"mixture -ELBO at {tag:38s} {m:12.1f} +/- {se:.1f}")
+m, se = mean_loss(p1, o1, s1)
+print(f"single  -ELBO at {'single solution':38s} {m:12.1f} +/- {se:.1f}")
+
+# Same comparison with the guide's scales shrunk toward zero (near-point
+# evaluation): if the ordering flips, guide noise is what favors the drift.
+def shrink(p, f=1e-3):
+    return {k: (v * f if k.endswith("_scale") or k.endswith("_scales") else v)
+            for k, v in p.items()}
+for tag, params in (("single's solution, scales x1e-3", shrink(start)),
+                    ("mixture endpoint, scales x1e-3", shrink(p2))):
+    m, se = mean_loss(params, o2, s2, n=8)
+    print(f"mixture -ELBO at {tag:38s} {m:12.1f} +/- {se:.1f}")
diff --git a/planning/studies/congression-calibration/diagnosis/diag_geno.py b/planning/studies/congression-calibration/diagnosis/diag_geno.py
new file mode 100644
index 00000000..a9459fc5
--- /dev/null
+++ b/planning/studies/congression-calibration/diagnosis/diag_geno.py
@@ -0,0 +1,28 @@
+"""
+Per-genotype growth log-likelihood gained from the congressed classes in
+grid run 0002 (lambda 0, mixture, matched prior), at the fit's median latents.
+Writes geno_gain.csv. Run from this directory.
+"""
+import os
+import sys
+sys.argv = [sys.argv[0]]
+from diag_lam0 import *
+import pandas as pd
+
+(d2, o2, s2, st2) = load("run_0002")
+lat2, _ = median_latents(o2, s2, st2)
+off = dict(lat2); off["transformation_lam"] = jnp.array(1e-8)
+on_lp, off_lp = per_genotype(o2, lat2, off)
+drop = (on_lp - off_lp).reshape(-1, on_lp.shape[-1]).sum(axis=0)
+labels = list(o2.growth_tm.tensor_dim_labels[o2.growth_tm.tensor_dim_names.index("genotype")])
+df = pd.DataFrame({"genotype": labels, "loglik_gain_from_congressed": drop})
+par = pd.read_csv(glob.glob(d2 + "/summary/*_params_dk_geno.csv")[0])[["genotype", "q0.5", "ref"]]
+par.columns = ["genotype", "dk_fit", "dk_true"]
+df = df.merge(par, on="genotype", how="left").sort_values("loglik_gain_from_congressed", ascending=False)
+print("total gain", df.loglik_gain_from_congressed.sum().round(1))
+print("share of gain in top 10 / top 50 genotypes:",
+      (df.loglik_gain_from_congressed.head(10).sum() / df.loglik_gain_from_congressed.sum()).round(2),
+      (df.loglik_gain_from_congressed.head(50).sum() / df.loglik_gain_from_congressed.sum()).round(2))
+print(df.head(12).round(4).to_string(index=False))
+print("\nall genotypes: dk_fit - dk_true median %.4f" % (df.dk_fit - df.dk_true).median())
+df.to_csv("geno_gain.csv", index=False)
diff --git a/planning/studies/congression-calibration/diagnosis/diag_gradient.py b/planning/studies/congression-calibration/diagnosis/diag_gradient.py
new file mode 100644
index 00000000..2862fe0f
--- /dev/null
+++ b/planning/studies/congression-calibration/diagnosis/diag_gradient.py
@@ -0,0 +1,77 @@
+"""
+Is the mixture's SVI gradient biased along the k/dk_geno direction?
+
+Build the mixture fit's guide parameters from the single fit's solution
+(keeping the mixture fit's lambda parameters), then average the ELBO
+gradient over many draws with no optimization. Also the deterministic
+gradient of the mixture's joint log density at single's median latents.
+Run from this directory; GRID selects the grid (default: the unmasked one).
+"""
+import sys
+sys.argv = [sys.argv[0]]
+from diag_lam0 import *
+from numpyro.infer import Trace_ELBO
+from numpyro.infer.util import log_density
+
+N_DRAWS = 64
+
+(d1, o1, s1, st1), (d2, o2, s2, st2) = load("run_0001"), load("run_0002")
+p1 = s1.get_params(st1)
+p2 = s2.get_params(st2)
+
+shared = sorted(set(p1) & set(p2))
+only2 = sorted(set(p2) - set(p1))
+print("mixture-only params:", only2)
+start = {k: (p1[k] if k in p1 else p2[k]) for k in p2}
+
+data = full_batch(o2)
+elbo = Trace_ELBO(num_particles=1)
+
+
+def mean_grad(params, orch, svi, n=N_DRAWS):
+    def loss(p, key):
+        return elbo.loss(key, p, orch.jax_model, svi.guide,
+                         data=full_batch(orch), priors=orch.priors)
+    g = jax.jit(jax.grad(loss))
+    grads = [g(params, jax.random.PRNGKey(i)) for i in range(n)]
+    mean = jax.tree_util.tree_map(lambda *x: jnp.mean(jnp.stack(x), 0), *grads)
+    se = jax.tree_util.tree_map(lambda *x: jnp.std(jnp.stack(x), 0) / np.sqrt(n), *grads)
+    return mean, se
+
+
+def show(tag, grads, keys):
+    mean, se = grads
+    print(f"\n{tag}: mean d(loss)/d(param) +/- SE (SVI moves against the sign)")
+    for k in keys:
+        m = np.ravel(np.asarray(mean[k])); s = np.ravel(np.asarray(se[k]))
+        print(f"  {k:40s} " + "  ".join(f"{a:+.3g}+/-{b:.2g}" for a, b in zip(m, s)))
+
+
+keys = [k for k in p2 if k.startswith("condition_growth_k")] + \
+       [k for k in p2 if k.startswith("dk_geno") and np.size(p2[k]) <= 4] + only2
+show("mixture model, guide at SINGLE's solution", mean_grad(start, o2, s2), keys)
+show("mixture model, guide at MIXTURE's own solution", mean_grad(p2, o2, s2), keys)
+k1 = [k for k in keys if k in p1]
+show("single model, guide at SINGLE's solution", mean_grad(p1, o1, s1), k1)
+
+# Deterministic check: gradient of the joint log density wrt the k latent.
+lat1, _ = median_latents(o1, s1, st1)
+lat = dict(lat1)
+lat["transformation_lam"] = jnp.asarray(np.exp(float(p2["transformation_lam_loc"])))
+
+
+def joint(l, orch):
+    lp, _ = log_density(seed(orch.jax_model, 0), (),
+                        dict(data=full_batch(orch), priors=orch.priors), l)
+    return lp
+
+
+for tag, orch, l in (("mixture", o2, lat), ("single", o1, lat1)):
+    g = jax.grad(joint)(l, orch)
+    kname = [k for k in g if k.startswith("condition_growth_k")]
+    print(f"\n{tag} joint log density at single's median latents, d/d(latent):")
+    for k in kname:
+        print(f"  {k:30s}", np.round(np.ravel(np.asarray(g[k])), 1))
+    dk = np.asarray(g["dk_geno_offset"]) if "dk_geno_offset" in g else None
+    if dk is not None:
+        print(f"  dk_geno_offset (sum over genotypes)   {dk.sum():.1f}")
diff --git a/planning/studies/congression-calibration/diagnosis/diag_lam0.py b/planning/studies/congression-calibration/diagnosis/diag_lam0.py
new file mode 100644
index 00000000..87d4a2ff
--- /dev/null
+++ b/planning/studies/congression-calibration/diagnosis/diag_lam0.py
@@ -0,0 +1,119 @@
+"""
+Diagnose why mixture fits with lambda ~ 0 do worse than single on the same data.
+
+Runs 0001 (single) and 0002 (mixture, matched prior, lambda -> ~0.004) were
+fit to identical simulated data. Evaluate both models' log-likelihood and
+predictions at both fits' posterior-median latents.
+"""
+
+import glob
+import os
+import sys
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+from numpyro.handlers import seed, substitute, trace
+
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.inference.run_inference import RunInference
+
+GRID = os.environ.get("GRID", os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "congression_calibration"))
+RUNS = sys.argv[1:] or ["run_0001", "run_0002"]
+
+
+def load(run):
+    d = glob.glob(os.path.join(GRID, run + "_*"))[0]
+    cwd = os.getcwd()
+    os.chdir(d)
+    try:
+        orch, init_params = read_configuration("tfs_configure_config.yaml")
+        ri = RunInference(orch, 0)
+        svi, state = ri.restore_svi_from_checkpoint(
+            "tfs_fit_model_checkpoint.pkl", init_params=init_params)
+    finally:
+        os.chdir(cwd)
+    return d, orch, svi, state
+
+
+def full_batch(orch):
+    return orch.get_batch(orch.data, jnp.arange(orch.data.num_genotype))
+
+
+def median_latents(orch, svi, state, n=60):
+    params = svi.get_params(state)
+    data = full_batch(orch)
+    guide = substitute(svi.guide, data=params)
+    draws = {}
+    for i in range(n):
+        tr = trace(seed(guide, i)).get_trace(data=data, priors=orch.priors)
+        for name, site in tr.items():
+            if site["type"] == "sample" and not name.startswith("_"):
+                draws.setdefault(name, []).append(np.asarray(site["value"]))
+    return {k: jnp.asarray(np.median(np.stack(v), axis=0)) for k, v in draws.items()}, params
+
+
+def evaluate(orch, latents):
+    data = full_batch(orch)
+    model = substitute(seed(orch.jax_model, 0), data=latents)
+    tr = trace(model).get_trace(data=data, priors=orch.priors)
+    obs = {name: float(jnp.sum(site["fn"].log_prob(site["value"])
+                               * (site["mask"] if site.get("mask") is not None else 1.0)))
+           for name, site in tr.items()
+           if site["type"] == "sample" and site.get("is_observed", False)}
+    return obs, np.asarray(tr["growth_pred"]["value"])
+
+
+if __name__ == "__main__":
+    (d1, o1, s1, st1), (d2, o2, s2, st2) = load(RUNS[0]), load(RUNS[1])
+    print("single run :", os.path.basename(d1))
+    print("mixture run:", os.path.basename(d2))
+
+    lat1, _ = median_latents(o1, s1, st1)
+    lat2, p2 = median_latents(o2, s2, st2)
+    print("\nmixture guide lam params:",
+          {k: np.asarray(v).round(4).tolist() for k, v in p2.items() if k.startswith("transformation")})
+    print("mixture median lam:", float(lat2["transformation_lam"]))
+
+    only1 = sorted(set(lat1) - set(lat2)); only2 = sorted(set(lat2) - set(lat1))
+    print("latents only in single:", only1, " only in mixture:", only2)
+
+    rows = []
+    obs, g_s_s = evaluate(o1, lat1)
+    rows.append(("single model @ single fit", obs))
+    obs, g_m_m = evaluate(o2, lat2)
+    rows.append(("mixture model @ mixture fit", obs))
+
+    for lam in (float(lat2["transformation_lam"]), 1e-8):
+        l = dict(lat1); l["transformation_lam"] = jnp.array(lam)
+        obs, g = evaluate(o2, l)
+        rows.append((f"mixture model @ single fit, lam={lam:.3g}", obs))
+        diff = g - g_s_s
+        print(f"\ngrowth_pred mixture@single-fit(lam={lam:.3g}) - single@single-fit: "
+              f"max|d| {np.nanmax(np.abs(diff)):.4g}, rms {np.sqrt(np.nanmean(diff**2)):.4g}")
+
+    l = dict(lat2); l.pop("transformation_lam")
+    obs, _ = evaluate(o1, l)
+    rows.append(("single model @ mixture fit", obs))
+
+    print("\nobserved-site log-likelihoods (sum):")
+    sites = sorted({k for _, o in rows for k in o})
+    print(f"{'':48s}" + "".join(f"{s:>18s}" for s in sites) + f"{'total':>18s}")
+    for name, o in rows:
+        print(f"{name:48s}" + "".join(f"{o.get(s, np.nan):18.1f}" for s in sites)
+              + f"{sum(o.values()):18.1f}")
+
+
+def per_genotype(orch, latents, off_latents):
+    """Growth log-lik per genotype with and without the congressed classes."""
+    data = full_batch(orch)
+    out = []
+    for lat in (latents, off_latents):
+        tr = trace(substitute(seed(orch.jax_model, 0), data=lat)).get_trace(
+            data=data, priors=orch.priors)
+        site = tr["growth_obs"]
+        lp = site["fn"].log_prob(site["value"])
+        if site.get("mask") is not None:
+            lp = lp * site["mask"]
+        out.append(np.asarray(lp))
+    return out
diff --git a/planning/studies/congression-calibration/diagnosis/diag_population.py b/planning/studies/congression-calibration/diagnosis/diag_population.py
new file mode 100644
index 00000000..20963906
--- /dev/null
+++ b/planning/studies/congression-calibration/diagnosis/diag_population.py
@@ -0,0 +1,29 @@
+"""
+Is the population dk_geno / activity / theta the mixture looks co-residents
+up in the same as each genotype's own value? Compare the component's focal
+output with population[batch_idx] at the mixture fit's median latents.
+Run from this directory; GRID selects the grid.
+"""
+import sys
+sys.argv = [sys.argv[0]]
+from diag_lam0 import *
+
+(d2, o2, s2, st2) = load("run_0002")
+lat2, _ = median_latents(o2, s2, st2)
+data = full_batch(o2)
+tr = trace(substitute(seed(o2.jax_model, 0), data=lat2)).get_trace(data=data, priors=o2.priors)
+print("deterministic sites:", [k for k, v in tr.items() if v["type"] == "deterministic"][:40])
+
+from tfscreen.tfmodel.generative.registry import model_registry
+comp = model_registry["dk_geno"][o2.settings["dk_geno"]]
+def run_dk():
+    return comp.define_model("dk_geno", data.growth, o2.priors.growth.dk_geno,
+                             return_population=True)
+focal, pop = substitute(seed(run_dk, 0), data=lat2)()
+bi = np.asarray(data.growth.batch_idx)
+f = np.ravel(np.asarray(focal)); p = np.asarray(pop)
+print("focal dk_geno: shape", np.asarray(focal).shape, " population:", p.shape)
+print("focal - population[batch_idx]: max|d| %.3g" % np.max(np.abs(f - p[bi])))
+print("focal mean %.4f  population mean %.4f" % (f.mean(), p.mean()))
+if "dk_geno" in tr:
+    print("traced dk_geno site mean %.4f" % float(np.mean(tr["dk_geno"]["value"])))
diff --git a/planning/studies/congression-calibration/diagnosis/diag_sites.py b/planning/studies/congression-calibration/diagnosis/diag_sites.py
new file mode 100644
index 00000000..d3bf78ad
--- /dev/null
+++ b/planning/studies/congression-calibration/diagnosis/diag_sites.py
@@ -0,0 +1,27 @@
+"""Per-site joint log density of the single model at the single and mixture fits."""
+import sys
+sys.argv = [sys.argv[0]]
+from diag_lam0 import *
+import pandas as pd
+from numpyro.infer.util import log_density
+
+(d1, o1, s1, st1), (d2, o2, s2, st2) = load("run_0001"), load("run_0002")
+lat1, _ = median_latents(o1, s1, st1)
+lat2, _ = median_latents(o2, s2, st2)
+lat2.pop("transformation_lam")
+rows = {}
+for tag, lat in (("single_fit", lat1), ("mixture_fit", lat2)):
+    _, tr = log_density(seed(o1.jax_model, 0), (), dict(data=full_batch(o1), priors=o1.priors), lat)
+    for name, site in tr.items():
+        if site["type"] != "sample":
+            continue
+        lp = site["fn"].log_prob(site["value"])
+        if site.get("scale") is not None:
+            lp = lp * site["scale"]
+        if site.get("mask") is not None:
+            lp = jnp.where(site["mask"], lp, 0.0)
+        rows.setdefault(name, {})[tag] = float(jnp.sum(lp))
+df = pd.DataFrame(rows).T
+df["diff"] = df.mixture_fit - df.single_fit
+pd.set_option("display.width", 200)
+print(df.reindex(df["diff"].abs().sort_values(ascending=False).index).head(12).round(1).to_string())
diff --git a/planning/studies/congression-calibration/grid.yaml b/planning/studies/congression-calibration/grid.yaml
new file mode 100644
index 00000000..85d4521e
--- /dev/null
+++ b/planning/studies/congression-calibration/grid.yaml
@@ -0,0 +1,70 @@
+# ---------------------------------------------------------------------------
+# Congression calibration grid (step 3.5 of planning/congression-physics-plan.md)
+#
+#   tfs-setup-sim-grid grid.yaml --out_prefix congression_calibration
+#   for d in congression_calibration/*/; do (cd "$d" && sbatch run.srun); done
+#
+# 3 lambda x 2 dk spreads x 3 seeds x 3 fits = 54 runs. See README.md.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__dkloc{{ dk_geno_hyper_loc }}__seed{{ seed }}__{{ fit }}_{{ lam_prior }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  # --- Simulated congression rate ------------------------------------------
+  # 0 = no congression (null arm); 0.357 = measured; 1.0 = stress.
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+      - transformation_poisson_lambda: 0.357
+      - transformation_poisson_lambda: 1.0
+
+  # --- dk_geno spread --------------------------------------------------------
+  # dk_geno = shift - exp(Normal(loc, scale)), wt = 0.
+  #   wide  (loc -3.5): SD 0.018/min, 5th-95th percentile -0.049 to 0.007
+  #                     (the example config)
+  #   tight (loc -7.0): SD 0.0006/min, mean ~0 (the measured regime, dk SD
+  #                     ~0.001/min)
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+      - dk_geno_hyper_loc: -7.0
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.001
+
+  # --- Simulation seed (also the fit seed) -----------------------------------
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+      - seed: 3
+
+
+template:
+
+  # --- Fit ------------------------------------------------------------------
+  # single: no congression model (the paired baseline).
+  # mixture/matched: lambda prior centered on the simulated value (0.01 when
+  #   the simulated value is 0), SD 0.05.
+  # mixture/measured: lambda prior 0.357 +/- 0.05 whatever the truth, so
+  #   lambda = 0 and 1.0 test a misspecified prior. At lambda = 0.357 it is
+  #   the same fit as matched (a determinism check).
+
+  - name: fit
+    variants:
+      - fit: single
+        lam_prior: "off"
+      - fit: mixture
+        lam_prior: matched
+      - fit: mixture
+        lam_prior: measured
diff --git a/planning/studies/congression-calibration/grid_anchor.yaml b/planning/studies/congression-calibration/grid_anchor.yaml
new file mode 100644
index 00000000..4a833312
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_anchor.yaml
@@ -0,0 +1,55 @@
+# ---------------------------------------------------------------------------
+# Anchored follow-up to grid.yaml (congression calibration, step 3.5)
+#
+#   tfs-setup-sim-grid grid_anchor.yaml --out_prefix congression_calibration_anchor
+#   for d in congression_calibration_anchor/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Tests the slide diagnosis (README "Third diagnosis"): the k/dk_geno slide
+# is held only by wt's dk_geno pin, which the Student-t growth likelihood
+# makes cheap to abandon, and the prefit's k prior never engaged (its
+# Hessian scale hit the 0.1 ceiling). Pin k with a tight prefit prior
+# (k_scale_ceiling 0.005), at binding_weight 1. Same simulations as grid.yaml
+# runs 0001-0003 (lambda 0) and 0037-0039 (lambda 1.0): wide dk, seed 1.
+# 2 lambda x 3 fits = 6 runs.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__dkloc{{ dk_geno_hyper_loc }}__seed{{ seed }}__{{ fit }}_{{ lam_prior }}__bw{{ binding_weight }}__kceil{{ k_scale_ceiling }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+      - transformation_poisson_lambda: 1.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: single
+        lam_prior: "off"
+      - fit: mixture
+        lam_prior: matched
+      - fit: mixture
+        lam_prior: measured
+
+  - name: weight
+    variants:
+      - binding_weight: 1.0
+        k_scale_ceiling: 0.005
diff --git a/planning/studies/congression-calibration/grid_baseline.yaml b/planning/studies/congression-calibration/grid_baseline.yaml
new file mode 100644
index 00000000..99ccffdb
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_baseline.yaml
@@ -0,0 +1,53 @@
+# ---------------------------------------------------------------------------
+# Baseline on the new convergence code and corrected library (congression
+# calibration, step 3.5)
+#
+#   tfs-setup-sim-grid grid_baseline.yaml --out_prefix congression_calibration_baseline
+#   for d in congression_calibration_baseline/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Checks that the noise-referenced stop rule (5bf5b78) ends runs cleanly on
+# its own, and whether the converged anchored results (README "Resumed to
+# convergence") hold on the corrected library enumeration (bff2c51).
+# Anchored settings: binding_weight 1, prefit k_scale_ceiling 0.005.
+# 2 lambda x 2 fits x 2 seeds = 8 runs.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__seed{{ seed }}__{{ fit }}_{{ lam_prior }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+      - transformation_poisson_lambda: 1.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: single
+        lam_prior: "off"
+      - fit: mixture
+        lam_prior: matched
+
+  - name: anchor
+    variants:
+      - binding_weight: 1.0
+        k_scale_ceiling: 0.005
diff --git a/planning/studies/congression-calibration/grid_bw1.yaml b/planning/studies/congression-calibration/grid_bw1.yaml
new file mode 100644
index 00000000..8f94715c
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_bw1.yaml
@@ -0,0 +1,53 @@
+# ---------------------------------------------------------------------------
+# Binding-weight follow-up to grid.yaml (congression calibration, step 3.5)
+#
+#   tfs-setup-sim-grid grid_bw1.yaml --out_prefix congression_calibration_bw1
+#   for d in congression_calibration_bw1/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Tests the binding-weight diagnosis (README "Diagnosis, revised"): the
+# default binding_weight (growth rows / binding rows, ~251 here) lets the
+# mixture trade growth fit for binding fit through its congressed classes.
+# Refit with binding_weight = 1. Same simulations as grid.yaml runs 0001-0003
+# (lambda 0) and 0037-0039 (lambda 1.0): wide dk, seed 1, all growth rows.
+# 2 lambda x 3 fits = 6 runs.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__dkloc{{ dk_geno_hyper_loc }}__seed{{ seed }}__{{ fit }}_{{ lam_prior }}__bw{{ binding_weight }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+      - transformation_poisson_lambda: 1.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: single
+        lam_prior: "off"
+      - fit: mixture
+        lam_prior: matched
+      - fit: mixture
+        lam_prior: measured
+
+  - name: weight
+    variants:
+      - binding_weight: 1.0
diff --git a/planning/studies/congression-calibration/grid_controls.yaml b/planning/studies/congression-calibration/grid_controls.yaml
new file mode 100644
index 00000000..1ec10518
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_controls.yaml
@@ -0,0 +1,65 @@
+# ---------------------------------------------------------------------------
+# Controlled fits for the lambda-0 mixture anomaly (congression calibration,
+# step 3.5)
+#
+#   tfs-setup-sim-grid grid_controls.yaml --out_prefix congression_calibration_controls
+#   for d in congression_calibration_controls/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# In every follow-up grid the lambda-0 mixture fit ends in a solution that
+# leans on congressed classes of weight ~0.2%, with k ~0.08 too high and wt
+# abandoned, while single does not. Two controls on the same simulation as
+# grid.yaml runs 0001-0003 (lambda 0, wide dk, seed 1), with the anchored
+# settings of grid_anchor.yaml (binding_weight 1, prefit k_scale_ceiling
+# 0.005):
+#
+#   mixture / fixed0 / svi  lambda pinned at ~1e-6, so the mixture is single.
+#                           Drift here means a bug in the mixture code path
+#                           during training.
+#   mixture / matched / map MAP instead of SVI. The same solution here means a
+#                           real mode of the model; single-like means an SVI
+#                           artifact.
+#   single / off / map      the MAP baseline for the MAP mixture fit.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__seed{{ seed }}__{{ fit }}_{{ lam_prior }}__{{ analysis_method }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: mixture
+        lam_prior: fixed0
+        analysis_method: svi
+      - fit: mixture
+        lam_prior: matched
+        analysis_method: map
+      - fit: single
+        lam_prior: "off"
+        analysis_method: map
+
+  - name: anchor
+    variants:
+      - binding_weight: 1.0
+        k_scale_ceiling: 0.005
diff --git a/planning/studies/congression-calibration/grid_crossrule.yaml b/planning/studies/congression-calibration/grid_crossrule.yaml
new file mode 100644
index 00000000..a2b9f990
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_crossrule.yaml
@@ -0,0 +1,73 @@
+# ---------------------------------------------------------------------------
+# Cross-rule validation (congression physics plan, step 4)
+#
+#   tfs-setup-sim-grid grid_crossrule.yaml --out_prefix congression_calibration_crossrule
+#   for d in congression_calibration_crossrule/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Simulate with the max or the homodimer theta rule; fit each simulation
+# with `single` and with the mixture under each rule (matched lambda prior).
+# Lambda 0.357 (the real library) and 1.0 x seeds 1-2; otherwise the
+# settings of grid_baseline.yaml. 24 runs. The simulate-max / fit-max cells
+# repeat runs from grid_measured.yaml and grid_baseline.yaml, so the grid
+# stands on its own.
+#
+# Summaries: against `single` (fit_theta_rule na), and homodimer against
+# max on the same simulation:
+#
+#   tfs-summarize-calibration congression_calibration_crossrule \
+#       --out_prefix calib/crossrule \
+#       --baseline fit=single lam_prior=off fit_theta_rule=na
+#   tfs-summarize-calibration congression_calibration_crossrule \
+#       --out_prefix calib/crossrule_vs_max --baseline fit_theta_rule=max
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__sim{{ congression_theta_rule }}__seed{{ seed }}__{{ fit }}_{{ fit_theta_rule }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.357
+      - transformation_poisson_lambda: 1.0
+
+  - name: sim_rule
+    variants:
+      - congression_theta_rule: max
+      - congression_theta_rule: homodimer
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+
+
+template:
+
+  # fit_theta_rule "na": `single` has no theta rule (run.srun ignores it).
+  - name: fit
+    variants:
+      - fit: single
+        lam_prior: "off"
+        fit_theta_rule: na
+      - fit: mixture
+        lam_prior: matched
+        fit_theta_rule: max
+      - fit: mixture
+        lam_prior: matched
+        fit_theta_rule: homodimer
+
+  - name: anchor
+    variants:
+      - binding_weight: 1.0
+        k_scale_ceiling: 0.005
diff --git a/planning/studies/congression-calibration/grid_dkrule.yaml b/planning/studies/congression-calibration/grid_dkrule.yaml
new file mode 100644
index 00000000..312855c7
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_dkrule.yaml
@@ -0,0 +1,84 @@
+# ---------------------------------------------------------------------------
+# dk rule sensitivity (congression physics plan, step 5)
+#
+#   tfs-setup-sim-grid grid_dkrule.yaml --out_prefix congression_calibration_dkrule
+#   for d in congression_calibration_dkrule/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Simulate with the dilution or the min dk rule (the two ends of the soft-min
+# family); fit each simulation with `single` and with the mixture under each
+# rule (matched lambda prior). Theta rule homodimer on both sides (the
+# default; step 4 found the theta rule does not matter here).
+#
+# Designs: the study's wide dk_geno spread (SD ~0.018/min, a worst case) at
+# lambda 0.357 and 1.0, and a realistic tight spread (bench: 0.024 +/- 0.001)
+# at lambda 0.357 only. x seeds 1-2 = 36 runs.
+#
+# Summaries: against `single` (fit_dk_rule na), and min against dilution on
+# the same simulation:
+#
+#   tfs-summarize-calibration congression_calibration_dkrule \
+#       --out_prefix calib/dkrule \
+#       --baseline fit=single lam_prior=off fit_dk_rule=na
+#   tfs-summarize-calibration congression_calibration_dkrule \
+#       --out_prefix calib/dkrule_vs_dilution --baseline fit_dk_rule=dilution
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__dk{{ dk_geno_hyper_loc }}__sim{{ congression_dk_rule }}__seed{{ seed }}__{{ fit }}_{{ fit_dk_rule }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  # lambda and dk spread move together: wide at 0.357 and 1.0, tight at 0.357.
+  - name: design
+    variants:
+      - transformation_poisson_lambda: 0.357
+        dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+      - transformation_poisson_lambda: 1.0
+        dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+      - transformation_poisson_lambda: 0.357
+        dk_geno_hyper_loc: -7.0
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.001
+
+  - name: theta_rule
+    variants:
+      - congression_theta_rule: homodimer
+
+  - name: sim_dk_rule
+    variants:
+      - congression_dk_rule: dilution
+      - congression_dk_rule: min
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+
+
+template:
+
+  # fit_dk_rule "na": `single` has no dk rule (run.srun ignores it).
+  - name: fit
+    variants:
+      - fit: single
+        lam_prior: "off"
+        fit_dk_rule: na
+      - fit: mixture
+        lam_prior: matched
+        fit_dk_rule: dilution
+      - fit: mixture
+        lam_prior: matched
+        fit_dk_rule: min
+
+  - name: anchor
+    variants:
+      - binding_weight: 1.0
+        k_scale_ceiling: 0.005
diff --git a/planning/studies/congression-calibration/grid_masked.yaml b/planning/studies/congression-calibration/grid_masked.yaml
new file mode 100644
index 00000000..a6678c3f
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_masked.yaml
@@ -0,0 +1,51 @@
+# ---------------------------------------------------------------------------
+# Masked follow-up to grid.yaml (congression calibration, step 3.5)
+#
+#   tfs-setup-sim-grid grid_masked.yaml --out_prefix congression_calibration_masked
+#   for d in congression_calibration_masked/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Tests the detection-floor diagnosis (README "Diagnosis"): growth rows with
+# fewer than min_counts reads are dropped before fitting. Same simulations as
+# grid.yaml runs 0001-0003 (lambda 0) and 0037-0039 (lambda 1.0): wide dk,
+# seed 1. 2 lambda x 3 fits = 6 runs.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__dkloc{{ dk_geno_hyper_loc }}__seed{{ seed }}__{{ fit }}_{{ lam_prior }}__min{{ min_counts }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+      - transformation_poisson_lambda: 1.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: single
+        lam_prior: "off"
+      - fit: mixture
+        lam_prior: matched
+      - fit: mixture
+        lam_prior: measured
+
+  - name: mask
+    variants:
+      - min_counts: 5
diff --git a/planning/studies/congression-calibration/grid_measured.yaml b/planning/studies/congression-calibration/grid_measured.yaml
new file mode 100644
index 00000000..5af577aa
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_measured.yaml
@@ -0,0 +1,50 @@
+# ---------------------------------------------------------------------------
+# Measured-regime confirmation (congression calibration, step 3.5)
+#
+#   tfs-setup-sim-grid grid_measured.yaml --out_prefix congression_calibration_measured
+#   for d in congression_calibration_measured/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# The real library's lambda (0.357), with the matched prior (0.357 +/- 0.05,
+# as on real data), {single, mixture} x seeds 1-3. Same settings as
+# grid_baseline.yaml. 6 runs.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__seed{{ seed }}__{{ fit }}_{{ lam_prior }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.357
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+      - seed: 3
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: single
+        lam_prior: "off"
+      - fit: mixture
+        lam_prior: matched
+
+  - name: anchor
+    variants:
+      - binding_weight: 1.0
+        k_scale_ceiling: 0.005
diff --git a/planning/studies/congression-calibration/grid_profile.yaml b/planning/studies/congression-calibration/grid_profile.yaml
new file mode 100644
index 00000000..a3a5270b
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_profile.yaml
@@ -0,0 +1,59 @@
+# ---------------------------------------------------------------------------
+# Lambda profile on the lambda-1 simulation (congression calibration,
+# step 3.5)
+#
+#   tfs-setup-sim-grid grid_profile.yaml --out_prefix congression_calibration_profile
+#   for d in congression_calibration_profile/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Converged mixture fits put lambda at 1.30 on data simulated at 1.0 (README
+# "Resumed to convergence"). Hold lambda fixed (lam_prior "fixed", prior SD
+# 0.1% of the value) at five values and fit everything else, on the same
+# simulation as grid_baseline.yaml's lambda-1 seed-1 runs. The loss against
+# lambda says whether the data prefer lambda > 1; theta error against lambda
+# says whether theta is recovered at the true lambda. Anchored settings as in
+# grid_baseline.yaml. 5 runs.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__seed{{ seed }}__{{ fit }}_{{ lam_prior }}{{ lam_fixed }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 1.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: mixture
+        lam_prior: fixed
+
+  - name: lam_value
+    variants:
+      - lam_fixed: 0.6
+      - lam_fixed: 0.8
+      - lam_fixed: 1.0
+      - lam_fixed: 1.2
+      - lam_fixed: 1.4
+
+  - name: anchor
+    variants:
+      - binding_weight: 1.0
+        k_scale_ceiling: 0.005
diff --git a/planning/studies/congression-calibration/grid_seed3.yaml b/planning/studies/congression-calibration/grid_seed3.yaml
new file mode 100644
index 00000000..8214bda5
--- /dev/null
+++ b/planning/studies/congression-calibration/grid_seed3.yaml
@@ -0,0 +1,48 @@
+# ---------------------------------------------------------------------------
+# Third seed for grid_baseline.yaml (congression calibration, step 3.5)
+#
+#   tfs-setup-sim-grid grid_seed3.yaml --out_prefix congression_calibration_seed3
+#   for d in congression_calibration_seed3/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Lambda 0 and 1.0 x {single, mixture}, seed 3; otherwise identical to
+# grid_baseline.yaml. 4 runs.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "lam{{ transformation_poisson_lambda }}__seed{{ seed }}__{{ fit }}_{{ lam_prior }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+      - transformation_poisson_lambda: 1.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 3
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: single
+        lam_prior: "off"
+      - fit: mixture
+        lam_prior: matched
+
+  - name: anchor
+    variants:
+      - binding_weight: 1.0
+        k_scale_ceiling: 0.005
diff --git a/planning/studies/congression-calibration/hill_params.csv b/planning/studies/congression-calibration/hill_params.csv
new file mode 100644
index 00000000..a3568471
--- /dev/null
+++ b/planning/studies/congression-calibration/hill_params.csv
@@ -0,0 +1,5 @@
+genotype,log_hill_K,hill_n,theta_high,theta_low
+wt,-4.1,2,0.01,0.99
+M42I,-2.2104779610300698,1.5156149571654287,0.5054105302329879,0.9538933267956679
+H74A,-5.872705439673401,1.2001278450518955,0.05,0.7115380202706272
+K84L,-5.755115159475512,1.0593068151944411,0.1,0.856776050040223
diff --git a/planning/studies/congression-calibration/inputs/hill_params.csv b/planning/studies/congression-calibration/inputs/hill_params.csv
new file mode 100644
index 00000000..a3568471
--- /dev/null
+++ b/planning/studies/congression-calibration/inputs/hill_params.csv
@@ -0,0 +1,5 @@
+genotype,log_hill_K,hill_n,theta_high,theta_low
+wt,-4.1,2,0.01,0.99
+M42I,-2.2104779610300698,1.5156149571654287,0.5054105302329879,0.9538933267956679
+H74A,-5.872705439673401,1.2001278450518955,0.05,0.7115380202706272
+K84L,-5.755115159475512,1.0593068151944411,0.1,0.856776050040223
diff --git a/planning/studies/congression-calibration/resume.srun b/planning/studies/congression-calibration/resume.srun
new file mode 100644
index 00000000..48b9702f
--- /dev/null
+++ b/planning/studies/congression-calibration/resume.srun
@@ -0,0 +1,70 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=cong_resume
+#SBATCH --output=resume_%j.out
+#SBATCH --error=resume_%j.err
+#SBATCH --partition=gpu
+#SBATCH --time=00-02:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --ntasks-per-node=1
+
+# Resume one finished grid run to real convergence, in a copy of its
+# directory (step 3.5 convergence check; see
+# planning/studies/congression-calibration-convergence/README.md, route 1).
+#
+#   sbatch resume.srun <grid_dir> <run_prefix>
+#   e.g. sbatch resume.srun congression_calibration_anchor run_0002
+#
+# The copy goes to <grid_dir>_resumed/<run name>. Resuming overwrites
+# tfs_fit_model_checkpoint.pkl and tfs_fit_model_losses.txt, hence the copy.
+# Written for fits from before the noise-referenced stop rule (5bf5b78),
+# whose stop rule fired early; the current rule stops on its own.
+
+# Swap lines if on cluster (module load) or local (XLA_FLAGS to use CPU)
+module load cuda/12.4.1
+#export XLA_FLAGS="--xla_force_host_platform_device_count=8"
+
+set -e
+
+GRID_DIR="${1%/}"
+RUN_PREFIX="${2}"
+if [[ -z "${GRID_DIR}" || -z "${RUN_PREFIX}" ]]; then
+    echo "Usage: sbatch resume.srun <grid_dir> <run_prefix>"
+    exit 1
+fi
+
+SRC=$(ls -d "${GRID_DIR}/${RUN_PREFIX}"_* | head -1)
+DEST="${GRID_DIR}_resumed/$(basename "${SRC}")"
+mkdir -p "${GRID_DIR}_resumed"
+rsync -a --exclude '*.h5' --exclude 'summary/' "${SRC}/" "${DEST}/"
+cd "${DEST}"
+
+SEED=$(python -c "import yaml; print(yaml.safe_load(open('tfs_sim_config.yaml'))['seed'])")
+METHOD=$(grep -o '^ANALYSIS_METHOD="[a-z]*"' run.srun | cut -d'"' -f2)
+METHOD=${METHOD:-svi}
+
+echo ">>> Resume fit (${METHOD}) from ${SRC}"
+tfs-fit-model tfs_configure_config.yaml \
+    --seed "${SEED}" \
+    --analysis_method "${METHOD}" \
+    --checkpoint_file tfs_fit_model_checkpoint.pkl
+
+echo ">>> Sample posterior"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl
+
+echo ">>> Extract parameters"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict theta"
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict growth"
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+    --num_marginal_samples=500
+
+echo ">>> Summarize fit"
+tfs-summarize-fit .
+
+echo ">>> Done"
diff --git a/planning/studies/congression-calibration/run.srun b/planning/studies/congression-calibration/run.srun
new file mode 100644
index 00000000..d7be6a8e
--- /dev/null
+++ b/planning/studies/congression-calibration/run.srun
@@ -0,0 +1,171 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=cong_calib
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpu
+#SBATCH --time=00-02:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --ntasks-per-node=1
+
+# Congression calibration study, one run (rendered by tfs-setup-sim-grid from
+# planning/studies/congression-calibration/run.srun). Submit from the run
+# directory: sbatch run.srun
+
+# Swap lines if on cluster (module load) or local (XLA_FLAGS to use CPU)
+module load cuda/12.4.1
+#export XLA_FLAGS="--xla_force_host_platform_device_count=8"
+
+set -e
+
+FIT="{{ fit }}"
+LAM_PRIOR="{{ lam_prior }}"
+LAM_PRIOR_SD=0.05
+LAM_MEASURED=0.357
+# Growth rows with fewer reads than this are dropped before configure (0 keeps
+# everything). grid_masked.yaml sets it; grid.yaml leaves the default.
+MIN_COUNTS={{ min_counts | default(0) }}
+# Binding likelihood weight passed to tfs-configure-model (empty = its default,
+# growth rows / binding rows). grid_bw1.yaml sets it to 1.
+BINDING_WEIGHT="{{ binding_weight | default('') }}"
+# Ceiling on the prefit's per-condition k prior scale (empty = its default,
+# 0.1). grid_anchor.yaml sets it low to pin the k/dk_geno slide.
+K_SCALE_CEILING="{{ k_scale_ceiling | default('') }}"
+# tfs-fit-model --analysis_method (svi or map). grid_controls.yaml sets map.
+ANALYSIS_METHOD="{{ analysis_method | default('svi') }}"
+# Lambda value for lam_prior "fixed" (grid_profile.yaml).
+LAM_FIXED="{{ lam_fixed | default('') }}"
+# Congression theta rule for the fit (mixture only). Empty = the rule the
+# simulation used (tfs_sim_config.yaml's congression_theta_rule); set it to
+# fit with a different rule (cross-rule grids).
+FIT_THETA_RULE="{{ fit_theta_rule | default('') }}"
+# Congression dk rule (and softmin alpha) for the fit (mixture only). Empty =
+# the simulation's (congression_dk_rule, congression_dk_alpha).
+FIT_DK_RULE="{{ fit_dk_rule | default('') }}"
+FIT_DK_ALPHA="{{ fit_dk_alpha | default('') }}"
+
+# The simulated lambda and the seed live in tfs_sim_config.yaml.
+read -r SEED LAM_SIM SIM_THETA_RULE SIM_DK_RULE SIM_DK_ALPHA < <(python -c "
+import yaml
+cf = yaml.safe_load(open('tfs_sim_config.yaml'))
+print(cf['seed'], cf.get('transformation_poisson_lambda') or 0.0,
+      cf.get('congression_theta_rule') or 'homodimer',
+      cf.get('congression_dk_rule') or 'dilution',
+      cf.get('congression_dk_alpha') or '')")
+FIT_THETA_RULE=${FIT_THETA_RULE:-${SIM_THETA_RULE}}
+if [[ -z "${FIT_DK_RULE}" ]]; then
+    FIT_DK_RULE=${SIM_DK_RULE}
+    FIT_DK_ALPHA=${SIM_DK_ALPHA}
+fi
+
+# ---------------------------------------------------------------------------
+# 1. Simulate
+# ---------------------------------------------------------------------------
+echo ">>> Simulate (lambda=${LAM_SIM}, seed=${SEED})"
+tfs-simulate tfs_sim_config.yaml . --seed "${SEED}"
+
+GROWTH_CSV=tfs_sim_growth.csv
+if [[ "${MIN_COUNTS}" -gt 0 ]]; then
+    GROWTH_CSV=tfs_sim_growth_min${MIN_COUNTS}.csv
+    python -c "
+import pandas as pd
+df = pd.read_csv('tfs_sim_growth.csv')
+kept = df[df['counts'] >= ${MIN_COUNTS}]
+kept.to_csv('${GROWTH_CSV}', index=False)
+print(f'Dropped {len(df) - len(kept)} of {len(df)} growth rows with counts < ${MIN_COUNTS}')"
+fi
+
+# ---------------------------------------------------------------------------
+# 2. Configure
+# ---------------------------------------------------------------------------
+if [[ "${FIT}" == "single" ]]; then
+    TRANSFORMATION_ARGS=(--transformation_model single)
+else
+    if [[ "${LAM_PRIOR}" == "matched" ]]; then
+        LAM_MEAN=$(python -c "print(max(${LAM_SIM}, 0.01))")
+    elif [[ "${LAM_PRIOR}" == "fixed" ]]; then
+        # lambda held at LAM_FIXED (prior SD 0.1% of the value).
+        LAM_MEAN=${LAM_FIXED}
+        LAM_PRIOR_SD=$(python -c "print(${LAM_FIXED} * 1e-3)")
+    elif [[ "${LAM_PRIOR}" == "fixed0" ]]; then
+        # lambda pinned essentially at zero: the mixture is then single.
+        LAM_MEAN=1e-6
+        LAM_PRIOR_SD=1e-9
+    else
+        LAM_MEAN=${LAM_MEASURED}
+    fi
+    TRANSFORMATION_ARGS=(--transformation_model mixture
+                         --transformation_lambda "${LAM_MEAN}" "${LAM_PRIOR_SD}"
+                         --congression_theta_rule "${FIT_THETA_RULE}"
+                         --congression_dk_rule "${FIT_DK_RULE}")
+    if [[ -n "${FIT_DK_ALPHA}" ]]; then
+        TRANSFORMATION_ARGS+=(--congression_dk_alpha "${FIT_DK_ALPHA}")
+    fi
+fi
+
+BINDING_WEIGHT_ARGS=()
+if [[ -n "${BINDING_WEIGHT}" ]]; then
+    BINDING_WEIGHT_ARGS=(--binding_weight "${BINDING_WEIGHT}")
+fi
+
+echo ">>> Configure (${FIT}, lambda prior ${LAM_PRIOR}, theta rule ${FIT_THETA_RULE}, dk rule ${FIT_DK_RULE}${FIT_DK_ALPHA:+ alpha ${FIT_DK_ALPHA}})"
+tfs-configure-model \
+    --binding_df tfs_sim_binding.csv \
+    --growth_df "${GROWTH_CSV}" \
+    --presplit_df tfs_sim_presplit.csv \
+    --condition_growth_model linear \
+    --growth_transition_model instant \
+    --ln_cfu0_model hierarchical \
+    --dk_geno_model hierarchical_geno \
+    --activity_model fixed \
+    --theta_model hill_mut \
+    --theta_rescale_model passthrough \
+    --theta_growth_noise_model logit_normal \
+    --theta_binding_noise_model zero \
+    --growth_noise_model normal_kt \
+    --growth_likelihood lncfu \
+    --sample_offset_model zero \
+    --library_config tfs_sim_config.yaml \
+    --growth_shares_replicates \
+    --epistasis \
+    "${TRANSFORMATION_ARGS[@]}" \
+    "${BINDING_WEIGHT_ARGS[@]}"
+
+# ---------------------------------------------------------------------------
+# 3. Pre-fit calibration, fit, posterior
+# ---------------------------------------------------------------------------
+PREFIT_ARGS=()
+if [[ -n "${K_SCALE_CEILING}" ]]; then
+    PREFIT_ARGS=(--k_scale_ceiling "${K_SCALE_CEILING}")
+fi
+
+echo ">>> Pre-fit calibration"
+tfs-prefit-calibration tfs_configure_config.yaml --seed "${SEED}" "${PREFIT_ARGS[@]}"
+
+echo ">>> Fit model"
+tfs-fit-model tfs_configure_config.yaml \
+    --seed "${SEED}" \
+    --analysis_method "${ANALYSIS_METHOD}"
+
+echo ">>> Sample posterior"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl
+
+# ---------------------------------------------------------------------------
+# 4. Extract, predict, summarize
+# ---------------------------------------------------------------------------
+echo ">>> Extract parameters"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict theta"
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict growth"
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+    --num_marginal_samples=500
+
+echo ">>> Summarize fit"
+tfs-summarize-fit .
+
+echo ">>> Done"
diff --git a/planning/studies/congression-calibration/simulate_config.yaml b/planning/studies/congression-calibration/simulate_config.yaml
new file mode 100644
index 00000000..53893ce4
--- /dev/null
+++ b/planning/studies/congression-calibration/simulate_config.yaml
@@ -0,0 +1,277 @@
+# Base simulate config for the congression calibration study (step 3.5 of
+# planning/congression-physics-plan.md). Copied from
+# examples/simulate-and-analyze/simulate_config.yaml, with these changes:
+#   - library_mixture is the real design (single-1 100, single-2 100,
+#     double-1-2 1000, spiked 1), as in planning/studies/congression-estimator;
+#   - library_binding adds 20 in-library (bulk) binding anchors;
+#   - transformation_poisson_lambda and the dk_geno hyperparameters are set
+#     per run by grid.yaml;
+#   - from 2026-09-26, binding noise 0.025 (the experimental theta error; was
+#     0.001) and a Hill coefficient limit of 4 (theta_sim_priors.max_hill_n).
+# binding_data.spiked_binding.choose_by names hill_params.csv relative to this
+# file; tfs-setup-sim-grid copies it into the grid's inputs/ directory.
+
+
+# ---------------------------------------------------------------------------
+# Library genetic information
+# ---------------------------------------------------------------------------
+
+# Reading frame 0, 1, 2. No reverse (negative) strand allowed.
+reading_frame: 0
+
+# Amino acid residue number for the first in-frame residue of the amplicon.
+first_amplicon_residue: 27
+
+# Wildtype DNA sequence of the amplicon.
+wt_seq: gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctgcacgcgccgtcgcaaattgtcgcggcgattaaatctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg
+
+# Degenerate codon pattern for library positions.  '.' = wildtype; 'nnt' = NNT
+# codon (encodes all 20 amino acids + stop with reduced synonymous redundancy).
+# Must be the same length as wt_seq.
+degen_sites: |
+  .............................................nnt.............................................................................................nnt...........................nnt..........................................
+# ......nntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnnt......nntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnnt
+
+
+# tiles groups contiguous degenerate positions that were cloned together.
+# '.' = wildtype; any other character defines a tile block.  Blocks must
+# be contiguous (......111... ok; ....11.1... not ok).
+tiles: |
+  ......111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111......222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222
+
+# Flanking sequences immediately upstream and downstream of the amplicon.
+expected_5p: cgcgtggtgaaccag
+expected_3p: gtagaacgaagcggc
+
+# Which genotype classes to include in the library.  'single-x' = all
+# single-mutation variants in tile x.  'double-x-y' = all pairwise
+# combinations between tiles x and y.  'double-x-x' = internal
+# doubles within tile x.
+tile_combos: ["single-1", "single-2", "double-1-2"]
+
+# Control sequences (e.g. known variants) added as spikes.  Must be the same
+# length as wt_seq.
+spiked_seqs:
+- ........................................................................................................................................................................................................................  # wt
+- .............................................ata........................................................................................................................................................................  # m42i
+- .............................................................................................................................................gcg........................................................................  # h74a
+- ...........................................................................................................................................................................ctg..........................................  # k84l
+- .............................................ata.............................................................................................gcg........................................................................  # m42i/h74a
+- .............................................ata...........................................................................................................................ctg..........................................  # m42i/k84l
+- .............................................................................................................................................gcg...........................ctg..........................................  # h74a/k84l
+- .............................................ata.............................................................................................gcg...........................ctg..........................................  # m42i/h74a/k84l
+- ........................................................................................................................................................................................cc..............................  # d88a
+
+# ---------------------------------------------------------------------------
+# Phenotype calculation
+# ---------------------------------------------------------------------------
+
+# Registered theta component name.  Any key in model_registry["theta"] is
+# valid except "_simple" (calibration-only).  Examples: "hill_geno", "thermo.O2_C12_K5_U0_a.PK".
+theta_component: "hill_mut"
+
+# Pairwise epistasis (regularized horseshoe; used when library has doubles):
+theta_sim_priors: 
+   epi_tau_scale:        0.1    # HalfCauchy scale for global sparsity τ
+   max_hill_n:           4.0    # physical upper limit on the Hill coefficient
+
+# Optional: path to structural/thermodynamic data file required by PnnC and
+# PddG theta components.  Omit (or set to null) for all other models.
+#   .h5 / .hdf5  → HDF5 structural ensemble (PnnC)
+#   .csv         → per-mutation per-structure ddG prior means (PddG)
+# thermo_data: null
+
+# Optional: overrides for the theta component's default hyperparameters.
+# Any key returned by the component's get_hyperparameters() can be overridden.
+# Omit this block to use all component defaults.
+# theta_priors:
+#   theta_tf_total_M: 3.3e-7
+
+# ---------------------------------------------------------------------------
+# Conditions
+# ---------------------------------------------------------------------------
+
+# Each block defines one set of growth conditions.  The Cartesian product of
+# titrant_conc × t_sel gives one sample per entry.  condition_pre and
+# condition_sel must also appear as keys in the 'growth' section below.
+condition_blocks:
+  - library: "kanR"
+    titrant_name: "iptg"
+    titrant_conc: [0, 0.0001, 0.001, 0.003]
+    condition_pre: "kanR-kan"
+    t_pre: 30
+    condition_sel: "kanR+kan"
+    t_sel: [160, 180, 200]
+
+  - library: "kanR"
+    titrant_name: "iptg"
+    titrant_conc: [0.01, 0.03, 0.1, 1.0]
+    condition_pre: "kanR-kan"
+    t_pre: 30
+    condition_sel: "kanR+kan"
+    t_sel: [145, 165, 180]
+
+  - library: "kanR"
+    titrant_name: "iptg"
+    titrant_conc: [0, 1.0]
+    condition_pre: "kanR-kan"
+    t_pre: 30
+    condition_sel: "kanR-kan"
+    t_sel: [160, 180, 200]
+
+  - library: "pheS"
+    titrant_name: "iptg"
+    titrant_conc: [0, 0.0001, 0.001, 0.003, 0.01, 0.03, 0.1, 1.0]
+    condition_pre: "pheS-4CP"
+    t_pre: 30
+    condition_sel: "pheS+4CP"
+    t_sel: [95, 110, 125]
+
+  - library: "pheS"
+    titrant_name: "iptg"
+    titrant_conc: [0, 1.0]
+    condition_pre: "pheS-4CP"
+    t_pre: 30
+    condition_sel: "pheS-4CP"
+    t_sel: [95, 110, 125]
+
+# ---------------------------------------------------------------------------
+# Growth rate parameters
+# ---------------------------------------------------------------------------
+
+# Linear mapping from operator occupancy (theta) to growth rate k for each
+# condition:  k = m * theta + b.  Every condition_pre and condition_sel that
+# appears in condition_blocks must have an entry here, and vice versa.
+growth:
+  kanR+kan: {m: -0.009933, b: 0.010696}
+  kanR-kan: {m:  0.000808, b: 0.015366}
+  pheS+4CP: {m:  0.006226, b: 0.021437}
+  pheS-4CP: {m: -0.000344, b: 0.028558}
+
+# Per-genotype pleiotropic growth cost is sampled from a shifted lognormal:
+#   dk_geno = hyper_shift - exp(Normal(hyper_loc, hyper_scale))
+# Most variants are mildly deleterious; a short right tail (at most hyper_shift)
+# allows beneficial mutations.  Matches the hierarchical tfmodel inference prior.
+dk_geno_hyper_loc: -3.5
+dk_geno_hyper_scale: 0.5
+dk_geno_hyper_shift: 0.02
+dk_geno_zero: false
+
+# Per-genotype TF activity (A) — a scalar that multiplies the theta contribution
+# to growth: k = b + A * m * theta.  The wild-type is fixed at activity_wt=1.0
+# by inference convention.  Set activity_mut_scale > 0 to draw each mutant
+# genotype's log(A) independently from Normal(log(activity_wt), activity_mut_scale).
+# activity_mut_scale=0 (default) gives all genotypes identical activity, matching
+# the 'fixed' activity inference component.
+activity_wt: 1.0
+activity_mut_scale: 0.0
+
+# ---------------------------------------------------------------------------
+# Experimental simulation parameters
+# ---------------------------------------------------------------------------
+
+# Number of transformants per tile combo.  Keys must match the entries in
+# tile_combos (e.g. 'single-1', 'double-1-2') plus 'spiked'.
+transform_sizes:
+  single-1: 10_000
+  single-2: 10_000
+  double-1-2: 1_000_000
+  spiked: 1000
+
+# Mixing ratio of tile combos in the pooled library.  Only the ratio matters.
+library_mixture:
+  single-1: 100
+  single-2: 100
+  double-1-2: 1000
+  spiked: 1
+
+# Sigma of the log-normal used to draw initial genotype frequencies.  0 = flat
+# distribution; 1–2 is realistic for a typical transformation.
+lib_assembly_skew_sigma: 1.25
+
+# Each bacterium receives a number of plasmids drawn from a zero-truncated
+# Poisson with this lambda.  Set to 0 or null for exactly one plasmid per cell.
+transformation_poisson_lambda: 0
+
+# How a cell carrying several plasmids gets its growth rate. The cell's theta
+# and TF activity come from congression_theta_rule ('max': the highest-theta
+# plasmid sets both, at each condition); its dk_geno from congression_dk_rule
+# ('dilution': share-weighted mean; also 'softmin' with congression_dk_alpha,
+# or 'min'). Growth is then computed once from these cell-level values. The
+# study keeps 'max' so its recorded grids stay reproducible (the default is
+# now 'homodimer'); grids set the rules they test.
+congression_theta_rule: max
+congression_dk_rule: dilution
+
+# Colony-forming units per mL at the start of pre-growth.
+cfu0: 6.5e7
+
+# Per-tube environmental growth-rate noise (hr⁻¹).  Each physical tube gets its
+# own delta_k ~ Normal(0, tube_noise_sigma); the kt contribution per tube is
+# delta_k * (t_pre + t_sel), shared by all genotypes in that tube.  A value of
+# ~0.001–0.002 hr⁻¹ is typical for well-controlled liquid cultures.
+# Set to 0 or null to disable.
+tube_noise_sigma: 0.002
+
+# ---------------------------------------------------------------------------
+# Growth transition model (optional)
+# ---------------------------------------------------------------------------
+
+# Models the lag phase when bacteria switch from pre-selection to selection medium.
+# Every condition_pre in condition_blocks must have an entry.  Use model: "instant"
+# for conditions with no detectable lag.  Omit this block to use instant everywhere.
+#
+# "memory" model:  tau = tau0 + k1 / (theta + k2)
+#   tau0 = baseline lag (minutes)
+#   k1   = occupancy-dependence of lag (higher = more effect)
+#   k2   = curvature constant (> 0; prevents division by zero)
+growth_transition:
+  - condition_pre: "pheS-4CP"
+    model: "instant"
+
+  - condition_pre: "kanR-kan"
+    model: "instant"
+
+# ---------------------------------------------------------------------------
+# Data collection parameters
+# ---------------------------------------------------------------------------
+
+# Total sequencing reads across all samples.
+total_num_reads: 100_000_000
+
+# Fraction of reads re-assigned to a randomly chosen genotype (index hopping).
+# Set to 0 or null to disable.
+prob_index_hop: 0.00
+
+# Random seed for reproducibility.  Set to null for a random seed each run.
+seed: 12345
+
+# ---------------------------------------------------------------------------
+# Binding data simulation (optional)
+# ---------------------------------------------------------------------------
+
+# If present, tfs-simulate also writes a binding CSV with per-genotype theta
+# curves.  
+
+binding_data:
+  # The binding assay (shared by both genotype sets).
+  titrant_name: iptg
+  titrant_conc: [0, 0.0001, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1.0]
+  noise: 0.025    # experimental theta error (user, 2026-09-26)
+
+  # Clean, monoclonal (congression-free) controls. Pool = spiked_seqs.
+  #   choose_by: <params file> | stratified | random
+  #   num: optional [1, num_spiked], default all (forbidden with a file)
+  spiked_binding:
+    choose_by: hill_params.csv
+
+  # In-library controls, drawn from the bulk (congression-affected growth).
+  # Pool = library genotypes NOT in spiked_seqs. Omit to disable.
+  #   choose_by: stratified | random | <params file>
+  #   num: required for stratified/random (forbidden with a file)
+  library_binding:
+    num: 20
+    choose_by: stratified
+
+presplit_data: 
+  noise: 0.00
diff --git a/planning/studies/congression-estimator/README.md b/planning/studies/congression-estimator/README.md
new file mode 100644
index 00000000..32ba0fbc
--- /dev/null
+++ b/planning/studies/congression-estimator/README.md
@@ -0,0 +1,136 @@
+# Congression estimator study
+
+**Question.** In the observable-level congression mixture, how should the fit
+evaluate a genotype's congressed-cell term `E_S exp(G_{g,S}(t))`, the average
+over random co-resident sets S of the congressed cells' exponentiated
+log-growth? And do co-residents need per-origin backgrounds?
+
+**Decision it fed.** Step 3.0 of
+[`planning/congression-physics-plan.md`](../../congression-physics-plan.md):
+the fit uses fixed Monte Carlo quadrature (K per-genotype co-resident sets,
+stratified by plasmid count, drawn once), with a pooled co-resident
+background. See the plan's "Fit design (step 3)".
+
+## How to run
+
+From a scratch directory (outputs are written to the working directory):
+
+```bash
+python /path/to/tfscreen/planning/studies/congression-estimator/study.py
+```
+
+An optional argument replaces the simulate config. Runtime is about 2.5 min
+and peak memory about 1.3 GB.
+
+Outputs: `study_summary.csv` (one row per dk scale x estimator x subset) and
+`study_by_time.csv` (RMS per growth condition and selection time). Not
+committed.
+
+## Inputs
+
+- `examples/simulate/simulate_config.yaml` (default) for the library, theta
+  prior, growth conditions and growth transition: 843 genotypes, thermo theta
+  prior, memory growth transition, 48 growth conditions, t_sel 95 to 200 min.
+- Overridden in the script: `library_mixture` = the real design
+  (single-1 100, single-2 100, double-1-2 1000, spiked 1),
+  `transformation_poisson_lambda` = 0.357, seed 11.
+- Cell rules as in `simulate/cell_rules.py`: max theta, activity from the max
+  plasmid, dilution dk.
+
+## Method
+
+For each bulk genotype g and growth condition, the observable relative to
+`ln_cfu0` is
+
+```
+L_g = log[ w_clean exp(G_clean) + sum_o w_o E_{S~o} exp(G_{g,S}) ]
+```
+
+with co-residents S drawn as N ~ Poisson(lambda) | N >= 1 plasmids from
+sub-library o's plasmid distribution (each sub-library is transformed
+separately). "Exact" enumerates N = 1 over every genotype in the sub-library
+and uses Monte Carlo for N >= 2 (16% of congressed cells). dk_geno deviations
+are scaled by s; at s = 1 (the sim prior) dk_geno has SD 0.068/min, with 5th
+to 95th percentiles -0.13 and 0.014/min. s = 0.015 corresponds to the bench
+measurement for the current library (SD about 0.001/min).
+
+Estimators of each congressed term, plugged into the same mixture:
+
+| name | estimator |
+|---|---|
+| `rate_avg` | `exp(E_S[G])` |
+| `cumulant2` | `exp(E_S[G] + Var_S[G] / 2)` |
+| `n1_only` | exact, but every congressed cell has exactly one co-resident |
+| `mcK` | K fixed co-resident sets per sub-library, shared by all genotypes |
+| `pooled` | exact, but co-residents from the pooled bulk library |
+| `current` | today's fit: one trajectory, theta -> `E[max(theta_g, M)]` over an unweighted all-genotype background, own dk (reference) |
+| `none` | no congression (reference) |
+
+Error = approximate minus exact `L_g`, in natural-log `ln_cfu` units, over
+genotype x condition cells within 7 ln units of wt ("detectable"). `mcK`
+pools 10 independent draw sets.
+
+## Commit
+
+Produced on `abec95f`; reproduced exactly on `fc0f2b9` (no simulator changes
+in between).
+
+## Results
+
+RMS error (detectable):
+
+| estimator | s = 0 | s = 0.015 | s = 0.03 | s = 0.1 | s = 0.3 | s = 1 |
+|---|---|---|---|---|---|---|
+| none | 0.041 | 0.053 | 0.097 | 0.287 | 0.758 | 1.507 |
+| current | 0.019 | 0.041 | 0.093 | 0.293 | 0.770 | 1.524 |
+| rate_avg | 0.006 | 0.006 | 0.008 | 0.036 | 0.206 | 1.086 |
+| cumulant2 | 0.000 | 0.000 | 0.001 | 0.025 | 0.509 | 12.599 |
+| n1_only | 0.003 | 0.004 | 0.010 | 0.030 | 0.080 | 0.127 |
+| mc1 | 0.032 | 0.043 | 0.049 | 0.104 | 0.391 | 1.113 |
+| mc2 | 0.018 | 0.028 | 0.038 | 0.075 | 0.207 | 0.515 |
+| mc4 | 0.016 | 0.016 | 0.032 | 0.072 | 0.181 | 0.379 |
+| mc8 | 0.013 | 0.017 | 0.020 | 0.037 | 0.136 | 0.360 |
+| mc16 | 0.008 | 0.011 | 0.010 | 0.028 | 0.072 | 0.187 |
+| mc32 | 0.005 | 0.006 | 0.010 | 0.019 | 0.064 | 0.142 |
+| pooled | 0.002 | 0.002 | 0.002 | 0.004 | 0.008 | 0.024 |
+
+95th percentile |error| (detectable):
+
+| estimator | s = 0 | s = 0.015 | s = 0.03 | s = 0.1 | s = 0.3 | s = 1 |
+|---|---|---|---|---|---|---|
+| none | 0.099 | 0.105 | 0.121 | 0.376 | 1.671 | 3.882 |
+| current | 0.048 | 0.064 | 0.108 | 0.414 | 1.707 | 3.900 |
+| rate_avg | 0.014 | 0.016 | 0.018 | 0.057 | 0.460 | 2.418 |
+| mc16 | 0.021 | 0.028 | 0.020 | 0.044 | 0.147 | 0.391 |
+| mc32 | 0.013 | 0.013 | 0.020 | 0.034 | 0.113 | 0.296 |
+| pooled | 0.002 | 0.002 | 0.003 | 0.006 | 0.016 | 0.053 |
+
+Mean error (detectable) of `mcK` is within ±0.01 at s <= 0.1 and within ±0.09
+at s = 1, for every K >= 2; `rate_avg` is biased low (-0.15 at s = 0.3,
+-0.76 at s = 1).
+
+Error grows with selection time. At s = 0.3, `rate_avg` RMS rises from 0.20
+to 0.31 over kanR t_sel 145 to 200 min, and `mc16` from 0.07 to 0.09.
+
+## Conclusions
+
+- Once dk varies, the congression effect is large (`none`), and today's fit
+  rule removes almost none of it: averaging theta cannot represent a mixture
+  of growth rates.
+- `rate_avg` is adequate only for a narrow dk spread and worsens with time;
+  `cumulant2` fails on heavy dk tails.
+- `mcK` has near-zero mean error at every spread, with RMS falling roughly as
+  1/sqrt(K). It is the only estimator that holds across the dk range.
+- A pooled co-resident background is adequate. Caveat: in the sim prior,
+  singles and doubles have similar theta/dk distributions; real doubles may be
+  more often broken.
+- At the measured spread for the current library (s ~ 0.015), the whole effect
+  is modest (0.053 RMS, 0.105 p95) and every estimator except small-K MC is
+  accurate.
+
+## Limits
+
+- One simulated library (843 genotypes, 2 sites) and one theta prior.
+- This study shares draw sets across genotypes; the adopted fit design uses
+  per-genotype sets, which gives the same per-genotype error with errors
+  independent across genotypes.
diff --git a/planning/studies/congression-estimator/study.py b/planning/studies/congression-estimator/study.py
new file mode 100644
index 00000000..ef479e60
--- /dev/null
+++ b/planning/studies/congression-estimator/study.py
@@ -0,0 +1,340 @@
+"""
+Step 3.0 of planning/congression-physics-plan.md: how should the fit evaluate
+the congressed class's E_S exp(G_{g,S}(t))?
+
+Ground truth comes from the simulator's own machinery (library_prediction on
+examples/simulate/simulate_config.yaml, with the real design's
+library_mixture and lambda = 0.357). For each bulk genotype g and each
+growth condition (titrant, condition_pre/sel, t_pre, t_sel), the observable
+(relative to ln_cfu0) is
+
+    L_g = log[ w_clean exp(G_clean) + sum_o w_o E_{S~o} exp(G_{g,S}) ]
+
+with the cell rules of simulate/cell_rules.py (max theta, activity from the
+max plasmid, dilution dk) and the same growth pipeline as the simulator
+(growth_rate_one_condition + growth transition). Co-residents S: N ~
+Poisson(lambda) | N >= 1 plasmids drawn from origin o's plasmid
+distribution (each sub-library is transformed separately).
+
+"Exact": N = 1 enumerated exactly over the origin's genotypes; N >= 2 by
+Monte Carlo (16% of congressed cells at lambda = 0.357).
+
+Estimators of each congressed term (plugged into the same mixture):
+  rate_avg   exp(E_S[G])
+  cumulant2  exp(E_S[G] + Var_S[G]/2)
+  n1_only    exact, but every congressed cell has exactly one co-resident
+  mcK        K fixed co-resident sets per origin, shared by all focal
+             genotypes (common random numbers, as a fit would use)
+  pooled     exact, but co-residents from the pooled bulk library
+References:
+  current    today's fit: one trajectory, theta -> E[max(theta_g, M)] over
+             an unweighted all-genotype background (spiked-origin genotypes
+             uncorrected), own dk and activity
+  none       no congression at all (G_clean)
+
+Errors are in ln_cfu units (natural log), approx - exact.
+"""
+
+import sys
+import numpy as np
+import pandas as pd
+
+from tfscreen.util.io import read_yaml
+from tfscreen.simulate.library_prediction import library_prediction
+from tfscreen.simulate.thermo_to_growth import growth_rate_one_condition
+from tfscreen.simulate.selection_experiment import _compute_kt_arrays
+
+LAM = 0.357
+REAL_MIXTURE = {"single-1": 100, "single-2": 100, "double-1-2": 1000,
+                "spiked": 1}
+DK_SCALES = [1.0, 0.3, 0.1, 0.03, 0.015, 0.0]
+MC_N2_DRAWS = 400          # N >= 2 draws per (focal, origin) for "exact"
+K_LIST = [1, 2, 4, 8, 16, 32]
+K_REPEATS = 10
+DETECT = -7.0              # detectable: L_g - L_wt >= DETECT
+SEED = 11
+
+rng = np.random.default_rng(SEED)
+
+# ---------------------------------------------------------------------------
+# Ground truth phenotypes and design
+# ---------------------------------------------------------------------------
+
+# Default input: the repository's example simulate config.
+_REPO = __import__("pathlib").Path(__file__).resolve().parents[3]
+cfg_path = (sys.argv[1] if len(sys.argv) > 1
+            else str(_REPO / "examples" / "simulate" / "simulate_config.yaml"))
+cf = read_yaml(cfg_path)
+cf["transformation_poisson_lambda"] = LAM
+cf["library_mixture"] = dict(REAL_MIXTURE)
+cf["seed"] = SEED
+library_df, phenotype_df, genotype_theta_df, parameters_df = library_prediction(cf)[:4]
+growth_params = cf["growth"]
+theta_rescale = cf.get("theta_rescale", "passthrough")
+growth_transition = cf.get("growth_transition")
+
+genotypes = list(parameters_df["genotype"].astype(str))
+gidx = {g: i for i, g in enumerate(genotypes)}
+G = len(genotypes)
+
+# theta per genotype per titrant point
+th = genotype_theta_df.copy()
+th["genotype"] = th["genotype"].astype(str)
+titr = (th[["titrant_name", "titrant_conc"]].drop_duplicates()
+        .sort_values(["titrant_name", "titrant_conc"]).reset_index(drop=True))
+titr["t_idx"] = np.arange(len(titr))
+th = th.merge(titr, on=["titrant_name", "titrant_conc"])
+theta_gt = np.full((G, len(titr)), np.nan)
+theta_gt[th["genotype"].map(gidx).to_numpy(), th["t_idx"].to_numpy()] = th["theta"].to_numpy()
+
+dk0 = parameters_df["dk_geno"].to_numpy(dtype=float)
+act = parameters_df["activity"].to_numpy(dtype=float)
+
+# growth conditions
+cond = (phenotype_df[phenotype_df["replicate"] == phenotype_df["replicate"].min()]
+        [["library", "titrant_name", "titrant_conc", "condition_pre",
+          "condition_sel", "t_pre", "t_sel"]]
+        .drop_duplicates().reset_index(drop=True))
+cond = cond.merge(titr, on=["titrant_name", "titrant_conc"])
+C = len(cond)
+cond_t = cond["t_idx"].to_numpy()
+
+# per-origin plasmid distributions (design, no assembly skew)
+lib = library_df.copy()
+lib["genotype"] = lib["genotype"].astype(str)
+lib["library_origin"] = lib["library_origin"].astype(str)
+lib = lib[lib["genotype"].isin(gidx)]
+origins = sorted(lib["library_origin"].unique())
+bulk_origins = [o for o in origins if o != "spiked"]
+probs = {}
+for o in origins:
+    sub = lib[lib["library_origin"] == o]
+    p = np.zeros(G)
+    np.add.at(p, sub["genotype"].map(gidx).to_numpy(), sub["weight"].to_numpy())
+    probs[o] = p / p.sum()
+mass = {o: REAL_MIXTURE[o] * probs[o] for o in origins}
+total_mass = sum(mass.values())
+in_spiked = mass["spiked"] > 0
+pooled_bulk = sum(mass[o] for o in bulk_origins)
+pooled_bulk = pooled_bulk / pooled_bulk.sum()
+
+p_cong = 1.0 - np.exp(-LAM)
+# weight of each (genotype, bulk origin) congressed class, and clean
+w_orig = {o: np.divide(mass[o], total_mass, out=np.zeros(G), where=total_mass > 0) * p_cong
+          for o in bulk_origins}
+w_clean = 1.0 - sum(w_orig.values())
+f_g = np.divide(sum(mass[o] for o in bulk_origins), total_mass,
+                out=np.zeros(G), where=total_mass > 0)
+present = total_mass > 0
+
+# Poisson(lambda) | N >= 1 split into N = 1 and N >= 2
+p_n = np.array([np.exp(-LAM) * LAM**n / np.prod(np.arange(1, n + 1)) for n in range(0, 12)])
+p1 = p_n[1] / (1 - p_n[0])
+p_n2 = p_n[2:] / p_n[2:].sum()          # distribution of N given N >= 2
+n2_values = np.arange(2, 12)
+
+# ---------------------------------------------------------------------------
+# Growth
+# ---------------------------------------------------------------------------
+
+def kt(theta_c, act_c, dk):
+    """theta_c, act_c: (..., C) over growth conditions; dk: (...). -> (..., C)"""
+    shape = theta_c.shape
+    out = np.empty(shape)
+    dk_b = np.broadcast_to(np.asarray(dk)[..., None], shape)
+    for c in range(C):
+        row = cond.iloc[c]
+        th_c = theta_c[..., c].ravel()
+        a_c = act_c[..., c].ravel()
+        d_c = dk_b[..., c].ravel()
+        k_pre = growth_rate_one_condition(row["condition_pre"], th_c, a_c, d_c,
+                                          growth_params, theta_rescale)
+        k_sel = growth_rate_one_condition(row["condition_sel"], th_c, a_c, d_c,
+                                          growth_params, theta_rescale)
+        n = len(th_c)
+        v = _compute_kt_arrays(k_pre, k_sel, np.full(n, float(row["t_pre"])),
+                               np.full(n, float(row["t_sel"])), th_c,
+                               np.full(n, row["condition_pre"], dtype=object),
+                               growth_transition)
+        out[..., c] = v.reshape(shape[:-1])
+    return out
+
+
+def cell_G(focal, co_sets, dk):
+    """
+    focal: (F,) genotype idx. co_sets: (F, D, Nmax) co-resident idx, -1 = none.
+    Returns G (F, D, C) for cells {focal} + co_sets under max theta /
+    activity-follows-theta / dilution dk.
+    """
+    F, D, Nmax = co_sets.shape
+    slots = np.concatenate([np.broadcast_to(focal[:, None, None], (F, D, 1)), co_sets], axis=2)
+    valid = slots >= 0
+    sl = np.where(valid, slots, 0)
+    th_s = theta_gt[sl][..., cond_t]                     # (F,D,P,C)
+    th_s = np.where(valid[..., None], th_s, -np.inf)
+    best = np.argmax(th_s, axis=2)                        # (F,D,C)
+    th_cell = np.take_along_axis(th_s, best[:, :, None, :], axis=2)[:, :, 0, :]
+    a_s = np.broadcast_to(act[sl][..., None], th_s.shape)
+    a_cell = np.take_along_axis(a_s, best[:, :, None, :], axis=2)[:, :, 0, :]
+    n_in = valid.sum(axis=2)
+    dk_cell = np.where(valid, dk[sl], 0.0).sum(axis=2) / n_in
+    return kt(th_cell, a_cell, dk_cell)
+
+
+def logmeanexp(x, axis, w=None):
+    m = np.max(x, axis=axis, keepdims=True)
+    if w is None:
+        return (m + np.log(np.mean(np.exp(x - m), axis=axis, keepdims=True))).squeeze(axis)
+    return (m + np.log(np.sum(w * np.exp(x - m), axis=axis, keepdims=True))).squeeze(axis)
+
+
+def draw_sets(p, F, D, n_values, n_probs, rng):
+    n = rng.choice(n_values, size=(F, D), p=n_probs)
+    nmax = int(n.max())
+    co = rng.choice(G, size=(F, D, nmax), p=p)
+    co = np.where(np.arange(nmax)[None, None, :] < n[..., None], co, -1)
+    return co
+
+
+def congressed_terms(focal, p, dk, rng, chunk=60):
+    parts = [_congressed_terms(focal[i:i + chunk], p, dk, rng)
+             for i in range(0, len(focal), chunk)]
+    return {k: np.concatenate([q[k] for q in parts]) for k in parts[0]}
+
+
+def _congressed_terms(focal, p, dk, rng):
+    """
+    For focal genotypes and one co-resident distribution p, return
+    dict of per-(focal, condition) log E exp(G) for exact / n1_only, and the
+    moments E[G], Var[G] (exact mixture of N=1 enumeration and N>=2 MC).
+    """
+    F = len(focal)
+    support = np.where(p > 0)[0]
+    # N = 1: enumerate
+    co1 = np.broadcast_to(support[None, :, None], (F, len(support), 1)).copy()
+    G1 = cell_G(focal, co1, dk)                                # (F,S,C)
+    w1 = p[support][None, :, None]
+    log_e1 = logmeanexp(G1, 1, w1)
+    m1 = np.sum(w1 * G1, axis=1)
+    s1 = np.sum(w1 * G1**2, axis=1)
+    # N >= 2: Monte Carlo
+    co2 = draw_sets(p, F, MC_N2_DRAWS, n2_values, p_n2, rng)
+    G2 = cell_G(focal, co2, dk)
+    log_e2 = logmeanexp(G2, 1)
+    m2 = G2.mean(axis=1)
+    s2 = (G2**2).mean(axis=1)
+    exact = np.logaddexp(np.log(p1) + log_e1, np.log(1 - p1) + log_e2)
+    mean = p1 * m1 + (1 - p1) * m2
+    var = p1 * s1 + (1 - p1) * s2 - mean**2
+    return {"exact": exact, "n1_only": log_e1, "mean": mean, "var": var}
+
+
+def mixture(clean_G, terms_by_origin, focal):
+    """log[w_clean e^Gclean + sum_o w_o e^{term_o}] for focal genotypes."""
+    acc = np.log(w_clean[focal])[:, None] + clean_G
+    for o, term in terms_by_origin.items():
+        wo = w_orig[o][focal]
+        with np.errstate(divide="ignore"):
+            acc = np.logaddexp(acc, np.log(wo)[:, None] + term)
+    return acc
+
+
+# today's fit rule: E[max(theta_g, M)], M = max of Poisson(lambda) draws
+# from the unweighted all-genotype theta distribution (per titrant point)
+def current_theta():
+    grid = np.linspace(0, 1, 2001)
+    out = theta_gt.copy()
+    for t in range(theta_gt.shape[1]):
+        pop = np.sort(theta_gt[present, t])
+        F = np.searchsorted(pop, grid, side="right") / len(pop)
+        P_M_le = np.exp(LAM * (F - 1.0))                    # includes N = 0
+        tail = 1.0 - P_M_le                                  # P(M > t)
+        cum = np.concatenate([[0], np.cumsum((tail[1:] + tail[:-1]) / 2 * np.diff(grid))])
+        G_tail = cum[-1] - np.interp(theta_gt[:, t], grid, cum)
+        out[:, t] = theta_gt[:, t] + G_tail
+    return out
+
+theta_current = current_theta()
+
+# ---------------------------------------------------------------------------
+# Run
+# ---------------------------------------------------------------------------
+
+focal = np.where(present & (f_g > 0))[0]
+wt = gidx["wt"]
+rows = []
+by_time = []
+for s in DK_SCALES:
+    dk = s * dk0
+    clean_G = kt(theta_gt[focal][:, cond_t], np.broadcast_to(act[focal][:, None], (len(focal), C)),
+                 dk[focal])
+    per_origin = {o: congressed_terms(focal, probs[o], dk, rng) for o in bulk_origins
+                  if np.any(w_orig[o][focal] > 0)}
+    pooled = congressed_terms(focal, pooled_bulk, dk, rng)
+
+    exact = mixture(clean_G, {o: t["exact"] for o, t in per_origin.items()}, focal)
+    est = {
+        "rate_avg": mixture(clean_G, {o: t["mean"] for o, t in per_origin.items()}, focal),
+        "cumulant2": mixture(clean_G, {o: t["mean"] + t["var"] / 2 for o, t in per_origin.items()}, focal),
+        "n1_only": mixture(clean_G, {o: t["n1_only"] for o, t in per_origin.items()}, focal),
+        "pooled": mixture(clean_G, {o: pooled["exact"] for o in per_origin}, focal),
+        "none": clean_G,
+    }
+    cur_theta = np.where(in_spiked[focal][:, None], theta_gt[focal], theta_current[focal])
+    est["current"] = kt(cur_theta[:, cond_t], np.broadcast_to(act[focal][:, None], (len(focal), C)),
+                        dk[focal])
+    for K in K_LIST:
+        errs = []
+        for r in range(K_REPEATS):
+            terms = {}
+            for o in per_origin:
+                co = draw_sets(probs[o], 1, K, np.arange(1, 12),
+                               p_n[1:] / p_n[1:].sum(), rng)
+                co = np.broadcast_to(co, (len(focal),) + co.shape[1:]).copy()
+                terms[o] = logmeanexp(cell_G(focal, co, dk), 1)
+            errs.append(mixture(clean_G, terms, focal) - exact)
+        est[f"mc{K}"] = errs   # list of repeats
+
+    # detectable: relative to wt's exact observable in the same condition
+    wt_pos = np.where(focal == wt)[0]
+    ref = exact[wt_pos[0]] if len(wt_pos) else np.median(exact, axis=0)
+    detect = (exact - ref[None, :]) >= DETECT
+
+    for name, val in est.items():
+        reps = val if isinstance(val, list) else [val - exact]
+        for subset, mask in (("all", np.ones_like(detect)), ("detectable", detect)):
+            e = np.concatenate([r[mask] for r in reps])
+            rows.append({"dk_scale": s, "estimator": name, "subset": subset,
+                         "rms": np.sqrt(np.mean(e**2)),
+                         "mean": np.mean(e),
+                         "p95_abs": np.quantile(np.abs(e), 0.95),
+                         "max_abs": np.max(np.abs(e)),
+                         "n": e.size})
+        for c in range(C):
+            e = np.concatenate([r[detect[:, c], c] for r in reps])
+            if e.size:
+                by_time.append({"dk_scale": s, "estimator": name,
+                                "library": cond.loc[c, "library"],
+                                "t_sel": cond.loc[c, "t_sel"],
+                                "rms": np.sqrt(np.mean(e**2))})
+
+    # size of the congression effect itself (exact vs none), detectable
+    print(f"dk_scale={s}: done", flush=True)
+
+res = pd.DataFrame(rows)
+res.to_csv("study_summary.csv", index=False)
+bt = (pd.DataFrame(by_time).groupby(["dk_scale", "estimator", "library", "t_sel"])
+      ["rms"].mean().reset_index())
+bt.to_csv("study_by_time.csv", index=False)
+
+pd.set_option("display.width", 200)
+print(f"\n{len(focal)} bulk genotypes x {C} conditions; lambda={LAM}; "
+      f"dk_geno SD (scale 1) = {np.std(dk0[present]):.4f}/min")
+print(res[res.subset == "detectable"].pivot_table(index="estimator", columns="dk_scale",
+                                                   values="rms").round(3).to_string())
+print("\np95 |error| (detectable)")
+print(res[res.subset == "detectable"].pivot_table(index="estimator", columns="dk_scale",
+                                                   values="p95_abs").round(3).to_string())
+print("\nmean error (detectable)")
+print(res[res.subset == "detectable"].pivot_table(index="estimator", columns="dk_scale",
+                                                   values="mean").round(3).to_string())
diff --git a/planning/studies/count-likelihood/.gitignore b/planning/studies/count-likelihood/.gitignore
new file mode 100644
index 00000000..b8072a76
--- /dev/null
+++ b/planning/studies/count-likelihood/.gitignore
@@ -0,0 +1 @@
+count_likelihood*
diff --git a/planning/studies/count-likelihood/README.md b/planning/studies/count-likelihood/README.md
new file mode 100644
index 00000000..0a6ce1e6
--- /dev/null
+++ b/planning/studies/count-likelihood/README.md
@@ -0,0 +1,277 @@
+# Count-likelihood study
+
+**Question.** Does observing read counts directly (a negative binomial with
+learned dispersion, no pseudocount; `growth_likelihood: counts`) fit better
+and give better-calibrated theta than observing `ln_cfu`, both on data whose
+counts are Poisson and on data with the real data's extra count noise? And,
+at a true lambda of 0, does the congression mixture still find lambda
+around 0.12 once the read-count floor is modeled?
+
+**Decisions it feeds.**
+- Roadmap step 7 (`planning/analysis-roadmap.md`): whether the count
+  likelihood becomes the default for real data.
+- Congression plan step 6 (`planning/congression-physics-plan.md`, on main):
+  whether the mixture's theta gain at a true lambda of 0 was the read-count
+  floor. That step also re-runs `grid_baseline.yaml` and `grid_dkrule.yaml`
+  on the count likelihood; this grid covers the lambda-0 question directly.
+
+## Design
+
+Base config: `../congression-calibration/simulate_config.yaml` (483
+genotypes, hill_mut theta, instant growth transitions, kanR and pheS, 20
+in-library binding anchors), at lambda 0 and the wide dk spread, with the
+anchored fit settings of that study's baseline (binding weight 1, prefit
+`k_scale_ceiling` 0.005).
+
+[`grid.yaml`](grid.yaml): 2 noise x 3 seeds x 4 fits = 24 runs.
+
+| axis | levels |
+|---|---|
+| simulated noise | `poisson` (the simulator's old behavior: Poisson counts); `realistic` (founder sampling and demographic growth with about 300 founders per genotype per tube, `cfu0` 150,000; shared transformation; 150,000 template molecules for about 830k reads per tube with amplification CV 0.5, so count variance about 8x Poisson, the middle of the 5-18x measured on real data in `../noise-anatomy/`) |
+| seed | 1, 2, 3 |
+| fit | `lncfu`/`zero` (today's fit: Student-t on `ln_cfu`, `normal_kt` growth noise); `lncfu`/`level` (plus a per-tube level offset); `counts`/`level` (the count likelihood; growth noise off, as it requires); `counts`/`level`/`mixture` with the measured lambda prior (0.357 +/- 0.05) |
+
+Each run ([`run.srun`](run.srun)) is the congression-calibration pipeline
+with `--growth_likelihood` and `--sample_offset_model` from the fit arm.
+
+## How to run
+
+On the cluster, from a scratch directory, with this repository checked out:
+
+```bash
+tfs-setup-sim-grid grid.yaml --out_prefix count_likelihood_v4
+for d in count_likelihood_v4/run_*/; do (cd "$d" && sbatch run.srun); done
+```
+
+When the runs finish:
+
+```bash
+tfs-summarize-calibration count_likelihood_v4 --out_prefix calib/count_likelihood_v4 \
+    --baseline likelihood=lncfu sample_offset=zero fit=single \
+    --facet_by founder_sampling
+```
+
+## What to look at
+
+- theta (`theta_test`): coverage, calibration bias, width and RMSE by
+  `purity` x `has_binding`, paired against `lncfu`/`zero`, separately for
+  the two noise levels. Under `realistic` noise the `lncfu` fits treat 5-18x
+  too little variance as known, so their coverage should fall; the count
+  fit should hold it.
+- `growth_phi` and `growth_inv_r`: near 0 under `poisson`; `phi` near the
+  simulated multiplier (about 8) under `realistic`.
+- The `mixture` arm's `lam` against the true 0.
+- Convergence (every run should stop on the noise-referenced rule, not the
+  epoch cap).
+
+## Inputs
+
+Repository paths only: `grid.yaml`, `run.srun`,
+`../congression-calibration/simulate_config.yaml` and its
+`hill_params.csv`. Seeds fixed in the grid.
+
+## Commit
+
+First run: commit 775f8b7 (2026-09-26/27), results in `count_likelihood/`.
+Second run: commit c65390aa (2026-09-27), results in `count_likelihood_v2/`.
+Third run: commit 740ebc19 (2026-09-27), results in `count_likelihood_v3/`.
+Fourth run: 722393cf (2026-09-28, inferred from the file times of its
+`run.out` files; the runs do not log a commit), results in
+`count_likelihood_v4/`. None of the result directories are committed.
+
+## Results
+
+**Fourth run (`count_likelihood_v4`, 2026-09-28): `ln_cfu0: hierarchical`.**
+All 24 runs finished. This is the first run after the SVI start fix
+(`../relative-fit/`) and the ln_cfu0 fix (`../svi-overconfidence/`). Every
+earlier run fit `hierarchical_factored`, which shares each genotype's
+starting abundance across the separately grown kanR and pheS libraries.
+
+Pooled theta (test genotypes), mean over 3 seeds, 95% coverage / RMSE, with
+the third run's numbers for comparison:
+
+| noise | fit | v4 | v3 |
+|---|---|---|---|
+| poisson | lncfu / zero | 0.72 / 0.100 | 0.76 / 0.101 |
+| poisson | lncfu / level | 0.74 / 0.100 | 0.56 / 0.100 |
+| poisson | counts / level / single | 0.83 / 0.035 | 0.65 / 0.041 |
+| poisson | counts / level / mixture | 0.82 / 0.037 | 0.65 / 0.043 |
+| realistic | lncfu / zero | 0.83 / 0.109 | 0.82 / 0.113 |
+| realistic | lncfu / level | 0.76 / 0.109 | 0.69 / 0.110 |
+| realistic | counts / level / single | 0.84 / 0.072 | 0.79 / 0.073 |
+| realistic | counts / level / mixture | 0.84 / 0.073 | 0.78 / 0.074 |
+
+- **Counts now wins on coverage as well as accuracy.** Under Poisson noise
+  it covers 0.83 against 0.72 for `lncfu`/`zero`, at a third of the RMSE.
+  Its coverage gain over v3 came from the two fixes. RMSE barely moved.
+- **dk_geno:** counts/single covers 0.86 (poisson) and 0.71 (realistic),
+  against 0.53-0.55 for `lncfu`/`zero`.
+- **k and m are still overconfident in every arm:** `growth_m` 95%
+  coverage is 0-0.58. This is the guides' shared-parameter collapse
+  (`../svi-overconfidence/`).
+- **Lambda at a true 0** is unchanged: 0.019-0.028 (poisson), 0.056-0.063
+  (realistic), with intervals that exclude 0.
+
+**Third run (`count_likelihood_v3`, commit 740ebc19, 2026-09-27): the
+answer.** All 24 runs finished; no NaN, no wrong optimum (run_0012's theta
+RMSE is 0.035, was 0.51). 16 stopped on the convergence rule. 8 reached
+the 100k-epoch cap, all poisson: the loss was flat at the floor step size
+and one global noise parameter whose true value is 0 (`growth_inv_r` for
+counts, `growth_noise` sigma for `lncfu`/`level`) kept sliding toward its
+boundary just past the movement tolerance (excess 0.05-0.08) -- a benign,
+honestly reported non-convergence.
+
+Pooled theta (test genotypes), mean over 3 seeds:
+
+| noise | fit | 95% coverage | 95% width | RMSE |
+|---|---|---|---|---|
+| poisson | lncfu / zero | 0.76 | 0.156 | 0.101 |
+| poisson | lncfu / level | 0.56 | 0.073 | 0.100 |
+| poisson | counts / level / single | 0.65 | 0.067 | 0.041 |
+| poisson | counts / level / mixture | 0.65 | 0.067 | 0.043 |
+| realistic | lncfu / zero | 0.82 | 0.207 | 0.113 |
+| realistic | lncfu / level | 0.69 | 0.139 | 0.110 |
+| realistic | counts / level / single | 0.79 | 0.152 | 0.073 |
+| realistic | counts / level / mixture | 0.78 | 0.154 | 0.074 |
+
+- **Accuracy: counts wins everywhere.** Theta RMSE falls 60% (poisson) and
+  35% (realistic); dk_geno RMSE 85% (poisson) and 40% (realistic); Hill K
+  and n improve too (log K RMSE 0.41 against 0.68 for `lncfu`/`zero`,
+  poisson). By stratum the gain holds for bulk, spiked and binding-anchored
+  genotypes; spiked genotypes gain the most under realistic noise (RMSE
+  0.13 against 0.26, coverage 0.93 against 0.50).
+- **Calibration: counts is about as overconfident as `lncfu`/`zero`, not
+  less.** Coverage 0.79 against 0.82 (realistic), 0.65 against 0.76
+  (poisson): intervals shrink with the error but not as far. The shortfall
+  is concentrated where the data say least: `mixed`-purity genotypes (0.41
+  poisson, 0.55 realistic) and theta near saturation (0.46 poisson, 0.69
+  realistic). So the remaining overconfidence is the fit's (mean-field
+  guide, the k pin), not the likelihood's; `growth_k` coverage is ~0 in
+  every arm (95% width 1e-4 against RMSE 6e-4-1.3e-3).
+- **Lambda at a true 0** (prior 0.357 +/- 0.05): 0.019-0.027 (poisson),
+  0.055-0.064 (realistic), against ~0.12 for the `lncfu` mixture in the
+  congression study. Most of that spurious lambda was the read-count floor;
+  the rest remains, and its intervals (width 0.002-0.006) exclude 0. The
+  mixture costs nothing on theta against `single`.
+- **Longer runs changed little.** Run for run against v2 (early cuts),
+  theta RMSE moved by at most 0.0015 and 95% coverage by at most 0.03
+  (poisson counts runs lost ~0.025), except run_0012, which the early cut
+  had broken. The pooled check mattered for robustness, not for the
+  typical run.
+- Dispersion and per-tube offsets as in v2: poisson phi ~0.05, realistic
+  phi ~28 (the realistic arm is ~29x Poisson, more than intended);
+  `sample_offset` SD ~0.36 absorbing the simulator's tube growth noise.
+
+**Second run (`count_likelihood_v2`, commit c65390aa, 2026-09-27): the
+count likelihood is clearly more accurate; two failures remain, neither in
+the likelihood itself.** 23 of 24 runs finished; 18 stopped on the
+convergence rule, 5 at the epoch cap (3 `counts`, 2 `lncfu`/`level`).
+
+Pooled theta (test genotypes), mean over seeds:
+
+| noise | fit | 95% coverage | 95% width | RMSE |
+|---|---|---|---|---|
+| poisson | lncfu / zero | 0.76 | 0.157 | 0.101 |
+| poisson | lncfu / level | 0.56 | 0.076 | 0.100 |
+| poisson | counts / level / single (2 runs) | 0.70 | 0.068 | 0.037 |
+| poisson | counts / level / mixture (seeds 1, 2) | 0.66 | 0.072 | 0.047 |
+| realistic | lncfu / zero | 0.81 | 0.207 | 0.114 |
+| realistic | lncfu / level | 0.68 | 0.140 | 0.110 |
+| realistic | counts / level / single | 0.78 | 0.153 | 0.073 |
+| realistic | counts / level / mixture | 0.78 | 0.155 | 0.074 |
+
+- **Accuracy.** Counts cut theta RMSE by a third (realistic) to two thirds
+  (poisson) against either `lncfu` arm, with coverage close to `lncfu`/
+  `zero` and better than `lncfu`/`level`. dk_geno RMSE also falls
+  (poisson single: 0.0003 against 0.002). Every arm still under-covers
+  (0.66-0.81 at 95%): the overconfidence is not the likelihood's.
+- **Lambda at a true 0** (prior 0.357 +/- 0.05): 0.023 and 0.027
+  (poisson), 0.054-0.064 (realistic), against ~0.12 for the `lncfu`
+  mixture in the congression study. Counts removes most of the spurious
+  lambda but not all, and the intervals (width ~0.01) exclude 0. The
+  mixture arms lose nothing against `single` on theta.
+- **Dispersion.** poisson: phi 0.03-0.06, inv_r 0.0014 (Poisson, as
+  simulated). realistic: phi 27-29, inv_r 0.007, i.e. ~29x Poisson,
+  well above the ~8x the arm was designed for (real data: 5-18x); the
+  realistic arm is noisier than intended.
+- **`sample_offset` level SD ~0.36 in every count run**, poisson included:
+  the base config's `tube_noise_sigma` (0.002/min over ~230 min, ~0.4 ln
+  units) shifts each tube's genotypes and its supplied total together, and
+  the model's predictions carry no tube term; the offset absorbs it, as
+  intended until step 6. The same explains `lncfu`/`level` under-covering:
+  in `lncfu`/`zero` the `normal_kt` row noise soaked up this tube noise and
+  with it the understated `ln_cfu` noise; the structural offset removes that
+  cover and the intervals halve at the same RMSE.
+- **`growth_k` coverage ~0 in every arm** (95% widths ~1e-4 against RMSE
+  ~1e-3): the pre-fit's k pin, not the likelihood.
+- **Failure 1, `run_0007` (poisson, seed 2, counts, single): NaN at step
+  ~12,400.** Not the likelihood: hill_mut's horseshoe local scales for the
+  lowest-read doubles (200-800 reads over all tubes, mostly zeros under
+  selection; typical doubles 3,600-200,000) widened (guide LogNormal scale
+  up to 11.5 on 41 elements; every other run stays at ~1.5), because beyond
+  saturation theta, and so the likelihood, no longer changes, while the
+  half-Cauchy tail is heavy. A draw of ~e^90 overflows float32 and the
+  epistasis term, theta, growth and the likelihood go NaN. Reproduced by
+  replaying from the step-12,000 checkpoint.
+- **Failure 2, `run_0012` (poisson, seed 3, counts, mixture): converged to
+  a wrong answer** (theta RMSE 0.51; m shrunk to ~40% and 4CP's m of the
+  wrong sign; ELBO 5.4e5 against 2.6e5 for the same-sized seed-2 mixture;
+  its `single` twin on the same data is fine). The step size was cut at
+  step 20,000 during a noisy but steady descent: window-median losses fell
+  1.5e5-1.9e5 per window, but each window's own trend was t = 1.9, 3.0,
+  1.0, so three windows counted as a plateau. It also showed some lambda
+  widening (scales to 4.9), perhaps from the same low-read doubles.
+- **Both fixed on this branch (2026-09-27).** Horseshoe: overflow-safe
+  `regularized_scale` and `HalfCauchy` (`components/_horseshoe.py`); a
+  replay of run_0007 from its step-12,000 checkpoint, which went NaN at step
+  12,370 before, ran 3,000 steps clean. Convergence: before a cut or stop,
+  the stalled windows are pooled into one trend (`pooled_loss_trend`).
+  Replaying every v2 run's loss trace up to its first cut, 20 of 23 first
+  cuts were made mid-descent (pooled t 4.4-14.2; run_0012: 14.2) and would
+  now continue; the other 3 (pooled t -0.5 to 1.4) are still cut. So the
+  early cut was the norm, not a one-off, and the v2 fits (and the earlier
+  congression grids) mostly finished their fast phase at too small a step.
+  A third run with both fixes is needed; runs will be longer.
+
+**First run (775f8b7): 11 of the 12 `counts` runs failed; not usable for
+the comparison.** All 12 `lncfu` runs and one `counts` run
+(`poisson`/seed 2/`mixture`) finished.
+
+- 8 `counts` runs crashed at the fit on a NaN `k_scale` written by the
+  pre-fit. Cause: `RunInference.compute_hessian_sigmas` took the constrained
+  MAP values it was given as unconstrained, so every positive site was
+  evaluated at `exp(value)`; the count likelihood's `growth_phi` (MAP 49
+  from the calibration model's misfit, dk_geno fixed at 0) became e^49 and
+  overflowed. Reproduced from `run_0003`'s saved MAP; fixed. The same bug
+  loosened the `lncfu` arms' pre-fit `k_scale` (0.0028 written against
+  k sigmas of 0.0003-0.0007 at the right point, which the 0.002 floor
+  now overrides), so the `lncfu` arms need the rerun too.
+- 3 `counts` runs (`poisson` seeds 2 and 3) went NaN within 250 SVI steps
+  after the pre-MAP. Cause: the negative binomial's concentration `mu / phi`
+  underflows float32 for genotypes predicted near extinction (all-zero
+  genotypes drift there), and the gradient goes NaN. Reproduced by
+  replaying `run_0007`'s pre-MAP hand-off (NaN by step 250); with the
+  concentration floor (`c + e^-30`) the same replay runs normally.
+- The one finished `counts` run (true lambda 0, prior 0.357 +/- 0.05)
+  found lambda 0.027 (95% 0.026-0.028): far from the prior and well below
+  the ~0.12 the `lncfu` mixture found at true lambda 0 in the congression
+  study, as the floor hypothesis predicts; one run, and the interval is
+  overconfident. Its theta RMSE was 0.054 against 0.10 for the `lncfu`
+  arms at the same noise.
+- `lncfu` arms, pooled theta 95% coverage (3 seeds each): `poisson`
+  0.76 (`zero`) and 0.55 (`level`); `realistic` 0.82 (`zero`) and 0.68
+  (`level`). The `level` offset narrows the theta intervals by about half
+  at the same RMSE, so it makes `lncfu` more overconfident, not less.
+  `growth_k` coverage is about 0 in every arm (intervals ~1e-4 wide, RMSE
+  0.001): the pre-fit's k prior dominates.
+- Counts runs start SVI at a much higher ELBO than `lncfu` (about 3e8 at
+  the pre-MAP point here): at 1e4-1e5 reads the likelihood is sharp and the
+  component guide's initial location scales (0.1) cost ~(0.1 mu)^2 / var per
+  observation. It falls as the scales shrink; a smaller
+  `--guide_init_scale` may suit counts fits if this slows convergence.
+  **Update (2026-09-27):** it did more than slow convergence. SVI lost the
+  pre-MAP point and settled into other optima (`../relative-fit/`,
+  Results), and every arm here, `lncfu` included, started 2e6-5e8 above
+  its pre-MAP. The default is now 1e-4 with no jitter. The comparisons
+  between arms ran under the same handicap. The absolute coverage numbers
+  above, `growth_k` included, need a rerun.
diff --git a/planning/studies/count-likelihood/grid.yaml b/planning/studies/count-likelihood/grid.yaml
new file mode 100644
index 00000000..6eb2dcd6
--- /dev/null
+++ b/planning/studies/count-likelihood/grid.yaml
@@ -0,0 +1,93 @@
+# ---------------------------------------------------------------------------
+# Count-likelihood validation (roadmap step 7, planning/analysis-roadmap.md)
+#
+#   tfs-setup-sim-grid grid.yaml --out_prefix count_likelihood
+#   for d in count_likelihood/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Does fitting read counts (negative binomial, no pseudocount) beat fitting
+# ln_cfu, on data with Poisson-only counts and on data with the real data's
+# extra count noise? And at a true lambda of 0, does the congression mixture
+# still find lambda ~0.12 once the read-count floor is modeled (the open
+# question from the congression plan's step 3.5)?
+#
+# Simulated noise:
+#   poisson    the simulator's old behavior: deterministic growth, one
+#              multinomial draw of reads (count variance = Poisson).
+#   realistic  founder sampling and demographic growth with ~300 founders
+#              per genotype per tube (cfu0 150,000 over 483 genotypes, as in
+#              the real 215k-genotype library at 6.5e7 cells), one shared
+#              transformation, and a template bottleneck with PCR
+#              jackpotting (150,000 templates for ~830k reads per tube, CV
+#              0.5): count variance ~8x Poisson, the middle of the 5-18x
+#              measured on real data (study noise-anatomy).
+#
+# Fits (all with the anchored settings of the congression 3.5 baseline):
+#   lncfu  / zero   today's production fit (Student-t on ln_cfu, normal_kt)
+#   lncfu  / level  the same with a per-tube level offset
+#   counts / level  the count likelihood with a per-tube level offset
+#   counts / level / mixture (measured lambda prior): the floor question
+#
+# 2 noise x 3 seeds x 4 fits = 24 runs.
+# ---------------------------------------------------------------------------
+
+base_config: ../congression-calibration/simulate_config.yaml
+
+run_name: "{{ 'realistic' if founder_sampling else 'poisson' }}__seed{{ seed }}__{{ likelihood }}_{{ sample_offset }}_{{ fit }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: noise
+    variants:
+      - founder_sampling: false
+      - founder_sampling: true
+        demographic_growth: true
+        shared_transformation: true
+        cfu0: 150_000.0
+        pcr_template_molecules: 150_000
+        pcr_amplification_cv: 0.5
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+      - seed: 3
+
+
+template:
+
+  - name: fit
+    variants:
+      - likelihood: lncfu
+        sample_offset: zero
+        fit: single
+        lam_prior: "off"
+      - likelihood: lncfu
+        sample_offset: level
+        fit: single
+        lam_prior: "off"
+      - likelihood: counts
+        sample_offset: level
+        fit: single
+        lam_prior: "off"
+      - likelihood: counts
+        sample_offset: level
+        fit: mixture
+        lam_prior: measured
+
+  - name: anchor
+    variants:
+      - binding_weight: 1.0
+        k_scale_ceiling: 0.005
diff --git a/planning/studies/count-likelihood/run.srun b/planning/studies/count-likelihood/run.srun
new file mode 100644
index 00000000..e825a1dd
--- /dev/null
+++ b/planning/studies/count-likelihood/run.srun
@@ -0,0 +1,183 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=count_lik
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpu
+#SBATCH --time=00-02:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --ntasks-per-node=1
+
+# Count-likelihood study, one run (rendered by tfs-setup-sim-grid from
+# planning/studies/count-likelihood/run.srun, which is the congression-
+# calibration study's run.srun plus the likelihood and sample-offset arms).
+# Submit from the run directory: sbatch run.srun
+
+# Swap lines if on cluster (module load) or local (XLA_FLAGS to use CPU)
+module load cuda/12.4.1
+#export XLA_FLAGS="--xla_force_host_platform_device_count=8"
+
+set -e
+
+FIT="{{ fit }}"
+# Growth likelihood (lncfu or counts) and per-tube offset (zero or level).
+LIKELIHOOD="{{ likelihood }}"
+SAMPLE_OFFSET="{{ sample_offset }}"
+LAM_PRIOR="{{ lam_prior }}"
+LAM_PRIOR_SD=0.05
+LAM_MEASURED=0.357
+# Growth rows with fewer reads than this are dropped before configure (0 keeps
+# everything). grid_masked.yaml sets it; grid.yaml leaves the default.
+MIN_COUNTS={{ min_counts | default(0) }}
+# Binding likelihood weight passed to tfs-configure-model (empty = its default,
+# growth rows / binding rows). grid_bw1.yaml sets it to 1.
+BINDING_WEIGHT="{{ binding_weight | default('') }}"
+# Ceiling on the prefit's per-condition k prior scale (empty = its default,
+# 0.1). grid_anchor.yaml sets it low to pin the k/dk_geno slide.
+K_SCALE_CEILING="{{ k_scale_ceiling | default('') }}"
+# tfs-fit-model --analysis_method (svi or map). grid_controls.yaml sets map.
+ANALYSIS_METHOD="{{ analysis_method | default('svi') }}"
+# Lambda value for lam_prior "fixed" (grid_profile.yaml).
+LAM_FIXED="{{ lam_fixed | default('') }}"
+# Congression theta rule for the fit (mixture only). Empty = the rule the
+# simulation used (tfs_sim_config.yaml's congression_theta_rule); set it to
+# fit with a different rule (cross-rule grids).
+FIT_THETA_RULE="{{ fit_theta_rule | default('') }}"
+# Congression dk rule (and softmin alpha) for the fit (mixture only). Empty =
+# the simulation's (congression_dk_rule, congression_dk_alpha).
+FIT_DK_RULE="{{ fit_dk_rule | default('') }}"
+FIT_DK_ALPHA="{{ fit_dk_alpha | default('') }}"
+
+# The simulated lambda and the seed live in tfs_sim_config.yaml.
+read -r SEED LAM_SIM SIM_THETA_RULE SIM_DK_RULE SIM_DK_ALPHA < <(python -c "
+import yaml
+cf = yaml.safe_load(open('tfs_sim_config.yaml'))
+print(cf['seed'], cf.get('transformation_poisson_lambda') or 0.0,
+      cf.get('congression_theta_rule') or 'homodimer',
+      cf.get('congression_dk_rule') or 'dilution',
+      cf.get('congression_dk_alpha') or '')")
+FIT_THETA_RULE=${FIT_THETA_RULE:-${SIM_THETA_RULE}}
+if [[ -z "${FIT_DK_RULE}" ]]; then
+    FIT_DK_RULE=${SIM_DK_RULE}
+    FIT_DK_ALPHA=${SIM_DK_ALPHA}
+fi
+
+# ---------------------------------------------------------------------------
+# 1. Simulate
+# ---------------------------------------------------------------------------
+echo ">>> Simulate (lambda=${LAM_SIM}, seed=${SEED})"
+tfs-simulate tfs_sim_config.yaml . --seed "${SEED}"
+
+GROWTH_CSV=tfs_sim_growth.csv
+if [[ "${MIN_COUNTS}" -gt 0 ]]; then
+    GROWTH_CSV=tfs_sim_growth_min${MIN_COUNTS}.csv
+    python -c "
+import pandas as pd
+df = pd.read_csv('tfs_sim_growth.csv')
+kept = df[df['counts'] >= ${MIN_COUNTS}]
+kept.to_csv('${GROWTH_CSV}', index=False)
+print(f'Dropped {len(df) - len(kept)} of {len(df)} growth rows with counts < ${MIN_COUNTS}')"
+fi
+
+# ---------------------------------------------------------------------------
+# 2. Configure
+# ---------------------------------------------------------------------------
+if [[ "${FIT}" == "single" ]]; then
+    TRANSFORMATION_ARGS=(--transformation_model single)
+else
+    if [[ "${LAM_PRIOR}" == "matched" ]]; then
+        LAM_MEAN=$(python -c "print(max(${LAM_SIM}, 0.01))")
+    elif [[ "${LAM_PRIOR}" == "fixed" ]]; then
+        # lambda held at LAM_FIXED (prior SD 0.1% of the value).
+        LAM_MEAN=${LAM_FIXED}
+        LAM_PRIOR_SD=$(python -c "print(${LAM_FIXED} * 1e-3)")
+    elif [[ "${LAM_PRIOR}" == "fixed0" ]]; then
+        # lambda pinned essentially at zero: the mixture is then single.
+        LAM_MEAN=1e-6
+        LAM_PRIOR_SD=1e-9
+    else
+        LAM_MEAN=${LAM_MEASURED}
+    fi
+    TRANSFORMATION_ARGS=(--transformation_model mixture
+                         --transformation_lambda "${LAM_MEAN}" "${LAM_PRIOR_SD}"
+                         --congression_theta_rule "${FIT_THETA_RULE}"
+                         --congression_dk_rule "${FIT_DK_RULE}")
+    if [[ -n "${FIT_DK_ALPHA}" ]]; then
+        TRANSFORMATION_ARGS+=(--congression_dk_alpha "${FIT_DK_ALPHA}")
+    fi
+fi
+
+# The count likelihood carries its own per-row dispersion, so it refuses the
+# extra ln_cfu-scale growth noise the lncfu arms use.
+if [[ "${LIKELIHOOD}" == "counts" ]]; then
+    GROWTH_NOISE=zero
+else
+    GROWTH_NOISE=normal_kt
+fi
+
+BINDING_WEIGHT_ARGS=()
+if [[ -n "${BINDING_WEIGHT}" ]]; then
+    BINDING_WEIGHT_ARGS=(--binding_weight "${BINDING_WEIGHT}")
+fi
+
+echo ">>> Configure (${LIKELIHOOD}, offset ${SAMPLE_OFFSET}, ${FIT}, lambda prior ${LAM_PRIOR}, theta rule ${FIT_THETA_RULE}, dk rule ${FIT_DK_RULE}${FIT_DK_ALPHA:+ alpha ${FIT_DK_ALPHA}})"
+tfs-configure-model \
+    --binding_df tfs_sim_binding.csv \
+    --growth_df "${GROWTH_CSV}" \
+    --presplit_df tfs_sim_presplit.csv \
+    --condition_growth_model linear \
+    --growth_transition_model instant \
+    --ln_cfu0_model hierarchical \
+    --dk_geno_model hierarchical_geno \
+    --activity_model fixed \
+    --theta_model hill_mut \
+    --theta_rescale_model passthrough \
+    --theta_growth_noise_model logit_normal \
+    --theta_binding_noise_model zero \
+    --growth_noise_model "${GROWTH_NOISE}" \
+    --growth_likelihood "${LIKELIHOOD}" \
+    --sample_offset_model "${SAMPLE_OFFSET}" \
+    --library_config tfs_sim_config.yaml \
+    --growth_shares_replicates \
+    --epistasis \
+    "${TRANSFORMATION_ARGS[@]}" \
+    "${BINDING_WEIGHT_ARGS[@]}"
+
+# ---------------------------------------------------------------------------
+# 3. Pre-fit calibration, fit, posterior
+# ---------------------------------------------------------------------------
+PREFIT_ARGS=()
+if [[ -n "${K_SCALE_CEILING}" ]]; then
+    PREFIT_ARGS=(--k_scale_ceiling "${K_SCALE_CEILING}")
+fi
+
+echo ">>> Pre-fit calibration"
+tfs-prefit-calibration tfs_configure_config.yaml --seed "${SEED}" "${PREFIT_ARGS[@]}"
+
+echo ">>> Fit model"
+tfs-fit-model tfs_configure_config.yaml \
+    --seed "${SEED}" \
+    --analysis_method "${ANALYSIS_METHOD}"
+
+echo ">>> Sample posterior"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl
+
+# ---------------------------------------------------------------------------
+# 4. Extract, predict, summarize
+# ---------------------------------------------------------------------------
+echo ">>> Extract parameters"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict theta"
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict growth"
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+    --num_marginal_samples=500
+
+echo ">>> Summarize fit"
+tfs-summarize-fit .
+
+echo ">>> Done"
diff --git a/planning/studies/fresh-install/README.md b/planning/studies/fresh-install/README.md
new file mode 100644
index 00000000..f833c77c
--- /dev/null
+++ b/planning/studies/fresh-install/README.md
@@ -0,0 +1,65 @@
+# Fresh install
+
+**Question.** Does a new lab member's install of tfscreen work: a clean
+clone, a new virtual environment, `pip install -e ".[cuda12]"`, on a
+cluster GPU node?
+
+**Decision fed.** `planning/release-0.5.0.md`, items 11 and 13: whether
+0.5.0's install instructions and dependency floors are right before
+Natalie's dry run.
+
+**How to run.** On the cluster, after pulling the commit that adds this
+study and pushing it to GitHub (the job clones from GitHub, not from the
+cluster checkout):
+
+```bash
+sbatch --export=ALL,OUT=/gpfs/projects/harmslab/harms/studies/fresh-install/run1 /gpfs/home/harms/tfscreen/planning/studies/fresh-install/fresh_install.srun
+```
+
+`OUT` must not hold `tfscreen/` or `venv/` already. Set `PY` to a Python
+3.12 or newer if `python3` on the node is older, and `REF` to test a tag.
+
+**What it checks, in order:** the clone at `REF`; the environment
+(`installed.txt` is its `pip freeze`); that jax sees the GPU (the job
+fails here if pip installed CPU-only jax); the unit tests
+(`unit_tests.txt`); the quickstart example on the GPU
+(`quickstart/run.log`, `quickstart/out/`).
+
+**Inputs.** None beyond the repository.
+
+**Pass.** jax on the GPU, the unit tests as on CI, and the quickstart
+running to its summary.
+
+## Results
+
+### Local fresh install (2026-10-20)
+
+A new venv on the maintainer's laptop (macOS, Python 3.13.11),
+`pip install -e ".[test]"` with no constraints, took the newest releases
+(pandas 3.0.6, numpy 2.5.4, jax 0.12.0, scipy 1.18.1, numba 0.68.0).
+The full unit suite passed (6,127 passed, 18 skipped, `pytest -n 4`,
+8.7 min). Its `pip freeze` was briefly kept as
+`constraints/tfscreen-0.5.0.txt`, then removed (user, 2026-10-20: no
+top-level directory for it; the manuscript records its own versions).
+
+A dry-run resolve of that set with `jax[cuda12]` for Linux x86-64
+(`pip install --dry-run --platform manylinux... --only-binary=:all:`)
+gives 83 packages for Python 3.12, with jax, jaxlib, jax-cuda12-plugin
+and jax-cuda12-pjrt all at 0.12.0 (pybktree left out of the check: it
+ships only a source package, which pip does not cross-resolve). For
+Python 3.11 it fails: contourpy 1.4.0, at least, has dropped 3.11.
+
+### Cluster fresh install (2026-10-20)
+
+The user wiped and reinstalled by hand in a new environment on the
+cluster: the install works. `fresh_install.srun` has not been run.
+
+### Python 3.12-3.14 (2026-10-20)
+
+CI's 3.11 jobs then failed at install: jax 0.11 and newer need Python
+3.12, so the new jax floor has no 3.11 wheel. tfscreen now needs Python
+3.12 or newer, and CI tests 3.12, 3.13 and 3.14. A dry-run resolve of the
+fresh install's versions found wheels for every package on 3.12 and 3.14,
+Linux x86-64 and macOS arm64.
+
+
diff --git a/planning/studies/fresh-install/fresh_install.srun b/planning/studies/fresh-install/fresh_install.srun
new file mode 100644
index 00000000..928c6583
--- /dev/null
+++ b/planning/studies/fresh-install/fresh_install.srun
@@ -0,0 +1,62 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_fresh
+#SBATCH --partition=gpu
+#SBATCH --time=00-06:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=64G
+#SBATCH --ntasks-per-node=1
+
+# A fresh install of tfscreen as a new lab member would make it, on a GPU
+# node (planning/release-0.5.0.md item 11; README.md here). Clones the
+# repository at REF into OUT, makes a new virtual environment there,
+# installs with the GPU extra, checks that
+# jax sees the GPU, runs the unit tests and the quickstart example.
+#
+#   sbatch --export=ALL,OUT=/path/to/empty/dir fresh_install.srun
+#
+#   OUT   an empty directory (required; absolute)
+#   REPO  repository URL (default https://github.com/harmsm/tfscreen)
+#   REF   branch, tag or commit (default main)
+#   PY    the Python that makes the environment (default python3; >= 3.12)
+
+module load cuda/12.4.1
+set -e
+OUT="${OUT:?set OUT to an empty directory}"
+REPO="${REPO:-https://github.com/harmsm/tfscreen}"
+REF="${REF:-main}"
+mkdir -p "${OUT}" && cd "${OUT}"
+if [ -e tfscreen ] || [ -e venv ]; then
+    echo "OUT must not hold tfscreen/ or venv/ already" >&2; exit 1
+fi
+
+echo ">>> Clone ${REPO} ${REF} $(date)"
+git clone -q "${REPO}" tfscreen
+git -C tfscreen checkout -q "${REF}"
+git -C tfscreen log --oneline -1
+
+echo ">>> Environment $(date)"
+PY="${PY:-python3}"
+"${PY}" -c "import sys; print(sys.version); assert sys.version_info >= (3, 12), 'tfscreen needs Python 3.12 or newer; set PY'"
+"${PY}" -m venv venv
+source venv/bin/activate
+pip install -q --upgrade pip
+pip install -q -e "tfscreen[cuda12,test]"
+pip freeze --exclude-editable > installed.txt
+python -c "import jax; d = jax.devices(); print('jax', jax.__version__, d); assert d[0].platform == 'gpu', 'jax does not see the GPU'"
+
+echo ">>> Unit tests $(date)"
+(cd tfscreen && NUMBA_DISABLE_JIT=1 pytest -q -n 8 tests/tfscreen) > unit_tests.txt 2>&1 || true
+tail -3 unit_tests.txt
+
+echo ">>> Quickstart $(date)"
+cp -r tfscreen/examples/simulate-and-analyze quickstart
+cd quickstart
+# run.sh forces 8 virtual CPU devices for laptops; on a GPU node drop it
+sed -i 's/^export XLA_FLAGS=/#export XLA_FLAGS=/' run.sh
+bash run.sh simulate_config.yaml out 1 > run.log 2>&1
+tail -5 run.log
+ls out | head -40
+echo ">>> Done $(date)"
diff --git a/planning/studies/full-size-sim/README.md b/planning/studies/full-size-sim/README.md
new file mode 100644
index 00000000..a6b400c2
--- /dev/null
+++ b/planning/studies/full-size-sim/README.md
@@ -0,0 +1,367 @@
+# Full-size simulation of the dev-data design
+
+**Question.** Does the documented chain (simulate, process counts,
+configure, staged MAP, arrowhead Laplace, extract, summarize) run on a
+simulation the size and shape of the real screen, with no hand step, and
+how well does the growth-only relative fit recover the truth there?
+
+**Decisions it feeds.**
+- Pipeline plan step 6's last item and step 7
+  (`planning/experiment-pipeline.md`): this is the full-size simulation
+  that runs through the same chain as the real data, with only the data
+  paths changed.
+- The footing for the science prioritization (pipeline step 8) and for
+  `planning/offset-mode-growth-transition.md`: every later model study
+  starts from this design, on files a user can regenerate.
+
+## Design
+
+`make_sim_config.py` builds the simulate config from the real experiment;
+its docstring says what is taken from the data and what is chosen. In
+short:
+
+- **Design:** the real tube table, 118 tubes in 3 replicates, with the real
+  names and per-replicate selection times. `tfs-simulate` follows it
+  exactly through the `design` key (added for this study), so the
+  simulated tube table has the real layout.
+- **Library:** the real genetics and `transform_sizes` (the nine-spike
+  config), about 218,000 genotypes. Since 2026-10-08 the `library_mixture`
+  is the realized one, estimated from the real pre-split composition
+  (`estimate_library_mixture`; doubles 0.54, single-1 0.20, single-2 0.25,
+  against the design's 0.83/0.08/0.08), and wt is repeated in the simulated
+  `spiked_seqs` until it is 11% of the pool, as in the real one. The excess
+  wt is simulation-only: the fit is configured with the real library config
+  (`library_config.yaml`), as the real data's fit is.
+- **Growth:** linear, b and m per condition from the wt monoculture rates
+  (the monokan rule); control conditions m = 0.
+- **Population:** `cfu0` per library, calibrated so the simulated tube
+  totals match the real ones on average (below); total reads equal to the
+  real experiment's (5.1e9, 4.3e7 per tube; runs before 2026-10-08 had 3
+  times that, a simulator bug), with the real share of each library's reads
+  unassigned (`unassigned_read_fraction`: kanR 0.66, pheS 0.22); OD600
+  through the real calibration, totals estimated from it.
+- **Congression:** lambda 0.357, measured.
+- **Chosen:** noise (`realistic`, the default, or `poisson`), phenotypes
+  (hill_geno prior draws, or `--phenotype_model` from `tfs-build-empirical`),
+  the growth transition (`instant`; `memory` as a test arm), dk_geno's
+  hyperparameters and `tube_noise_sigma`.
+
+The fit is the real data's: growth-only `hill_relative`, level tube offsets
+held at SD 0.17, log(n) population SD held at 0.5, loose k/m priors
+(`growth_priors_loose.csv`), MAP (staged automatically), arrowhead Laplace.
+
+## How to run
+
+A full-size simulation is large: about 85 minutes, 10 GB resident and a
+68 GB peak memory footprint on a laptop for one seed (2026-10-05), and
+several GB of files. Run it on the cluster, in `planning/dev-data/`
+(gitignored, not pulled), after `prep_dev_data.py` has written
+`processed/`. Copy the study files there (the staged-map study's
+`score_map.py` too):
+
+```bash
+cp <repo>/planning/studies/full-size-sim/{make_sim_config.py,run_pilot.srun,run_full_size.srun,growth_priors_loose.csv} <repo>/planning/studies/staged-map/score_map.py .
+```
+
+First a pilot at the first-guess `cfu0`, to calibrate it and to check the
+read depth against the real screen (raw count files, no growth table):
+
+```bash
+sbatch run_pilot.srun
+```
+
+It runs `make_sim_config.py --out_dir pilot_s1 --seed 1`, then
+`tfs-simulate` with `--no_write_growth` inside `pilot_s1`.
+
+Then the calibrated config and the run (same seed, so the same phenotypes):
+
+```bash
+python make_sim_config.py --out_dir sim_realistic_s1 --seed 1 --calibrate_from pilot_s1
+```
+
+```bash
+cd sim_realistic_s1 && sbatch ../run_full_size.srun
+```
+
+A Poisson-noise arm for comparison:
+
+```bash
+python make_sim_config.py --out_dir sim_poisson_s1 --seed 1 --noise poisson --calibrate_from pilot_s1
+```
+
+```bash
+cd sim_poisson_s1 && sbatch ../run_full_size.srun
+```
+
+## Inputs
+
+`planning/dev-data/processed/`: `sample_df.csv` (the tube table),
+`library_config.yaml`, `od600_calibration.yaml`,
+`wt_control_rate_summary.csv`. Nothing outside `planning/dev-data`.
+
+## Commit
+
+The commit that adds the simulator's `design` key. Every step writes its
+provenance (`*_provenance.json`).
+
+## Results
+
+Local pilot (2026-10-05, the uncommitted working tree, seed 1, first-guess
+`cfu0` 1.5e7 per tube): it ran in 85 minutes (10 GB resident, 68 GB peak
+footprint) and followed the 118-tube design. The simulated tubes were
+about half as dense as the real ones (median OD600 kanR 0.16 against 0.22,
+pheS 0.13 against 0.26), and six pheS tubes read below the detection
+threshold. Calibrating the level per library from it gave `cfu0` x1.74 for
+kanR and x3.58 for pheS (mean log real/simulated total 0.55 and 1.28).
+
+A per-library level leaves a structured residual (log real/simulated
+total, SD 0.6-0.7 over tubes), which the calibration does not try to
+remove, because it is the model question itself:
+
+- **By IPTG.** kanR+kan: about +1 at low IPTG and -0.3 at 1 mM; pheS+4CP:
+  +1 rising to +2.1 at high IPTG. The simulated growth difference across
+  IPTG (wt monoculture rates on the theta scale) is stronger than the real
+  library's, most of which is mutants.
+- **Over time.** Within nearly every condition, controls included, real
+  totals fall behind the simulation by about 0.01-0.02 per minute: the
+  screen populations grow slower than the wt monoculture rates say.
+
+Both bear on `planning/offset-mode-growth-transition.md` (the tube offsets
+carried an IPTG pattern) and on roadmap step 6 (the population model), and
+go to the science prioritization (pipeline plan step 8).
+
+Cluster runs, first attempt (2026-10-06): both arms simulated and
+processed, then stopped at configure. Seed 1 drew dk_geno -0.1 per minute
+for the spike H74A/K84L. At that rate the spike falls about e^-12 behind by
+the first sampled tube (30 minutes of pre-growth plus about 90 of
+selection), so it has no reads, and configure refused a spike without
+growth data. A spike can die in a simulation, so `run_full_size.srun` now
+passes `--allow_missing_spikes`. It also skips the simulation and the
+processing when their outputs exist.
+
+Rerun from configure (2026-10-06): both arms ran through the whole chain
+with no hand step (staged MAP converged at every stage; arrowhead Laplace
+held 45 and 55 shared directions, all k/m/dk_geno shift combinations), and
+the tube-offset diagnostic found no structure (no condition trends with
+IPTG or time; offset SD 1.8 to 1.9 times the held 0.17). The recovery
+numbers are not usable: **the simulated wt did not respond to IPTG**.
+hill_geno's simulation sampler drew wt's phenotype like any genotype's,
+and seed 1 gave it "never binds" (theta 0.0063 at 0 to 0.0060 at 1 mM).
+The relative fit gauges X on wt's own change, so the mapped X truth was
+about 3,600 times too wide (theta test RMSE about 2,700), and the fit
+itself was forced to put a wt response where there was none.
+`thermo_to_growth` now gives wt the hill_geno reference curve
+(`_pin_wt_reference`; an explicit wt override still wins). Every earlier
+hill_geno simulation had a perturbed wt, 12% of seeds a non-normal
+category. Rerun: new directories, same pilot calibration (wt is one
+spike, a negligible share of the tube totals):
+
+```bash
+python make_sim_config.py --out_dir sim_realistic_s1b --seed 1 --calibrate_from pilot_s1
+```
+
+```bash
+python make_sim_config.py --out_dir sim_poisson_s1b --seed 1 --noise poisson --calibrate_from pilot_s1
+```
+
+Corrected arms (2026-10-06/07, commit 8399fb6b; `sim_realistic_s1b`,
+`sim_poisson_s1b`): wt carries the reference curve (0.99 to 0.01, log K
+-4.1, n 2). **The chain is clean**: simulate, process, configure, staged
+MAP (every stage converged on the exact loss), arrowhead Laplace (9 and 11
+held shared directions), extract, predict and summarize, with no hand
+step. **The offset diagnostic is clean** (smallest BH q 0.63 realistic,
+0.31 Poisson), as it should be with no selection-onset transient in the
+simulator. The offsets' SD (0.31, 0.34) is about twice the held 0.17: the
+simulator's tube noise is a per-tube growth-rate shift
+(`tube_noise_sigma` 0.002 per minute, so ~0.4 ln units over a selection),
+larger than the real fit's offsets (SD 0.20) and not a level.
+
+Recovery, the baseline for pipeline plan step 8 (realistic / Poisson;
+genotypes 124k / 186k with growth data):
+
+| | realistic | Poisson |
+|---|---|---|
+| X: Pearson r, RMSE, mean error | 0.34, 0.94, +0.06 | 0.76, 0.37, -0.21 |
+| X: 95% coverage, median width | 0.66, 0.39 | 0.29, 0.15 |
+| log K: r, 95% coverage | 0.28, 0.80 | 0.45, 0.82 |
+| log n: r, 95% coverage | 0.43, 0.94 | 0.66, 0.94 |
+| dk_geno: r, bias, 95% coverage | 0.69, -0.007, 0.66 | 0.79, -0.011, 0.23 |
+| growth k, bias per minute | +0.006 | +0.022 |
+| growth m, kanR+kan / pheS+4CP (truth -0.0138 / +0.0141) | -0.0192 / +0.0187 | -0.0168 / +0.0164 |
+| k and m 95% coverage | 0 | 0 |
+
+X coverage by genotype reads (95%; realistic / Poisson): <=100 reads
+1.00 / 0.93, 1e2-1e3 0.90 / 0.62, 1e3-1e4 0.81 / 0.24, 1e4-1e5 0.61 /
+0.10, >1e5 0.20 / 0.04. Coverage falls with depth, and under Poisson
+noise the X error is a near-constant -0.22 at every depth: a shared error,
+not per-genotype noise. The shared parameters are off and carry no
+interval. k slid up with dk_geno down (the k/dk_geno slide,
+"Per-condition growth priors" in CLAUDE.md; the loose priors, k SD 0.01,
+allow it), |m| came out 20-40% too large, and the arrowhead Laplace held
+exactly those directions (k, m, dk_geno_hyper_shift) at the MAP, so k and
+m have zero-width intervals and every deep genotype inherits their error.
+The relative-fit study's run 4 (small library) covered X 0.97 / 0.94 with
+the same route; at full size the shared error dominates. These go to step
+8: anchoring k against dk_geno (tighter or measured k priors, base-growth
+or wt monoculture data), the held Schur directions, and a simulator tube
+noise that matches the real offsets.
+
+**What limits the correlation is depth, not k and m.** The realistic arm's
+overall X r of 0.34 is set by shallow genotypes. Reads below are summed
+over all 118 tubes (every condition, IPTG, time and replicate, both
+libraries); 1,000 reads is about 8 per tube. By genotype-concentration
+point, realistic arm:
+
+| total reads | r | RMSE | RMSE after the best affine map | SD of truth |
+|---|---|---|---|---|
+| <=100 | 0.11 | 2.64 | 0.43 | 0.43 |
+| 1e2-1e3 | 0.30 | 1.26 | 0.41 | 0.43 |
+| 1e3-1e4 | 0.73 | 0.33 | 0.30 | 0.43 |
+| 1e4-1e5 | 0.96 | 0.14 | 0.13 | 0.43 |
+| >1e5 | 0.99 | 0.11 | 0.06 | 0.43 |
+
+Deep genotypes rank well; their error is a shared rescaling (slope 1.2,
+|m| too large), which the k/m work fixes and r does not see. Below 1e3
+reads the realistic noise leaves no information on X (best affine error =
+the truth's spread), and the MAP points are wild (RMSE 1.3-2.6) because
+the X population SDs are not held (only log n is; `hill_relative`'s
+`theta_{X_low,X_delta,log_hill_K}_hyper_scale_fixed` would shrink them).
+Their intervals cover (0.996), so they are honest but uninformative.
+Above 1e3 reads the realistic arm's r is 0.87 (Spearman 0.82). Report
+recovery by depth, not one all-genotype r.
+
+**The simulated depth distribution does not match the real screen.**
+Genotypes with growth data by total reads (doubles / singles):
+
+| total reads | realistic sim | Poisson sim | real (recipe) |
+|---|---|---|---|
+| 1-100 | 10,948 / 25 | 17,870 / 17 | 43,713 / 0 |
+| 1e2-1e3 | 18,616 / 36 | 30,618 / 33 | 95,717 / 0 |
+| 1e3-1e4 | 30,460 / 90 | 48,356 / 82 | 60,542 / 7 |
+| 1e4-1e5 | 39,771 / 159 | 59,435 / 145 | 15,634 / 40 |
+| >1e5 | 23,630 / 608 | 28,474 / 644 | 1,387 / 903 |
+| all (incl. wt, one triple) | 124,345 | 185,676 | 217,945 |
+
+Median total reads per double: 11,136 / 8,348 / 480 (94 / 71 / 4 per
+tube); per single 441,080 / 590,799 / 867,829. The total reads match, but
+the simulation concentrates them: many doubles deep, many below the
+10-read minimum and dropped (124k of 218k survive under realistic noise),
+where the real reads spread thinly over nearly every double (64% of real
+doubles, 139,430, have fewer than 1,000 reads). The likely cause is the
+simulated pool's unevenness (`lib_assembly_skew_sigma` 1.25 and the
+design's `transform_sizes` bottleneck). So:
+
+- The simulation is not yet a stand-in for this screen. Calibrate its
+  library composition to the real depth distribution (this table is the
+  target) before quoting its recovery; every other result depends on it.
+- Most real doubles sit where the realistic arm had no information on X.
+  If the real counts are as overdispersed as that arm, those curves are
+  not usable one genotype at a time (the noise model is calibrated to the
+  real overdispersion, planning/studies/noise-anatomy/, so this is a
+  warning, not a measurement). Pooling is the lever there: held X
+  population SDs, and mutation-level structure (`hill_mut`) that shares
+  information across doubles with the same single.
+
+**Depth calibration (2026-10-08).** Three causes of the depth mismatch,
+found by comparing the real count files with the simulation's raw files
+kind by kind, and fixed:
+
+1. A simulator bug gave every replicate the full `total_num_reads`, so
+   the runs above had 3 times the configured reads (fixed in
+   `simulate_cli.replicate_read_totals`).
+2. No unassigned reads (real: kanR 66%, pheS 22%; the new
+   `unassigned_read_fraction`) and the design's pool composition instead
+   of the realized one (doubles 83% of the pool against 54%; wt 1% against
+   11%). `make_sim_config.py` now takes both from the real files.
+3. Too few clones per double and too harsh a dk_geno prior. The real
+   initial pool is very uneven among doubles (CV 5-7, 23-30% at zero out
+   of a mean of 20-50 reads) yet only 2.7% of real doubles end below 10
+   reads, and abundance is shared between the two separately transformed
+   libraries (log-count r 0.69): a wide assembly skew (sigma 2.0 for
+   doubles, 0.8 for singles) with ~300 clones per double (6.7e7
+   transformants), which the simulator now carries cheaply by merging
+   identical clones into weighted rows (`_sim_transform_collapsed`). The
+   old dk_geno prior put 31% of genotypes below -0.03 per minute (they died;
+   real well-measured genotypes span about -0.018 to -0.002); scale 0.15
+   instead of 1.0. With slower-growing mutants the pool grows less, so the
+   cfu0 calibration rose about 14-fold (2.1e8 per library).
+
+Check (local, seed 1, `--calibrate_from` a pilot; 14 minutes, 6.5 GB peak;
+every tube detectable):
+
+| doubles by total reads | real | simulated |
+|---|---|---|
+| <10 (dropped) | 5,887 | 15,327 |
+| 10-100 | 43,759 | 37,987 |
+| 1e2-1e3 | 96,201 | 89,233 |
+| 1e3-1e4 | 60,549 | 64,362 |
+| 1e4-1e5 | 15,634 | 15,283 |
+| >1e5 | 1,387 | 1,225 |
+
+Median double 446 real, 472 simulated (3.8 and 4.0 reads per tube); median
+single 868k and 623k (10th-90th percentile 166k-3.2M and 171k-2.2M). Read
+shares by kind match within a few points, except wt: 4% (kanR) and 12%
+(pheS) of real reads against 12% and 23% simulated. Real wt falls from 11%
+of the initial pool to those shares, so it grows slower than the library
+average in the screen; the simulated wt does not. Left: the dropout tail
+(7% against 2.7%; a skew of 1.9 instead of 2.0 fitted the dropout better
+in the composition fit) and the wt's growth. The tube totals' residual
+after the cfu0 level is now mostly control against selection conditions
+(control tubes -1.2 kanR, -0.7 pheS in log real/simulated).
+
+**Calibrated arms on the cluster (2026-10-08/09, `sim_realistic_s2`,
+`sim_poisson_s2`, downloaded to the gitignored
+`planning/studies/full-size-sim-3/`).** Both ran end to end; every MAP
+stage converged on the exact loss; the arrowhead Laplace held 9 and 10
+shared directions; the tube offsets are unstructured (smallest BH q 0.45
+and 0.21; SD 0.32 and 0.35, about twice the held 0.17, from the
+simulator's per-tube rate noise). The realistic arm's read depth
+reproduces the local check exactly (same seed and code). The Poisson arm
+is evener (median double 925 reads, 614 dropped), since it has no founder
+or PCR noise.
+
+| | realistic | Poisson |
+|---|---|---|
+| X: Pearson r (all), r above 1e3 reads | 0.36, 0.94 | 0.94, 0.99 |
+| X: 95% coverage (all), above 1e3 reads | 0.66, 0.50 | 0.29, 0.14 |
+| log K: r, 95% coverage | 0.27, 0.81 | 0.59, 0.82 |
+| log n: r, bias, 95% coverage | 0.23, +0.23, 0.96 | 0.61, +0.00, 0.92 |
+| dk_geno: r, bias, 95% coverage | 0.25, -0.010, 0.67 | 0.72, -0.004, 0.39 |
+| growth k bias per minute | +0.009 | +0.003 |
+| growth m kanR+kan, pheS+4CP (truth -0.0138, +0.0141) | -0.0203, +0.0202 | -0.0182, +0.0164 |
+| k and m 95% coverage | 0 | 0 (m 0.25) |
+
+X by total reads per genotype (realistic / Poisson):
+
+| reads | genotypes | r | RMSE after affine map | slope | bias | 95% coverage |
+|---|---|---|---|---|---|---|
+| <=100 | 37,707 / 17,526 | 0.26 / 0.72 | 0.42 / 0.30 | 0.05 / 0.60 | -0.07 / -0.12 | 0.97 / 0.87 |
+| 1e2-1e3 | 89,022 / 97,548 | 0.62 / 0.96 | 0.34 / 0.12 | 0.49 / 1.10 | -0.09 / -0.12 | 0.82 / 0.51 |
+| 1e3-1e4 | 64,362 / 89,313 | 0.93 / 0.99 | 0.16 / 0.06 | 1.19 / 1.16 | -0.10 / -0.12 | 0.56 / 0.16 |
+| 1e4-1e5 | 15,325 / 17,129 | 0.99 / 0.99 | 0.07 / 0.06 | 1.36 / 1.17 | -0.10 / -0.12 | 0.29 / 0.02 |
+| >1e5 | 2,135 / 1,869 | 0.99 / 0.99 | 0.06 / 0.06 | 1.37 / 1.17 | -0.09 / -0.12 | 0.08 / 0.01 |
+
+What this says, at realistic depth:
+
+- **Ranking above 1e3 reads is good in both arms** (r 0.93-0.99). Their
+  error is shared, not per genotype: X is compressed (slope 1.2-1.4;
+  |m| 30-45% too large) and shifted (-0.09 to -0.12), and k slid up
+  against dk_geno. The arrowhead Laplace holds exactly those directions,
+  so k and m have no interval and coverage falls with depth to near 0.
+  This is the step 8 item "anchor k and m, give the held directions an
+  interval"; fixing it should bring deep coverage up without changing r.
+- **Realistic noise, not depth alone, is what costs the shallow
+  genotypes.** At 1e2-1e3 reads (89k-98k genotypes in both arms, the
+  bulk of the real doubles) Poisson counts give r 0.96 and the realistic
+  arm 0.62; at <=100 reads 0.72 against 0.26. The realistic arm's extra
+  variance is founder sampling and the PCR template bottleneck (calibrated
+  to the real counts' 5-18x overdispersion). For the next experiment's
+  design that points at the noise sources, not only at depth: with
+  templates at a tenth of the reads, the PCR bottleneck alone multiplies
+  the count variance about 13-fold, so more template DNA into PCR is
+  likely the larger lever than more reads. Not yet tested: a sweep of
+  `pcr_template_molecules` and `cfu0` on this simulation would say how
+  much each buys.
+- log n is biased up (+0.23) under realistic noise only; dk_geno is
+  poorly recovered at depth-limited genotypes and undercovers in both.
+
diff --git a/planning/studies/full-size-sim/growth_priors_loose.csv b/planning/studies/full-size-sim/growth_priors_loose.csv
new file mode 100644
index 00000000..4cfb4e48
--- /dev/null
+++ b/planning/studies/full-size-sim/growth_priors_loose.csv
@@ -0,0 +1,5 @@
+condition_rep,k_loc,k_scale,m_loc,m_scale
+kanR+kan,0.015,0.01,0.0,0.01
+kanR-kan,0.015,0.01,0.0,0.01
+pheS+4CP,0.015,0.01,0.0,0.01
+pheS-4CP,0.015,0.01,0.0,0.01
diff --git a/planning/studies/full-size-sim/make_sim_config.py b/planning/studies/full-size-sim/make_sim_config.py
new file mode 100644
index 00000000..04ab7e37
--- /dev/null
+++ b/planning/studies/full-size-sim/make_sim_config.py
@@ -0,0 +1,359 @@
+"""
+Build a full-size simulate config that follows the dev-data screen's design.
+
+    python make_sim_config.py [--out_dir full_size_sim] [--seed 1]
+                              [--noise realistic|poisson]
+                              [--phenotype_model <emp_phenotype_model.json>]
+                              [--transition instant|memory]
+                              [--calibrate_from <pilot run directory>]
+
+Run from planning/dev-data (gitignored; the lab's data never enter the
+repository, C10). Reads processed/ (written by prep_dev_data.py) and writes
+<out_dir>/design.csv, <out_dir>/simulate_config.yaml and a copy of the OD600
+calibration, all inside planning/dev-data.
+
+What comes from the real experiment, and how:
+
+- design.csv: the real tube table (processed/sample_df.csv): every
+  sequenced tube with its real name, replicate, conditions, IPTG and
+  selection time. tfs-simulate follows it exactly (the ``design`` key), so
+  the simulated tube table has the real layout.
+- Library: the genetics keys, transform_sizes and library_mixture of
+  processed/library_config.yaml (the nine-spike config).
+- Growth (linear, rate = b + m * theta): for each selection condition the
+  wt monoculture rates (processed/wt_control_rate_summary.csv) at 0 and
+  1 mM, with wt taken as theta 1 at 0 mM and 0 at 1 mM (the monokan rule):
+  b = rate at 1 mM, m = rate at 0 minus b. Control conditions: b = their
+  mean rate over IPTG, m = 0.
+- cfu0 (cells per tube at -t_pre): first guess from the pre-split culture's
+  OD600 (0.35, diluted 0.2 into 15.2 mL; prep_dev_data.py) through the real
+  calibration, times the 5 mL tube. That guess left the simulated tubes about
+  half as dense as the real ones (pheS 0.13 against 0.26 median OD600;
+  2026-10-05), so the cfu0 is calibrated per library from a pilot run
+  (--calibrate_from): with the phenotypes fixed by the seed, a tube's total
+  scales with cfu0, so each library's cfu0 is multiplied by the geometric
+  mean of real / simulated tube totals over its tubes. The script prints
+  what is left by condition; a per-library level cannot fix a spread that
+  comes from the growth rates.
+- Reads: total_num_reads = the sum of the real tubes' called reads, spread
+  evenly over the tubes (the simulator does not vary depth per tube).
+  unassigned_read_fraction, per library, is the share of the real tubes'
+  reads in the __unknown__ row (kanR about 0.66, pheS 0.20: the kanR
+  library's structural variants), so a simulated genotype gets the reads a
+  real one does.
+- Pool composition: library_mixture is the realized one, estimated from the
+  real pre-split (initial) composition with estimate_library_mixture and
+  averaged over the two libraries (they agree within ~0.05), not the
+  design's (which gives doubles 83% of the pool against about 54%
+  realized). wt is about 11% of the real pool, ~90 times what the spike
+  design gives it, for reasons not yet known (being worked on at the bench,
+  2026-10-08). The simulation reproduces it by repeating the wt sequence in
+  the simulated spiked_seqs (a repeated sequence is degeneracy within the
+  spiked origin) and scaling the spiked origin's mixture and transformants
+  so the other spikes keep their share. This is simulation-only: the fit is
+  configured with the real library config (copied to library_config.yaml),
+  as the real data's fit is.
+- OD600: the real calibration, totals estimated from OD600 as the lab does
+  (sample_cfu_from_od600).
+- Congression: transformation_poisson_lambda 0.357, the measured value.
+- Pool evenness (2026-10-08), fitted to the real initial composition and
+  the screen's dropout: doubles are very uneven before any selection (CV 5
+  to 7 in the initial sample, 23-30% at zero reads out of a mean of 20-50),
+  yet only 2.7% of real doubles end below 10 reads over the screen, and
+  their abundance is shared between the separately transformed libraries
+  (log-count correlation 0.69). That is a wide assembly skew with many
+  clones per genotype: doubles skew sigma 2.0 and ~300 clones per double
+  (transform_sizes double-1-2 = 300 x its genotypes, about 6.7e7); singles
+  are even (CV 0.8-1.2), skew 0.8. The simulator merges identical clones
+  into weighted rows, so this costs one row per genotype, and holds the
+  congressed cells to max_congressed_cells (2e6).
+- dk_geno: the real fit's well-measured genotypes (singles, doubles above
+  1e4 reads) spread over about 0.016 per minute (1st-99th percentile), with
+  none below -0.03; the old hyperparameters (scale 1.0) put 31% of
+  genotypes below -0.03 and killed them. Scale 0.15 keeps the median
+  (-0.010) and gives a 1st-99th percentile range of about -0.023 to -0.001.
+
+Chosen, not measured: the noise model (``realistic``: founder sampling,
+demographic growth, one shared transformation, PCR templates at assigned
+reads per tube / 10 with CV 0.5, the setting that matched the real counts' 5-18x
+overdispersion, planning/studies/noise-anatomy/; or ``poisson``), the
+phenotypes (hill_geno prior draws, or resampled from a tfs-build-empirical
+model with --phenotype_model), dk_geno's location and shift (the example
+config's; its scale is fitted, above), tube_noise_sigma (0.002 per minute) and the growth transition
+(instant; ``memory`` with the example config's parameters as a test arm).
+"""
+
+import argparse
+import glob
+import os
+import shutil
+
+import numpy as np
+import pandas as pd
+import yaml
+
+from tfscreen.genetics import UNKNOWN_GENOTYPE
+from tfscreen.genetics.library_design import (
+    estimate_library_mixture,
+    library_composition_table,
+)
+from tfscreen.genetics.library_manager import LibraryManager
+from tfscreen.process_raw.od600 import od600_to_cfu_per_mL
+from tfscreen.simulate.build_sample_dataframes import read_design
+
+PROCESSED = "processed"
+TUBE_VOLUME_ML = 5.0
+PRESPLIT_OD600 = 0.35
+PRESPLIT_DILUTION = 0.2 / 15.2
+LAMBDA = 0.357
+ASSEMBLY_SKEW = {"double-1-2": 2.0, "single-1": 0.8, "single-2": 0.8,
+                 "spiked": 0.5}
+CLONES_PER_DOUBLE = 300
+MAX_CONGRESSED_CELLS = 2_000_000
+DK_HYPER_SCALE = 0.15
+
+GENETICS_KEYS = ("reading_frame", "first_amplicon_residue", "wt_seq",
+                 "degen_sites", "tiles", "expected_5p", "expected_3p",
+                 "tile_combos", "spiked_seqs", "transform_sizes",
+                 "library_mixture")
+
+
+def growth_block(rates):
+    """Linear growth (b, m) per condition from the wt monoculture rates."""
+    out = {}
+    for cond, g in rates.groupby("condition_sel"):
+        r = g.set_index("titrant_conc")["rate_mean"]
+        if "+" in cond:
+            b = float(r.loc[1.0])
+            m = float(r.loc[0.0]) - b
+        else:
+            b, m = float(r.mean()), 0.0
+        out[cond] = {"b": round(b, 6), "m": round(m, 6)}
+    return out
+
+
+def unassigned_fractions(tubes):
+    """Share of each library's reads in the real count files' __unknown__ row."""
+    out = {}
+    for lib, g in tubes.groupby("library"):
+        unknown = total = 0
+        for name in g["sample"]:
+            path = os.path.join(PROCESSED, "counts", f"counts__{name}__.csv")
+            c = pd.read_csv(path)
+            unknown += c.loc[c["genotype"] == UNKNOWN_GENOTYPE, "counts"].sum()
+            total += c["counts"].sum()
+        out[lib] = round(float(unknown / total), 4)
+    return out
+
+
+def realized_pool(lib):
+    """
+    The realized library_mixture (mean over libraries of
+    estimate_library_mixture on the initial composition) and wt's share of
+    the pool.
+    """
+    library_df = LibraryManager(lib).build_library_df()
+    ic = pd.read_csv(os.path.join(PROCESSED, "initial_composition.csv"))
+    ic = ic[ic["genotype"] != UNKNOWN_GENOTYPE]
+    mixes, wt_shares = [], []
+    for _, g in ic.groupby("library"):
+        mix, _, _ = estimate_library_mixture(
+            library_df, g.set_index("genotype")["counts"])
+        mixes.append(mix)
+        wt_shares.append(g.loc[g["genotype"] == "wt", "counts"].sum()
+                         / g["counts"].sum())
+    mixture = {k: float(np.mean([m[k] for m in mixes])) for k in mixes[0]}
+    return mixture, float(np.mean(wt_shares))
+
+
+def with_wt_excess(lib, mixture, wt_share):
+    """
+    Genetics keys, mixture and transform sizes with wt repeated in the
+    spiked origin so wt makes up wt_share of the pool, the other spikes
+    keeping theirs (simulation only).
+
+    The spiked origin's n sequences share its mixture by degeneracy. Giving
+    wt d copies and scaling the origin's mixture and transformants by
+    (n - 1 + d) / n leaves every other spike's mass unchanged; d is the
+    smallest integer that brings wt's pool fraction (spiked plus bulk, from
+    library_composition_table) up to wt_share.
+    """
+    wt_seq = lib["wt_seq"]
+    spiked = list(lib["spiked_seqs"])
+    library_df = LibraryManager(lib).build_library_df()
+    n_doubles = int((library_df["library_origin"] == "double-1-2").sum())
+    n = len(spiked)
+    if spiked.count(wt_seq) != 1:
+        raise ValueError("expected wt exactly once in spiked_seqs")
+
+    def build(d):
+        cf = dict(lib)
+        cf["spiked_seqs"] = spiked + [wt_seq] * (d - 1)
+        scale = (n - 1 + d) / n
+        cf["library_mixture"] = dict(mixture, spiked=mixture["spiked"] * scale)
+        sizes = dict(lib["transform_sizes"])
+        sizes["double-1-2"] = CLONES_PER_DOUBLE * n_doubles
+        sizes["spiked"] = int(round(sizes["spiked"] * scale))
+        cf["transform_sizes"] = sizes
+        comp = library_composition_table(cf)
+        wt = float(comp.loc[comp["genotype"] == "wt", "pool_fraction"].iloc[0])
+        return cf, wt
+
+    lo, hi = 1, 2
+    while build(hi)[1] < wt_share:
+        lo, hi = hi, hi * 2
+    while hi - lo > 1:
+        mid = (lo + hi) // 2
+        if build(mid)[1] < wt_share:
+            lo = mid
+        else:
+            hi = mid
+    cf, wt = build(hi)
+    return cf, hi, wt
+
+
+TUBE_KEY = ["replicate", "library", "condition_pre", "t_pre", "condition_sel",
+            "t_sel", "titrant_name", "titrant_conc"]
+
+
+def calibrated_cfu0(pilot_dir, real_tubes):
+    """
+    Per-library cfu0 that brings a pilot run's tube totals to the real ones.
+
+    Matches each real tube to the pilot's simulated tube by design, takes
+    log(real total / simulated true total), and multiplies each library's
+    pilot cfu0 by exp(mean log ratio). Prints the residual log ratio by
+    library and selection condition after the correction.
+    """
+    sim = pd.read_csv(os.path.join(pilot_dir, "tfs_sim_od600.csv"))
+    with open(os.path.join(pilot_dir, "tfs_sim_input-config.yaml")) as fh:
+        pilot_cfu0 = yaml.safe_load(fh)["cfu0"]
+    real = real_tubes.rename(columns={"rep": "replicate"})
+    m = real[TUBE_KEY + ["sample_cfu"]].merge(
+        sim[TUBE_KEY + ["sample_cfu_true"]], on=TUBE_KEY, how="inner")
+    if len(m) < len(real):
+        print(f"warning: {len(real) - len(m)} real tubes have no pilot tube")
+    m["log_ratio"] = np.log(m["sample_cfu"] / m["sample_cfu_true"])
+    out = {}
+    for lib, g in m.groupby("library"):
+        base = (float(str(pilot_cfu0[lib]).replace("_", ""))
+                if isinstance(pilot_cfu0, dict)
+                else float(str(pilot_cfu0).replace("_", "")))
+        out[lib] = base * float(np.exp(g["log_ratio"].mean()))
+    m["residual"] = m["log_ratio"] - m.groupby("library")["log_ratio"].transform("mean")
+    print("cfu0 calibration: log(real / pilot) total by library, then the "
+          "residual by selection condition (mean, SD over tubes):")
+    print(m.groupby("library")["log_ratio"].agg(["mean", "std", "count"]).round(3)
+          .to_string())
+    print(m.groupby(["library", "condition_sel"])["residual"]
+          .agg(["mean", "std", "count"]).round(3).to_string())
+    return out
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--out_dir", default="full_size_sim")
+    ap.add_argument("--seed", type=int, default=1)
+    ap.add_argument("--noise", choices=("realistic", "poisson"),
+                    default="realistic")
+    ap.add_argument("--phenotype_model", default=None)
+    ap.add_argument("--transition", choices=("instant", "memory"),
+                    default="instant")
+    ap.add_argument("--calibrate_from", default=None,
+                    help="a pilot run's directory (tfs_sim_od600.csv and "
+                         "tfs_sim_input-config.yaml) to calibrate cfu0 from")
+    args = ap.parse_args()
+    os.makedirs(args.out_dir, exist_ok=True)
+
+    tubes = pd.read_csv(os.path.join(PROCESSED, "sample_df.csv"))
+    design = read_design(tubes)
+    design.to_csv(os.path.join(args.out_dir, "design.csv"), index=False)
+
+    with open(os.path.join(PROCESSED, "library_config.yaml")) as fh:
+        lib = yaml.safe_load(fh)
+    # the fit is configured with the real library config, as the real data's
+    shutil.copyfile(os.path.join(PROCESSED, "library_config.yaml"),
+                    os.path.join(args.out_dir, "library_config.yaml"))
+    mixture, wt_share = realized_pool(lib)
+    sim_lib, wt_copies, wt_pool = with_wt_excess(lib, mixture, wt_share)
+    unassigned = unassigned_fractions(tubes)
+
+    cal_src = os.path.join(PROCESSED, "od600_calibration.yaml")
+    cal_dst = os.path.join(args.out_dir, "od600_calibration.yaml")
+    shutil.copyfile(cal_src, cal_dst)
+
+    rates = pd.read_csv(os.path.join(PROCESSED,
+                                     "wt_control_rate_summary.csv"))
+    presplit = float(od600_to_cfu_per_mL(np.array([PRESPLIT_OD600]),
+                                         cal_src)[0][0])
+    cfu0 = presplit * PRESPLIT_DILUTION * TUBE_VOLUME_ML
+    if args.calibrate_from:
+        cfu0 = calibrated_cfu0(args.calibrate_from, tubes)
+    total_reads = int(tubes["called_reads"].sum())
+    reads_per_tube = total_reads / len(design)
+
+    cf = {k: sim_lib[k] for k in GENETICS_KEYS if k in sim_lib}
+    cf.update({
+        "design": "design.csv",
+        "growth": growth_block(rates),
+        "dk_geno_hyper_loc": -3.5,
+        "dk_geno_hyper_scale": DK_HYPER_SCALE,
+        "dk_geno_hyper_shift": 0.02,
+        "activity_wt": 1.0,
+        "activity_mut_scale": 0.0,
+        "lib_assembly_skew_sigma": dict(ASSEMBLY_SKEW),
+        "max_congressed_cells": MAX_CONGRESSED_CELLS,
+        "transformation_poisson_lambda": LAMBDA,
+        "congression_theta_rule": "homodimer",
+        "congression_dk_rule": "dilution",
+        "cfu0": ({k: float(round(v, -3)) for k, v in cfu0.items()}
+                 if isinstance(cfu0, dict) else float(round(cfu0, -3))),
+        "tube_noise_sigma": 0.002,
+        "total_num_reads": total_reads,
+        "unassigned_read_fraction": unassigned,
+        "prob_index_hop": 0.0,
+        "seed": args.seed,
+        # bare file names: tfs-simulate runs from inside out_dir
+        "od600": {"calibration": os.path.basename(cal_dst),
+                  "tube_volume_mL": TUBE_VOLUME_ML,
+                  "sample_cfu_from_od600": True},
+    })
+    if args.phenotype_model:
+        cf["phenotype_source"] = "empirical"
+        cf["empirical"] = {"phenotype_model": os.path.abspath(args.phenotype_model)}
+    else:
+        cf["theta_component"] = "hill_geno"
+    if args.noise == "realistic":
+        cf.update({"founder_sampling": True, "demographic_growth": True,
+                   "shared_transformation": True,
+                   # templates = assigned reads per tube / 10, the ratio
+                   # that matched the real counts' overdispersion
+                   # (noise-anatomy, measured on called reads)
+                   "pcr_template_molecules": int(round(
+                       reads_per_tube * (1 - np.mean(list(unassigned.values())))
+                       / 10)),
+                   "pcr_amplification_cv": 0.5})
+    if args.transition == "memory":
+        cf["growth_transition"] = [
+            {"condition_pre": "pheS-4CP", "model": "memory",
+             "tau0": 90.0, "k1": 5.0, "k2": 0.2},
+            {"condition_pre": "kanR-kan", "model": "memory",
+             "tau0": 60.0, "k1": 2.0, "k2": 0.2}]
+
+    path = os.path.join(args.out_dir, "simulate_config.yaml")
+    with open(path, "w") as fh:
+        fh.write("# Written by planning/studies/full-size-sim/make_sim_config.py\n"
+                 f"# from {os.path.abspath(PROCESSED)}; noise {args.noise}, "
+                 f"transition {args.transition}, seed {args.seed}.\n")
+        yaml.safe_dump(cf, fh, sort_keys=False)
+    print(f"{path}: {len(design)} tubes in {design['replicate'].nunique()} "
+          f"replicates; cfu0 {cf['cfu0']}; {total_reads:.3g} reads "
+          f"({reads_per_tube:.3g} per tube); growth {cf['growth']}")
+    print(f"realized library_mixture {cf['library_mixture']} (design "
+          f"{lib['library_mixture']}); wt {wt_share:.3f} of the real pool, "
+          f"{wt_copies} wt copies in spiked_seqs give {wt_pool:.3f}; "
+          f"unassigned_read_fraction {unassigned}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/full-size-sim/run_full_size.srun b/planning/studies/full-size-sim/run_full_size.srun
new file mode 100644
index 00000000..e0be704b
--- /dev/null
+++ b/planning/studies/full-size-sim/run_full_size.srun
@@ -0,0 +1,86 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_fullsim
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpulong
+#SBATCH --time=02-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# The full-size simulation of the dev-data design, through the same chain as
+# real data (planning/studies/full-size-sim). Run from a directory made by
+# make_sim_config.py (it holds design.csv, simulate_config.yaml and the
+# calibration), inside planning/dev-data:
+#
+#   python ../make_sim_config.py --out_dir sim_realistic_s1 --seed 1
+#   cd sim_realistic_s1 && sbatch ../run_full_size.srun
+#
+# Each step is the documented command; nothing between them is edited by hand.
+# The fit is configured with the real library config (library_config.yaml,
+# copied by make_sim_config.py), as the real data's fit is; the simulated
+# pool's realized composition and excess wt stay in the simulation.
+# The fit is the growth-only relative model of the real fit
+# (planning/studies/staged-map/run_staged.srun), so the simulation tests the
+# recipe the real data use.
+
+module load cuda/12.4.1
+
+set -e
+
+SEED="${SEED:-1}"
+
+# A rerun skips the simulation and the processing when their outputs exist
+# (the simulation alone takes about 85 minutes).
+if [[ -f tfs_sim_tubes.csv && -f tfs_sim_input-config.yaml ]]; then
+    echo ">>> Simulate: tfs_sim_tubes.csv exists, skipped"
+else
+    echo ">>> Simulate $(date)"
+    tfs-simulate simulate_config.yaml --out_prefix tfs_sim --seed "${SEED}"
+fi
+
+if [[ -f tfs_growth.csv ]]; then
+    echo ">>> Process counts: tfs_growth.csv exists, skipped"
+else
+    echo ">>> Process counts $(date)"
+    tfs-process-counts tfs_sim_tubes.csv tfs_sim_counts \
+        --od600_file tfs_sim_tube_od600.csv \
+        --od600_calibration_file tfs_sim_od600_calibration.yaml \
+        --tube_volume_mL 5 \
+        --out_prefix tfs_growth --no_verbose
+fi
+
+# --allow_missing_spikes: a spike's simulated phenotype can kill it (seed 1
+# left H74A/K84L with no growth data, 2026-10-06).
+echo ">>> Configure $(date)"
+tfs-configure-model \
+    --allow_missing_spikes \
+    --growth_df tfs_growth.csv \
+    --library_config library_config.yaml \
+    --theta_model hill_relative \
+    --growth_shares_replicates \
+    --batch_size 4096 \
+    --growth_priors ../growth_priors_loose.csv \
+    --set_priors sigma_fixed=0.17 theta_log_hill_n_hyper_scale_fixed=0.5
+
+echo ">>> Fit (staged MAP) $(date)"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" --analysis_method map
+
+echo ">>> Arrowhead Laplace $(date)"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    --laplace arrowhead --num_posterior_samples 500 --skip_growth_observations
+
+echo ">>> Extract and predict $(date)"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Score (exact, full batch) $(date)"
+python ../score_map.py .
+
+echo ">>> Summarize against the truth $(date)"
+tfs-summarize-fit .
+
+echo ">>> Done $(date)"
diff --git a/planning/studies/full-size-sim/run_pilot.srun b/planning/studies/full-size-sim/run_pilot.srun
new file mode 100644
index 00000000..6337de69
--- /dev/null
+++ b/planning/studies/full-size-sim/run_pilot.srun
@@ -0,0 +1,29 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_pilot
+#SBATCH --output=pilot.out
+#SBATCH --error=pilot.err
+#SBATCH --partition=gpulong
+#SBATCH --time=0-06:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# The pilot simulation that calibrates cfu0 (planning/studies/full-size-sim):
+# the first-guess cfu0, with the raw count files (for the depth check
+# against the real screen) but no growth table. About 85 minutes and a 68 GB peak; numpy only, so no GPU is
+# needed. Submit from the study's working directory (processed/ beside it):
+#
+#   sbatch run_pilot.srun
+#
+# Then make_sim_config.py --calibrate_from pilot_s1 for each arm.
+
+set -e
+
+SEED="${SEED:-1}"
+
+python make_sim_config.py --out_dir "pilot_s${SEED}" --seed "${SEED}"
+cd "pilot_s${SEED}"
+tfs-simulate simulate_config.yaml --out_prefix tfs_sim --seed "${SEED}" \
+    --no_write_growth
diff --git a/planning/studies/growth-binding-map/README.md b/planning/studies/growth-binding-map/README.md
new file mode 100644
index 00000000..075a9d67
--- /dev/null
+++ b/planning/studies/growth-binding-map/README.md
@@ -0,0 +1,123 @@
+# Study 0a: growth vs in vitro binding, model-free
+
+**Status: provisional (2026-09-26).** Run on the 2026-07-23 snapshot:
+counts from a FASTQ processor that assigned every unknown read to wt (about
+two thirds of kan reads), a stringent Q cutoff, and binding data for five
+genotypes at the original (high) protein concentration. The wt-relative
+pass is unusable in kan and wt's own points in the absolute pass are
+suspect; the mutants' absolute slopes are unaffected. The user has since
+collected binding at two lower repressor concentrations (curves much closer
+to the in vivo response, wt no longer capped at theta = 1) for 10 genotypes
+with diverse behavior, which confirms the concentration offset and should
+give this study real leverage. The 4CP null result conflicts with an
+earlier per-genotype MLE analysis that showed reasonable pheS response
+curves; re-check it on the new data before drawing conclusions.
+
+## Question
+
+For the genotypes with in vitro binding data, is growth rate a single
+linear function of the binding occupancy theta (the joint fit's
+assumptions 1 and 2), a monotone but nonlinear one, or neither? Decides the
+base case for Track G (`planning/growth-only-relative-fit.md`, step 0;
+`planning/analysis-roadmap.md`, steps 5, 8, 9).
+
+## How to run
+
+```bash
+python growth_binding_map.py ~/work/programming/git-clones/tfscreen/dev/real-2026-07-23 out/ \
+    ~/Desktop/2026-07-23_onward/combined_fit_sample_df.csv
+```
+
+Inputs from `planning/studies/step0-data/`. The third argument enables the
+second pass (absolute slopes and the concentration-scale scan).
+
+## Method
+
+Five genotypes have binding data: wt, M42I, H74A, K84L, M42I/H74A (all
+spiked; 22,000-56,000 reads per tube). Binding and growth use the same
+eight IPTG concentrations (0 to 1 mM).
+
+1. **wt-relative, total-free.** Slope over `t_sel` of
+   `ln(reads_g / reads_wt)` per replicate x condition x concentration:
+   `dk_g + m_c (X_g - X_wt)`, with no tube total and no tube noise.
+2. **Absolute.** Slope of `ln(reads_g / D) + fit_lncfu` (the global
+   smoothed total): `k_c + dk_g + m_c X_g`. Uses theta itself, which for wt
+   spans 1.00 to 0.33, but trusts the total of each IPTG column: an error in
+   a column's total shifts every genotype at that concentration together.
+3. **Concentration scale.** Hill fits to the binding data, then theta_g
+   evaluated at `s x` (in vivo effective [IPTG] = s times external) for
+   s = 0.3-300.
+
+Slope noise is taken from replicate differences (counting SEs understate it
+about 2.7-fold; study 0b).
+
+## Commit
+
+Scripts as in this directory on top of `b949095`.
+
+## Results (2026-09-26)
+
+**1. wt-relative: no leverage.** Slope noise 0.0032-0.0037 per min
+(replicate differences; counting alone predicts 0.0016). The four mutants'
+binding curves differ from wt's by at most a few tenths across IPTG, so the
+common-m fits are consistent with the linear map and measure nothing (kan
+m = +0.005 +/- 0.006, 4CP m = +0.002 +/- 0.009 per min per unit theta).
+
+**2. Absolute, s = 1: the common linear map fails.**
+
+| condition | m (per min) | reduced chi2 | genotype-specific m | curvature |
+|---|---|---|---|---|
+| kan+ | -0.0124 +/- 0.0019 | 6.7 | p = 2e-5 | p = 4e-4 |
+| 4CP+ | +0.0006 +/- 0.0019 | 2.0 | p = 6e-4 | p = 0.08 |
+
+The curves (mean over replicates, per min):
+
+- kan+: every genotype's growth rises from about 0 to 0.015-0.027 between
+  0.001 and 0.01 mM IPTG, then flattens or falls slightly. In vitro, wt,
+  M42I and M42I/H74A are still fully bound (theta 1.00) at 0.01 mM, and wt
+  falls only to 0.33 at 1 mM. H74A (theta 0.57 at 0 mM) grows like wt at
+  0 mM.
+- 4CP+: growth falls with IPTG to a minimum near 0.01-0.03 mM and partly
+  recovers, for every genotype; no ordering by binding.
+- Total-free check of the same point: at 0.01 mM in kan, all four mutants
+  outgrow wt (replicate 1: +0.006 to +0.015 per min) although M42I and
+  M42I/H74A bind like wt there in vitro.
+
+**3. An in vivo concentration scale helps in kan.** Reduced chi2 of the
+common linear map by s:
+
+| s | 0.3 | 1 | 3 | 10 | 30 | 100 | 300 |
+|---|---|---|---|---|---|---|---|
+| kan+ | 7.1 | 6.7 | 6.0 | 4.8 | 3.4 | 3.0 | 3.7 |
+| 4CP+ | 2.0 | 2.0 | 2.0 | 1.9 | 1.8 | 1.8 | 1.8 |
+
+At s = 30-100, kan m = -0.024 to -0.026 per min. 4CP stays weak
+(m = +0.005 to +0.006).
+
+The no-drug conditions (only 0 and 1 mM) differ between the two
+concentrations for every genotype (kanR library faster at 1 mM, pheS
+library slower), in line with the OD600 population slopes (study 0c):
+marker expression changes growth even without the drug, so "no drug" is not
+a pure `dk_geno` control.
+
+## What it means
+
+- **The base case (one linear map at s = 1) is not supported.** In kan the
+  in vivo response sits at roughly 30-100x lower external IPTG than the in
+  vitro assay, with genotype-specific structure left over; in 4CP growth
+  does not track in vitro binding for these five genotypes. The joint fit's
+  assumption 1 (in vitro binding = in vivo occupancy at the same [IPTG])
+  is likely violated, which is what Track G was built to find out. The map
+  in step 8 must include the concentration scale s.
+- Plausible sources of s: active IPTG uptake (LacY) concentrating IPTG in
+  cells; the in vitro assay's conditions (22 uM protein; not all curves
+  taken with the final protocol, per the snapshot README).
+- **Caveats.** Five genotypes, all similar to wt in vitro; the absolute
+  pass depends on per-column totals (roadmap C4), and the non-monotone
+  shapes shared by every genotype (the kan peak at 0.01 mM, the 4CP dip)
+  are what a column-total error would look like.
+- **The direct test is a bench measurement:** monoculture growth rates of
+  these five genotypes across the eight IPTG concentrations in kan and 4CP
+  (`planning/absolute-abundance-measurements.md`, experiment 1, extended
+  from the gauge concentrations to the full titration). It measures the
+  growth-binding map with no library and no totals problem.
diff --git a/planning/studies/growth-binding-map/growth_binding_map.py b/planning/studies/growth-binding-map/growth_binding_map.py
new file mode 100644
index 00000000..44ec9fce
--- /dev/null
+++ b/planning/studies/growth-binding-map/growth_binding_map.py
@@ -0,0 +1,301 @@
+"""
+Step 0a of planning/analysis-roadmap.md: a model-free look at how growth
+relates to in vitro binding, for the genotypes with binding data.
+
+For genotype g and wt in the same tube, ln(reads_g / reads_wt) does not
+depend on the tube's total population, so its slope over t_sel is
+
+    s_g(c, x) = dk_g + m_c * (X_g(x) - X_wt(x))
+
+for condition c and IPTG concentration x, with no OD600 and no tube
+noise. If growth is linear in the in vitro occupancy theta, then for each
+selective condition the points (theta_g(x) - theta_wt(x), s_g(c, x))
+from every genotype lie on one line per genotype with a common slope m_c,
+offset by dk_g. Genotype-specific slopes or curvature mean the linear map
+does not hold.
+
+Second pass (optional SAMPLE_DF): absolute slopes. The wt-relative
+design removes the tube total but also most of the signal, because the
+binding genotypes' curves differ from wt's by only a few tenths of theta.
+With the tube totals from the global smoothed fit (SAMPLE_DF's fit_lncfu),
+ln_cfu_g = ln(reads_g / D) + fit_lncfu, and each genotype's absolute slope
+
+    s_g(c, x) = k_c + dk_g + m_c * X_g(x)
+
+can be tested against theta_g(x) itself, which for wt spans nearly 0-1.
+
+Usage:
+    python growth_binding_map.py DATA_DIR OUT_DIR [SAMPLE_DF]
+
+DATA_DIR holds counts.parquet, samples.parquet and binding.csv from
+planning/studies/step0-data/extract_snapshot.py.
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pyarrow.parquet as pq
+import statsmodels.formula.api as smf
+from matplotlib import pyplot as plt
+
+
+def wt_relative_slopes(counts, samples, genotypes):
+    """Weighted slope of ln(reads_g / reads_wt) on t_sel per genotype x
+    replicate x condition_sel x titrant_conc (Poisson weights)."""
+    c = counts.pivot_table(index="sample", columns="genotype",
+                           values="counts", aggfunc="sum", observed=True)
+    rows = []
+    s = samples.set_index("sample")
+    for g in genotypes:
+        if g == "wt":
+            continue
+        d = pd.DataFrame({"cg": c[g], "cw": c["wt"]}).join(s)
+        d = d[(d["cg"] > 0) & (d["cw"] > 0)]
+        d["y"] = np.log(d["cg"] / d["cw"])
+        d["w"] = 1.0 / (1.0 / d["cg"] + 1.0 / d["cw"])
+        for key, grp in d.groupby(["replicate", "condition_sel",
+                                    "titrant_conc"]):
+            if grp["t_sel"].nunique() < 2:
+                continue
+            f = smf.wls("y ~ t_sel", data=grp, weights=grp["w"]).fit()
+            rows.append({"genotype": g, "replicate": key[0],
+                         "condition_sel": key[1], "titrant_conc": key[2],
+                         "slope": f.params["t_sel"],
+                         "slope_se": f.bse["t_sel"] if len(grp) > 2 else np.nan,
+                         "n_tubes": len(grp),
+                         "min_reads": int(grp["cg"].min())})
+    return pd.DataFrame(rows)
+
+
+def absolute_slopes(counts, samples, sample_df, genotypes):
+    """Slope of ln(reads_g / D) + fit_lncfu on t_sel per genotype x
+    replicate x condition_sel x titrant_conc."""
+    tot = pd.read_csv(sample_df, index_col=0)
+    tot["condition_sel"] = tot["condition_sel"].str.strip()
+    tot = tot.rename(columns={"rep": "replicate"})
+    keys = ["replicate", "condition_sel", "titrant_conc", "t_sel"]
+    smp = samples.merge(tot[keys + ["fit_lncfu"]], on=keys, how="inner")
+    print(f"\nsecond pass: {len(smp)} of {len(samples)} tubes matched to a "
+          f"smoothed total")
+    c = counts.pivot_table(index="sample", columns="genotype",
+                           values="counts", aggfunc="sum", observed=True)
+    smp = smp.set_index("sample")
+    rows = []
+    for g in genotypes:
+        d = smp.join(c[g].rename("cg"), how="inner")
+        d = d[d["cg"] > 0]
+        d["y"] = np.log(d["cg"] / d["freq_denominator"]) + d["fit_lncfu"]
+        for key, grp in d.groupby(["replicate", "condition_sel",
+                                    "titrant_conc"]):
+            if grp["t_sel"].nunique() < 2:
+                continue
+            f = smf.ols("y ~ t_sel", data=grp).fit()
+            rows.append({"genotype": g, "replicate": key[0],
+                         "condition_sel": key[1], "titrant_conc": key[2],
+                         "slope": f.params["t_sel"], "n_tubes": len(grp)})
+    return pd.DataFrame(rows)
+
+
+def test_map(sl, theta_col, label):
+    """Per condition: common m vs genotype-specific m vs curvature, with the
+    slope noise estimated from replicate differences."""
+    w = sl.pivot_table(index=["genotype", "condition_sel", "titrant_conc"],
+                       columns="replicate", values="slope")
+    diff = (w.iloc[:, 1] - w.iloc[:, 0]).dropna()
+    sd = np.sqrt(diff.var() / 2)
+    print(f"{label}: slope SD from replicate differences {sd:.5f} per min")
+    out = []
+    for cond, d in sl.groupby("condition_sel"):
+        common = smf.ols(f"slope ~ C(genotype) + {theta_col}", d).fit()
+        per_g = smf.ols(f"slope ~ C(genotype) + C(genotype):{theta_col}", d).fit()
+        curved = smf.ols(f"slope ~ C(genotype) + {theta_col} + "
+                         f"I({theta_col}**2)", d).fit()
+        # F-tests with the replicate-based noise as the known variance.
+        def chi_test(big, small):
+            from scipy import stats
+            dchi = (small.ssr - big.ssr) / sd ** 2
+            ddf = small.df_resid - big.df_resid
+            return stats.chi2.sf(dchi, ddf)
+        m, m_se = common.params[theta_col], sd * np.sqrt(
+            common.normalized_cov_params.loc[theta_col, theta_col])
+        red = common.ssr / sd ** 2 / common.df_resid
+        out.append({"condition_sel": cond, "m": m, "m_se": m_se,
+                    "red_chi2": red,
+                    "p_genotype_slopes": chi_test(per_g, common),
+                    "p_curvature": chi_test(curved, common), "n": len(d)})
+        print(f"  {cond:10s} m = {m:+.5f} +/- {m_se:.5f}; reduced chi2 "
+              f"{red:.1f}; genotype-specific m p={out[-1]['p_genotype_slopes']:.3g}; "
+              f"curvature p={out[-1]['p_curvature']:.3g}")
+    return pd.DataFrame(out)
+
+
+def hill_fits(binding):
+    """Four-parameter Hill fit (in log concentration) per genotype, so
+    theta can be evaluated off the measured grid."""
+    from scipy.optimize import least_squares
+    x0 = binding["titrant_conc"][binding["titrant_conc"] > 0].min() / 10
+    out = {}
+    for g, d in binding.groupby("genotype"):
+        lx = np.log(np.maximum(d["titrant_conc"].to_numpy(), x0))
+        y = d["theta_obs"].to_numpy()
+
+        def f(p, lx=lx):
+            lo, hi, lk, n = p
+            return hi + (lo - hi) / (1 + np.exp(-np.exp(n) * (lx - lk)))
+        r = least_squares(lambda p: f(p) - y,
+                          [y.min(), y.max(), np.log(0.1), 0.0],
+                          bounds=([-0.2, 0, -20, -3], [1, 1.2, 10, 3]))
+        out[g] = (f, r.x)
+    return out, x0
+
+
+def scan_concentration_scale(ab, binding, scales):
+    """For each s, theta_g evaluated at s * x (in vivo effective
+    concentration), then the common linear map's residual sum of squares
+    per selective condition."""
+    fits, x0 = hill_fits(binding)
+    rows = []
+    for sc in scales:
+        d = ab.copy()
+        d["theta_s"] = [fits[g][0](fits[g][1], np.log(max(x * sc, x0)))
+                        for g, x in zip(d["genotype"], d["titrant_conc"])]
+        for cond, dd in d.groupby("condition_sel"):
+            f = smf.ols("slope ~ C(genotype) + theta_s", dd).fit()
+            rows.append({"s": sc, "condition_sel": cond, "ssr": f.ssr,
+                         "m": f.params["theta_s"], "df": f.df_resid})
+    return pd.DataFrame(rows)
+
+
+def main(data_dir, out_dir, sample_df=None):
+    data_dir, out_dir = Path(data_dir), Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    binding = pd.read_csv(data_dir / "binding.csv")
+    genotypes = sorted(binding["genotype"].unique())
+    counts = pq.read_table(data_dir / "counts.parquet",
+                           filters=[("genotype", "in", genotypes)]).to_pandas()
+    samples = pd.read_parquet(data_dir / "samples.parquet")
+    print(f"binding genotypes: {genotypes}")
+    print(f"binding concentrations: {sorted(binding['titrant_conc'].unique())}")
+    print(f"growth concentrations:  {sorted(samples['titrant_conc'].unique())}")
+
+    sl = wt_relative_slopes(counts, samples, genotypes)
+
+    th = binding.pivot_table(index="titrant_conc", columns="genotype",
+                             values="theta_obs")
+    th_sd = binding.pivot_table(index="titrant_conc", columns="genotype",
+                                values="theta_std")
+    sl["dtheta"] = [th.loc[x, g] - th.loc[x, "wt"]
+                    for g, x in zip(sl["genotype"], sl["titrant_conc"])]
+    sl["dtheta_sd"] = [np.hypot(th_sd.loc[x, g], th_sd.loc[x, "wt"])
+                       for g, x in zip(sl["genotype"], sl["titrant_conc"])]
+    sl.to_csv(out_dir / "wt_relative_slopes.csv", index=False)
+
+    print(f"\nslope SE (per min): median {sl['slope_se'].median():.5f}; "
+          f"min reads in a tube: median {sl['min_reads'].median():.0f}")
+
+    # Replicate agreement.
+    w = sl.pivot_table(index=["genotype", "condition_sel", "titrant_conc"],
+                       columns="replicate", values="slope")
+    diff = w.iloc[:, 1] - w.iloc[:, 0]
+    print(f"replicate difference in slope: SD {diff.std():.5f} "
+          f"(expected from SEs ~ "
+          f"{np.sqrt(2) * sl['slope_se'].median():.5f})")
+
+    # Counting SEs miss most of the noise (study 0b: per-tube variance is
+    # 5-10x Poisson). Add an excess slope variance tau^2, estimated from
+    # the replicate differences, to every slope's SE.
+    tau2 = max(diff.var() / 2 - (sl["slope_se"] ** 2).mean(), 0.0)
+    sl["slope_se_counting"] = sl["slope_se"]
+    sl["slope_se"] = np.sqrt(sl["slope_se"] ** 2 + tau2)
+    print(f"excess slope SD tau = {np.sqrt(tau2):.5f} per min, added in "
+          f"quadrature to every slope SE")
+
+    # Per selective condition: common m vs genotype-specific m vs curvature.
+    print("\nper condition (both replicates pooled; genotype = dk offset):")
+    results = []
+    for cond, d in sl.groupby("condition_sel"):
+        d = d.copy()
+        d["w"] = 1.0 / d["slope_se"] ** 2
+        common = smf.wls("slope ~ C(genotype) + dtheta", d, weights=d["w"]).fit()
+        per_g = smf.wls("slope ~ C(genotype) + C(genotype):dtheta", d,
+                        weights=d["w"]).fit()
+        curved = smf.wls("slope ~ C(genotype) + dtheta + I(dtheta**2)", d,
+                         weights=d["w"]).fit()
+        f_g = per_g.compare_f_test(common)
+        f_c = curved.compare_f_test(common)
+        red_chi2 = common.ssr / common.df_resid
+        results.append({"condition_sel": cond, "m_common": common.params["dtheta"],
+                        "m_se": common.bse["dtheta"], "red_chi2_common": red_chi2,
+                        "p_genotype_slopes": f_g[1], "p_curvature": f_c[1],
+                        "n": len(d)})
+        print(f"  {cond:10s} m = {common.params['dtheta']:+.5f} "
+              f"+/- {common.bse['dtheta']:.5f}; reduced chi2 {red_chi2:.1f}; "
+              f"genotype-specific m p={f_g[1]:.3g}; curvature p={f_c[1]:.3g}")
+        for g in sorted(d["genotype"].unique()):
+            dg = d[d["genotype"] == g]
+            if dg["dtheta"].std() > 0.05:
+                fg = smf.wls("slope ~ dtheta", dg, weights=dg["w"]).fit()
+                print(f"      {g:10s} m_g = {fg.params['dtheta']:+.5f} "
+                      f"+/- {fg.bse['dtheta']:.5f} "
+                      f"(dtheta range {dg['dtheta'].min():+.2f}.."
+                      f"{dg['dtheta'].max():+.2f})")
+    pd.DataFrame(results).to_csv(out_dir / "map_tests.csv", index=False)
+
+    # Figure: slope vs dtheta per condition, coloured by genotype.
+    conds = sorted(sl["condition_sel"].unique())
+    fig, axes = plt.subplots(1, len(conds), figsize=(4 * len(conds), 3.6))
+    for ax, cond in zip(np.atleast_1d(axes), conds):
+        d = sl[sl["condition_sel"] == cond]
+        for g, dg in d.groupby("genotype"):
+            ax.errorbar(dg["dtheta"], dg["slope"], xerr=dg["dtheta_sd"],
+                        yerr=dg["slope_se"], fmt="o", ms=3, label=g)
+        ax.axhline(0, color="gray", lw=0.5)
+        ax.set_title(cond)
+        ax.set_xlabel("theta_g - theta_wt (binding)")
+    np.atleast_1d(axes)[0].set_ylabel("d ln(reads_g/reads_wt) / dt (per min)")
+    np.atleast_1d(axes)[0].legend(fontsize=7)
+    fig.tight_layout()
+    fig.savefig(out_dir / "growth_vs_binding.pdf")
+
+    if sample_df is None:
+        return
+    ab = absolute_slopes(counts, samples, sample_df, genotypes)
+    ab["theta"] = [th.loc[x, g] for g, x in zip(ab["genotype"], ab["titrant_conc"])]
+    ab.to_csv(out_dir / "absolute_slopes.csv", index=False)
+    test_map(ab, "theta", "absolute slopes vs binding theta").to_csv(
+        out_dir / "map_tests_absolute.csv", index=False)
+
+    scales = [0.3, 1, 3, 10, 30, 100, 300]
+    sc = scan_concentration_scale(ab, binding, scales)
+    sc.to_csv(out_dir / "concentration_scale_scan.csv", index=False)
+    w = ab.pivot_table(index=["genotype", "condition_sel", "titrant_conc"],
+                       columns="replicate", values="slope")
+    sd2 = (w.iloc[:, 1] - w.iloc[:, 0]).dropna().var() / 2
+    print("\nconcentration scale s (in vivo [IPTG] = s * in vitro): "
+          "reduced chi2 of the common linear map")
+    tab = sc.assign(red=sc["ssr"] / sd2 / sc["df"]).pivot(
+        index="s", columns="condition_sel", values="red")
+    print(tab.round(2).to_string())
+    print("m at each s:")
+    print(sc.pivot(index="s", columns="condition_sel", values="m")
+          .round(4).to_string())
+
+    conds = sorted(ab["condition_sel"].unique())
+    fig, axes = plt.subplots(1, len(conds), figsize=(4 * len(conds), 3.6))
+    for ax, cond in zip(np.atleast_1d(axes), conds):
+        d = ab[ab["condition_sel"] == cond]
+        for g, dg in d.groupby("genotype"):
+            ax.plot(dg["theta"], dg["slope"], "o", ms=3, label=g)
+        ax.set_title(cond)
+        ax.set_xlabel("theta_g (binding)")
+    np.atleast_1d(axes)[0].set_ylabel("d ln_cfu_g / dt (per min)")
+    np.atleast_1d(axes)[0].legend(fontsize=7)
+    fig.tight_layout()
+    fig.savefig(out_dir / "absolute_growth_vs_binding.pdf")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:4])
diff --git a/planning/studies/joint-fit-real/README.md b/planning/studies/joint-fit-real/README.md
new file mode 100644
index 00000000..c1587dc1
--- /dev/null
+++ b/planning/studies/joint-fit-real/README.md
@@ -0,0 +1,128 @@
+# Joint stage 1 on the real screen
+
+**Question.** With the recipe from `planning/studies/joint-fit-sim/`
+(absolute theta from `hill_geno`, binding at weight 1, the plain model
+first and the full model started from it, 75 co-resident sets), what do
+the real data say: is kanR's growth a switch-like function of occupancy
+and pheS's graded, what is the in vivo titrant scale s, which binding
+genotypes disagree with their growth (M42I), and does the full model
+explain growth and binding better than the plain one?
+
+**Decision it feeds.** `planning/joint-fit-return.md` step 6, and through
+it step 7 (stage 2 with the full library) and which model pieces stay.
+
+## Inputs
+
+- Growth: the recipe's growth table (`dev-data/real_fit/recipe/tfs_growth.csv`
+  on the cluster), wt, spikes and singles (`two_stage.stage1_genotypes`),
+  with the probably swapped pheS+4CP replicate 2, t_sel 82 row left out
+  (`planning/studies/relative-m/`, tube QC; the lab judged the swap
+  probable, and the 1 uM tube is ambiguous, so the row is dropped rather
+  than relabeled).
+- Library: `dev-data/processed/library_config.yaml`.
+- Binding: the 2026-10-15 table through `tfs-process-binding` at 2 uM:
+  110 points over seven genotypes (wt, M42I, H74A, K84L, M42I/H74A,
+  H74A/K84L, G65H), theta_std from the pooled replicate SD (0.13, wt's
+  day-to-day scatter; the mutants have one replicate each). D88A, V95N and
+  the other spikes have no 2 uM data. The lab's data stay out of the
+  repository: the table is made locally in `planning/dev-data` (gitignored)
+  and copied to the cluster.
+
+## Model
+
+`stage1_real.py`: `hill_geno`; the mixture with the homodimer rule, lambda
+prior 0.38 +- 0.15, co-resident sets 50 20 5 (`--congression_sets`,
+joint-fit-sim run 9); binding at weight 1; counts with level tube offsets
+(SD 0.17); log(n) population SD 0.5; one k and m per condition with loose
+priors. `plain`: linear growth, s = 1, no offsets. `full`: warps on both
+selection conditions, s, and one offset per binding genotype; started from
+the plain fit. The Laplace uses a Hessian chunk of 8 (75 sets ran the
+default 64 out of GPU memory on the simulation).
+
+## What to expect from the simulations
+
+The real data have fewer binding anchors than the simulations: wt and
+five spikes at 2 uM (the simulations had wt and eight) plus one in-library
+genotype (G65H). At 0 to 5 extra binding singles the congression
+simulation put stage 1's m 17-21% steep and the growth-only singles'
+theta RMSE at 0.046-0.056, intervals 3-6 times too narrow
+(`planning/studies/joint-fit-sim/`, runs 8 and 10). Treat the real
+fit's theta intervals as provisional; m, the warps, s and the offsets are
+the results here.
+
+## How to run
+
+Locally, make the binding table (already done on 2026-10-18):
+
+```bash
+cd planning/dev-data && tfs-process-binding binding/2026-10-15_binding.csv --out_prefix binding/processed/2026-10-15_binding_2uM
+```
+
+Copy it to the cluster:
+
+```bash
+ssh harms@login2.talapas.uoregon.edu 'mkdir -p /gpfs/projects/harmslab/harms/studies/dev-data/binding'
+```
+
+```bash
+scp planning/dev-data/binding/processed/2026-10-15_binding_2uM.csv harms@login2.talapas.uoregon.edu:/gpfs/projects/harmslab/harms/studies/dev-data/binding/
+```
+
+Verify:
+
+```bash
+ssh harms@login2.talapas.uoregon.edu 'wc -l /gpfs/projects/harmslab/harms/studies/dev-data/binding/2026-10-15_binding_2uM.csv'
+```
+
+(111 lines.) Then on the cluster, the plain fit and the full fit started
+from it:
+
+```bash
+mkdir -p /gpfs/projects/harmslab/harms/studies/joint-fit-real && cd /gpfs/projects/harmslab/harms/studies/joint-fit-real && R=/gpfs/home/harms/tfscreen/planning/studies/joint-fit-real/stage1_real.srun && j=$(sbatch --parsable --export=ALL,MODEL=plain,OUT=plain --output=plain.out $R) && sbatch --dependency=afterok:$j --export=ALL,MODEL=full,OUT=full,INIT=plain --output=full.out $R
+```
+
+Then locally, after downloading each fit's `tfs_params_*.csv`,
+`tfs_pred_theta*.csv` and convergence tables into `runs/`:
+
+```bash
+python report.py runs/plain runs/full --binding ../../dev-data/binding/processed/2026-10-15_binding_2uM.csv --out out
+```
+
+## Results
+
+### Run 1 (2026-10-18): 2 uM binding
+
+Both fits stopped at the epoch cap still descending slowly (about 20 nats
+over the last five windows at step 1e-5); the Laplace ran (chunk 8).
+Final MAP losses: plain 813,484, full 804,973.
+
+| | plain | full |
+|---|---|---|
+| m kanR+kan (within-tube -0.0046) | -0.0280 | -0.063 [-0.089, -0.035] |
+| m pheS+4CP (within-tube +0.0125) | +0.0171 | +0.0156 |
+| kanR warp a / x0 | - | 8.9 / 0.81 |
+| pheS warp a / x0 | - | 6.6 / 0.52 |
+| s | (1) | 0.53 [0.23, 1.26] |
+| lambda (prior 0.38 +- 0.15) | 2.10 | 0.82 |
+| offset scale tau | - | 0.34 |
+
+Binding offsets (full): G65H -0.18, H74A -0.23, H74A/K84L -0.27, K84L
+-0.28, M42I -0.07, M42I/H74A -0.20, wt -0.17: every one negative, and in
+the plain fit every genotype's binding sits below its fitted curve (mean
+residual -0.03 to -0.26).
+
+wt shows why. Its 2 uM binding falls from 0.75 at 0 IPTG to about 0 by
+0.01 mM (midpoint near 0.003 mM); the curve growth wants has its midpoint
+near 0.03 mM and a floor of 0.15-0.24. An IPTG scale of about 0.1 would
+reconcile the two for wt; the fit keeps s near 0.5 and leaves wt's binding
+misfit (RMS residual 2.9-4.0 measurement SDs), since growth's rows
+outweigh eighteen binding points. The offsets then absorb a common level
+difference (a hierarchical offset with mean zero is the wrong shape for
+it), and lambda runs to 0.8-2.1 against the measured 0.36 to absorb the
+rest. So m, the warps and lambda here are not results.
+
+The 2 uM binding and growth disagree by more than s plus per-genotype
+offsets can express. This matches the open question of
+planning/joint-fit-return.md (wt's IPTG midpoint matched 20 uM binding,
+the mutants' levels 2 uM): the in vivo occupancy may correspond to more
+than 2 uM of repressor.
diff --git a/planning/studies/joint-fit-real/report.py b/planning/studies/joint-fit-real/report.py
new file mode 100644
index 00000000..88adad61
--- /dev/null
+++ b/planning/studies/joint-fit-real/report.py
@@ -0,0 +1,105 @@
+"""
+Report joint stage 1 fits on the real screen (README).
+
+    python report.py runs/plain runs/full --binding BINDING_CSV --out out
+
+For each fit: its final MAP loss; k and m per condition (with the
+within-tube m of planning/studies/within-tube-m for reference); the warps,
+log s and the offset scale tau when present; and per binding genotype its
+offset and binding residuals (theta_obs minus the fitted curve at the
+binding concentrations plus the offset), as RMS and RMS in units of
+theta_std. Writes out/params.csv and out/binding_residuals.csv.
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+
+Q = ["q0.025", "q0.5", "q0.975"]
+# kanR+kan and pheS+4CP, change of the wt/flat-reference slope from 0 to 1 mM
+# (planning/studies/within-tube-m, run 3, swapped row left out).
+WITHIN_TUBE_M = {"kanR+kan": -0.0046, "pheS+4CP": 0.0125}
+
+
+def _read(path):
+    return pd.read_csv(path) if os.path.exists(path) else None
+
+
+def params(run, name):
+    rows = []
+    for p in ("growth_k", "growth_m", "warp_log_a", "warp_x0",
+              "growth_log_conc_shift", "offset_scale"):
+        d = _read(os.path.join(run, f"tfs_params_{p}.csv"))
+        if d is None:
+            continue
+        key = ("condition_rep" if "condition_rep" in d.columns
+               else "parameter" if "parameter" in d.columns else d.columns[0])
+        for _, r in d.iterrows():
+            rows.append(dict(run=name, parameter=p, key=r[key],
+                             **{q: r[q] for q in Q}))
+    out = pd.DataFrame(rows)
+    out["within_tube_m"] = np.where(out["parameter"] == "growth_m",
+                                    out["key"].map(WITHIN_TUBE_M), np.nan)
+    return out
+
+
+def residuals(run, name, binding):
+    pred = pd.read_csv(os.path.join(run, "tfs_pred_theta_binding.csv"))
+    pred["genotype"] = pred["genotype"].astype(str)
+    b = binding.merge(pred[["genotype", "titrant_conc", "q0.5"]],
+                      on=["genotype", "titrant_conc"], how="inner")
+    off = _read(os.path.join(run, "tfs_params_offset.csv"))
+    offset = ({} if off is None
+              else dict(zip(off["genotype"].astype(str), off["q0.5"])))
+    b["offset"] = b["genotype"].map(offset).fillna(0.0)
+    b["resid"] = b["theta_obs"] - (b["q0.5"] + b["offset"])
+    b["z"] = b["resid"] / b["theta_std"]
+    out = (b.groupby("genotype")
+           .agg(n=("resid", "size"), offset=("offset", "first"),
+                mean_resid=("resid", "mean"),
+                rms_resid=("resid", lambda x: float(np.sqrt(np.mean(x ** 2)))),
+                rms_z=("z", lambda x: float(np.sqrt(np.mean(x ** 2)))))
+           .reset_index())
+    out.insert(0, "run", name)
+    return out
+
+
+def final_loss(run):
+    c = _read(os.path.join(run, "tfs_fit_model_convergence.csv"))
+    if c is None or c.empty:
+        return np.nan, "no convergence windows"
+    r = c.iloc[-1]
+    return float(r["loss_exact"]), str(r["decision"])
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("runs", nargs="+")
+    ap.add_argument("--binding", required=True)
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+    binding = pd.read_csv(args.binding)
+    binding["genotype"] = binding["genotype"].astype(str)
+    ps, rs = [], []
+    for run in args.runs:
+        name = os.path.basename(os.path.normpath(run))
+        loss, decision = final_loss(run)
+        print(f"{name}: final MAP loss {loss:.1f} ({decision})")
+        ps.append(params(run, name))
+        rs.append(residuals(run, name, binding))
+    p = pd.concat(ps, ignore_index=True)
+    r = pd.concat(rs, ignore_index=True)
+    p.to_csv(os.path.join(args.out, "params.csv"), index=False)
+    r.to_csv(os.path.join(args.out, "binding_residuals.csv"), index=False)
+    pd.set_option("display.width", 200)
+    print()
+    print(p.to_string(index=False, float_format="%+.4f"))
+    print()
+    print(r.to_string(index=False, float_format="%+.3f"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/joint-fit-real/stage1_real.py b/planning/studies/joint-fit-real/stage1_real.py
new file mode 100644
index 00000000..34399f4d
--- /dev/null
+++ b/planning/studies/joint-fit-real/stage1_real.py
@@ -0,0 +1,164 @@
+"""
+Joint stage 1 on the real screen (README; planning/joint-fit-return.md
+step 6): wt, spikes and singles, growth and binding together.
+
+    python stage1_real.py GROWTH_CSV LIBRARY_CONFIG BINDING_CSV OUT_DIR --model plain|full
+                          [--init_from OTHER/tfs_fit_model_params.npz]
+                          [--drop_tubes COND:REP:T_SEL ...] [--congression_sets 50 20 5]
+                          [--seed 1] [--hessian_chunk_size 8]
+
+The recipe from planning/studies/joint-fit-sim: hill_geno (absolute theta,
+shared by both markers), the congression mixture with the homodimer rule
+(lambda prior 0.38 +- 0.15, the lab's measurement), binding at weight 1,
+the count likelihood with level tube offsets (SD 0.17), log(n) population
+SD 0.5, one k and m per condition with loose priors.
+
+- plain: linear growth in theta, s = 1, binding as measured.
+- full: plus a logistic warp of occupancy on both selection conditions,
+  the growth titrant scale s (log s ~ Normal(0, 1)) and one binding offset
+  per binding genotype (genotype_offset). Start it from the plain fit
+  (--init_from): from scratch the full model can stop in a wrong mode
+  (joint-fit-sim, runs 1-3).
+
+Each step is skipped when its output exists, so a rerun resumes. theta is
+predicted at the growth concentrations (tfs_pred_theta.csv) and at the
+binding concentrations (tfs_pred_theta_binding.csv), both in binding units.
+"""
+
+import argparse
+import os
+
+import pandas as pd
+
+from tfscreen.genetics.library_design import library_composition_table
+from tfscreen.tfmodel import two_stage as ts
+from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+from tfscreen.tfmodel.scripts.extract_params_cli import extract_params
+from tfscreen.tfmodel.scripts.fit_model_cli import fit_model
+from tfscreen.tfmodel.scripts.predict_theta_cli import predict_theta
+from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+
+CONFIG = "tfs_configure_config.yaml"
+CHECKPOINT = "tfs_fit_model_checkpoint.pkl"
+LOOSE = dict(k_loc=0.015, k_scale=0.01, m_loc=0.0, m_scale=0.01)
+
+
+def growth_priors(growth_csv, warp):
+    """Loose k/m priors for every condition; m_warp on selection conditions."""
+    conds = set()
+    for chunk in pd.read_csv(growth_csv, usecols=["condition_pre", "condition_sel"],
+                             chunksize=1_000_000):
+        conds |= set(chunk["condition_pre"]) | set(chunk["condition_sel"])
+    t = pd.DataFrame({"condition_rep": sorted(conds)}).assign(**LOOSE)
+    if warp:
+        t["m_warp"] = t["condition_rep"].str.contains(r"\+").astype(int)
+    return t
+
+
+def drop_rows(df, specs):
+    """Drop the tubes named condition_sel:replicate:t_sel."""
+    keep = pd.Series(True, index=df.index)
+    for spec in specs:
+        parts = spec.split(":")
+        if len(parts) != 3:
+            raise ValueError(f"--drop_tubes {spec!r}: expected condition_sel:replicate:t_sel")
+        cond, rep, t_sel = parts
+        hit = ((df["condition_sel"] == cond)
+               & (df["replicate"].astype(float) == float(rep))
+               & (df["t_sel"].astype(float) == float(t_sel)))
+        if not hit.any():
+            raise ValueError(f"--drop_tubes {spec!r} matches no rows")
+        keep &= ~hit
+    return df[keep]
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("growth_csv")
+    ap.add_argument("library_config")
+    ap.add_argument("binding_csv")
+    ap.add_argument("out_dir")
+    ap.add_argument("--model", choices=["full", "plain"], required=True)
+    ap.add_argument("--init_from", default=None)
+    ap.add_argument("--drop_tubes", nargs="*", default=[])
+    ap.add_argument("--congression_sets", type=int, nargs="+", default=None)
+    ap.add_argument("--seed", type=int, default=1)
+    ap.add_argument("--hessian_chunk_size", type=int, default=8)
+    ap.add_argument("--max_num_epochs", type=int, default=None)
+    args = ap.parse_args()
+    growth_csv = os.path.abspath(args.growth_csv)
+    library_config = os.path.abspath(args.library_config)
+    binding_csv = os.path.abspath(args.binding_csv)
+    init_from = None if args.init_from is None else os.path.abspath(args.init_from)
+    full = args.model == "full"
+    os.makedirs(args.out_dir, exist_ok=True)
+    os.chdir(args.out_dir)
+
+    if not os.path.exists("tfs_growth.csv"):
+        lib = library_composition_table(library_config)
+        keep = ts.stage1_genotypes(lib, ts.growth_genotypes(growth_csv))
+        ts.filter_growth_csv(growth_csv, "tfs_growth.csv.partial", genotypes=keep)
+        if args.drop_tubes:
+            g = pd.read_csv("tfs_growth.csv.partial")
+            n = len(g)
+            g = drop_rows(g, args.drop_tubes)
+            print(f"dropped {n - len(g)} rows ({', '.join(args.drop_tubes)})", flush=True)
+            g.to_csv("tfs_growth.csv.partial", index=False)
+        os.replace("tfs_growth.csv.partial", "tfs_growth.csv")
+        print(f"stage 1: {len(keep)} genotypes (wt, spikes, singles)", flush=True)
+
+    if not os.path.exists(CONFIG):
+        growth_priors("tfs_growth.csv", warp=full).to_csv("growth_priors.csv", index=False)
+        set_priors = ["sigma_fixed=0.17", "theta_log_hill_n_hyper_scale_fixed=0.5"]
+        if full:
+            set_priors.append("theta_growth_log_conc_shift_scale=1.0")
+        extra = {} if args.congression_sets is None else {
+            "congression_sets": list(args.congression_sets)}
+        configure_model(
+            binding_df=binding_csv,
+            growth_df=os.path.abspath("tfs_growth.csv"),
+            library_config=library_config,
+            theta_model="hill_geno",
+            transformation_model="mixture",
+            transformation_lambda=[0.38, 0.15],
+            congression_theta_rule="homodimer",
+            theta_binding_noise_model="genotype_offset" if full else "zero",
+            growth_shares_replicates=True,
+            batch_size=4096,
+            allow_missing_spikes=True,
+            growth_priors=os.path.abspath("growth_priors.csv"),
+            set_priors=set_priors,
+            skip_model_stats=True,
+            out_prefix="tfs_configure",
+            **extra)
+
+    fit_kw = {} if args.max_num_epochs is None else {"max_num_epochs": args.max_num_epochs}
+    if init_from is not None:
+        fit_kw["init_from"] = init_from
+    if not os.path.exists("tfs_fit_model_params.npz"):
+        fit_model(CONFIG, seed=args.seed, analysis_method="map", **fit_kw)
+    if not os.path.exists("tfs_posterior.h5"):
+        sample_posterior(CONFIG, CHECKPOINT, out_prefix="tfs_posterior",
+                         num_posterior_samples=500, skip_growth_observations=True,
+                         hessian_chunk_size=args.hessian_chunk_size)
+    if not os.path.exists("tfs_params_provenance.json"):
+        extract_params(CONFIG, "tfs_posterior.h5", out_prefix="tfs_params")
+    if not os.path.exists("tfs_pred_theta.csv"):
+        predict_theta(CONFIG, "tfs_posterior.h5", out_prefix="tfs_pred_theta")
+    if not os.path.exists("tfs_pred_theta_binding.csv"):
+        b = pd.read_csv(binding_csv)
+        with open("binding_genotypes.txt", "w") as fh:
+            fh.write("\n".join(sorted(b["genotype"].astype(str).unique())) + "\n")
+        with open("binding_concs.txt", "w") as fh:
+            fh.write("\n".join(str(c) for c in sorted(b["titrant_conc"].unique())) + "\n")
+        with open("binding_titrants.txt", "w") as fh:
+            fh.write("\n".join(sorted(b["titrant_name"].astype(str).unique())) + "\n")
+        predict_theta(CONFIG, "tfs_posterior.h5", out_prefix="tfs_pred_theta_binding",
+                      genotypes_file="binding_genotypes.txt",
+                      titrant_names_file="binding_titrants.txt",
+                      titrant_concs_file="binding_concs.txt", only_files=True)
+    print("stage 1 done", flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/joint-fit-real/stage1_real.srun b/planning/studies/joint-fit-real/stage1_real.srun
new file mode 100644
index 00000000..435e06b2
--- /dev/null
+++ b/planning/studies/joint-fit-real/stage1_real.srun
@@ -0,0 +1,46 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=jfreal_fit
+#SBATCH --partition=gpu
+#SBATCH --time=06:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# Joint stage 1 on the real screen (planning/studies/joint-fit-real). Run
+# from the study's cluster directory:
+#
+#   sbatch --export=ALL,MODEL=plain,OUT=plain --output=plain.out stage1_real.srun
+#
+#   MODEL    plain | full
+#   OUT      output directory
+#   INIT     optional: a fit directory to start from (its MAP params)
+#   BINDING  binding_df (default: dev-data/binding/2026-10-15_binding_2uM.csv)
+#   CSETS    co-resident sets, ':'-separated (default 50:20:5)
+#   DROP     tubes to leave out, ' '-separated condition_sel:replicate:t_sel
+#            (default the probably swapped pheS+4CP replicate 2, t_sel 82 row)
+#
+# A rerun resumes.
+
+module load cuda/12.4.1
+set -e
+MODEL="${MODEL:?set MODEL}"
+OUT="${OUT:?set OUT}"
+DEV=/gpfs/projects/harmslab/harms/studies/dev-data
+GROWTH="${GROWTH:-${DEV}/real_fit/recipe/tfs_growth.csv}"
+LIB="${LIB:-${DEV}/processed/library_config.yaml}"
+BINDING="${BINDING:-${DEV}/binding/2026-10-15_binding_2uM.csv}"
+CSETS="${CSETS:-50:20:5}"
+DROP="${DROP:-pheS+4CP:2:82}"
+STUDY=/gpfs/home/harms/tfscreen/planning/studies/joint-fit-real
+IFS=':' read -r -a SETS <<< "${CSETS}"
+read -r -a DROPS <<< "${DROP}"
+EXTRA=(--congression_sets "${SETS[@]}" --drop_tubes "${DROPS[@]}")
+if [[ -n "${INIT:-}" ]]; then
+    EXTRA+=(--init_from "${INIT}/tfs_fit_model_params.npz")
+fi
+echo ">>> stage 1 real ${MODEL} -> ${OUT} $(date)"
+python "${STUDY}/stage1_real.py" "${GROWTH}" "${LIB}" "${BINDING}" "${OUT}" --model "${MODEL}" "${EXTRA[@]}"
+echo ">>> done $(date)"
diff --git a/planning/studies/joint-fit-sim/README.md b/planning/studies/joint-fit-sim/README.md
new file mode 100644
index 00000000..a132e8f3
--- /dev/null
+++ b/planning/studies/joint-fit-sim/README.md
@@ -0,0 +1,610 @@
+# Joint stage 1 on a simulation
+
+**Question.** With binding back at weight 1 and the joint model's pieces
+(absolute theta from `hill_geno`, a per-marker warp of occupancy, the
+binding-to-growth titrant scale s, one binding offset per binding
+genotype), does stage 1 (wt, spikes and singles, with the congression
+mixture) recover m, the maps, s and theta on a simulation where we know
+them? Does binding at weight 1 leave growth undistorted, and does the full
+model return the null when there is nothing to find?
+
+**Decision it feeds.** `planning/joint-fit-return.md` step 5: whether to
+run step 6 (the real stage 1) with this model, and which pieces it needs.
+
+## Simulations (`make_sim.py`)
+
+Both start from the full-size realistic configuration
+(`planning/studies/full-size-sim/make_sim_config.py`; on the cluster
+`full-sized-sims-3/sim_realistic_s2`), seed 1, so the library, the
+phenotypes and the spikes' curves are the same. Every spike gets binding
+at the lab's 2 uM IPTG grid (16 concentrations, 0 to 5 mM) with noise 0.04,
+the scatter within one real curve. Its curve comes from `spike_hill.csv`:
+wt at the simulator's reference, the other eight drawn by the script.
+
+| arm | kanR+kan | pheS+4CP | s | binding offsets |
+|---|---|---|---|---|
+| `joint` | switch-like, warp a = 8, x0 = 0.5 | graded, a = 1.5, x0 = 0.4 | 0.5 | SD 0.1 per spike |
+| `null` | linear in theta | linear | 1 | none |
+
+New simulator keys (2026-10-16): a linear growth condition's `warp_a` and
+`warp_x0`, the top-level `growth_titrant_scale` and
+`binding_data.genotype_offset_sd`. Their truths are in
+`tfs_sim_growth_parameters.csv` (`warp_log_a`, `warp_x0`),
+`tfs_sim_growth_titrant_scale.csv` (log s) and
+`tfs_sim_binding_offsets.csv`.
+
+## Fits (`stage1.py`)
+
+Stage 1 of the two-stage fit, joint: wt, spikes and singles; `hill_geno`;
+the mixture with the homodimer rule (lambda prior 0.357 +- 0.15); binding
+at weight 1; counts with level tube offsets (SD 0.17); log(n) population
+SD 0.5; one k and m per condition with loose priors. MAP (staged), Laplace
+with 500 draws, extraction, theta predictions.
+
+- `full`: plus the warp on both selection conditions, s (log s ~
+  Normal(0, 1)) and the binding offsets (`genotype_offset`).
+- `plain`: none of those.
+
+| run | simulation | model | asks |
+|---|---|---|---|
+| `fit_joint_s1_full` | joint | full | recovery of m, warps, s, offsets, theta |
+| `fit_joint_s1_plain` | joint | plain | what the pieces buy (the misspecified model) |
+| `fit_null_s1_full` | null | full | the null comes back (a small, s near 1, tau near 0) without costing theta |
+
+**Pass.** On `fit_joint_s1_full`: m within its 95% interval and within
+about 10% of the truth in both selection conditions; warp log a and x0
+covered; log s covered; the offsets covered and tau's interval holding
+0.1; theta coverage near 0.95 for wt, spikes and singles. On
+`fit_null_s1_full`: kanR's and pheS's warps compatible with near-linear,
+log s covering 0, tau's interval reaching toward 0, and theta no worse than
+`fit_joint_s1_full`'s. `fit_joint_s1_plain` should miss somewhere; where
+it does says which piece matters.
+
+## How to run
+
+On the cluster, after pulling the commit that adds this study:
+
+```bash
+mkdir -p /gpfs/projects/harmslab/harms/studies/joint-fit-sim && cd /gpfs/projects/harmslab/harms/studies/joint-fit-sim
+```
+
+```bash
+for a in joint null; do sbatch --export=ALL,ARM=$a,SEED=1 --output=sim_$a.out /gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/simulate.srun; done
+```
+
+Once both finish (about 90 minutes each; check `ls sim_*_s1/tfs_sim_binding.csv`):
+
+```bash
+for r in joint_s1:full joint_s1:plain null_s1:full; do sbatch --export=ALL,SIM=sim_${r%%:*},MODEL=${r##*:} --output=fit_${r%%:*}_${r##*:}.out /gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/stage1.srun; done
+```
+
+Then, locally after downloading the fits' outputs and each simulation's
+truth files (`simulate_config.yaml`, `tfs_sim_parameters.csv`,
+`tfs_sim_growth_parameters.csv`, `tfs_sim_growth_titrant_scale.csv`,
+`tfs_sim_binding_offsets.csv`):
+
+```bash
+python analyze.py runs/fit_joint_s1_full:runs/sim_joint_s1 runs/fit_joint_s1_plain:runs/sim_joint_s1 runs/fit_null_s1_full:runs/sim_null_s1 --out out
+```
+
+`analyze.py` writes `out/params.csv` (each fitted parameter against its
+truth, with its 95% interval) and `out/theta.csv` (theta RMSE, bias and
+95% coverage by group: wt, spikes, singles). Theta is compared in binding
+units, the fitted curve against the truth curve at the growth
+concentrations.
+
+## Results
+
+### Run 1 (2026-10-17, commit of 2026-10-16)
+
+Simulations from `sim_realistic_s2`'s config, seed 1; 955 stage-1
+genotypes (wt, 8 spikes with binding, 946 singles); M42I/H74A survived
+this time. Final MAP losses: joint/full 780,800 (converged), joint/plain
+798,807 (converged), null/full 784,304 (still descending at step 1e-5).
+
+Shared parameters, median [95%] against the truth:
+
+| | truth | joint/full | joint/plain | null/full (truth) |
+|---|---|---|---|---|
+| m kanR+kan | -0.0141 | -0.0144 [-0.0145, -0.0143] | -0.0187 | -0.0167 (-0.0141) |
+| m pheS+4CP | +0.0144 | +0.0164 [0.0160, 0.0167] | +0.0155 | +0.0169 (+0.0144) |
+| kanR warp log a / x0 | 2.08 / 0.50 | 2.20 / 0.57 | - | 2.78 / 0.70 (linear) |
+| pheS warp log a / x0 | 0.41 / 0.40 | -0.14 [-0.68, 0.44] / 1.25 [0.28, 2.16] | - | 2.71 / 0.70 (linear) |
+| s | 0.50 | 0.42 [0.37, 0.48] | (1) | 0.25 [0.17, 0.35] (1) |
+| offset scale tau | 0.10 | 0.25 [0.11, 0.51] | - | 0.34 (0) |
+| k, every condition | | 0.002-0.005 high | 0.003-0.005 high | 0.000-0.003 high |
+
+Theta RMSE of the median (95% coverage), binding units, and the RMSE as
+growth sees it (each curve at its own s times nominal):
+
+| | joint/full | joint/plain | null/full |
+|---|---|---|---|
+| singles (946) | 0.072 (0.18); in vivo 0.074 | 0.130 (0.04); in vivo 0.091 | 0.293 (0.04); in vivo 0.302 |
+| spikes (8) | 0.059 (0.31) | 0.047 (0.31) | 0.226 (0.09) |
+| wt | 0.055 (0.25) | 0.137 (0.00) | 0.151 (0.13) |
+
+What it says:
+
+- **On the joint simulation the pieces work and are needed.** The full
+  model gets kanR's m within 2% and its warp close (a 9.0 against 8,
+  x0 0.57 against 0.50), s within 16%, and halves the singles' theta
+  error of the plain model, which steepens kanR's m by 33% to bend a
+  linear map round a switch. pheS's graded warp is weakly identified
+  (wide, covers) and its m is 14% steep. Most offsets are near their
+  truth; tau is high (0.25 against 0.1).
+- **The null fit finds a wrong mode.** With linear truth, both markers get
+  the same steep warp (a about 16, x0 0.70), s drops to 0.25 and the
+  spikes take large offsets (K84L -0.32, M42I/K84L -0.38); theta is off by
+  0.3 whether read in binding or growth units. A warp shared by both
+  markers trades exactly with every single's curve (growth sees f(theta),
+  and singles have no binding), so only the binding genotypes pin it, and
+  the offsets let those same genotypes disagree with their binding. On
+  the joint arm the two markers' warps differ, so the singles pin their
+  difference and binding only sets the common scale.
+- **Intervals are far too narrow everywhere** (theta 18-31% coverage
+  where the median error is small; m's interval excludes the truth),
+  the MAP plus Laplace holding the shared parameters' error out of each
+  genotype, as before.
+- k sits high in every condition, the k/dk_geno slide, as in every stage
+  1 so far.
+
+### Run 2 (2026-10-17): trap or degeneracy?
+
+`fit_null_s1_plain` (the null simulation's own model) converged at a MAP
+loss of 783,286, about 1,000 nats below `fit_null_s1_full` (784,304, still
+descending at step 1e-5 when it hit the epoch cap). The full model nests
+the plain one at a cost of tens of nats in prior terms, so run 1's null
+fit was trapped, not unidentified: the data do separate the linear truth
+from the steep shared warp. It reached the steep warps early, while the
+step size was large.
+
+The plain fit's theta is good (singles RMSE 0.056, bias -0.002; spikes
+0.036) but its intervals still undercover (singles 0.10), and m is about
+20% steep in both markers with the true model (kanR -0.0173 against
+-0.0141, pheS +0.0173 against +0.0144) while k sits 0.005 high: an open
+question for stage 1 itself, separate from the warp.
+
+Next (run 3): the full model started from the plain fit's MAP
+(`stage1.py --init_from`, `stage1.srun` `INIT=`), on both simulations. Pass:
+the null fit returns to the plain fit's loss or below with near-linear
+warps, s near 1 and small offsets; the joint fit reaches run 1's 780,800
+or below.
+
+```bash
+for s in null joint; do sbatch --export=ALL,SIM=sim_${s}_s1,MODEL=full,INIT=fit_${s}_s1_plain --output=fit_${s}_s1_full_from_plain.out /gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/stage1.srun; done
+```
+
+### Run 3 (2026-10-17): the full model started from the plain fit
+
+Both converged below the fits they had to beat: null 783,021 (plain
+783,286; run 1's full 784,304), joint 780,782 (run 1's full 780,800).
+Starting the full model from the plain one is the recipe.
+
+| | truth | null, full from plain | truth | joint, full from plain |
+|---|---|---|---|---|
+| m kanR+kan | -0.0141 | -0.0176 | -0.0141 | -0.0144 |
+| m pheS+4CP | +0.0144 | +0.0180 | +0.0144 | +0.0166 |
+| kanR warp a / x0 | linear | 2.6 / 0.59 | 8 / 0.50 | 9.0 [8.7, 9.5] / 0.53 [0.50, 0.56] |
+| pheS warp a / x0 | linear | 1.7 / 0.57 | 1.5 / 0.40 | 0.9 [0.2, 4.3] / 0.59 [-0.24, 1.32] |
+| log s | 0 | -0.05 [-0.22, 0.11] | -0.69 | -0.80 [-0.94, -0.66] |
+| offsets covered | 0 | 9 of 9 | | 8 of 9 |
+| tau | 0 | 0.17 [0.05, 0.52] | 0.10 | 0.22 [0.09, 0.50] |
+| singles theta RMSE (95% cov.) | | 0.077 (0.14) | | 0.070 (0.17) |
+| spikes theta RMSE (95% cov.) | | 0.056 (0.64) | | 0.039 (0.48) |
+
+- **The null comes back.** The warps sit at about the prior's median
+  steepness (a about 2.7, close to linear on [0, 1]), s covers 1, every
+  offset covers 0. The extra freedom costs theta a little against the
+  plain fit (singles 0.077 against 0.056).
+- **The joint truth comes back.** kanR's switch, s and the offsets are
+  recovered; pheS's graded warp is weakly identified (wide, covers).
+- **Still open, both independent of the warp:**
+  1. m is steep: 22-25% in both markers on the null simulation, with the
+     plain (true) model as well; pheS 15% on the joint one (kanR right).
+     The simulator and the fit use the same congression rules (homodimer,
+     dilution). k is high by 0.002-0.005, the k/dk_geno slide.
+  2. Theta intervals undercover badly (singles 14-17%, wt 13-25%) while
+     the median error is small: the MAP plus Laplace leaves the shared
+     parameters' error (m above all) out of each genotype's interval.
+
+### Run 4 (2026-10-17): the loss at the true k and m
+
+The null simulation, plain (true) model, k and m pinned at their truth:
+from `fit_null_s1_plain`'s MAP, converged at 784,031; from scratch,
+784,068 (still descending at step 1e-5). The free fit is 783,286, so the
+truth costs about 745 nats: stage 1 prefers the steep m, by far more
+than four parameters' noise. At the truth, theta is much better
+(singles RMSE 0.020 against 0.056, coverage 0.70 against 0.10; spikes
+0.012, wt 0.011): the steep m is most of stage 1's theta error.
+
+Attribution (`attribute.py`, pinned minus free at each MAP; total -720):
+
+| | nats |
+|---|---|
+| growth, pheS+4CP | -396 |
+| growth, pheS-4CP | -106 |
+| growth, kanR+kan / kanR-kan | -64 / -31 |
+| log prior (theta population scales and offsets, mostly) | -161 |
+| binding | +37 |
+
+- pheS+4CP's loss is in the deep singles (> 1e5 reads), at 0.1 and 1 mM
+  IPTG (-261 together), growing with selection time, and in rows whose
+  true theta is below 0.1 (-366 of -396): the cells that grow slowest
+  under 4CP, whose counts their co-transformed partners come to
+  dominate. Fitted lambda is 0.335 free and 0.301 pinned, against the
+  simulated 0.357.
+- The prior's share: at the true m the singles' curves must spread wider,
+  which the theta population scales charge for; shrinkage of the theta
+  range is part of what a steeper m buys.
+- Binding agrees with the truth better (+37).
+
+So the steep m most likely compensates for how the fit's congression
+mixture (fixed co-resident sets, `congression_sets` [12, 3, 1]) describes
+slow growers, plus some shrinkage. The simulator and the fit share the
+congression rules (homodimer, dilution); what differs is that the fit
+approximates each cell's co-residents by a few fixed draws.
+
+### Run 5 (2026-10-17): without congression
+
+The control: the null simulation without congression
+(`make_sim.py --arm nocong`, `transformation_poisson_lambda` 0, same seed
+and library), fitted with the plain model without the mixture
+(`stage1.py --no_mixture`, `NOMIX=1`), free and with k and m pinned at the
+truth. If m comes back unbiased and the truth costs a few nats, the
+mixture's approximation is the cause.
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/joint-fit-sim && sim=$(sbatch --parsable --export=ALL,ARM=nocong,SEED=1 --output=sim_nocong.out /gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/simulate.srun) && echo "sim $sim"
+```
+
+```bash
+sbatch --dependency=afterok:$sim --export=ALL,SIM=sim_nocong_s1,MODEL=plain,NOMIX=1 --output=fit_nocong_s1_plain_single.out /gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/stage1.srun && sbatch --dependency=afterok:$sim --export=ALL,SIM=sim_nocong_s1,MODEL=plain,NOMIX=1,PIN=1 --output=fit_nocong_s1_plain_single_pinkm.out /gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/stage1.srun
+```
+
+Results. Free 776,488, pinned at the truth 776,871 (both converged): the
+truth still costs 383 nats, half of run 4's 745. m is 14% steep in both
+markers (kanR -0.0161, pheS +0.0164), against 22-25% with congression. At
+the truth theta is nearly exact (singles RMSE 0.010, 95% coverage 0.91;
+spikes 0.006, wt 0.002); free, singles 0.046 (coverage 0.11).
+
+Attribution (pinned minus free, total -357): growth -235, spread over all
+four conditions and the deep singles with no hot spot; log prior -144, of
+which the theta population scales and offsets take about -130 (logit low,
+logit delta and log K, about -21 to -23 each for scale and offsets);
+binding +22.
+
+So the bias has two parts of about equal size:
+
+1. **The MAP over-shrinks the theta population** (no congression needed).
+   The population scales of the singles' Hill parameters run small, which
+   compresses their theta range, and a steeper m restores the growth swing;
+   at the true m the curves need their real spread, which the population
+   prior charges for. The doubles' pull on m (the m-bias study) works the
+   same way. A hierarchical MAP has no reason to get those scales right
+   (the funnel); `hill_relative` holds them for the same reason
+   (`theta_*_hyper_scale_fixed`), `hill_geno` only holds log(n)'s.
+2. **The congression mixture's approximation** adds the rest (run 4's
+   pheS slow growers).
+
+Correction to the reading above (checked before run 6): the MAP's
+population SDs are not small. Free, logit theta_low's is 6.8, logit
+delta's 8.4 and log K's 1.22, against the truth's spread among singles
+(SD 2.6, 3.9 and 0.50; robust SD 0.56, 0.63, 0.49: a bulk with outliers,
+the simulator's stuck-bound, never-binds and inverted genotypes); pinned
+at the true k and m they are larger still (9.5, 10.8, 1.38). What
+compresses theta is the population location: the free fit's logit
+theta_low location is 2.04, the default hyperprior's location (2.0 +- 2),
+so its typical single runs from theta 0.88 to 0.085 where the truth runs
+from 0.99 to 0.01, about 20% compression. Growth alone cannot tell those
+ranges apart while m is free, and eight binding genotypes set the scale,
+so the location rides its prior; the SDs' tight HalfNormal priors (log
+K's at 0.1) add their cost at the truth.
+
+### Run 6 (2026-10-17): held SDs, moved locations
+
+`hill_geno` now holds any population SD (`theta_*_hyper_scale_fixed`).
+The no-congression simulation, plain model without the mixture, free k
+and m, three arms (`stage1.py --set_priors`, `stage1.srun` `PRIORS`/`TAG`):
+
+| arm | priors |
+|---|---|
+| `heldsd` | SDs held at the truth's spread: logit low 2.6, logit delta 3.9, log K 0.5, log n 0.3 |
+| `loc` | location priors at the truth's medians: logit low loc 4.5, logit delta loc -9.0 (scales 2 as before) |
+| `both` | both |
+
+Pass: m within a few percent of the truth and theta near run 5's pinned
+fit (singles RMSE 0.010). Compare each arm's MAP loss with run 5's free
+fit (776,488).
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/joint-fit-sim
+```
+
+```bash
+SD=theta_logit_low_hyper_scale_fixed=2.6:theta_logit_delta_hyper_scale_fixed=3.9:theta_log_hill_K_hyper_scale_fixed=0.5:theta_log_hill_n_hyper_scale_fixed=0.3; LOC=theta_logit_low_hyper_loc_loc=4.5:theta_logit_delta_hyper_loc_loc=-9.0; for a in heldsd:$SD loc:$LOC both:$SD:$LOC; do sbatch --export=ALL,SIM=sim_nocong_s1,MODEL=plain,NOMIX=1,TAG=${a%%:*},PRIORS=${a#*:} --output=fit_nocong_s1_plain_single_${a%%:*}.out /gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/stage1.srun; done
+```
+
+Results (priors checked in each `tfs_configure_priors.csv`; `heldsd` hit
+the epoch cap still descending at step 1e-5, the others converged; MAP
+losses are not comparable across arms, whose priors differ):
+
+| arm | m kanR (truth -0.0141) | m pheS (truth +0.0144) | singles theta RMSE (cov.) |
+|---|---|---|---|
+| free (run 5) | -0.0161 | +0.0164 | 0.046 (0.11) |
+| `loc` | -0.0160 | +0.0164 | 0.046 (0.11) |
+| `heldsd` | -0.0184 | +0.0187 | 0.085 (0.04) |
+| `both` | -0.0184 | +0.0188 | 0.088 (0.04) |
+
+Neither fixes it; holding the SDs at the truth's spread makes it worse.
+What the free fit does, by regression on the truth: the singles' theta is
+0.048 + 0.90 theta_true (the fit pinned at the true k and m: 0.000 +
+0.997), and m is stretched by about 1/0.90; dk_geno is right in both
+(slope 0.99). For a single without binding this is close to a symmetry of
+the growth model: an affine squeeze of a Hill curve is a Hill curve, k
+absorbs the constant, m the slope. Only the binding genotypes (wt and
+eight spikes) resist it. What pushes along it is the theta population
+prior in logit space: plateaus near 0.99 and 0.01 (logit +-4.6, outliers
+further) cost prior density, a squeezed curve less. That is why holding
+the SDs at 2.6 / 3.9 / 0.5 (smaller than the free fit's 6.8 / 8.4 / 1.2)
+squeezes harder, and why moving the location does nothing at SDs near 7.
+The congression mixture's approximation (run 4) adds to the same
+direction.
+
+So the remedies are along that direction, not at the priors' values:
+pooling weak enough not to penalize saturated plateaus (held SDs larger
+than the free fit's), more genotypes whose binding pins theta's scale
+(in-library binding genotypes, like the real data's G65H and V95N), or a
+parameterization whose population prior does not prefer the middle of
+[0, 1].
+
+### Run 7 (2026-10-17): weaker pooling, more binding
+
+The no-congression simulation, plain model without the mixture, free k
+and m, against run 5's free fit:
+
+| arm | change |
+|---|---|
+| `weakpool` | theta population SDs held large: logit low 10, logit delta 10, log K 2 (log n 0.5, as before) |
+| `lib2` | 2 extra binding singles (the real data have two in-library binding genotypes, G65H and V95N) |
+| `lib20` | 20 extra binding singles |
+| `weakpool_lib20` | both |
+
+The extra binding singles are random singles with growth data, measured on
+the lab's 2 uM grid from their true Hill curves with the spikes' noise
+(`stage1.py --add_binding_singles`, `stage1.srun` `ADDB=`; the table is
+`binding.csv` in the fit's folder, and `analyze.py` scores them as their
+own group). The growth data are run 5's, so the comparison is clean.
+Pass: m within a few percent of the truth and the singles' theta slope on
+the truth near 1 (run 5 free: 0.90).
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/joint-fit-sim
+```
+
+```bash
+W=theta_logit_low_hyper_scale_fixed=10:theta_logit_delta_hyper_scale_fixed=10:theta_log_hill_K_hyper_scale_fixed=2; for a in weakpool:$W:0 lib2::2 lib20::20 weakpool_lib20:$W:20; do t=${a%%:*}; r=${a#*:}; n=${r##*:}; p=${r%:*}; sbatch --export=ALL,SIM=sim_nocong_s1,MODEL=plain,NOMIX=1,TAG=$t,PRIORS=$p,ADDB=$([ "$n" = 0 ] || echo $n) --output=fit_nocong_s1_plain_single_$t.out /gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/stage1.srun; done
+```
+
+Results (all converged; settings checked in each fit's priors and binding
+table):
+
+| arm | m steep, kanR / pheS | singles' theta slope on truth | singles theta RMSE (95% cov.) | spikes | wt |
+|---|---|---|---|---|---|
+| free (run 5) | 14% / 14% | 0.900 | 0.046 (0.11) | 0.023 | 0.041 |
+| `weakpool` | 11% / 11% | 0.914 | 0.039 (0.13) | 0.020 | 0.035 |
+| `lib2` | 11% / 10% | 0.924 | 0.036 (0.15) | 0.018 | 0.031 |
+| `lib20` | 6% / 6% | 0.961 | 0.021 (0.36) | 0.012 | 0.014 |
+| `weakpool_lib20` | 5% / 5% | 0.964 | 0.019 (0.38) | 0.011 | 0.013 |
+| pinned at truth (run 5) | 0 | 0.997 | 0.010 (0.91) | 0.006 | 0.002 |
+
+- **Binding genotypes are the lever.** Twenty binding singles (spanning
+  the library's theta range at random) cut the m bias from 14% to 6% and
+  halve the singles' theta error; two do about as much as weak pooling.
+  Each binding curve pins one genotype's theta on an absolute scale, and
+  the squeeze of run 6 has to fight all of them.
+- **Weak pooling helps a little** (14% to 11% alone, 6% to 5% with 20
+  binding singles): the logit population prior is part of the push, not
+  most of it.
+- **Not yet enough:** 5% left and theta coverage 0.36-0.38, against 0.91
+  at the true k and m. The remaining m error is shared by every genotype
+  and missing from their intervals (the Laplace at the MAP), as in every
+  run so far.
+
+For the real data, this argues for measuring binding on more singles,
+about twenty spread over the theta range, beyond the nine spikes and the
+two in-library genotypes. The congression part (run 4) is untested with
+binding singles.
+
+### Run 8 (2026-10-17): binding singles with congression, more of them, and the whole recipe
+
+No separate interval study: at the true k and m the singles' intervals
+cover 0.91 (run 5), so the per-genotype uncertainty is right and the
+undercoverage is the shared parameters' bias (the truth sits hundreds of
+nats from the MAP, outside any honest posterior). Fixing the bias is the
+interval fix. Config-only runs on the existing simulations:
+
+| run | asks |
+|---|---|
+| `fit_null_s1_plain_lib20` | 20 binding singles with congression (null; plain model with the mixture; run 4's bias 22-25%) |
+| `fit_null_s1_plain_weakpool_lib20` | the same with weak pooling |
+| `fit_nocong_s1_plain_single_lib50` | 50 binding singles: does more binding keep closing the last 5%? |
+| `fit_joint_s1_plain_lib20` then `fit_joint_s1_full_from_plain_lib20` | the recipe on the joint truth (warps, s, offsets) with 20 binding singles |
+
+The chained full fit builds its own `binding.csv` with the same seed from
+the same stage-1 genotypes, so it measures the same twenty singles.
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/joint-fit-sim && R=/gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/stage1.srun && W=theta_logit_low_hyper_scale_fixed=10:theta_logit_delta_hyper_scale_fixed=10:theta_log_hill_K_hyper_scale_fixed=2
+```
+
+```bash
+sbatch --export=ALL,SIM=sim_null_s1,MODEL=plain,TAG=lib20,ADDB=20 --output=fit_null_s1_plain_lib20.out $R && sbatch --export=ALL,SIM=sim_null_s1,MODEL=plain,TAG=weakpool_lib20,PRIORS=$W,ADDB=20 --output=fit_null_s1_plain_weakpool_lib20.out $R && sbatch --export=ALL,SIM=sim_nocong_s1,MODEL=plain,NOMIX=1,TAG=lib50,ADDB=50 --output=fit_nocong_s1_plain_single_lib50.out $R && j=$(sbatch --parsable --export=ALL,SIM=sim_joint_s1,MODEL=plain,TAG=lib20,ADDB=20 --output=fit_joint_s1_plain_lib20.out $R) && sbatch --dependency=afterok:$j --export=ALL,SIM=sim_joint_s1,MODEL=full,INIT=fit_joint_s1_plain_lib20,ADDB=20 --output=fit_joint_s1_full_from_plain_lib20.out $R
+```
+
+Results (all converged except `fit_joint_s1_full_from_plain_lib20`, at the
+floor step size 1e-6 when it hit the epoch cap, still creeping):
+
+| run | m steep, kanR / pheS | singles theta RMSE (95% cov.) | wt |
+|---|---|---|---|
+| null, plain (run 2) | 23% / 20% | 0.056 (0.10) | 0.040 |
+| null, plain, 20 binding singles | 13% / 11% | 0.037 (0.31) | 0.018 |
+| null, plain, weak pooling, 20 binding singles | 13% / 11% | 0.036 (0.33) | 0.017 |
+| no congression, 20 binding singles (run 7) | 6% / 6% | 0.021 (0.35) | 0.014 |
+| no congression, 50 binding singles | 5% / 5% | 0.017 (0.51) | 0.010 |
+| joint, full from plain (run 3) | 2% / 15% | 0.070 (0.17) | 0.057 |
+| joint, plain, 20 binding singles | 19% / -3% | 0.123 (0.13) | 0.129 |
+| joint, full from plain, 20 binding singles | 1% / 7% | 0.048 (0.37) | 0.029 |
+
+On the joint truth with 20 binding singles: log s -0.74 [-0.78, -0.69]
+against -0.69 (covered, much tighter than without); kanR's warp a 8.3
+[8.2, 8.4] against 8 and x0 0.535 [0.52, 0.55] against 0.50 (tight, near,
+not covered); pheS's graded warp flips to log a -0.83 [-1.8, 0.05]
+against 0.41 (not covered; it was wide and covered without); tau 0.23
+against 0.10.
+
+- **With congression, binding singles halve the bias** (21% to 12%) but
+  leave about twice the no-congression bias: the congression mixture's
+  approximation is now the largest piece. Weak pooling adds nothing here.
+- **More binding has diminishing returns:** 50 singles take the
+  no-congression bias from 6% to 5%, a floor of about 5% from the
+  population prior and the squeeze.
+- **The recipe on the joint truth works best with binding singles:**
+  kanR's m within 1%, s covered, theta error down a third; pheS's m still
+  7% steep and its weak warp the least identified piece.
+- Coverage rises with binding (0.10 to 0.37, 0.51 with 50) but stays far
+  from 0.95, as the bias it tracks.
+
+Next: the congression mixture's approximation (`congression_sets`, the
+fixed co-resident draws), on the null simulation with 20 binding singles.
+
+### Run 9 (2026-10-18): more co-resident draws
+
+`tfs-configure-model --congression_sets` (new) sets the mixture's fixed
+co-resident draws; `stage1.py --congression_sets`, `stage1.srun`
+`CSETS=` (':'-separated). The congression null simulation, plain model
+with the mixture and 20 binding singles, against
+`fit_null_s1_plain_lib20` (default 12 3 1, 16 sets: m 13% / 11% steep):
+
+| arm | sets (1, 2, 3 co-residents) | total |
+|---|---|---|
+| `cs75` | 50 20 5 | 75 |
+| `cs140` | 100 30 10 | 140 |
+| `cs430` | 300 100 30 | 430 |
+
+On a subsampled stage 1, 140 sets took 2.1x the default's wall time
+(fixed overheads included); the default fits took 20-35 minutes here.
+Pass: the bias falls toward the no-congression fit's 6% with 20 binding
+singles (run 7). If it does not move, the error is in the mixture's form
+(the fixed-set quadrature, the homodimer rule's equal shares), not in the
+number of draws.
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/joint-fit-sim && R=/gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/stage1.srun && for a in cs75:50:20:5 cs140:100:30:10 cs430:300:100:30; do sbatch --export=ALL,SIM=sim_null_s1,MODEL=plain,ADDB=20,TAG=${a%%:*},CSETS=${a#*:} --output=fit_null_s1_plain_${a%%:*}.out $R; done
+```
+
+To check each arm's sets once it has configured:
+
+```bash
+for t in cs75 cs140 cs430; do echo "$t: $(python -c "import yaml; print(yaml.safe_load(open('fit_null_s1_plain_$t/tfs_configure_config.yaml'))['components']['congression_sets'])")"; done
+```
+
+Results. All three MAPs finished (75 and 140 sets at the epoch cap, still
+creeping; 430 converged), and all three then ran the GPU out of memory in
+the Laplace Hessian (`tfs-sample-posterior`, `_chunked_hessian`: every
+cell class is evaluated per Hessian probe), so there are no posteriors or
+theta predictions; m comes from each MAP (`two_stage.km_from_map`).
+
+| sets | total | m steep, kanR / pheS |
+|---|---|---|
+| 12 3 1 (run 8, `fit_null_s1_plain_lib20`) | 16 | 13.4% / 11.0% |
+| 50 20 5 | 75 | 9.7% / 8.8% |
+| 100 30 10 | 140 | 9.5% / 8.8% |
+| 300 100 30 | 430 | 8.8% / 8.4% |
+| no congression, same 20 binding singles (run 7) | - | 5.7% / 5.9% |
+
+- **The number of draws is about a third of the congression excess,** and
+  nearly all of that third comes by 75 sets.
+- **The rest (about 3 points) does not shrink with more draws:** it is in
+  the mixture's form, not its sample size. Candidates: the simulator's
+  cells carry their co-residents' real abundances, while the fit's sets
+  are drawn from the pool composition and weighted by the design; the
+  homodimer rule's equal plasmid shares; the classes beyond three
+  co-residents the fit leaves out.
+- **Memory:** many sets need the Laplace Hessian chunked smaller (or a
+  posterior that does not take one) before they can be the default.
+
+### Theta so far (2026-10-18)
+
+Growth-only singles (no binding of their own), from the fits with
+predictions; SD of z is the SD of (error / posterior SD), 1 for honest
+intervals:
+
+| simulation | binding singles | theta RMSE | slope on truth | 95% coverage | median 95% width | SD of z |
+|---|---|---|---|---|---|---|
+| no congression | 0 | 0.046 | 0.90 | 0.11 | 0.039 | 4.3 |
+| | 2 | 0.036 | 0.92 | 0.15 | 0.033 | 4.2 |
+| | 20 | 0.021 | 0.96 | 0.36 | 0.031 | 2.7 |
+| | 50 | 0.017 | 0.97 | 0.51 | 0.032 | 2.2 |
+| | 0, true k and m | 0.010 | 1.00 | 0.91 | 0.047 | 0.9 |
+| congression (null) | 0 | 0.056 | 0.89 | 0.10 | 0.033 | 6.4 |
+| | 20 | 0.037 | 0.94 | 0.31 | 0.032 | 4.3 |
+| | 0, true k and m | 0.020 | 0.98 | 0.70 | 0.042 | 1.8 |
+| joint truth | 0 | 0.070 | 0.85 | 0.17 | 0.053 | 5.2 |
+| | 20 | 0.048 | 0.92 | 0.37 | 0.044 | 3.8 |
+
+Theta's median is good to a few hundredths and its error is mostly the
+shared affine squeeze (slope below 1); its intervals are 2-6 times too
+narrow. With congression the intervals stay about 1.8 times too narrow
+even at the true k and m: the mixture's approximation also adds
+per-genotype error the model does not carry.
+
+### Run 10 (2026-10-18): how theta scales with binding singles
+
+`analyze.py` now also reports each group's median 95% width, SD of z and
+slope on the truth; `stage1.srun` takes `BSEED` (which random singles).
+Arms: the no-congression simulation with 2, 5, 10 and 20 binding singles,
+three random choices each (`lib2` and `lib20` from run 7 are choice 1),
+and the congression null with 5 and 10 (choice 1; 20 is run 8's). Twelve
+fits of about 30 minutes.
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/joint-fit-sim && R=/gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim/stage1.srun
+```
+
+```bash
+for n in 2 5 10 20; do for b in 1 2 3; do if [[ ($n == 2 || $n == 20) && $b == 1 ]]; then continue; fi; sbatch --export=ALL,SIM=sim_nocong_s1,MODEL=plain,NOMIX=1,ADDB=$n,BSEED=$b,TAG=lib${n}_b${b} --output=fit_nocong_s1_plain_single_lib${n}_b${b}.out $R; done; done; for n in 5 10; do sbatch --export=ALL,SIM=sim_null_s1,MODEL=plain,ADDB=$n,TAG=lib$n --output=fit_null_s1_plain_lib$n.out $R; done
+```
+
+Results (`binding_scaling.py runs --out out`: `out/binding_scaling.csv`
+and `out/YYYY-MM-DD_binding-scaling.pdf`). Growth-only singles; mean over
+the three random choices (range in brackets), one choice on the
+congression simulation:
+
+| simulation | binding singles | m steep | theta RMSE | slope | 95% coverage | SD of z |
+|---|---|---|---|---|---|---|
+| no congression | 0 | 14% | 0.046 | 0.90 | 0.11 | 4.3 |
+| | 2 | 11% | 0.036 [0.036, 0.037] | 0.92 | 0.15 | 4.1 |
+| | 5 | 9% | 0.030 [0.029, 0.032] | 0.94 | 0.20 | 3.7 |
+| | 10 | 8% | 0.027 [0.025, 0.029] | 0.95 | 0.23 | 3.6 |
+| | 20 | 6% | 0.021 [0.021, 0.022] | 0.96 | 0.34 | 2.9 |
+| | 50 | 5% | 0.017 | 0.97 | 0.51 | 2.2 |
+| | true k and m | 0 | 0.010 | 1.00 | 0.91 | 0.9 |
+| congression | 0 | 21% | 0.056 | 0.89 | 0.10 | 6.4 |
+| | 5 | 17% | 0.046 | 0.91 | 0.18 | 5.4 |
+| | 10 | 17% | 0.046 | 0.92 | 0.20 | 5.2 |
+| | 20 | 12% | 0.037 | 0.94 | 0.31 | 4.3 |
+| | true k and m | 0 | 0.020 | 0.98 | 0.70 | 1.8 |
+
+- **Which singles are chosen barely matters** (RMSE within 0.003 across
+  choices at every count).
+- **The error falls as one over the square root:** without congression,
+  theta RMSE = 0.010 + 0.053 / sqrt(n + 2) fits every count from 0 to 50
+  (the 0.010 is the fit at the true k and m; the "+2" is what wt and the
+  eight spikes are worth in binding singles). The binding curves average
+  down the shared scale error. Halving the excess at 20 would take about
+  90 binding singles.
+- **Coverage climbs slowly** (0.11 to 0.51 at 50) and SD of z falls only
+  to 2.2: binding alone does not give honest intervals at any realistic
+  count. With congression everything is worse and the intervals stay about
+  1.8 times too narrow even at the true k and m.
+
diff --git a/planning/studies/joint-fit-sim/analyze.py b/planning/studies/joint-fit-sim/analyze.py
new file mode 100644
index 00000000..c39ba93b
--- /dev/null
+++ b/planning/studies/joint-fit-sim/analyze.py
@@ -0,0 +1,138 @@
+"""
+Score joint stage 1 fits against the simulation's truth (README).
+
+    python analyze.py RUN_DIR:SIM_DIR [RUN_DIR:SIM_DIR ...] --out out
+
+For each fit: k, m, the warps, log s, the binding offsets and their scale
+against the truth files, and theta (in binding units, from the fitted
+curves at the growth concentrations) against the truth Hill curves for
+wt, the spikes and the singles: RMSE of the median and 95% coverage.
+Writes out/params.csv and out/theta.csv and prints both.
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+
+from tfscreen.tfmodel import two_stage as ts
+
+Q_LO, Q_MID, Q_HI = "q0.025", "q0.5", "q0.975"
+
+
+def _read(path):
+    return pd.read_csv(path) if os.path.exists(path) else None
+
+
+def hill(p, conc):
+    c = np.where(np.asarray(conc) == 0, 1e-20, conc)
+    occ = 1 / (1 + np.exp(-p["hill_n"] * (np.log(c) - p["log_hill_K"])))
+    return p["theta_low"] + (p["theta_high"] - p["theta_low"]) * occ
+
+
+def param_rows(run, sim, name):
+    rows = []
+
+    def add(what, key, est, truth):
+        if est is None:
+            return
+        for _, r in est.iterrows():
+            t = truth(r) if callable(truth) else truth
+            rows.append(dict(run=name, parameter=what, key=key(r), truth=t,
+                             q0_5=r[Q_MID], lo=r[Q_LO], hi=r[Q_HI],
+                             covered=bool(r[Q_LO] <= t <= r[Q_HI])))
+
+    gp = pd.read_csv(os.path.join(sim, "tfs_sim_growth_parameters.csv")).set_index("condition_rep")
+    for p, col in (("growth_k", "growth_k"), ("growth_m", "growth_m"),
+                   ("warp_log_a", "warp_log_a"), ("warp_x0", "warp_x0")):
+        if col not in gp.columns:
+            continue
+        add(p, lambda r: r["condition_rep"],
+            _read(os.path.join(run, f"tfs_params_{p}.csv")),
+            lambda r, col=col: gp.loc[r["condition_rep"], col])
+    scale = _read(os.path.join(sim, "tfs_sim_growth_titrant_scale.csv"))
+    log_s = 0.0 if scale is None else float(scale["ref"].iloc[0])
+    add("log_s", lambda r: "global",
+        _read(os.path.join(run, "tfs_params_growth_log_conc_shift.csv")), log_s)
+    offs = _read(os.path.join(sim, "tfs_sim_binding_offsets.csv"))
+    off_truth = {} if offs is None else dict(zip(offs["genotype"], offs["offset"]))
+    add("binding_offset", lambda r: r["genotype"],
+        _read(os.path.join(run, "tfs_params_offset.csv")),
+        lambda r: off_truth.get(r["genotype"], 0.0))
+    import yaml
+    with open(os.path.join(sim, "simulate_config.yaml")) as fh:
+        tau = float((yaml.safe_load(fh).get("binding_data") or {})
+                    .get("genotype_offset_sd", 0.0))
+    add("binding_offset_scale", lambda r: "global",
+        _read(os.path.join(run, "tfs_params_offset_scale.csv")), tau)
+    return rows
+
+
+def theta_rows(run, sim, name):
+    pred = pd.read_csv(os.path.join(run, "tfs_pred_theta.csv"))
+    truth = pd.read_csv(os.path.join(sim, "tfs_sim_parameters.csv"))
+    truth = truth.assign(genotype=truth["genotype"].astype(str)).set_index("genotype")
+    pred["genotype"] = pred["genotype"].astype(str)
+    pred = pred[pred["genotype"].isin(truth.index)].copy()
+    t = truth.loc[pred["genotype"]]
+    pred["truth"] = hill(t[["theta_low", "theta_high", "log_hill_K", "hill_n"]]
+                         .reset_index(drop=True),
+                         pred["titrant_conc"].to_numpy()).to_numpy()
+    lib = pd.read_csv(os.path.join(run, "tfs_configure_library.csv"))
+    spiked = set(lib.loc[lib["in_spiked_origin"].astype(bool), "genotype"].astype(str))
+    bfile = os.path.join(run, "binding.csv")
+    bound = (set(pd.read_csv(bfile)["genotype"].astype(str)) - spiked
+             if os.path.exists(bfile) else set())
+    pred["group"] = np.where(pred["genotype"] == "wt", "wt",
+                             np.where(pred["genotype"].isin(spiked), "spike",
+                                      np.where(pred["genotype"].isin(bound),
+                                               "single, binding",
+                                               np.where(pred["genotype"].map(ts.is_single),
+                                                        "single", "other"))))
+    pred["err"] = pred[Q_MID] - pred["truth"]
+    pred["covered"] = (pred[Q_LO] <= pred["truth"]) & (pred["truth"] <= pred[Q_HI])
+    pred["width95"] = pred[Q_HI] - pred[Q_LO]
+    pred["z"] = pred["err"] / ((pred["q0.841"] - pred["q0.159"]) / 2)
+
+    def slope(d):
+        return float(np.polyfit(d["truth"], d[Q_MID], 1)[0]) if len(d) > 2 else np.nan
+
+    out = (pred.groupby("group")
+           .agg(n_genotypes=("genotype", "nunique"),
+                rmse=("err", lambda e: float(np.sqrt(np.mean(e ** 2)))),
+                bias=("err", "mean"),
+                coverage95=("covered", "mean"),
+                median_width95=("width95", "median"),
+                sd_z=("z", "std"))
+           .reset_index())
+    out["slope"] = out["group"].map(
+        {g: slope(d) for g, d in pred.groupby("group")})
+    out.insert(0, "run", name)
+    return out
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("pairs", nargs="+", help="RUN_DIR:SIM_DIR")
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+    params, thetas = [], []
+    for pair in args.pairs:
+        run, sim = pair.split(":")
+        name = os.path.basename(os.path.normpath(run))
+        params.extend(param_rows(run, sim, name))
+        thetas.append(theta_rows(run, sim, name))
+    p = pd.DataFrame(params)
+    th = pd.concat(thetas, ignore_index=True)
+    p.to_csv(os.path.join(args.out, "params.csv"), index=False)
+    th.to_csv(os.path.join(args.out, "theta.csv"), index=False)
+    pd.set_option("display.width", 200)
+    print(p.to_string(index=False, float_format="%+.4f"))
+    print()
+    print(th.to_string(index=False, float_format="%.3f"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/joint-fit-sim/attribute.py b/planning/studies/joint-fit-sim/attribute.py
new file mode 100644
index 00000000..8e9c04b5
--- /dev/null
+++ b/planning/studies/joint-fit-sim/attribute.py
@@ -0,0 +1,140 @@
+"""
+Where two stage 1 fits differ in log density (README, run 4): the count
+log likelihood of every growth row, the binding log likelihood of every
+binding genotype and the log prior of every latent site, at each fit's
+MAP; their differences summed by marker, genotype class, IPTG and tube.
+
+    python attribute.py runs/fit_null_s1_plain runs/fit_null_s1_plain_from_plain_pinkm --out out/attr
+
+Each folder is a stage1.py fit downloaded under runs/ (its config,
+priors, guesses, library, tfs_growth.csv and MAP params), next to the
+simulation folders its config names. The difference is the second minus
+the first: negative where the second fit explains the data worse.
+"""
+
+import argparse
+import os
+import warnings
+
+warnings.filterwarnings("ignore")
+
+import jax  # noqa: E402
+import jax.numpy as jnp  # noqa: E402
+import numpy as np  # noqa: E402
+import pandas as pd  # noqa: E402
+from numpyro import handlers  # noqa: E402
+
+from tfscreen.tfmodel.configuration_io import read_configuration  # noqa: E402
+from tfscreen.tfmodel.inference.run_inference import RunInference  # noqa: E402
+
+CLUSTER = "/gpfs/projects/harmslab/harms/studies/joint-fit-sim/"
+IDX = ["replicate_idx", "time_idx", "condition_pre_idx", "condition_sel_idx",
+       "titrant_name_idx", "titrant_conc_idx", "genotype_idx"]
+KEY = ["genotype", "replicate", "condition_sel", "titrant_conc", "t_sel"]
+
+
+def local_config(fit):
+    """The fit's config with cluster paths pointed at the local runs/ copy."""
+    runs = os.path.dirname(os.path.abspath(fit))
+    text = open(os.path.join(fit, "tfs_configure_config.yaml")).read()
+    out = os.path.join(fit, "config_local.yaml")
+    open(out, "w").write(text.replace(CLUSTER, runs + "/"))
+    return out
+
+
+def score(fit):
+    """Per-row growth and per-genotype binding log likelihood, per-site prior."""
+    cwd = os.getcwd()
+    cfg = local_config(fit)
+    os.chdir(fit)
+    try:
+        orch, _ = read_configuration(os.path.basename(cfg))
+        ri = RunInference(orch, 0)
+        with np.load("tfs_fit_model_params.npz") as p:
+            values = {k[:-len("_auto_loc")]: jnp.asarray(p[k]) for k in p.files
+                      if k.endswith("_auto_loc")}
+        G = orch.data.num_genotype
+        batch = ri._unscaled_batch(jax.device_put(orch.data), jnp.arange(G))
+        model = handlers.substitute(orch.jax_model, data=values)
+        tr = handlers.trace(handlers.seed(model, 0)).get_trace(
+            data=batch, priors=orch.priors)
+        site = tr["growth_obs"]
+        lp = np.asarray(site["fn"].log_prob(site["value"]))
+        df = orch.growth_df
+        rows = df[KEY].copy()
+        rows["ll"] = lp[tuple(df[c].to_numpy() for c in IDX)]
+        good = np.asarray(orch.data.growth.good_mask)
+        growth_total = float(lp[good].sum())
+
+        b = tr["binding_obs"]
+        blp = np.where(np.asarray(batch.binding.good_mask),
+                       np.asarray(b["fn"].log_prob(b["value"])), 0.0)
+        names = list(orch.binding_tm.tensor_dim_names)
+        labels = orch.binding_tm.tensor_dim_labels[names.index("genotype")]
+        binding = pd.Series(blp.sum(axis=(0, 1)), index=[str(g) for g in labels])
+
+        priors = {n: float(s["fn"].log_prob(s["value"]).sum())
+                  for n, s in tr.items()
+                  if s["type"] == "sample" and not s["is_observed"]}
+        return rows, binding, pd.Series(priors), growth_total
+    finally:
+        os.chdir(cwd)
+
+
+def genotype_class(g, spikes, reads):
+    if g == "wt":
+        return "wt"
+    if g in spikes:
+        return "spike"
+    r = reads.get(g, 0)
+    for edge, name in ((1e4, "single <1e4"), (1e5, "single 1e4-1e5"),
+                       (1e6, "single 1e5-1e6")):
+        if r < edge:
+            return name
+    return "single >=1e6"
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("first")
+    ap.add_argument("second")
+    ap.add_argument("--out", default="out/attr")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+    a, ba, pa, ga = score(args.first)
+    b, bb, pb, gb = score(args.second)
+    m = a.merge(b, on=KEY, suffixes=("_1", "_2"))
+    m["d"] = m["ll_2"] - m["ll_1"]
+    lib = pd.read_csv(os.path.join(args.first, "tfs_configure_library.csv"))
+    spikes = set(lib.loc[lib["in_spiked_origin"].astype(bool), "genotype"].astype(str))
+    reads = pd.read_csv(os.path.join(args.first, "tfs_growth.csv"),
+                        usecols=["genotype", "counts"]).groupby("genotype")["counts"].sum()
+    m["class"] = m["genotype"].astype(str).map(lambda g: genotype_class(g, spikes, reads))
+    m.to_csv(os.path.join(args.out, "per_row.csv"), index=False)
+
+    db = (bb - ba)
+    dp = (pb - pa).dropna()
+    print(f"growth log likelihood: second - first {gb - ga:+.1f} ({len(m)} rows)")
+    print(f"binding log likelihood: second - first {db.sum():+.1f}")
+    print(f"log prior: second - first {dp.sum():+.1f}")
+    print(f"total: {gb - ga + db.sum() + dp.sum():+.1f}")
+    print("\n== binding, second - first, by genotype")
+    print(db.sort_values().round(1).to_string())
+    print("\n== log prior, largest sites")
+    print(dp[dp.abs().sort_values(ascending=False).index].head(10).round(1).to_string())
+    for by in (["condition_sel"], ["condition_sel", "class"],
+               ["condition_sel", "titrant_conc"],
+               ["condition_sel", "replicate", "t_sel"]):
+        t = m.groupby(by, observed=True)["d"].agg(["sum", "size"]).round(1)
+        t = t[t["size"] > 0]
+        print(f"\n== growth, second - first, by {', '.join(by)}")
+        print(t.to_string())
+    g = m.groupby("genotype")["d"].sum().sort_values()
+    print("\n== genotypes losing most under the second fit")
+    print(g.head(10).round(1).to_string())
+    print("\n== genotypes gaining most under the second fit")
+    print(g.tail(5).round(1).to_string())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/joint-fit-sim/binding_scaling.py b/planning/studies/joint-fit-sim/binding_scaling.py
new file mode 100644
index 00000000..2be1a0d2
--- /dev/null
+++ b/planning/studies/joint-fit-sim/binding_scaling.py
@@ -0,0 +1,119 @@
+"""
+Theta of growth-only singles against the number of binding singles
+(README, run 10).
+
+    python binding_scaling.py runs --out out
+
+Scores every stage 1 fit in RUNS_DIR named fit_nocong_s1_plain_single[_libN[_bK]]
+or fit_null_s1_plain[_libN[_bK]] (analyze.theta_rows; growth-only singles),
+writes out/binding_scaling.csv (one row per fit: sim, n, pick, m_steep,
+rmse, slope, cov95, width, sd_z) and draws RMSE, 95% coverage and SD of z
+against n, each pick a point and the mean a line, with the fit at the true
+k and m (run 5) as the floor and the curve floor + a / sqrt(n + 2).
+"""
+
+import argparse
+import datetime
+import os
+
+import numpy as np
+import pandas as pd
+
+import analyze
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+
+plt.rc("pdf", fonttype=42)
+plt.rc("font", size=14)
+plt.rc("axes", titlesize=14, labelsize=16)
+plt.rc("legend", fontsize=12)
+
+FLOOR = {"nocong": dict(rmse=0.010, cov95=0.910, sd_z=0.909),
+         "null": dict(rmse=0.020, cov95=0.699, sd_z=1.845)}
+COLOR = {"nocong": "#0072B2", "null": "#D55E00"}
+LABEL = {"nocong": "no congression", "null": "congression"}
+
+
+BASES = {"nocong": "fit_nocong_s1_plain_single", "null": "fit_null_s1_plain"}
+
+
+def parse(name, base):
+    """(n, pick) for base, base_libN or base_libN_bK; None otherwise."""
+    rest = name[len(base):]
+    if rest == "":
+        return 0, 1
+    if not rest.startswith("_lib"):
+        return None
+    parts = rest[4:].split("_b")
+    if not parts[0].isdigit() or (len(parts) > 1 and not parts[1].isdigit()):
+        return None
+    return int(parts[0]), (int(parts[1]) if len(parts) > 1 else 1)
+
+
+def build_table(runs_dir):
+    rows = []
+    for sim, base in BASES.items():
+        sim_dir = os.path.join(runs_dir, f"sim_{sim}_s1")
+        truth = pd.read_csv(os.path.join(sim_dir, "tfs_sim_growth_parameters.csv"))
+        truth = truth.set_index("condition_rep")["growth_m"]
+        for d in sorted(os.listdir(runs_dir)):
+            run = os.path.join(runs_dir, d)
+            got = parse(d, base) if d.startswith(base) else None
+            if got is None or not os.path.exists(os.path.join(run, "tfs_pred_theta.csv")):
+                continue
+            th = analyze.theta_rows(run, sim_dir, d)
+            s = th[th["group"] == "single"].iloc[0]
+            m = pd.read_csv(os.path.join(run, "tfs_params_growth_m.csv"))
+            m = m.set_index("condition_rep")["q0.5"]
+            steep = np.mean([abs(m[c] / truth[c] - 1) for c in ("kanR+kan", "pheS+4CP")])
+            rows.append(dict(sim=sim, n=got[0], pick=got[1], m_steep=100 * steep,
+                             rmse=s["rmse"], slope=s["slope"], cov95=s["coverage95"],
+                             width=s["median_width95"], sd_z=s["sd_z"]))
+    return pd.DataFrame(rows).sort_values(["sim", "n", "pick"])
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("runs_dir")
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+    r = build_table(args.runs_dir)
+    r.to_csv(os.path.join(args.out, "binding_scaling.csv"), index=False)
+    print(r.round(3).to_string(index=False))
+    nocong = r[r["sim"] == "nocong"].groupby("n")["rmse"].mean()
+    a = float(np.mean((nocong - FLOOR["nocong"]["rmse"]) * np.sqrt(nocong.index + 2)))
+    print(f"no congression: excess RMSE = {a:.4f} / sqrt(n + 2)")
+    fig, axes = plt.subplots(1, 3, figsize=(15, 4.5))
+    for ax, col, name in zip(axes, ["rmse", "cov95", "sd_z"],
+                             ["theta RMSE", "95% coverage", "SD of z"]):
+        for sim, d in r.groupby("sim"):
+            x = d["n"] + 1
+            ax.plot(x, d[col], "o", color=COLOR[sim], alpha=0.5, ms=5)
+            mean = d.groupby("n")[col].mean()
+            ax.plot(mean.index + 1, mean.values, "-", color=COLOR[sim], label=LABEL[sim])
+            ax.axhline(FLOOR[sim][col], color=COLOR[sim], ls=":", lw=1)
+        if col == "rmse":
+            n = np.linspace(0, 60, 200)
+            ax.plot(n + 1, FLOOR["nocong"]["rmse"] + a / np.sqrt(n + 2), "--",
+                    color="0.4", lw=1, label=f"{FLOOR['nocong']['rmse']} + {a:.3f}/sqrt(n+2)")
+        if col == "cov95":
+            ax.axhline(0.95, color="0.6", lw=0.8)
+        if col == "sd_z":
+            ax.axhline(1.0, color="0.6", lw=0.8)
+        ax.set_xscale("log")
+        ax.set_xticks([1, 3, 6, 11, 21, 51])
+        ax.set_xticklabels(["0", "2", "5", "10", "20", "50"])
+        ax.set_xlabel("binding singles")
+        ax.set_ylabel(name)
+    axes[0].legend(fontsize=10)
+    fig.suptitle("growth-only singles; dotted: fit at the true k and m", fontsize=13)
+    fig.tight_layout()
+    out = os.path.join(args.out, f"{datetime.date.today().isoformat()}_binding-scaling.pdf")
+    fig.savefig(out)
+    print(f"wrote {out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/joint-fit-sim/make_sim.py b/planning/studies/joint-fit-sim/make_sim.py
new file mode 100644
index 00000000..216b1e1f
--- /dev/null
+++ b/planning/studies/joint-fit-sim/make_sim.py
@@ -0,0 +1,129 @@
+"""
+Simulate configs for the joint-fit simulation test (README).
+
+    python make_sim.py BASE_CONFIG OUT_DIR --arm joint|null|nocong [--seed 1]
+
+BASE_CONFIG is a full-size realistic simulate config
+(planning/studies/full-size-sim/make_sim_config.py; on the cluster
+full-sized-sims-3/sim_realistic_s2/simulate_config.yaml). Writes
+OUT_DIR/simulate_config.yaml and OUT_DIR/spike_hill.csv: the base config
+with its relative paths made absolute, plus
+
+- binding for every spike at the lab's 2 uM IPTG grid, from Hill curves
+  in spike_hill.csv (wt at the simulator's reference; the others drawn
+  here, seeded), noise 0.04 (the scatter within one real curve);
+- arm joint: kanR+kan's growth a switch-like function of occupancy
+  (warp a = 8, x0 = 0.5), pheS+4CP's graded (a = 1.5, x0 = 0.4), growth
+  seeing IPTG at s = 0.5 times nominal, and one binding offset per spike
+  (SD 0.1);
+- arm null: none of those (linear growth, s = 1, no offsets);
+- arm nocong: null without congression (transformation_poisson_lambda 0),
+  the control for run 4's attribution, which put the steep m in pheS+4CP's
+  slow-growing, congression-dominated rows.
+
+The spikes' curves come from a params file so that their Hill truth is
+the curve simulated (the stratified path pastes pool curves over the
+spikes without patching tfs_sim_parameters.csv).
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+import yaml
+
+from tfscreen.genetics import library_composition_table
+
+# The lab's 2 uM binding grid (mM), from the 2026-10-15 binding table.
+BINDING_IPTG = [0.0, 1e-6, 6e-6, 1e-5, 6e-5, 1e-4, 6e-4, 1e-3, 6e-3, 0.01,
+                0.06, 0.1, 0.6, 1.0, 3.0, 5.0]
+BINDING_NOISE = 0.04
+
+ARMS = {
+    "joint": dict(warp={"kanR+kan": (8.0, 0.5), "pheS+4CP": (1.5, 0.4)},
+                  growth_titrant_scale=0.5, genotype_offset_sd=0.1),
+    "null": dict(warp={}, growth_titrant_scale=None, genotype_offset_sd=0.0),
+    "nocong": dict(warp={}, growth_titrant_scale=None, genotype_offset_sd=0.0,
+                   transformation_poisson_lambda=0.0),
+}
+
+# The simulator's hill_geno wt reference (get_sim_hyperparameters).
+WT = dict(theta_low=0.99, theta_high=0.01, log_hill_K=-4.1, hill_n=2.0)
+
+
+def absolute_paths(cf, base_dir):
+    for keys in (("design",), ("od600", "calibration"), ("library_genotypes",),
+                 ("thermo_data",)):
+        d = cf
+        for k in keys[:-1]:
+            d = d.get(k) or {}
+        v = d.get(keys[-1]) if isinstance(d, dict) else None
+        if isinstance(v, str) and not os.path.isabs(v):
+            d[keys[-1]] = os.path.abspath(os.path.join(base_dir, v))
+    return cf
+
+
+def spike_curves(spikes, seed):
+    """wt at the reference; each other spike a random repressor-like curve."""
+    rng = np.random.default_rng([seed, 2026])
+    rows = []
+    for g in spikes:
+        if g == "wt":
+            rows.append(dict(genotype=g, **WT))
+            continue
+        low = rng.uniform(0.3, 0.99)
+        rows.append(dict(genotype=g, theta_low=low,
+                         theta_high=rng.uniform(0.01, low),
+                         log_hill_K=rng.normal(-4.1, 1.0),
+                         hill_n=rng.uniform(1.0, 3.0)))
+    return pd.DataFrame(rows)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("base_config")
+    ap.add_argument("out_dir")
+    ap.add_argument("--arm", choices=sorted(ARMS), required=True)
+    ap.add_argument("--seed", type=int, default=1)
+    args = ap.parse_args()
+
+    with open(args.base_config) as fh:
+        cf = yaml.safe_load(fh)
+    cf = absolute_paths(cf, os.path.dirname(os.path.abspath(args.base_config)))
+    cf["seed"] = args.seed
+    os.makedirs(args.out_dir, exist_ok=True)
+
+    lib = library_composition_table(args.base_config)
+    spikes = sorted(set(lib.loc[lib["in_spiked_origin"], "genotype"].astype(str)))
+    hill_path = os.path.abspath(os.path.join(args.out_dir, "spike_hill.csv"))
+    spike_curves(spikes, args.seed).to_csv(hill_path, index=False)
+
+    arm = ARMS[args.arm]
+    for cond, (a, x0) in arm["warp"].items():
+        if cond not in cf["growth"]:
+            raise ValueError(f"{cond} is not a growth condition of the base config")
+        cf["growth"][cond]["warp_a"] = a
+        cf["growth"][cond]["warp_x0"] = x0
+    if "transformation_poisson_lambda" in arm:
+        cf["transformation_poisson_lambda"] = arm["transformation_poisson_lambda"]
+    if arm["growth_titrant_scale"] is not None:
+        cf["growth_titrant_scale"] = arm["growth_titrant_scale"]
+    cf["binding_data"] = {
+        "titrant_name": "iptg",
+        "titrant_conc": BINDING_IPTG,
+        "noise": BINDING_NOISE,
+        "genotype_offset_sd": arm["genotype_offset_sd"],
+        "spiked_binding": {"choose_by": hill_path},
+    }
+
+    out = os.path.join(args.out_dir, "simulate_config.yaml")
+    with open(out, "w") as fh:
+        fh.write(f"# joint-fit simulation test, arm {args.arm}, seed {args.seed} "
+                 f"(planning/studies/joint-fit-sim)\n")
+        yaml.safe_dump(cf, fh, sort_keys=False)
+    print(f"wrote {out} and {hill_path} ({len(spikes)} spikes: {', '.join(spikes)})")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/joint-fit-sim/out_null/params.csv b/planning/studies/joint-fit-sim/out_null/params.csv
new file mode 100644
index 00000000..69ef6779
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_null/params.csv
@@ -0,0 +1,28 @@
+run,parameter,key,truth,q0_5,lo,hi,covered
+fit_null_s1_plain,growth_k,kanR+kan,0.015042,0.019639313,0.01776531,0.021214087,False
+fit_null_s1_plain,growth_k,kanR-kan,0.023923,0.029058484,0.026518818,0.03145352,False
+fit_null_s1_plain,growth_k,pheS+4CP,0.003107,0.0037996653,0.0013952442,0.0064904196,True
+fit_null_s1_plain,growth_k,pheS-4CP,0.022042,0.02379319,0.020922774,0.027036328,True
+fit_null_s1_plain,growth_m,kanR+kan,-0.014122,-0.017343588,-0.01765266,-0.016980322,False
+fit_null_s1_plain,growth_m,kanR-kan,0.0,2.2137636e-05,-2.0148449e-05,6.657838e-05,True
+fit_null_s1_plain,growth_m,pheS+4CP,0.014379,0.017263267,0.016902821,0.017571792,False
+fit_null_s1_plain,growth_m,pheS-4CP,0.0,2.6166083e-05,-5.8999135e-06,5.760233e-05,True
+fit_null_s1_full,growth_k,kanR+kan,0.015042,0.018222786,0.016564487,0.019878788,False
+fit_null_s1_full,growth_k,kanR-kan,0.023923,0.02736361,0.024703149,0.0301056,False
+fit_null_s1_full,growth_k,pheS+4CP,0.003107,0.003581851,0.0014208781,0.005962828,True
+fit_null_s1_full,growth_k,pheS-4CP,0.022042,0.023629753,0.021096645,0.026247969,True
+fit_null_s1_full,growth_m,kanR+kan,-0.014122,-0.016731359,-0.016920654,-0.016567638,False
+fit_null_s1_full,growth_m,kanR-kan,0.0,1.9714233e-05,-8.185913e-05,0.000114059236,True
+fit_null_s1_full,growth_m,pheS+4CP,0.014379,0.016879555,0.016714346,0.01707239,False
+fit_null_s1_full,growth_m,pheS-4CP,0.0,0.00011692887,2.695735e-05,0.0002005057,False
+fit_null_s1_full,log_s,global,0.0,-1.3931419,-1.7649902,-1.0404953,False
+fit_null_s1_full,binding_offset,wt,0.0,0.024139058,-0.0062584644,0.053019974,True
+fit_null_s1_full,binding_offset,M42I,0.0,-0.0752502,-0.12573017,-0.029268635,False
+fit_null_s1_full,binding_offset,H74A,0.0,-0.060534805,-0.10915214,-0.016090112,False
+fit_null_s1_full,binding_offset,K84L,0.0,-0.3232484,-0.37575576,-0.2427785,False
+fit_null_s1_full,binding_offset,D88A,0.0,-0.1492683,-0.19367711,-0.09986605,False
+fit_null_s1_full,binding_offset,M42I/H74A,0.0,-0.20263562,-0.25535667,-0.14406282,False
+fit_null_s1_full,binding_offset,M42I/K84L,0.0,-0.38100737,-0.44027537,-0.303622,False
+fit_null_s1_full,binding_offset,H74A/K84L,0.0,-0.049540114,-0.11304207,0.023334747,True
+fit_null_s1_full,binding_offset,M42I/H74A/K84L,0.0,-0.05620514,-0.10274805,-0.011132386,False
+fit_null_s1_full,binding_offset_scale,global,0.0,0.34289342,0.19601426,0.59310955,False
diff --git a/planning/studies/joint-fit-sim/out_null/theta.csv b/planning/studies/joint-fit-sim/out_null/theta.csv
new file mode 100644
index 00000000..09efc92a
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_null/theta.csv
@@ -0,0 +1,7 @@
+run,group,n_genotypes,rmse,bias,coverage95
+fit_null_s1_plain,single,946,0.056310164351737704,-0.002109800618809592,0.102669133192389
+fit_null_s1_plain,spike,8,0.03564173580072257,0.005594124974061381,0.421875
+fit_null_s1_plain,wt,1,0.039885856632109154,-0.008674691594757433,0.0
+fit_null_s1_full,single,946,0.29316910442072874,0.09677065014736853,0.038319238900634246
+fit_null_s1_full,spike,8,0.225658662648604,0.15255901831150648,0.09375
+fit_null_s1_full,wt,1,0.15129934306821724,-0.03213419625712716,0.125
diff --git a/planning/studies/joint-fit-sim/out_run3/params.csv b/planning/studies/joint-fit-sim/out_run3/params.csv
new file mode 100644
index 00000000..a25aafa1
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run3/params.csv
@@ -0,0 +1,43 @@
+run,parameter,key,truth,q0_5,lo,hi,covered
+fit_null_s1_full_from_plain,growth_k,kanR+kan,0.015042,0.019716062,0.017876662,0.021552911,False
+fit_null_s1_full_from_plain,growth_k,kanR-kan,0.023923,0.028928714,0.026172755,0.031623535,False
+fit_null_s1_full_from_plain,growth_k,pheS+4CP,0.003107,0.003445853,0.0020440621,0.0048898384,True
+fit_null_s1_full_from_plain,growth_k,pheS-4CP,0.022042,0.02417104,0.021403147,0.026987113,True
+fit_null_s1_full_from_plain,growth_m,kanR+kan,-0.014122,-0.01760393,-0.01799401,-0.017248493,False
+fit_null_s1_full_from_plain,growth_m,kanR-kan,0.0,3.3065917e-05,-1.9288647e-05,7.71631e-05,True
+fit_null_s1_full_from_plain,growth_m,pheS+4CP,0.014379,0.018049337,0.017655252,0.018451113,False
+fit_null_s1_full_from_plain,growth_m,pheS-4CP,0.0,3.2067503e-05,-2.4998012e-06,6.6250024e-05,True
+fit_null_s1_full_from_plain,log_s,global,0.0,-0.051260404,-0.21699598,0.109757304,True
+fit_null_s1_full_from_plain,binding_offset,wt,0.0,-0.00874895,-0.049160227,0.030390788,True
+fit_null_s1_full_from_plain,binding_offset,M42I,0.0,0.0037606945,-0.08931366,0.07740348,True
+fit_null_s1_full_from_plain,binding_offset,H74A,0.0,0.0050335433,-0.07902607,0.061427925,True
+fit_null_s1_full_from_plain,binding_offset,K84L,0.0,-0.07332194,-0.13243912,0.053684596,True
+fit_null_s1_full_from_plain,binding_offset,D88A,0.0,-0.01866046,-0.107220404,0.06887053,True
+fit_null_s1_full_from_plain,binding_offset,M42I/H74A,0.0,-0.027172983,-0.115051314,0.05755074,True
+fit_null_s1_full_from_plain,binding_offset,M42I/K84L,0.0,-0.08818379,-0.13815251,0.029590687,True
+fit_null_s1_full_from_plain,binding_offset,H74A/K84L,0.0,-0.0089068655,-0.07606615,0.060055368,True
+fit_null_s1_full_from_plain,binding_offset,M42I/H74A/K84L,0.0,0.0125195645,-0.061470274,0.059541415,True
+fit_null_s1_full_from_plain,binding_offset_scale,global,0.0,0.16763236,0.052113824,0.5166569,False
+fit_joint_s1_full_from_plain,growth_k,kanR+kan,0.015042,0.017957725,0.016482297,0.019431103,False
+fit_joint_s1_full_from_plain,growth_k,kanR-kan,0.023923,0.02928865,0.027100608,0.031431463,False
+fit_joint_s1_full_from_plain,growth_k,pheS+4CP,0.003107,0.0051531717,0.0032590851,0.0072666015,False
+fit_joint_s1_full_from_plain,growth_k,pheS-4CP,0.022042,0.027519124,0.025027083,0.03014029,False
+fit_joint_s1_full_from_plain,growth_m,kanR+kan,-0.014122,-0.014448609,-0.014514818,-0.01439054,False
+fit_joint_s1_full_from_plain,growth_m,kanR-kan,0.0,-1.0012214e-05,-5.552533e-05,2.6604968e-05,True
+fit_joint_s1_full_from_plain,growth_m,pheS+4CP,0.014379,0.01656733,0.016284943,0.016876321,False
+fit_joint_s1_full_from_plain,growth_m,pheS-4CP,0.0,1.2326492e-05,-1.8744171e-05,4.392883e-05,True
+fit_joint_s1_full_from_plain,warp_log_a,kanR+kan,2.0794415416798357,2.2021394,2.1576385,2.248794,False
+fit_joint_s1_full_from_plain,warp_log_a,pheS+4CP,0.4054651081081644,-0.08407947,-1.5147213,1.4601364,True
+fit_joint_s1_full_from_plain,warp_x0,kanR+kan,0.5,0.53383434,0.49808156,0.5636751,True
+fit_joint_s1_full_from_plain,warp_x0,pheS+4CP,0.4,0.58642846,-0.23549522,1.322586,True
+fit_joint_s1_full_from_plain,log_s,global,-0.6931471805599453,-0.79566884,-0.9376141,-0.6642408,True
+fit_joint_s1_full_from_plain,binding_offset,wt,-0.1056078950314186,-0.10702185,-0.1374449,-0.042688668,True
+fit_joint_s1_full_from_plain,binding_offset,M42I,0.0282415842244401,0.009926669,-0.043777168,0.05783087,True
+fit_joint_s1_full_from_plain,binding_offset,H74A,-0.0420857002548871,-0.043082345,-0.081066534,0.011327274,True
+fit_joint_s1_full_from_plain,binding_offset,K84L,0.0425148735331908,-0.017117418,-0.0507347,0.02963588,False
+fit_joint_s1_full_from_plain,binding_offset,D88A,0.0511536419917619,0.037221782,-0.022255406,0.081511445,True
+fit_joint_s1_full_from_plain,binding_offset,M42I/H74A,-0.1159296726932429,-0.13883291,-0.18324412,-0.043825623,True
+fit_joint_s1_full_from_plain,binding_offset,M42I/K84L,-0.0590434943289043,-0.11167629,-0.14532866,-0.03547845,True
+fit_joint_s1_full_from_plain,binding_offset,H74A/K84L,-0.0228535367478158,-0.023133399,-0.06173983,0.037326477,True
+fit_joint_s1_full_from_plain,binding_offset,M42I/H74A/K84L,0.0833342596669661,0.07477276,0.014866954,0.10796515,True
+fit_joint_s1_full_from_plain,binding_offset_scale,global,0.1,0.2238241,0.0949375,0.49544805,True
diff --git a/planning/studies/joint-fit-sim/out_run3/theta.csv b/planning/studies/joint-fit-sim/out_run3/theta.csv
new file mode 100644
index 00000000..cc6bd50c
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run3/theta.csv
@@ -0,0 +1,7 @@
+run,group,n_genotypes,rmse,bias,coverage95
+fit_null_s1_full_from_plain,single,946,0.07655127606143827,0.007179007491373085,0.13860993657505286
+fit_null_s1_full_from_plain,spike,8,0.05580013936709474,0.02425916185497325,0.640625
+fit_null_s1_full_from_plain,wt,1,0.05699302949382643,-0.0024569782542474654,0.125
+fit_joint_s1_full_from_plain,single,946,0.07012804425293707,0.004091526192144923,0.16596194503171247
+fit_joint_s1_full_from_plain,spike,8,0.038620122460039444,0.020425483904325994,0.484375
+fit_joint_s1_full_from_plain,wt,1,0.05676126023676298,-0.0053830364664834325,0.25
diff --git a/planning/studies/joint-fit-sim/out_run4/params.csv b/planning/studies/joint-fit-sim/out_run4/params.csv
new file mode 100644
index 00000000..03318771
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run4/params.csv
@@ -0,0 +1,17 @@
+run,parameter,key,truth,q0_5,lo,hi,covered
+fit_null_s1_plain_from_plain_pinkm,growth_k,kanR+kan,0.015042,0.015042,0.015042,0.015042,True
+fit_null_s1_plain_from_plain_pinkm,growth_k,kanR-kan,0.023923,0.023923,0.023923,0.023923,True
+fit_null_s1_plain_from_plain_pinkm,growth_k,pheS+4CP,0.003107,0.003107,0.003107,0.003107,True
+fit_null_s1_plain_from_plain_pinkm,growth_k,pheS-4CP,0.022042,0.022042,0.022042,0.022042,True
+fit_null_s1_plain_from_plain_pinkm,growth_m,kanR+kan,-0.014122,-0.014122,-0.014122,-0.014122,True
+fit_null_s1_plain_from_plain_pinkm,growth_m,kanR-kan,0.0,0.0,0.0,0.0,True
+fit_null_s1_plain_from_plain_pinkm,growth_m,pheS+4CP,0.014379,0.014379,0.014379,0.014379,True
+fit_null_s1_plain_from_plain_pinkm,growth_m,pheS-4CP,0.0,0.0,0.0,0.0,True
+fit_null_s1_plain,growth_k,kanR+kan,0.015042,0.019639313,0.01776531,0.021214087,False
+fit_null_s1_plain,growth_k,kanR-kan,0.023923,0.029058484,0.026518818,0.03145352,False
+fit_null_s1_plain,growth_k,pheS+4CP,0.003107,0.0037996653,0.0013952442,0.0064904196,True
+fit_null_s1_plain,growth_k,pheS-4CP,0.022042,0.02379319,0.020922774,0.027036328,True
+fit_null_s1_plain,growth_m,kanR+kan,-0.014122,-0.017343588,-0.01765266,-0.016980322,False
+fit_null_s1_plain,growth_m,kanR-kan,0.0,2.2137636e-05,-2.0148449e-05,6.657838e-05,True
+fit_null_s1_plain,growth_m,pheS+4CP,0.014379,0.017263267,0.016902821,0.017571792,False
+fit_null_s1_plain,growth_m,pheS-4CP,0.0,2.6166083e-05,-5.8999135e-06,5.760233e-05,True
diff --git a/planning/studies/joint-fit-sim/out_run4/theta.csv b/planning/studies/joint-fit-sim/out_run4/theta.csv
new file mode 100644
index 00000000..14c0ebc9
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run4/theta.csv
@@ -0,0 +1,7 @@
+run,group,n_genotypes,rmse,bias,coverage95
+fit_null_s1_plain_from_plain_pinkm,single,946,0.02023168631524801,0.006409369313332721,0.6993921775898521
+fit_null_s1_plain_from_plain_pinkm,spike,8,0.012441048865219476,0.004809608444669076,0.78125
+fit_null_s1_plain_from_plain_pinkm,wt,1,0.011436729538816675,0.0083255390645486,0.625
+fit_null_s1_plain,single,946,0.056310164351737704,-0.002109800618809592,0.102669133192389
+fit_null_s1_plain,spike,8,0.03564173580072257,0.005594124974061381,0.421875
+fit_null_s1_plain,wt,1,0.039885856632109154,-0.008674691594757433,0.0
diff --git a/planning/studies/joint-fit-sim/out_run5/params.csv b/planning/studies/joint-fit-sim/out_run5/params.csv
new file mode 100644
index 00000000..103e8a36
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run5/params.csv
@@ -0,0 +1,17 @@
+run,parameter,key,truth,q0_5,lo,hi,covered
+fit_nocong_s1_plain_single,growth_k,kanR+kan,0.015042,0.013801275,0.012038811,0.015553157,True
+fit_nocong_s1_plain_single,growth_k,kanR-kan,0.023923,0.01879035,0.016206073,0.021419218,False
+fit_nocong_s1_plain_single,growth_k,pheS+4CP,0.003107,0.0015561959,-0.0005445291,0.0034262936,True
+fit_nocong_s1_plain_single,growth_k,pheS-4CP,0.022042,0.020876065,0.017976936,0.023448763,True
+fit_nocong_s1_plain_single,growth_m,kanR+kan,-0.014122,-0.016061418,-0.016527396,-0.015459808,False
+fit_nocong_s1_plain_single,growth_m,kanR-kan,0.0,-4.444495e-07,-3.4166256e-05,3.535745e-05,True
+fit_nocong_s1_plain_single,growth_m,pheS+4CP,0.014379,0.016373929,0.01574738,0.016852725,False
+fit_nocong_s1_plain_single,growth_m,pheS-4CP,0.0,-1.2868813e-06,-2.9207054e-05,2.2709995e-05,True
+fit_nocong_s1_plain_single_pinkm,growth_k,kanR+kan,0.015042,0.015042,0.015042,0.015042,True
+fit_nocong_s1_plain_single_pinkm,growth_k,kanR-kan,0.023923,0.023923,0.023923,0.023923,True
+fit_nocong_s1_plain_single_pinkm,growth_k,pheS+4CP,0.003107,0.003107,0.003107,0.003107,True
+fit_nocong_s1_plain_single_pinkm,growth_k,pheS-4CP,0.022042,0.022042,0.022042,0.022042,True
+fit_nocong_s1_plain_single_pinkm,growth_m,kanR+kan,-0.014122,-0.014122,-0.014122,-0.014122,True
+fit_nocong_s1_plain_single_pinkm,growth_m,kanR-kan,0.0,0.0,0.0,0.0,True
+fit_nocong_s1_plain_single_pinkm,growth_m,pheS+4CP,0.014379,0.014379,0.014379,0.014379,True
+fit_nocong_s1_plain_single_pinkm,growth_m,pheS-4CP,0.0,0.0,0.0,0.0,True
diff --git a/planning/studies/joint-fit-sim/out_run5/theta.csv b/planning/studies/joint-fit-sim/out_run5/theta.csv
new file mode 100644
index 00000000..4c2c847c
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run5/theta.csv
@@ -0,0 +1,7 @@
+run,group,n_genotypes,rmse,bias,coverage95
+fit_nocong_s1_plain_single,single,946,0.04600651406708678,-0.010776528232766088,0.10782241014799154
+fit_nocong_s1_plain_single,spike,8,0.023051857667078703,-0.003161633681085658,0.46875
+fit_nocong_s1_plain_single,wt,1,0.04132453085322089,-0.013980366024244472,0.0
+fit_nocong_s1_plain_single_pinkm,single,946,0.010026984980318813,-0.0017261095541820563,0.9100158562367865
+fit_nocong_s1_plain_single_pinkm,spike,8,0.006414977708462745,-0.002008960226691008,0.984375
+fit_nocong_s1_plain_single_pinkm,wt,1,0.0019766868091285602,-0.0019484390082925964,1.0
diff --git a/planning/studies/joint-fit-sim/out_run6/params.csv b/planning/studies/joint-fit-sim/out_run6/params.csv
new file mode 100644
index 00000000..a0786011
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run6/params.csv
@@ -0,0 +1,33 @@
+run,parameter,key,truth,q0_5,lo,hi,covered
+fit_nocong_s1_plain_single,growth_k,kanR+kan,0.015042,0.013801275,0.012038811,0.015553157,True
+fit_nocong_s1_plain_single,growth_k,kanR-kan,0.023923,0.01879035,0.016206073,0.021419218,False
+fit_nocong_s1_plain_single,growth_k,pheS+4CP,0.003107,0.0015561959,-0.0005445291,0.0034262936,True
+fit_nocong_s1_plain_single,growth_k,pheS-4CP,0.022042,0.020876065,0.017976936,0.023448763,True
+fit_nocong_s1_plain_single,growth_m,kanR+kan,-0.014122,-0.016061418,-0.016527396,-0.015459808,False
+fit_nocong_s1_plain_single,growth_m,kanR-kan,0.0,-4.444495e-07,-3.4166256e-05,3.535745e-05,True
+fit_nocong_s1_plain_single,growth_m,pheS+4CP,0.014379,0.016373929,0.01574738,0.016852725,False
+fit_nocong_s1_plain_single,growth_m,pheS-4CP,0.0,-1.2868813e-06,-2.9207054e-05,2.2709995e-05,True
+fit_nocong_s1_plain_single_heldsd,growth_k,kanR+kan,0.015042,0.015109595,0.013546642,0.016911933,True
+fit_nocong_s1_plain_single_heldsd,growth_k,kanR-kan,0.023923,0.01908277,0.016755098,0.021597177,False
+fit_nocong_s1_plain_single_heldsd,growth_k,pheS+4CP,0.003107,0.00043212692,-0.0009298731,0.0016626724,False
+fit_nocong_s1_plain_single_heldsd,growth_k,pheS-4CP,0.022042,0.02102251,0.018773993,0.02310895,True
+fit_nocong_s1_plain_single_heldsd,growth_m,kanR+kan,-0.014122,-0.018351812,-0.018829105,-0.017876515,False
+fit_nocong_s1_plain_single_heldsd,growth_m,kanR-kan,0.0,-2.695143e-06,-4.473586e-05,4.2335116e-05,True
+fit_nocong_s1_plain_single_heldsd,growth_m,pheS+4CP,0.014379,0.018708836,0.018215489,0.01919493,False
+fit_nocong_s1_plain_single_heldsd,growth_m,pheS-4CP,0.0,-1.6029747e-06,-3.200255e-05,2.505449e-05,True
+fit_nocong_s1_plain_single_loc,growth_k,kanR+kan,0.015042,0.01380008,0.012327066,0.01526425,True
+fit_nocong_s1_plain_single_loc,growth_k,kanR-kan,0.023923,0.018815354,0.016599998,0.021009775,False
+fit_nocong_s1_plain_single_loc,growth_k,pheS+4CP,0.003107,0.001565194,-6.083904e-05,0.0028944348,False
+fit_nocong_s1_plain_single_loc,growth_k,pheS-4CP,0.022042,0.020891104,0.01818344,0.023047762,True
+fit_nocong_s1_plain_single_loc,growth_m,kanR+kan,-0.014122,-0.01604757,-0.016499398,-0.015521235,False
+fit_nocong_s1_plain_single_loc,growth_m,kanR-kan,0.0,1.8007813e-07,-3.5932873e-05,3.7369908e-05,True
+fit_nocong_s1_plain_single_loc,growth_m,pheS+4CP,0.014379,0.016371025,0.015813284,0.016805565,False
+fit_nocong_s1_plain_single_loc,growth_m,pheS-4CP,0.0,-3.2650047e-07,-3.1479118e-05,2.145867e-05,True
+fit_nocong_s1_plain_single_both,growth_k,kanR+kan,0.015042,0.015178808,0.013595535,0.017004577,True
+fit_nocong_s1_plain_single_both,growth_k,kanR-kan,0.023923,0.019120358,0.016526548,0.021932742,False
+fit_nocong_s1_plain_single_both,growth_k,pheS+4CP,0.003107,0.00036602712,-0.0018512164,0.0024347708,False
+fit_nocong_s1_plain_single_both,growth_k,pheS-4CP,0.022042,0.021021845,0.018108612,0.023728538,True
+fit_nocong_s1_plain_single_both,growth_m,kanR+kan,-0.014122,-0.018444527,-0.01890321,-0.01800809,False
+fit_nocong_s1_plain_single_both,growth_m,kanR-kan,0.0,-1.8536489e-06,-4.3988308e-05,4.3155676e-05,True
+fit_nocong_s1_plain_single_both,growth_m,pheS+4CP,0.014379,0.01880873,0.01834993,0.019277444,False
+fit_nocong_s1_plain_single_both,growth_m,pheS-4CP,0.0,-2.406667e-06,-3.215514e-05,2.4122559e-05,True
diff --git a/planning/studies/joint-fit-sim/out_run6/theta.csv b/planning/studies/joint-fit-sim/out_run6/theta.csv
new file mode 100644
index 00000000..03a80a48
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run6/theta.csv
@@ -0,0 +1,13 @@
+run,group,n_genotypes,rmse,bias,coverage95
+fit_nocong_s1_plain_single,single,946,0.04600651406708678,-0.010776528232766088,0.10782241014799154
+fit_nocong_s1_plain_single,spike,8,0.023051857667078703,-0.003161633681085658,0.46875
+fit_nocong_s1_plain_single,wt,1,0.04132453085322089,-0.013980366024244472,0.0
+fit_nocong_s1_plain_single_heldsd,single,946,0.08540881392358195,-0.018294370036810996,0.04373678646934461
+fit_nocong_s1_plain_single_heldsd,spike,8,0.04221698927449781,-0.004739749984244781,0.171875
+fit_nocong_s1_plain_single_heldsd,wt,1,0.07880600956917144,-0.024576634203527545,0.0
+fit_nocong_s1_plain_single_loc,single,946,0.04563101607446293,-0.010643469689389149,0.10689746300211417
+fit_nocong_s1_plain_single_loc,spike,8,0.022944694706117076,-0.0029301735532101456,0.484375
+fit_nocong_s1_plain_single_loc,wt,1,0.04091447279596791,-0.014013389128223145,0.0
+fit_nocong_s1_plain_single_both,single,946,0.08761831526429646,-0.01872845471482825,0.04254756871035941
+fit_nocong_s1_plain_single_both,spike,8,0.04332452652241193,-0.004835552177489268,0.140625
+fit_nocong_s1_plain_single_both,wt,1,0.08108988569030989,-0.025144357819514025,0.0
diff --git a/planning/studies/joint-fit-sim/out_run7/params.csv b/planning/studies/joint-fit-sim/out_run7/params.csv
new file mode 100644
index 00000000..2e34169c
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run7/params.csv
@@ -0,0 +1,41 @@
+run,parameter,key,truth,q0_5,lo,hi,covered
+fit_nocong_s1_plain_single,growth_k,kanR+kan,0.015042,0.013801275,0.012038811,0.015553157,True
+fit_nocong_s1_plain_single,growth_k,kanR-kan,0.023923,0.01879035,0.016206073,0.021419218,False
+fit_nocong_s1_plain_single,growth_k,pheS+4CP,0.003107,0.0015561959,-0.0005445291,0.0034262936,True
+fit_nocong_s1_plain_single,growth_k,pheS-4CP,0.022042,0.020876065,0.017976936,0.023448763,True
+fit_nocong_s1_plain_single,growth_m,kanR+kan,-0.014122,-0.016061418,-0.016527396,-0.015459808,False
+fit_nocong_s1_plain_single,growth_m,kanR-kan,0.0,-4.444495e-07,-3.4166256e-05,3.535745e-05,True
+fit_nocong_s1_plain_single,growth_m,pheS+4CP,0.014379,0.016373929,0.01574738,0.016852725,False
+fit_nocong_s1_plain_single,growth_m,pheS-4CP,0.0,-1.2868813e-06,-2.9207054e-05,2.2709995e-05,True
+fit_nocong_s1_plain_single_weakpool,growth_k,kanR+kan,0.015042,0.013628845,0.011500317,0.016083382,True
+fit_nocong_s1_plain_single_weakpool,growth_k,kanR-kan,0.023923,0.01892056,0.01560307,0.022467064,False
+fit_nocong_s1_plain_single_weakpool,growth_k,pheS+4CP,0.003107,0.0017348563,-0.0013139428,0.004589546,True
+fit_nocong_s1_plain_single_weakpool,growth_k,pheS-4CP,0.022042,0.020952094,0.016725298,0.02493233,True
+fit_nocong_s1_plain_single_weakpool,growth_m,kanR+kan,-0.014122,-0.015718333,-0.016057761,-0.01538869,False
+fit_nocong_s1_plain_single_weakpool,growth_m,kanR-kan,0.0,-1.20813e-06,-3.727754e-05,3.714259e-05,True
+fit_nocong_s1_plain_single_weakpool,growth_m,pheS+4CP,0.014379,0.016023008,0.015685845,0.016374357,False
+fit_nocong_s1_plain_single_weakpool,growth_m,pheS-4CP,0.0,-2.3394914e-06,-2.7781969e-05,1.9868905e-05,True
+fit_nocong_s1_plain_single_lib2,growth_k,kanR+kan,0.015042,0.013561364,0.011840986,0.015271412,True
+fit_nocong_s1_plain_single_lib2,growth_k,kanR-kan,0.023923,0.0187679,0.016024807,0.021588586,False
+fit_nocong_s1_plain_single_lib2,growth_k,pheS+4CP,0.003107,0.0018079232,-0.00046474964,0.0036126454,True
+fit_nocong_s1_plain_single_lib2,growth_k,pheS-4CP,0.022042,0.02095231,0.017813662,0.02331125,True
+fit_nocong_s1_plain_single_lib2,growth_m,kanR+kan,-0.014122,-0.0156019665,-0.015870579,-0.015286126,False
+fit_nocong_s1_plain_single_lib2,growth_m,kanR-kan,0.0,-1.2407652e-06,-3.2953893e-05,3.317217e-05,True
+fit_nocong_s1_plain_single_lib2,growth_m,pheS+4CP,0.014379,0.015908673,0.015576741,0.016182851,False
+fit_nocong_s1_plain_single_lib2,growth_m,pheS-4CP,0.0,-4.3367936e-07,-2.7493896e-05,2.2053584e-05,True
+fit_nocong_s1_plain_single_lib20,growth_k,kanR+kan,0.015042,0.013215949,0.012228213,0.014197754,False
+fit_nocong_s1_plain_single_lib20,growth_k,kanR-kan,0.023923,0.018749062,0.016980382,0.020398566,False
+fit_nocong_s1_plain_single_lib20,growth_k,pheS+4CP,0.003107,0.0020643813,0.0009044189,0.0030294056,False
+fit_nocong_s1_plain_single_lib20,growth_k,pheS-4CP,0.022042,0.0209004,0.018916024,0.02242581,True
+fit_nocong_s1_plain_single_lib20,growth_m,kanR+kan,-0.014122,-0.014929175,-0.015063497,-0.01479034,False
+fit_nocong_s1_plain_single_lib20,growth_m,kanR-kan,0.0,-4.4176616e-07,-3.27292e-05,3.3555803e-05,True
+fit_nocong_s1_plain_single_lib20,growth_m,pheS+4CP,0.014379,0.015220229,0.0150832655,0.015350807,False
+fit_nocong_s1_plain_single_lib20,growth_m,pheS-4CP,0.0,-1.3181095e-06,-2.6742477e-05,2.0230633e-05,True
+fit_nocong_s1_plain_single_weakpool_lib20,growth_k,kanR+kan,0.015042,0.0131746335,0.01162946,0.014956468,False
+fit_nocong_s1_plain_single_weakpool_lib20,growth_k,kanR-kan,0.023923,0.018806567,0.016182827,0.021627234,False
+fit_nocong_s1_plain_single_weakpool_lib20,growth_k,pheS+4CP,0.003107,0.0020984784,0.00034642438,0.0036215098,True
+fit_nocong_s1_plain_single_weakpool_lib20,growth_k,pheS-4CP,0.022042,0.02089941,0.018101713,0.023452591,True
+fit_nocong_s1_plain_single_weakpool_lib20,growth_m,kanR+kan,-0.014122,-0.0148265865,-0.014932872,-0.014723662,False
+fit_nocong_s1_plain_single_weakpool_lib20,growth_m,kanR-kan,0.0,-2.0679395e-06,-3.5970006e-05,3.4226574e-05,True
+fit_nocong_s1_plain_single_weakpool_lib20,growth_m,pheS+4CP,0.014379,0.015116502,0.015009742,0.015218562,False
+fit_nocong_s1_plain_single_weakpool_lib20,growth_m,pheS-4CP,0.0,-1.5685268e-06,-2.5498137e-05,1.9750209e-05,True
diff --git a/planning/studies/joint-fit-sim/out_run7/theta.csv b/planning/studies/joint-fit-sim/out_run7/theta.csv
new file mode 100644
index 00000000..af22ac2f
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run7/theta.csv
@@ -0,0 +1,19 @@
+run,group,n_genotypes,rmse,bias,coverage95
+fit_nocong_s1_plain_single,single,946,0.04600651406708678,-0.010776528232766088,0.10782241014799154
+fit_nocong_s1_plain_single,spike,8,0.023051857667078703,-0.003161633681085658,0.46875
+fit_nocong_s1_plain_single,wt,1,0.04132453085322089,-0.013980366024244472,0.0
+fit_nocong_s1_plain_single_weakpool,single,946,0.0394620077243776,-0.008725156518903036,0.1261892177589852
+fit_nocong_s1_plain_single_weakpool,spike,8,0.01980491592948896,-0.002717789717918243,0.46875
+fit_nocong_s1_plain_single_weakpool,wt,1,0.0350033887773782,-0.01130932117445482,0.0
+fit_nocong_s1_plain_single_lib2,single,944,0.03573471290021584,-0.0080530998358043,0.1481726694915254
+fit_nocong_s1_plain_single_lib2,"single, binding",2,0.022768642812501568,4.6763618178457655e-05,0.25
+fit_nocong_s1_plain_single_lib2,spike,8,0.018451340792210596,-0.0018307648317920575,0.515625
+fit_nocong_s1_plain_single_lib2,wt,1,0.03056125393936438,-0.010622998827147592,0.0
+fit_nocong_s1_plain_single_lib20,single,926,0.020627103634744764,-0.002397537332113211,0.35461663066954646
+fit_nocong_s1_plain_single_lib20,"single, binding",20,0.018520133288800207,8.424415839208539e-05,0.4
+fit_nocong_s1_plain_single_lib20,spike,8,0.012215949671842655,0.0008572381069769292,0.703125
+fit_nocong_s1_plain_single_lib20,wt,1,0.014084600170187629,-0.003690915351246332,0.0
+fit_nocong_s1_plain_single_weakpool_lib20,single,926,0.0193553533381434,-0.0022052652264256027,0.3752699784017279
+fit_nocong_s1_plain_single_weakpool_lib20,"single, binding",20,0.01712178379393477,0.00047100702239295756,0.425
+fit_nocong_s1_plain_single_weakpool_lib20,spike,8,0.010915790643631365,4.878429160192757e-05,0.625
+fit_nocong_s1_plain_single_weakpool_lib20,wt,1,0.013225308865114584,-0.0032218532631413106,0.0
diff --git a/planning/studies/joint-fit-sim/out_run8/params.csv b/planning/studies/joint-fit-sim/out_run8/params.csv
new file mode 100644
index 00000000..8310b626
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run8/params.csv
@@ -0,0 +1,115 @@
+run,parameter,key,truth,q0_5,lo,hi,covered
+fit_null_s1_plain,growth_k,kanR+kan,0.015042,0.019639313,0.01776531,0.021214087,False
+fit_null_s1_plain,growth_k,kanR-kan,0.023923,0.029058484,0.026518818,0.03145352,False
+fit_null_s1_plain,growth_k,pheS+4CP,0.003107,0.0037996653,0.0013952442,0.0064904196,True
+fit_null_s1_plain,growth_k,pheS-4CP,0.022042,0.02379319,0.020922774,0.027036328,True
+fit_null_s1_plain,growth_m,kanR+kan,-0.014122,-0.017343588,-0.01765266,-0.016980322,False
+fit_null_s1_plain,growth_m,kanR-kan,0.0,2.2137636e-05,-2.0148449e-05,6.657838e-05,True
+fit_null_s1_plain,growth_m,pheS+4CP,0.014379,0.017263267,0.016902821,0.017571792,False
+fit_null_s1_plain,growth_m,pheS-4CP,0.0,2.6166083e-05,-5.8999135e-06,5.760233e-05,True
+fit_null_s1_plain_lib20,growth_k,kanR+kan,0.015042,0.019513223,0.018394718,0.020453129,False
+fit_null_s1_plain_lib20,growth_k,kanR-kan,0.023923,0.029919986,0.028066596,0.031689644,False
+fit_null_s1_plain_lib20,growth_k,pheS+4CP,0.003107,0.004991972,0.0034726348,0.0065823984,False
+fit_null_s1_plain_lib20,growth_k,pheS-4CP,0.022042,0.024593122,0.022723252,0.026306747,False
+fit_null_s1_plain_lib20,growth_m,kanR+kan,-0.014122,-0.016021106,-0.016161803,-0.015866857,False
+fit_null_s1_plain_lib20,growth_m,kanR-kan,0.0,2.108172e-05,-1.7717397e-05,6.212296e-05,True
+fit_null_s1_plain_lib20,growth_m,pheS+4CP,0.014379,0.015963431,0.015811132,0.01611957,False
+fit_null_s1_plain_lib20,growth_m,pheS-4CP,0.0,2.3652243e-05,-5.4952243e-06,5.275869e-05,True
+fit_null_s1_plain_weakpool_lib20,growth_k,kanR+kan,0.015042,0.019537132,0.017989652,0.021163287,False
+fit_null_s1_plain_weakpool_lib20,growth_k,kanR-kan,0.023923,0.029986436,0.027413847,0.033001345,False
+fit_null_s1_plain_weakpool_lib20,growth_k,pheS+4CP,0.003107,0.005170502,0.003279138,0.0071089477,False
+fit_null_s1_plain_weakpool_lib20,growth_k,pheS-4CP,0.022042,0.024631048,0.022674588,0.026770502,False
+fit_null_s1_plain_weakpool_lib20,growth_m,kanR+kan,-0.014122,-0.015952587,-0.016091129,-0.01580104,False
+fit_null_s1_plain_weakpool_lib20,growth_m,kanR-kan,0.0,2.0243358e-05,-2.3737131e-05,6.384833e-05,True
+fit_null_s1_plain_weakpool_lib20,growth_m,pheS+4CP,0.014379,0.015898272,0.015745746,0.01606892,False
+fit_null_s1_plain_weakpool_lib20,growth_m,pheS-4CP,0.0,2.5069487e-05,-7.2455196e-06,5.3015283e-05,True
+fit_nocong_s1_plain_single_lib20,growth_k,kanR+kan,0.015042,0.013215949,0.012228213,0.014197754,False
+fit_nocong_s1_plain_single_lib20,growth_k,kanR-kan,0.023923,0.018749062,0.016980382,0.020398566,False
+fit_nocong_s1_plain_single_lib20,growth_k,pheS+4CP,0.003107,0.0020643813,0.0009044189,0.0030294056,False
+fit_nocong_s1_plain_single_lib20,growth_k,pheS-4CP,0.022042,0.0209004,0.018916024,0.02242581,True
+fit_nocong_s1_plain_single_lib20,growth_m,kanR+kan,-0.014122,-0.014929175,-0.015063497,-0.01479034,False
+fit_nocong_s1_plain_single_lib20,growth_m,kanR-kan,0.0,-4.4176616e-07,-3.27292e-05,3.3555803e-05,True
+fit_nocong_s1_plain_single_lib20,growth_m,pheS+4CP,0.014379,0.015220229,0.0150832655,0.015350807,False
+fit_nocong_s1_plain_single_lib20,growth_m,pheS-4CP,0.0,-1.3181095e-06,-2.6742477e-05,2.0230633e-05,True
+fit_nocong_s1_plain_single_lib50,growth_k,kanR+kan,0.015042,0.013170511,0.011740442,0.014591995,False
+fit_nocong_s1_plain_single_lib50,growth_k,kanR-kan,0.023923,0.018710695,0.016430056,0.021022294,False
+fit_nocong_s1_plain_single_lib50,growth_k,pheS+4CP,0.003107,0.002142149,-0.00020314584,0.004049731,True
+fit_nocong_s1_plain_single_lib50,growth_k,pheS-4CP,0.022042,0.020901732,0.018200245,0.023223246,True
+fit_nocong_s1_plain_single_lib50,growth_m,kanR+kan,-0.014122,-0.014778586,-0.014881822,-0.014663564,False
+fit_nocong_s1_plain_single_lib50,growth_m,kanR-kan,0.0,-2.481416e-06,-3.1933774e-05,3.0242873e-05,True
+fit_nocong_s1_plain_single_lib50,growth_m,pheS+4CP,0.014379,0.0150651205,0.014947455,0.015167577,False
+fit_nocong_s1_plain_single_lib50,growth_m,pheS-4CP,0.0,8.895906e-07,-2.4735891e-05,2.2106657e-05,True
+fit_joint_s1_full_from_plain,growth_k,kanR+kan,0.015042,0.017957725,0.016482297,0.019431103,False
+fit_joint_s1_full_from_plain,growth_k,kanR-kan,0.023923,0.02928865,0.027100608,0.031431463,False
+fit_joint_s1_full_from_plain,growth_k,pheS+4CP,0.003107,0.0051531717,0.0032590851,0.0072666015,False
+fit_joint_s1_full_from_plain,growth_k,pheS-4CP,0.022042,0.027519124,0.025027083,0.03014029,False
+fit_joint_s1_full_from_plain,growth_m,kanR+kan,-0.014122,-0.014448609,-0.014514818,-0.01439054,False
+fit_joint_s1_full_from_plain,growth_m,kanR-kan,0.0,-1.0012214e-05,-5.552533e-05,2.6604968e-05,True
+fit_joint_s1_full_from_plain,growth_m,pheS+4CP,0.014379,0.01656733,0.016284943,0.016876321,False
+fit_joint_s1_full_from_plain,growth_m,pheS-4CP,0.0,1.2326492e-05,-1.8744171e-05,4.392883e-05,True
+fit_joint_s1_full_from_plain,warp_log_a,kanR+kan,2.0794415416798357,2.2021394,2.1576385,2.248794,False
+fit_joint_s1_full_from_plain,warp_log_a,pheS+4CP,0.4054651081081644,-0.08407947,-1.5147213,1.4601364,True
+fit_joint_s1_full_from_plain,warp_x0,kanR+kan,0.5,0.53383434,0.49808156,0.5636751,True
+fit_joint_s1_full_from_plain,warp_x0,pheS+4CP,0.4,0.58642846,-0.23549522,1.322586,True
+fit_joint_s1_full_from_plain,log_s,global,-0.6931471805599453,-0.79566884,-0.9376141,-0.6642408,True
+fit_joint_s1_full_from_plain,binding_offset,wt,-0.1056078950314186,-0.10702185,-0.1374449,-0.042688668,True
+fit_joint_s1_full_from_plain,binding_offset,M42I,0.0282415842244401,0.009926669,-0.043777168,0.05783087,True
+fit_joint_s1_full_from_plain,binding_offset,H74A,-0.0420857002548871,-0.043082345,-0.081066534,0.011327274,True
+fit_joint_s1_full_from_plain,binding_offset,K84L,0.0425148735331908,-0.017117418,-0.0507347,0.02963588,False
+fit_joint_s1_full_from_plain,binding_offset,D88A,0.0511536419917619,0.037221782,-0.022255406,0.081511445,True
+fit_joint_s1_full_from_plain,binding_offset,M42I/H74A,-0.1159296726932429,-0.13883291,-0.18324412,-0.043825623,True
+fit_joint_s1_full_from_plain,binding_offset,M42I/K84L,-0.0590434943289043,-0.11167629,-0.14532866,-0.03547845,True
+fit_joint_s1_full_from_plain,binding_offset,H74A/K84L,-0.0228535367478158,-0.023133399,-0.06173983,0.037326477,True
+fit_joint_s1_full_from_plain,binding_offset,M42I/H74A/K84L,0.0833342596669661,0.07477276,0.014866954,0.10796515,True
+fit_joint_s1_full_from_plain,binding_offset_scale,global,0.1,0.2238241,0.0949375,0.49544805,True
+fit_joint_s1_plain_lib20,growth_k,kanR+kan,0.015042,0.018819705,0.016253246,0.020976365,False
+fit_joint_s1_plain_lib20,growth_k,kanR-kan,0.023923,0.029214837,0.025166273,0.032829322,False
+fit_joint_s1_plain_lib20,growth_k,pheS+4CP,0.003107,0.006786953,0.003960558,0.009375356,False
+fit_joint_s1_plain_lib20,growth_k,pheS-4CP,0.022042,0.02691646,0.023299571,0.030369096,False
+fit_joint_s1_plain_lib20,growth_m,kanR+kan,-0.014122,-0.016758459,-0.016872358,-0.016635366,False
+fit_joint_s1_plain_lib20,growth_m,kanR-kan,0.0,-0.00010611255,-0.00015000328,-5.9442915e-05,False
+fit_joint_s1_plain_lib20,growth_m,pheS+4CP,0.014379,0.013973749,0.013877165,0.014074243,False
+fit_joint_s1_plain_lib20,growth_m,pheS-4CP,0.0,-0.0001146983,-0.00015081318,-7.984436e-05,False
+fit_joint_s1_full_from_plain_lib20,growth_k,kanR+kan,0.015042,0.017779581,0.016860586,0.018680673,False
+fit_joint_s1_full_from_plain_lib20,growth_k,kanR-kan,0.023923,0.029037233,0.027302386,0.030687595,False
+fit_joint_s1_full_from_plain_lib20,growth_k,pheS+4CP,0.003107,0.0056578703,0.0041585388,0.0071753864,False
+fit_joint_s1_full_from_plain_lib20,growth_k,pheS-4CP,0.022042,0.027364645,0.025275411,0.0294674,False
+fit_joint_s1_full_from_plain_lib20,growth_m,kanR+kan,-0.014122,-0.014273547,-0.014312269,-0.014231038,False
+fit_joint_s1_full_from_plain_lib20,growth_m,kanR-kan,0.0,-1.1004556e-05,-5.270699e-05,2.7652484e-05,True
+fit_joint_s1_full_from_plain_lib20,growth_m,pheS+4CP,0.014379,0.015366033,0.015236356,0.015508619,False
+fit_joint_s1_full_from_plain_lib20,growth_m,pheS-4CP,0.0,7.2036037e-06,-2.1223015e-05,3.653898e-05,True
+fit_joint_s1_full_from_plain_lib20,warp_log_a,kanR+kan,2.0794415416798357,2.1142178,2.102064,2.1257231,False
+fit_joint_s1_full_from_plain_lib20,warp_log_a,pheS+4CP,0.4054651081081644,-0.82945144,-1.798458,0.044557422,False
+fit_joint_s1_full_from_plain_lib20,warp_x0,kanR+kan,0.5,0.5354536,0.52431077,0.5475014,False
+fit_joint_s1_full_from_plain_lib20,warp_x0,pheS+4CP,0.4,0.4527818,-0.44477156,1.380398,True
+fit_joint_s1_full_from_plain_lib20,log_s,global,-0.6931471805599453,-0.73761225,-0.77925617,-0.69210213,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,wt,-0.1056078950314186,-0.12130986,-0.14442615,-0.03954602,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,R35V,0.0,0.0066577913,-0.027562032,0.035075385,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,E39G,0.0,-0.014689369,-0.058493417,0.04300828,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,M42I,0.0282415842244401,-0.0024007813,-0.033313878,0.02533397,False
+fit_joint_s1_full_from_plain_lib20,binding_offset,M42R,0.0,-0.028005581,-0.059273943,0.0022332526,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,I48T,0.0,-0.006387547,-0.044981755,0.038059637,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,P49R,0.0,0.006776956,-0.029772121,0.035331957,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,R51S,0.0,0.0026306093,-0.02836745,0.03084597,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,Q54N,0.0,0.0031762463,-0.037408613,0.03518658,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,G58P,0.0,0.009631575,-0.024174592,0.042895533,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,I64S,0.0,0.004168044,-0.03434479,0.036891572,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,T68P,0.0,-0.06675541,-0.09764926,-0.011501151,False
+fit_joint_s1_full_from_plain_lib20,binding_offset,S69Y,0.0,-0.010365965,-0.04579498,0.020010848,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,A72L,0.0,-0.002680641,-0.034203846,0.024273165,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,L73A,0.0,-0.032399192,-0.05919222,0.0023936494,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,L73T,0.0,-0.02334304,-0.051212806,0.013600973,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,H74A,-0.0420857002548871,-0.05864109,-0.08231991,-0.01892689,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,I79R,0.0,-0.0011421873,-0.028837943,0.029050298,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,K84L,0.0425148735331908,-0.006878795,-0.033184737,0.019459294,False
+fit_joint_s1_full_from_plain_lib20,binding_offset,A87Y,0.0,-0.02838487,-0.055748343,0.010387307,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,D88A,0.0511536419917619,0.030758113,-0.0026694841,0.06234709,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,L90F,0.0,-0.037861533,-0.07017803,0.014069745,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,A92T,0.0,-0.015601618,-0.041187625,0.015074927,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,S93D,0.0,-0.032036737,-0.06792169,0.008784562,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,M98C,0.0,-0.020271339,-0.04684171,0.006408527,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,M42I/H74A,-0.1159296726932429,-0.14236295,-0.16812178,-0.06912915,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,M42I/K84L,-0.0590434943289043,-0.09321104,-0.11819824,-0.045555465,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,H74A/K84L,-0.0228535367478158,-0.034255706,-0.07295843,0.011829977,True
+fit_joint_s1_full_from_plain_lib20,binding_offset,M42I/H74A/K84L,0.0833342596669661,0.062235218,-0.0064693503,0.08571177,True
+fit_joint_s1_full_from_plain_lib20,binding_offset_scale,global,0.1,0.23241907,0.104634695,0.5254489,False
diff --git a/planning/studies/joint-fit-sim/out_run8/theta.csv b/planning/studies/joint-fit-sim/out_run8/theta.csv
new file mode 100644
index 00000000..d1ac7a50
--- /dev/null
+++ b/planning/studies/joint-fit-sim/out_run8/theta.csv
@@ -0,0 +1,31 @@
+run,group,n_genotypes,rmse,bias,coverage95
+fit_null_s1_plain,single,946,0.056310164351737704,-0.002109800618809592,0.102669133192389
+fit_null_s1_plain,spike,8,0.03564173580072257,0.005594124974061381,0.421875
+fit_null_s1_plain,wt,1,0.039885856632109154,-0.008674691594757433,0.0
+fit_null_s1_plain_lib20,single,926,0.03742019087357716,0.006280591514255646,0.31128509719222464
+fit_null_s1_plain_lib20,"single, binding",20,0.02948787173413293,0.002053183470534506,0.23125
+fit_null_s1_plain_lib20,spike,8,0.028131534122881947,0.009055072049138246,0.484375
+fit_null_s1_plain_lib20,wt,1,0.01777155343423133,0.0017060284501499686,0.125
+fit_null_s1_plain_weakpool_lib20,single,926,0.03641671775598344,0.006851190370131859,0.3334233261339093
+fit_null_s1_plain_weakpool_lib20,"single, binding",20,0.028394803008836775,0.0025508700755496713,0.24375
+fit_null_s1_plain_weakpool_lib20,spike,8,0.026920229825424385,0.008794566164607782,0.390625
+fit_null_s1_plain_weakpool_lib20,wt,1,0.0169375881728929,0.0025135766127508197,0.125
+fit_nocong_s1_plain_single_lib20,single,926,0.020627103634744764,-0.002397537332113211,0.35461663066954646
+fit_nocong_s1_plain_single_lib20,"single, binding",20,0.018520133288800207,8.424415839208539e-05,0.4
+fit_nocong_s1_plain_single_lib20,spike,8,0.012215949671842655,0.0008572381069769292,0.703125
+fit_nocong_s1_plain_single_lib20,wt,1,0.014084600170187629,-0.003690915351246332,0.0
+fit_nocong_s1_plain_single_lib50,single,896,0.017310637101799704,0.0003807505451593771,0.5129743303571429
+fit_nocong_s1_plain_single_lib50,"single, binding",50,0.014311556027633864,1.7586267188102147e-06,0.58
+fit_nocong_s1_plain_single_lib50,spike,8,0.011325124552406352,0.0024836520634945914,0.765625
+fit_nocong_s1_plain_single_lib50,wt,1,0.009545868703611097,-0.0008252714673179138,0.125
+fit_joint_s1_full_from_plain,single,946,0.07012804425293707,0.004091526192144923,0.16596194503171247
+fit_joint_s1_full_from_plain,spike,8,0.038620122460039444,0.020425483904325994,0.484375
+fit_joint_s1_full_from_plain,wt,1,0.05676126023676298,-0.0053830364664834325,0.25
+fit_joint_s1_plain_lib20,single,926,0.1234452092416617,0.03522000583807315,0.1345842332613391
+fit_joint_s1_plain_lib20,"single, binding",20,0.11332281121631436,0.02392954237848126,0.15
+fit_joint_s1_plain_lib20,spike,8,0.0560554303999554,0.009719645536402303,0.25
+fit_joint_s1_plain_lib20,wt,1,0.1294237078331936,0.02906087291808343,0.125
+fit_joint_s1_full_from_plain_lib20,single,926,0.04834778668533071,0.016795755583481432,0.37405507559395246
+fit_joint_s1_full_from_plain_lib20,"single, binding",20,0.03720021546543201,0.01387537335109203,0.4125
+fit_joint_s1_full_from_plain_lib20,spike,8,0.03157813125665683,0.025924187186855078,0.40625
+fit_joint_s1_full_from_plain_lib20,wt,1,0.029264566946553074,0.010577851409671971,0.125
diff --git a/planning/studies/joint-fit-sim/simulate.srun b/planning/studies/joint-fit-sim/simulate.srun
new file mode 100644
index 00000000..03152780
--- /dev/null
+++ b/planning/studies/joint-fit-sim/simulate.srun
@@ -0,0 +1,42 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=jfsim_sim
+#SBATCH --partition=gpu
+#SBATCH --time=06:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# One simulation of the joint-fit simulation test
+# (planning/studies/joint-fit-sim). Run from the study's cluster directory:
+#
+#   sbatch --export=ALL,ARM=joint,SEED=1 --output=sim_joint.out simulate.srun
+#
+#   ARM   joint | null (make_sim.py)
+#   SEED  simulation seed (default 1)
+#   BASE  base simulate config (default: full-sized-sims-3/sim_realistic_s2)
+#
+# Writes sim_${ARM}_s${SEED}/. The direct growth table only (--no_write_raw):
+# stage 1 reads tfs_sim_growth.csv, which tfs-process-counts reproduces row
+# for row from the raw output. The simulation takes about 90 minutes.
+
+module load cuda/12.4.1
+set -e
+ARM="${ARM:?set ARM}"
+SEED="${SEED:-1}"
+BASE="${BASE:-/gpfs/projects/harmslab/harms/studies/full-sized-sims-3/sim_realistic_s2/simulate_config.yaml}"
+STUDY=/gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim
+OUT="sim_${ARM}_s${SEED}"
+
+if [[ -f "${OUT}/tfs_sim_binding.csv" ]]; then
+    echo ">>> ${OUT}: simulated, skipped"
+    exit 0
+fi
+echo ">>> make_sim ${ARM} ${SEED} $(date)"
+python "${STUDY}/make_sim.py" "${BASE}" "${OUT}" --arm "${ARM}" --seed "${SEED}"
+cd "${OUT}"
+echo ">>> simulate $(date)"
+tfs-simulate simulate_config.yaml --out_prefix tfs_sim --seed "${SEED}" --no_write_raw
+echo ">>> done $(date)"
diff --git a/planning/studies/joint-fit-sim/stage1.py b/planning/studies/joint-fit-sim/stage1.py
new file mode 100644
index 00000000..09e79a94
--- /dev/null
+++ b/planning/studies/joint-fit-sim/stage1.py
@@ -0,0 +1,218 @@
+"""
+Joint stage 1 on a simulation (README): wt, spikes and singles, growth and
+binding together.
+
+    python stage1.py SIM_DIR OUT_DIR --model full|plain [--seed 1] [--max_num_epochs N]
+                     [--init_from OTHER_FIT/tfs_fit_model_params.npz]
+                     [--pin_km_truth] [--no_mixture] [--set_priors NAME=VALUE ...]
+                     [--add_binding_singles N] [--binding_seed 1]
+                     [--congression_sets N1 N2 N3]
+
+SIM_DIR holds a tfs-simulate run of make_sim.py's config (simulate_config.yaml,
+tfs_sim_growth.csv, tfs_sim_binding.csv). Each step is skipped when its
+output exists, so a rerun resumes.
+
+Both models: hill_geno (absolute theta, shared by both markers), the
+congression mixture with the homodimer rule (lambda prior 0.357 +- 0.15),
+binding at weight 1, the count likelihood with level tube offsets
+(SD 0.17), log(n) population SD 0.5, one k and m per condition, loose
+k/m priors (growth_priors_loose.csv's values).
+
+- full: plus a logistic warp of occupancy on both selection conditions,
+  the growth titrant scale s (log s ~ Normal(0, 1)) and one binding offset
+  per binding genotype (genotype_offset).
+- plain: none of those (linear growth in theta, s = 1, binding as
+  measured).
+
+--init_from starts the MAP at another fit's MAP (tfs-fit-model --init_from;
+not staged): run 1's full fit on the null simulation stopped in a wrong
+mode about 1,000 nats above the plain fit, so the full model is started
+from the plain one, its extra sites (warps, s, offsets) at their guesses.
+
+--pin_km_truth pins every condition's k and m at the simulation's truth
+(tfs_sim_growth_parameters.csv; growth_priors k_pinned/m_pinned): run 3's
+fits put m 15-25% steep even with the true model, and the loss at the
+true k and m against the free fit's says whether stage 1 prefers that m
+or only drifts along a flat direction.
+
+--no_mixture fits without the congression mixture (transformation
+single), for a simulation without congression (make_sim.py --arm nocong).
+
+--set_priors adds tfs-configure-model --set_priors items to the study's
+own (run 6: the theta population SDs held, or their locations' priors
+moved).
+
+--add_binding_singles N measures N random singles with growth data as
+well, on the lab's 2 uM grid from their true Hill curves with the spikes'
+noise (make_sim.BINDING_IPTG, BINDING_NOISE), appended to the simulated
+binding table in OUT_DIR/binding.csv (run 7: run 6 found the singles'
+theta squeezed along a near-symmetry of the growth model that only the
+binding genotypes resist).
+
+--congression_sets sets the mixture's co-resident draws (tfs-configure-model
+--congression_sets; default 12 3 1): run 8 left the congression simulation's
+m bias at twice the no-congression one with binding singles.
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+
+from tfscreen.genetics.library_design import library_composition_table
+from make_sim import BINDING_IPTG, BINDING_NOISE
+from tfscreen.tfmodel import two_stage as ts
+from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+from tfscreen.tfmodel.scripts.extract_params_cli import extract_params
+from tfscreen.tfmodel.scripts.fit_model_cli import fit_model
+from tfscreen.tfmodel.scripts.predict_theta_cli import predict_theta
+from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+
+CONFIG = "tfs_configure_config.yaml"
+CHECKPOINT = "tfs_fit_model_checkpoint.pkl"
+LOOSE = dict(k_loc=0.015, k_scale=0.01, m_loc=0.0, m_scale=0.01)
+
+
+def growth_priors(growth_csv, warp, truth=None):
+    """
+    Loose k/m priors for every condition; m_warp on selection conditions;
+    with ``truth`` (a growth-parameters table), k and m pinned at it.
+    """
+    conds = set()
+    for chunk in pd.read_csv(growth_csv, usecols=["condition_pre", "condition_sel"],
+                             chunksize=1_000_000):
+        conds |= set(chunk["condition_pre"]) | set(chunk["condition_sel"])
+    t = pd.DataFrame({"condition_rep": sorted(conds)}).assign(**LOOSE)
+    if warp:
+        t["m_warp"] = t["condition_rep"].str.contains(r"\+").astype(int)
+    if truth is not None:
+        tr = truth.set_index("condition_rep")
+        missing = sorted(set(t["condition_rep"]) - set(tr.index))
+        if missing:
+            raise ValueError(f"no growth truth for {missing}")
+        t["k_loc"] = t["condition_rep"].map(tr["growth_k"])
+        t["m_loc"] = t["condition_rep"].map(tr["growth_m"])
+        t["k_pinned"] = 1
+        t["m_pinned"] = 1
+    return t
+
+
+def binding_singles(sim, genotypes, num, seed):
+    """Binding rows for num random singles, from their true Hill curves."""
+    truth = pd.read_csv(os.path.join(sim, "tfs_sim_parameters.csv"))
+    truth = truth.assign(genotype=truth["genotype"].astype(str)).set_index("genotype")
+    spiked = pd.read_csv(os.path.join(sim, "tfs_sim_binding.csv"))["genotype"]
+    pool = sorted(g for g in genotypes
+                  if ts.is_single(g) and g in truth.index and g not in set(spiked))
+    if num > len(pool):
+        raise ValueError(f"only {len(pool)} singles to choose from")
+    rng = np.random.default_rng([seed, 7])
+    chosen = rng.choice(pool, size=num, replace=False)
+    c = np.asarray(BINDING_IPTG, dtype=float)
+    lc = np.log(np.where(c == 0, 1e-20, c))
+    rows = []
+    for g in chosen:
+        r = truth.loc[g]
+        th = r["theta_low"] + (r["theta_high"] - r["theta_low"]) / (
+            1 + np.exp(-r["hill_n"] * (lc - r["log_hill_K"])))
+        obs = th + rng.normal(0.0, BINDING_NOISE, size=len(c))
+        rows.extend(dict(genotype=g, titrant_name="iptg", titrant_conc=float(ci),
+                         theta_obs=float(o), theta_std=BINDING_NOISE)
+                    for ci, o in zip(c, obs))
+    return pd.DataFrame(rows)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("sim_dir")
+    ap.add_argument("out_dir")
+    ap.add_argument("--model", choices=["full", "plain"], required=True)
+    ap.add_argument("--seed", type=int, default=1)
+    ap.add_argument("--max_num_epochs", type=int, default=None)
+    ap.add_argument("--init_from", default=None)
+    ap.add_argument("--pin_km_truth", action="store_true")
+    ap.add_argument("--no_mixture", action="store_true")
+    ap.add_argument("--set_priors", nargs="*", default=[])
+    ap.add_argument("--add_binding_singles", type=int, default=0)
+    ap.add_argument("--binding_seed", type=int, default=1)
+    ap.add_argument("--congression_sets", type=int, nargs="+", default=None)
+    args = ap.parse_args()
+    sim = os.path.abspath(args.sim_dir)
+    init_from = None if args.init_from is None else os.path.abspath(args.init_from)
+    full = args.model == "full"
+    os.makedirs(args.out_dir, exist_ok=True)
+    os.chdir(args.out_dir)
+
+    library_config = os.path.join(sim, "simulate_config.yaml")
+    if not os.path.exists("tfs_growth.csv"):
+        lib = library_composition_table(library_config)
+        src = os.path.join(sim, "tfs_sim_growth.csv")
+        keep = ts.stage1_genotypes(lib, ts.growth_genotypes(src))
+        ts.filter_growth_csv(src, "tfs_growth.csv.partial", genotypes=keep)
+        os.replace("tfs_growth.csv.partial", "tfs_growth.csv")
+        print(f"stage 1: {len(keep)} genotypes (wt, spikes, singles)", flush=True)
+
+    binding_csv = os.path.join(sim, "tfs_sim_binding.csv")
+    if args.add_binding_singles > 0:
+        binding_csv = os.path.abspath("binding.csv")
+        if not os.path.exists(binding_csv):
+            extra = binding_singles(sim, ts.growth_genotypes("tfs_growth.csv"),
+                                    args.add_binding_singles, args.binding_seed)
+            base = pd.read_csv(os.path.join(sim, "tfs_sim_binding.csv"))
+            pd.concat([base, extra[base.columns]], ignore_index=True).to_csv(
+                binding_csv, index=False)
+            print(f"binding: {base['genotype'].nunique()} spikes plus "
+                  f"{extra['genotype'].nunique()} singles", flush=True)
+
+    if not os.path.exists(CONFIG):
+        truth = (pd.read_csv(os.path.join(sim, "tfs_sim_growth_parameters.csv"))
+                 if args.pin_km_truth else None)
+        growth_priors("tfs_growth.csv", warp=full, truth=truth).to_csv(
+            "growth_priors.csv", index=False)
+        set_priors = ["sigma_fixed=0.17", "theta_log_hill_n_hyper_scale_fixed=0.5"]
+        if full:
+            set_priors.append("theta_growth_log_conc_shift_scale=1.0")
+        extra = {i.split("=")[0] for i in args.set_priors}
+        set_priors = [i for i in set_priors if i.split("=")[0] not in extra]
+        set_priors += list(args.set_priors)
+        mixture = ({} if args.no_mixture else
+                   dict(transformation_model="mixture",
+                        transformation_lambda=[0.357, 0.15],
+                        congression_theta_rule="homodimer"))
+        if args.congression_sets is not None:
+            if args.no_mixture:
+                raise ValueError("--congression_sets needs the mixture")
+            mixture["congression_sets"] = list(args.congression_sets)
+        configure_model(
+            binding_df=binding_csv,
+            growth_df=os.path.abspath("tfs_growth.csv"),
+            library_config=library_config,
+            theta_model="hill_geno",
+            **mixture,
+            theta_binding_noise_model="genotype_offset" if full else "zero",
+            growth_shares_replicates=True,
+            batch_size=4096,
+            allow_missing_spikes=True,
+            growth_priors=os.path.abspath("growth_priors.csv"),
+            set_priors=set_priors,
+            skip_model_stats=True,
+            out_prefix="tfs_configure")
+
+    fit_kw = {} if args.max_num_epochs is None else {"max_num_epochs": args.max_num_epochs}
+    if init_from is not None:
+        fit_kw["init_from"] = init_from
+    if not os.path.exists("tfs_fit_model_params.npz"):
+        fit_model(CONFIG, seed=args.seed, analysis_method="map", **fit_kw)
+    if not os.path.exists("tfs_posterior.h5"):
+        sample_posterior(CONFIG, CHECKPOINT, out_prefix="tfs_posterior",
+                         num_posterior_samples=500, skip_growth_observations=True)
+    if not os.path.exists("tfs_params_provenance.json"):
+        extract_params(CONFIG, "tfs_posterior.h5", out_prefix="tfs_params")
+    if not os.path.exists("tfs_pred_theta.csv"):
+        predict_theta(CONFIG, "tfs_posterior.h5", out_prefix="tfs_pred_theta")
+    print("stage 1 done", flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/joint-fit-sim/stage1.srun b/planning/studies/joint-fit-sim/stage1.srun
new file mode 100644
index 00000000..2b5e972a
--- /dev/null
+++ b/planning/studies/joint-fit-sim/stage1.srun
@@ -0,0 +1,74 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=jfsim_fit
+#SBATCH --partition=gpu
+#SBATCH --time=06:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# Joint stage 1 on one simulation (planning/studies/joint-fit-sim). Run from
+# the study's cluster directory after simulate.srun:
+#
+#   sbatch --export=ALL,SIM=sim_joint_s1,MODEL=full --output=fit_joint_full.out stage1.srun
+#
+#   SIM    simulation directory (from simulate.srun)
+#   MODEL  full | plain
+#   SEED   fit seed (default 1)
+#   INIT   optional: start at this fit's MAP (e.g. fit_null_s1_plain); the
+#          output is then fit_${SIM#sim_}_${MODEL}_from_${INIT#fit_*_s1_}
+#   PIN    optional: 1 pins k and m at the simulation's truth (stage1.py
+#          --pin_km_truth); output suffix _pinkm
+#   NOMIX  optional: 1 fits without the congression mixture (stage1.py
+#          --no_mixture); output suffix _single
+#   PRIORS optional: extra --set_priors items, separated by ':' (sbatch
+#          --export splits on commas); give TAG with it
+#   TAG    optional: output suffix _${TAG}
+#   CSETS  optional: the mixture's co-resident sets, ':'-separated (e.g.
+#          50:20:5; stage1.py --congression_sets); name the arm with TAG
+#   ADDB   optional: N extra binding singles (stage1.py
+#          --add_binding_singles); name the arm with TAG
+#   BSEED  optional: which random singles (stage1.py --binding_seed,
+#          default 1)
+#
+# Writes fit_${SIM#sim_}_${MODEL}/; a rerun resumes.
+
+module load cuda/12.4.1
+set -e
+SIM="${SIM:?set SIM}"
+MODEL="${MODEL:?set MODEL}"
+SEED="${SEED:-1}"
+STUDY=/gpfs/home/harms/tfscreen/planning/studies/joint-fit-sim
+OUT="fit_${SIM#sim_}_${MODEL}"
+EXTRA=()
+if [[ -n "${INIT:-}" ]]; then
+    OUT="${OUT}_from_${INIT#fit_*_s1_}"
+    EXTRA=(--init_from "${INIT}/tfs_fit_model_params.npz")
+fi
+if [[ "${NOMIX:-0}" == 1 ]]; then
+    OUT="${OUT}_single"
+    EXTRA+=(--no_mixture)
+fi
+if [[ -n "${PRIORS:-}" ]]; then
+    IFS=':' read -r -a ITEMS <<< "${PRIORS}"
+    EXTRA+=(--set_priors "${ITEMS[@]}")
+fi
+if [[ -n "${ADDB:-}" ]]; then
+    EXTRA+=(--add_binding_singles "${ADDB}" --binding_seed "${BSEED:-1}")
+fi
+if [[ -n "${CSETS:-}" ]]; then
+    IFS=':' read -r -a SETS <<< "${CSETS}"
+    EXTRA+=(--congression_sets "${SETS[@]}")
+fi
+if [[ -n "${TAG:-}" ]]; then
+    OUT="${OUT}_${TAG}"
+fi
+if [[ "${PIN:-0}" == 1 ]]; then
+    OUT="${OUT}_pinkm"
+    EXTRA+=(--pin_km_truth)
+fi
+echo ">>> stage 1 ${SIM} ${MODEL} -> ${OUT} $(date)"
+python "${STUDY}/stage1.py" "${SIM}" "${OUT}" --model "${MODEL}" --seed "${SEED}" "${EXTRA[@]}"
+echo ">>> done $(date)"
diff --git a/planning/studies/km-anchor/README.md b/planning/studies/km-anchor/README.md
new file mode 100644
index 00000000..834c6e3b
--- /dev/null
+++ b/planning/studies/km-anchor/README.md
@@ -0,0 +1,159 @@
+# Anchoring k and m with the wt monoculture rates
+
+**Question.** On the full-size simulation the deep genotypes rank well (X r
+0.99 above 1e4 reads) but their intervals do not cover (95% coverage 0.29
+realistic, 0.02 Poisson), because k and m are off and held at the MAP: X is
+compressed by |m| 30-45% too large, and k slid up against dk_geno. Counts
+measure relative frequencies, so only the tube totals pin the absolute
+scale, weakly. The experiment has one more measurement of that scale, the
+wt monoculture growth rates. Does a k/m prior from them bring deep coverage
+up while keeping the ranking? Does the real data accept that anchor?
+
+**Decision it feeds.** `planning/deep-coverage.md`, step 1: whether the
+real fit's k and m get their priors from the monoculture, and what the
+deep genotypes' intervals can claim.
+
+**Why it matters for the real data.** In kanR+kan the real fit and the
+simulation are off the same way against the monoculture rates, which are
+the simulation's truth:
+
+| kanR+kan | k | m |
+|---|---|---|
+| wt monoculture (= sim truth) | 0.0150 | -0.0141 |
+| real fit (recipe) | 0.0298 | -0.0204 |
+| simulated fit, realistic | 0.0274 | -0.0203 |
+| simulated fit, Poisson | 0.0189 | -0.0182 |
+
+In the simulation that is a property of the fit, so the real kanR curves
+are likely compressed the same way. pheS+4CP differs: the real fit's k is
+0.023 against 0.003 for the monoculture, and its |m| is smaller (0.0097
+against 0.0144), which may be the real selection-onset effect
+(`planning/offset-mode-growth-transition.md`).
+
+## Arms
+
+All three refit an existing growth table with the recipe's model (growth-
+only relative fit, level offsets at SD 0.17, log(n) population SD 0.5,
+loose k/m priors in the control conditions), adding k/m priors in the two
+selection conditions from the wt monoculture rates
+(`--growth_priors_wt_rates`: k = wt rate at 1 mM, m = rate at 0 minus rate
+at 1 mM, SD = standard error of the replicate mean floored at 0.002 per
+minute).
+
+| arm | growth table | rates |
+|---|---|---|
+| `anchor_sim` | `sim_realistic_s2` | the monoculture rates (the simulation's truth, at the monoculture's SD) |
+| `anchor_sim_perturbed` | `sim_realistic_s2` | the same, each rate moved once by a draw of its SD (seed 1) |
+| `anchor_real` | the recipe's `tfs_growth.csv` | the monoculture rates |
+
+Only the selection conditions get monoculture priors (`make_wt_rates.py`):
+in the control conditions wt's monoculture rate varies with IPTG in ways
+an m of 0 cannot carry, and the simulation sets their m to 0. The rate
+tables are derived from lab data, so they are generated on the cluster and
+never committed.
+
+## How to run
+
+On the cluster, after pulling this commit. Simulation arms, from the
+full-size simulation's working directory (it holds `sim_realistic_s2/` and
+the `processed/` link):
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/km-anchor/{run_anchor.srun,make_wt_rates.py} .
+```
+
+```bash
+python make_wt_rates.py processed/wt_control_rate_summary.csv --seed 1
+```
+
+It prints the 0 and 1 mM rates and their perturbed values; check that it
+wrote `wt_rates_selection.csv` and `wt_rates_selection_perturbed.csv`.
+`growth_priors_loose.csv` is already in this directory from the full-size
+run.
+
+```bash
+mkdir anchor_sim && cd anchor_sim && sbatch --export=ALL,GROWTH=../sim_realistic_s2/tfs_growth.csv,LIBRARY=../sim_realistic_s2/library_config.yaml,WT_RATES=../wt_rates_selection.csv,TRUTH_DIR=../sim_realistic_s2 ../run_anchor.srun && cd ..
+```
+
+```bash
+mkdir anchor_sim_perturbed && cd anchor_sim_perturbed && sbatch --export=ALL,GROWTH=../sim_realistic_s2/tfs_growth.csv,LIBRARY=../sim_realistic_s2/library_config.yaml,WT_RATES=../wt_rates_selection_perturbed.csv,TRUTH_DIR=../sim_realistic_s2 ../run_anchor.srun && cd ..
+```
+
+Real arm, next to the recipe run:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/km-anchor/{run_anchor.srun,make_wt_rates.py} .
+```
+
+```bash
+python make_wt_rates.py ../processed/wt_control_rate_summary.csv --seed 1
+```
+
+```bash
+mkdir anchor_real && cd anchor_real && sbatch --export=ALL,GROWTH=../recipe/tfs_growth.csv,LIBRARY=../../processed/library_config.yaml,WT_RATES=../wt_rates_selection.csv ../run_anchor.srun && cd ..
+```
+
+`growth_priors_loose.csv` is already in `real_fit/` from the recipe. Each
+arm ends with `>>> Done` in its `run.out` and a
+`summary/tfs_summarize_fit_summary.json`.
+
+## Inputs
+
+`sim_realistic_s2` (`planning/studies/full-size-sim/`), the recipe's growth
+table (`planning/studies/real-data-fit/`), and
+`processed/wt_control_rate_summary.csv` (the wt monoculture rates, from
+`prep_dev_data.py`).
+
+## Commit
+
+The commit that adds this study. Every step writes its provenance.
+
+## Results
+
+Run 2026-10-09 (all three arms converged at every stage; offsets clean in
+all). **The data reject the anchor, in the simulation and in the real
+data.**
+
+| arm | condition | k prior (SD) | k fit | m prior (SD) | m fit | truth k, m |
+|---|---|---|---|---|---|---|
+| unanchored sim (`sim_realistic_s2`) | kanR+kan | 0.015 (0.01) | 0.0274 | 0 (0.01) | -0.0203 | 0.0149, -0.0138 |
+| `anchor_sim` | kanR+kan | 0.0150 (0.002) | 0.0247 | -0.0141 (0.002) | -0.0184 | 0.0149, -0.0138 |
+| `anchor_sim_perturbed` | kanR+kan | 0.0156 (0.002) | 0.0255 | -0.0140 (0.002) | -0.0199 | 0.0149, -0.0138 |
+| `anchor_sim` | pheS+4CP | 0.0031 (0.002) | 0.0065 | 0.0144 (0.002) | 0.0183 | 0.0033, 0.0141 |
+| recipe (real) | kanR+kan | 0.015 (0.01) | 0.0298 | 0 (0.01) | -0.0204 | |
+| `anchor_real` | kanR+kan | 0.0150 (0.002) | 0.0249 | -0.0141 (0.002) | -0.0202 | |
+| recipe (real) | pheS+4CP | 0.015 (0.01) | 0.0226 | 0 (0.01) | 0.0097 | |
+| `anchor_real` | pheS+4CP | 0.0031 (0.002) | 0.0172 | 0.0144 (0.002) | 0.0097 | |
+
+- k moves by a pure slide: on the real data it fell by 0.005 in every
+  condition, the unanchored controls included, with dk_geno up by 0.0048
+  everywhere; it still ends five prior SDs above the monoculture.
+- m does not move. The real kanR m stays at -0.020 against a prior of
+  -0.014 +/- 0.002 (pheS 0.0097 against 0.0144), so the likelihood pins it.
+  In the simulation, given the truth as its prior, the fit still puts m at
+  -0.018 to -0.020 against -0.0138: the fitted model prefers the wrong m.
+- Deep coverage barely improves (`anchor_sim`, X 95% coverage 0.65 /
+  0.35 / 0.22 at 1e3-1e4 / 1e4-1e5 / >1e5 reads, against 0.56 / 0.29 /
+  0.08 unanchored); X slope 1.08-1.23 against 1.19-1.37.
+- The real fit is otherwise unchanged (X shifts by 0.03, log K and n by
+  under 0.01, 9 held directions).
+
+So the |m| inflation is a misspecification of the fit against how the
+simulation makes its data, not a weakly pinned ridge, and the simulation
+reproduces the real kanR numbers, so the real kanR X is most likely
+compressed by the same factor (~1.4). Ruled out: the OD-derived tube totals
+(unbiased in the simulation, drift <= 0.001 per minute, 96% of tubes in the
+calibrated range) and the 2026-10-08 simulator features (the bias is in the
+s1b arms, which had neither unassigned reads nor the excess wt). Suspects:
+congression (the simulation's lambda 0.357 with the homodimer rule; the fit
+uses `single`), per-tube rate noise (the fit has level offsets), and the
+dk_geno prior's shape. Next: a reduced-library factorial that switches each
+off (`planning/deep-coverage.md`, step 1b).
diff --git a/planning/studies/km-anchor/growth_priors_loose.csv b/planning/studies/km-anchor/growth_priors_loose.csv
new file mode 100644
index 00000000..4cfb4e48
--- /dev/null
+++ b/planning/studies/km-anchor/growth_priors_loose.csv
@@ -0,0 +1,5 @@
+condition_rep,k_loc,k_scale,m_loc,m_scale
+kanR+kan,0.015,0.01,0.0,0.01
+kanR-kan,0.015,0.01,0.0,0.01
+pheS+4CP,0.015,0.01,0.0,0.01
+pheS-4CP,0.015,0.01,0.0,0.01
diff --git a/planning/studies/km-anchor/make_wt_rates.py b/planning/studies/km-anchor/make_wt_rates.py
new file mode 100644
index 00000000..6f2ab0fe
--- /dev/null
+++ b/planning/studies/km-anchor/make_wt_rates.py
@@ -0,0 +1,50 @@
+"""
+wt monoculture rate tables for the k/m anchor arms.
+
+    python make_wt_rates.py <wt_control_rate_summary.csv> [--seed 1]
+
+Writes, in the current directory:
+
+- wt_rates_selection.csv: the selection conditions only (condition_sel
+  containing '+'). The control conditions are left to the loose priors: in
+  them wt's monoculture rate varies with IPTG for reasons the fit's m = 0
+  cannot carry, and the full-size simulation sets their m to 0.
+- wt_rates_selection_perturbed.csv: the same, with each rate_mean moved
+  once by a Normal draw of the SD the prior assumes (the standard error of
+  the replicate mean, floored at 0.002 per minute, as
+  priors_edit.growth_priors_from_wt_rates does). The honest-anchor arm: a
+  monoculture is a measurement, not the truth.
+"""
+
+import argparse
+
+import numpy as np
+import pandas as pd
+
+SD_FLOOR = 0.002
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("rates")
+    ap.add_argument("--seed", type=int, default=1)
+    args = ap.parse_args()
+
+    r = pd.read_csv(args.rates)
+    sel = r[r["condition_sel"].str.contains("+", regex=False)].copy()
+    sel.to_csv("wt_rates_selection.csv", index=False)
+
+    rng = np.random.default_rng(args.seed)
+    se = sel["rate_sd"] / np.sqrt(sel["num_replicates"])
+    sd = np.maximum(se.to_numpy(), SD_FLOOR)
+    pert = sel.copy()
+    pert["rate_mean"] = sel["rate_mean"].to_numpy() + rng.normal(0.0, sd)
+    pert.to_csv("wt_rates_selection_perturbed.csv", index=False)
+
+    show = sel[["condition_sel", "titrant_conc", "rate_mean"]].assign(
+        perturbed=pert["rate_mean"].to_numpy(), sd=sd)
+    print(show[show["titrant_conc"].isin([0.0, 1.0])].round(5).to_string(index=False))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/km-anchor/run_anchor.srun b/planning/studies/km-anchor/run_anchor.srun
new file mode 100644
index 00000000..fbf593a8
--- /dev/null
+++ b/planning/studies/km-anchor/run_anchor.srun
@@ -0,0 +1,68 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_anchor
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpulong
+#SBATCH --time=02-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# One k/m anchor arm (planning/studies/km-anchor): the recipe's model and
+# chain, refit on an existing growth table with k and m priors in the
+# selection conditions from the wt monoculture rates. Run from a new arm
+# directory; the paths below are relative to it.
+#
+#   GROWTH      growth table (tfs-process-counts output) to refit
+#   LIBRARY     library config the fit is configured with
+#   WT_RATES    wt_rates_selection.csv or wt_rates_selection_perturbed.csv
+#   TRUTH_DIR   (simulation arms) directory holding tfs_sim_*.csv; they are
+#               linked in so tfs-summarize-fit scores against the truth
+#
+# See README.md for the exact sbatch lines.
+
+module load cuda/12.4.1
+
+set -e
+
+GROWTH="${GROWTH:?set GROWTH}"
+LIBRARY="${LIBRARY:?set LIBRARY}"
+WT_RATES="${WT_RATES:?set WT_RATES}"
+SEED="${SEED:-1}"
+
+if [[ -n "${TRUTH_DIR:-}" ]]; then
+    for f in "${TRUTH_DIR}"/tfs_sim_*.csv; do
+        ln -sf "${f}" .
+    done
+fi
+
+echo ">>> Configure (k/m priors from ${WT_RATES}) $(date)"
+tfs-configure-model \
+    --allow_missing_spikes \
+    --growth_df "${GROWTH}" \
+    --library_config "${LIBRARY}" \
+    --theta_model hill_relative \
+    --growth_shares_replicates \
+    --batch_size 4096 \
+    --growth_priors ../growth_priors_loose.csv \
+    --growth_priors_wt_rates "${WT_RATES}" \
+    --set_priors sigma_fixed=0.17 theta_log_hill_n_hyper_scale_fixed=0.5
+
+echo ">>> Fit (staged MAP) $(date)"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" --analysis_method map
+
+echo ">>> Arrowhead Laplace $(date)"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    --laplace arrowhead --num_posterior_samples 500 --skip_growth_observations
+
+echo ">>> Extract and predict $(date)"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Summarize $(date)"
+tfs-summarize-fit .
+
+echo ">>> Done $(date)"
diff --git a/planning/studies/m-bias-factorial/README.md b/planning/studies/m-bias-factorial/README.md
new file mode 100644
index 00000000..83c0f0a3
--- /dev/null
+++ b/planning/studies/m-bias-factorial/README.md
@@ -0,0 +1,1515 @@
+# What makes the fit's m too steep
+
+**Question.** On the calibrated full-size simulation the fit's growth slope
+m is about 40% too steep in kanR+kan (-0.018 to -0.020 against a true
+-0.0138), even with the truth as a 0.002-SD prior
+(`planning/studies/km-anchor/`); the real fit has the same m against the
+wt monoculture. The fitted likelihood prefers the wrong m, so something
+the simulation does is missing from the fit. Which of the simulation's
+features the fit does not model causes it?
+
+**Decision it feeds.** `planning/deep-coverage.md`, step 1b: what the fit
+has to model, or correct for, before the deep genotypes' intervals can be
+honest.
+
+## Round 1: reduced-library simulations
+
+A reduced library (5 degenerate sites per tile, every spike site kept:
+5,256 doubles instead of 223,000), scaled with
+`genetics.library_design.scale_library_design` so each sequence keeps its
+reads, cells, transformants and PCR templates, on the real design (118
+tubes) with the calibrated simulation of `planning/studies/full-size-sim/`.
+The fit is the real recipe's (growth-only relative model, level offsets at
+SD 0.17, loose k/m priors), MAP only; the slope does not need the Laplace.
+
+| arm | changes from the calibrated simulation |
+|---|---|
+| `base` | none |
+| `no_cong` | no co-transformed cells (`transformation_poisson_lambda` 0) |
+| `no_tube` | no per-tube rate noise (`tube_noise_sigma` 0) |
+| `poisson` | Poisson counts (no founder, demographic or PCR noise) |
+| `clean` | all three off |
+
+Two seeds each. `score_arms.py` puts the truth on the fit's X scale (wt's
+true curve at the gauge concentrations) and reports k, m, the ratio
+m_fit / m_true, and the median dk_geno error.
+
+`base` must reproduce the bias first; if it does not, the reduced library
+changes the problem and the factorial says nothing.
+
+A local test (2026-10-09) ran the simulation, processing and configure in
+minutes, but the staged MAP's first stage was still descending (about 1.8
+nats per window, t = 12) at step 80,000 after an hour on a laptop CPU, so
+the arms run on the cluster.
+
+### How to run round 1
+
+On the cluster, after pulling this commit, from the full-size simulation's
+working directory (it holds `processed/` and `pilot_s1/`):
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/make_factorial.py --pilot pilot_s1 --out_dir m_bias
+```
+
+It builds the two full-size configs (a few minutes) and prints one line per
+arm: 5256 doubles, scale 0.1612, reads 8.17e+08. Then:
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/{run_arm.sh,run_arm.srun,score_arms.py} m_bias/
+```
+
+```bash
+cd m_bias && for d in base_s1 base_s2 no_cong_s1 no_cong_s2 no_tube_s1 no_tube_s2 poisson_s1 poisson_s2 clean_s1 clean_s2; do (cd $d && sbatch ../run_arm.srun); done
+```
+
+Each arm ends with `>>> Done` in its `run.out` and a
+`tfs_params_growth_m.csv`. Then, in `m_bias/`:
+
+```bash
+python score_arms.py *_s1 *_s2
+```
+
+It prints the table and writes `factorial_scores.csv`.
+
+Inputs: `processed/` (the dev data, via make_sim_config.py) and the
+full-size pilot (`pilot_s1/`, for the cfu0 calibration).
+
+### Round 1 results (2026-10-09)
+
+**Inconclusive: `base` did not reproduce the bias.** m_fit / m_true on the
+X scale (truth from wt's true curve at the gauge):
+
+| arm | kanR+kan s1 / s2 | pheS+4CP s1 / s2 |
+|---|---|---|
+| full size, realistic (`sim_realistic_s2`) | 1.47 | 1.43 |
+| full size, Poisson (`sim_poisson_s2`) | 1.32 | 1.16 |
+| `base` | 1.03 / 1.14 | 0.96 / 1.06 |
+| `no_cong` | 0.99 / 0.96 | 1.03 / 0.99 |
+| `no_tube` | 1.05 / 1.21 | 1.00 / 1.12 |
+| `poisson` | 1.13 / 1.14 | 1.01 / 1.02 |
+| `clean` | 1.04 / 1.05 | 1.04 / 1.05 |
+
+Every arm sits between 0.96 and 1.21. The seed-to-seed spread (`base` 1.03
+against 1.14) is as large as any difference between arms. Every fit
+converged except `clean`, whose stage 1 and joint stage both reached the
+epoch cap while still dropping a few nats per window.
+
+The reduced library changed the problem in two ways:
+
+- **Composition.** `scale_library_design` keeps each sequence's abundance,
+  so the spiked origin (wt repeated 271 times) keeps its mass while the
+  bulk shrinks. wt starts at 55% of the reduced pool against 11% at full
+  size, and doubles at 6% against 44% (`library_composition_table`). In
+  the sequenced tubes wt is 81% of reads against 31%, and doubles are 3%
+  against 35%.
+- **Detection.** cfu0 scales with the pool, so each tube's OD600 falls by
+  the same factor. 25 of 118 tubes read below the detection threshold and
+  dropped out (93 sequenced against 118).
+
+The bias weakens when wt dominates the tubes. That fits the tube totals
+pinning the absolute scale through wt when wt is most of the population,
+and the library's genotypes setting it when they are. It does not say
+which feature is misspecified.
+
+## Round 2: fit-side subsets of the full-size simulation
+
+Given the shared parameters (k, m, tube offsets, population hypers), each
+genotype's count likelihood is independent: the count model observes each
+genotype's reads against its predicted share of the tube's cells, with no
+constraint that the shares sum to one. So refitting the full-size
+`sim_realistic_s2` growth table (m ratio 1.47) with only some genotypes is a
+valid fit of the same simulated data. It changes only which genotypes
+inform k and m. `make_subsets.py` writes three arms:
+
+| arm | genotypes |
+|---|---|
+| `no_doubles` | wt, spikes and singles (952) |
+| `deep_doubles` | those plus every double with at least 1,000 total reads (80,915) |
+| `shallow_doubles` | those plus 80,915 of the 126,684 doubles below 1,000 reads, at random (seed 1) |
+
+Same chain as round 1 (`run_arm.sh` skips simulate and process because the
+arm directory links the simulation's `tfs_sim_*` files and has its own
+`tfs_growth.csv`). Readout: if `no_doubles` recovers m (ratio near 1), the
+doubles drive the bias; `deep_doubles` against `shallow_doubles` says
+whether it is the well-measured doubles or the noisy ones. If `no_doubles`
+is still at about 1.4, the bias comes from how wt, spikes and singles are
+modeled (or from the design), and congression and the dk_geno prior's
+shape become the next arms at full size.
+
+### How to run round 2
+
+On the cluster, after pulling this commit, from the full-size simulation's
+working directory (it holds `sim_realistic_s2/`):
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/make_subsets.py sim_realistic_s2 --out_dir m_subsets
+```
+
+It reads the 8 GB growth table twice (about 6 minutes locally) and prints
+`952 wt/spike/single genotypes, 80915 doubles >= 1000 reads, 80915 of
+126684 below`, then one genotype count per arm (952, 81867, 81867).
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/{run_arm.sh,run_arm.srun,score_arms.py} m_subsets/
+```
+
+```bash
+cd m_subsets && for d in no_doubles deep_doubles shallow_doubles; do (cd $d && sbatch ../run_arm.srun); done
+```
+
+When each `run.out` ends with `>>> Done`, in `m_subsets/`:
+
+```bash
+python score_arms.py no_doubles deep_doubles shallow_doubles
+```
+
+Inputs: `sim_realistic_s2/` (`planning/studies/full-size-sim/`).
+
+## Round 3: the full library with k and m held
+
+If the doubles bias m and wt, spikes and singles do not, then fitting the
+full library with k and m held at the no-doubles MAP should recover X for
+the doubles. `held_priors.py` turns an arm's fitted k and m into a
+`--growth_priors` table, and `run_held.srun` runs the full-size chain
+(staged MAP, arrowhead Laplace, predict, `tfs-summarize-fit`) with both
+clamped (`--set_priors m_pinned=1 k_pinned=1`). The intervals then leave
+out k and m's own uncertainty, so this checks X's slope and the coverage
+given k and m, against `sim_realistic_s2`'s summary (X slope 1.19-1.37,
+95% coverage 0.56 / 0.29 / 0.08 at 1e3-1e4 / 1e4-1e5 / >1e5 reads). A
+20-epoch smoke test of the chain with both pins ran locally (configure, MAP,
+arrowhead Laplace, extract).
+
+### How to run round 3
+
+On the cluster, from the full-size simulation's working directory:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+mkdir held_s2 && cd held_s2
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../m_subsets/no_doubles --out growth_priors_held.csv
+```
+
+It prints the four conditions' k and m; kanR+kan should read k 0.0182,
+m -0.0142. Then link the simulation's data and truth, and submit:
+
+```bash
+ln -s ../sim_realistic_s2/tfs_growth.csv ../sim_realistic_s2/tfs_sim_* . && cp ../sim_realistic_s2/library_config.yaml .
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun . && sbatch run_held.srun
+```
+
+It ends with `>>> Done` in `run.out` and a `summary/` directory, like the
+full-size runs (several hours: the full library and the Laplace).
+
+## Round 4: hold kanR only
+
+The real pheS+4CP departs from its monoculture with or without the doubles
+(`planning/deep-coverage.md` step 3), so the real fit holds kanR only:
+kanR+kan and kanR-kan at the no-doubles MAP by a tight prior (SD 1e-4;
+the doubles' pull has a likelihood SD near 0.0014, so this gives up about
+0.5% of it), pheS at the recipe's loose priors, and no pins (they act on
+every condition). X is shared across markers, so the simulated arm checks
+that holding kanR alone fixes X while pheS's m stays free: does pheS's m
+follow the kanR-pinned X, or do the doubles pull it, and X with it, steep
+again? The real arm is read only if the simulated one passes.
+
+| arm | data | held |
+|---|---|---|
+| `held_kanR_s2` | `sim_realistic_s2` | kanR at the simulated `no_doubles` MAP |
+| `held_kanR_real` | the recipe's growth table | kanR at the real `no_doubles` MAP |
+
+### How to run round 4
+
+`held_priors.py --hold` has since changed to write pin columns (round 5);
+round 4 ran it at commit 75611b08, where `--hold` held the named
+conditions by a tight prior (`--scale`, default 1e-4).
+
+Simulated arm, in the full-size simulation's working directory:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+mkdir held_kanR_s2 && cd held_kanR_s2
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../m_subsets/no_doubles --hold kanR --loose ../growth_priors_loose.csv --out growth_priors_held.csv
+```
+
+Check: kanR+kan k 0.018182, m -0.014221 at scale 0.0001; pheS rows 0.015,
+0.01, 0, 0.01.
+
+```bash
+ln -s ../sim_realistic_s2/tfs_growth.csv ../sim_realistic_s2/tfs_sim_* . && cp ../sim_realistic_s2/library_config.yaml .
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun . && sbatch --export=ALL,PINS= run_held.srun
+```
+
+Real arm, next to the recipe run:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit
+```
+
+```bash
+mkdir held_kanR_real && cd held_kanR_real
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../m_subsets_real/no_doubles --hold kanR --loose ../growth_priors_loose.csv --out growth_priors_held.csv
+```
+
+Check: kanR+kan k 0.016018, m -0.014136 at scale 0.0001.
+
+```bash
+ln -s ../recipe/tfs_growth.csv . && cp ../../processed/library_config.yaml .
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun . && sbatch --export=ALL,PINS= run_held.srun
+```
+
+The log's configure line reads `pins: none`. Each run ends with `>>> Done`
+in `run.out` and a `summary/` directory (about 2 hours, as round 3).
+
+## Round 5: kanR pinned per condition
+
+Round 4's tight prior leaked (see its result below), so round 5 repeats it
+with `linear`'s per-condition pins: `held_priors.py --hold kanR` now writes
+`k_pinned`/`m_pinned` columns (1 for kanR, 0 for pheS) into the growth
+priors table, which `tfs-configure-model --growth_priors` turns into
+per-condition clamps. kanR's k and m are then exact and leave the
+Laplace; pheS's are fitted. It needs the commit that adds per-condition
+pins on the cluster.
+
+### How to run round 5
+
+Simulated arm:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+mkdir held_kanR_pin_s2 && cd held_kanR_pin_s2
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../m_subsets/no_doubles --hold kanR --loose ../growth_priors_loose.csv --out growth_priors_held.csv
+```
+
+Check: the table has `k_pinned` and `m_pinned` columns, 1 for both kanR
+rows and 0 for pheS.
+
+```bash
+ln -s ../sim_realistic_s2/tfs_growth.csv ../sim_realistic_s2/tfs_sim_* . && cp ../sim_realistic_s2/library_config.yaml .
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun . && sbatch --export=ALL,PINS= run_held.srun
+```
+
+Real arm:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit
+```
+
+```bash
+mkdir held_kanR_pin_real && cd held_kanR_pin_real
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../m_subsets_real/no_doubles --hold kanR --loose ../growth_priors_loose.csv --out growth_priors_held.csv
+```
+
+```bash
+ln -s ../recipe/tfs_growth.csv . && cp ../../processed/library_config.yaml .
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun . && sbatch --export=ALL,PINS= run_held.srun
+```
+
+In `tfs_configure_priors.csv`, `growth.condition_growth.m_pinned` has one
+row per condition (1, 1, 0, 0 by name). After the fit,
+`tfs_params_growth_m.csv` reads kanR's held values exactly.
+
+## Round 6: the real data with every condition pinned
+
+Round 5 showed a free k on either marker reopens the k/dk_geno slide, and
+the real pheS m with X set by kanR matches pheS's own no-doubles fit. So
+the real fit pins all four conditions at the real no-doubles MAP, round
+3's setup (`held_priors.py` without `--hold`, `run_held.srun` with its
+default pins), in `real_fit/held_all_real`. The pheS curves stay
+provisional until `planning/deep-coverage.md` step 3: the pool's pheS
+selection is about half the wt monoculture's, and the simulation cannot
+say whether that is biology or a misspecification it lacks.
+
+## Step 2: k and m's uncertainty in X
+
+`planning/deep-coverage.md` step 2. A held fit gives X intervals given k
+and m. To carry their uncertainty, take joint draws of every condition's k
+and m from the stage-1 fit's arrowhead Laplace, refit the full library
+pinned at each draw (round 3's setup, starting from round 3's MAP with
+`--init_from`), and pool the draws' posteriors into one equal-weight
+mixture. The pooled intervals should cover the deepest genotypes better
+than round 3 (0.65 above 1e5 reads) if stage 1's own error is what they
+miss. The Laplace's k/m spread is only as honest as stage 1: on seed 1
+its pheS m was 11% off the truth, so if the spread comes out near 1%, the
+draws cannot cover that and the second seed (below) says whether it was
+luck.
+
+`km_draws.py` stops if the stage-1 Laplace holds a k or m direction at
+zero width (it would give those draws no spread). `score_x.py` scores
+any run directory or pooled table by depth; `depth.py` makes its read
+table.
+
+### How to run step 2 (simulation, `sim_realistic_s2`)
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+sbatch /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_km_draws.srun
+```
+
+`run_km_draws.out` prints each condition's k and m SD across the stage-1
+posterior and ends with `wrote 10 draws to km_draws/draw_*/`. If it stops
+on held k/m directions instead, send me the table it prints. Then submit
+the draws, each starting from round 3's MAP:
+
+```bash
+cd km_draws && for d in draw_*; do (cd $d && ln -s ../../sim_realistic_s2/tfs_growth.csv ../../sim_realistic_s2/tfs_sim_* . && cp ../../sim_realistic_s2/library_config.yaml . && sbatch --export=ALL,INIT=../../held_s2/tfs_fit_model_params.npz /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun); done
+```
+
+When every `draw_*/run.out` ends with `>>> Done`, pool them (in
+`km_draws/`):
+
+```bash
+sbatch /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_pool.srun
+```
+
+Then score `km_draws/pooled_aggregate.csv` against round 3 with
+`score_x.py` and round 3's truth table.
+
+## Second seed
+
+Stage 1's pheS m came out 11% low on the one simulation so far.
+`sim_realistic_s2` is seed 1 (the "s2" is the second calibration of the
+simulator). A fresh seed with the same calibration, run through both
+stages by `run_two_stage.srun`, says whether that was noise and whether the
+two-stage fit's X coverage holds on new data. The stage-1 Laplace is part
+of the job, so step 2 can run on it too.
+
+### How to run the second seed
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/full-size-sim/make_sim_config.py --out_dir sim_realistic_seed2 --seed 2 --calibrate_from pilot_s1
+```
+
+Check that `sim_realistic_seed2/simulate_config.yaml` reads `seed: 2`.
+
+```bash
+cd sim_realistic_seed2 && sbatch --export=ALL,SEED=2 /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_two_stage.srun
+```
+
+The simulation alone takes about 85 minutes; the whole job ends with
+`>>> Done` in `run_two_stage.out` and `stage2/summary/`. To also get this
+seed's unheld full-library fit (the bias at 1.45 should reproduce), submit
+`run_full_size.srun` in the same directory after it (it reuses
+`tfs_growth.csv`):
+
+```bash
+sbatch --export=ALL,SEED=2 --dependency=afterok:<the two-stage job id> ../run_full_size.srun
+```
+
+## Commit
+
+The commit that adds this study.
+
+## Results
+
+Round 1 above (inconclusive).
+
+### Round 2 results (2026-10-10)
+
+All three arms converged at every stage (about 15 minutes for
+`no_doubles`, 1.7 and 2.3 hours for the others on one GPU). m_fit / m_true
+on the X scale, `sim_realistic_s2`:
+
+| genotypes in the fit | doubles | kanR+kan | pheS+4CP |
+|---|---|---|---|
+| `no_doubles` | 0 | 1.03 | 0.89 |
+| `deep_doubles` | 80,915, all >= 1e3 reads | 1.23 | 1.16 |
+| `shallow_doubles` | 80,915, all < 1e3 reads | 1.20 | 1.10 |
+| all (the full-size fit) | 207,599 | 1.47 | 1.43 |
+
+**The doubles cause the bias, in proportion to how many there are, whatever
+their depth.** With only wt, spikes and singles the fit recovers m in kanR
+(pheS 11% shallow, within about twice round 1's seed spread). Adding 81,000
+doubles moves it to about 1.2 whether they are all deep or all shallow, and
+all 208,000 move it to 1.47. k follows m along the ridge (kanR k 0.018,
+0.022, 0.022 and 0.027 against a true 0.0149). The tube offsets look alike
+in every arm (SD 0.31-0.35).
+
+The genotypes' data are independent given the shared parameters, so each
+double adds a small, consistent pull on m, and the pulls add. A pull that
+does not depend on depth is not plain count noise. Two candidates:
+
+- **The joint MAP over many per-genotype latents.** The MAP optimizes each
+  genotype's X, K, n and dk_geno jointly with m, along the m·X ridge. The
+  hierarchical priors on those latents add one term per genotype. If a
+  steeper m with compressed X fits the population priors slightly better
+  per genotype, the preference grows with the number of genotypes, while the
+  information on the true m (wt, spikes, the tube totals) stays fixed. This
+  is the classic Neyman-Scott bias of joint estimation.
+- **A per-genotype misspecification of the doubles' model**, such as their
+  shared bulk transformation (congression; the fit uses `single`), whose
+  per-genotype effect would add the same way. Full-size Poisson counts
+  still give 1.32, so count noise accounts for at most part of it.
+
+**Practical route, whatever the mechanism:** estimate k and m (and the tube
+offsets) from wt, spikes and singles alone, then hold them for the full
+library. The first step is the same subset of the real data: does the real
+kanR m move from -0.020 to the monoculture's -0.014 when the doubles are
+dropped?
+
+### How to run the real-data `no_doubles` arm
+
+On the cluster, after pulling, next to the recipe run:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/make_subsets.py recipe --out_dir m_subsets_real --arms no_doubles --library_config ../processed/library_config.yaml
+```
+
+Check that it prints about 950 wt/spike/single genotypes, then:
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/{run_arm.sh,run_arm.srun} m_subsets_real/
+```
+
+```bash
+cd m_subsets_real/no_doubles && sbatch ../run_arm.srun
+```
+
+It ends with `>>> Done` in `run.out` (about 15 minutes) and writes
+`tfs_params_growth_k.csv` and `tfs_params_growth_m.csv`. There is no truth,
+so compare by hand against the recipe and the monoculture (kanR+kan k
+0.0150, m -0.0141; pheS+4CP k 0.0031, m 0.0144).
+
+### Real-data `no_doubles` result (2026-10-10)
+
+The recipe's growth table without the doubles (15 minutes on one GPU).
+Stage 1 and the joint stage reached the 100,000-epoch cap still descending
+about 1 nat per window in the joint stage, led by an ln_cfu0 hyper scale
+(the hierarchical MAP's funnel, which has no finite optimum). The tube
+offsets are centered and small (SD 0.23).
+
+| condition | fit | k | m |
+|---|---|---|---|
+| kanR+kan | wt monoculture | 0.0150 | -0.0141 |
+| | recipe, all genotypes | 0.0298 | -0.0204 |
+| | recipe, monoculture priors (`anchor_real`) | 0.0249 | -0.0202 |
+| | **no doubles** | **0.0160** | **-0.0141** |
+| pheS+4CP | wt monoculture | 0.0031 | 0.0144 |
+| | recipe, all genotypes | 0.0226 | 0.0097 |
+| | recipe, monoculture priors (`anchor_real`) | 0.0172 | 0.0097 |
+| | **no doubles** | **0.0117** | **0.0061** |
+
+**In kanR+kan the real data behave as the simulation does.** Without the
+doubles, k and m land on the wt monoculture with no prior pulling them
+there (loose priors, SD 0.01), so the real kanR curves fitted with all
+genotypes are compressed by about 1.45, as in the simulation.
+
+**pheS+4CP does not.** Dropping the doubles lowers m, as in the simulation,
+but from a starting point already below the monoculture, so it moves away
+from it (0.0061 against 0.0144) and k stays high. Something in the real
+pheS data that the simulation lacks sets pheS's scale: the selection-onset
+transient (`planning/offset-mode-growth-transition.md`) or the known
+mismatch between the 4CP wt in the screen and in monoculture. That is
+`planning/deep-coverage.md` step 3.
+
+### Round 3 result (2026-10-10)
+
+`held_s2`: the full `sim_realistic_s2` library with k and m clamped at the
+simulated `no_doubles` MAP (kanR+kan k 0.0182, m -0.0142; pheS+4CP k
+0.0057, m 0.0126). Every stage converged; about 2 hours on one GPU. X of
+the doubles against the truth, by total reads, unheld (the original
+full-size fit) against held:
+
+| reads | doubles | r | slope (truth on fit) | RMSE | 95% coverage | median 95% width |
+|---|---|---|---|---|---|---|
+| <= 1e2 | 37,707 | 0.26 / 0.28 | 0.05 / 0.05 | 2.16 / 2.25 | 0.97 / 0.98 | 4.08 / 5.74 |
+| 1e2-1e3 | 89,022 | 0.62 / 0.64 | 0.49 / 0.38 | 0.45 / 0.57 | 0.82 / 0.90 | 0.85 / 1.29 |
+| 1e3-1e4 | 64,362 | 0.93 / 0.93 | 1.19 / 0.82 | 0.20 / 0.19 | 0.56 / 0.89 | 0.35 / 0.53 |
+| 1e4-1e5 | 15,283 | 0.99 / 0.99 | 1.36 / 0.95 | 0.17 / 0.07 | 0.29 / 0.90 | 0.14 / 0.22 |
+| > 1e5 | 1,225 | 0.99 / 1.00 | 1.37 / 0.96 | 0.16 / 0.05 | 0.10 / 0.65 | 0.05 / 0.08 |
+
+**Holding k and m removes the compression.** Above 1e4 reads the slope
+goes from 1.36 to 0.95, RMSE falls about threefold and 95% coverage rises
+from 0.29 to 0.90 (1e4-1e5) and from 0.10 to 0.65 (above 1e5). The
+k/dk_geno slide is gone too: the median dk_geno error falls from -0.0057
+to -0.0008, and the held directions shrink from the k/dk_geno slide
+(eigenvalue -1.4e10) to a small dk_geno hyperparameter direction
+(-2.9e3). Tube offsets are unchanged (SD 0.32).
+
+What is left:
+
+- **The deepest genotypes still undercover (0.65 above 1e5 reads)**,
+  because the intervals leave out k and m's own error. The no-doubles m is
+  3% steep in kanR and 11% shallow in pheS, and at widths of 0.08 that is
+  as large as the interval.
+- **Below 1e3 reads the held fit is a little worse** (RMSE 0.57 against
+  0.45 at 1e2-1e3, slope 0.38): the shallow points scatter more and their
+  intervals widen to cover it (0.90). That is
+  `planning/deep-coverage.md` step 4 (hold the X population SDs).
+- At 1e3-1e4 a slope below 1 (0.82) is the expected regression dilution
+  of a noisy point, not a scale bias.
+
+### Round 4 result (2026-10-10)
+
+Both arms converged at every stage (4.9 and 5.7 hours). The configure
+lines read `pins: none`.
+
+| condition | truth (sim) | unheld | all pinned (round 3) | kanR held (round 4) |
+|---|---|---|---|---|
+| kanR+kan m | -0.0138 | -0.0203 | -0.0142 (held) | -0.0149 |
+| pheS+4CP m (free in round 4) | 0.0141 | 0.0202 | 0.0126 (held) | 0.0147 |
+
+| doubles' X (sim) | unheld | all pinned | kanR held |
+|---|---|---|---|
+| 1e4-1e5 reads: slope / RMSE / 95% coverage | 1.36 / 0.17 / 0.29 | 0.95 / 0.07 / 0.90 | 1.00 / 0.08 / 0.82 |
+| > 1e5 reads: slope / RMSE / 95% coverage | 1.37 / 0.16 / 0.10 | 0.96 / 0.05 / 0.65 | 1.01 / 0.07 / 0.42 |
+
+- **Holding kanR alone is enough.** With X fixed through kanR, the free
+  pheS m settles at 1.04 of the truth, closer than when it was held at its
+  own no-doubles value (0.89).
+- **The tight prior leaked.** kanR m moved from the held -0.0142 to
+  -0.0149, seven prior SDs and about 11% of the doubles' pull, not the
+  0.5% estimated from the k/m anchor study. That costs deep coverage
+  against the pinned run, and k and m came back as held Laplace directions
+  (eigenvalues near -1e10).
+- **Real data.** kanR m leaked shallow (-0.0133 against the held -0.0141,
+  k 0.0162), so the real kanR X comes out about 1.5 times wider than in the
+  recipe fit (0.0204 / 0.0133). The free pheS m settled at 0.0064 (k
+  0.0099), as in the no-doubles fit: with X set by kanR, the real pool's
+  pheS selection is about half the wt monoculture's, a property of the
+  data (`planning/deep-coverage.md` step 3).
+
+So round 5 pins kanR per condition instead.
+
+### Round 5 result (2026-10-11)
+
+Both arms converged at every stage (about 3.5 and 7 hours). The pins held
+exactly: kanR's k and m equal the no-doubles values in both arms.
+
+| sim, doubles above 1e4 reads | all pinned (round 3) | kanR prior (round 4) | kanR pinned (round 5) |
+|---|---|---|---|
+| 1e4-1e5: slope / RMSE / 95% coverage | 0.95 / 0.07 / 0.90 | 1.00 / 0.08 / 0.82 | 0.99 / 0.10 / 0.67 |
+| > 1e5: slope / RMSE / 95% coverage | 0.96 / 0.05 / 0.65 | 1.01 / 0.07 / 0.42 | 0.99 / 0.09 / 0.20 |
+| mean X error at 0 / 1 mM IPTG | +0.02 / -0.05 | +0.06 / 0.00 | +0.09 / +0.04 |
+| pheS+4CP k (truth 0.0033) | 0.0057 (held) | 0.0005 | -0.0009 |
+| pheS+4CP m (truth 0.0141) | 0.0126 (held) | 0.0147 | 0.0140 |
+
+**Freeing pheS's k reopens the k/dk_geno slide.** The free pheS m is
+right (1.00 of the truth), and X's scale is right (slope 0.99), but every
+deep double's X shifts up by a near-constant amount. A uniform shift in X
+is compensated by dk_geno and pheS's k together. kanR's pinned k no longer
+closes the slide on its own, because dk_geno is shared and pheS's k can
+absorb the difference. Only wt's gauge resists, weakly. The Laplace
+holds that direction (pheS+4CP k, eigenvalue -1.4e10) at zero width, so
+the intervals do not widen to cover the shift. Round 4 left pheS's k
+free too; its MAP stopped at a smaller shift. Round 3, with every
+condition pinned, has no slide.
+
+**Real arm:** kanR k 0.0160, m -0.0141 (pinned); pheS+4CP k 0.0093, m
+0.0068, against its own no-doubles fit's 0.0117 and 0.0061. pheS's k slid
+the same way, and its held direction is again pheS+4CP k (eigenvalue
+-4.7e9).
+
+**What the data support for pheS.** In the real data, pheS's m with X
+set by kanR (0.0064 in round 4, 0.0068 here) matches pheS's own
+no-doubles fit (0.0061). So the half-strength pheS selection is the pool's
+property, the same with or without the doubles, not an artifact of the
+no-doubles fit. Pinning pheS at its no-doubles values (round 3's setup) is
+what the data support, and it is the setup that closes the slide on the
+simulation.
+
+### Round 6 result (2026-10-11)
+
+`held_all_real` converged at every stage. k and m sit at the real
+no-doubles values in all four conditions; the only held Laplace direction
+left is a small one on the dk_geno population spread (eigenvalue -3.6e3;
+no k, m or slide direction). Tube offsets SD 0.21.
+
+Against the recipe fit, by total reads (the recipe's `tfs_pred_theta.csv`
+and per-genotype reads from its growth table):
+
+| genotypes | reads | n | r with recipe | slope (pinned on recipe) | median 95% width, recipe / pinned |
+|---|---|---|---|---|---|
+| doubles | <= 1e2 | 43,713 | 0.977 | 1.48 | 1.61 / 2.44 |
+| doubles | 1e2-1e3 | 95,717 | 0.990 | 1.47 | 0.63 / 0.94 |
+| doubles | 1e3-1e4 | 60,542 | 0.997 | 1.47 | 0.31 / 0.46 |
+| doubles | 1e4-1e5 | 15,634 | 0.999 | 1.48 | 0.18 / 0.27 |
+| doubles | > 1e5 | 1,387 | 0.999 | 1.48 | 0.13 / 0.21 |
+| singles | > 1e5 | 904 | 1.000 | 1.47 | 0.13 / 0.19 |
+
+**The real X stretches by a uniform 1.47**, at every depth, singles and
+doubles alike: between kanR's m ratio (0.0204 / 0.0141 = 1.45) and pheS's
+(0.0097 / 0.0061 = 1.59), as a single X shared across markers should be,
+and the factor the simulation predicted for the recipe's compression
+(about 1.45). Ranking is unchanged (r 0.99-1.00 above 100 reads) and the
+widths stretch with X. Deep doubles' median X runs from 0.53 at no IPTG to
+0.13 at 1 mM, against 0.42 to 0.14 in the recipe.
+
+The intervals still leave out k and m's own uncertainty
+(`planning/deep-coverage.md` step 2), and the pheS-derived part of X waits
+on step 3.
+
+### Step 2: the stage-1 Laplace (2026-10-11)
+
+`run_km_draws.srun` on `m_subsets/no_doubles` (seed 1). The arrowhead
+Laplace held no k or m direction, and its k/m spread is far smaller than
+stage 1's actual error:
+
+| condition | stage-1 k (SD) | k truth | stage-1 m (SD) | m truth (X scale) |
+|---|---|---|---|---|
+| kanR+kan | 0.0182 (0.0008) | 0.0149 | -0.01422 (0.00008) | -0.0138 |
+| pheS+4CP | 0.0056 (0.0011) | 0.0033 | 0.01261 (0.00007) | 0.0141 |
+
+m is off by about 5 SDs in kanR and 21 in pheS; k by 4 and 2. So draws
+from this Laplace vary m by about 0.6% and cannot cover a 3-11% error.
+Either stage 1 is biased, or its error is noise much larger than its
+Laplace (the simulation's per-tube rate noise, which the fit's level offsets do not model, is
+a likely source). The second seed tells the two apart. If it is noise,
+the k/m uncertainty should come from resampling stage 1 (refits over
+tubes or replicates), not from its Laplace. The ten draw fits were
+cancelled before they ran; `km_draws/` holds only the draw tables.
+
+### Second seed: stage 1 (2026-10-11)
+
+`sim_realistic_seed2/stage1/no_doubles` converged at every stage (about 10
+minutes); the Laplace held no k or m direction (k SD 0.0003-0.0006, m SD
+under 0.0001).
+
+| | seed 1 | seed 2 |
+|---|---|---|
+| kanR+kan m / truth | 1.028 | 1.017 |
+| pheS+4CP m / truth | 0.895 | 0.897 |
+| kanR+kan k - truth | +0.0033 | -0.0024 |
+| pheS+4CP k - truth | +0.0023 | -0.0007 |
+
+**Stage 1's m error is a bias, not noise**: the same on both seeds, kanR
+about 2% steep and pheS about 10% shallow. Wider intervals cannot fix a
+bias, so step 2's draws would not help even with an honest spread. k's
+error is mostly noise: it changes sign between seeds, and its seed-to-seed
+spread (about 0.003) is several times its Laplace SD. Through the
+k/dk_geno slide that noise moves X by a constant. Next: find the pheS bias
+with stage-1-only fits (about 10 minutes each), starting with the Poisson
+simulation (`sim_poisson_s2`) to test the realistic count noise.
+
+### Stage 1 under Poisson counts (2026-10-11)
+
+`m_subsets_poisson/no_doubles` (the `sim_poisson_s2` growth table, seed
+1; the joint stage reached the epoch cap still creeping about 30 nats per
+window, led by `growth_phi` drifting toward the Poisson limit): kanR+kan m
+1.004 of the truth, pheS+4CP 0.874. The realistic count noise is not the
+cause, and neither is founder or demographic noise (the Poisson simulation
+has none). The OD600-derived tube totals are not either: against the
+simulation's true totals they are within 2% in every condition and IPTG
+level, with no trend.
+
+**Suspect: congression's asymmetry between markers.** The homodimer rule
+gives a co-transformed cell a θ that leans toward its higher-θ plasmid. In
+pheS+4CP (m > 0) higher θ grows faster, so a low-X mutant sharing a cell
+with a high-X partner grows like the partner and those cells take over:
+low-X genotypes look higher, the response compresses, and m reads
+shallow. In kanR+kan (m < 0) the shared cells grow slower and fall
+behind, so the clean cells dominate and the bias stays small. The test:
+the seed-1 simulation with `transformation_poisson_lambda` 0 (same seed,
+so the same phenotypes), stage 1 only (`run_two_stage.srun`, `STAGES=1`).
+
+### How to run the no-congression stage 1
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+mkdir sim_realistic_nocong && cp sim_realistic_s2/{simulate_config.yaml,design.csv,od600_calibration.yaml,library_config.yaml} sim_realistic_nocong/
+```
+
+```bash
+sed -i 's/^transformation_poisson_lambda: .*/transformation_poisson_lambda: 0.0/' sim_realistic_nocong/simulate_config.yaml && grep -n "lambda\|^seed" sim_realistic_nocong/simulate_config.yaml
+```
+
+Check: `transformation_poisson_lambda: 0.0` and `seed: 1`.
+
+```bash
+cd sim_realistic_nocong && sbatch --export=ALL,SEED=1,STAGES=1 /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_two_stage.srun
+```
+
+About 85 minutes to simulate, then about 10 for stage 1. Read
+`stage1/no_doubles/tfs_params_growth_m.csv` against the truth with
+`score_arms.py`.
+
+### No-congression stage 1 (2026-10-11)
+
+`sim_realistic_nocong` (seed 1, `transformation_poisson_lambda` 0, else
+as `sim_realistic_s2`); every stage converged; no k or m direction held.
+
+| stage-1 m / truth | congression on | congression off |
+|---|---|---|
+| kanR+kan | 1.028 | 1.032 |
+| pheS+4CP | 0.895 | 1.035 |
+
+**Congression causes stage 1's pheS bias.** kanR does not move, as the
+asymmetry predicts. Both markers keep a small steep bias (about 3%)
+without congression.
+
+Fitting congression (`transformation: mixture`) does not fit the
+two-stage idea: a mixture needs each co-resident partner's θ, so the
+partners, mostly doubles, have to be in the fit, which brings back their
+pull on m; and the relative-X fit accepts only the `max` rule, not the
+simulation's `homodimer`. A stage 1 of clones with little congression
+avoids both: wt and the spikes. In the simulated pool (with the 271
+repeated wt sequences), about 17% of a bulk genotype's cells are
+congressed (`transformation_poisson_lambda` 0.357); wt is 77% spiked, so
+about 4% of its cells are congressed, single spikes about 10%, double
+spikes almost none. wt alone sets the X gauge and, through the tube
+totals, its growth difference between 0 and 1 mM is m. `make_subsets.py
+--arms spikes` keeps wt and the library config's spiked genotypes (about
+six with growth data).
+
+### How to run the spikes-only stage 1
+
+Simulation, seed 1 (congression on), in the full-size working directory:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/make_subsets.py sim_realistic_s2 --arms spikes --out_dir m_subsets
+```
+
+```bash
+cd m_subsets/spikes && sbatch ../run_arm.srun && cd ../..
+```
+
+Seed 2 (`run_arm.srun` and `run_arm.sh` are not in its `stage1/` yet):
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/make_subsets.py sim_realistic_seed2 --arms spikes --out_dir sim_realistic_seed2/stage1
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/{run_arm.sh,run_arm.srun} sim_realistic_seed2/stage1/ && cd sim_realistic_seed2/stage1/spikes && sbatch ../run_arm.srun
+```
+
+Real data, next to the recipe:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/make_subsets.py recipe --arms spikes --out_dir m_subsets_real --library_config ../processed/library_config.yaml
+```
+
+```bash
+cd m_subsets_real/spikes && sbatch ../run_arm.srun
+```
+
+Each `make_subsets.py` reads its 8 GB growth table (about 6 minutes) and
+prints `spikes: N genotypes`; each fit takes about 10 minutes. On the
+real data, whether pheS m rises toward the monoculture's 0.0144 says how
+much of the real pheS gap is congression (the simulation's congression
+alone takes about 14% off pheS m; the real gap is about 2x).
+
+### Second seed: stage 2 (2026-10-11)
+
+`sim_realistic_seed2/stage2` (every condition pinned at seed 2's stage 1)
+converged at every stage (about 1.7 hours). The doubles' X by total reads
+(`score_x.py`, reads from `depth.py` on seed 2's growth table):
+
+| doubles | seed 1 (round 3) | seed 2 |
+|---|---|---|
+| 1e3-1e4: slope / RMSE / 95% coverage | 0.82 / 0.19 / 0.89 | 0.83 / 0.19 / 0.88 |
+| 1e4-1e5: slope / RMSE / 95% coverage | 0.95 / 0.07 / 0.90 | 0.97 / 0.07 / 0.87 |
+| > 1e5: slope / RMSE / 95% coverage | 0.96 / 0.05 / 0.65 | 0.98 / 0.06 / 0.51 |
+| deep X error at 0 / 1 mM | +0.02 / -0.05 | +0.05 / +0.01 |
+
+The two-stage fit's gain reproduces on a new seed: slope near 1 and RMSE
+0.06-0.07 above 1e4 reads, against 1.36 and 0.16 for the one-stage fit.
+What is left is an X offset of up to about 0.05 that changes between
+seeds (stage 1's k noise, through the k/dk_geno slide) on top of pheS's
+congression bias; the deepest genotypes' intervals (widths about 0.08)
+do not cover it (0.65, 0.51).
+
+### Spikes-only stage 1 (2026-10-11)
+
+wt plus the spiked genotypes (`make_subsets.py --arms spikes`), about 10
+minutes each. Simulation (m / truth):
+
+| | seed 1 | seed 2 |
+|---|---|---|
+| all singles, kanR+kan | 1.028 | 1.017 |
+| all singles, pheS+4CP | 0.895 | 0.897 |
+| spikes only, kanR+kan | 1.040 | 0.969 |
+| spikes only, pheS+4CP | 1.037 | 0.988 |
+
+**In the simulation, spikes only removes the congression bias** (pheS as
+with congression off), at the cost of noise: about ±4% between seeds
+against about ±1% with all singles. Seed 1's joint stage reached the
+epoch cap still descending about 8 nats per window (a count-noise
+parameter moving), so its numbers are provisional.
+
+**On the real data it fails.** All nine spikes are deep (wt 3.9e8 reads,
+each spike 1.3-6.2e6), but the fit settled in another solution: kanR+kan
+m -0.0051, the control kanR-kan m -0.0123 with that condition's tube
+offsets spread to SD 0.64 (the others 0.14-0.23), and pheS+4CP m about 0
+(stage 1 at the epoch cap, nearly flat). With nine genotypes the model
+can trade slope between the selection and control conditions, and the
+loose priors give the controls' m the same SD (0.01) as selection's. Next:
+the same fit with the control slopes held near 0 (SD 0.001, `linear`'s
+default for control conditions).
+
+### How to run the real spikes-only fit with tight controls
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit/m_subsets_real
+```
+
+```bash
+mkdir spikes_tightctl && cd spikes_tightctl && ln -s ../spikes/tfs_growth.csv . && cp ../spikes/library_config.yaml .
+```
+
+```bash
+printf "condition_rep,k_loc,k_scale,m_loc,m_scale\nkanR+kan,0.015,0.01,0.0,0.01\nkanR-kan,0.015,0.01,0.0,0.001\npheS+4CP,0.015,0.01,0.0,0.01\npheS-4CP,0.015,0.01,0.0,0.001\n" > growth_priors_loose.csv && cat growth_priors_loose.csv
+```
+
+Check that the two control rows (`-kan`, `-4CP`) have m_scale 0.001.
+`run_arm.sh` reads the file by that name.
+
+```bash
+sbatch ../run_arm.srun
+```
+
+### Real spikes-only with tight controls, and wt's own trajectories (2026-10-11)
+
+`m_subsets_real/spikes_tightctl` (control m SD 0.001): the same solution
+as before (kanR+kan m -0.0042, kanR-kan m -0.0093, nine prior SDs from 0,
+pheS+4CP m about -0.001; kanR-kan tube offsets SD 0.54). With nine
+genotypes there is no population of X values to pin X's scale, so the fit
+slides along the m·X ridge to a small m. The spikes-only stage 1 is
+dropped for the real data.
+
+Reading wt's growth straight off the growth tables (slope of ln_cfu over
+time per condition and IPTG level) is too noisy to use: each replicate
+has 2-3 sequenced tubes over about 40 minutes per level, and tube-level
+noise of about 0.3 ln units gives a slope error near 0.008 per minute, the
+size of m. On the simulation it gives kanR m of +0.039 and -0.026 in the
+two replicates against a truth of -0.014, and pooling replicates
+confounds their starting levels with time. The real pool's wt happened
+to come out near the monoculture (kanR m -0.016, pheS +0.013, pooled over
+replicates), but that is not evidence. The real pheS gap stays open.
+
+Options for congression in stage 1: correct the all-singles stage 1's
+pheS m by the simulation's factor (about 1.15; assumes the simulation's
+lambda and homodimer rule); a middle-sized stage 1 (the spikes plus a few
+hundred of the deepest singles); or fit congression (`mixture`, which
+needs the partners and the `max` rule).
+
+### Option 2 and a test of whether congression is the doubles' pull
+
+Option 2: `make_subsets.py --arms spikes_deep` keeps wt, the spikes and
+the 300 deepest singles (`--n_deep_singles`), on both simulation seeds.
+
+Option 3 is feasible: the relative fit configures and runs with
+`--transformation_model mixture --congression_theta_rule max` (40 steps
+on a round 1 table, about 1.8 s per step on a laptop). The lab's estimate
+of lambda is 0.357 with a likelihood-ratio 95% interval of 0.156-0.674
+(log-SD about 0.37; as the (mean, std) that `--transformation_lambda`
+takes, about 0.38 0.15). The simulation's 0.357 sits mid-interval, but the
+real lambda could be nearly twice that, and congression's pheS bias grows
+with lambda.
+
+Before building option 3 into stage 1: round 1's reduced library hinted
+that congression also drives the doubles' pull (no_cong m 0.96-0.99
+against base 1.03-1.14). If it does, one full-library fit with `mixture`
+could fix both and the two stages would not be needed. The test is the
+one-stage full-library fit on `sim_realistic_nocong`: m near 1.0 means
+the pull is congression; m near 1.45 means it is separate.
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/sim_realistic_nocong && sbatch --export=ALL,SEED=1 ../run_full_size.srun
+```
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/make_subsets.py sim_realistic_s2 --arms spikes_deep --out_dir m_subsets && (cd m_subsets/spikes_deep && sbatch ../run_arm.srun)
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/make_subsets.py sim_realistic_seed2 --arms spikes_deep --out_dir sim_realistic_seed2/stage1 && (cd sim_realistic_seed2/stage1/spikes_deep && sbatch ../run_arm.srun)
+```
+
+### Option 2 result (2026-10-11)
+
+| stage-1 m / truth | all singles | spikes only | spikes + 300 deepest singles |
+|---|---|---|---|
+| seed 1, kanR+kan | 1.028 | 1.040 | 1.014 |
+| seed 1, pheS+4CP | 0.895 | 1.037 | 0.889 |
+| seed 2, kanR+kan | 1.017 | 0.969 | 1.024 |
+| seed 2, pheS+4CP | 0.897 | 0.988 | 0.908 |
+
+The deepest singles are as congressed as the rest; once they are in, the
+pheS bias is back at the all-singles level (seed 1's joint stage at the
+epoch cap, seed 2 converged). A stage 1 large enough to pin X's scale
+carries congression, so congression has to be modeled (option 3). The
+one-stage no-congression fit decides whether that goes into one full
+fit or into stage 1.
+
+### One-stage fit without congression (2026-10-11)
+
+`sim_realistic_nocong`, the full library through `run_full_size.srun`;
+every stage converged.
+
+| full-library m / truth | congression on | congression off |
+|---|---|---|
+| kanR+kan | 1.47 | 1.37 |
+| pheS+4CP | 1.43 | 1.51 |
+
+**The doubles' pull is not congression.** Two separate problems: the
+doubles steepen m about 1.45-fold (fixed by the two stages), and
+congression flattens pheS's m about 0.9-fold in any stage 1 large enough to
+pin X's scale. So congression is modeled inside the two-stage fit, in
+stage 1.
+
+## Option 3: stage 1 with the congression mixture
+
+Stage 1 without doubles, with `--transformation_model mixture
+--transformation_lambda 0.357 0.15 --congression_theta_rule max`
+(`run_arm.sh` reads extra configure flags from the arm's
+`configure_extra.txt`). Two approximations: the fit draws each genotype's
+co-resident partners from the genotypes with growth data, so in stage 1
+from wt, the spikes and the singles, while the real partners are mostly
+doubles (in the simulation singles and doubles draw their phenotypes from
+the same distribution); and the fit's `max` rule stands in for the
+simulation's `homodimer`. The arm is configured with the simulation's own
+composition (`simulate_config.yaml` as the library config: wt's 271
+spiked copies, so wt is mostly clean, as in the simulated pool), so the test
+is of modeling congression, not of knowing wt's purity. On real data, wt's
+purity is unknown (the excess wt is being worked out at the bench).
+
+### How to run option 3 (both seeds, reusing the no-doubles tables)
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/m_subsets
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_arm.sh . && mkdir no_doubles_mix && cd no_doubles_mix && ln -s ../no_doubles/tfs_growth.csv ../no_doubles/tfs_sim_* . && cp ../no_doubles/{simulate_config.yaml,growth_priors_loose.csv} . && cp simulate_config.yaml library_config.yaml && echo "--transformation_model mixture --transformation_lambda 0.357 0.15 --congression_theta_rule max" > configure_extra.txt && sbatch ../run_arm.srun
+```
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/sim_realistic_seed2/stage1
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_arm.sh . && mkdir no_doubles_mix && cd no_doubles_mix && ln -s ../no_doubles/tfs_growth.csv ../no_doubles/tfs_sim_* . && cp ../no_doubles/{simulate_config.yaml,growth_priors_loose.csv} . && cp simulate_config.yaml library_config.yaml && echo "--transformation_model mixture --transformation_lambda 0.357 0.15 --congression_theta_rule max" > configure_extra.txt && sbatch ../run_arm.srun
+```
+
+The `cp run_arm.sh` replaces the copy already in each folder with the one
+that reads `configure_extra.txt`. In each arm's `configure.log` the
+command line shows the mixture flags.
+
+### Option 3 result: stage 1 (2026-10-11)
+
+| stage-1 m / truth | seed 1 | seed 2 |
+|---|---|---|
+| without mixture, kanR+kan | 1.028 | 1.017 |
+| without mixture, pheS+4CP | 0.895 | 0.897 |
+| with mixture, kanR+kan | 1.056 | 1.047 |
+| with mixture, pheS+4CP | 1.037 | 1.023 |
+
+**Modeling congression removes stage 1's pheS bias** on both seeds. Both
+markers keep a small steep bias (2-6%). Seed 2 converged at every stage;
+seed 1's first stage reached its cap, the later ones converged. The
+fitted lambda is 0.27 and 0.25 against the simulation's 0.357, inside the
+prior: the `max` rule lets the higher-θ plasmid set a shared cell's θ
+completely, where `homodimer` only leans toward it, so fewer congressed
+cells reproduce the same effect. On real data the fitted lambda is a
+model parameter, not the measured one; keep it fitted, with the
+measurement as its prior.
+
+## Stage 2 after a mixture stage 1
+
+k and m pinned at the mixture stage 1, on both seeds, with stage 2 fitted
+without the mixture (`single`, as rounds 3 and 6) and with it (every
+genotype's partners are in the full library; a few cell classes per
+genotype, so heavier). `run_held.srun` reads `configure_extra.txt` like
+`run_arm.sh`. All four use the simulation's composition as the library
+config, as the stage 1 did.
+
+### How to run
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+Seed 1 (`sim_realistic_s2`), stage 2 without and with the mixture:
+
+```bash
+for v in single mix; do mkdir stage2_${v}_seed1 && (cd stage2_${v}_seed1 && python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../m_subsets/no_doubles_mix --out growth_priors_held.csv && ln -s ../sim_realistic_s2/tfs_growth.csv ../sim_realistic_s2/tfs_sim_* . && cp ../sim_realistic_s2/simulate_config.yaml library_config.yaml && if [ $v = mix ]; then echo "--transformation_model mixture --transformation_lambda 0.357 0.15 --congression_theta_rule max" > configure_extra.txt; fi && sbatch /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun); done
+```
+
+Seed 2:
+
+```bash
+for v in single mix; do mkdir stage2_${v}_seed2 && (cd stage2_${v}_seed2 && python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../sim_realistic_seed2/stage1/no_doubles_mix --out growth_priors_held.csv && ln -s ../sim_realistic_seed2/tfs_growth.csv ../sim_realistic_seed2/tfs_sim_* . && cp ../sim_realistic_seed2/simulate_config.yaml library_config.yaml && if [ $v = mix ]; then echo "--transformation_model mixture --transformation_lambda 0.357 0.15 --congression_theta_rule max" > configure_extra.txt; fi && sbatch /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun); done
+```
+
+Each `held_priors.py` prints the pinned table (seed 1 kanR+kan m about
+-0.0146). Each `run.out` names the extra flags on its configure line
+(`extra: none` for `single`). Score with `score_x.py` against
+`held_s2` (seed 1) and `sim_realistic_seed2/stage2` (seed 2).
+
+### Stage 2 after a mixture stage 1: first results (2026-10-12)
+
+`stage2_single_seed1` (k and m pinned at the mixture stage 1, stage 2
+`single`) converged; against round 3 (stage 1 without the mixture):
+
+| doubles, seed 1 | round 3 | mixture stage 1, `single` stage 2 |
+|---|---|---|
+| 1e4-1e5: slope / RMSE / 95% coverage | 0.95 / 0.07 / 0.90 | 1.02 / 0.07 / 0.90 |
+| > 1e5: slope / RMSE / 95% coverage | 0.96 / 0.05 / 0.65 | 1.03 / 0.05 / 0.76 |
+| deep X error at 0 / 1 mM | +0.02 / -0.05 | +0.005 / -0.02 |
+
+The better stage 1 roughly halves the X offset and raises the deepest
+genotypes' coverage from 0.65 to 0.76.
+
+`stage2_mix_seed1`'s MAP converged (162,000 steps, no memory trouble), but
+the arrowhead Laplace refuses it: "genotype blocks are coupled (response
+on unprobed genotypes 1.10e-02 of the probed ones)". The mixture couples
+each genotype to its co-resident partners' X, which the block Laplace
+assumes away; the coupling is about 1% of a genotype's own response. Seed
+2's mixture run's MAP converged too (102,000 steps); its Laplace ran out
+of GPU memory (a 26.6 GB allocation) before reaching the coupling check. `run_point.srun` finishes such a
+fit at the MAP point (`--laplace point`, predict, summarize), enough to
+compare X points with `single`. If the mixture stage 2 is worth keeping,
+its intervals need a Laplace that tolerates (or drops) couplings this small.
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/stage2_mix_seed1 && sbatch /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_point.srun
+```
+
+Once `stage2_mix_seed2/run.err` shows the same error, the same in
+`stage2_mix_seed2`.
+
+`stage2_single_seed2` converged; against seed 2's first two-stage fit
+(stage 1 without the mixture):
+
+| doubles, seed 2 | stage 1 without mixture | mixture stage 1, `single` stage 2 |
+|---|---|---|
+| 1e4-1e5: slope / RMSE / 95% coverage | 0.97 / 0.07 / 0.87 | 1.04 / 0.07 / 0.88 |
+| > 1e5: slope / RMSE / 95% coverage | 0.98 / 0.06 / 0.51 | 1.05 / 0.06 / 0.67 |
+| deep X error at 0 / 1 mM | +0.05 / +0.01 | +0.03 / +0.03 |
+
+On both seeds the mixture stage 1 raises the deepest genotypes' coverage
+(0.65 to 0.76, 0.51 to 0.67). The slope now overshoots slightly
+(1.03-1.05): the small steep bias the mixture stage 1 keeps in m (kanR
+about 5%, pheS 2-4%) is now the main error in X's scale, plus a constant
+offset near 0.03.
+
+### Stage 2 with the mixture, at the MAP point (2026-10-12)
+
+`run_point.srun` on both mixture stage 2 fits. Seed 1 finished; seed 2
+ran out of GPU memory at a 4.1 GB allocation even for the one-draw
+posterior (seed 1's identical job succeeded, probably on a larger card).
+Seed 1, X of the doubles:
+
+| doubles, seed 1 | `single` stage 2 | mixture stage 2 (MAP point) |
+|---|---|---|
+| 1e4-1e5: slope / RMSE / error at 0, 1 mM | 1.02 / 0.067 / +0.005, -0.020 | 1.02 / 0.070 / -0.002, -0.010 |
+| > 1e5: slope / RMSE / error at 0, 1 mM | 1.03 / 0.050 / +0.013, -0.025 | 1.03 / 0.044 / +0.006, -0.013 |
+| 1e2-1e3: r / RMSE | 0.65 / 0.51 | 0.50 / 0.86 |
+
+For the deep genotypes the mixture stage 2 trims the offsets by about
+0.01 and leaves slope and RMSE unchanged. At low depth it looks worse, but
+the `single` column is a Laplace median and the mixture's a bare MAP
+point, which scatters more there. Not worth a coupled Laplace.
+
+**The recipe: a mixture stage 1, then a `single` stage 2.** Stage 1 needs
+congression modeled to get pheS's m right; with k and m pinned, stage 2
+gets X right without it.
+
+## The real data: mixture stage 1 under two assumptions about wt
+
+The mixture needs the pool's composition, and wt's purity (how much of wt
+is clean spike rather than bulk) is not known for these data. Two
+variants of the real no-doubles stage 1 with the mixture (max rule,
+lambda prior 0.38 +- 0.15, the lab's estimate 0.357 with 95% CI
+0.156-0.674 in log space):
+
+| variant | library config | wt in the fit's pool |
+|---|---|---|
+| `cfg` | the real config as written (design mixture 100 / 100 / 1000 / 1) | 1.2% of the pool, 99% bulk (about 17% of its cells congressed) |
+| `wtspiked` | the simulation's config (the realized mixture from the initial composition, plus wt repeated in the spiked origin) | 11% of the pool, 23% bulk (about 4% congressed) |
+
+`wtspiked` is the composition the simulation was built to match (the
+realized starting pool has wt at about 11%); its config has the same
+genetics keys as the real one, so it serves as the library config. Both
+stay on the cluster (lab-derived). Readout: kanR+kan m against the
+monoculture's -0.0141 (the all-singles stage 1 without the mixture sat on
+it; in the simulation the mixture moved kanR about 3% steeper), and pheS+4CP
+m against the monoculture's 0.0144. kanR may not separate the variants
+strongly, since its congression bias is small.
+
+### How to run the two real variants
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit/m_subsets_real
+```
+
+```bash
+cp /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_arm.sh . && for v in cfg wtspiked; do mkdir no_doubles_mix_${v} && (cd no_doubles_mix_${v} && ln -s ../no_doubles/tfs_growth.csv . && cp ../no_doubles/growth_priors_loose.csv . && if [ $v = cfg ]; then cp ../no_doubles/library_config.yaml .; else cp /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/sim_realistic_s2/simulate_config.yaml library_config.yaml; fi && echo "--transformation_model mixture --transformation_lambda 0.38 0.15 --congression_theta_rule max" > configure_extra.txt && sbatch ../run_arm.srun); done
+```
+
+Check: `grep -c "" no_doubles_mix_wtspiked/library_config.yaml` is about
+350 lines (the full simulate config) and `no_doubles_mix_cfg`'s is the
+short real config; each `configure.log` shows the mixture flags.
+
+### The two real variants (2026-10-12)
+
+Both joint stages ended at the epoch cap creeping about 1 nat per window
+(nuisance parameters moving, not k or m).
+
+| real stage 1 | no mixture | mixture, `cfg` | mixture, `wtspiked` | wt monoculture |
+|---|---|---|---|---|
+| kanR+kan k / m | 0.0160 / -0.0141 | 0.0167 / -0.0147 | 0.0160 / -0.0139 | 0.0150 / -0.0141 |
+| pheS+4CP k / m | 0.0117 / 0.0061 | 0.0120 / 0.0068 | 0.0122 / 0.0062 | 0.0031 / 0.0144 |
+| fitted lambda | | 0.365 | 0.174 | measured 0.357 (0.16-0.67) |
+| final exact loss | | 856,117 | 856,142 | |
+
+- **Congression barely moves the real pheS m** (0.0061 to 0.0068 or
+  0.0062; it moved the simulation's about 15%). The real pheS gap with the
+  monoculture (about 2x) is mostly something else: step 3, and the pheS
+  curves stay provisional.
+- **The data lean toward wt mostly bulk (`cfg`):** about 25 nats better
+  on the same data (about 2 of them the lambda prior), and its fitted
+  lambda lands on the measurement while `wtspiked` drives it to the low
+  end of the interval. Neither is decisive (the `max` rule fit lambda
+  about 25% low in the simulation), but both point the same way.
+- **kanR stays within about 4% of the monoculture** either way (the
+  variants differ by about 6%).
+
+Next: the real stage 2 (`single`, every condition pinned at the `cfg`
+mixture stage 1), `held_mix_real`, to compare with round 6
+(`held_all_real`):
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit
+```
+
+```bash
+mkdir held_mix_real && cd held_mix_real && python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../m_subsets_real/no_doubles_mix_cfg --out growth_priors_held.csv && ln -s ../recipe/tfs_growth.csv . && cp ../../processed/library_config.yaml . && sbatch /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun
+```
+
+Check: kanR+kan k 0.016722, m -0.014744 in the printed table.
+
+### The real stage 2 under the full recipe (2026-10-12)
+
+`held_mix_real`: every condition pinned at the real mixture stage 1 (wt
+mostly bulk), stage 2 `single`. Every stage converged; one small held
+direction (dk_geno's population mean, eigenvalue -6.8e3), none on k, m or
+the slide; tube offsets SD 0.20.
+
+| real X, full recipe against | round 6 | the original recipe (one stage) |
+|---|---|---|
+| r, doubles above 100 reads | 0.999-1.000 | 0.991-0.999 |
+| slope | 0.94 | 1.39 |
+| deep doubles' median X, 0 to 1 mM | 0.53 to 0.13, now 0.53 to 0.15 | 0.42 to 0.14, now 0.53 to 0.15 |
+
+Against round 6 the real X compresses about 6%, as the mixture stage 1's
+slightly steeper m predicts (kanR -0.0147 against -0.0141, pheS 0.0068
+against 0.0061); ranking and shapes are unchanged and the widths shrink
+with X. Against the original recipe the real X stretches about 1.39 (1.47
+in round 6). The kanR part of the scale sits within 4% of the monoculture;
+the pheS part waits on step 3.
+
+## The kanR-only check
+
+Stage 2 on the kanR tubes alone, with kanR's k and m pinned at the same
+mixture stage 1, against the both-marker fit. If the pheS gap's cause
+depends on genotype, the pheS data bend the X curves and the two
+disagree. The seed-1 simulation shows what agreement looks like when
+pheS is fine. `resample_growth.py --library kanR` keeps one library's
+tubes; `held_priors.py --only kanR` keeps kanR's pinned conditions.
+
+Real (about 6 minutes to filter the growth table, then about 5 hours):
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit
+```
+
+```bash
+mkdir kanR_only_real && cd kanR_only_real && python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/resample_growth.py ../recipe/tfs_growth.csv --library kanR --out tfs_growth.csv && cp ../../processed/library_config.yaml . && python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../m_subsets_real/no_doubles_mix_cfg --only kanR --out growth_priors_held.csv && sbatch /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun
+```
+
+Simulation, seed 1:
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+mkdir kanR_only_seed1 && cd kanR_only_seed1 && python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/resample_growth.py ../sim_realistic_s2/tfs_growth.csv --library kanR --out tfs_growth.csv && ln -s ../sim_realistic_s2/tfs_sim_* . && cp ../sim_realistic_s2/simulate_config.yaml library_config.yaml && python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/held_priors.py ../m_subsets/no_doubles_mix --only kanR --out growth_priors_held.csv && sbatch /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun
+```
+
+Each `resample_growth.py` prints the kanR tube count; each
+`held_priors.py` prints two rows (kanR+kan, kanR-kan). Compare with
+`held_mix_real` and `stage2_single_seed1`.
+
+## Stage 1's k and m uncertainty: delete-d jackknife
+
+`run_jackknife.srun` (an array of 30, at most 10 at once) drops a random
+20% of the tubes from the stage-1 growth table, stratified by condition and
+IPTG (`resample_growth.py --drop_frac`, seed = task number), and refits
+the mixture stage 1 in `jk_NN/` with the same model (it copies the full
+stage 1's `library_config.yaml`, `growth_priors_loose.csv` and
+`configure_extra.txt`). Tubes cannot be resampled with replacement: a
+duplicated tube lands on the original's tensor cell. `jackknife_summary.py`
+scales the subsample spread by (n - d) / d and, on the simulation,
+compares the full stage 1's error with the jackknife SE. Seed 1 first;
+seed 2 the same way for a second check of the SE against a known error.
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+mkdir jk_seed1 && cd jk_seed1 && sbatch --export=ALL,SRC=$PWD/../m_subsets/no_doubles/tfs_growth.csv,CONF=$PWD/../m_subsets/no_doubles_mix /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_jackknife.srun
+```
+
+Each task writes `jk_NN.out` ending `>>> Done jk_NN` (about 20-30 minutes
+each). Then, in `jk_seed1/`:
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/jackknife_summary.py . --full ../m_subsets/no_doubles_mix --truth ../sim_realistic_s2
+```
+
+### The kanR-only check (2026-10-12)
+
+Both kanR-only stage 2 fits converged at every stage. On the simulation,
+where pheS is modeled correctly, dropping pheS loses much of X: at 0 IPTG
+wt barely grows in kanR+kan, so genotypes near the top of X are nearly
+static there and pheS carries most of that information. Seed 1's doubles
+above 1e5 reads: RMSE 0.15 (0.05 with both markers), 95% coverage 0.36
+(0.76), widths 2.7 times wider, X error +0.17 at 0 IPTG. So the check is
+read against the simulation's agreement, not against perfect agreement.
+
+| deep doubles (> 1e4 reads), kanR-only against both markers | seed 1 | real |
+|---|---|---|
+| r, 1e4-1e5 / > 1e5 | 0.96 / 0.99 | 0.84 / 0.94 |
+| slope, 1e4-1e5 / > 1e5 | 1.09 / 1.08 | 1.11 / 1.05 |
+| scatter after the best linear map | 0.13 | 0.21 |
+| that scatter in combined SDs (RMS) | 0.89 | 1.16 |
+| share beyond 2 combined SDs | 2.5% | 8.2% |
+| per-genotype scatter, median / 90th / 99th percentile | 0.08 / 0.21 / 0.38 | 0.11 / 0.32 / 0.68 |
+
+**The median real genotype agrees across markers about as the simulation
+does; a tail does not.** About 8% of the deep real doubles disagree
+beyond 2 SD, against 2.5% in the simulation. The check cannot say which
+marker is off. A candidate inside the model: dk_geno is shared by the
+kanR and pheS conditions, while a real mutation may slow growth
+differently in kan and in 4CP; that would bend exactly such genotypes and
+cannot show in a simulation that shares dk_geno too.
+
+For the paper: the pheS gap stays a limitation, with a per-genotype
+marker-consistency flag (the z-score between the kanR-only and both-marker
+fits) marking the genotypes whose curves rest on a cross-marker
+inconsistency. If needed later, a marker-specific dk_geno tests whether the
+tail is genotype-specific fitness.
+
+### The seed-1 jackknife (2026-10-12)
+
+30 subsamples (20 of 118 tubes dropped each, variance factor 4.90); 29
+converged, one at the epoch cap.
+
+| seed 1, mixture stage 1 | k, kanR+kan | k, pheS+4CP | m, kanR+kan | m, pheS+4CP |
+|---|---|---|---|---|
+| jackknife SE | 0.0048 | 0.0028 | 0.00009 | 0.00008 |
+| Laplace SD | 0.0008 | 0.0011 | 0.00008 | 0.00007 |
+| actual error | +0.0037 | +0.0014 | -0.0008 | +0.0005 |
+| error in jackknife SEs | 0.8 | 0.5 | 8.8 | 6.5 |
+
+**The jackknife covers k; m's error is the bias.** k's jackknife SE is
+about five times its Laplace SD and covers k's actual errors. m's agrees
+with the Laplace (about 0.6%), and its error, 6-9 SEs, is the small steep
+bias accepted as a limitation.
+
+One direction carries 94% of k's jackknife variance: all four conditions'
+k move together (one-SD step -0.0049 kanR+kan, -0.0079 kanR-kan, -0.0023
+pheS+4CP, -0.0027 pheS-4CP). A common shift of every k is absorbed by
+dk_geno and leaves X alone; only the uneven part moves X. So two stage 2
+refits measure X's sensitivity: k at plus and minus one SD along that
+direction (`jackknife_summary.py --pc1_tables`), m at the full fit,
+started from `stage2_single_seed1`'s MAP. `k_band.py` takes each
+genotype's k-driven SD as |X(plus) - X(minus)| / 2 and adds it in
+quadrature to the stage 2 Laplace SD; `score_x.py` then scores the
+widened intervals against the truth.
+
+### How to run the k-band refits (seed 1)
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/jk_seed1
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/jackknife_summary.py . --full ../m_subsets/no_doubles_mix --truth ../sim_realistic_s2 --pc1_tables pc1
+```
+
+It prints `k variance on the first component: 0.941` and writes
+`pc1/plus/` and `pc1/minus/`. Then:
+
+```bash
+for v in plus minus; do (cd pc1/$v && ln -s ../../../sim_realistic_s2/tfs_growth.csv ../../../sim_realistic_s2/tfs_sim_* . && cp ../../../sim_realistic_s2/simulate_config.yaml library_config.yaml && sbatch --export=ALL,INIT=$PWD/../../../stage2_single_seed1/tfs_fit_model_params.npz /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/run_held.srun); done
+```
+
+Each `pc1/*/run.out` ends with `>>> Done` and a `summary/`. Then, locally:
+`k_band.py stage2_single_seed1 jk_seed1/pc1/plus jk_seed1/pc1/minus`, and
+`score_x.py` on its output with `stage2_single_seed1`'s truth table.
+
+### Is the marker disagreement a library-specific dk_geno?
+
+Split into a per-genotype level and a shape part, the kanR-only against
+both-marker disagreement of the deep doubles is 92% level on seed 1 and
+91% on the real data: without pheS, a genotype's X level is weakly
+pinned, so dropping a marker produces level-shaped noise even in the
+simulation, which shares dk_geno across libraries. The real tail is
+larger (median level offset of the worst 8% 0.45 against 0.28).
+
+A dk_geno that differs between the kanR and pheS libraries (backbone or
+marker-cassette effects, GxE visible in the library's own control) is
+identifiable: each library's control condition (m near 0) measures its
+own dk directly, the two-stage fit closes both libraries' k/dk slides,
+and a bivariate prior with a learned correlation pools shallow genotypes.
+A dk that differs only under the stressor (kan or 4CP present) is not:
+inside a selection condition it trades with the level of the genotype's
+X curve.
+
+`control_dk_check.py` tests the first: per deep double, the slope over
+time of ln(counts relative to wt) in each library's control condition
+estimates its dk there (weighted least squares per series, combined by
+inverse variance), and delta_dk = dk_kanR - dk_pheS. If the tail's X
+offsets come from a library-specific dk, they track delta_dk with a
+slope near 1 / (2 |m_kanR|) (about 34). The simulation, with a shared
+dk, is the no-signal benchmark; it also shows how well the two controls
+recover the same dk at this depth.
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/dev-data/real_fit
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/control_dk_check.py recipe/tfs_growth.csv held_mix_real/tfs_pred_theta.csv kanR_only_real/tfs_pred_theta.csv --out control_dk_real.csv
+```
+
+```bash
+cd /gpfs/projects/harmslab/harms/studies/full-sized-sims-3
+```
+
+```bash
+python /gpfs/home/harms/tfscreen/planning/studies/m-bias-factorial/control_dk_check.py sim_realistic_s2/tfs_growth.csv stage2_single_seed1/tfs_pred_theta.csv kanR_only_seed1/tfs_pred_theta.csv --m_kan -0.0146 --out control_dk_seed1.csv
+```
+
+Each reads its 8 GB growth table twice (about 10 minutes) and prints the
+two libraries' dk agreement and the offset-delta_dk correlations.
+
+#### Result (2026-10-12)
+
+| | seed 1 (shared dk) | real |
+|---|---|---|
+| deep doubles with both controls | 16,509 | 16,982 |
+| r(offset, delta_dk), all / worst 8% | 0.40 / 0.65 | 0.40 / 0.52 |
+| slope, all / worst 8% (about 34 expected for a real dk effect) | 5.9 / 15.7 | 6.7 / 14.1 |
+| delta_dk SD across genotypes / median SE | 0.0086 / 0.0026 | 0.0118 / 0.0041 |
+| r(dk_kanR, dk_pheS) | 0.26 | 0.04 |
+
+**No evidence for a library-specific dk_geno.** The offset-delta_dk
+correlation is as strong on the simulation, where none exists: the
+kanR-only fit takes its dk from the same kanR control tubes `dk_kanR` is
+estimated from, so their noise is shared. The real data do not exceed
+that benchmark, and the slopes are far below what a real dk difference
+would give. delta_dk's spread is about three times its Poisson SE in
+both (tube-level noise the counting SE does not see). The real controls
+agree less on each genotype's dk (r 0.04 against 0.26), with larger
+SEs; that fits a smaller real dk spread relative to the noise and does
+not single out a library effect. A marker-specific dk_geno is not
+supported and would be poorly constrained. The tail stays unexplained;
+the per-genotype marker-consistency flag is the handling.
+
+### k's uncertainty in X (seed 1, 2026-10-13)
+
+Both k-band refits (k at plus and minus one jackknife SD along the main
+direction, from `stage2_single_seed1`'s MAP) converged. `k_band.py` adds
+X's k-driven SD in quadrature to the stage 2 Laplace SD:
+
+| seed-1 doubles | stage 2 Laplace only | with k's uncertainty |
+|---|---|---|
+| 1e3-1e4: 95% coverage / width | 0.883 / 0.48 | 0.897 / 0.50 |
+| 1e4-1e5: 95% coverage / width | 0.896 / 0.20 | 0.939 / 0.24 |
+| > 1e5: 95% coverage / width | 0.761 / 0.07 | 0.905 / 0.15 |
+
+**The deep genotypes' intervals come close to nominal.** Points, slope
+and RMSE are unchanged; only the widths grow, and only where k matters.
+What is left above 1e5 reads (0.905) is the accepted steep m bias (slope
+1.03).
+
+**k's uncertainty is a shift shared by every genotype.** One SD moves
+every non-wt genotype's X by -0.032 (low IPTG) to -0.038 (1 mM), with a
+genotype-to-genotype SD of about 0.005 and no dependence on the genotype's
+own X; wt is fixed by the gauge. In quadrature it is about 0.034 for
+every genotype, against a Laplace SD of 1.37 at <= 100 reads and 0.018
+above 1e5. Because it is shared, it should not widen comparisons between
+genotypes; epistasis on X (X_ab - X_a - X_b + X_wt) keeps one copy of it,
+since wt does not shift. The pipeline should therefore carry it as one
+shared random offset per posterior draw (per IPTG level) added to every
+non-wt genotype, not as independent width, so that downstream draws
+(`tfs-predict-epistasis`, rankings) get the correlation right.
+
diff --git a/planning/studies/m-bias-factorial/control_dk_check.py b/planning/studies/m-bias-factorial/control_dk_check.py
new file mode 100644
index 00000000..6a48db21
--- /dev/null
+++ b/planning/studies/m-bias-factorial/control_dk_check.py
@@ -0,0 +1,139 @@
+"""
+Do genotypes whose X disagrees between markers also grow differently in
+the two libraries' control conditions? (planning/deep-coverage.md step 3)
+
+    python control_dk_check.py tfs_growth.csv both/tfs_pred_theta.csv \
+        kanR_only/tfs_pred_theta.csv --out control_dk.csv
+
+A library-specific dk_geno (the genotype growing differently on the kanR
+and pheS backbones) shows up directly in each library's control condition
+(kanR-kan, pheS-4CP), where m is near 0: there a genotype's growth
+relative to wt is its dk. For each deep double (at least --min_reads total
+reads) and each control series (replicate x condition x IPTG level), the
+slope over time of ln((counts_g + 0.5) / (counts_wt + 0.5)) estimates dk_g
+(weighted least squares, Poisson weights 1 / (1/c_g + 1/c_wt)); series
+combine by inverse variance into dk_kanR and dk_pheS, and delta_dk =
+dk_kanR - dk_pheS.
+
+The X disagreement is each genotype's mean offset between the kanR-only
+and both-marker fits after the best affine map (the level part, 91-92% of
+the disagreement). If the kanR library's dk differs from the shared value
+by delta, the kanR-only fit's X moves by about -delta / m_kanR relative to
+the shared fit; with the shared dk near the two libraries' mean, the
+offset is about delta_dk / (2 |m_kanR|) (--m_kan, default -0.0147).
+
+Prints the correlation between the two libraries' dk estimates (with a
+shared dk, how well the estimator recovers it), the correlation of offset
+and delta_dk over all deep doubles and
+over the worst --tail share by disagreement, the regression slope against
+the expected 1 / (2 |m_kanR|), and the noise in delta_dk. Writes one row
+per genotype. A simulation that shares dk_geno across libraries gives the
+no-signal benchmark.
+"""
+
+import argparse
+
+import numpy as np
+import pandas as pd
+
+CHUNK = 5_000_000
+CONTROLS = {"kanR": "kanR-kan", "pheS": "pheS-4CP"}
+
+
+def depth_and_controls(path, min_reads):
+    tot = None
+    for c in pd.read_csv(path, usecols=["genotype", "counts"], chunksize=CHUNK):
+        s = c.groupby("genotype")["counts"].sum()
+        tot = s if tot is None else tot.add(s, fill_value=0)
+    g = tot.index.to_series().astype(str)
+    deep = set(tot.index[(g.str.count("/") == 1) & (tot >= min_reads)])
+    keep = deep | {"wt"}
+    cols = ["genotype", "library", "replicate", "condition_sel",
+            "titrant_conc", "t_sel", "sample", "counts"]
+    parts = []
+    for c in pd.read_csv(path, usecols=cols, chunksize=CHUNK):
+        c = c[c["condition_sel"].isin(CONTROLS.values()) & c["genotype"].isin(keep)]
+        parts.append(c)
+    return deep, pd.concat(parts)
+
+
+def control_dk(rows):
+    """Per genotype and library: dk relative to wt and its SE."""
+    wt = rows[rows["genotype"] == "wt"][["sample", "counts"]].rename(
+        columns={"counts": "c_wt"})
+    d = rows[rows["genotype"] != "wt"].merge(wt, on="sample")
+    d["y"] = np.log((d["counts"] + 0.5) / (d["c_wt"] + 0.5))
+    d["w"] = 1.0 / (1.0 / (d["counts"] + 0.5) + 1.0 / (d["c_wt"] + 0.5))
+    key = ["genotype", "library", "replicate", "condition_sel", "titrant_conc"]
+    d["wt_"] = d["w"] * d["t_sel"]
+    d["wy"] = d["w"] * d["y"]
+    s = d.groupby(key).agg(sw=("w", "sum"), swt=("wt_", "sum"),
+                           swy=("wy", "sum"), n=("y", "size"))
+    d = d.join(s, on=key)
+    d["tc"] = d["t_sel"] - d["swt"] / d["sw"]
+    d["yc"] = d["y"] - d["swy"] / d["sw"]
+    d["wtt"] = d["w"] * d["tc"] ** 2
+    d["wty"] = d["w"] * d["tc"] * d["yc"]
+    s = d.groupby(key).agg(stt=("wtt", "sum"), sty=("wty", "sum"),
+                           n=("y", "size")).reset_index()
+    s = s[(s["n"] >= 2) & (s["stt"] > 0)]
+    s["slope"] = s["sty"] / s["stt"]
+    s["var"] = 1.0 / s["stt"]
+    s["iv"] = 1.0 / s["var"]
+    s["iv_slope"] = s["iv"] * s["slope"]
+    out = s.groupby(["genotype", "library"]).agg(
+        iv=("iv", "sum"), ivs=("iv_slope", "sum"), n_series=("slope", "size"))
+    out["dk"] = out["ivs"] / out["iv"]
+    out["dk_se"] = 1.0 / np.sqrt(out["iv"])
+    return out[["dk", "dk_se", "n_series"]].unstack("library")
+
+
+def x_offsets(both, kan, genotypes):
+    cols = ["genotype", "titrant_conc", "q0.5"]
+    m = pd.read_csv(both, usecols=cols).merge(
+        pd.read_csv(kan, usecols=cols), on=["genotype", "titrant_conc"],
+        suffixes=("_b", "_k"))
+    m = m[m["genotype"].isin(genotypes)]
+    p = np.polyfit(m["q0.5_b"], m["q0.5_k"], 1)
+    m["res"] = m["q0.5_k"] - np.polyval(p, m["q0.5_b"])
+    g = m.groupby("genotype")["res"]
+    return pd.DataFrame({"offset": g.mean(),
+                         "rms": g.apply(lambda r: np.sqrt(np.mean(r ** 2)))})
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("growth_csv")
+    ap.add_argument("both_pred")
+    ap.add_argument("kan_pred")
+    ap.add_argument("--min_reads", type=float, default=1e4)
+    ap.add_argument("--tail", type=float, default=0.08)
+    ap.add_argument("--m_kan", type=float, default=-0.0147)
+    ap.add_argument("--out", default="control_dk.csv")
+    args = ap.parse_args()
+
+    deep, rows = depth_and_controls(args.growth_csv, args.min_reads)
+    dk = control_dk(rows)
+    dk.columns = [f"{a}_{b}" for a, b in dk.columns]
+    off = x_offsets(args.both_pred, args.kan_pred, deep)
+    df = off.join(dk, how="inner").dropna(subset=["dk_kanR", "dk_pheS"])
+    df["delta_dk"] = df["dk_kanR"] - df["dk_pheS"]
+    df["delta_dk_se"] = np.hypot(df["dk_se_kanR"], df["dk_se_pheS"])
+    df.to_csv(args.out)
+
+    expect = 1.0 / (2 * abs(args.m_kan))
+    tail = df[df["rms"] >= df["rms"].quantile(1 - args.tail)]
+    print(f"{len(df)} deep doubles with both controls; tail {len(tail)}")
+    print(f"delta_dk: SD across genotypes {df['delta_dk'].std():.4f}, "
+          f"median SE {df['delta_dk_se'].median():.4f} per minute")
+    print(f"r(dk_kanR, dk_pheS) {np.corrcoef(df['dk_kanR'], df['dk_pheS'])[0, 1]:.3f} "
+          f"(how well the two controls agree on each genotype's dk)")
+    for name, sub in (("all", df), ("tail", tail)):
+        r = np.corrcoef(sub["offset"], sub["delta_dk"])[0, 1]
+        slope = np.polyfit(sub["delta_dk"], sub["offset"], 1)[0]
+        print(f"{name}: r(offset, delta_dk) {r:.3f}, slope {slope:.1f} "
+              f"(expected about {expect:.0f})")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/m-bias-factorial/depth.py b/planning/studies/m-bias-factorial/depth.py
new file mode 100644
index 00000000..b04781c8
--- /dev/null
+++ b/planning/studies/m-bias-factorial/depth.py
@@ -0,0 +1,29 @@
+"""
+Total reads per genotype from a growth table (for score_x.py's depth bins).
+
+    python depth.py sim_realistic_s2/tfs_growth.csv --out depth.csv
+
+Reads the table in chunks (a full-size one is 8 GB).
+"""
+
+import argparse
+
+import pandas as pd
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("growth_csv")
+    ap.add_argument("--out", default="depth.csv")
+    args = ap.parse_args()
+    tot = None
+    for chunk in pd.read_csv(args.growth_csv, usecols=["genotype", "counts"],
+                             chunksize=5_000_000):
+        s = chunk.groupby("genotype")["counts"].sum()
+        tot = s if tot is None else tot.add(s, fill_value=0)
+    tot.rename("reads").to_csv(args.out)
+    print(f"{len(tot)} genotypes -> {args.out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/m-bias-factorial/held_priors.py b/planning/studies/m-bias-factorial/held_priors.py
new file mode 100644
index 00000000..e9d57539
--- /dev/null
+++ b/planning/studies/m-bias-factorial/held_priors.py
@@ -0,0 +1,72 @@
+"""
+Growth priors that hold k and m at a subset fit's MAP (m-bias factorial).
+
+    python held_priors.py no_doubles --out growth_priors_held.csv
+    python held_priors.py no_doubles --hold kanR --loose growth_priors_loose.csv \
+        --out growth_priors_held.csv
+    python held_priors.py no_doubles_mix --only kanR --out growth_priors_held.csv
+
+Reads the arm's tfs_params_growth_k.csv and tfs_params_growth_m.csv (one
+row per condition_rep with --growth_shares_replicates) and writes a
+--growth_priors table with those values as k_loc and m_loc.
+
+Without --hold every condition is held: configure with --set_priors
+m_pinned=1 k_pinned=1 to clamp them (the scales are then unused). With
+--hold, only conditions whose name starts with one of the given prefixes
+are held, clamped through the table's k_pinned/m_pinned columns (linear's
+per-condition pins); every other condition takes its loc and scale from
+--loose and is fitted. Do not add the --set_priors pins then: they act on
+every condition. (Round 4 held kanR by a tight prior instead, SD 1e-4; it
+leaked about 11% of the doubles' pull.) --only keeps just the conditions
+starting with the given prefixes, for a model fitted to one library's
+data (the kanR-only check).
+"""
+
+import argparse
+import os
+
+import pandas as pd
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("arm_dir")
+    ap.add_argument("--out", default="growth_priors_held.csv")
+    ap.add_argument("--hold", nargs="+", default=None,
+                    help="condition_rep prefixes to hold (default: all)")
+    ap.add_argument("--only", nargs="+", default=None,
+                    help="keep only conditions starting with these prefixes")
+    ap.add_argument("--loose", default=None,
+                    help="growth priors for the conditions not held")
+    args = ap.parse_args()
+    km = {}
+    for p in ("k", "m"):
+        df = pd.read_csv(os.path.join(args.arm_dir, f"tfs_params_growth_{p}.csv"))
+        km[p] = df.groupby("condition_rep")["q0.5"].mean()
+    out = pd.DataFrame({"k_loc": km["k"], "k_scale": 0.002,
+                        "m_loc": km["m"], "m_scale": 0.002})
+    out.index.name = "condition_rep"
+    if args.only is not None:
+        out = out[[any(c.startswith(p) for p in args.only) for c in out.index]]
+        if out.empty:
+            ap.error(f"no condition starts with {args.only}")
+    if args.hold is not None:
+        if args.loose is None:
+            ap.error("--hold needs --loose for the conditions not held")
+        loose = pd.read_csv(args.loose).set_index("condition_rep")
+        held = [c for c in out.index if any(c.startswith(h) for h in args.hold)]
+        if not held:
+            ap.error(f"no condition starts with {args.hold}: {list(out.index)}")
+        free = [c for c in out.index if c not in held]
+        missing = [c for c in free if c not in loose.index]
+        if missing:
+            ap.error(f"{args.loose} has no row for {missing}")
+        out = pd.concat([out.loc[held], loose.loc[free, out.columns]])
+        out["k_pinned"] = [int(c in held) for c in out.index]
+        out["m_pinned"] = out["k_pinned"]
+    out.to_csv(args.out)
+    print(out.to_string())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/m-bias-factorial/jackknife_summary.py b/planning/studies/m-bias-factorial/jackknife_summary.py
new file mode 100644
index 00000000..03b03101
--- /dev/null
+++ b/planning/studies/m-bias-factorial/jackknife_summary.py
@@ -0,0 +1,135 @@
+"""
+k and m standard errors from a delete-d jackknife of a stage-1 fit.
+
+    python jackknife_summary.py jk_dir --full ../m_subsets/no_doubles_mix \
+        --truth ../sim_realistic_s2 --out jackknife_km.csv
+
+jk_dir holds jk_NN/ from run_jackknife.srun (each with resample.log and
+tfs_params_growth_{k,m}.csv). For n tubes, d dropped per subsample and the
+subsample estimates t_i with mean t_bar, the jackknife variance is
+(n - d) / d * mean((t_i - t_bar)^2). Prints, per condition, the full fit's
+k and m (--full), the jackknife SE and, with --truth (a simulation
+directory holding tfs_sim_parameters.csv and tfs_sim_growth_parameters.csv),
+the truth on the X scale and the error in jackknife SEs.
+
+--pc1_tables DIR also writes DIR/plus/ and DIR/minus/growth_priors_held.csv:
+k at the full fit's value plus and minus one jackknife SD along the first
+principal component of the k covariance (m at the full fit's value), for
+two stage 2 refits that measure how X moves with k. It prints the share
+of k's variance on that component.
+"""
+
+import argparse
+import glob
+import os
+import re
+
+import numpy as np
+import pandas as pd
+import yaml
+
+
+def read_km(d):
+    out = {}
+    for p in ("k", "m"):
+        f = os.path.join(d, f"tfs_params_growth_{p}.csv")
+        out[p] = pd.read_csv(f).groupby("condition_rep")["q0.5"].mean()
+    return pd.DataFrame(out)
+
+
+def n_and_d(d):
+    text = open(os.path.join(d, "resample.log")).read()
+    m = re.search(r"n (\d+) tubes, dropped d (\d+)", text)
+    return int(m.group(1)), int(m.group(2))
+
+
+def truth_x(sim_dir, config):
+    """k and m on the fit's X scale (wt's true curve at the gauge)."""
+    cfg = yaml.safe_load(open(config))
+    c_lo, c_hi = (float(x) for x in cfg["components"]["theta_gauge_conc"])
+    par = pd.read_csv(os.path.join(sim_dir, "tfs_sim_parameters.csv"))
+    wt = par[par["genotype"] == "wt"].iloc[0]
+
+    def th(c):
+        if c <= 0:
+            return wt["theta_low"]
+        occ = 1 / (1 + np.exp(-wt["hill_n"] * (np.log(c) - wt["log_hill_K"])))
+        return wt["theta_low"] + (wt["theta_high"] - wt["theta_low"]) * occ
+
+    tr = pd.read_csv(os.path.join(sim_dir, "tfs_sim_growth_parameters.csv"))
+    tr = tr.set_index("condition_rep")
+    m = tr["growth_m"] * (th(c_lo) - th(c_hi))
+    k = tr["growth_k"] + tr["growth_m"] * th(c_hi)
+    return pd.DataFrame({"k": k, "m": m})
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("jk_dir")
+    ap.add_argument("--full", default=None, help="the full stage-1 fit's dir")
+    ap.add_argument("--truth", default=None, help="simulation dir")
+    ap.add_argument("--out", default="jackknife_km.csv")
+    ap.add_argument("--pc1_tables", default=None,
+                    help="write plus/ and minus/ pinned tables here (needs --full)")
+    args = ap.parse_args()
+
+    dirs = sorted(d for d in glob.glob(os.path.join(args.jk_dir, "jk_*"))
+                  if os.path.exists(os.path.join(d, "tfs_params_growth_m.csv")))
+    if len(dirs) < 2:
+        raise SystemExit(f"{len(dirs)} finished subsamples in {args.jk_dir}")
+    nd = {n_and_d(d) for d in dirs}
+    if len(nd) != 1:
+        raise SystemExit(f"subsamples differ in n and d: {sorted(nd)}")
+    n, d = nd.pop()
+    est = pd.concat({os.path.basename(x): read_km(x) for x in dirs})
+    est.index.names = ["subsample", "condition_rep"]
+
+    rows = []
+    for c, g in est.groupby(level="condition_rep"):
+        row = {"condition_rep": c, "n_sub": len(g)}
+        for p in ("k", "m"):
+            t = g[p].to_numpy()
+            row[f"{p}_jk_mean"] = t.mean()
+            row[f"{p}_jk_se"] = np.sqrt((n - d) / d * np.mean((t - t.mean()) ** 2))
+        rows.append(row)
+    out = pd.DataFrame(rows).set_index("condition_rep")
+
+    if args.full:
+        full = read_km(args.full)
+        for p in ("k", "m"):
+            out[f"{p}_full"] = full[p]
+        if args.truth:
+            truth = truth_x(args.truth,
+                            os.path.join(args.full, "tfs_configure_config.yaml"))
+            for p in ("k", "m"):
+                out[f"{p}_truth"] = truth[p]
+                out[f"{p}_err_in_se"] = (out[f"{p}_full"] - out[f"{p}_truth"]) \
+                    / out[f"{p}_jk_se"]
+    if args.pc1_tables:
+        if not args.full:
+            raise SystemExit("--pc1_tables needs --full")
+        k = est["k"].unstack("condition_rep")
+        dev = (k - k.mean()) * np.sqrt((n - d) / d)
+        cov = np.cov(dev.to_numpy().T)
+        w, v = np.linalg.eigh(cov)
+        top = int(np.argmax(w))
+        step = pd.Series(v[:, top] * np.sqrt(w[top]), index=k.columns)
+        full = read_km(args.full)
+        print(f"k variance on the first component: {w[top] / w.sum():.3f}; "
+              f"one-SD step {step.round(5).to_dict()}")
+        for name, sign in (("plus", 1), ("minus", -1)):
+            dd = os.path.join(args.pc1_tables, name)
+            os.makedirs(dd, exist_ok=True)
+            table = pd.DataFrame({
+                "condition_rep": full.index,
+                "k_loc": full["k"] + sign * step.reindex(full.index),
+                "k_scale": 0.002, "m_loc": full["m"], "m_scale": 0.002})
+            table.to_csv(os.path.join(dd, "growth_priors_held.csv"), index=False)
+    out.to_csv(args.out)
+    print(f"{len(dirs)} subsamples, n {n} tubes, d {d} dropped "
+          f"(variance factor {(n - d) / d:.2f})")
+    print(out.to_string(float_format="%.5f"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/m-bias-factorial/k_band.py b/planning/studies/m-bias-factorial/k_band.py
new file mode 100644
index 00000000..754dcb9f
--- /dev/null
+++ b/planning/studies/m-bias-factorial/k_band.py
@@ -0,0 +1,53 @@
+"""
+X intervals that carry stage 1's k uncertainty (planning/deep-coverage.md
+step 2).
+
+    python k_band.py stage2_single_seed1 pc1/plus pc1/minus --out x_kband.csv
+
+The first directory is the stage 2 fit at stage 1's k and m; the other two
+are stage 2 refits with k moved one jackknife SD up and down along the
+first principal component of k's jackknife covariance (jackknife_summary.py
+--pc1_tables; it carries most of k's variance). Per genotype and
+concentration, X's k-driven SD is |X(plus) - X(minus)| / 2 (the medians),
+added in quadrature to the stage 2 Laplace SD ((q0.975 - q0.025) / 3.92).
+Writes genotype, titrant_conc, q0.025, q0.5 (the first fit's), q0.975 and
+both SDs, for score_x.py.
+"""
+
+import argparse
+import os
+
+import pandas as pd
+
+COLS = ["genotype", "titrant_name", "titrant_conc", "q0.025", "q0.5", "q0.975"]
+KEY = ["genotype", "titrant_name", "titrant_conc"]
+
+
+def pred(d):
+    return pd.read_csv(os.path.join(d, "tfs_pred_theta.csv"), usecols=COLS)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("base")
+    ap.add_argument("plus")
+    ap.add_argument("minus")
+    ap.add_argument("--out", default="x_kband.csv")
+    args = ap.parse_args()
+    base = pred(args.base)
+    plus = pred(args.plus)[KEY + ["q0.5"]].rename(columns={"q0.5": "x_plus"})
+    minus = pred(args.minus)[KEY + ["q0.5"]].rename(columns={"q0.5": "x_minus"})
+    df = base.merge(plus, on=KEY).merge(minus, on=KEY)
+    df["sd_laplace"] = (df["q0.975"] - df["q0.025"]) / 3.92
+    df["sd_k"] = (df["x_plus"] - df["x_minus"]).abs() / 2
+    sd = (df["sd_laplace"] ** 2 + df["sd_k"] ** 2) ** 0.5
+    df["q0.025"] = df["q0.5"] - 1.96 * sd
+    df["q0.975"] = df["q0.5"] + 1.96 * sd
+    df[KEY + ["q0.025", "q0.5", "q0.975", "sd_laplace", "sd_k"]].to_csv(
+        args.out, index=False)
+    print(f"{len(df)} rows; median sd_laplace {df['sd_laplace'].median():.4f}, "
+          f"median sd_k {df['sd_k'].median():.4f}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/m-bias-factorial/km_draws.py b/planning/studies/m-bias-factorial/km_draws.py
new file mode 100644
index 00000000..eeaf04fc
--- /dev/null
+++ b/planning/studies/m-bias-factorial/km_draws.py
@@ -0,0 +1,105 @@
+"""
+Draws of k and m from a stage-1 fit's Laplace, one held-k/m arm per draw
+(planning/deep-coverage.md step 2).
+
+    python km_draws.py ../m_subsets/no_doubles --num_draws 10 --out_dir km_draws
+
+The stage-1 directory needs tfs_posterior.h5 (tfs-sample-posterior on its
+MAP checkpoint) and tfs_configure_priors.csv (whose per-condition rows name
+the conditions in the model's order). Each draw is one joint sample of every
+condition's k and m, so their correlations carry over. Each draw directory
+gets growth_priors_held.csv with that draw as k_loc and m_loc; run_held.srun
+pins every condition at it (its default PINS).
+
+The k/m uncertainty is only as good as the Laplace: a k or m direction the
+arrowhead Laplace held at zero width (tfs_posterior_held_directions.csv)
+has no spread in the draws, so the script stops if one does, unless
+--allow_held.
+"""
+
+import argparse
+import os
+
+import h5py
+import numpy as np
+import pandas as pd
+
+PREFIX = "growth.condition_growth."
+
+
+def condition_order(priors_csv):
+    """condition_rep labels in the model's order, from the indexed k_loc rows."""
+    df = pd.read_csv(priors_csv)
+    rows = df[df["parameter"] == PREFIX + "k_loc"].dropna(subset=["flat_index"])
+    if rows.empty or "condition_rep" not in rows:
+        raise ValueError(f"{priors_csv} has no per-condition k_loc rows to "
+                         "name the conditions (configure with --growth_priors).")
+    if "replicate" in rows and rows["replicate"].notna().any():
+        raise ValueError("per-replicate conditions are not supported; configure "
+                         "with --growth_shares_replicates.")
+    return list(rows.sort_values("flat_index")["condition_rep"].astype(str))
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("stage1_dir")
+    ap.add_argument("--num_draws", type=int, default=10)
+    ap.add_argument("--out_dir", default="km_draws")
+    ap.add_argument("--seed", type=int, default=1)
+    ap.add_argument("--allow_held", action="store_true")
+    args = ap.parse_args()
+    s1 = args.stage1_dir
+
+    held_file = os.path.join(s1, "tfs_posterior_held_directions.csv")
+    if os.path.exists(held_file):
+        held = pd.read_csv(held_file)
+        km = held[held["parameter"].str.startswith("condition_growth_")
+                  & (held["loading"].abs() > 0.1)]
+        if not km.empty:
+            print("Held directions with k or m loadings above 0.1:")
+            print(km.to_string(index=False))
+            if not args.allow_held:
+                raise SystemExit("k/m directions are held at zero width; the "
+                                 "draws would understate their uncertainty "
+                                 "(--allow_held to go on anyway).")
+
+    labels = condition_order(os.path.join(s1, "tfs_configure_priors.csv"))
+    with h5py.File(os.path.join(s1, "tfs_posterior.h5"), "r") as hf:
+        k = np.asarray(hf["condition_growth_k"]).reshape(-1, len(labels))
+        m = np.asarray(hf["condition_growth_m"]).reshape(-1, len(labels))
+
+    # The extracted medians must match the posterior's columns by name.
+    for name, arr in (("k", k), ("m", m)):
+        ext = pd.read_csv(os.path.join(s1, f"tfs_params_growth_{name}.csv"))
+        ext = ext.groupby("condition_rep")["q0.5"].mean()
+        med = pd.Series(np.median(arr, axis=0), index=labels)
+        off = (med - ext.reindex(labels)).abs().max()
+        if not off < 1e-3:
+            raise ValueError(f"{name}: posterior medians {med.round(5).to_dict()} "
+                             f"do not match tfs_params_growth_{name}.csv "
+                             f"{ext.round(5).to_dict()}; condition order?")
+
+    summary = pd.DataFrame({"k_sd": k.std(axis=0), "m_sd": m.std(axis=0),
+                            "k_median": np.median(k, axis=0),
+                            "m_median": np.median(m, axis=0)}, index=labels)
+    print(f"{k.shape[0]} posterior draws of k and m:")
+    print(summary.to_string(float_format="%.5f"))
+
+    rng = np.random.default_rng(args.seed)
+    pick = rng.choice(k.shape[0], size=args.num_draws, replace=False)
+    rows = []
+    for i, j in enumerate(pick):
+        d = os.path.join(args.out_dir, f"draw_{i:02d}")
+        os.makedirs(d, exist_ok=True)
+        table = pd.DataFrame({"condition_rep": labels, "k_loc": k[j],
+                              "k_scale": 0.002, "m_loc": m[j], "m_scale": 0.002})
+        table.to_csv(os.path.join(d, "growth_priors_held.csv"), index=False)
+        rows.append(table.assign(draw=i, sample=int(j)))
+    pd.concat(rows).to_csv(os.path.join(args.out_dir, "km_draws.csv"),
+                           index=False)
+    print(f"wrote {args.num_draws} draws to {args.out_dir}/draw_*/ "
+          f"(table: {args.out_dir}/km_draws.csv)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/m-bias-factorial/make_factorial.py b/planning/studies/m-bias-factorial/make_factorial.py
new file mode 100644
index 00000000..afc019a7
--- /dev/null
+++ b/planning/studies/m-bias-factorial/make_factorial.py
@@ -0,0 +1,137 @@
+"""
+Reduced-library factorial for the |m| bias (planning/deep-coverage.md, step 1b).
+
+    python make_factorial.py --pilot <pilot run dir> [--out_dir m_bias] [--seeds 1 2]
+
+Run from planning/dev-data (needs processed/; the lab's data stay there,
+C10). Builds the full-size calibrated configs with
+planning/studies/full-size-sim/make_sim_config.py (realistic and Poisson
+noise, cfu0 calibrated from --pilot), then shrinks the library to 5
+degenerate sites per tile (tile 1 residues 32, 37, 42, 47, 52; tile 2
+residues 74, 79, 84, 88, 93, every spike site kept: about 5,000 doubles
+instead of 223,000) with genetics.library_design.scale_library_design, so
+every sequence keeps its reads, cells, transformants and PCR templates. The
+fit is configured with the real library config shrunk the same way.
+
+Arms (each switches off one feature of the simulation the fit does not
+model):
+
+- base: the calibrated realistic simulation (congression lambda 0.357,
+  per-tube rate noise 0.002 per minute, founder/demographic/PCR noise)
+- no_cong: transformation_poisson_lambda 0 (no co-transformed cells)
+- no_tube: tube_noise_sigma 0
+- poisson: Poisson counts (the Poisson config: no founder, demographic or
+  PCR noise)
+- clean: all three off
+
+Each arm directory gets simulate_config.yaml, library_config.yaml and a
+copy of growth_priors_loose.csv; run_arm.sh runs the chain in it.
+"""
+
+import argparse
+import copy
+import os
+import shutil
+import subprocess
+import sys
+
+import yaml
+
+from tfscreen.genetics.library_design import scale_library_design
+from tfscreen.genetics.library_manager import LibraryManager
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+MAKE_SIM = os.path.join(HERE, "..", "full-size-sim", "make_sim_config.py")
+LOOSE = os.path.join(HERE, "..", "full-size-sim", "growth_priors_loose.csv")
+KEEP_RESIDUES = {32, 37, 42, 47, 52, 74, 79, 84, 88, 93}
+ARMS = {
+    "base": ("realistic", {}),
+    "no_cong": ("realistic", {"transformation_poisson_lambda": 0.0}),
+    "no_tube": ("realistic", {"tube_noise_sigma": 0.0}),
+    "poisson": ("poisson", {}),
+    "clean": ("poisson", {"transformation_poisson_lambda": 0.0,
+                          "tube_noise_sigma": 0.0}),
+}
+
+
+def shrink_degen(cfg):
+    """degen_sites with only KEEP_RESIDUES left degenerate."""
+    d, r0 = cfg["degen_sites"], int(cfg["first_amplicon_residue"])
+    codons = [d[i:i + 3] for i in range(0, len(d), 3)]
+    out = [c if (c == "..." or r0 + i in KEEP_RESIDUES) else "..."
+           for i, c in enumerate(codons)]
+    return "".join(out)
+
+
+def shrink(sim_cf, real_lib):
+    """The reduced-library simulate config and library config."""
+    cf = copy.deepcopy(sim_cf)
+    cf["degen_sites"] = shrink_degen(cf)
+    ref = LibraryManager(sim_cf).build_library_df()
+    tgt = LibraryManager(cf).build_library_df()
+    scaled = scale_library_design(ref, tgt, sim_cf["library_mixture"],
+                                  sim_cf["transform_sizes"],
+                                  total_num_reads=sim_cf["total_num_reads"])
+    ratio = scaled["total_num_reads"] / sim_cf["total_num_reads"]
+    cf["library_mixture"] = scaled["library_mixture"]
+    cf["transform_sizes"] = scaled["transform_sizes"]
+    cf["total_num_reads"] = scaled["total_num_reads"]
+    if isinstance(cf["cfu0"], dict):
+        cf["cfu0"] = {k: float(v) * ratio for k, v in cf["cfu0"].items()}
+    else:
+        cf["cfu0"] = float(cf["cfu0"]) * ratio
+    if cf.get("pcr_template_molecules"):
+        cf["pcr_template_molecules"] = max(
+            1, int(round(cf["pcr_template_molecules"] * ratio)))
+    if cf.get("max_congressed_cells"):
+        cf["max_congressed_cells"] = max(
+            1000, int(round(cf["max_congressed_cells"] * ratio)))
+    lib = copy.deepcopy(real_lib)
+    lib["degen_sites"] = shrink_degen(lib)
+    n = (tgt["library_origin"] == "double-1-2").sum()
+    return cf, lib, ratio, n
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--pilot", required=True)
+    ap.add_argument("--out_dir", default="m_bias")
+    ap.add_argument("--seeds", type=int, nargs="+", default=[1, 2])
+    args = ap.parse_args()
+    os.makedirs(args.out_dir, exist_ok=True)
+
+    full = {}
+    for noise in ("realistic", "poisson"):
+        d = os.path.join(args.out_dir, f"_full_{noise}")
+        if not os.path.exists(os.path.join(d, "simulate_config.yaml")):
+            subprocess.run([sys.executable, MAKE_SIM, "--out_dir", d,
+                            "--seed", "1", "--noise", noise,
+                            "--calibrate_from", args.pilot], check=True)
+        with open(os.path.join(d, "simulate_config.yaml")) as fh:
+            full[noise] = (d, yaml.safe_load(fh))
+    with open(os.path.join("processed", "library_config.yaml")) as fh:
+        real_lib = yaml.safe_load(fh)
+
+    for arm, (noise, changes) in ARMS.items():
+        d_full, sim_cf = full[noise]
+        cf, lib, ratio, n = shrink(sim_cf, real_lib)
+        cf.update(changes)
+        for seed in args.seeds:
+            d = os.path.join(args.out_dir, f"{arm}_s{seed}")
+            os.makedirs(d, exist_ok=True)
+            cf["seed"] = seed
+            with open(os.path.join(d, "simulate_config.yaml"), "w") as fh:
+                fh.write(f"# make_factorial.py arm {arm}: {noise} noise, "
+                         f"changes {changes}, seed {seed}\n")
+                yaml.safe_dump(cf, fh, sort_keys=False)
+            with open(os.path.join(d, "library_config.yaml"), "w") as fh:
+                yaml.safe_dump(lib, fh, sort_keys=False)
+            for f in ("design.csv", "od600_calibration.yaml"):
+                shutil.copyfile(os.path.join(d_full, f), os.path.join(d, f))
+            shutil.copyfile(LOOSE, os.path.join(d, "growth_priors_loose.csv"))
+        print(f"{arm}: {n} doubles, scale {ratio:.4f}, reads "
+              f"{cf['total_num_reads']:.3g}, changes {changes}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/m-bias-factorial/make_subsets.py b/planning/studies/m-bias-factorial/make_subsets.py
new file mode 100644
index 00000000..0b1ebce5
--- /dev/null
+++ b/planning/studies/m-bias-factorial/make_subsets.py
@@ -0,0 +1,135 @@
+"""
+Fit-side subsets of a full-size simulation (m-bias factorial, round 2).
+
+    python make_subsets.py sim_realistic_s2 --out_dir m_subsets
+    python make_subsets.py recipe --out_dir m_subsets_real --arms no_doubles \
+        --library_config ../processed/library_config.yaml
+
+Each arm refits the same simulated growth table with only some genotypes.
+Given the shared parameters (k, m, tube offsets, population hypers) each
+genotype's count likelihood is independent, so a subset is a valid fit of
+the same data; what changes is which genotypes inform k and m.
+
+    no_doubles       wt, spikes and singles
+    spikes           wt and the spiked genotypes only (the library config's
+                     spiked_seqs): the clones least affected by congression
+    spikes_deep      spikes plus the --n_deep_singles deepest singles: enough
+                     genotypes to pin X's scale, less congression than every
+                     single
+    deep_doubles     no_doubles plus every double with >= DEEP total reads
+    shallow_doubles  no_doubles plus as many doubles below DEEP, at random
+
+Each arm directory gets the filtered tfs_growth.csv, links to the
+simulation's tfs_sim_* files (truth, and so run_arm.sh skips simulate and
+process), and copies of simulate_config.yaml (its seed), library_config.yaml
+and growth_priors_loose.csv. Submit run_arm.srun from inside each.
+
+A real-data growth table works the same way: there are no tfs_sim_* files
+or simulate_config.yaml to carry over, so pass --library_config;
+run_arm.sh then starts at configure, with seed $SEED or 1.
+"""
+
+import argparse
+import os
+import shutil
+
+import numpy as np
+import pandas as pd
+import yaml
+
+from tfscreen.genetics.library_design import library_composition_table
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+LOOSE = os.path.join(HERE, "..", "full-size-sim", "growth_priors_loose.csv")
+DEEP = 1000
+CHUNK = 5_000_000
+
+
+def genotype_class(g):
+    g = g.astype(str)
+    return np.select([g == "wt", g.str.count("/") == 1], ["wt", "double"],
+                     "single")
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("sim_dir")
+    ap.add_argument("--out_dir", default="m_subsets")
+    ap.add_argument("--seed", type=int, default=1)
+    ap.add_argument("--arms", nargs="+",
+                    default=["no_doubles", "deep_doubles", "shallow_doubles"],
+                    choices=["no_doubles", "deep_doubles", "shallow_doubles",
+                             "spikes", "spikes_deep"])
+    ap.add_argument("--n_deep_singles", type=int, default=300)
+    ap.add_argument("--library_config", default=None,
+                    help="library config (default: sim_dir's)")
+    args = ap.parse_args()
+    growth = os.path.join(args.sim_dir, "tfs_growth.csv")
+
+    tot = None
+    for chunk in pd.read_csv(growth, usecols=["genotype", "counts"],
+                             chunksize=CHUNK):
+        s = chunk.groupby("genotype")["counts"].sum()
+        tot = s if tot is None else tot.add(s, fill_value=0)
+    cls = pd.Series(genotype_class(tot.index.to_series()), index=tot.index)
+    doubles = tot[cls == "double"]
+    deep = doubles.index[doubles >= DEEP]
+    shallow_all = doubles.index[doubles < DEEP]
+    rng = np.random.default_rng(args.seed)
+    shallow = rng.choice(shallow_all, size=min(len(deep), len(shallow_all)),
+                         replace=False)
+    core = tot.index[cls != "double"]
+    arms = {"no_doubles": set(core),
+            "deep_doubles": set(core) | set(deep),
+            "shallow_doubles": set(core) | set(shallow)}
+    if "spikes" in args.arms or "spikes_deep" in args.arms:
+        lib_path = (args.library_config
+                    or os.path.join(args.sim_dir, "library_config.yaml"))
+        with open(lib_path) as fh:
+            comp = library_composition_table(yaml.safe_load(fh))
+        spiked = set(comp.loc[comp["in_spiked_origin"].astype(bool), "genotype"])
+        arms["spikes"] = (spiked | {"wt"}) & set(tot.index)
+        singles = tot[(cls == "single") & ~tot.index.isin(arms["spikes"])]
+        deepest = singles.sort_values(ascending=False).index[:args.n_deep_singles]
+        arms["spikes_deep"] = arms["spikes"] | set(deepest)
+        print(f"spikes_deep: the {len(deepest)} deepest singles have "
+              f">= {int(singles.loc[deepest].min())} reads")
+    arms = {a: arms[a] for a in args.arms}
+    print(f"{len(core)} wt/spike/single genotypes, {len(deep)} doubles >= "
+          f"{DEEP} reads, {len(shallow)} of {len(shallow_all)} below")
+
+    os.makedirs(args.out_dir, exist_ok=True)
+    out = {}
+    for arm in arms:
+        d = os.path.join(args.out_dir, arm)
+        os.makedirs(d, exist_ok=True)
+        out[arm] = os.path.join(d, "tfs_growth.csv")
+        if os.path.exists(out[arm]):
+            os.remove(out[arm])
+    for chunk in pd.read_csv(growth, chunksize=CHUNK):
+        for arm, keep in arms.items():
+            sub = chunk[chunk["genotype"].isin(keep)]
+            sub.to_csv(out[arm], mode="a", index=False,
+                       header=not os.path.exists(out[arm]))
+
+    sim = os.path.abspath(args.sim_dir)
+    for arm in arms:
+        d = os.path.join(args.out_dir, arm)
+        for f in os.listdir(sim):
+            if f.startswith("tfs_sim_"):
+                link = os.path.join(d, f)
+                if not os.path.lexists(link):
+                    os.symlink(os.path.join(sim, f), link)
+        if os.path.exists(os.path.join(sim, "simulate_config.yaml")):
+            shutil.copyfile(os.path.join(sim, "simulate_config.yaml"),
+                            os.path.join(d, "simulate_config.yaml"))
+        shutil.copyfile(args.library_config
+                        or os.path.join(sim, "library_config.yaml"),
+                        os.path.join(d, "library_config.yaml"))
+        shutil.copyfile(LOOSE, os.path.join(d, "growth_priors_loose.csv"))
+        n = pd.read_csv(out[arm], usecols=["genotype"])["genotype"].nunique()
+        print(f"{arm}: {n} genotypes")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/m-bias-factorial/resample_growth.py b/planning/studies/m-bias-factorial/resample_growth.py
new file mode 100644
index 00000000..0dd46160
--- /dev/null
+++ b/planning/studies/m-bias-factorial/resample_growth.py
@@ -0,0 +1,81 @@
+"""
+Filter or resample a growth table by tube (planning/deep-coverage.md).
+
+    python resample_growth.py tfs_growth.csv --library kanR --out kanR/tfs_growth.csv
+    python resample_growth.py tfs_growth.csv --drop_frac 0.2 --seed 3 --out boot_03/tfs_growth.csv
+
+--library keeps one library's tubes (the kanR-only check). --drop_frac
+drops that share of the tubes at random, stratified by condition_sel and
+titrant_conc and keeping at least one tube per stratum: a delete-d
+jackknife. Tubes cannot be resampled with replacement, since a duplicated
+tube lands on the same tensor cell as the original. For a delete-d
+jackknife with n tubes, d dropped and N subsamples, the variance of an
+estimate is (n - d) / d times the mean squared deviation of the
+subsample estimates from their mean (boot_summary.py applies it).
+
+Writes the kept tubes' rows and prints n, d and the dropped tubes.
+Reads in chunks (a full-size table is 8 GB).
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+
+CHUNK = 5_000_000
+
+
+def tube_table(path):
+    """One row per tube: sample, library, condition_sel, titrant_conc."""
+    cols = ["sample", "library", "condition_sel", "titrant_conc"]
+    parts = [c.drop_duplicates() for c in
+             pd.read_csv(path, usecols=cols, chunksize=CHUNK)]
+    return pd.concat(parts).drop_duplicates("sample").reset_index(drop=True)
+
+
+def choose_drops(tubes, frac, seed):
+    """Tubes to drop: frac of each condition_sel x titrant_conc stratum,
+    rounded, leaving at least one."""
+    rng = np.random.default_rng(seed)
+    drop = []
+    for _, g in tubes.groupby(["condition_sel", "titrant_conc"]):
+        k = min(int(round(frac * len(g))), len(g) - 1)
+        if k > 0:
+            drop.extend(rng.choice(g["sample"].to_numpy(), size=k,
+                                   replace=False))
+    return set(drop)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("growth_csv")
+    ap.add_argument("--out", required=True)
+    ap.add_argument("--library", default=None)
+    ap.add_argument("--drop_frac", type=float, default=0.0)
+    ap.add_argument("--seed", type=int, default=1)
+    args = ap.parse_args()
+
+    tubes = tube_table(args.growth_csv)
+    if args.library is not None:
+        if args.library not in set(tubes["library"]):
+            raise SystemExit(f"no library {args.library!r}: "
+                             f"{sorted(set(tubes['library']))}")
+        tubes = tubes[tubes["library"] == args.library]
+    drop = choose_drops(tubes, args.drop_frac, args.seed) if args.drop_frac else set()
+    keep = set(tubes["sample"]) - drop
+
+    os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
+    if os.path.exists(args.out):
+        os.remove(args.out)
+    for chunk in pd.read_csv(args.growth_csv, chunksize=CHUNK):
+        chunk[chunk["sample"].isin(keep)].to_csv(
+            args.out, mode="a", index=False, header=not os.path.exists(args.out))
+    print(f"n {len(tubes)} tubes, dropped d {len(drop)}, kept {len(keep)}"
+          + (f" (library {args.library})" if args.library else ""))
+    if drop:
+        print("dropped:", " ".join(sorted(drop)))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/m-bias-factorial/run_arm.sh b/planning/studies/m-bias-factorial/run_arm.sh
new file mode 100755
index 00000000..069a1753
--- /dev/null
+++ b/planning/studies/m-bias-factorial/run_arm.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+# One factorial arm (planning/studies/m-bias-factorial), run inside the arm's
+# directory: simulate, process, configure, MAP, extract. The chain and model
+# are the full-size simulation's (run_full_size.srun), without the Laplace.
+set -e
+# Seed from the simulation's config; a real-data arm (no simulate_config.yaml,
+# its own tfs_growth.csv) uses $SEED or 1.
+if [[ -f simulate_config.yaml ]]; then
+    SEED="$(grep '^seed:' simulate_config.yaml | awk '{print $2}')"
+fi
+SEED="${SEED:-1}"
+
+[[ -f tfs_sim_tubes.csv || -f tfs_growth.csv ]] || tfs-simulate simulate_config.yaml \
+    --out_prefix tfs_sim --seed "${SEED}" --no_write_growth > sim.log 2>&1
+
+[[ -f tfs_growth.csv ]] || tfs-process-counts tfs_sim_tubes.csv tfs_sim_counts \
+    --od600_file tfs_sim_tube_od600.csv \
+    --od600_calibration_file tfs_sim_od600_calibration.yaml \
+    --tube_volume_mL 5 --out_prefix tfs_growth --no_verbose > process.log 2>&1
+
+# configure_extra.txt (optional, one line): extra tfs-configure-model flags
+# for this arm, e.g. the congression mixture.
+EXTRA=""
+[[ -f configure_extra.txt ]] && EXTRA="$(cat configure_extra.txt)"
+
+[[ -f tfs_configure_config.yaml ]] || tfs-configure-model ${EXTRA} \
+    --allow_missing_spikes \
+    --growth_df tfs_growth.csv \
+    --library_config library_config.yaml \
+    --theta_model hill_relative \
+    --growth_shares_replicates \
+    --batch_size 4096 \
+    --growth_priors growth_priors_loose.csv \
+    --set_priors sigma_fixed=0.17 theta_log_hill_n_hyper_scale_fixed=0.5 \
+    --skip_model_stats > configure.log 2>&1
+
+[[ -f tfs_fit_model_params.npz ]] || tfs-fit-model tfs_configure_config.yaml \
+    --seed "${SEED}" --analysis_method map > fit.log 2>&1
+
+tfs-extract-params tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    > extract.log 2>&1
+echo "done"
diff --git a/planning/studies/m-bias-factorial/run_arm.srun b/planning/studies/m-bias-factorial/run_arm.srun
new file mode 100644
index 00000000..4929fbc9
--- /dev/null
+++ b/planning/studies/m-bias-factorial/run_arm.srun
@@ -0,0 +1,22 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_mbias
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpulong
+#SBATCH --time=01-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# One m-bias factorial arm (planning/studies/m-bias-factorial), submitted
+# from inside the arm's directory; run_arm.sh (one level up) runs the chain
+# and skips any step whose output exists, so a resubmit resumes.
+
+module load cuda/12.4.1
+set -e
+echo ">>> Start $(date)"
+bash ../run_arm.sh
+echo ">>> Done $(date)"
diff --git a/planning/studies/m-bias-factorial/run_held.srun b/planning/studies/m-bias-factorial/run_held.srun
new file mode 100644
index 00000000..6359d644
--- /dev/null
+++ b/planning/studies/m-bias-factorial/run_held.srun
@@ -0,0 +1,68 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_held
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpulong
+#SBATCH --time=02-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# The full library with k and m held at a no-doubles fit's MAP
+# (planning/studies/m-bias-factorial, round 3): the full-size chain
+# (run_full_size.srun) with k and m clamped (m_pinned, k_pinned) at the
+# values in growth_priors_held.csv, from held_priors.py. Run from an arm
+# directory holding tfs_growth.csv, library_config.yaml and
+# growth_priors_held.csv, plus links to the simulation's tfs_sim_* files
+# for tfs-summarize-fit to score against. See README.md.
+#
+#   PINS   the --set_priors pins (default "m_pinned=1 k_pinned=1", every
+#          condition). When held_priors.py --hold pins some conditions
+#          through the table's pin columns, submit with --export=ALL,PINS=
+#          so the global pins do not override them.
+#   INIT   optional params.npz of an earlier MAP of the same model (e.g. a
+#          held fit at nearby k and m) to start the MAP from (--init_from;
+#          that also skips the staged MAP). Step 2's draws start from the
+#          round 3 MAP this way.
+#   configure_extra.txt (optional file, one line): extra tfs-configure-model
+#          flags, e.g. the congression mixture.
+
+module load cuda/12.4.1
+
+set -e
+SEED="${SEED:-1}"
+PINS="${PINS-m_pinned=1 k_pinned=1}"
+EXTRA=""
+[[ -f configure_extra.txt ]] && EXTRA="$(cat configure_extra.txt)"
+
+echo ">>> Configure (k, m held; pins: ${PINS:-none}; extra: ${EXTRA:-none}) $(date)"
+tfs-configure-model ${EXTRA} \
+    --allow_missing_spikes \
+    --growth_df tfs_growth.csv \
+    --library_config library_config.yaml \
+    --theta_model hill_relative \
+    --growth_shares_replicates \
+    --batch_size 4096 \
+    --growth_priors growth_priors_held.csv \
+    --set_priors sigma_fixed=0.17 theta_log_hill_n_hyper_scale_fixed=0.5 \
+        ${PINS}
+
+echo ">>> Fit (staged MAP) $(date)"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" --analysis_method map \
+    ${INIT:+--init_from "${INIT}"}
+
+echo ">>> Arrowhead Laplace $(date)"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    --laplace arrowhead --num_posterior_samples 500 --skip_growth_observations
+
+echo ">>> Extract and predict $(date)"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Summarize against the truth $(date)"
+tfs-summarize-fit .
+
+echo ">>> Done $(date)"
diff --git a/planning/studies/m-bias-factorial/run_jackknife.srun b/planning/studies/m-bias-factorial/run_jackknife.srun
new file mode 100644
index 00000000..64f08a19
--- /dev/null
+++ b/planning/studies/m-bias-factorial/run_jackknife.srun
@@ -0,0 +1,52 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_jk
+#SBATCH --output=jk_%a.out
+#SBATCH --error=jk_%a.err
+#SBATCH --partition=gpulong
+#SBATCH --time=00-08:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+#SBATCH --array=1-30%10
+
+
+# Delete-d jackknife of a stage-1 fit (planning/deep-coverage.md, k/m
+# uncertainty): array task i drops a random DROP_FRAC of the tubes
+# (stratified by condition and IPTG; resample_growth.py, seed i) from the
+# stage-1 growth table and refits stage 1 with the same model, in jk_NN/.
+# Submit from the directory that should hold jk_*/; boot_summary.py then
+# turns the fits into k and m standard errors.
+#
+#   SRC       the stage-1 growth table (absolute path)
+#   CONF      the stage-1 fit's directory: its library_config.yaml,
+#             growth_priors_loose.csv and configure_extra.txt (if any) are
+#             copied, so every subsample fits the same model
+#   DROP_FRAC share of tubes dropped (default 0.2)
+#   TFS       the tfscreen checkout (default /gpfs/home/harms/tfscreen)
+#
+# The fit seed is 1 for every task, so the spread is the data's.
+
+module load cuda/12.4.1
+
+set -e
+SRC="${SRC:?set SRC}"
+CONF="${CONF:?set CONF}"
+DROP_FRAC="${DROP_FRAC:-0.2}"
+TFS="${TFS:-/gpfs/home/harms/tfscreen}"
+STUDY="${TFS}/planning/studies/m-bias-factorial"
+i="${SLURM_ARRAY_TASK_ID:?run as an array job}"
+d="$(printf 'jk_%02d' "${i}")"
+
+mkdir -p "${d}" && cd "${d}"
+if [[ ! -f tfs_growth.csv ]]; then
+    python "${STUDY}/resample_growth.py" "${SRC}" --drop_frac "${DROP_FRAC}" \
+        --seed "${i}" --out tfs_growth.csv > resample.log
+fi
+cp "${CONF}/library_config.yaml" "${CONF}/growth_priors_loose.csv" .
+[[ -f "${CONF}/configure_extra.txt" ]] && cp "${CONF}/configure_extra.txt" .
+echo ">>> Stage 1 fit ${d} $(date)"
+SEED=1 bash "${STUDY}/run_arm.sh"
+echo ">>> Done ${d} $(date)"
diff --git a/planning/studies/m-bias-factorial/run_km_draws.srun b/planning/studies/m-bias-factorial/run_km_draws.srun
new file mode 100644
index 00000000..e982af2a
--- /dev/null
+++ b/planning/studies/m-bias-factorial/run_km_draws.srun
@@ -0,0 +1,40 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_kmdraw
+#SBATCH --output=run_km_draws.out
+#SBATCH --error=run_km_draws.err
+#SBATCH --partition=gpulong
+#SBATCH --time=00-08:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# Step 2 of planning/deep-coverage.md, part 1: the arrowhead Laplace of a
+# stage-1 (no-doubles) fit, then km_draws.py, which writes one directory per
+# joint draw of every condition's k and m (README "Step 2"). Run from the
+# directory that will hold km_draws/.
+#
+#   STAGE1  the stage-1 fit's directory (default m_subsets/no_doubles)
+#   DRAWS   number of draws (default 10)
+#   TFS     the tfscreen checkout (default /gpfs/home/harms/tfscreen)
+
+module load cuda/12.4.1
+
+set -e
+STAGE1="${STAGE1:-m_subsets/no_doubles}"
+DRAWS="${DRAWS:-10}"
+TFS="${TFS:-/gpfs/home/harms/tfscreen}"
+
+if [[ ! -f "${STAGE1}/tfs_posterior.h5" ]]; then
+    echo ">>> Stage 1 Laplace $(date)"
+    (cd "${STAGE1}" && tfs-sample-posterior tfs_configure_config.yaml \
+        tfs_fit_model_checkpoint.pkl --laplace arrowhead \
+        --num_posterior_samples 1000 --skip_growth_observations)
+fi
+
+echo ">>> Draws $(date)"
+python "${TFS}/planning/studies/m-bias-factorial/km_draws.py" "${STAGE1}" \
+    --num_draws "${DRAWS}" --out_dir km_draws
+echo ">>> Done $(date)"
diff --git a/planning/studies/m-bias-factorial/run_point.srun b/planning/studies/m-bias-factorial/run_point.srun
new file mode 100644
index 00000000..0ef303d3
--- /dev/null
+++ b/planning/studies/m-bias-factorial/run_point.srun
@@ -0,0 +1,30 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_point
+#SBATCH --output=run_point.out
+#SBATCH --error=run_point.err
+#SBATCH --partition=gpulong
+#SBATCH --time=00-08:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# Finish a held fit whose arrowhead Laplace refuses the model (a congression
+# mixture couples genotypes through their co-resident partners): the MAP
+# point as a one-draw posterior, then predict and summarize, as run_held.srun
+# would. X points only (no intervals). Submit from the fit's directory.
+
+module load cuda/12.4.1
+
+set -e
+echo ">>> MAP point $(date)"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    --laplace point --skip_growth_observations
+echo ">>> Extract and predict $(date)"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+echo ">>> Summarize against the truth $(date)"
+tfs-summarize-fit .
+echo ">>> Done $(date)"
diff --git a/planning/studies/m-bias-factorial/run_pool.srun b/planning/studies/m-bias-factorial/run_pool.srun
new file mode 100644
index 00000000..cce54c70
--- /dev/null
+++ b/planning/studies/m-bias-factorial/run_pool.srun
@@ -0,0 +1,24 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_pool
+#SBATCH --output=run_pool.out
+#SBATCH --error=run_pool.err
+#SBATCH --partition=gpulong
+#SBATCH --time=00-04:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# Step 2 of planning/deep-coverage.md, part 3: pool the draws' X posteriors
+# into one equal-weight mixture (tfs-compare-runs' aggregate), run from
+# km_draws/ once every draw_*/run.out ends with ">>> Done". Writes
+# pooled_aggregate.csv (the pooled quantiles) and pooled.csv (the draws'
+# disagreement per genotype).
+
+set -e
+ls draw_*/tfs_pred_theta.csv > pooled_inputs.txt
+echo ">>> Pool $(wc -l < pooled_inputs.txt) draws $(date)"
+tfs-compare-runs pooled_inputs.txt --out_prefix pooled
+echo ">>> Done $(date)"
diff --git a/planning/studies/m-bias-factorial/run_two_stage.srun b/planning/studies/m-bias-factorial/run_two_stage.srun
new file mode 100644
index 00000000..2a85a726
--- /dev/null
+++ b/planning/studies/m-bias-factorial/run_two_stage.srun
@@ -0,0 +1,88 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_2stage
+#SBATCH --output=run_two_stage.out
+#SBATCH --error=run_two_stage.err
+#SBATCH --partition=gpulong
+#SBATCH --time=02-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# The two-stage fit of one full-size simulation (planning/studies/
+# m-bias-factorial, README "Second seed"), run from a directory made by
+# full-size-sim/make_sim_config.py:
+#
+#   1. simulate and process (skipped when tfs_growth.csv exists),
+#   2. stage 1: wt, spikes and singles only (make_subsets.py no_doubles,
+#      run_arm.sh: configure, staged MAP, extract), plus its arrowhead
+#      Laplace for step 2's k/m draws,
+#   3. stage 2: the full library with every condition's k and m pinned at
+#      stage 1's MAP (held_priors.py, run_held.srun's chain).
+#
+# Every step is skipped when its output exists, so a resubmit resumes.
+#
+#   SEED  the simulation and fit seed (default 1; match make_sim_config's)
+#   TFS   the tfscreen checkout (default /gpfs/home/harms/tfscreen)
+#   STAGES  2 (default) runs both stages; 1 stops after stage 1 and its
+#           Laplace (enough to read stage 1's k and m)
+
+module load cuda/12.4.1
+
+set -e
+SEED="${SEED:-1}"
+TFS="${TFS:-/gpfs/home/harms/tfscreen}"
+STAGES="${STAGES:-2}"
+STUDY="${TFS}/planning/studies/m-bias-factorial"
+
+if [[ -f tfs_growth.csv ]]; then
+    echo ">>> Simulate and process: tfs_growth.csv exists, skipped"
+else
+    if [[ ! -f tfs_sim_tubes.csv ]]; then
+        echo ">>> Simulate $(date)"
+        tfs-simulate simulate_config.yaml --out_prefix tfs_sim --seed "${SEED}"
+    fi
+    echo ">>> Process counts $(date)"
+    tfs-process-counts tfs_sim_tubes.csv tfs_sim_counts \
+        --od600_file tfs_sim_tube_od600.csv \
+        --od600_calibration_file tfs_sim_od600_calibration.yaml \
+        --tube_volume_mL 5 \
+        --out_prefix tfs_growth --no_verbose
+fi
+
+S1=stage1/no_doubles
+if [[ ! -f ${S1}/tfs_growth.csv ]]; then
+    echo ">>> Stage 1 subset $(date)"
+    python "${STUDY}/make_subsets.py" . --out_dir stage1 --arms no_doubles
+fi
+if [[ ! -f ${S1}/tfs_params_growth_m.csv ]]; then
+    echo ">>> Stage 1 fit $(date)"
+    (cd "${S1}" && bash "${STUDY}/run_arm.sh")
+fi
+if [[ ! -f ${S1}/tfs_posterior.h5 ]]; then
+    echo ">>> Stage 1 Laplace $(date)"
+    (cd "${S1}" && tfs-sample-posterior tfs_configure_config.yaml \
+        tfs_fit_model_checkpoint.pkl --laplace arrowhead \
+        --num_posterior_samples 1000 --skip_growth_observations \
+        > laplace.log 2>&1)
+fi
+
+if [[ "${STAGES}" == "1" ]]; then
+    echo ">>> Stage 2: skipped (STAGES=1)"
+elif [[ -f stage2/summary/tfs_summarize_fit_summary.json ]]; then
+    echo ">>> Stage 2: done, skipped"
+else
+    echo ">>> Stage 2 setup $(date)"
+    mkdir -p stage2
+    (cd stage2 \
+        && python "${STUDY}/held_priors.py" "../${S1}" --out growth_priors_held.csv \
+        && ln -sf ../tfs_growth.csv . \
+        && for f in ../tfs_sim_*; do ln -sf "${f}" .; done \
+        && cp ../library_config.yaml .)
+    echo ">>> Stage 2 fit (every condition pinned) $(date)"
+    (cd stage2 && SEED="${SEED}" bash "${STUDY}/run_held.srun" > run.out 2> run.err)
+fi
+
+echo ">>> Done $(date)"
diff --git a/planning/studies/m-bias-factorial/score_arms.py b/planning/studies/m-bias-factorial/score_arms.py
new file mode 100644
index 00000000..b43123ed
--- /dev/null
+++ b/planning/studies/m-bias-factorial/score_arms.py
@@ -0,0 +1,65 @@
+"""
+Score the factorial arms: k and m against the truth on the X scale.
+
+    python score_arms.py m_bias/*_s*
+    python score_arms.py no_doubles deep_doubles shallow_doubles
+
+Truth on the fit's X scale (theta: hill_relative gauges wt to X = 1 at the
+low and 0 at the high gauge concentration): m_X = m (theta_wt(c_lo) -
+theta_wt(c_hi)), k_X = b + m theta_wt(c_hi), with wt's true Hill curve. Also
+the median dk_geno error over genotypes, when tfs_params_dk_geno.csv exists
+(a MAP checkpoint does not give it). Writes factorial_scores.csv.
+"""
+
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+import yaml
+
+
+def wt_theta(p, conc):
+    lo, hi = p["theta_low"], p["theta_high"]
+    if conc <= 0:
+        return lo
+    occ = 1.0 / (1.0 + np.exp(-p["hill_n"] * (np.log(conc) - p["log_hill_K"])))
+    return lo + (hi - lo) * occ
+
+
+rows = []
+for d in sys.argv[1:]:
+    if not os.path.exists(os.path.join(d, "tfs_params_growth_m.csv")):
+        print("skip (not done):", d)
+        continue
+    cfg = yaml.safe_load(open(os.path.join(d, "tfs_configure_config.yaml")))
+    gauge = None
+    for key in ("theta_gauge_conc",):
+        gauge = cfg.get("components", {}).get(key) or cfg.get(key)
+    c_lo, c_hi = (float(x) for x in gauge)
+    par = pd.read_csv(os.path.join(d, "tfs_sim_parameters.csv"))
+    wt = par[par.genotype == "wt"].iloc[0]
+    t_lo, t_hi = wt_theta(wt, c_lo), wt_theta(wt, c_hi)
+    truth = pd.read_csv(os.path.join(d, "tfs_sim_growth_parameters.csv")).set_index("condition_rep")
+    k = pd.read_csv(os.path.join(d, "tfs_params_growth_k.csv")).groupby("condition_rep")["q0.5"].mean()
+    m = pd.read_csv(os.path.join(d, "tfs_params_growth_m.csv")).groupby("condition_rep")["q0.5"].mean()
+    dk_file = os.path.join(d, "tfs_params_dk_geno.csv")
+    dk_bias = np.nan
+    if os.path.exists(dk_file):
+        dk = pd.read_csv(dk_file)[["genotype", "q0.5"]].merge(par[["genotype", "dk_geno"]], on="genotype")
+        dk_bias = float(np.median(dk["q0.5"] - dk["dk_geno"]))
+    arm = os.path.basename(os.path.normpath(d))
+    for c in ("kanR+kan", "pheS+4CP"):
+        b, mm = truth.loc[c, "growth_k"], truth.loc[c, "growth_m"]
+        m_x, k_x = mm * (t_lo - t_hi), b + mm * t_hi
+        name, _, seed = arm.rpartition("_s")
+        if not seed.isdigit():
+            name, seed = arm, ""
+        rows.append(dict(arm=name, seed=seed,
+                         condition=c, k_true=k_x, k_fit=k[c], m_true=m_x, m_fit=m[c],
+                         m_ratio=m[c] / m_x, dk_bias=dk_bias))
+df = pd.DataFrame(rows)
+df.to_csv("factorial_scores.csv", index=False)
+print(df.round(4).to_string(index=False))
+print()
+print(df.groupby(["arm", "condition"])[["m_ratio", "dk_bias"]].mean().round(3).to_string())
diff --git a/planning/studies/m-bias-factorial/score_x.py b/planning/studies/m-bias-factorial/score_x.py
new file mode 100644
index 00000000..0b79be37
--- /dev/null
+++ b/planning/studies/m-bias-factorial/score_x.py
@@ -0,0 +1,79 @@
+"""
+X against the simulation's truth by read depth (the tables in README.md).
+
+    python score_x.py depth.csv held_s2 km_draws/pooled_aggregate.csv \
+        --truth held_s2/summary/tfs_summarize_theta_corr_test.csv
+
+Each input is a run directory (its summary/tfs_summarize_theta_corr_test.csv,
+which carries the truth as `ref`) or a quantile table (q0.025, q0.5, q0.975
+by genotype and titrant_conc, such as tfs-compare-runs' aggregate), scored
+against --truth. Doubles only unless --all. Per depth bin: r, the slope of
+truth on fit, RMSE, 95% coverage, median 95% width, and the mean error at
+the lowest and highest concentration.
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+
+BINS = [0, 1e2, 1e3, 1e4, 1e5, 1e13]
+LABELS = ["<=1e2", "1e2-1e3", "1e3-1e4", "1e4-1e5", ">1e5"]
+COLS = ["genotype", "titrant_conc", "q0.025", "q0.5", "q0.975"]
+
+
+def load(path, truth):
+    if os.path.isdir(path):
+        return pd.read_csv(os.path.join(path, "summary",
+                                        "tfs_summarize_theta_corr_test.csv"),
+                           usecols=COLS + ["ref"])
+    if truth is None:
+        raise SystemExit(f"{path} is a table; give --truth for its reference")
+    df = pd.read_csv(path, usecols=COLS)
+    return df.merge(truth, on=["genotype", "titrant_conc"])
+
+
+def score(df, depth, doubles_only=True):
+    g = df["genotype"].astype(str)
+    df = df[g != "wt"]
+    if doubles_only:
+        df = df[df["genotype"].astype(str).str.count("/") == 1]
+    df = df.assign(bin=pd.cut(df["genotype"].map(depth), BINS, labels=LABELS))
+    lo, hi = df["titrant_conc"].min(), df["titrant_conc"].max()
+    rows = []
+    for b, d in df.groupby("bin", observed=True):
+        x, y = d["q0.5"].to_numpy(), d["ref"].to_numpy()
+        err = d["q0.5"] - d["ref"]
+        rows.append(dict(
+            bin=b, n_geno=d["genotype"].nunique(), r=np.corrcoef(x, y)[0, 1],
+            slope=np.polyfit(x, y, 1)[0], rmse=np.sqrt(np.mean((x - y) ** 2)),
+            cover95=np.mean((d["q0.025"] <= y) & (y <= d["q0.975"])),
+            width95=np.median(d["q0.975"] - d["q0.025"]),
+            err_lo=err[d["titrant_conc"] == lo].mean(),
+            err_hi=err[d["titrant_conc"] == hi].mean()))
+    return pd.DataFrame(rows)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("depth_csv")
+    ap.add_argument("inputs", nargs="+")
+    ap.add_argument("--truth", default=None,
+                    help="a run's summary/tfs_summarize_theta_corr_test.csv")
+    ap.add_argument("--all", action="store_true", help="singles too")
+    args = ap.parse_args()
+    depth = pd.read_csv(args.depth_csv).set_index("genotype")["reads"]
+    truth = None
+    if args.truth:
+        truth = pd.read_csv(args.truth,
+                            usecols=["genotype", "titrant_conc", "ref"])
+    for path in args.inputs:
+        print(path)
+        print(score(load(path, truth), depth, not args.all)
+              .round(3).to_string(index=False))
+        print()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/noise-anatomy/README.md b/planning/studies/noise-anatomy/README.md
new file mode 100644
index 00000000..f089783f
--- /dev/null
+++ b/planning/studies/noise-anatomy/README.md
@@ -0,0 +1,135 @@
+# Study 0b: anatomy of read-count noise
+
+**Status: provisional (2026-09-26).** Run on the 2026-07-23 snapshot, whose
+counts came from a FASTQ processor that assigned every unknown read to wt
+and used a very stringent Q cutoff; a new processing batch is running.
+Affected here: section 3 (ratios to wt) and the `__unknown__` shares, which
+are wrong (unknown reads sat in wt). Expected to survive, to be re-checked:
+the low-count prevalence (may improve with a looser Q cutoff), the
+composition offset, and the 5-18x excess count variance (uniform read
+thinning does not create overdispersion). Next analysis: the variance
+multiplier by condition and IPTG column against the tube's fold-growth
+since the split; it rises with growth if founder sampling dominates and is
+flat if a template bottleneck does.
+
+## Question
+
+How much of the real data sits in the low-count regime, how large is the
+per-tube composition offset `u_s`, and what does per-tube count noise look
+like compared with Poisson? Feeds roadmap steps 4 (simulator), 6 (`u_s`
+prior) and 7 (the count likelihood's dispersion model)
+(`planning/analysis-roadmap.md`).
+
+## How to run
+
+```bash
+python noise_anatomy.py ~/work/programming/git-clones/tfscreen/dev/real-2026-07-23 out/
+```
+
+Inputs from `planning/studies/step0-data/`. About 15 s.
+
+## Method
+
+Each replicate x condition x IPTG concentration has three tubes
+(timepoints). A per-genotype straight line in `t_sel` through three points
+leaves one residual degree of freedom; its projection `z` on the unit
+vector orthogonal to `[1, t]` carries whatever straight-line growth does not
+explain. The shared composition offset is the mean of `z` over the 494
+genotypes with at least 1000 reads in every tube; its variance is corrected
+for the finite number of genotypes. Count noise compares `E[z^2]` with the
+Poisson expectation `sum_i v_i^2 / c_i`, on ln frequency with the shared
+offset removed, using lines with at least 100 reads in every tube (below
+that, requiring nonzero counts truncates the draws and ln counts are
+biased).
+
+## Commit
+
+Scripts as in this directory on top of `b949095`.
+
+## Results (2026-09-26)
+
+**1. The library is overwhelmingly low-count.** 113 tubes, median 17.9M
+reads each (listed genotypes); `__unknown__` 0.4-3% of reads.
+
+| class | median reads per tube | 0 reads | < 5 | < 20 |
+|---|---|---|---|---|
+| doubles (kan+ / 4CP+) | 4 / 3 | 23% / 33% | 53% / 55% | 79% / 76% |
+| singles | ~5,300 | 0.5-1% | 1-3% | 1.5-4.5% |
+| spiked (5 genotypes) | 29,000-56,000 | 0 | 0 | 0 |
+
+Doubles are about 97% of rows. At 3-5 reads, `ln(counts + pseudocount)` is
+far from Gaussian and the pseudocount dominates.
+
+**2. The per-tube composition offset is small:** SD 0.018 ln units, shared
+by every genotype in a tube (consistent with the earlier denominator review,
+0.026). Correlation with the tube's `__unknown__` share: -0.34. Per-genotype
+residual SD for well-measured genotypes: 0.047.
+
+**3. Per-tube count noise is 5-10 times Poisson at high counts.** On ln
+frequency, lines with >= 100 reads:
+
+`E[z^2] = 0.0055 + 8.4 x Poisson` (pooled): a variance multiplier of about
+8 on top of counting, plus a floor of about 0.07 ln units. By condition the
+split between the two trades off (multiplier 3-5 with floors 0.07-0.15)
+because each line has one residual degree of freedom; the robust statement
+is that at 100-3000 reads the observed variance is 5-18 times the Poisson
+variance. The ratio to wt gives the same picture (13 times Poisson at ~500
+reads).
+
+**4. Low counts are mostly low abundance, not crashes.** Each double's
+row in a selective tube, classified by the genotype's median reads in the
+no-drug tubes of the same library and replicate (its abundance before
+selection):
+
+| reads without drug | share of selective double rows | of which < 5 reads (kan+ / 4CP+) |
+|---|---|---|
+| < 5 | 59% | 74% / 88% |
+| 5-20 | 22% | 19% / 38% |
+| 20-100 | 13% | 2% / 9% |
+| >= 100 | 6% | 0% / 0.2% |
+
+About 88% of the low-count (< 5) double rows belong to genotypes that are
+already under 5 reads without the drug. Of the 69,245 doubles with >= 20
+reads without the drug, 39% dip below 5 reads in at least one selective
+tube, but only 0.1% do so in half of their selective tubes. The double
+sub-library's design gives about 1.5 transformants per double
+(`transform_sizes` 300,000 for ~200,000 doubles), so a double's abundance
+is set early and varies widely between doubles.
+
+## What it means
+
+- **The count likelihood (roadmap step 7) is the main event, not a
+  refinement.** The doubles, which carry the mutant cycles, are almost all
+  at 0-20 reads.
+- **Today's `ln_cfu_var` (binomial counting) understates the noise 5-10
+  fold** for well-measured genotypes, so fits are overconfident by roughly
+  2-3x in SD. This is a leading candidate for the low theta coverage seen
+  in simulation-based calibration being worse on real data.
+- **The dispersion model must allow variance proportional to the mean**
+  (NB1 / quasi-Poisson, `var = phi * mu`), not only a constant-CV floor
+  (NB2, `var = mu + mu^2 / r`). Variance scaling with 1/reads at 5-10x
+  Poisson is the signature of a bottleneck upstream of the reads: fewer
+  template molecules than reads (PCR input), and/or few founder cells per
+  genotype per tube.
+- **Founder sampling is a physical per-tube noise the model and simulator
+  both lack.** A typical double has ~4 reads at harvest, ~15 cells in the
+  tube, and so only a few cells when the tube was seeded from the split.
+  Each tube draws its own founders, so a rare genotype's starting abundance
+  differs from tube to tube, independently across timepoints; `ln_cfu0`
+  shared by all tubes of a replicate x `condition_pre` is wrong at low
+  abundance. The simulator seeds every tube deterministically
+  (`_sim_growth`: `total_cfu0 * freq`) and samples reads once
+  (multinomial), so simulated noise is pure Poisson.
+- **`u_s` needs only a tight prior** (SD ~0.02).
+- **Most of the doubles' weak data is weak everywhere.** Low counts come
+  from low abundance before selection, not from genotypes crashing under
+  it, so a count likelihood mostly makes their uncertainty honest (and
+  lets the hierarchical priors carry them) rather than recovering a lost
+  crash signal. The crash information exists for the better-represented
+  doubles (39% of them dip below 5 reads somewhere) and is where the count
+  likelihood beats a pseudocount. The design lever is more cells per double:
+  deeper transformation of the double sub-library and more founders per
+  tube at the split (which also shrinks founder noise).
+- Separating template bottleneck from founder sampling needs a technical
+  replicate: re-amplify and re-sequence the same extracted DNA for a few
+  tubes (see the roadmap).
diff --git a/planning/studies/noise-anatomy/noise_anatomy.py b/planning/studies/noise-anatomy/noise_anatomy.py
new file mode 100644
index 00000000..0798e2c4
--- /dev/null
+++ b/planning/studies/noise-anatomy/noise_anatomy.py
@@ -0,0 +1,260 @@
+"""
+Step 0b of planning/analysis-roadmap.md: the anatomy of read-count noise.
+
+Within one replicate x condition x concentration there are three tubes
+(timepoints). A per-genotype straight line in t_sel through three points
+leaves one residual degree of freedom per line; its projection
+z = r . v (v the unit vector orthogonal to [1, t]) carries everything the
+straight-growth model does not explain in that group. That gives:
+
+  1. Low-count prevalence: fraction of rows at 0, < 5 and < 20 reads, by
+     genotype class and selection condition.
+  2. The per-tube composition offset u_s (PCR, calling efficiency): a shift
+     shared by every genotype's ln(frequency) in a tube. Its projection is
+     the same for every genotype in a group, so the mean of z over many
+     well-measured genotypes estimates it, and
+         var(u) ~ mean_groups(zbar^2) - mean_groups(var(z)) / K.
+     Compared with the tube's __unknown__ share.
+  3. The count noise model: for ln(reads_g / reads_wt) (tube total and u
+     cancel), E[z^2] against the Poisson expectation
+     sum_i v_i^2 (1/c_g,i + 1/c_wt,i). A fit E[z^2] = a + b * poisson gives
+     b (1 = Poisson; > 1 = effective depth smaller than the reads) and a
+     (a floor independent of counts: PCR jackpotting and real
+     genotype-by-tube differences).
+
+Usage:
+    python noise_anatomy.py DATA_DIR OUT_DIR
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+SPIKED = {"wt", "M42I", "H74A", "K84L", "M42I/H74A"}
+GROUP = ["replicate", "condition_sel", "titrant_conc"]
+
+
+def genotype_class(g):
+    if g in SPIKED:
+        return "spiked"
+    return "double" if "/" in g else "single"
+
+
+def residual_projector(t):
+    """Unit vector orthogonal to [1, t] (3 points -> exactly one)."""
+    X = np.column_stack([np.ones_like(t), t])
+    q, _ = np.linalg.qr(X, mode="complete")
+    return q[:, 2]
+
+
+def main(data_dir, out_dir):
+    data_dir, out_dir = Path(data_dir), Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    samples = pd.read_parquet(data_dir / "samples.parquet")
+    counts = pd.read_parquet(data_dir / "counts.parquet")
+    genos = counts["genotype"].cat.categories
+    samples["unknown_share"] = 1 - samples["reads_listed"] / samples["freq_denominator"]
+    print(f"{len(samples)} samples, {len(genos):,} genotypes, "
+          f"{len(counts):,} rows")
+    print(f"reads per tube (listed genotypes): median "
+          f"{samples['reads_listed'].median():,.0f}; __unknown__ share by "
+          f"condition:")
+    print(samples.groupby("condition_sel")["unknown_share"]
+          .describe()[["mean", "min", "max"]].to_string())
+
+    # --- 1. low-count prevalence -------------------------------------------
+    counts = counts.merge(samples[["sample", "condition_sel"]], on="sample")
+    cls = pd.Series({g: genotype_class(g) for g in genos})
+    counts["class"] = counts["genotype"].map(cls).astype("category")
+    low = counts.groupby(["class", "condition_sel"], observed=True)["counts"].agg(
+        rows="size",
+        zero=lambda c: (c == 0).mean(),
+        lt5=lambda c: (c < 5).mean(),
+        lt20=lambda c: (c < 20).mean(),
+        median="median")
+    print("\n1. low-count prevalence (fraction of rows):")
+    print(low.to_string(float_format=lambda x: f"{x:.3f}"))
+    low.to_csv(out_dir / "low_counts.csv")
+
+    # Matrix: tube x genotype.
+    mat = counts.pivot_table(index="sample", columns="genotype",
+                             values="counts", aggfunc="sum", observed=True,
+                             fill_value=0)
+    s = samples.set_index("sample").loc[mat.index]
+    lnf = np.log(mat.to_numpy(dtype=float) + 0.5) - \
+        np.log(s["freq_denominator"].to_numpy())[:, None]
+
+    # --- 2. composition offset u_s ------------------------------------------
+    rows = []
+    well = mat.columns[(mat.to_numpy() >= 1000).all(axis=0)]
+    wi = mat.columns.get_indexer(well)
+    print(f"\n2. genotypes with >= 1000 reads in every tube: {len(well)}")
+    for key, g in s.reset_index().groupby(GROUP):
+        if len(g) != 3:
+            continue
+        idx = mat.index.get_indexer(g["sample"])
+        v = residual_projector(g["t_sel"].to_numpy(dtype=float))
+        z = v @ lnf[idx][:, wi]              # one z per well-measured genotype
+        uz = v @ g["unknown_share"].to_numpy()
+        rows.append({**dict(zip(GROUP, key)), "zbar": z.mean(),
+                     "var_z": z.var(ddof=1), "K": len(z), "unknown_proj": uz})
+    u = pd.DataFrame(rows)
+    var_u = (u["zbar"] ** 2).mean() - (u["var_z"] / u["K"]).mean()
+    print(f"   groups with 3 tubes: {len(u)}")
+    print(f"   composition offset SD (shared by all genotypes in a tube): "
+          f"{np.sqrt(max(var_u, 0)):.4f} ln units")
+    print(f"   per-genotype residual SD (well-measured, projection): "
+          f"{np.sqrt(u['var_z'].mean()):.4f}")
+    r = np.corrcoef(u["zbar"], u["unknown_proj"])[0, 1]
+    print(f"   correlation of the shared offset with the __unknown__ share "
+          f"(projected): {r:+.2f}")
+    u.to_csv(out_dir / "composition_offset.csv", index=False)
+
+    # --- 3. count noise: ln(reads_g / reads_wt) -----------------------------
+    wt = mat["wt"].to_numpy(dtype=float)
+    M = mat.to_numpy(dtype=float)
+    rec = []
+    for key, g in s.reset_index().groupby(GROUP):
+        if len(g) != 3:
+            continue
+        idx = mat.index.get_indexer(g["sample"])
+        v = residual_projector(g["t_sel"].to_numpy(dtype=float))
+        c = M[idx]
+        ok = (c > 0).all(axis=0)
+        cg = c[:, ok]
+        y = np.log(cg) - np.log(wt[idx])[:, None]
+        z = v @ y
+        pois = (v[:, None] ** 2 * (1 / cg + 1 / wt[idx][:, None])).sum(axis=0)
+        rec.append(pd.DataFrame({"z2": z ** 2, "poisson": pois,
+                                 "min_reads": cg.min(axis=0),
+                                 "condition_sel": key[1]}))
+    rec = pd.concat(rec, ignore_index=True)
+    rec = rec[rec["poisson"] > 0]
+    bins = np.quantile(rec["poisson"], np.linspace(0, 1, 21))
+    rec["bin"] = pd.cut(rec["poisson"], bins, include_lowest=True)
+    b = rec.groupby("bin", observed=True).agg(poisson=("poisson", "mean"),
+                                              z2=("z2", "mean"),
+                                              min_reads=("min_reads", "median"),
+                                              n=("z2", "size"))
+    b["ratio"] = b["z2"] / b["poisson"]
+    print(f"\n3. ln(reads_g/reads_wt), {len(rec):,} genotype-group lines "
+          f"(all 3 tubes > 0 reads)")
+    print(b.to_string(float_format=lambda x: f"{x:.4g}"))
+    A = np.column_stack([np.ones(len(b)), b["poisson"]])
+    coef, *_ = np.linalg.lstsq(A, b["z2"], rcond=None)
+    print(f"   E[z^2] = {coef[0]:.4g} + {coef[1]:.3f} * poisson  "
+          f"(floor SD {np.sqrt(max(coef[0], 0)):.3f} ln units; "
+          f"Poisson multiplier {coef[1]:.2f})")
+    for cond, d in rec.groupby("condition_sel"):
+        bb = d.groupby(pd.cut(d["poisson"], bins, include_lowest=True),
+                       observed=True).agg(p=("poisson", "mean"), z2=("z2", "mean"))
+        A = np.column_stack([np.ones(len(bb)), bb["p"]])
+        cc, *_ = np.linalg.lstsq(A, bb["z2"], rcond=None)
+        print(f"   {cond:10s} floor SD {np.sqrt(max(cc[0], 0)):.3f}, "
+              f"Poisson multiplier {cc[1]:.2f}, n={len(d):,}")
+    b.to_csv(out_dir / "count_noise_bins.csv")
+
+    # --- 3b. same, on ln(frequency) with the shared offset removed ----------
+    # The ratio to wt carries wt's own residual in every line. Here each
+    # genotype's projection has the group's shared offset (zbar over the
+    # well-measured genotypes) subtracted instead, and only lines with at
+    # least MIN_READS reads in every tube are used (below that, ln counts
+    # are biased and the Poisson expansion fails).
+    MIN_READS = 30
+    zbar = u.set_index(GROUP)["zbar"]
+    rec = []
+    for key, g in s.reset_index().groupby(GROUP):
+        if len(g) != 3 or key not in zbar.index:
+            continue
+        idx = mat.index.get_indexer(g["sample"])
+        v = residual_projector(g["t_sel"].to_numpy(dtype=float))
+        c = M[idx]
+        ok = (c >= MIN_READS).all(axis=0)
+        cg = c[:, ok]
+        lnD = np.log(s.loc[g["sample"], "freq_denominator"].to_numpy())
+        z = v @ (np.log(cg) - lnD[:, None]) - zbar.loc[key]
+        pois = (v[:, None] ** 2 / cg).sum(axis=0)
+        rec.append(pd.DataFrame({"z2": z ** 2, "poisson": pois,
+                                 "min_reads": cg.min(axis=0),
+                                 "condition_sel": key[1],
+                                 "genotype": mat.columns[ok]}))
+    rec = pd.concat(rec, ignore_index=True)
+    rec["class"] = rec["genotype"].map(cls)
+    bins = np.quantile(rec["poisson"], np.linspace(0, 1, 16))
+    rec["bin"] = pd.cut(rec["poisson"], bins, include_lowest=True)
+    b = rec.groupby("bin", observed=True).agg(poisson=("poisson", "mean"),
+                                              z2=("z2", "mean"),
+                                              min_reads=("min_reads", "median"),
+                                              n=("z2", "size"))
+    b["ratio"] = b["z2"] / b["poisson"]
+    print(f"\n3b. ln(frequency) minus the shared offset, lines with >= "
+          f"{MIN_READS} reads in every tube ({len(rec):,} lines)")
+    print(b.to_string(float_format=lambda x: f"{x:.4g}"))
+    # Fit only where the >= MIN_READS selection does not truncate the draws.
+    FIT_READS = 100
+    bf = b[b["min_reads"] >= FIT_READS]
+    rec = rec[rec["min_reads"] >= FIT_READS]
+    print(f"   fit on bins with median min reads >= {FIT_READS} "
+          f"({len(bf)} bins)")
+    A = np.column_stack([np.ones(len(bf)), bf["poisson"]])
+    coef, *_ = np.linalg.lstsq(A, bf["z2"], rcond=None)
+    print(f"   E[z^2] = {coef[0]:.4g} + {coef[1]:.2f} * poisson: variance "
+          f"multiplier (phi) {coef[1]:.2f}, floor SD "
+          f"{np.sqrt(max(coef[0], 0)):.3f} ln units")
+    for (cond), d in rec.groupby("condition_sel"):
+        bb = d.groupby(pd.cut(d["poisson"], bins, include_lowest=True),
+                       observed=True).agg(p=("poisson", "mean"), z2=("z2", "mean"))
+        bb = bb.dropna()
+        A = np.column_stack([np.ones(len(bb)), bb["p"]])
+        cc, *_ = np.linalg.lstsq(A, bb["z2"], rcond=None)
+        print(f"   {cond:10s} phi {cc[1]:.2f}, floor SD "
+              f"{np.sqrt(max(cc[0], 0)):.3f}, n={len(d):,}")
+    b.to_csv(out_dir / "count_noise_bins_lnfreq.csv")
+
+    # --- 4. why is a low count low? -----------------------------------------
+    # A double that is rare everywhere carries little information in any
+    # tube; one that is abundant without the drug and crashes under
+    # selection carries its signal in the crash. Classify each low-count
+    # row in a selective tube by the genotype's median reads in the no-drug
+    # tubes of the same library (same replicate), which stand in for its
+    # abundance before selection.
+    low_rows = counts.merge(samples[["sample", "library", "replicate"]],
+                            on="sample")
+    nodrug = low_rows[low_rows["condition_sel"].isin(["kanR-kan", "pheS-4CP"])]
+    base = (nodrug.groupby(["library", "replicate", "genotype"], observed=True)
+            ["counts"].median().rename("nodrug_median"))
+    sel = low_rows[low_rows["condition_sel"].isin(["kanR+kan", "pheS+4CP"])]
+    sel = sel.join(base, on=["library", "replicate", "genotype"])
+    edges = [-0.1, 5, 20, 100, np.inf]
+    labels = ["rare (<5)", "low (5-20)", "moderate (20-100)", "abundant (>=100)"]
+    sel["abundance_without_drug"] = pd.cut(sel["nodrug_median"], edges,
+                                           labels=labels)
+    print("\n4. doubles in selective tubes: reads in the tube vs the "
+          "genotype's median reads without the drug")
+    d = sel[sel["class"] == "double"]
+    tab = pd.crosstab(d["abundance_without_drug"],
+                      pd.cut(d["counts"], [-1, 0, 4, 19, np.inf],
+                             labels=["0", "1-4", "5-19", ">=20"]),
+                      normalize="all")
+    print("   fraction of all double rows in selective tubes:")
+    print(tab.round(3).to_string())
+    by_cond = (d.assign(low=d["counts"] < 5)
+                .groupby(["condition_sel", "abundance_without_drug"],
+                         observed=True)["low"].agg(["mean", "size"]))
+    print("   fraction below 5 reads, by condition and abundance without drug:")
+    print(by_cond.round(3).to_string())
+    g = (d.assign(low=d["counts"] < 5)
+          .groupby(["library", "genotype"], observed=True)
+          .agg(nodrug=("nodrug_median", "median"), frac_low=("low", "mean")))
+    g = g[g["nodrug"] >= 20]
+    print(f"   doubles with >= 20 median reads without the drug: {len(g):,}; "
+          f"of these, share with some selective tube below 5 reads: "
+          f"{(g['frac_low'] > 0).mean():.3f}; with at least half below 5: "
+          f"{(g['frac_low'] >= 0.5).mean():.3f}")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:3])
diff --git a/planning/studies/od600-population/README.md b/planning/studies/od600-population/README.md
new file mode 100644
index 00000000..0d6ed610
--- /dev/null
+++ b/planning/studies/od600-population/README.md
@@ -0,0 +1,72 @@
+# Study 0c: what per-tube OD600 says about each tube's total
+
+**Status: first pass (2026-09-26)** on the two sequenced replicates'
+per-tube OD600; waiting on the third bioreplicate, the repeated OD600 runs
+and the presplit OD600 and dilution.
+
+## Question
+
+How do bioreplicates differ (level vs slope), how far do tubes scatter
+about a smooth curve compared with the reader's own noise, which tubes are
+near or below the detection threshold, and how fast does the total grow in
+each condition? Feeds roadmap steps 2, 4 and 6 and open item D9 (the
+population curve form) (`planning/analysis-roadmap.md`).
+
+## How to run
+
+```bash
+python od600_anatomy.py ~/Desktop/2026-07-23_onward/combined_fit_sample_df.csv \
+    ~/Desktop/tfscreen-notebooks/od600-to-cfu/od600_to_cfu.yaml out/
+```
+
+## Method
+
+Per-tube OD600 through the lab calibration to ln(cfu); reading noise from
+the calibration's 2% OD600 SD alone (the calibration curve's own error is
+shared by every tube and left out). Nested least-squares models over the
+118 sequenced tubes (2 replicates x 20 conditions x 3 timepoints, a few
+missing): one curve per condition (a line in `t_sel`), plus a replicate
+level, plus replicate x condition levels, plus replicate-specific slopes.
+
+## Commit
+
+Scripts as in this directory on top of `b949095`.
+
+## Results (2026-09-26)
+
+- **Replicates differ by a level:** replicate 2 reads +0.24 ln units above
+  replicate 1 (SE 0.03; F = 52, p = 3e-10). Separate levels per condition
+  add nothing (p = 0.14), consistent with one split density per replicate.
+- **Replicate-specific slopes are significant** (F = 4.2, p = 7e-5, residual
+  SD 0.167 -> 0.115). This contradicts slopes being shared across
+  bioreplicates for these two replicates; with three tubes per line it
+  could also be structured noise.
+- **Tube scatter dwarfs reading noise:** 0.176 ln units about a shared
+  curve plus replicate level, against 0.031 from OD600 reading noise. The
+  excess, 0.17, is tube-to-tube (growth or handling). It does not grow
+  with time (0.14, 0.11, 0.18 by tercile of `t_sel`), so it is not
+  `sigma * t` tube growth noise alone.
+- **No tube is below the threshold** (0.096). The lowest are kan+ at 0 mM
+  (1.5x threshold), kan- at 1 mM and kan+ at 0.001 mM (1.6-1.7x).
+- **Population growth per condition** (per min, shared across replicates
+  with a replicate level): kan+ rises from -0.001 +/- 0.006 at 0 mM to
+  about 0.011 at >= 0.01 mM; 4CP+ falls from 0.020 at 0 mM to 0.014-0.015
+  at >= 0.01 mM; no drug 0.016-0.030. Standard errors are 0.003-0.007 per
+  condition: three tubes per replicate over 45-120 min.
+
+## What it means
+
+- **Per-tube OD600 pins condition growth rates only to +/- 0.003-0.007 per
+  min,** comparable to `m` (~0.01-0.025). The absolute scale (`k_c`, `m_c`;
+  roadmap C4) cannot rest on these tubes alone; the earlier smoothed total
+  drew on repeated OD600 runs, and those data (and the third bioreplicate)
+  are the next input for this study.
+- **The population model needs a replicate level** and probably
+  per-replicate slope deviations (D9), and the OD600 likelihood a per-tube
+  scatter term of about 0.17 ln units that is not proportional to time.
+- **The sequenced tubes were chosen to be visible;** strong selection
+  shows up as low but readable OD600 (kan+ 0 mM barely grows), not as
+  censored tubes, in this dataset.
+- Missing for the rest of 0c: OD600 for the third (unsequenced)
+  bioreplicate and the repeated OD600 runs behind `fit_lncfu`, and the
+  presplit OD600 and dilution factor (the known start).
diff --git a/planning/studies/od600-population/od600_anatomy.py b/planning/studies/od600-population/od600_anatomy.py
new file mode 100644
index 00000000..00c6f35e
--- /dev/null
+++ b/planning/studies/od600-population/od600_anatomy.py
@@ -0,0 +1,138 @@
+"""
+Step 0c of planning/analysis-roadmap.md: what the per-tube OD600 readings
+say about each tube's total population.
+
+Questions:
+  1. How much of the difference between bioreplicates is a level (shared
+     split density) and how much a slope?
+  2. How far do tubes scatter about a smooth curve, against what the
+     reader's own noise (2% of OD600, through the calibration) predicts?
+     The excess is tube-to-tube growth noise (or protocol noise).
+  3. How close are tubes to the detection threshold, by condition?
+  4. The population growth rate per condition (does strong selection
+     stall or shrink the total?).
+
+Usage:
+    python od600_anatomy.py SAMPLE_DF CALIBRATION_YAML OUT_DIR
+
+SAMPLE_DF has one row per sequenced tube with od600, rep, library,
+condition_sel, titrant_conc, t_sel (combined_fit_sample_df.csv).
+"""
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import statsmodels.formula.api as smf
+import yaml
+from matplotlib import pyplot as plt
+
+
+def od_to_ln_cfu(od, cal):
+    """ln(cfu/mL) and the SD of ln(cfu/mL) from OD600 reading noise alone
+    (the calibration curve's own error is excluded: it is shared by every
+    tube, roadmap C11)."""
+    cfu = cal["A_CFU"] + cal["B_CFU"] * od + cal["C_CFU"] * od ** 2
+    dcfu_dod = cal["B_CFU"] + 2 * cal["C_CFU"] * od
+    read_sd = np.abs(dcfu_dod) * cal["OD600_PCT_STD"] * od / cfu
+    return np.log(cfu), read_sd
+
+
+def main(sample_df, cal_yaml, out_dir):
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    cal = yaml.safe_load(open(cal_yaml))
+
+    df = pd.read_csv(sample_df, index_col=0)
+    df["condition_sel"] = df["condition_sel"].str.strip()
+    df["cond"] = (df["condition_sel"] + "@" + df["titrant_conc"].astype(str))
+    df["rep"] = df["rep"].astype(str)
+    df["lnN"], df["read_sd"] = od_to_ln_cfu(df["od600"].to_numpy(), cal)
+    df["t"] = df["t_sel"].astype(float)
+
+    print(f"{len(df)} tubes, {df['cond'].nunique()} conditions, "
+          f"reps {sorted(df['rep'].unique())}")
+    print(f"OD600 reading noise in ln units: median "
+          f"{df['read_sd'].median():.4f} (range {df['read_sd'].min():.4f}-"
+          f"{df['read_sd'].max():.4f})")
+
+    # --- 1 and 2: nested models -------------------------------------------
+    forms = {
+        "shared curve":            "lnN ~ C(cond) + C(cond):t",
+        "+ replicate level":       "lnN ~ C(rep) + C(cond) + C(cond):t",
+        "+ replicate x cond level": "lnN ~ C(rep)*C(cond) + C(cond):t",
+        "+ replicate slopes":      "lnN ~ C(rep)*C(cond) + C(rep):C(cond):t",
+    }
+    fits = {k: smf.ols(f, data=df).fit() for k, f in forms.items()}
+    print("\nmodel                     dof  resid SD   ")
+    for k, f in fits.items():
+        print(f"{k:25s} {int(f.df_resid):4d}  {np.sqrt(f.scale):.4f}")
+    names = list(fits)
+    for a, b in zip(names[:-1], names[1:]):
+        ft = fits[b].compare_f_test(fits[a])
+        print(f"F-test {b!r} vs {a!r}: F={ft[0]:.2f} p={ft[1]:.3g} "
+              f"df={int(ft[2])}")
+
+    rep_level = fits["+ replicate level"].params.filter(like="C(rep)")
+    print(f"\nreplicate level offset (rep 2 - rep 1): "
+          f"{rep_level.iloc[0]:+.4f} ln units "
+          f"(SE {fits['+ replicate level'].bse.filter(like='C(rep)').iloc[0]:.4f})")
+
+    resid_sd = np.sqrt(fits["+ replicate level"].scale)
+    read_sd = df["read_sd"].median()
+    excess = np.sqrt(max(resid_sd ** 2 - read_sd ** 2, 0))
+    print(f"scatter about shared curve + replicate level: {resid_sd:.4f}; "
+          f"reading noise {read_sd:.4f}; excess (tube noise) {excess:.4f}")
+
+    # does the scatter grow with time (tube growth noise ~ sigma * t)?
+    r = fits["+ replicate level"].resid
+    by_t = df.assign(r2=r ** 2).groupby(pd.cut(df["t"], 3), observed=True)["r2"]
+    print("residual SD by t_sel tercile:")
+    for k, v in by_t:
+        print(f"  {str(k):16s} n={len(v):3d} SD={np.sqrt(v.mean()):.4f}")
+
+    # --- 3: distance from the detection threshold --------------------------
+    thr = cal["OD600_MEAS_THRESHOLD"]
+    near = (df.groupby(["cond"])["od600"].min()
+              .rename("min_od600").to_frame())
+    near["fold_above_threshold"] = near["min_od600"] / thr
+    print(f"\ndetection threshold {thr:.4f}; tubes below: "
+          f"{(df['od600'] < thr).sum()}")
+    print("lowest OD600 per condition (fold above threshold):")
+    print(near.sort_values("min_od600").head(10).to_string())
+
+    # --- 4: population growth rate per condition ---------------------------
+    slopes = []
+    for cond, g in df.groupby("cond"):
+        f = smf.ols("lnN ~ C(rep) + t", data=g).fit()
+        slopes.append({"cond": cond, "slope_per_min": f.params["t"],
+                       "slope_se": f.bse["t"], "n": len(g)})
+    slopes = pd.DataFrame(slopes).sort_values("cond")
+    slopes.to_csv(out_dir / "population_slopes.csv", index=False)
+    print("\npopulation growth rate (per min) by condition, shared across "
+          "replicates with a replicate level:")
+    print(slopes.to_string(index=False, float_format=lambda x: f"{x:.5f}"))
+
+    # --- figure --------------------------------------------------------------
+    conds = sorted(df["cond"].unique())
+    ncol = 8
+    nrow = int(np.ceil(len(conds) / ncol))
+    fig, axes = plt.subplots(nrow, ncol, figsize=(2.4 * ncol, 2.2 * nrow),
+                             sharex=True, sharey=True)
+    for ax, cond in zip(axes.flat, conds):
+        g = df[df["cond"] == cond]
+        for rep, gg in g.groupby("rep"):
+            ax.errorbar(gg["t"], gg["lnN"], yerr=gg["read_sd"], fmt="o",
+                        ms=3, label=f"rep {rep}")
+        ax.set_title(cond, fontsize=7)
+    axes.flat[0].legend(fontsize=6)
+    fig.supxlabel("t_sel (min)")
+    fig.supylabel("ln(cfu) from OD600")
+    fig.tight_layout()
+    fig.savefig(out_dir / "od600_by_condition.pdf")
+    df.to_csv(out_dir / "tubes.csv", index=False)
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:4])
diff --git a/planning/studies/real-data-fit/README.md b/planning/studies/real-data-fit/README.md
new file mode 100644
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+# Real-data fit of the dev-data screen
+
+**Question.** Does the growth-only relative fit (`theta: hill_relative`),
+with the count likelihood and tube offsets, run on a full screen? What do
+its curves say about the relationship between in vitro binding and in vivo
+growth?
+
+**Decisions it fed.**
+- Roadmap step 5: the full-library inference route. The answer is the MAP
+  plus the arrowhead Laplace; the SVI guides slid along the m·X ridge.
+- Roadmap step 8: whether a simple growth-binding map exists. It does not.
+- The new options added along the way: `--init_from`, `sigma_fixed`, the
+  held Hill population SDs, per-condition m prior scales, and the block
+  and arrowhead Laplace.
+
+The results and their reasoning are in `planning/analysis-roadmap-summary.md`.
+The full lab notebook, with every run including the dead ends, is
+`planning/dev-data/README.md` and `planning/dev-data/real_fit/README.md`.
+Those are gitignored, because they sit with the data.
+
+## Layout: tracked scripts, untracked data
+
+The lab's data never enter the repository (roadmap C10). They live in
+`planning/dev-data/` (gitignored), and these scripts expect that layout:
+
+```
+planning/dev-data/
+  counts/                    tfs-process-fastq output, one dir per tube
+  counts/run_config.yaml     processing library config (3 spikes; see below)
+  sample_df.csv, all_reps_screen_od600.xlsx, 2025-10-02_control-experiments.xlsx
+  binding/                   anisotropy tables
+  od600-to-cfu/              calibration spreadsheets
+  processed/                 written by processing/prep_dev_data.py
+  real_fit/inputs/           growth.csv.gz, library_config.yaml,
+                             wt_control_rate_summary.csv, init_*.npz
+  real_fit/<run>/            one directory per fit
+```
+
+In `planning/dev-data/` the scripts are symlinks to the copies here, so they
+run in place: `prep_dev_data.py` from `dev-data/`, the `fit/` and
+`analysis/` scripts from `real_fit/`, as `../<script>` from inside a run
+directory. On the cluster, copy `fit/*` into `real_fit/` by hand. The data
+directory is gitignored and is not pulled. Check the copy with
+`grep -c HS_FIXED run_relative.srun`.
+
+- `processing/`
+  - `process_fastq.srun`: `tfs-process-fastq` per tube pair.
+  - `prep_dev_data.py`: the tube table, OD600 to tube totals through
+    `tfs-calibrate-od600`, the swapped initial samples, pooling of
+    resequenced tubes, failed tubes dropped, the nine-spike library config
+    (`SPIKE_EDITS`), and the wt monoculture rates. Since 2026-10-05 it also
+    writes `tube_table.csv` and `tube_od600.csv`, the recipe's inputs to
+    `tfs-process-counts` (the old `sample_df.csv` carries OD600 and totals
+    both, which the command refuses).
+  - `binding_fit.py`, `binding_plot.py`: the joint anisotropy-to-theta and
+    Hill fit, interpolated to 10 µM protein.
+- `fit/`
+  - `run_recipe.srun` and `growth_priors_loose.csv`: **the recipe** (below).
+  - `run.srun`: the joint fits.
+  - `run_relative.srun`: the growth-only relative fits.
+  - `run_blap.srun`: the block Laplace, or the arrowhead Laplace with
+    `SHARED=1`.
+  - `set_growth_priors.py`: k/m priors for growth-only models, which the
+    pre-fit refuses.
+  - `make_warm_start.py`, `make_init_npz.py`, `make_init_hs.py`: build
+    `--init_from` start points.
+  - `check_warm.py`: stops a job whose start is not where it should be.
+  - Retired 2026-10-05 (pipeline plan step 7): `make_warm_start.py`,
+    `make_init_npz.py` and `check_warm.py` built the staged start by hand;
+    `tfs-fit-model` stages a level-offset MAP itself now. They stay as the
+    record of the runs below and are part of no recipe. `make_init_hs.py`
+    (a warm start at held Hill SDs) and `set_growth_priors.py` (replaced
+    by `tfs-configure-model --growth_priors`) are kept for the same reason.
+- `analysis/`
+  - `score_counts.py`: the count log-likelihood by tube, traced from the
+    model, so it works for any model.
+  - `residual_compare.py`: two single-transformation MAPs compared
+    observation by observation.
+  - `small_offsets.py`: the best per-tube offset with everything else held.
+  - `profile_offsets.py`: SVI against MAP.
+  - `plot_x_vs_binding.py`, `scan_protein.py`, `scan_monotone.py`: the
+    binding comparison.
+
+## How to run
+
+Processing, from `planning/dev-data/`, with `tfs-process-fastq` already
+run:
+
+```bash
+python prep_dev_data.py
+```
+
+```bash
+python binding_fit.py
+```
+
+Then `tfs-process-counts` per library on `processed/sample_df_{kanR,pheS}.csv`,
+concatenated into `real_fit/inputs/growth.csv.gz`.
+`processed/library_config.yaml` becomes `real_fit/inputs/library_config.yaml`.
+`counts/run_config.yaml` lists only three spikes, with the wrong M42I codon,
+so it must not be used for configure.
+
+**The final fit and its start chain, as it ran.** A level-offset MAP
+started cold lands in a ±2.8 offset mode (see "Starting point" in the
+summary). Each offset fit therefore started from an earlier point. All
+commands run from `real_fit/`, each in its own new run directory:
+
+| Run | Command (`sbatch --export=ALL,...`) | Start |
+|---|---|---|
+| `map_nooffset_b` | `ARM=map,OFFSET_MODEL=zero,BATCH_SIZE=4096 ../run.srun` | cold |
+| `map_sig017b` | `ARM=map,SIGMA_FIXED=0.17,BATCH_SIZE=4096 ../run.srun` | cold; only its config is used |
+| `map_warm` | `ARM=map,WARM=1,ADAM_STEP=1e-4 ../run.srun` | `make_warm_start.py map_sig017b map_nooffset_b map_warm`, after `residual_compare.py map_sig017b map_nooffset_b resid/sig_vs_nooff` and `resid/small_offsets.py` |
+| `map_nooffset_n05` | `ARM=map,OFFSET_MODEL=zero,BATCH_SIZE=4096,N_SCALE=0.5 ../run.srun` | cold |
+| `map_off_n05` | `ARM=map,SIGMA_FIXED=0.17,N_SCALE=0.5,BATCH_SIZE=4096,INIT_FROM=../inputs/init_n05_warm.npz,INIT_REF=../map_nooffset_n05,ADAM_STEP=1e-4 ../run.srun` | `make_init_npz.py map_nooffset_n05 map_warm inputs/init_n05_warm.npz` |
+| `rel_loose_b` | `PRIORS=loose ../run_relative.srun` | cold |
+| **`rel_off_n05`** | `PRIORS=loose,N_SCALE=0.5,OFFSET_MODEL=level,SIGMA_FIXED=0.17,INIT_FROM=../inputs/init_rel_off.npz,ADAM_STEP=1e-4 ../run_relative.srun` | `make_init_npz.py rel_loose_b map_off_n05 inputs/init_rel_off.npz` |
+| `rel_off_n05_blap` | `MAP_DIR=../rel_off_n05 ../run_blap.srun` | |
+| **`rel_off_n05_alap`** | `MAP_DIR=../rel_off_n05,SHARED=1 ../run_blap.srun` | |
+| `rel_off_hs` | `rel_off_n05`'s settings plus `HS_FIXED=X_low=0.5:X_delta=0.5:log_hill_K=1.0` and `INIT_FROM=../inputs/init_rel_off_hs.npz` | `make_init_hs.py rel_off_n05 inputs/init_rel_off_hs.npz X_low=0.5 X_delta=0.5 log_hill_K=1.0` |
+| `rel_off_hs_mix` | `rel_off_hs`'s settings plus `TRANSFORMATION=mixture` and `INIT_FROM=../inputs/init_rel_off_hs_map.npz` | a copy of `rel_off_hs/tfs_fit_model_params.npz` |
+
+The chain is long because each step fixed a problem found by the one
+before. The pipeline plan (`planning/experiment-pipeline.md`) built the
+staged start into `tfs-fit-model`; the recipe below replaces the chain.
+
+## Recipe
+
+`fit/run_recipe.srun` is the official recipe for this fit (pipeline plan
+step 7, 2026-10-05): `prep_dev_data.py` (the lab-specific cleanup, a
+script), then `tfs-process-counts` (tube totals from OD600 through the
+calibration), `tfs-configure-model` (every model choice a flag),
+`tfs-fit-model` (MAP, staged automatically), `tfs-sample-posterior`
+(`--laplace auto`, the arrowhead at this size), `tfs-extract-params`,
+`tfs-predict-theta` and `tfs-summarize-fit`, whose tube-offset diagnostic
+flags a fit in the offset mode. No file is edited by hand and no start is
+built by hand. The model is `rel_off_n05`'s and `staged_auto2`'s
+(`planning/studies/staged-map/`). The full-size simulation of this design
+(`planning/studies/full-size-sim/`) runs the same commands with only the
+data paths changed.
+
+Recipe run (2026-10-06, commit 47b02e63; the provenance's dirty flag is
+a local path edit in `wipe.sh`, nothing the run used): ran end to end with
+no hand step. All three MAP stages converged on the exact loss; the arrowhead
+Laplace held 6 shared directions. The tube-offset diagnostic is clean: no
+condition trends with titrant or time (smallest BH q 0.20), offset SD 0.20
+against the held 0.17, range -0.79 to +0.44. It reproduces `staged_auto2`
+(`planning/studies/staged-map/`, the same model run by hand-assembled
+commands): every genotype matched; Pearson r 0.992 for log K, 0.982 for n,
+0.998 for X_low and dk_geno, median absolute differences 0.04 (log K) and
+0.12 (n); growth k within 7e-4 and m within 2e-4 per minute in every
+condition. The differences come from the growth table, now built by
+`tfs-process-counts` from the OD600 table rather than read from the older
+`inputs/growth.csv.gz`.
+
+Every other run directory in `real_fit/` is a dead end recorded in the
+notebook: the first offset fits, the SVI arms, `rel_monokan`,
+`rel_loose_n05` (stale script), `local_offsets/` and the `smoke_*` tests.
+
+## Inputs
+
+Only the gitignored files above. No input path points outside
+`planning/dev-data/`.
+
+## Commit
+
+The runs do not log a commit. Each run below needed the option its commit
+added, and its `run.out` start time falls after that commit and before the
+next one it would need.
+
+| Runs | Commit |
+|---|---|
+| `map_nooffset_b`, `map_sig017b` | d451faa9 |
+| `map_warm` | faa5b116 |
+| `rel_loose_b` | e4320c7f |
+| `map_nooffset_n05`, `map_off_n05`, `rel_off_n05` | beacad0e |
+| `rel_off_n05_blap` | cd208d15 |
+| `rel_off_n05_alap`, `rel_off_hs`, `rel_off_hs_mix` | a874ac38 |
+
+## Results
+
+See `planning/analysis-roadmap-summary.md`, Part 1, "Fitting the real
+screen" onward. In short:
+- The growth curves are broader than binding (n 0.6-1.2 against 1.6-2.5).
+- M42I's K is below wt's in growth (6.4 against 12 µM) and about 10x above
+  it in binding.
+- Every mutant falls to a common floor near X -0.5.
+- No shared protein concentration or monotone map carries binding onto
+  growth.
+- K and n held up when the Hill population SDs were held, when the
+  congression mixture was added, and with the k/m uncertainty included.
+- m and the absolute X scale are under-constrained.
+
+## Next
+
+- **Full-size simulations of this design,** to answer reviewers: the same
+  library size, read depth, noise and fit recipe, with known truth, to show
+  that the curves, the intervals and the binding comparison come out
+  right. Run 2026-10-07 in `planning/studies/full-size-sim/` through the
+  recipe's commands: the curves' ranks come out, but X intervals
+  undercover badly at depth because k and m are off with no interval.
+  Not yet reviewer-ready; pipeline plan step 8 takes it up.
+- Run the recipe (above) on the cluster and record it here. One
+  experiment file (the orchestrator, pipeline plan step 5) is deferred.
diff --git a/planning/studies/real-data-fit/analysis/plot_x_vs_binding.py b/planning/studies/real-data-fit/analysis/plot_x_vs_binding.py
new file mode 100644
index 00000000..068d8e53
--- /dev/null
+++ b/planning/studies/real-data-fit/analysis/plot_x_vs_binding.py
@@ -0,0 +1,54 @@
+"""
+Growth-only X (rel_off_n05; 95% band from rel_off_n05_blap, the
+per-genotype Laplace, when present) against binding theta at 10 uM protein for the
+seven binding genotypes, both against IPTG. Binding theta is put on the X
+gauge through wt (theta -> (theta - theta_wt(1 mM)) / (theta_wt(0) -
+theta_wt(1 mM))), so wt's two curves share their ends.
+
+    python plot_x_vs_binding.py   ->  resid/2026-10-02_x-vs-binding.pdf
+"""
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+plt.rc("pdf", fonttype=42)
+plt.rc("font", size=14)
+plt.rc("axes", titlesize=14, labelsize=16)
+OKABE = ["#000000", "#E69F00", "#56B4E9", "#009E73", "#0072B2", "#D55E00", "#CC79A7"]
+ORDER = ["wt", "M42I", "M42I/H74A", "G65H", "H74A", "K84L", "H74A/K84L"]
+ZERO_X = 3e-5   # where 0 IPTG is drawn on the log axis (mM)
+
+j = pd.read_csv("resid/X_vs_binding_off_n05.csv")
+try:
+    band = pd.read_parquet("resid/blap_X.parquet")[["genotype", "titrant_conc",
+                                                   "q0.025", "q0.975"]]
+    j = j.merge(band, on=["genotype", "titrant_conc"], how="left")
+except FileNotFoundError:
+    j["q0.025"] = j["q0.975"] = np.nan
+w = j[j.genotype == "wt"].set_index("titrant_conc").theta_obs
+j["theta_gauged"] = (j.theta_obs - w[1.0]) / (w[0.0] - w[1.0])
+j["x_plot"] = j.titrant_conc.where(j.titrant_conc > 0, ZERO_X)
+
+fig, axes = plt.subplots(2, 4, figsize=(15, 7.5), sharex=True, sharey=True)
+for ax, g, c in zip(axes.flat, ORDER, OKABE):
+    d = j[j.genotype == g].sort_values("x_plot")
+    ax.fill_between(d.x_plot, d["q0.025"], d["q0.975"], color=c, alpha=0.2,
+                    lw=0, label="X 95% (block Laplace)")
+    ax.plot(d.x_plot, d.X, "o-", color=c, label="growth X")
+    ax.plot(d.x_plot, d.theta_gauged, "s--", color=c, mfc="white",
+            label="binding θ (wt gauge)")
+    ax.axhline(0, color="0.8", lw=1, zorder=0)
+    ax.axhline(1, color="0.8", lw=1, zorder=0)
+    ax.set_xscale("log")
+    ax.set_title(g)
+axes.flat[-1].axis("off")
+axes.flat[0].legend(frameon=False, fontsize=11, loc="lower left")
+for ax in axes[1]:
+    ax.set_xlabel("IPTG (mM; 0 drawn at 3e-5)")
+for ax in axes[:, 0]:
+    ax.set_ylabel("X")
+fig.tight_layout()
+fig.savefig("resid/2026-10-02_x-vs-binding.pdf")
+print("wrote resid/2026-10-02_x-vs-binding.pdf")
diff --git a/planning/studies/real-data-fit/analysis/profile_offsets.py b/planning/studies/real-data-fit/analysis/profile_offsets.py
new file mode 100644
index 00000000..3af20965
--- /dev/null
+++ b/planning/studies/real-data-fit/analysis/profile_offsets.py
@@ -0,0 +1,24 @@
+"""Fair count-likelihood comparison of two fits whose tube offsets are not
+both readable: rebuild each from parameter medians (residual_compare.predict),
+then refit one offset per tube under N(0, 0.17) with everything else held.
+Usage: python resid/profile_offsets.py armA armB"""
+import sys
+import numpy as np, pandas as pd
+from scipy.optimize import minimize_scalar
+sys.path.insert(0, ".")
+from residual_compare import nb_logpmf, predict, dispersion, GROWTH, TUBE
+SIG = 0.17
+obs = pd.read_csv(GROWTH, usecols=TUBE + ["genotype", "t_pre", "counts", "sample_reads", "sample_ln_cfu"])
+obs = obs[obs.genotype != "__unknown__"].reset_index(drop=True)
+obs["ln_sample_reads"] = np.log(obs.sample_reads)
+groups = obs.groupby(TUBE).indices
+for arm in sys.argv[1:]:
+    phi, inv_r = dispersion(sys.argv[1])  # first arm's dispersion for every arm
+    lm = obs.ln_sample_reads.to_numpy() + predict(arm, obs) - obs.sample_ln_cfu.to_numpy()
+    k = obs.counts.to_numpy(float)
+    tot, deltas = 0.0, []
+    for key, idx in groups.items():
+        f = lambda x: -(nb_logpmf(k[idx], lm[idx] + x, phi, inv_r).sum() - 0.5 * (x / SIG) ** 2)
+        r = minimize_scalar(f, bounds=(-3, 3), method="bounded", options={"xatol": 1e-4})
+        tot += -r.fun; deltas.append(r.x)
+    print(f"{arm}: phi {phi:.3g} inv_r {inv_r:.3g}; ll + offset prior, offsets refit: {tot:.6g}; offsets sd {np.std(deltas):.3f}")
diff --git a/planning/studies/real-data-fit/analysis/residual_compare.py b/planning/studies/real-data-fit/analysis/residual_compare.py
new file mode 100644
index 00000000..dae1a1e5
--- /dev/null
+++ b/planning/studies/real-data-fit/analysis/residual_compare.py
@@ -0,0 +1,208 @@
+"""
+Where does the tube-offset fit gain its likelihood over the no-offset fit?
+
+    python residual_compare.py <arm_a> <arm_b> [out_prefix]
+
+Rebuilds each MAP arm's ln_cfu prediction for every observation in the full
+library (linear growth, instant transition, single transformation, fixed
+activity):
+
+    ln_cfu = ln_cfu0[rep, cond_pre, g] + (k_pre + dk + m_pre θ) t_pre
+             + (k_sel + dk + m_sel θ) t_sel + offset[tube]
+
+from the extracted parameters and tfs_pred_theta.csv. The tube offset is
+read off tfs_pred_growth.csv (its subset block): the median of q0.5 minus
+the rebuilt prediction, per tube. The rebuild is checked against q0.5 there.
+Then it scores every observation under each arm's count likelihood
+(observe/growth_counts.count_distribution, the arm's own phi and inv_r) and
+reports where ll_a - ll_b comes from: by tube, by genotype, by read depth.
+"""
+
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+from scipy.special import gammaln
+
+pd.set_option("display.width", 250)
+
+GROWTH = "inputs/growth.csv.gz"
+TUBE = ["replicate", "condition_pre", "condition_sel", "titrant_conc", "t_sel"]
+TUBE_INDEX = "resid/tube_index.csv"
+TUBE_SHAPE = (3, 3, 2, 2, 1, 8)
+
+
+def nb_logpmf(k, log_mu, phi, inv_r):
+    log_c = -np.logaddexp(np.log(phi) - log_mu, np.log(inv_r))
+    log_c = np.logaddexp(log_c, -30.0)
+    c = np.exp(log_c)
+    log_p = -np.logaddexp(0, log_mu - log_c)
+    log_q = -np.logaddexp(0, log_c - log_mu)
+    return (gammaln(k + c) - gammaln(c) - gammaln(k + 1)
+            + c * log_p + k * log_q)
+
+
+def q50(path, keys):
+    return pd.read_csv(path, usecols=keys + ["q0.5"]).set_index(keys)["q0.5"]
+
+
+def predict(arm, obs):
+    k = q50(f"{arm}/tfs_params_growth_k.csv", ["condition_rep"])
+    m = q50(f"{arm}/tfs_params_growth_m.csv", ["condition_rep"])
+    dk = q50(f"{arm}/tfs_params_dk_geno.csv", ["genotype"])
+    l0 = q50(f"{arm}/tfs_params_ln_cfu0.csv",
+             ["replicate", "condition_pre", "genotype"])
+    th = q50(f"{arm}/tfs_pred_theta.csv", ["genotype", "titrant_conc"])
+
+    d = obs
+    lo = l0.reindex(pd.MultiIndex.from_arrays(
+        [d.replicate, d.condition_pre, d.genotype])).to_numpy()
+    t = th.reindex(pd.MultiIndex.from_arrays(
+        [d.genotype, d.titrant_conc])).to_numpy()
+    g = dk.reindex(d.genotype).to_numpy()
+    kp, mp = k.reindex(d.condition_pre).to_numpy(), m.reindex(d.condition_pre).to_numpy()
+    ks, ms = k.reindex(d.condition_sel).to_numpy(), m.reindex(d.condition_sel).to_numpy()
+    pred = (lo + (kp + g + mp * t) * d.t_pre.to_numpy()
+            + (ks + g + ms * t) * d.t_sel.to_numpy())
+    assert np.isfinite(pred).all(), f"{arm}: missing parameters"
+    return pred
+
+
+def tube_offsets(arm, obs, pred):
+    """
+    Per-tube offsets from the fitted params, mapped onto tubes through the
+    tensor indices in tfs_pred_growth.csv. tfs-predict-growth zeroes the
+    offsets (a prediction is for a typical tube), so its q0.5 must equal the
+    rebuilt prediction without them; that is checked here.
+    """
+    idx = ["replicate_idx", "time_idx", "condition_pre_idx",
+           "condition_sel_idx", "titrant_name_idx", "titrant_conc_idx"]
+    pg = pd.read_csv(f"{arm}/tfs_pred_growth.csv",
+                     usecols=TUBE + ["genotype", "ln_cfu", "q0.5"])
+    pg = pg[pg.ln_cfu.notna()]
+    o = obs[TUBE + ["genotype"]].copy()
+    o["pred"] = pred
+    j = pg.merge(o, on=TUBE + ["genotype"])
+    diff = (j["q0.5"] - j["pred"]).abs()
+    print(f"  {arm}: rebuilt vs q0.5 (no offsets) on {len(j)} rows: "
+          f"median |diff| {diff.median():.2e}, q99 {diff.quantile(0.99):.2e}, "
+          f"max {diff.max():.2e}")
+    # a MAP rebuild is exact; an SVI rebuild uses parameter medians, which
+    # only approximate the median prediction
+    if os.path.exists(f"{arm}/tfs_fit_model_params.npz"):
+        assert diff.max() < 1e-3
+    else:
+        return pd.Series(0.0, index=pd.read_csv(TUBE_INDEX).set_index(TUBE).index)
+    z = np.load(f"{arm}/tfs_fit_model_params.npz")
+    # training-grid indices (from smoke_map's config; same 118 tubes)
+    tubes = pd.read_csv(TUBE_INDEX).set_index(TUBE)
+    if "sample_offset_offset_auto_loc" not in z.files:
+        return pd.Series(0.0, index=tubes.index)
+    off = np.asarray(z["sample_offset_offset_auto_loc"])
+    shape = TUBE_SHAPE
+    assert np.prod(shape) == off.size, (shape, off.size)
+    off = off.reshape(shape)
+    v = off[tuple(tubes[c].to_numpy() for c in idx)]
+    print(f"  {arm}: {len(v)} tube offsets, sd {v.std():.3f}, "
+          f"range {v.min():.2f} to {v.max():.2f}")
+    return pd.Series(v, index=tubes.index)
+
+
+def dispersion(arm):
+    """phi and inv_r: the MAP values, or an SVI guide's LogNormal medians."""
+    f = f"{arm}/tfs_fit_model_params.npz"
+    if os.path.exists(f):
+        z = np.load(f)
+        return float(z["growth_phi_auto_loc"]), float(z["growth_inv_r_auto_loc"])
+    import dill
+    from numpyro.optim import Adam
+    with open(f"{arm}/tfs_fit_model_checkpoint.pkl", "rb") as fh:
+        ck = dill.load(fh)
+    p = Adam(1e-3).get_params(ck["svi_state"].optim_state)
+    # SVI arms carry no tube offsets here (svi_nooffset); refuse otherwise
+    assert "sample_offset_offset_loc" not in p, "SVI offsets not handled"
+    return float(np.exp(p["growth_phi_loc"])), float(np.exp(p["growth_inv_r_loc"]))
+
+
+def score(arm, obs):
+    phi, inv_r = dispersion(arm)
+    pred = predict(arm, obs)
+    off = tube_offsets(arm, obs, pred)
+    o = off.reindex(pd.MultiIndex.from_frame(obs[TUBE])).to_numpy()
+    assert np.isfinite(o).all(), f"{arm}: tube without offset"
+    pred = pred + o
+    log_mu = obs.ln_sample_reads.to_numpy() + pred - obs.sample_ln_cfu.to_numpy()
+    ll = nb_logpmf(obs.counts.to_numpy(float), log_mu, phi, inv_r)
+    print(f"  {arm}: phi {phi:.3g}, inv_r {inv_r:.3g}, sum ll {ll.sum():.6g}")
+    return pred, ll, off
+
+
+def main():
+    a, b = sys.argv[1], sys.argv[2]
+    out = sys.argv[3] if len(sys.argv) > 3 else "resid"
+    obs = pd.read_csv(GROWTH, usecols=TUBE + ["genotype", "t_pre", "counts",
+                                              "sample_reads", "sample_ln_cfu"])
+    obs = obs[obs.genotype != "__unknown__"].reset_index(drop=True)
+    obs["ln_sample_reads"] = np.log(obs.sample_reads)
+    print(f"{len(obs)} observations, {obs.genotype.nunique()} genotypes, "
+          f"{obs.groupby(TUBE).ngroups} tubes")
+
+    pa, lla, offa = score(a, obs)
+    pb, llb, offb = score(b, obs)
+    obs["pred_a"], obs["pred_b"] = pa, pb
+    obs["dll"] = lla - llb
+    obs["ll_b"] = llb
+    tot = obs.dll.sum()
+    print(f"\nll_a - ll_b, whole library: {tot:.4g} nats")
+
+    tube = (obs.groupby(TUBE)
+            .agg(dll=("dll", "sum"), reads=("counts", "sum"),
+                 dpred=("pred_a", "median"))
+            .reset_index())
+    tube["dpred"] = (obs.pred_a - obs.pred_b).groupby(
+        [obs[c] for c in TUBE]).median().to_numpy()
+    tube["offset_a"] = offa.reindex(pd.MultiIndex.from_frame(tube[TUBE])).to_numpy()
+    tube = tube.sort_values("dll", ascending=False)
+    tube.to_csv(f"{out}_tube.csv", index=False)
+    print("\nby condition x IPTG (sum dll, 1e3 nats):")
+    print((tube.pivot_table(index=["condition_pre", "condition_sel"],
+                            columns="titrant_conc", values="dll",
+                            aggfunc="sum") / 1e3).round(1).to_string())
+    print("\nmean offset_a by condition x IPTG:")
+    print(tube.pivot_table(index=["condition_pre", "condition_sel"],
+                           columns="titrant_conc", values="offset_a",
+                           aggfunc="mean").round(2).to_string())
+    print("\ntubes, dll vs offset: corr(dll, |offset_a|) = "
+          f"{np.corrcoef(tube.dll, tube.offset_a.abs())[0, 1]:.2f}")
+    s = tube.dll.sort_values(ascending=False)
+    for f in (0.1, 0.25):
+        print(f"top {f:.0%} of tubes carry {s.head(int(f * len(s))).sum() / tot:.2f} of the gain")
+    print(tube.head(12).round(3).to_string(index=False))
+
+    geno = (obs.groupby("genotype")
+            .agg(dll=("dll", "sum"), reads=("counts", "sum"), n=("dll", "size"))
+            .reset_index().sort_values("dll", ascending=False))
+    geno.to_csv(f"{out}_geno.csv", index=False)
+    print("\ntop 15 genotypes:")
+    print(geno.head(15).round(1).to_string(index=False))
+    print("bottom 8 genotypes:")
+    print(geno.tail(8).round(1).to_string(index=False))
+    s = geno.dll.sort_values(ascending=False)
+    for n in (10, 100, 1000, 10000):
+        print(f"top {n} genotypes: {s.head(n).sum() / tot:.2f} of the gain")
+    print(f"genotypes with dll > 0: {(geno.dll > 0).mean():.2f}")
+    geno["read_bin"] = pd.cut(geno.reads, [-1, 0, 10, 100, 1e3, 1e4, 1e5, 1e9])
+    print("\nby genotype total reads:")
+    print(geno.groupby("read_bin", observed=True).dll
+          .agg(["sum", "mean", "size"]).round(1).to_string())
+
+    obs["count_bin"] = pd.cut(obs.counts, [-1, 0, 1, 5, 20, 100, 1e3, 1e9])
+    print("\nby observation count:")
+    print(obs.groupby("count_bin", observed=True).dll
+          .agg(["sum", "mean", "size"]).round(2).to_string())
+    obs.drop(columns="count_bin").to_parquet(f"{out}_obs.parquet")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/analysis/scan_monotone.py b/planning/studies/real-data-fit/analysis/scan_monotone.py
new file mode 100644
index 00000000..1a8660ad
--- /dev/null
+++ b/planning/studies/real-data-fit/analysis/scan_monotone.py
@@ -0,0 +1,36 @@
+"""
+Is there a shared monotone (not necessarily affine) map X = f(theta(P))
+from binding theta at apparent protein P to growth-only X, across the
+binding genotypes? Isotonic regression (decreasing X with decreasing theta,
+i.e. increasing) per P; also with M42I left out.
+
+    python scan_monotone.py
+"""
+import numpy as np
+import pandas as pd
+from sklearn.isotonic import IsotonicRegression
+import scan_protein as sp
+
+
+def iso_rms(th, X):
+    f = IsotonicRegression(increasing=True).fit(th, X)
+    return np.sqrt(np.mean((X - f.predict(th)) ** 2))
+
+
+curves = pd.read_csv(sp.CURVES)
+xd = pd.read_csv(sp.X_FILE)[["genotype", "titrant_conc", "X"]]
+by_g = {g: d.set_index("protein_uM_dimer").sort_index() for g, d in curves.groupby("genotype")}
+rows = []
+for P in sp.P_GRID[::3]:
+    parts = []
+    for g in sp.GENOS:
+        lo, hi, logK, n, ex = sp.params_at(by_g[g], P)
+        d = xd[xd.genotype == g]
+        parts.append(pd.DataFrame(dict(genotype=g, X=d.X.values,
+                                       theta=sp.theta(lo, hi, logK, n, d.titrant_conc))))
+    t = pd.concat(parts)
+    wo = t[t.genotype != "M42I"]
+    _, aff = sp.affine_rms(t.theta.values, t.X.values)
+    rows.append(dict(P=P, affine=aff, monotone=iso_rms(t.theta.values, t.X.values),
+                     monotone_without_M42I=iso_rms(wo.theta.values, wo.X.values)))
+print(pd.DataFrame(rows).round(3).to_string(index=False))
diff --git a/planning/studies/real-data-fit/analysis/scan_protein.py b/planning/studies/real-data-fit/analysis/scan_protein.py
new file mode 100644
index 00000000..ddbc2a3d
--- /dev/null
+++ b/planning/studies/real-data-fit/analysis/scan_protein.py
@@ -0,0 +1,148 @@
+"""
+Is there one apparent protein concentration P at which a single affine map,
+X = a + b theta(P, IPTG), carries the binding curves onto the growth-only X
+(rel_off_n05) for all binding genotypes?
+
+    python scan_protein.py
+
+theta(P) comes from ../processed/binding_curves.csv (Hill per genotype and
+protein), interpolated linearly in ln P exactly as binding_fit.interpolate
+does (logit plateaus, ln K, ln n). Mutants were measured at 2 and 20 uM
+only, so P outside 2-20 uM is a linear extrapolation in ln P and is
+flagged; wt has 0.02-80 uM. Writes resid/protein_scan.csv and
+resid/2026-10-01_protein-scan.pdf.
+"""
+
+import numpy as np
+import pandas as pd
+from scipy.special import expit, logit
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+plt.rc("pdf", fonttype=42)
+plt.rc("font", size=14)
+plt.rc("axes", labelsize=16)
+
+CURVES = "../processed/binding_curves.csv"
+X_FILE = "resid/X_vs_binding_off_n05.csv"
+GENOS = ["wt", "M42I", "M42I/H74A", "G65H", "H74A", "K84L", "H74A/K84L"]
+P_GRID = np.exp(np.linspace(np.log(0.5), np.log(80), 61))
+
+
+def clip(v):
+    return np.clip(v, 1e-4, 1 - 1e-4)
+
+
+def params_at(d, P):
+    """Hill parameters at P, linear in ln P between (or beyond) the two
+    nearest measured protein concentrations."""
+    ps = np.array(sorted(d.index))
+    if P <= ps[0]:
+        p0, p1 = ps[0], ps[1]
+    elif P >= ps[-1]:
+        p0, p1 = ps[-2], ps[-1]
+    else:
+        p1 = ps[ps >= P][0]
+        p0 = ps[ps <= P][-1]
+        if p0 == p1:
+            r = d.loc[p0]
+            return r.theta_lo, r.theta_hi, r.log_K_mM, r.hill_n, False
+    t = (np.log(P) - np.log(p0)) / (np.log(p1) - np.log(p0))
+    r0, r1 = d.loc[p0], d.loc[p1]
+    lg = lambda k: (1 - t) * logit(clip(r0[k])) + t * logit(clip(r1[k]))
+    lo, hi = expit(lg("theta_lo")), expit(lg("theta_hi"))
+    logK = (1 - t) * r0.log_K_mM + t * r1.log_K_mM
+    n = np.exp((1 - t) * np.log(r0.hill_n) + t * np.log(r1.hill_n))
+    return lo, hi, logK, n, not (ps[0] <= P <= ps[-1])
+
+
+def theta(lo, hi, logK, n, c):
+    c = np.asarray(c, float)
+    frac = np.where(c > 0, (np.maximum(c, 1e-300) / np.exp(logK)) ** n, 0.0)
+    return hi + (lo - hi) / (1.0 + frac)
+
+
+def affine_rms(th, X):
+    A = np.vstack([np.ones_like(th), th]).T
+    coef = np.linalg.lstsq(A, X, rcond=None)[0]
+    return coef, np.sqrt(np.mean((X - A @ coef) ** 2))
+
+
+def main():
+    curves = pd.read_csv(CURVES)
+    xd = pd.read_csv(X_FILE)[["genotype", "titrant_conc", "X"]]
+    by_g = {g: d.set_index("protein_uM_dimer").sort_index()
+            for g, d in curves.groupby("genotype")}
+
+    def table(P_of):
+        rows = []
+        for g in GENOS:
+            lo, hi, logK, n, extrap = params_at(by_g[g], P_of(g))
+            d = xd[xd.genotype == g]
+            rows.append(pd.DataFrame(dict(genotype=g, titrant_conc=d.titrant_conc,
+                                          X=d.X, theta=theta(lo, hi, logK, n, d.titrant_conc),
+                                          extrap=extrap)))
+        return pd.concat(rows)
+
+    # 1. one shared P
+    out = []
+    for P in P_GRID:
+        t = table(lambda g: P)
+        coef, rms = affine_rms(t.theta.values, t.X.values)
+        per = t.assign(res=t.X - (coef[0] + coef[1] * t.theta)).groupby("genotype").res.apply(
+            lambda r: np.sqrt(np.mean(r ** 2)))
+        no_m42i = t[t.genotype != "M42I"]
+        _, rms_wo = affine_rms(no_m42i.theta.values, no_m42i.X.values)
+        out.append(dict(P=P, a=coef[0], b=coef[1], rms=rms, rms_without_M42I=rms_wo,
+                        mutant_extrap=bool(t[t.genotype != "wt"].extrap.any()),
+                        **{f"rms_{g}": per[g] for g in GENOS}))
+    s = pd.DataFrame(out)
+    s.to_csv("resid/protein_scan.csv", index=False)
+    best = s.loc[s.rms.idxmin()]
+    best_in = s[~s.mutant_extrap].loc[lambda d: d.rms.idxmin()]
+    best_wo = s.loc[s.rms_without_M42I.idxmin()]
+    pd.set_option("display.width", 250)
+    show = s.iloc[::6][["P", "a", "b", "rms", "rms_without_M42I", "mutant_extrap"]
+                       + [f"rms_{g}" for g in GENOS]]
+    print(show.round(3).to_string(index=False))
+    print(f"\nbest shared P: {best.P:.2f} uM (rms {best.rms:.3f}, a {best.a:.2f}, b {best.b:.2f}, "
+          f"mutants extrapolated: {best.mutant_extrap})")
+    print(f"best within 2-20 uM: {best_in.P:.2f} uM (rms {best_in.rms:.3f})")
+    print(f"best without M42I: {best_wo.P:.2f} uM (rms {best_wo.rms_without_M42I:.3f})")
+
+    # 2. one P per genotype, shared affine map (alternate to convergence)
+    Pg = {g: best.P for g in GENOS}
+    for _ in range(20):
+        t = table(lambda g: Pg[g])
+        coef, rms = affine_rms(t.theta.values, t.X.values)
+        for g in GENOS:
+            errs = []
+            for P in P_GRID:
+                lo, hi, logK, n, _ = params_at(by_g[g], P)
+                d = xd[xd.genotype == g]
+                th = theta(lo, hi, logK, n, d.titrant_conc)
+                errs.append(np.sqrt(np.mean((d.X - coef[0] - coef[1] * th) ** 2)))
+            Pg[g] = P_GRID[int(np.argmin(errs))]
+    t = table(lambda g: Pg[g])
+    coef, rms = affine_rms(t.theta.values, t.X.values)
+    print(f"\nper-genotype P, shared map: rms {rms:.3f}, a {coef[0]:.2f}, b {coef[1]:.2f}")
+    for g in GENOS:
+        d = t[t.genotype == g]
+        r = np.sqrt(np.mean((d.X - coef[0] - coef[1] * d.theta) ** 2))
+        print(f"  {g:10s} P {Pg[g]:6.2f} uM{'  (extrapolated)' if d.extrap.iloc[0] else ''}  rms {r:.3f}")
+
+    fig, ax = plt.subplots(figsize=(7, 5))
+    ax.plot(s.P, s.rms, "k-", label="all seven")
+    ax.plot(s.P, s.rms_without_M42I, "--", color="#E69F00", label="without M42I")
+    ax.axvspan(2, 20, color="0.92", zorder=0, label="mutants measured 2-20 µM")
+    ax.set_xscale("log")
+    ax.set_xlabel("apparent protein (µM dimer)")
+    ax.set_ylabel("rms(X − (a + b θ))")
+    ax.legend(frameon=False, fontsize=12)
+    fig.tight_layout()
+    fig.savefig("resid/2026-10-01_protein-scan.pdf")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/analysis/score_counts.py b/planning/studies/real-data-fit/analysis/score_counts.py
new file mode 100644
index 00000000..a33d5254
--- /dev/null
+++ b/planning/studies/real-data-fit/analysis/score_counts.py
@@ -0,0 +1,84 @@
+"""
+Count log-likelihood of a MAP run, by tube, from the model itself.
+
+    python score_counts.py <run_dir> [genotype_chunk]
+
+Traces the run's model at its MAP (tfs_fit_model_params.npz, constrained
+values) over genotype chunks of the full batch with the mini-batch scale
+removed, and sums the log-probability of every observed count site. Unlike
+residual_compare.py, which rebuilds the single-transformation prediction by
+hand, this works for any model, the congression mixture included.
+
+Writes <run_dir>/score_tubes.csv: one row per tube (replicate x time x
+condition_pre x condition_sel x titrant_conc index) with its summed ll, and
+prints the total. Compare two runs with compare_scores.py.
+"""
+import os
+import sys
+import warnings
+
+warnings.filterwarnings("ignore")
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+from numpyro import handlers
+
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.inference.run_inference import RunInference
+
+
+def main():
+    run = sys.argv[1]
+    chunk = int(sys.argv[2]) if len(sys.argv) > 2 else 20000
+    os.chdir(run)  # config paths are relative to the run directory
+    run = "."
+    orch, _ = read_configuration(f"{run}/tfs_configure_config.yaml")
+    ri = RunInference(orch, 0)
+    p = np.load(f"{run}/tfs_fit_model_params.npz")
+    values = {k[:-len("_auto_loc")]: jnp.asarray(p[k]) for k in p.files
+              if k.endswith("_auto_loc")}
+    data = jax.device_put(orch.data)
+    G = orch.data.num_genotype
+
+    @jax.jit
+    def tube_ll(batch):
+        model = handlers.substitute(orch.jax_model, data=values)
+        tr = handlers.trace(handlers.seed(model, 0)).get_trace(
+            data=batch, priors=orch.priors)
+        out = {}
+        for name, site in tr.items():
+            if site["type"] != "sample" or not site["is_observed"]:
+                continue
+            if not name.startswith("growth"):
+                continue
+            lp = site["fn"].log_prob(site["value"])
+            mask = site.get("mask")
+            if mask is not None:
+                lp = jnp.where(jnp.broadcast_to(mask, lp.shape), lp, 0.0)
+            out[name] = lp.sum(axis=-1)
+        return out
+
+    total = None
+    for start in range(0, G, chunk):
+        idx = jnp.arange(start, min(start + chunk, G))
+        res = tube_ll(ri._unscaled_batch(data, idx))
+        res = {k: np.asarray(v, dtype=float) for k, v in res.items()}
+        total = res if total is None else {k: total[k] + res[k] for k in total}
+
+    for name, arr in total.items():
+        print(f"{run}: {name} sum ll {arr.sum():.6e}  (tube array {arr.shape})")
+    arr = total[[k for k in total if k.endswith("_obs")][0]]
+    idx = np.indices(arr.shape).reshape(arr.ndim, -1).T
+    df = pd.DataFrame(idx, columns=["replicate", "time", "condition_pre",
+                                    "condition_sel", "titrant_name",
+                                    "titrant_conc"][:arr.ndim])
+    df["ll"] = arr.ravel()
+    df = df[df.ll != 0]
+    df.to_csv(f"{run}/score_tubes.csv", index=False)
+    print(f"wrote {run}/score_tubes.csv ({len(df)} tubes)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/analysis/small_offsets.py b/planning/studies/real-data-fit/analysis/small_offsets.py
new file mode 100644
index 00000000..e09588c2
--- /dev/null
+++ b/planning/studies/real-data-fit/analysis/small_offsets.py
@@ -0,0 +1,31 @@
+"""No-offset MAP params + per-tube offsets only (N(0, 0.17) prior), each tube
+optimized alone: how much of the offset fit's likelihood gain does that get?"""
+import numpy as np, pandas as pd
+from scipy.optimize import minimize_scalar
+from scipy.special import gammaln
+import sys
+sys.path.insert(0, ".")
+from residual_compare import nb_logpmf, TUBE
+SIG = 0.17
+d = pd.read_parquet("resid/sig_vs_nooff_obs.parquet")
+z = np.load("map_nooffset_b/tfs_fit_model_params.npz")
+phi, inv_r = float(z["growth_phi_auto_loc"]), float(z["growth_inv_r_auto_loc"])
+d["log_mu"] = d.ln_sample_reads + d.pred_b - d.sample_ln_cfu
+rows = []
+for key, g in d.groupby(TUBE):
+    k = g.counts.to_numpy(float); lm = g.log_mu.to_numpy()
+    base = nb_logpmf(k, lm, phi, inv_r).sum()
+    f = lambda x: -(nb_logpmf(k, lm + x, phi, inv_r).sum() - 0.5 * (x / SIG) ** 2)
+    r = minimize_scalar(f, bounds=(-3, 3), method="bounded", options={"xatol": 1e-4})
+    gain = -r.fun - base
+    rows.append(dict(zip(TUBE, key), delta=r.x, gain=gain))
+r = pd.DataFrame(rows)
+t = pd.read_csv("resid/sig_vs_nooff_tube.csv")
+r = r.merge(t[TUBE + ["dll", "offset_a"]], on=TUBE)
+r.to_csv("resid/small_offsets.csv", index=False)
+print(f"sum gain (ll + offset prior) {r.gain.sum():.4g}; offset-fit dll {r.dll.sum():.4g}")
+print(f"delta sd {r.delta.std():.3f}, range {r.delta.min():.2f} to {r.delta.max():.2f}")
+print(f"corr(delta, offset_a) {np.corrcoef(r.delta, r.offset_a)[0,1]:.2f}; "
+      f"corr(gain, dll) {np.corrcoef(r.gain, r.dll)[0,1]:.2f}")
+pd.set_option("display.width", 250)
+print(r.pivot_table(index=["condition_pre","condition_sel"], columns="titrant_conc", values="delta").round(2).to_string())
diff --git a/planning/studies/real-data-fit/fit/check_warm.py b/planning/studies/real-data-fit/fit/check_warm.py
new file mode 100644
index 00000000..52974158
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/check_warm.py
@@ -0,0 +1,69 @@
+"""
+Check that a warm-started run starts where it should.
+
+    python ../check_warm.py ../map_nooffset_b warm_init.npz
+    python ../check_warm.py warm_init.npz
+
+Run from the run directory. Reads the config and the --init_from npz the
+way tfs-fit-model does and turns them into site values
+(RunInference.site_values, what the MAP starts from).
+
+With a reference run, every site of its MAP must start at that MAP's value.
+With the npz alone, every site in the npz must start at the npz's value;
+sites the model does not sample (a hyperscale held fixed by the priors, say)
+are listed and skipped. Either way the tube offsets must be 288 values with
+118 nonzero. Exits nonzero on any failure, so the job stops before the fit.
+"""
+
+import sys
+
+import numpy as np
+
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.inference.run_inference import RunInference
+from tfscreen.tfmodel.scripts.fit_model_cli import _read_init_from
+
+
+def main():
+    if len(sys.argv) == 3:
+        ref_path = f"{sys.argv[1]}/tfs_fit_model_params.npz"
+        init_path = sys.argv[2]
+    else:
+        ref_path = init_path = sys.argv[1]
+    ref = np.load(ref_path)
+    orch, guesses = read_configuration("tfs_configure_config.yaml")
+    guesses = {**guesses, **_read_init_from(init_path)}
+    ri = RunInference(orch, 0)
+    start = ri.site_values(guesses)
+    bad, skipped, checked = [], [], 0
+    for key in ref.files:
+        site = key[:-len("_auto_loc")]
+        if site == "sample_offset_offset" and ref_path == init_path:
+            continue
+        want = np.asarray(ref[key], dtype=float)
+        if site not in start:
+            if ref_path == init_path:
+                skipped.append(site)
+            else:
+                bad.append(f"{site}: not in start values")
+            continue
+        checked += 1
+        got = np.asarray(start[site], dtype=float)
+        if got.shape != want.shape or not np.allclose(got, want, rtol=1e-5, atol=1e-6):
+            err = (np.abs(got - want).max() if got.shape == want.shape
+                   else f"shape {got.shape} vs {want.shape}")
+            bad.append(f"{site}: max diff {err}")
+    off = np.asarray(start.get("sample_offset_offset", np.zeros(0)))
+    nz = off[off != 0]
+    print(f"sample_offset_offset start: {off.size} values, {nz.size} nonzero, "
+          f"sd {nz.std() if nz.size else float('nan'):.3f}")
+    print(f"{checked - len(bad)} of {checked} sites start at {ref_path}")
+    if skipped:
+        print(f"not sampled by this model, skipped: {skipped}")
+    if bad or off.size != 288 or nz.size != 118:
+        print("\n".join(bad))
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/fit/growth_priors_loose.csv b/planning/studies/real-data-fit/fit/growth_priors_loose.csv
new file mode 100644
index 00000000..4cfb4e48
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/growth_priors_loose.csv
@@ -0,0 +1,5 @@
+condition_rep,k_loc,k_scale,m_loc,m_scale
+kanR+kan,0.015,0.01,0.0,0.01
+kanR-kan,0.015,0.01,0.0,0.01
+pheS+4CP,0.015,0.01,0.0,0.01
+pheS-4CP,0.015,0.01,0.0,0.01
diff --git a/planning/studies/real-data-fit/fit/make_init_hs.py b/planning/studies/real-data-fit/fit/make_init_hs.py
new file mode 100644
index 00000000..f64ecaee
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/make_init_hs.py
@@ -0,0 +1,37 @@
+"""
+Re-express a relative-fit MAP's Hill offsets for held population SDs, as an
+--init_from file.
+
+    python make_init_hs.py <map_run> <out.npz> X_low=0.5 X_delta=0.5 log_hill_K=1.0
+
+hill_relative is non-centered: value = hyper_loc + offset * hyper_scale. A
+run that holds a hyperscale at a new value would put every genotype
+somewhere else if it started from the old offsets, so each offset is
+rescaled by old_scale / new_scale (same per-genotype values at the start)
+and the learned hyperscale site, which the new model does not sample, is
+dropped. 2026-10-02: rel_off_n05 -> inputs/init_rel_off_hs.npz.
+"""
+import sys
+
+import numpy as np
+
+
+def main():
+    run, out = sys.argv[1:3]
+    held = {k: float(v) for k, v in (a.split("=") for a in sys.argv[3:])}
+    p = np.load(f"{run}/tfs_fit_model_params.npz")
+    init = {k: np.asarray(p[k]) for k in p.files}
+    for h, new in held.items():
+        scale_key = f"theta_{h}_hyper_scale_auto_loc"
+        off_key = f"theta_{h}_offset_auto_loc"
+        old = init.pop(scale_key)
+        init[off_key] = init[off_key] * (old[:, None] / new)
+        z = init[off_key]
+        print(f"{h}: scale {float(old[0]):.3f} -> {new}; offsets now "
+              f"median |z| {np.median(np.abs(z)):.2f}, 99th pct {np.percentile(np.abs(z), 99):.1f}")
+    np.savez(out, **init)
+    print(f"wrote {out} ({len(init)} sites)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/fit/make_init_npz.py b/planning/studies/real-data-fit/fit/make_init_npz.py
new file mode 100644
index 00000000..08e44ee0
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/make_init_npz.py
@@ -0,0 +1,34 @@
+"""
+Combine a MAP's parameters with another MAP's tube offsets into one
+--init_from file.
+
+    python make_init_npz.py <params_run> <offsets_run> <out.npz>
+
+params_run:  every {site}_auto_loc of its tfs_fit_model_params.npz
+offsets_run: its sample_offset_offset_auto_loc only (same 288-tube layout:
+             same tube grid and condition order)
+2026-10-01: map_nooffset_n05 (n held, nine spikes) + map_warm (small
+offsets, SD 0.19) -> inputs/init_n05_warm.npz, for run.srun INIT_FROM.
+"""
+import sys
+
+import numpy as np
+
+
+def main():
+    params_run, offsets_run, out = sys.argv[1:4]
+    p = np.load(f"{params_run}/tfs_fit_model_params.npz")
+    o = np.load(f"{offsets_run}/tfs_fit_model_params.npz")
+    init = {k: p[k] for k in p.files}
+    assert "sample_offset_offset_auto_loc" not in init
+    off = np.asarray(o["sample_offset_offset_auto_loc"])
+    assert off.shape == (288,), off.shape
+    init["sample_offset_offset_auto_loc"] = off
+    np.savez(out, **init)
+    nz = off[off != 0]
+    print(f"{out}: {len(p.files)} sites from {params_run} + tube offsets from "
+          f"{offsets_run} ({nz.size} nonzero, sd {nz.std():.3f})")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/fit/make_warm_start.py b/planning/studies/real-data-fit/fit/make_warm_start.py
new file mode 100644
index 00000000..92f0f116
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/make_warm_start.py
@@ -0,0 +1,58 @@
+"""
+Warm-start a level-offset MAP from the no-offset MAP.
+
+    python make_warm_start.py <offset_run> <nooffset_run> <out_dir>
+
+offset_run:   a finished level-offset run (sigma_fixed 0.17), for its config,
+              priors, guesses and library snapshot (e.g. map_sig017b)
+nooffset_run: the no-offset MAP whose point is the start (map_nooffset_b)
+out_dir:      new run directory, a sibling of the others (config paths are
+              ../inputs/...)
+
+Writes <out_dir>/warm_init.npz for tfs-fit-model --init_from: every
+{site}_auto_loc array of the no-offset MAP, plus the tube offsets
+(sample_offset_offset_auto_loc) at the per-tube values from
+resid/small_offsets.py (the best offset per tube with everything else held
+at the no-offset MAP). Run it with ARM=map WARM=1 (run.srun skips configure
+and prefit, checks the start with check_warm.py, then fits).
+"""
+
+import os
+import shutil
+import sys
+
+import numpy as np
+import pandas as pd
+
+TUBE = ["replicate", "condition_pre", "condition_sel", "titrant_conc", "t_sel"]
+TUBE_SHAPE = (3, 3, 2, 2, 1, 8)
+IDX = ["replicate_idx", "time_idx", "condition_pre_idx",
+       "condition_sel_idx", "titrant_name_idx", "titrant_conc_idx"]
+
+
+def main():
+    off_run, nooff_run, out = sys.argv[1:4]
+    os.makedirs(out, exist_ok=False)
+    for f in ("tfs_configure_config.yaml", "tfs_configure_priors.csv",
+              "tfs_configure_guesses.csv", "tfs_configure_library.csv"):
+        shutil.copy(os.path.join(off_run, f), os.path.join(out, f))
+
+    z = np.load(os.path.join(nooff_run, "tfs_fit_model_params.npz"))
+    init = {k: z[k] for k in z.files}
+
+    so = pd.read_csv("resid/small_offsets.csv")
+    ti = pd.read_csv("resid/tube_index.csv")
+    so = so.merge(ti, on=TUBE, how="left")
+    assert so[IDX].notna().all().all() and len(so) == 118
+    flat = np.ravel_multi_index(tuple(so[c].astype(int) for c in IDX), TUBE_SHAPE)
+    off = np.zeros(int(np.prod(TUBE_SHAPE)), dtype=np.float32)
+    off[flat] = so.delta.to_numpy()
+    init["sample_offset_offset_auto_loc"] = off
+
+    np.savez(os.path.join(out, "warm_init.npz"), **init)
+    print(f"{out}/warm_init.npz: {len(init)} sites from {nooff_run} "
+          f"+ tube offsets (118 set, sd {so.delta.std():.3f})")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/fit/run.srun b/planning/studies/real-data-fit/fit/run.srun
new file mode 100644
index 00000000..c66799e4
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/run.srun
@@ -0,0 +1,170 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_real
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpu
+#SBATCH --time=01-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=384G
+#SBATCH --ntasks-per-node=1
+
+# First real-data joint fit of the dev-data screen (planning/dev-data).
+# Submit from a run directory next to inputs/, one per arm:
+#
+#   mkdir svi && cd svi && sbatch --export=ALL,ARM=svi ../run.srun
+#   mkdir map && cd map && sbatch --export=ALL,ARM=map ../run.srun
+#
+# Offset variants (2026-09-29; see README):
+#   sbatch --export=ALL,ARM=map,SIGMA_FIXED=0.17,BATCH_SIZE=4096 ../run.srun
+#   sbatch --export=ALL,ARM=map,OFFSET_MODEL=zero,BATCH_SIZE=4096 ../run.srun
+#   sbatch --export=ALL,ARM=svi,OFFSET_MODEL=zero,BATCH_SIZE=4096 ../run.srun
+#   sbatch --export=ALL,ARM=map,OFFSET_MODEL=zero,BATCH_SIZE=4096,N_SCALE=0.5 ../run.srun
+#
+# INIT_FROM: an npz for tfs-fit-model --init_from; unlike WARM, configure
+#            and prefit still run. Offsets + held n from the best points
+#            (2026-10-01; make_init_npz.py). INIT_REF: a run whose MAP
+#            the start should equal (check_warm.py stops the job if not):
+#   sbatch --export=ALL,ARM=map,SIGMA_FIXED=0.17,N_SCALE=0.5,BATCH_SIZE=4096,INIT_FROM=../inputs/init_n05_warm.npz,INIT_REF=../map_nooffset_n05,ADAM_STEP=1e-4 ../run.srun
+#
+# Warm start (2026-09-30; see README): a level-offset MAP started at the
+# no-offset MAP's point. Build the directory first with make_warm_start.py,
+# then from inside it:
+#   sbatch --export=ALL,ARM=map,WARM=1,ADAM_STEP=1e-4 ../run.srun
+# WARM=1 skips configure and prefit (the directory already has them), checks
+# the start with check_warm.py and fits with --init_from warm_init.npz.
+#
+# Locally (smoke test): ARM=svi GROWTH=../inputs/growth_smoke.csv.gz
+#   MAX_EPOCHS=2000 bash ../run.srun  (drop the module line first)
+#
+# ARM:     svi (component-guide SVI) or map (MAP; point estimates, which
+#          put the high-IPTG plateau near truth on simulations where SVI
+#          collapses it; planning/studies/svi-overconfidence/)
+# GROWTH:  growth table (default the full library)
+# N_SCALE: hold the population SD of log(hill_n) at this value
+#          (theta_log_hill_n_hyper_scale_fixed). Learned, it ran to about 19
+#          and n to 0.01-13 on the first real-data fits (README, 2026-10-01).
+
+module load cuda/12.4.1
+
+set -e
+
+ARM="${ARM:-svi}"
+GROWTH="${GROWTH:-../inputs/growth.csv.gz}"
+MAX_EPOCHS="${MAX_EPOCHS:-}"
+SEED="${SEED:-20260929}"
+# per-tube offsets: level (default) or zero; SIGMA_FIXED holds the level
+# offsets' SD instead of learning it (e.g. 0.17, the per-tube OD600 scatter)
+OFFSET_MODEL="${OFFSET_MODEL:-level}"
+SIGMA_FIXED="${SIGMA_FIXED:-}"
+BATCH_SIZE="${BATCH_SIZE:-1024}"
+WARM="${WARM:-}"
+N_SCALE="${N_SCALE:-}"
+INIT_FROM="${INIT_FROM:-}"
+INIT_REF="${INIT_REF:-}"
+ADAM_STEP="${ADAM_STEP:-}"
+EPOCH_ARGS=()
+if [[ -n "${MAX_EPOCHS}" ]]; then
+    EPOCH_ARGS=(--max_num_epochs "${MAX_EPOCHS}")
+fi
+
+if [[ -z "${WARM}" ]]; then
+echo ">>> Configure (${ARM}; growth ${GROWTH}) $(date)"
+tfs-configure-model \
+    --growth_df "${GROWTH}" \
+    --binding_df ../inputs/binding_df_10uM.csv \
+    --library_config ../inputs/library_config.yaml \
+    --growth_likelihood counts \
+    --sample_offset_model "${OFFSET_MODEL}" \
+    --batch_size "${BATCH_SIZE}" \
+    --growth_noise_model zero \
+    --condition_growth_model linear \
+    --growth_transition_model instant \
+    --ln_cfu0_model hierarchical \
+    --dk_geno_model hierarchical_geno \
+    --activity_model fixed \
+    --theta_model hill_geno \
+    --theta_rescale_model passthrough \
+    --theta_growth_noise_model zero \
+    --theta_binding_noise_model zero \
+    --transformation_model single \
+    --growth_shares_replicates \
+    --binding_weight 1.0
+
+if [[ -n "${SIGMA_FIXED}" ]]; then
+    grep -q '^growth.sample_offset.sigma_fixed,' tfs_configure_priors.csv
+    sed -i "s/^growth.sample_offset.sigma_fixed,[^,]*/growth.sample_offset.sigma_fixed,${SIGMA_FIXED}/" \
+        tfs_configure_priors.csv
+    echo "sample_offset sigma held at ${SIGMA_FIXED}"
+    grep '^growth.sample_offset' tfs_configure_priors.csv
+fi
+
+if [[ -n "${N_SCALE}" ]]; then
+    grep -q '^theta.theta_log_hill_n_hyper_scale_fixed,' tfs_configure_priors.csv
+    sed -i "s/^theta.theta_log_hill_n_hyper_scale_fixed,[^,]*/theta.theta_log_hill_n_hyper_scale_fixed,${N_SCALE}/" \
+        tfs_configure_priors.csv
+    echo "log hill_n population SD held at ${N_SCALE}"
+    grep '^theta.theta_log_hill_n' tfs_configure_priors.csv
+fi
+
+echo ">>> Pre-fit calibration $(date)"
+tfs-prefit-calibration tfs_configure_config.yaml --seed "${SEED}" \
+    --k_scale_ceiling 0.005
+else
+echo ">>> Warm start: checking the start against ../map_nooffset_b $(date)"
+python ../check_warm.py ../map_nooffset_b warm_init.npz
+fi
+
+NUM_SAMPLES="${NUM_SAMPLES:-300}"
+case "${ARM}" in
+    svi) FIT_ARGS=()
+         SAMPLE_ARGS=(--num_posterior_samples "${NUM_SAMPLES}") ;;
+    # the MAP point only: a Laplace needs the full Hessian, out of reach at
+    # millions of parameters
+    map) FIT_ARGS=(--analysis_method map)
+         SAMPLE_ARGS=(--map_point) ;;
+    *) echo "unknown ARM '${ARM}'" >&2; exit 1 ;;
+esac
+
+if [[ -n "${WARM}" ]]; then
+    FIT_ARGS+=(--init_from warm_init.npz)
+fi
+if [[ -n "${INIT_FROM}" ]]; then
+    FIT_ARGS+=(--init_from "${INIT_FROM}")
+    if [[ -n "${INIT_REF}" ]]; then
+        echo ">>> Checking the start against ${INIT_REF} $(date)"
+        python ../check_warm.py "${INIT_REF}" "${INIT_FROM}"
+    fi
+fi
+if [[ -n "${ADAM_STEP}" ]]; then
+    FIT_ARGS+=(--adam_step_size "${ADAM_STEP}")
+fi
+
+echo ">>> Fit (${ARM}) $(date)"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" \
+    "${FIT_ARGS[@]}" "${EPOCH_ARGS[@]}"
+
+echo ">>> Sample posterior $(date)"
+# 25 draws per batch and a peak-memory log (/usr/bin/time -v, "Maximum
+# resident set size" in run.err): the first SVI run was OOM-killed on the
+# host at 128 GB assembling a 100-draw batch
+/usr/bin/time -v tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    --sampling_batch_size "${SAMPLING_BATCH:-25}" \
+    --skip_growth_observations \
+    "${SAMPLE_ARGS[@]}"
+
+echo ">>> Extract parameters $(date)"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict theta $(date)"
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+# one block of 2,000 genotypes (binding and spiked always in it), for
+# the observed-vs-predicted check; the whole library would be ~140 GB of CSV
+echo ">>> Predict growth (subset) $(date)"
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+    --num_marginal_samples=500 --subset_genotypes --genotype_batch_size 2000
+
+echo ">>> Done $(date)"
diff --git a/planning/studies/real-data-fit/fit/run_blap.srun b/planning/studies/real-data-fit/fit/run_blap.srun
new file mode 100644
index 00000000..d7667440
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/run_blap.srun
@@ -0,0 +1,68 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_blap
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpulong
+#SBATCH --time=01-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# Per-genotype (block) Laplace at a finished MAP (2026-10-02; README).
+# Given the shared parameters (k, m, hyperparameters, tube offsets) each
+# genotype's parameters couple to no other genotype, so the Hessian splits
+# into one small block per genotype: B Hessian-vector products per genotype
+# chunk instead of a full Hessian. Shared parameters stay at the MAP, so
+# their uncertainty is left out (k/m intervals are zero width).
+#
+# Submit from a new directory next to the MAP run:
+#   mkdir rel_off_n05_blap && cd rel_off_n05_blap && sbatch --export=ALL,MAP_DIR=../rel_off_n05 ../run_blap.srun
+#
+# With the shared parameters' uncertainty (2026-10-02): SHARED=1 adds one
+# Hessian-vector product per shared parameter (about 314 here) per genotype
+# chunk, draws the shared parameters from their Laplace marginal (the Schur
+# complement) and each genotype from its conditional given them. That is the
+# full Laplace; k/m intervals are no longer zero width.
+#   mkdir rel_off_n05_alap && cd rel_off_n05_alap && sbatch --export=ALL,MAP_DIR=../rel_off_n05,SHARED=1 ../run_blap.srun
+#
+# MAP_DIR: the finished MAP run (config, priors, guesses, library snapshot
+#          and checkpoint are copied from it)
+# GENOTYPE_CHUNK: genotypes per Hessian-vector-product pass (default 20000)
+
+module load cuda/12.4.1
+
+set -e
+
+MAP_DIR="${MAP_DIR:?set MAP_DIR to the MAP run directory}"
+GENOTYPE_CHUNK="${GENOTYPE_CHUNK:-20000}"
+NUM_SAMPLES="${NUM_SAMPLES:-300}"
+SHARED_ARGS=()
+if [[ -n "${SHARED:-}" ]]; then
+    SHARED_ARGS=(--laplace_shared)
+fi
+
+echo ">>> Copy MAP run from ${MAP_DIR} $(date)"
+cp "${MAP_DIR}"/tfs_configure_config.yaml "${MAP_DIR}"/tfs_configure_priors.csv \
+   "${MAP_DIR}"/tfs_configure_guesses.csv "${MAP_DIR}"/tfs_configure_library.csv \
+   "${MAP_DIR}"/tfs_fit_model_checkpoint.pkl .
+
+echo ">>> Block Laplace $(date)"
+/usr/bin/time -v tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    --laplace_blocks "${SHARED_ARGS[@]}" --genotype_chunk_size "${GENOTYPE_CHUNK}" \
+    --num_posterior_samples "${NUM_SAMPLES}" --sampling_batch_size 25 \
+    --skip_growth_observations
+
+echo ">>> Extract parameters $(date)"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict theta/X $(date)"
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict growth (subset) $(date)"
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+    --num_marginal_samples=500 --subset_genotypes --genotype_batch_size 2000
+
+echo ">>> Done $(date)"
diff --git a/planning/studies/real-data-fit/fit/run_recipe.srun b/planning/studies/real-data-fit/fit/run_recipe.srun
new file mode 100644
index 00000000..f5d2083c
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/run_recipe.srun
@@ -0,0 +1,80 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_recipe
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpulong
+#SBATCH --time=02-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# The official recipe for the dev-data fit (pipeline plan step 7): the
+# documented CLI chain from the processed experiment to the posterior and its
+# diagnostics, with no hand-edited file and no hand-built start. It replaces
+# the six-run chain in README.md. Every setting is a flag; the only tracked
+# input is growth_priors_loose.csv (loose k/m priors for a growth-only model).
+#
+# The lab-specific step stays a script: processing/prep_dev_data.py, run in
+# planning/dev-data, writes processed/ (tube table, OD600 table, calibration,
+# count files, library config), and planning/dev-data/tube_corrections.yaml
+# holds the tube corrections (tfs-correct-tubes; since 2026-10-19 the
+# pheS+4CP replicate 2, t_sel 82 row is excluded). Then, from a new directory in
+# planning/dev-data/real_fit:
+#
+#   mkdir recipe && cd recipe && sbatch ../run_recipe.srun
+#
+# Copy run_recipe.srun and growth_priors_loose.csv into real_fit/ first
+# (planning/dev-data is gitignored and not pulled); check with
+#   grep -c tube_od600 run_recipe.srun
+#
+# The same chain runs on the full-size simulation of this design
+# (planning/studies/full-size-sim/run_full_size.srun), with only the data
+# paths changed.
+
+module load cuda/12.4.1
+
+set -e
+
+PROCESSED="${PROCESSED:-../../processed}"
+CORRECTIONS="${CORRECTIONS:-../../tube_corrections.yaml}"
+SEED="${SEED:-20260930}"
+
+echo ">>> Correct tubes $(date)"
+tfs-correct-tubes "${PROCESSED}/tube_table.csv" "${CORRECTIONS}" \
+    --out_prefix tfs_tubes_corrected
+
+echo ">>> Process counts $(date)"
+tfs-process-counts tfs_tubes_corrected.csv "${PROCESSED}/counts" \
+    --od600_file "${PROCESSED}/tube_od600.csv" \
+    --od600_calibration_file "${PROCESSED}/od600_calibration.yaml" \
+    --tube_volume_mL 5 \
+    --out_prefix tfs_growth --no_verbose
+
+echo ">>> Configure $(date)"
+tfs-configure-model \
+    --growth_df tfs_growth.csv \
+    --library_config "${PROCESSED}/library_config.yaml" \
+    --theta_model hill_relative \
+    --growth_shares_replicates \
+    --batch_size 4096 \
+    --growth_priors ../growth_priors_loose.csv \
+    --set_priors sigma_fixed=0.17 theta_log_hill_n_hyper_scale_fixed=0.5
+
+echo ">>> Fit (staged MAP) $(date)"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" --analysis_method map
+
+echo ">>> Posterior (Laplace, auto: arrowhead at this size) $(date)"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    --num_posterior_samples 500 --skip_growth_observations
+
+echo ">>> Extract and predict $(date)"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Summarize (tube-offset diagnostic) $(date)"
+tfs-summarize-fit .
+
+echo ">>> Done $(date)"
diff --git a/planning/studies/real-data-fit/fit/run_relative.srun b/planning/studies/real-data-fit/fit/run_relative.srun
new file mode 100644
index 00000000..6fc0312b
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/run_relative.srun
@@ -0,0 +1,178 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_rel
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpulong
+#SBATCH --time=01-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# Growth-only relative fit (theta: hill_relative) of the dev-data screen:
+# no binding data, MAP, tube offsets optional (OFFSET_MODEL). wt is gauged to X = 1 at 0 IPTG
+# and X = 0 at 1 mM, so the fit's X is compared with the binding theta
+# afterwards instead of being tied to it (see README, 2026-09-30).
+# Submit from a run directory next to inputs/:
+#
+#   mkdir rel_loose && cd rel_loose && sbatch --export=ALL,PRIORS=loose ../run_relative.srun
+#   mkdir rel_monokan && cd rel_monokan && sbatch --export=ALL,PRIORS=monokan ../run_relative.srun
+#   mkdir rel_loose_n05 && cd rel_loose_n05 && sbatch --export=ALL,PRIORS=loose,N_SCALE=0.5 ../run_relative.srun
+#
+# With tube offsets (2026-10-01): level offsets at SD 0.17, n held, started
+# from rel_loose_b's point plus map_off_n05's offsets (make_init_npz.py;
+# check_warm.py stops the job if the start is off):
+#   mkdir rel_off_n05 && cd rel_off_n05 && sbatch --export=ALL,PRIORS=loose,N_SCALE=0.5,OFFSET_MODEL=level,SIGMA_FIXED=0.17,INIT_FROM=../inputs/init_rel_off.npz,ADAM_STEP=1e-4 ../run_relative.srun
+#
+# SVI uncertainty from the best growth-only MAP (2026-10-01): low-rank
+# guide (rank 20; numpyro's default sqrt(D) is about 1,500 here), started
+# at rel_off_n05's MAP point (inputs/init_rel_off_n05_map.npz, a copy of its
+# tfs_fit_model_params.npz):
+#   mkdir rel_off_n05_svi && cd rel_off_n05_svi && sbatch --export=ALL,ARM=svi,PRIORS=loose,N_SCALE=0.5,OFFSET_MODEL=level,SIGMA_FIXED=0.17,INIT_FROM=../inputs/init_rel_off_n05_map.npz ../run_relative.srun
+#
+# Shrinkage (2026-10-02): hold the X_low, X_delta and log K population SDs
+# too (learned they ran to 3.6, 4.3 and 11, so the MAP shrank nothing), from
+# rel_off_n05 with its offsets re-expressed for the held SDs
+# (make_init_hs.py -> inputs/init_rel_off_hs.npz):
+#   mkdir rel_off_hs && cd rel_off_hs && sbatch --export=ALL,PRIORS=loose,N_SCALE=0.5,HS_FIXED=X_low=0.5:X_delta=0.5:log_hill_K=1.0,OFFSET_MODEL=level,SIGMA_FIXED=0.17,INIT_FROM=../inputs/init_rel_off_hs.npz,ADAM_STEP=1e-4 ../run_relative.srun
+#
+# Congression test (2026-10-02): rel_off_hs with the congression mixture
+# (max rule, the only one the relative fit allows; lambda prior from the
+# measured 0.3572, likelihood-ratio 95% CI 0.156-0.674: lognormal, median
+# 0.357, log SD 0.373, entered as linear mean 0.383, SD 0.147), started
+# from rel_off_hs's MAP. Does the IPTG pattern in
+# the tube offsets move into the mixture?
+#   mkdir rel_off_hs_mix && cd rel_off_hs_mix && sbatch --export=ALL,PRIORS=loose,N_SCALE=0.5,HS_FIXED=X_low=0.5:X_delta=0.5:log_hill_K=1.0,OFFSET_MODEL=level,SIGMA_FIXED=0.17,TRANSFORMATION=mixture,INIT_FROM=../inputs/init_rel_off_hs_map.npz,ADAM_STEP=1e-4 ../run_relative.srun
+#
+# PRIORS: per-condition k and m priors written by set_growth_priors.py
+#         (tfs-prefit-calibration refuses growth-only models).
+#         loose: every condition k ~ N(0.015, 0.01), m ~ N(0, 0.01)
+#         monokan: kan conditions from the wt monoculture rates, pheS loose
+# N_SCALE: hold the population SD of log(hill_n) at this value
+#          (theta_log_hill_n_hyper_scale_fixed). Learned, it ran to about 19
+#          and n to 0.01-13 on the first real-data fits (README, 2026-10-01).
+# TRANSFORMATION: single (default) or mixture (congression_theta_rule max,
+#                 lambda prior LAMBDA as linear "mean sd", default "0.383 0.147")
+# HS_FIXED: colon-separated name=value pairs holding the other population
+#           SDs (theta_{name}_hyper_scale_fixed), e.g. X_low=0.5:log_hill_K=1.0
+
+module load cuda/12.4.1
+
+set -e
+
+PRIORS="${PRIORS:?set PRIORS=loose or PRIORS=monokan}"
+GROWTH="${GROWTH:-../inputs/growth.csv.gz}"
+MAX_EPOCHS="${MAX_EPOCHS:-}"
+SEED="${SEED:-20260930}"
+BATCH_SIZE="${BATCH_SIZE:-4096}"
+N_SCALE="${N_SCALE:-}"
+HS_FIXED="${HS_FIXED:-}"
+TRANSFORMATION="${TRANSFORMATION:-single}"
+LAMBDA="${LAMBDA:-0.383 0.147}"
+TRANSFORM_ARGS=(--transformation_model "${TRANSFORMATION}")
+if [[ "${TRANSFORMATION}" != "single" ]]; then
+    TRANSFORM_ARGS+=(--transformation_lambda ${LAMBDA} --congression_theta_rule max)
+fi
+# per-tube offsets: zero (default) or level; SIGMA_FIXED holds their SD
+OFFSET_MODEL="${OFFSET_MODEL:-zero}"
+SIGMA_FIXED="${SIGMA_FIXED:-}"
+INIT_FROM="${INIT_FROM:-}"
+ADAM_STEP="${ADAM_STEP:-}"
+# map (default) or svi: low-rank multivariate normal guide, started at the
+# INIT_FROM point with no pre-MAP (the start is already a MAP)
+ARM="${ARM:-map}"
+GUIDE_RANK="${GUIDE_RANK:-20}"
+NUM_SAMPLES="${NUM_SAMPLES:-300}"
+EPOCH_ARGS=()
+if [[ -n "${MAX_EPOCHS}" ]]; then
+    EPOCH_ARGS=(--max_num_epochs "${MAX_EPOCHS}")
+fi
+
+echo ">>> Configure (relative, ${PRIORS}; growth ${GROWTH}) $(date)"
+tfs-configure-model \
+    --growth_df "${GROWTH}" \
+    --library_config ../inputs/library_config.yaml \
+    --growth_likelihood counts \
+    --sample_offset_model "${OFFSET_MODEL}" \
+    --batch_size "${BATCH_SIZE}" \
+    --growth_noise_model zero \
+    --condition_growth_model linear \
+    --growth_transition_model instant \
+    --ln_cfu0_model hierarchical \
+    --dk_geno_model hierarchical_geno \
+    --activity_model fixed \
+    --theta_model hill_relative \
+    --theta_rescale_model passthrough \
+    --theta_growth_noise_model zero \
+    "${TRANSFORM_ARGS[@]}" \
+    --growth_shares_replicates
+
+# the library config must carry all nine spikes (2026-09-30)
+python -c "import sys, pandas as pd; n = int(pd.read_csv('tfs_configure_library.csv').in_spiked_origin.sum()); print(f'{n} spiked genotypes in the library snapshot'); sys.exit(n != 9)"
+
+echo ">>> Growth priors (${PRIORS}) $(date)"
+python ../set_growth_priors.py "${PRIORS}"
+if [[ -n "${N_SCALE}" ]]; then
+    grep -q '^theta.theta_log_hill_n_hyper_scale_fixed,' tfs_configure_priors.csv
+    sed -i "s/^theta.theta_log_hill_n_hyper_scale_fixed,[^,]*/theta.theta_log_hill_n_hyper_scale_fixed,${N_SCALE}/" \
+        tfs_configure_priors.csv
+    echo "log hill_n population SD held at ${N_SCALE}"
+    grep '^theta.theta_log_hill_n' tfs_configure_priors.csv
+fi
+
+for pair in ${HS_FIXED//:/ }; do
+    h="${pair%%=*}"; v="${pair#*=}"
+    grep -q "^theta.theta_${h}_hyper_scale_fixed," tfs_configure_priors.csv
+    sed -i "s/^theta.theta_${h}_hyper_scale_fixed,[^,]*/theta.theta_${h}_hyper_scale_fixed,${v}/" \
+        tfs_configure_priors.csv
+    grep "^theta.theta_${h}_hyper_scale_fixed," tfs_configure_priors.csv
+done
+
+if [[ -n "${SIGMA_FIXED}" ]]; then
+    grep -q '^growth.sample_offset.sigma_fixed,' tfs_configure_priors.csv
+    sed -i "s/^growth.sample_offset.sigma_fixed,[^,]*/growth.sample_offset.sigma_fixed,${SIGMA_FIXED}/" \
+        tfs_configure_priors.csv
+    echo "sample_offset sigma held at ${SIGMA_FIXED}"
+    grep '^growth.sample_offset' tfs_configure_priors.csv
+fi
+
+case "${ARM}" in
+    map) FIT_ARGS=(--analysis_method map)
+         SAMPLE_ARGS=(--map_point) ;;
+    svi) FIT_ARGS=(--analysis_method svi
+                   --guide_type auto_low_rank_multivariate_normal
+                   --guide_rank "${GUIDE_RANK}" --pre_map_num_epoch 0)
+         SAMPLE_ARGS=(--num_posterior_samples "${NUM_SAMPLES}"
+                      --sampling_batch_size "${SAMPLING_BATCH:-25}") ;;
+    *) echo "unknown ARM '${ARM}'" >&2; exit 1 ;;
+esac
+if [[ -n "${INIT_FROM}" ]]; then
+    echo ">>> Checking the start against ${INIT_FROM} $(date)"
+    python ../check_warm.py "${INIT_FROM}"
+    FIT_ARGS+=(--init_from "${INIT_FROM}")
+fi
+if [[ -n "${ADAM_STEP}" ]]; then
+    FIT_ARGS+=(--adam_step_size "${ADAM_STEP}")
+fi
+
+echo ">>> Fit (${ARM}) $(date)"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" \
+    "${FIT_ARGS[@]}" "${EPOCH_ARGS[@]}"
+
+echo ">>> Sample posterior (${ARM}) $(date)"
+/usr/bin/time -v tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    "${SAMPLE_ARGS[@]}" --skip_growth_observations
+
+echo ">>> Extract parameters $(date)"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict X $(date)"
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict growth (subset) $(date)"
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+    --num_marginal_samples=500 --subset_genotypes --genotype_batch_size 2000
+
+echo ">>> Done $(date)"
diff --git a/planning/studies/real-data-fit/fit/set_growth_priors.py b/planning/studies/real-data-fit/fit/set_growth_priors.py
new file mode 100644
index 00000000..48d0a224
--- /dev/null
+++ b/planning/studies/real-data-fit/fit/set_growth_priors.py
@@ -0,0 +1,88 @@
+"""
+Per-condition k and m priors for the growth-only relative fit (hill_relative).
+
+    python ../set_growth_priors.py loose|monokan [priors_csv]
+
+tfs-prefit-calibration refuses growth-only models, so these priors are
+written by hand into tfs_configure_priors.csv as indexed rows (one per
+condition_rep), which configuration_io joins to the model's conditions by
+name.
+
+In the X gauge (wt X = 1 at 0 IPTG, X = 0 at 1 mM; g = k + dk + m X, wt's
+dk pinned at 0), wt grows at k + m at 0 IPTG and at k at 1 mM, so
+
+    k = wt rate at 1 mM,   m = wt rate at 0 - wt rate at 1 mM.
+
+loose:   every condition k ~ N(0.015, 0.01), m ~ N(0, 0.01).
+monokan: kanR+kan and kanR-kan from the wt monoculture rates
+         (inputs/wt_control_rate_summary.csv, a copy of
+         ../processed/'s), SD the standard error
+         of the replicate mean floored at 0.002 per min (the prefit's k
+         scale; day-to-day and library-vs-monoculture differences are at
+         least that). pheS conditions loose, as above. Mike, 2026-09-30:
+         library wt grows on in pheS+4CP at high IPTG where the
+         monoculture stops, so the monoculture cannot be its prior.
+"""
+
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+
+CONDITIONS = ["kanR+kan", "kanR-kan", "pheS+4CP", "pheS-4CP"]
+LOOSE = dict(k=(0.015, 0.01), m=(0.0, 0.01))
+SD_FLOOR = 0.002
+MONO = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                    "inputs", "wt_control_rate_summary.csv")
+
+
+def monoculture(cond):
+    r = pd.read_csv(MONO)
+    r = r[r.condition_sel == cond].set_index("titrant_conc")
+    lo, hi = r.loc[0.0], r.loc[1.0]
+    se_lo = lo.rate_sd / np.sqrt(lo.num_replicates)
+    se_hi = hi.rate_sd / np.sqrt(hi.num_replicates)
+    k = (hi.rate_mean, max(se_hi, SD_FLOOR))
+    m = (lo.rate_mean - hi.rate_mean, max(np.hypot(se_lo, se_hi), SD_FLOOR))
+    return dict(k=k, m=m)
+
+
+def main():
+    which = sys.argv[1]
+    path = sys.argv[2] if len(sys.argv) > 2 else "tfs_configure_priors.csv"
+    assert which in ("loose", "monokan"), which
+    p = pd.read_csv(path)
+    for c in ("flat_index", "condition_rep"):
+        if c not in p.columns:
+            p[c] = np.nan
+
+    table = {}
+    for cond in CONDITIONS:
+        use_mono = which == "monokan" and cond.startswith("kanR")
+        table[cond] = monoculture(cond) if use_mono else LOOSE
+
+    pre = "growth.condition_growth."
+    fields = {
+        "k_loc": [table[c]["k"][0] for c in CONDITIONS],
+        "k_scale": [table[c]["k"][1] for c in CONDITIONS],
+        "m_loc": [table[c]["m"][0] for c in CONDITIONS],
+        # each condition reads the one its +/- flag names; both carry the
+        # same per-condition values so the flag cannot pick a wrong one
+        "m_scale_plus": [table[c]["m"][1] for c in CONDITIONS],
+        "m_scale_minus": [table[c]["m"][1] for c in CONDITIONS],
+    }
+    p = p[~p.parameter.isin([pre + f for f in fields])]
+    rows = [dict(parameter=pre + f, value=v, flat_index=float(i), condition_rep=c)
+            for f, vals in fields.items() for i, (c, v) in enumerate(zip(CONDITIONS, vals))]
+    p = pd.concat([p, pd.DataFrame(rows)], ignore_index=True)
+    p.to_csv(path, index=False)
+
+    show = pd.DataFrame({f: vals for f, vals in fields.items()}, index=CONDITIONS)
+    print(f"{which} priors written to {path}:")
+    print(show.drop(columns="m_scale_minus").rename(
+        columns={"m_scale_plus": "m_scale"}).round(5).to_string())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/processing/binding_fit.py b/planning/studies/real-data-fit/processing/binding_fit.py
new file mode 100644
index 00000000..84fa8e64
--- /dev/null
+++ b/planning/studies/real-data-fit/processing/binding_fit.py
@@ -0,0 +1,323 @@
+"""
+Joint fit of the anisotropy data: anisotropy -> theta and Hill curves at once.
+
+For every well (batch b = day x biorep, genotype g, protein P, IPTG c):
+
+    r = f_b + s_b * delta * theta_gP(c) + noise
+
+- f_b: the batch's free-DNA level. Free-DNA wells (protein 0, DNA present)
+  are observations with theta = 0.
+- s_b: the batch's scale, log s_b ~ N(0, 0.25). The two wt-titration
+  batches define s = 1.
+- delta: fully bound minus free, shared by all batches.
+- theta_gP(c) = theta_hi + (theta_lo - theta_hi) / (1 + (c / K)^n), a
+  repressor curve per genotype and protein concentration.
+- wt: theta_lo(P) = P / (Kd + P), a binding isotherm across 0.02-80 uM,
+  which is what sets delta. wt's control wells in the mutant file share
+  wt's curves at their protein concentration.
+
+Wells are the mean of their six reads. Residuals use a robust (soft L1)
+loss with a noise scale re-estimated from the fit, so a few bad wells do
+not move a batch. Excluded: buffer-only wells, DNA-free wells, V95N at
+20 uM (reads far above the bound level, no IPTG response: aggregation).
+
+Then each genotype's Hill parameters are interpolated to TARGET_PROTEIN_UM
+on log protein concentration, and the curve is written at the screen's
+IPTG concentrations as a binding_df for tfs-configure-model.
+
+Run from this directory:  python binding_fit.py
+"""
+
+import os
+
+import numpy as np
+import pandas as pd
+from scipy.optimize import least_squares
+from scipy.special import expit, logit
+
+OUT = "processed"
+TARGET_PROTEIN_UM = 10.0
+SCREEN_IPTG_MM = [0.0, 1e-4, 1e-3, 3e-3, 0.01, 0.03, 0.1, 1.0]
+EXCLUDE = [("V95N", 20.0)]
+REFERENCE_SOURCE = "wt_20260826"
+CURVE_PARAMS = 4       # theta_lo, theta_hi, K, n
+
+# weak priors, in the fit's parameter units
+PRIOR = {"a": (0.0, 3.0), "b": (0.0, 3.0),          # logit theta_lo / hi
+         "logK": (np.log(0.01), 3.0),               # ln mM
+         "logn": (np.log(1.5), 0.7),
+         "logs": (0.0, 0.25),
+         "f": (0.138, 0.005)}
+
+
+def load_wells():
+    w = pd.read_csv(os.path.join(OUT, "binding_wells.csv"))
+    w = w[w["Buffer_only"] != 1]
+    has_dna = (w["dna_nM"].isna() & (w["source"] == REFERENCE_SOURCE)) \
+        | (w["dna_nM"] > 0)
+    w = w[has_dna].copy()
+    w["batch"] = w["Day"].astype(str) + "_b" + w["Biorep"].astype(int).astype(str)
+    w["free"] = w["protein_uM_dimer"] == 0
+    w = w[w["free"] | (w["genotype"] != "blank")]
+    for g, p in EXCLUDE:
+        w = w[~((w["genotype"] == g) & (w["protein_uM_dimer"] == p))]
+    return w.reset_index(drop=True)
+
+
+class Model:
+    def __init__(self, wells):
+        self.w = wells
+        self.batches = sorted(wells["batch"].unique())
+        ref = wells.loc[wells["source"] == REFERENCE_SOURCE, "batch"].unique()
+        self.ref_batches = set(ref)
+        curves = (wells[~wells["free"]]
+                  .groupby(["genotype", "protein_uM_dimer"]).size().index)
+        self.curves = [c for c in curves if c[0] != "wt"]
+        self.wt_P = sorted(wells.loc[(wells["genotype"] == "wt")
+                                     & ~wells["free"], "protein_uM_dimer"]
+                           .unique())
+
+        names = ["log_delta", "log_Kd"]
+        for g, p in self.curves:
+            names += [f"{g}|{p}|{k}" for k in ("a", "b", "logK", "logn")]
+        for p in self.wt_P:
+            names += [f"wt|{p}|{k}" for k in ("b", "logK", "logn")]
+        for b in self.batches:
+            names.append(f"f|{b}")
+            if b not in self.ref_batches:
+                names.append(f"logs|{b}")
+        self.names = names
+        self.idx = {n: i for i, n in enumerate(names)}
+
+    def x0(self):
+        x = np.zeros(len(self.names))
+        x[self.idx["log_delta"]] = np.log(0.032)
+        x[self.idx["log_Kd"]] = np.log(1.0)
+        for n, i in self.idx.items():
+            key = n.split("|")[-1]
+            if key == "a":
+                x[i] = 1.0
+            elif key == "b":
+                x[i] = -2.0
+            elif key == "logK":
+                x[i] = np.log(0.01)
+            elif key == "logn":
+                x[i] = np.log(1.5)
+            elif n.startswith("f|"):
+                x[i] = 0.138
+        return x
+
+    def curve_params(self, x, g, p):
+        """theta_lo, theta_hi, K, n for one genotype and protein conc."""
+        if g == "wt":
+            kd = np.exp(x[self.idx["log_Kd"]])
+            lo = p / (kd + p)
+            hi = expit(x[self.idx[f"wt|{p}|b"]])
+            K = np.exp(x[self.idx[f"wt|{p}|logK"]])
+            n = np.exp(x[self.idx[f"wt|{p}|logn"]])
+        else:
+            lo = expit(x[self.idx[f"{g}|{p}|a"]])
+            hi = expit(x[self.idx[f"{g}|{p}|b"]])
+            K = np.exp(x[self.idx[f"{g}|{p}|logK"]])
+            n = np.exp(x[self.idx[f"{g}|{p}|logn"]])
+        return lo, hi, K, n
+
+    @staticmethod
+    def hill(lo, hi, K, n, c):
+        c = np.asarray(c, dtype=float)
+        frac = np.where(c > 0, (np.maximum(c, 1e-300) / K) ** n, 0.0)
+        return hi + (lo - hi) / (1.0 + frac)
+
+    def theta(self, x):
+        w = self.w
+        th = np.zeros(len(w))
+        for (g, p), rows in w[~w["free"]].groupby(["genotype",
+                                                   "protein_uM_dimer"]).groups.items():
+            lo, hi, K, n = self.curve_params(x, g, p)
+            th[rows] = self.hill(lo, hi, K, n, w.loc[rows, "iptg_mM"])
+        return th
+
+    def predict(self, x):
+        w = self.w
+        f = np.array([x[self.idx[f"f|{b}"]] for b in w["batch"]])
+        s = np.array([1.0 if b in self.ref_batches
+                      else np.exp(x[self.idx[f"logs|{b}"]]) for b in w["batch"]])
+        delta = np.exp(x[self.idx["log_delta"]])
+        return f + s * delta * self.theta(x)
+
+    def residuals(self, x, sigma):
+        res = [(self.w["r_mean"].to_numpy() - self.predict(x)) / sigma]
+        pri = []
+        for n, i in self.idx.items():
+            key = "f" if n.startswith("f|") else \
+                "logs" if n.startswith("logs|") else n.split("|")[-1]
+            if key in PRIOR:
+                mu, sd = PRIOR[key]
+                pri.append((x[i] - mu) / sd)
+        return np.concatenate(res + [np.array(pri)])
+
+
+def fit(model):
+    x = model.x0()
+    sigma = 0.002
+    nobs = len(model.w)
+    for _ in range(3):
+        sol = least_squares(model.residuals, x, args=(sigma,),
+                            loss="soft_l1", f_scale=2.0, method="trf",
+                            max_nfev=20000)
+        x = sol.x
+        resid = model.w["r_mean"].to_numpy() - model.predict(x)
+        sigma = 1.4826 * np.median(np.abs(resid - np.median(resid)))
+    J = sol.jac
+    cov = np.linalg.pinv(J.T @ J)
+    return x, cov, sigma, sol, resid[:nobs]
+
+
+def curve_table(model, x, cov):
+    rows = []
+    se = np.sqrt(np.diag(cov))
+    for g, p in list(model.curves) + [("wt", p) for p in model.wt_P]:
+        lo, hi, K, n = model.curve_params(x, g, p)
+        row = dict(genotype=g, protein_uM_dimer=p, theta_lo=lo, theta_hi=hi,
+                   log_K_mM=np.log(K), hill_n=n)
+        for k in ("a", "b", "logK", "logn"):
+            name = f"{g}|{p}|{k}"
+            if name in model.idx:
+                row[f"{k}_se"] = se[model.idx[name]]
+        rows.append(row)
+    return pd.DataFrame(rows)
+
+
+def interpolate(curves, target=TARGET_PROTEIN_UM):
+    """Hill parameters at the target protein conc, linear in ln(protein)."""
+    out = []
+    for g, d in curves.groupby("genotype"):
+        d = d.set_index("protein_uM_dimer").sort_index()
+        lower = [p for p in d.index if p <= target]
+        upper = [p for p in d.index if p >= target]
+        if not lower or not upper:
+            continue
+        p0, p1 = max(lower), min(upper)
+        t = 0.0 if p0 == p1 else (np.log(target) - np.log(p0)) / \
+            (np.log(p1) - np.log(p0))
+        r0, r1 = d.loc[p0], d.loc[p1]
+        clip = lambda v: np.clip(v, 1e-4, 1 - 1e-4)
+        lg = lambda k: (1 - t) * logit(clip(r0[k])) + t * logit(clip(r1[k]))
+        lin = lambda k: (1 - t) * r0[k] + t * r1[k]
+        out.append(dict(genotype=g, protein_uM_dimer=target,
+                        bracket=f"{p0:g}-{p1:g}",
+                        theta_lo=expit(lg("theta_lo")),
+                        theta_hi=expit(lg("theta_hi")),
+                        log_K_mM=lin("log_K_mM"),
+                        hill_n=np.exp(lin_log(r0, r1, t))))
+    return pd.DataFrame(out)
+
+
+def lin_log(r0, r1, t):
+    return (1 - t) * np.log(r0["hill_n"]) + t * np.log(r1["hill_n"])
+
+
+def curve_sd(model, x, cov, interp, well_sd, step=1e-5):
+    """
+    theta_std for each (genotype, screen IPTG) point of the 10 uM curves.
+
+    The curve's own uncertainty, by linear propagation of the fit's
+    covariance through the interpolation in theta itself (J C J^T; sampling
+    the logit parameters blew up where a plateau sits at theta = 1, M42I at
+    20 uM), added in quadrature to the well noise, then scaled by
+    sqrt(points / curve parameters): a curve's points are correlated,
+    carrying about CURVE_PARAMS independent values, and the binding
+    likelihood treats them as independent. The interpolation's model error
+    (linear in ln protein) is not included.
+    """
+    def values(xv):
+        ci = interpolate(curve_table(model, xv, cov)).set_index("genotype")
+        return np.concatenate([
+            Model.hill(ci.loc[g, "theta_lo"], ci.loc[g, "theta_hi"],
+                       np.exp(ci.loc[g, "log_K_mM"]), ci.loc[g, "hill_n"],
+                       SCREEN_IPTG_MM)
+            for g in interp["genotype"]])
+
+    base = values(x)
+    J = np.zeros((len(base), len(x)))
+    for k in range(len(x)):
+        xp = x.copy()
+        xp[k] += step
+        J[:, k] = (values(xp) - base) / step
+    var = np.einsum("ij,jk,ik->i", J, cov, J)
+    inflate = np.sqrt(len(SCREEN_IPTG_MM) / CURVE_PARAMS)
+    sd = inflate * np.sqrt(np.maximum(var, 0) + well_sd ** 2)
+    n = len(SCREEN_IPTG_MM)
+    return {g: sd[i * n:(i + 1) * n] for i, g in enumerate(interp["genotype"])}
+
+
+def binding_df(interp, sd_by_genotype):
+    rows = []
+    for _, r in interp.iterrows():
+        th = Model.hill(r["theta_lo"], r["theta_hi"], np.exp(r["log_K_mM"]),
+                        r["hill_n"], SCREEN_IPTG_MM)
+        sd = sd_by_genotype[r["genotype"]]
+        for c, t, s in zip(SCREEN_IPTG_MM, th, sd):
+            rows.append(dict(genotype=r["genotype"], titrant_name="iptg",
+                             titrant_conc=c, theta_obs=float(t),
+                             theta_std=float(s)))
+    return pd.DataFrame(rows)
+
+
+def main():
+    wells = load_wells()
+    model = Model(wells)
+    print(f"{len(wells)} wells, {len(model.batches)} batches, "
+          f"{len(model.curves)} mutant curves, wt at {model.wt_P} uM, "
+          f"{len(model.names)} parameters")
+    x, cov, sigma, sol, resid = fit(model)
+    print(f"converged: {sol.status > 0} ({sol.message}); well noise "
+          f"{sigma:.4f} in r; delta {np.exp(x[model.idx['log_delta']]):.4f}; "
+          f"wt Kd {np.exp(x[model.idx['log_Kd']]):.3g} uM")
+
+    se = np.sqrt(np.diag(cov))
+    delta = np.exp(x[model.idx["log_delta"]])
+    batches = []
+    for b in model.batches:
+        f = x[model.idx[f"f|{b}"]]
+        if b in model.ref_batches:
+            s, s_se = 1.0, 0.0
+        else:
+            i = model.idx[f"logs|{b}"]
+            s, s_se = np.exp(x[i]), np.exp(x[i]) * se[i]
+        batches.append(dict(batch=b, free=f, free_se=se[model.idx[f"f|{b}"]],
+                            scale=s, scale_se=s_se,
+                            num_wells=int((wells["batch"] == b).sum())))
+    batches = pd.DataFrame(batches)
+    print(batches.round(4).to_string(index=False))
+
+    # per-well theta on the common scale, for plots and checks
+    f = np.array([x[model.idx[f"f|{b}"]] for b in wells["batch"]])
+    s = wells["batch"].map(batches.set_index("batch")["scale"]).to_numpy()
+    wells["theta"] = (wells["r_mean"] - f) / (s * delta)
+    wells["theta_fit"] = model.theta(x)
+    wells["resid_r"] = resid
+    wells["resid_z"] = resid / sigma
+
+    curves = curve_table(model, x, cov)
+    interp = interpolate(curves)
+
+    bdf = binding_df(interp, curve_sd(model, x, cov, interp, sigma / delta))
+
+    batches.to_csv(os.path.join(OUT, "binding_batches.csv"), index=False)
+    wells.to_csv(os.path.join(OUT, "binding_wells_theta.csv"), index=False)
+    curves.to_csv(os.path.join(OUT, "binding_curves.csv"), index=False)
+    interp.to_csv(os.path.join(OUT, "binding_curves_10uM.csv"), index=False)
+    bdf.to_csv(os.path.join(OUT, "binding_df_10uM.csv"), index=False)
+    print(curves.round(3).to_string(index=False))
+    print(interp.round(3).to_string(index=False))
+    print(bdf.pivot(index="titrant_conc", columns="genotype",
+                    values="theta_std").round(3).to_string())
+    print("worst wells (|z| > 4):")
+    print(wells.loc[wells["resid_z"].abs() > 4,
+                    ["batch", "genotype", "protein_uM_dimer", "iptg_mM",
+                     "r_mean", "resid_z"]].round(4).to_string(index=False))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/processing/binding_plot.py b/planning/studies/real-data-fit/processing/binding_plot.py
new file mode 100644
index 00000000..e76a53b6
--- /dev/null
+++ b/planning/studies/real-data-fit/processing/binding_plot.py
@@ -0,0 +1,52 @@
+"""Per-genotype check of binding_fit.py: well theta, fitted curves, 10 uM."""
+import os
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("pdf")
+import matplotlib.pyplot as plt
+from binding_fit import Model, OUT
+
+plt.rc('pdf', fonttype=42)
+plt.rc('font', size=14)
+plt.rc('axes', titlesize=14, labelsize=16)
+COLORS = {0.02: "#CC79A7", 0.2: "#56B4E9", 2.0: "#E69F00", 20.0: "#0072B2",
+          80.0: "#009E73"}
+
+w = pd.read_csv(os.path.join(OUT, "binding_wells_theta.csv"))
+w = w[~w["free"]]
+curves = pd.read_csv(os.path.join(OUT, "binding_curves.csv"))
+interp = pd.read_csv(os.path.join(OUT, "binding_curves_10uM.csv"))
+genos = ["wt", "M42I", "H74A", "K84L", "M42I/H74A", "H74A/K84L", "G65H", "V95N"]
+c = np.logspace(-6.5, 0.8, 200)
+xfloor = 3e-7
+
+fig, axes = plt.subplots(2, 4, figsize=(18, 9), sharey=True)
+for ax, g in zip(axes.ravel(), genos):
+    for p, d in w[w["genotype"] == g].groupby("protein_uM_dimer"):
+        x = np.where(d["iptg_mM"] > 0, d["iptg_mM"], xfloor)
+        ax.scatter(x, d["theta"], s=14, color=COLORS[p], alpha=0.7,
+                   label=f"{p:g} uM")
+    for _, r in curves[curves["genotype"] == g].iterrows():
+        y = Model.hill(r.theta_lo, r.theta_hi, np.exp(r.log_K_mM), r.hill_n, c)
+        ax.plot(c, y, color=COLORS[r.protein_uM_dimer], lw=1.5)
+    for _, r in interp[interp["genotype"] == g].iterrows():
+        y = Model.hill(r.theta_lo, r.theta_hi, np.exp(r.log_K_mM), r.hill_n, c)
+        ax.plot(c, y, color="#000000", lw=2, ls="--", label="10 uM")
+    ax.set_xscale("log")
+    ax.set_title(g)
+    ax.axhline(0, color="0.7", lw=0.8)
+    ax.axhline(1, color="0.7", lw=0.8)
+for ax in axes[1]:
+    ax.set_xlabel("IPTG (mM); 0 at left edge")
+for ax in axes[:, 0]:
+    ax.set_ylabel("theta (fraction bound)")
+handles, labels = [], []
+for ax in axes.ravel():
+    for h, l in zip(*ax.get_legend_handles_labels()):
+        if l not in labels:
+            handles.append(h); labels.append(l)
+fig.legend(handles, labels, loc="upper right", ncol=6, frameon=False)
+fig.tight_layout(rect=(0, 0, 1, 0.95))
+fig.savefig(os.path.join(OUT, "binding_fit.pdf"))
+print("wrote", os.path.join(OUT, "binding_fit.pdf"))
diff --git a/planning/studies/real-data-fit/processing/prep_dev_data.py b/planning/studies/real-data-fit/processing/prep_dev_data.py
new file mode 100644
index 00000000..600681ae
--- /dev/null
+++ b/planning/studies/real-data-fit/processing/prep_dev_data.py
@@ -0,0 +1,349 @@
+"""
+Turn the raw dev-data into pipeline inputs, written to processed/.
+
+Lab-specific choices live here (see README.md, "Answers from Mike"):
+
+- initial-kan and initial-phe count files are swapped (data-level evidence;
+  origin unknown), so each is assigned to the other library.
+- Resequenced files of one tube are the same PCR product and are summed.
+- Tubes with fewer than MIN_READS called reads after pooling are dropped.
+- OD600 is the measurement; sample_df's lncfu columns are ignored.
+- Tube totals: calibrated CFU/mL times TUBE_VOLUME_ML.
+
+Writes processed/sample_df.csv (the tube table with OD600, the totals and
+read counts, for the study scripts) and, for the documented chain
+(fit/run_recipe.srun), processed/tube_table.csv and processed/tube_od600.csv,
+which tfs-process-counts turns into the growth table with the calibration.
+
+Run from this directory:  python prep_dev_data.py
+"""
+
+import glob
+import os
+import re
+
+import numpy as np
+import pandas as pd
+import yaml
+
+from tfscreen.process_raw.od600 import (
+    cfu_per_mL_error_components,
+    od600_to_cfu_per_mL,
+    read_calibration,
+)
+
+OUT = "processed"
+MIN_READS = 1_000_000
+TUBE_VOLUME_ML = 5.0
+
+# Split: shared culture at OD600 ~0.35, 200 uL into 15 mL (Mike, approximate)
+PRESPLIT_OD600 = 0.35
+PRESPLIT_DILUTION = 0.2 / 15.2
+
+LIBRARY_DESIGN = {
+    "transform_sizes": {"single-1": 100_000, "single-2": 100_000,
+                        "double-1-2": 300_000, "spiked": 1_000},
+    "library_mixture": {"single-1": 100, "single-2": 100,
+                        "double-1-2": 1_000, "spiked": 1},
+}
+
+
+def canonical(name):
+    """Sample name with '.' decimals as 'o' and any barcode suffix dropped."""
+    name = re.sub(r"(\d)\.(\d)", r"\1o\2", name)
+    name = re.sub(r"-[ACGT]{8,}-[ACGT]{8,}$", "", name)
+    return name.replace("initial_", "initial-")
+
+
+# ---------------------------------------------------------------------------
+# library config
+# ---------------------------------------------------------------------------
+
+# The spikes actually in the experiment (Mike, 2026-09-30), as codon edits
+# of wt_seq ('.' keeps the wt base). counts/run_config.yaml, the processing
+# config, lists only the three the library design cannot make (H74A/K84L,
+# M42I/H74A/K84L, D88A), and its M42I codon is att, not the spikes' ata.
+# The reads were still called right: tfs-process-fastq ran with
+# --allowed_diff_from_expected 2, and every spike is 0-2 mismatches from a
+# unique expected sequence of the same genotype. But the fit needs all nine
+# as spiked (in_spiked_origin: ln_cfu0 prior class, bulk_fraction).
+_P = "." * 45
+SPIKE_EDITS = {
+    "wt": "",
+    "M42I": _P + "ata",
+    "H74A": "." * 141 + "gcg",
+    "K84L": "." * 171 + "ctg",
+    "M42I/H74A": _P + "ata" + "." * 93 + "gcg",
+    "M42I/K84L": _P + "ata" + "." * 123 + "ctg",
+    "H74A/K84L": "." * 141 + "gcg" + "." * 27 + "ctg",
+    "M42I/H74A/K84L": _P + "ata" + "." * 93 + "gcg" + "." * 27 + "ctg",
+    "D88A": "." * 184 + "cc",
+}
+
+
+def spiked_seqs(wt_seq):
+    out = []
+    for edit in SPIKE_EDITS.values():
+        edit = edit.ljust(len(wt_seq), ".")
+        assert len(edit) == len(wt_seq)
+        out.append("".join(w if e == "." else e for w, e in zip(wt_seq, edit)))
+    return out
+
+
+def library_config():
+    with open("counts/run_config.yaml") as f:
+        cfg = yaml.safe_load(f)
+    cfg.update(LIBRARY_DESIGN)
+    cfg["spiked_seqs"] = spiked_seqs(cfg["wt_seq"])
+    path = os.path.join(OUT, "library_config.yaml")
+    with open(path, "w") as f:
+        f.write("# counts/run_config.yaml plus the realized library design\n"
+                "# (serial dilution matched the spec; Mike, 2026-09-28) and\n"
+                "# the nine spikes actually used (Mike, 2026-09-30; see\n"
+                "# SPIKE_EDITS in prep_dev_data.py)\n")
+        yaml.safe_dump(cfg, f, sort_keys=False)
+    return path
+
+
+# ---------------------------------------------------------------------------
+# OD600 calibration
+# ---------------------------------------------------------------------------
+
+def od600_calibration():
+    d = "od600-to-cfu/"
+    r = pd.read_excel(d + "2025-09-17_od600-serial-dilution-of-keio-cells-"
+                      "with-10x-technical-replicate.xlsx",
+                      sheet_name="1-3dilution", header=None)
+    reps = pd.DataFrame([(3.0 ** -i, v) for i, row in r.iterrows()
+                         for v in row.values], columns=["dilution", "od600"])
+    p = pd.read_excel(d + "2025-09-19_od600-vs-cfu-per-mL-by-plate-"
+                      "counting.xlsx")
+    plates = pd.DataFrame(dict(od600=p["OD600 plate reader"],
+                               colonies=p["CFU"], dilution=p["Dilution"],
+                               plated_volume_mL=p["Volume_mL"],
+                               num_dilutions=p["num_dilutions"],
+                               plating_steps=p["plating_steps"],
+                               day=p["replicate"]))
+    reps.to_csv(os.path.join(OUT, "od600_replicates.csv"), index=False)
+    plates.to_csv(os.path.join(OUT, "od600_plate_counts.csv"), index=False)
+    prefix = os.path.join(OUT, "od600_calibration")
+    from tfscreen.process_raw.scripts.calibrate_od600_cli import calibrate_od600
+    calibrate_od600(os.path.join(OUT, "od600_replicates.csv"),
+                    os.path.join(OUT, "od600_plate_counts.csv"),
+                    out_prefix=prefix)
+    return read_calibration(prefix + ".yaml")
+
+
+# ---------------------------------------------------------------------------
+# counts: pool resequencing, fix the initial swap, drop failed tubes
+# ---------------------------------------------------------------------------
+
+def pooled_counts():
+    files = {}
+    for f in sorted(glob.glob("counts/counts/counts_*.csv")):
+        name = canonical(re.match(r".*counts_(.*?)_S\d+_", f).group(1))
+        files.setdefault(name, []).append(f)
+
+    pooled = {}
+    for name, fs in files.items():
+        df = pd.concat([pd.read_csv(f) for f in fs])
+        pooled[name] = (df.groupby("genotype", as_index=False)["counts"].sum(),
+                        [os.path.basename(f) for f in fs])
+
+    # the two initial files are swapped (README: initial-kan has the pheS
+    # library's composition)
+    pooled["initial-kan"], pooled["initial-phe"] = (pooled["initial-phe"],
+                                                    pooled["initial-kan"])
+    return pooled
+
+
+# ---------------------------------------------------------------------------
+# sample table
+# ---------------------------------------------------------------------------
+
+def sample_table(cal, pooled):
+    s = pd.read_csv("sample_df.csv")
+    s["sample"] = s["sample"].map(canonical)
+    s = s[~s["sample"].str.startswith("initial")]
+
+    # phe-1-0o03-2-2 has counts and OD600 but no row: rebuild it from its
+    # replicate's other rows and the OD600 table
+    od = pd.read_excel("all_reps_screen_od600.xlsx")
+    od["condition_sel"] = od["condition_sel"].str.replace("4cp", "4CP")
+    if "phe-1-0o03-2-2" not in set(s["sample"]):
+        row = s[s["sample"] == "phe-1-0o03-1-2"].iloc[0].copy()
+        hit = od[(od["rep"] == 2) & (od["condition_sel"] == "pheS+4CP")
+                 & np.isclose(od["titrant_conc"], 0.03)]
+        t = sorted(hit["time"])[1]
+        row["sample"] = "phe-1-0o03-2-2"
+        row["t_sel"] = t
+        row["od600"] = float(hit.loc[hit["time"] == t, "od600"].iloc[0])
+        s = pd.concat([s, row.to_frame().T], ignore_index=True)
+
+    # the sheet's od600 must match the raw OD600 table
+    chk = s.merge(od, left_on=["rep", "condition_sel", "titrant_conc", "t_sel"],
+                  right_on=["rep", "condition_sel", "titrant_conc", "time"],
+                  suffixes=("", "_raw"))
+    assert len(chk) == len(s), "every sequenced tube has a raw OD600"
+    assert np.allclose(chk["od600"].astype(float), chk["od600_raw"])
+
+    od600 = s["od600"].astype(float).to_numpy()
+    cfu_mL, cfu_mL_sd, detectable = od600_to_cfu_per_mL(od600, cal)
+    curve_sd, reading_sd = cfu_per_mL_error_components(od600, cal)
+    assert detectable.all()
+
+    reads = {n: int(p[0]["counts"].sum()) for n, p in pooled.items()}
+    s["called_reads"] = s["sample"].map(reads)
+    s["count_files"] = s["sample"].map(lambda n: ";".join(pooled[n][1])
+                                       if n in pooled else "")
+
+    out = pd.DataFrame({
+        "sample": s["sample"],
+        "library": s["library"],
+        "replicate": s["rep"].astype(int),
+        "condition_pre": s["condition_pre"],
+        "t_pre": s["t_pre"].astype(int),
+        "condition_sel": s["condition_sel"],
+        "t_sel": s["t_sel"].astype(int),
+        "titrant_name": s["titrant_name"],
+        "titrant_conc": s["titrant_conc"].astype(float),
+        "od600": od600,
+        "sample_cfu": cfu_mL * TUBE_VOLUME_ML,
+        "sample_cfu_std": cfu_mL_sd * TUBE_VOLUME_ML,
+        "sample_cfu_curve_std": curve_sd * TUBE_VOLUME_ML,
+        "sample_cfu_reading_std": reading_sd * TUBE_VOLUME_ML,
+        "called_reads": s["called_reads"],
+        "count_files": s["count_files"],
+    })
+    dropped = out[out["called_reads"].fillna(0) < MIN_READS]
+    kept = out[out["called_reads"].fillna(0) >= MIN_READS]
+    return kept.sort_values("sample").reset_index(drop=True), dropped
+
+
+TUBE_TABLE_COLUMNS = ["sample", "library", "replicate", "condition_pre",
+                      "t_pre", "condition_sel", "t_sel", "titrant_name",
+                      "titrant_conc"]
+
+
+def write_counts(pooled, samples):
+    d = os.path.join(OUT, "counts")
+    os.makedirs(d, exist_ok=True)
+    for name in samples["sample"]:
+        pooled[name][0].to_csv(os.path.join(d, f"counts__{name}__.csv"),
+                               index=False)
+
+
+def initial_composition(pooled):
+    rows = []
+    for name, lib in (("initial-kan", "kanR"), ("initial-phe", "pheS")):
+        df = pooled[name][0].copy()
+        df["library"] = lib
+        called = df.loc[df["genotype"] != "__unknown__", "counts"].sum()
+        df["frequency"] = df["counts"] / called
+        df.loc[df["genotype"] == "__unknown__", "frequency"] = np.nan
+        df["source_files"] = ";".join(pooled[name][1])
+        rows.append(df)
+    return pd.concat(rows, ignore_index=True)
+
+
+# ---------------------------------------------------------------------------
+# wt monoculture controls
+# ---------------------------------------------------------------------------
+
+def wt_controls(cal):
+    c = pd.read_excel("2025-10-02_control-experiments.xlsx")
+    c.columns = ["condition_pre", "t_pre", "condition_pre_dup",
+                 "condition_sel", "t_sel", "titrant_name", "titrant_conc",
+                 "od600", "replicate", "library", "genotype"]
+    c = c.drop(columns="condition_pre_dup")
+    cfu_mL, cfu_mL_sd, detectable = od600_to_cfu_per_mL(c["od600"].to_numpy(),
+                                                        cal)
+    c["cfu_per_mL"] = cfu_mL
+    c["ln_cfu_per_mL"] = np.log(cfu_mL)
+    c["ln_cfu_per_mL_std"] = cfu_mL_sd / cfu_mL
+    c["detectable"] = detectable
+
+    rows = []
+    for key, g in c.groupby(["condition_sel", "titrant_conc", "replicate"]):
+        g = g[g["detectable"]]
+        if g["t_sel"].nunique() < 2:
+            continue
+        (slope, icpt), cov = np.polyfit(g["t_sel"], g["ln_cfu_per_mL"], 1,
+                                        cov=g["t_sel"].nunique() > 2)
+        se = np.sqrt(cov[0, 0]) if np.ndim(cov) == 2 else np.nan
+        rows.append(dict(zip(["condition_sel", "titrant_conc", "replicate"],
+                             key), rate=slope, rate_se=se,
+                         num_timepoints=g["t_sel"].nunique()))
+    rates = pd.DataFrame(rows)
+    summary = (rates.groupby(["condition_sel", "titrant_conc"])["rate"]
+               .agg(rate_mean="mean", rate_sd="std", num_replicates="size")
+               .reset_index())
+    return c, rates, summary
+
+
+# ---------------------------------------------------------------------------
+# binding: tidy anisotropy (no conversion to theta yet)
+# ---------------------------------------------------------------------------
+
+def binding_tidy():
+    w = pd.read_csv("binding/clean_anisotropy_df_20260826.csv")
+    w["Day"] = "20260826"
+    w["source"] = "wt_20260826"
+    m = pd.read_csv("binding/clean_anisotropy_df_20260923-muts.csv")
+    m["source"] = "muts_20260923"
+    m["genotype"] = m["genotype"].fillna("blank")
+    cols = ["source", "Day", "Biorep", "well", "genotype", "protein_conc",
+            "DNA", "Buffer_only", "IPTG_conc", "read", "r"]
+    both = pd.concat([w[cols], m[cols]], ignore_index=True)
+    both = both.rename(columns={"protein_conc": "protein_uM_dimer",
+                                "DNA": "dna_nM", "IPTG_conc": "iptg_mM"})
+    # wt file records DNA as a present/absent flag (1), not nM
+    both.loc[both["source"] == "wt_20260826", "dna_nM"] = np.nan
+    wells = (both.groupby(["source", "Day", "Biorep", "well", "genotype",
+                           "protein_uM_dimer", "dna_nM", "Buffer_only",
+                           "iptg_mM"], dropna=False)["r"]
+             .agg(r_mean="mean", r_sd="std", num_reads="size")
+             .reset_index())
+    return wells
+
+
+def main():
+    os.makedirs(OUT, exist_ok=True)
+    print("library config ->", library_config())
+
+    cal = od600_calibration()
+    presplit = od600_to_cfu_per_mL(np.array([PRESPLIT_OD600]), cal)[0][0]
+    print(f"presplit {presplit:.3g} CFU/mL; tubes start at "
+          f"{presplit * PRESPLIT_DILUTION:.3g} CFU/mL "
+          f"(ln per tube {np.log(presplit * PRESPLIT_DILUTION * TUBE_VOLUME_ML):.2f})")
+
+    pooled = pooled_counts()
+    samples, dropped = sample_table(cal, pooled)
+    samples.to_csv(os.path.join(OUT, "sample_df.csv"), index=False)
+    dropped.to_csv(os.path.join(OUT, "sample_df_dropped.csv"), index=False)
+    # the inputs of the documented chain (pipeline plan step 7):
+    # tfs-process-counts computes the totals from the OD600 table itself,
+    # so the tube table carries neither OD600 nor totals
+    samples[TUBE_TABLE_COLUMNS].to_csv(os.path.join(OUT, "tube_table.csv"),
+                                       index=False)
+    samples[["sample", "od600"]].to_csv(os.path.join(OUT, "tube_od600.csv"),
+                                        index=False)
+    write_counts(pooled, samples)
+    print(f"{len(samples)} tubes kept, {len(dropped)} dropped: "
+          f"{dropped['sample'].tolist()}")
+
+    initial_composition(pooled).to_csv(
+        os.path.join(OUT, "initial_composition.csv"), index=False)
+
+    tubes, rates, summary = wt_controls(cal)
+    tubes.to_csv(os.path.join(OUT, "wt_control_tubes.csv"), index=False)
+    rates.to_csv(os.path.join(OUT, "wt_control_rates.csv"), index=False)
+    summary.to_csv(os.path.join(OUT, "wt_control_rate_summary.csv"),
+                   index=False)
+
+    binding_tidy().to_csv(os.path.join(OUT, "binding_wells.csv"), index=False)
+    print("done")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/real-data-fit/processing/process_fastq.srun b/planning/studies/real-data-fit/processing/process_fastq.srun
new file mode 100644
index 00000000..10d24879
--- /dev/null
+++ b/planning/studies/real-data-fit/processing/process_fastq.srun
@@ -0,0 +1,19 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab      ### change this to your actual account for charging
+#SBATCH --job-name=tfscreen     ### job name
+#SBATCH --output=hostname.out   ### file in which to store job stdout
+#SBATCH --error=hostname.err    ### file in which to store job stderr
+#SBATCH --partition=memory
+#SBATCH --time=00-02:00:00
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --mem=0 
+
+f1=${1}
+f2=${2}
+out_dir=${3}
+cfg=${4}
+
+mkdir ${out_dir}
+
+tfs-process-fastq ${f1} ${f2} ${out_dir} ${cfg} --phred_cutoff 10 --min_read_length 50 --allowed_num_flank_diffs 1 --allowed_diff_from_expected 2
diff --git a/planning/studies/relative-fit/.gitignore b/planning/studies/relative-fit/.gitignore
new file mode 100644
index 00000000..71de47e5
--- /dev/null
+++ b/planning/studies/relative-fit/.gitignore
@@ -0,0 +1,7 @@
+calib
+relative_fit
+relative_fit_v2
+relative_fit_v3
+relative_fit_v4
+relative_fit_v4_block
+relative_fit_v4_arrowhead
diff --git a/planning/studies/relative-fit/README.md b/planning/studies/relative-fit/README.md
new file mode 100644
index 00000000..4e501fb7
--- /dev/null
+++ b/planning/studies/relative-fit/README.md
@@ -0,0 +1,451 @@
+# Relative-X fit study
+
+**Question.** Does a growth-only fit on the wt-relative scale X (theta
+component `hill_relative`: no binding data, no prefit, wt's X pinned to 1 at
+0 mM and 0 at 1 mM IPTG) recover X, the Hill K and n, and each condition's
+`k` and `m`, about as well as the anchored joint fit recovers theta? Does
+that hold under realistic count noise?
+
+**Decisions it feeds.**
+- Roadmap step 5 (`planning/analysis-roadmap.md`): whether the relative fit
+  is sound enough to run on real data and build the growth-binding map on
+  (step 8).
+- C4: whether `k_c`/`m_c` are recovered from growth alone, with totals
+  supplied (step 6 later replaces the supplied totals).
+
+**Current answer (2026-10-02).** The relative fit is sound. For intervals,
+use the MAP plus the arrowhead Laplace (`--laplace_blocks
+--laplace_shared`): on run 4 it covered X 0.97 / 0.94 (Poisson /
+realistic) and k/m about as well as the full Laplace, and it runs on a full
+library. The low-rank guide is the SVI choice (X 0.81 / 0.76), but on the
+real library it slid along the m·X ridge. k and m are recovered here only
+as well as the tube totals allow (C4). The sections below are in the order
+they were run; later runs supersede earlier ones.
+
+## Design
+
+Base config: `../congression-calibration/simulate_config.yaml` (483
+genotypes, hill_mut theta, instant growth transitions, kanR and pheS, 20
+in-library binding anchors), at lambda 0 and the wide dk spread, like
+`../count-likelihood/`. Lambda 0 keeps congression out of the comparison;
+both fits use `single`.
+
+[`grid.yaml`](grid.yaml): 2 noise x 4 fit/inference arms x 3 seeds = 24
+runs, all on the count likelihood (the default after `../count-likelihood/`;
+the `lncfu` arm of the first design was dropped, 2026-09-27). Run 1 had only
+the two component-guide arms. Run 2 added the `low_rank` and `map` arms for
+the relative fit after run 1 found the mean-field guide biased there
+(Results).
+
+| axis | levels |
+|---|---|
+| simulated noise | `poisson`; `realistic` (founder sampling, demographic growth, shared transformation, 150,000 PCR templates with CV 0.5; designed for ~8x Poisson, fitted at ~29x in `../count-likelihood/` v3) |
+| fit | `joint` (`hill_geno`, binding at weight 1, prefit with `k_scale_ceiling` 0.005); `relative` (`hill_relative`, growth only, no prefit) |
+| inference | `component` (mean-field component guide, the default); `low_rank` (`auto_low_rank_multivariate_normal`, numpyro's default rank); `map` (MAP, then a Laplace posterior in `tfs-sample-posterior`). The joint fit runs `component` only. |
+| seed | 1, 2, 3 |
+
+Both fits use `growth_likelihood: counts` with `sample_offset: level` and
+per-genotype Hill curves (the simulation's `hill_mut` truth
+is fit by neither), activity fixed and no theta noise (the relative fit
+refuses it; the joint fit leaves it off to match). Each run
+([`run.srun`](run.srun)) simulates, configures, prefits (joint only), fits,
+samples the posterior and summarizes.
+
+## How to run
+
+On the cluster, from a scratch directory, with this repository checked out:
+
+```bash
+tfs-setup-sim-grid grid.yaml --out_prefix relative_fit_v3
+for d in relative_fit_v3/run_*/; do (cd "$d" && sbatch run.srun); done
+```
+
+When the runs finish:
+
+```bash
+tfs-summarize-calibration relative_fit_v3 --out_prefix calib/relative_fit_v3 \
+    --baseline fit=joint inference=component --facet_by founder_sampling
+```
+
+## What to look at
+
+- `theta_test`: for the relative arms `tfs-summarize-fit` puts the truth on
+  the X gauge, `X = (A theta - A_wt theta_wt(1 mM)) / (A_wt (theta_wt(0) -
+  theta_wt(1 mM)))`, and the rows carry `theta_regime = X`. Coverage, PIT,
+  width by `purity` x `has_binding` (has_binding is held-out for the
+  relative fit: it saw no binding). Coverage and PIT are scale-free and
+  compare across arms; width and RMSE do not (X spans about 1 where theta
+  spans about 0.98 for the base config's wt, so close).
+- `log_hill_K`, `hill_n` (`*_params_theta_*`): the same quantity in both
+  fits (the gauge leaves them unchanged).
+- `growth_k`, `growth_m`: for the relative arms the truth is mapped to X
+  (`k_X = k + m theta_wt(1 mM)`, `m_X = m (theta_wt(0) - theta_wt(1 mM))`).
+  C4 asks for these explicitly: the joint fit gets them partly from the
+  prefit's binding anchors; the relative fit only from wt and the totals.
+- `dk_geno`: unaffected by the gauge; the X baselines trade against it
+  (idea file, "what stays assumed"), so compare its recovery across arms.
+- Genotypes whose true theta does not depend on IPTG (the simulator's
+  stuck-bound and never-binds classes): internal standards across columns
+  (C4).
+- Convergence: no prefit on the relative arm, so check that the fit stops
+  on the noise-referenced rule, not the epoch cap.
+
+## Not covered here
+
+- The kan-vs-4CP consistency check (D5): X is shared by construction, so a
+  fit cannot test it by itself. It needs either a variant with one X per
+  condition family or a model-free comparison of kan and 4CP wt-relative
+  slopes per genotype (as in `../growth-binding-map/`); decide when the
+  real-data run is set up.
+- Real data: waits for the new processing batch and step 2's smoothed total.
+
+## Inputs
+
+Repository paths only: `grid.yaml`, `run.srun`,
+`../congression-calibration/simulate_config.yaml` and its
+`hill_params.csv`. Seeds fixed in the grid.
+
+## Commit
+
+Grid runs do not log a commit. Where the README did not record one, the
+commit below is inferred: the last commit before the grid's runs finished,
+from the file times of the downloaded `run.out` files (2026-10-03).
+
+- Run 1 (`relative_fit`, 2026-09-27): before c57d9ee5 (the SVI start fix);
+  not recorded more closely.
+- Run 2 (`relative_fit_v2`, 2026-09-28): after e29c7395 (the low-rank and
+  MAP arms), with factored ln_cfu0; not recorded more closely.
+- Run 3 (`relative_fit_v3`, 2026-09-28): 722393cf (inferred).
+- Run 4 (`relative_fit_v4`, 2026-10-01): 0e220e7b (inferred).
+- Block Laplace tables (2026-10-02, local): cd208d15. Arrowhead Laplace
+  tables (2026-10-02, local): a874ac38. Both are reproduced by
+  `laplace_blocks.sh block|arrowhead` from a finished `relative_fit_v4/`.
+
+None of the result directories are committed (gitignored).
+
+## Results
+
+**Run 3 (`relative_fit_v3`, 2026-09-28): `ln_cfu0: hierarchical`.** All
+24 runs finished. Theta/X test set, 95% coverage / RMSE, mean over 3 seeds
+(run 2 in brackets):
+
+| noise | arm | run 3 | run 2 |
+|---|---|---|---|
+| poisson | joint component | 0.36 / 0.071 | 0.43 / 0.068 |
+| poisson | relative component | 0.07 / 0.254 | 0.17 / 0.130 |
+| poisson | relative low_rank | 0.78 / 0.065 | 0.64 / 0.057 |
+| realistic | joint component | 0.61 / 0.094 | 0.58 / 0.097 |
+| realistic | relative component | 0.38 / 0.288 | 0.43 / 0.270 |
+| realistic | relative low_rank | 0.78 / 0.122 | 0.78 / 0.115 |
+
+- **This library barely moved, unlike the small one.** Most of its
+  genotypes are low-read doubles (median 3-5 reads per tube), where the
+  ln_cfu0 mismatch was never the main error. The low-rank relative fit
+  gained coverage under Poisson noise (0.64 to 0.78).
+- **The relative component guide got worse** (RMSE 0.13 to 0.25 under
+  Poisson noise). The per-library ln_cfu0 adds free parameters per
+  genotype, which flattens the m·X ridge the mean-field guide already
+  slides along. The low-rank guide is unaffected, which keeps the
+  recommendation.
+- **MAP + Laplace:** m covers in every run (1.0). Theta is still unusable
+  because of the Laplace blowups.
+
+**Run 1 (2026-09-27): inconclusive; the SVI start is broken.** All 12 runs
+finished. Three hit the 100,000-epoch cap (0002, 0003, 0004). run_0011's
+summary came down incomplete, so we regenerated it locally from the run's
+CSVs.
+Pooled with `tfs-summarize-calibration` into `calib/` (not committed).
+
+The final fits looked bad in both arms:
+
+| | theta/X test coverage (95%) | r | notes |
+|---|---|---|---|
+| joint, Poisson | 0.37 | 0.29 | run 0005 in the mirror mode (m and theta sign-flipped); 0003 capped mid-descent |
+| joint, realistic | 0.59 | 0.97 | |
+| relative, Poisson | 0.08 | 0.99 | X shifted 0.2-0.3 below truth, library-wide |
+| relative, realistic | 0.33 | 0.96 | run 0010 fine (0.58), the others shifted |
+
+The relative fit's growth rates matched the truth within each tube to 1e-4.
+wt did not: the fit moved the whole library against wt, missing wt's
+ln_cfu by 0.1-0.27 per tube, while the joint fit held it within 0.03.
+
+**The pre-MAP was right in every run.** The pre-MAP recovered each
+condition's m to within 0.0002 of the X-scale truth (relative) and 0.001 of
+the theta-scale truth (joint), and put the relative fit's X on truth (slope
+1.00, intercept -0.02 to -0.08). SVI then lost it. It halved m in 0003,
+flipped it in 0005, and shifted X in the relative runs.
+
+**Cause: the pre-MAP to component-guide hand-off.**
+- **Starting loss.** SVI started at a loss of about 4e8 against the
+  pre-MAP's 2.5e5, then spent about 40,000 epochs descending from there
+  into whatever optimum it reached.
+- **The scale cap is in each site's own units.** `component_guide_start`
+  caps every guide scale at `guide_init_scale` (0.1). Evaluated at run
+  0002's hand-off (scratch script, one site at 0.1 and the rest at 1e-5,
+  base loss 2.8e5), SD 0.1 added:
+  - 6e8 on `dk_geno_hyper_shift`
+  - 4e7 on `condition_growth_k`
+  - 1e7 on `condition_growth_m`
+  - 9e6 on `ln_cfu0_tube_offset`
+  - 2e6 on `dk_geno_hyper_loc`
+
+  These are growth rates per minute or ln_cfu levels, where SD 0.1 is
+  enormous: 0.1/min over a 170-minute selection is 17 ln units.
+- **The jitter makes it worse.** `init_param_jitter` (0.1) is
+  multiplicative, so it moves locations with large values (ln_cfu0 about
+  10-18) by 1-2 ln units. At scale 0.001 it raised the starting loss from
+  1.0e6 to 1.6e7.
+- **Every earlier SVI grid has the same start.** The count-likelihood
+  v1/v2 runs, lncfu included, began at 2e6-5e8. Their between-arm
+  comparisons ran under the same handicap, but their absolute coverage
+  numbers are suspect.
+
+**Separately:** the joint arm's prefit wrote per-condition priors far from
+truth under the count likelihood (kanR+kan `k_loc` -0.0006 and `m_loc`
++0.0007, against 0.0107 and -0.0099, at scales 0.002 and 0.001). The
+pre-MAP overrode them here. This needs its own look before the prefit is
+trusted with counts.
+
+**Fix (2026-09-27).** `DEFAULT_GUIDE_INIT_SCALE` is now 1e-4, applied to
+the component guide and to fresh autoguides, and `init_param_jitter`
+defaults to 0. At run 0002's hand-off the starting loss fell from 5.9e8 to
+2.81e5, level with scales at 1e-5.
+
+**The start was not the only cause.** We checked run 0002 locally in two
+continuations (scratch scripts, not committed):
+- **SVI.** 8000 SVI steps from the fixed start held the loss near the
+  pre-MAP's but moved to the same shifted solution. The kanR+kan m went
+  from -0.0098 to -0.0087, and X to slope 1.12 and intercept -0.25.
+- **MAP.** Continuing the pre-MAP (AutoDelta) for 18,000 more epochs
+  stayed on truth: flat loss, m -0.00973 against an X-scale truth of
+  -0.00973.
+
+The tube-offset spread was the same, about 0.08, in both. So the mean-field
+ELBO, not the MAP optimum, prefers the shift. The likely mechanism: growth
+alone constrains mostly the product m·(X_g - X_wt). The posterior is
+therefore a curved ridge along m·X = constant. Only wt's absolute growth
+change across IPTG sets the position along it, and that reaches the fit
+only through the tube totals. A mean-field Gaussian cannot follow a curved
+ridge, and it slides toward smaller |m|. The joint fit escapes because
+binding observes theta for the anchors.
+
+**MAP + Laplace works on run 0002, once the MAP has converged.** We
+continued the pre-MAP at a fixed step size of 1e-3. The result sat far
+from a stationary point, with gradient norm 1.4e7. Its Hessian had 9
+negative eigenvalues, down to -767, all on per-genotype offsets.
+Clamping them made the Laplace X intervals useless (median 95% width
+2.6e10). Continuing with step-size cuts, the fit reached 1e-4 after about
+128,000 epochs in total. The loss was still falling about 3 nats per
+2000-epoch window. At that point:
+- **Gradient and Hessian.** The gradient norm was 2.8e4 and one tiny
+  negative eigenvalue remained (-5e-5, on `ln_cfu0_tube_scale`).
+- **m.** m stayed on truth (-0.00976).
+- **Laplace X.** X was unbiased: RMSE 0.109, slope 1.00, intercept
+  -0.01. 95% coverage was 0.99 and 50% coverage 0.77, so the intervals
+  are conservative. The run-1 SVI covered 8%.
+
+Run 2 therefore adds a `map` arm with `--max_num_epochs 200000` and a
+`low_rank` arm (`auto_low_rank_multivariate_normal`).
+
+**Run 2 (2026-09-28): `relative_fit_v2`, 24 runs, all finished.** Pooled
+in `calib/relative_fit_v2_*` (not committed).
+
+- **Start fix confirmed.** Every SVI run began within about 45k of its
+  pre-MAP loss (run 1 began about 4e8 above). No mirror modes. All
+  component-guide runs, joint included, hit the 100,000-epoch cap at step
+  size 1e-6 without being called converged. Five of the six `low_rank`
+  runs converged. All `map` runs hit the 200,000 cap at step size 1e-4 or
+  1e-5.
+- **Joint fit (component).** Much better than run 1: theta r 0.99/0.97
+  and RMSE 0.07/0.10 (Poisson/realistic), against run 1's 0.29 r under
+  Poisson. It still undercovers: 95% coverage 0.43/0.58.
+
+X test set (`theta_test`), per-arm means over 3 seeds, Poisson / realistic:
+
+| relative arm | 95% coverage | 50% coverage | RMSE | median 95% width |
+|---|---|---|---|---|
+| component | 0.17 / 0.43 | 0.06 / 0.18 | 0.13 / 0.27 | 0.045 / 0.23 |
+| low_rank | 0.64 / 0.78 | 0.29 / 0.39 | 0.058 / 0.115 | 0.034 / 0.18 |
+| map + Laplace | 1 of 6 runs usable | | | |
+
+- **`low_rank` removes most of the relative fit's bias.** Its X RMSE is
+  at or below the joint fit's (0.058 vs 0.068 Poisson), with coverage at
+  or above the joint fit's. The component guide stays biased, as in
+  run 1.
+- **`map` + Laplace is not usable as is.** Every MAP recovered m with
+  calibrated intervals (95% coverage 1.0/1.0). The Laplace covariance on
+  the genotype parameters blew up in 5 of 6 runs, though: 36-126
+  genotypes had `hill_n` 95% widths above 100, and X widths reached
+  1e8-1e14 even for the other genotypes. Only run 0004 (seed 1, Poisson)
+  gave sensible X (95% coverage 0.91).
+- **Every SVI arm is overconfident.** 50% coverage of X/theta is 0.2-0.4.
+  `growth_k`/`growth_m` intervals are about 1e-4 wide with coverage near
+  0, in the joint and relative arms alike. The MAP's Laplace intervals
+  on k and m cover (0.92-1.0). Remaining gap: the guides' variance, not
+  their location.
+
+> **Caveat (2026-10-01).** Runs 2 and 3 ran with two bugs fixed on
+> 2026-09-29 (`9db28286`). From `c57d9ee5` (2026-09-27 16:48) every
+> component-guide hyperparameter scale started on its `greater_than(1e-4)`
+> bound and never moved, so the `component` arms ran crippled while the
+> `low_rank` autoguide did not; the comparison between them is lopsided.
+> The Laplace floor clamped negative eigenvalues to 1e-3 (variance 1,000),
+> which is the `map` arm's blowups; the learned log(hill_n) spread, which
+> ran away on real data, was also free here. The svi-overconfidence
+> study's relative arm, in the same window on a smaller library, found
+> component and low-rank about equal (X 95% coverage 0.68 vs 0.74, RMSE
+> 0.071 vs 0.072). So the guide recommendation below is not established.
+> The post-fix rerun is run 4 (`grid_v4.yaml`, below). The k/m collapse
+> under every guide holds after the fixes.
+
+**Decision fed (step 5).** The relative fit is sound. Its point estimates
+match the anchored joint fit's without binding data. Fit it with
+`--guide_type auto_low_rank_multivariate_normal`, not the component guide.
+This is documented, not made the default: the default waits on studies
+with real data (user, 2026-09-28).
+Its intervals, like every SVI fit's here, are too narrow. That is a
+separate problem shared with the joint fit, to take up before trusting
+coverage from any arm.
+
+## Run 4: post-fix guide comparison (2026-10-01)
+
+**Question.** With the frozen-scale and Laplace-floor fixes in, which
+inference gives honest X intervals on the relative fit: the component
+guide, the low-rank guide (numpyro's default rank, and rank 20 as on the
+real-data run `planning/dev-data/real_fit/rel_off_n05_svi`), MAP + floored
+Laplace, or the two-stage fit?
+
+**Design** ([`grid_v4.yaml`](grid_v4.yaml), template
+[`run_v4.srun`](run_v4.srun)): runs 2-3's simulations (same base config,
+noise levels and seeds 1-3), relative fit only, 5 inference arms, so 2
+noise x 3 seeds x 5 = 30 runs. Every arm holds the log(hill_n) population
+SD at 0.5, as the real-data fits now do. `run_v4.srun` is run.srun plus
+the svi-overconfidence study's two-stage path and guide diagnostics
+(`../svi-overconfidence/two_stage.py`, `guide_diagnostics.py`, staged
+into the grid's inputs) and a `guide_rank` variable. 12 h per run.
+
+```bash
+tfs-setup-sim-grid grid_v4.yaml --out_prefix relative_fit_v4
+for d in relative_fit_v4/run_*/; do (cd "$d" && sbatch run_v4.srun); done
+```
+
+Afterwards:
+
+```bash
+tfs-summarize-calibration relative_fit_v4 --out_prefix calib/relative_fit_v4 \
+    --facet_by founder_sampling
+```
+
+Compare X coverage by depth, RMSE and width across arms, k and m coverage,
+and the guide diagnostics (log-weight SD, k-hat) for the two SVI arms. The
+low-rank rank-20 arm decides whether the real-data run's rank was enough.
+
+**Results (2026-10-01).** All 30 runs finished. Every MAP and the
+Poisson two-stage MAPs hit their caps (200,000 / 100,000 epochs); every
+other SVI fit converged. Pooled into `calib/relative_fit_v4_*` (not
+committed). Means over 3 seeds per arm.
+
+X test set:
+
+| noise | arm | 95% cov | 50% cov | median 95% width | RMSE |
+|---|---|---|---|---|---|
+| poisson | component | 0.06 | 0.02 | 0.16 | 0.297 |
+| poisson | low_rank | 0.81 | 0.38 | 0.11 | 0.068 |
+| poisson | low_rank rank 20 | 0.81 | 0.38 | 0.11 | 0.065 |
+| poisson | map + Laplace | 0.98 | 0.63 | 0.22 | 0.105 |
+| poisson | two_stage | 0.91 | 0.51 | 0.13 | 0.075 |
+| realistic | component | 0.38 | 0.14 | 0.31 | 0.289 |
+| realistic | low_rank | 0.76 | 0.38 | 0.27 | 0.126 |
+| realistic | low_rank rank 20 | 0.76 | 0.38 | 0.27 | 0.119 |
+| realistic | map + Laplace | 0.94 | 0.45 | 0.64 | 0.293 |
+| realistic | two_stage | 0.78 | 0.32 | 0.27 | 0.110 |
+
+95% coverage of the shared and per-genotype parameters (Poisson /
+realistic):
+
+| arm | growth_k | growth_m | dk_geno | log_hill_K | hill_n |
+|---|---|---|---|---|---|
+| component | 0 / 0 | 0 / 0.08 | 0.20 / 0.63 | 0.81 / 0.84 | 0.87 / 0.87 |
+| low_rank | 0 / 0 | 0.25 / 0.08 | 0.41 / 0.71 | 0.80 / 0.85 | 0.83 / 0.87 |
+| low_rank rank 20 | 0 / 0 | 0.25 / 0.08 | 0.38 / 0.70 | 0.79 / 0.85 | 0.82 / 0.86 |
+| map + Laplace | 0.92 / 0.58 | 0.92 / 0.50 | 0.98 / 0.88 | 0.92 / 0.86 | 0.91 / 0.85 |
+| two_stage | 0.75 / 0.25 | 0.83 / 0.33 | 0.75 / 0.88 | 0.85 / 0.84 | 0.87 / 0.86 |
+
+- **The component guide is biased even with the fixes**: X RMSE 0.29-0.30
+  against 0.07-0.13 for the low-rank guide; 95% coverage 0.06 / 0.38. The
+  frozen scales were not the cause, and runs 2-3's guide verdict stands.
+- **Rank 20 matches numpyro's default rank** in every number, so the
+  real-data run's rank is enough.
+- **The low-rank guide undercovers X moderately** (0.81 / 0.76 at 95%,
+  0.38 at 50%).
+- **Two-stage is the best calibrated under Poisson noise** (0.91 / 0.51)
+  but no better than low-rank under realistic noise (0.78 / 0.32). It is
+  the only SVI route that covers k and m at all, and only partly under
+  realistic noise.
+- **MAP + floored Laplace now works**: it covers X (0.98 / 0.94) and k/m
+  best, but with the widest intervals and the noisiest point (RMSE 0.105 /
+  0.293). The run-2 blowups were the old floor. Every MAP hit its 200,000
+  epoch cap.
+- **Every guide collapses k and m** (coverage 0-0.25).
+
+**Decision fed.** Fit the relative model with the low-rank guide (rank 20
+is enough). Expect X 95% intervals to cover about 0.76-0.81, and treat k
+and m intervals from any guide as too narrow. A full-library Laplace is
+out of reach (about 2M parameters), so on real data the k/m uncertainty
+needs the two-stage fit with the Laplace taken on a subset.
+*Superseded 2026-10-02:* the arrowhead Laplace below covers k/m on a full
+library, and the low-rank guide failed on real data, so see "Current
+answer" at the top.
+
+**Block Laplace on run 4's MAPs (2026-10-02, local).** The per-genotype
+Laplace (`tfs-sample-posterior --laplace_blocks`, new) on copies of the six
+`map` runs (500 draws each, then predict, extract and summarize;
+`laplace_blocks.sh block`). Given the shared parameters, run 0004's full Hessian (4,521
+parameters: 478 genotypes x 9, plus 219 shared) had every cross-genotype
+entry exactly 0, wt included. Each block's covariance matched the inverse
+of the full Hessian's diagonal block to 8e-6, except where the prior floor
+raised an eigenvalue.
+
+| noise | arm | X 95% cov | 50% cov | median 95% width | k / m cov |
+|---|---|---|---|---|---|
+| poisson | block Laplace | 0.93 | 0.49 | 0.17 | 0 / 0 |
+| poisson | full Laplace | 0.98 | 0.63 | 0.22 | 0.92 / 0.92 |
+| realistic | block Laplace | 0.84 | 0.40 | 0.55 | 0 / 0 |
+| realistic | full Laplace | 0.94 | 0.45 | 0.64 | 0.58 / 0.50 |
+
+log_hill_K, hill_n and dk_geno cover 0.80-0.91 under both. By genotype
+total reads, under realistic noise (block Laplace / low-rank X RMSE, block
+coverage): <= 100: 1.38 / 0.22, 0.99; 1e2-1e3: 0.63 / 0.24, 0.93; 1e3-1e4:
+0.23 / 0.16, 0.83; > 1e4: 0.057 / 0.091, 0.83 (low-rank covers 0.70
+there). The MAP point is poor at low depth, where nothing shrinks it, but
+its intervals cover; at high depth it beats the low-rank point. The
+missing k/m uncertainty costs coverage at high depth (0.83 against the
+full Laplace's 0.94).
+
+
+**Arrowhead Laplace on run 4's MAPs (2026-10-02, local).** The block
+Laplace plus each genotype's coupling to the shared parameters and the
+shared block (`--laplace_blocks --laplace_shared`, new), on the same six
+copies (500 draws each; `laplace_blocks.sh arrowhead`). The shared
+parameters come from their Schur complement and each genotype from its
+conditional, which is the full Laplace. Two rules were needed. Floored genotype directions carry
+no coupling. Negative Schur directions are held at the MAP: floored at the
+prior instead, the k/dk_geno slide (`dk_geno_hyper_shift` with k,
+eigenvalue about -1e7, runs 0009 and 0024) gave k its prior SD of 0.01 and
+X widths of 1-3, and hyperscale directions (run 0004) pulled X coverage
+above 1000 reads to 0.53. With both rules, 0-6 of 219 shared directions
+were held per run, always hyperparameters or the k/dk slide.
+
+| noise | arm | X 95% cov | 50% cov | k / m 95% cov |
+|---|---|---|---|---|
+| poisson | block Laplace | 0.93 | 0.49 | 0 / 0 |
+| poisson | arrowhead Laplace | 0.97 | 0.59 | 0.92 / 1.0 |
+| poisson | full Laplace | 0.98 | 0.63 | 0.92 / 0.92 |
+| realistic | block Laplace | 0.84 | 0.40 | 0 / 0 |
+| realistic | arrowhead Laplace | 0.94 | 0.49 | 0.50 / 0.42 |
+| realistic | full Laplace | 0.94 | 0.45 | 0.58 / 0.50 |
+
+Above 1000 reads X now covers 0.95-0.98 under realistic noise (block:
+0.83). log_hill_K and hill_n are unchanged (they barely depend on k and
+m); dk_geno rises to 0.93 / 0.95. The k/m shortfall under realistic noise
+is the full Laplace's too, so it is not the arrowhead's.
diff --git a/planning/studies/relative-fit/grid.yaml b/planning/studies/relative-fit/grid.yaml
new file mode 100644
index 00000000..a4b6d1f7
--- /dev/null
+++ b/planning/studies/relative-fit/grid.yaml
@@ -0,0 +1,86 @@
+# ---------------------------------------------------------------------------
+# Relative-X fit validation (roadmap step 5, planning/analysis-roadmap.md)
+#
+#   tfs-setup-sim-grid grid.yaml --out_prefix relative_fit
+#   for d in relative_fit/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Does a growth-only fit on the wt-relative scale X (theta: hill_relative;
+# wt's X pinned to 1 at 0 mM and 0 at 1 mM IPTG; no binding data, no prefit)
+# recover X, the Hill K and n, and each condition's k and m as well as the
+# joint fit recovers theta with binding anchors? Truth goes onto the X gauge
+# in tfs-summarize-fit (C8).
+#
+# Simulated noise (as in ../count-likelihood/grid.yaml):
+#   poisson    deterministic growth, one multinomial draw of reads.
+#   realistic  founder sampling, demographic growth, shared transformation,
+#              template bottleneck with PCR jackpotting (~8x Poisson).
+#
+# Fits (both hill-per-genotype, theta noise off, single transformation; the
+# simulation has lambda 0, so congression is out of the comparison), both on
+# the count likelihood with a per-tube level offset (the default since the
+# count-likelihood study, ../count-likelihood/):
+#   joint     hill_geno with binding at weight 1, prefit (k_scale_ceiling
+#             0.005), the anchored baseline of the congression study
+#   relative  hill_relative, growth only, no prefit
+#
+# Inference (run 2, 2026-09-27). Run 1 found the mean-field component guide
+# biased on the relative fit: from the pre-MAP point SVI slides along the
+# m*X ridge (smaller |m|, stretched X) while the MAP stays on truth. So the
+# relative fit gets three arms:
+#   component  mean-field component guide (the default; run 1's method)
+#   low_rank   auto_low_rank_multivariate_normal (numpyro's default rank),
+#              which can represent the ridge's correlations
+#   map        MAP, then a Laplace posterior from tfs-sample-posterior
+# The joint fit keeps the component guide as the anchored baseline.
+#
+# 2 noise x 4 fit/inference arms x 3 seeds = 24 runs.
+# ---------------------------------------------------------------------------
+
+base_config: ../congression-calibration/simulate_config.yaml
+
+run_name: "{{ 'realistic' if founder_sampling else 'poisson' }}__seed{{ seed }}__{{ fit }}__{{ inference }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: noise
+    variants:
+      - founder_sampling: false
+      - founder_sampling: true
+        demographic_growth: true
+        shared_transformation: true
+        cfu0: 150_000.0
+        pcr_template_molecules: 150_000
+        pcr_amplification_cv: 0.5
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+      - seed: 3
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: joint
+        inference: component
+      - fit: relative
+        inference: component
+      - fit: relative
+        inference: low_rank
+      - fit: relative
+        inference: map
diff --git a/planning/studies/relative-fit/grid_v4.yaml b/planning/studies/relative-fit/grid_v4.yaml
new file mode 100644
index 00000000..74ea26bb
--- /dev/null
+++ b/planning/studies/relative-fit/grid_v4.yaml
@@ -0,0 +1,86 @@
+# ---------------------------------------------------------------------------
+# Relative-X fit study, run 4: post-fix guide comparison (2026-10-01)
+#
+#   tfs-setup-sim-grid grid_v4.yaml --out_prefix relative_fit_v4
+#   for d in relative_fit_v4/run_*/; do (cd "$d" && sbatch run_v4.srun); done
+#
+# Runs 2 and 3 (2026-09-28) recommended the low-rank guide for the relative
+# fit, but ran with two bugs fixed on 2026-09-29: every component-guide
+# hyperparameter scale frozen at its 1e-4 bound (the low-rank autoguide
+# was unaffected) and the flat 1e-3 Laplace eigenvalue floor (the map
+# arm's blowups). This reruns the relative fit's inference arms on the same
+# simulations with the fixed code (README, "Run 4").
+#
+# Relative fit only (no joint baseline: the question is which inference
+# to trust on growth alone). Every arm holds the log(hill_n) population SD
+# at 0.5, as the real-data fits do.
+#   component           mean-field component guide
+#   low_rank            low-rank MVN, numpyro's default rank
+#   low_rank (rank 20)  the rank of the real-data run rel_off_n05_svi
+#   map                 MAP, then the floored Laplace posterior
+#   two_stage           MAP, floored Laplace, conditional component fits
+#
+# 2 noise x 3 seeds x 5 arms = 30 runs.
+# ---------------------------------------------------------------------------
+
+base_config: ../congression-calibration/simulate_config.yaml
+
+run_name: "{{ 'realistic' if founder_sampling else 'poisson' }}__seed{{ seed }}__{{ inference }}{{ ('_r' ~ guide_rank) if guide_rank else '' }}"
+
+output_file: run_v4.srun
+
+
+simulate:
+
+  - name: noise
+    variants:
+      - founder_sampling: false
+      - founder_sampling: true
+        demographic_growth: true
+        shared_transformation: true
+        cfu0: 150_000.0
+        pcr_template_molecules: 150_000
+        pcr_amplification_cv: 0.5
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+      - seed: 3
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: relative
+        n_scale: 0.5
+
+  - name: inference
+    variants:
+      - inference: component
+        guide_rank: ""
+      - inference: low_rank
+        guide_rank: ""
+      - inference: low_rank
+        guide_rank: 20
+      - inference: map
+        guide_rank: ""
+      - inference: two_stage
+        guide_rank: ""
+
+  # copied into the grid's inputs/ and named from each run
+  - name: scripts
+    variants:
+      - diagnostics_script: ../svi-overconfidence/guide_diagnostics.py
+        two_stage_script: ../svi-overconfidence/two_stage.py
diff --git a/planning/studies/relative-fit/laplace_blocks.sh b/planning/studies/relative-fit/laplace_blocks.sh
new file mode 100644
index 00000000..59704fde
--- /dev/null
+++ b/planning/studies/relative-fit/laplace_blocks.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+# Block and arrowhead Laplace on run 4's MAP runs (README, "Block Laplace on
+# run 4's MAPs" and "Arrowhead Laplace on run 4's MAPs").
+#
+#   bash laplace_blocks.sh block      # --laplace_blocks (shared params held)
+#   bash laplace_blocks.sh arrowhead  # --laplace_blocks --laplace_shared
+#
+# Run from this directory after relative_fit_v4/ is complete. Each
+# relative_fit_v4/run_*_map is copied (config, priors, guesses, library,
+# checkpoint, simulated truth) into relative_fit_v4_<mode>/, so the map arm's
+# own posterior is left alone. Then 500 draws, extract, predict, summarize,
+# and tfs-summarize-calibration pools the six runs into
+# relative_fit_v4_<mode>/calib_<mode>_*. About 5 minutes per run on a laptop
+# CPU; RUN_JOBS (default 3) runs that many at once.
+#
+# Tables in the README: block at cd208d15, arrowhead at a874ac38.
+
+set -euo pipefail
+
+mode="${1:?usage: laplace_blocks.sh block|arrowhead}"
+case "${mode}" in
+    block) laplace_args=(--laplace_blocks) ;;
+    arrowhead) laplace_args=(--laplace_blocks --laplace_shared) ;;
+    *) echo "unknown mode '${mode}'" >&2; exit 1 ;;
+esac
+
+src=relative_fit_v4
+out="relative_fit_v4_${mode}"
+mkdir -p "${out}"
+cp "${src}/grid_summary.json" "${out}/"
+
+one_run() {
+    local run="$1" dest
+    dest="${out}/$(basename "${run}")"
+    mkdir -p "${dest}"
+    cp "${run}"/combo.json "${run}"/tfs_configure_* "${run}"/tfs_sim_* \
+       "${run}"/tfs_fit_model_checkpoint.pkl "${dest}/"
+    (
+        cd "${dest}"
+        tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+            "${laplace_args[@]}" --num_posterior_samples 500 \
+            --sampling_batch_size 100 > sample.log 2>&1
+        tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5 > extract.log 2>&1
+        tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5 > theta.log 2>&1
+        tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+            --num_marginal_samples=500 > growth.log 2>&1
+        tfs-summarize-fit . > summary.log 2>&1
+    )
+    echo "done ${dest}"
+}
+export -f one_run
+export out mode
+export laplace_args_str="${laplace_args[*]}"
+
+ls -d "${src}"/run_*_map | xargs -P "${RUN_JOBS:-3}" -I{} bash -c \
+    'set -euo pipefail; laplace_args=(${laplace_args_str}); one_run "$1"' _ {}
+
+tfs-summarize-calibration "${out}" --out_prefix "${out}/calib_${mode}"
diff --git a/planning/studies/relative-fit/run.srun b/planning/studies/relative-fit/run.srun
new file mode 100644
index 00000000..dfc98275
--- /dev/null
+++ b/planning/studies/relative-fit/run.srun
@@ -0,0 +1,119 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=relative_fit
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpu
+#SBATCH --time=00-06:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --ntasks-per-node=1
+
+# Relative-X fit study, one run (rendered by tfs-setup-sim-grid from
+# planning/studies/relative-fit/run.srun). Submit from the run directory:
+# sbatch run.srun
+
+# Swap lines if on cluster (module load) or local (XLA_FLAGS to use CPU)
+module load cuda/12.4.1
+#export XLA_FLAGS="--xla_force_host_platform_device_count=8"
+
+set -e
+
+# joint (hill_geno + binding + prefit) or relative (hill_relative, growth only)
+FIT="{{ fit }}"
+# Growth likelihood: counts (default; with a per-tube level offset, and no
+# extra growth noise, which it refuses) or lncfu (with normal_kt growth
+# noise). grid.yaml runs counts only.
+LIKELIHOOD="{{ likelihood | default('counts') }}"
+# component (mean-field component guide), low_rank (low-rank multivariate
+# normal autoguide) or map (MAP, then a Laplace posterior)
+INFERENCE="{{ inference | default('component') }}"
+
+SEED=$(python -c "import yaml; print(yaml.safe_load(open('tfs_sim_config.yaml'))['seed'])")
+
+# ---------------------------------------------------------------------------
+# 1. Simulate
+# ---------------------------------------------------------------------------
+echo ">>> Simulate (seed=${SEED})"
+tfs-simulate tfs_sim_config.yaml . --seed "${SEED}"
+
+# ---------------------------------------------------------------------------
+# 2. Configure
+# ---------------------------------------------------------------------------
+if [[ "${LIKELIHOOD}" == "counts" ]]; then
+    LIKELIHOOD_ARGS=(--growth_likelihood counts --growth_noise_model zero
+                     --sample_offset_model level)
+else
+    LIKELIHOOD_ARGS=(--growth_likelihood lncfu --growth_noise_model normal_kt
+                     --sample_offset_model zero)
+fi
+
+if [[ "${FIT}" == "relative" ]]; then
+    FIT_ARGS=(--theta_model hill_relative)
+else
+    FIT_ARGS=(--theta_model hill_geno
+              --binding_df tfs_sim_binding.csv
+              --binding_weight 1.0)
+fi
+
+echo ">>> Configure (${FIT}, ${LIKELIHOOD})"
+tfs-configure-model \
+    --growth_df tfs_sim_growth.csv \
+    --presplit_df tfs_sim_presplit.csv \
+    --condition_growth_model linear \
+    --growth_transition_model instant \
+    --ln_cfu0_model hierarchical \
+    --dk_geno_model hierarchical_geno \
+    --activity_model fixed \
+    --theta_rescale_model passthrough \
+    --theta_growth_noise_model zero \
+    --theta_binding_noise_model zero \
+    --transformation_model single \
+    --library_config tfs_sim_config.yaml \
+    --growth_shares_replicates \
+    "${FIT_ARGS[@]}" \
+    "${LIKELIHOOD_ARGS[@]}"
+
+# ---------------------------------------------------------------------------
+# 3. Pre-fit calibration (joint only: it needs binding), fit, posterior
+# ---------------------------------------------------------------------------
+if [[ "${FIT}" == "joint" ]]; then
+    echo ">>> Pre-fit calibration"
+    tfs-prefit-calibration tfs_configure_config.yaml --seed "${SEED}" \
+        --k_scale_ceiling 0.005
+fi
+
+case "${INFERENCE}" in
+    component) INFERENCE_ARGS=() ;;
+    low_rank)  INFERENCE_ARGS=(--guide_type auto_low_rank_multivariate_normal) ;;
+    # a converged MAP took ~130,000 epochs on run 1's run 0002; Laplace
+    # needs it converged (a MAP stopped at a fixed step size had 9 negative
+    # Hessian eigenvalues and gave 95% intervals ~1e10 wide)
+    map)       INFERENCE_ARGS=(--analysis_method map --max_num_epochs 200000) ;;
+    *) echo "unknown inference '${INFERENCE}'" >&2; exit 1 ;;
+esac
+
+echo ">>> Fit model (${INFERENCE})"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" "${INFERENCE_ARGS[@]}"
+
+echo ">>> Sample posterior"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl
+
+# ---------------------------------------------------------------------------
+# 4. Extract, predict, summarize
+# ---------------------------------------------------------------------------
+echo ">>> Extract parameters"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict theta"
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict growth"
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+    --num_marginal_samples=500
+
+echo ">>> Summarize fit"
+tfs-summarize-fit .
+
+echo ">>> Done"
diff --git a/planning/studies/relative-fit/run_v4.srun b/planning/studies/relative-fit/run_v4.srun
new file mode 100644
index 00000000..c93f55ad
--- /dev/null
+++ b/planning/studies/relative-fit/run_v4.srun
@@ -0,0 +1,164 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=relative_v4
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpu
+#SBATCH --time=00-12:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --ntasks-per-node=1
+
+# Relative-X fit study, run 4 (post-fix guide comparison), one run (rendered
+# by tfs-setup-sim-grid from planning/studies/relative-fit/run_v4.srun;
+# built on ../svi-overconfidence/run.srun for the two-stage path and guide
+# diagnostics). Submit from the run directory: sbatch run_v4.srun
+
+# Swap lines if on cluster (module load) or local (XLA_FLAGS to use CPU)
+module load cuda/12.4.1
+#export XLA_FLAGS="--xla_force_host_platform_device_count=8"
+
+set -e
+
+# joint (hill_geno + binding + prefit) or relative (hill_relative, growth only)
+FIT="{{ fit }}"
+# Growth likelihood: counts (default; with a per-tube level offset, and no
+# extra growth noise, which it refuses) or lncfu (with normal_kt growth
+# noise). grid.yaml runs counts only.
+LIKELIHOOD="{{ likelihood | default('counts') }}"
+# component (mean-field component guide), low_rank (low-rank multivariate
+# normal autoguide), map (MAP, then a Laplace posterior), or two_stage /
+# two_stage_low_rank (MAP, Laplace, then conditional SVI refits)
+INFERENCE="{{ inference | default('component') }}"
+# rank of the low-rank guide; empty = numpyro's default (round(sqrt(D)))
+GUIDE_RANK="{{ guide_rank | default('') }}"
+# population SD of log(hill_n), held (theta_log_hill_n_hyper_scale_fixed);
+# empty = learned. Learned, it ran to about 19 on real data (2026-10-01).
+N_SCALE="{{ n_scale | default('') }}"
+
+SEED=$(python -c "import yaml; print(yaml.safe_load(open('tfs_sim_config.yaml'))['seed'])")
+
+# ---------------------------------------------------------------------------
+# 1. Simulate
+# ---------------------------------------------------------------------------
+echo ">>> Simulate (seed=${SEED})"
+tfs-simulate tfs_sim_config.yaml . --seed "${SEED}"
+
+# ---------------------------------------------------------------------------
+# 2. Configure
+# ---------------------------------------------------------------------------
+if [[ "${LIKELIHOOD}" == "counts" ]]; then
+    LIKELIHOOD_ARGS=(--growth_likelihood counts --growth_noise_model zero
+                     --sample_offset_model level)
+else
+    LIKELIHOOD_ARGS=(--growth_likelihood lncfu --growth_noise_model normal_kt
+                     --sample_offset_model zero)
+fi
+
+if [[ "${FIT}" == "relative" ]]; then
+    FIT_ARGS=(--theta_model hill_relative)
+else
+    FIT_ARGS=(--theta_model hill_geno
+              --binding_df tfs_sim_binding.csv
+              --binding_weight 1.0)
+fi
+
+echo ">>> Configure (${FIT}, ${LIKELIHOOD})"
+tfs-configure-model \
+    --growth_df tfs_sim_growth.csv \
+    --presplit_df tfs_sim_presplit.csv \
+    --condition_growth_model linear \
+    --growth_transition_model instant \
+    --ln_cfu0_model hierarchical \
+    --dk_geno_model hierarchical_geno \
+    --activity_model fixed \
+    --theta_rescale_model passthrough \
+    --theta_growth_noise_model zero \
+    --theta_binding_noise_model zero \
+    --transformation_model single \
+    --library_config tfs_sim_config.yaml \
+    --growth_shares_replicates \
+    "${FIT_ARGS[@]}" \
+    "${LIKELIHOOD_ARGS[@]}"
+
+if [[ -n "${N_SCALE}" ]]; then
+    grep -q '^theta.theta_log_hill_n_hyper_scale_fixed,' tfs_configure_priors.csv
+    sed -i "s/^theta.theta_log_hill_n_hyper_scale_fixed,[^,]*/theta.theta_log_hill_n_hyper_scale_fixed,${N_SCALE}/" \
+        tfs_configure_priors.csv
+    echo "log hill_n population SD held at ${N_SCALE}"
+fi
+
+# ---------------------------------------------------------------------------
+# 3. Pre-fit calibration (joint only: it needs binding), fit, posterior
+# ---------------------------------------------------------------------------
+if [[ "${FIT}" == "joint" ]]; then
+    echo ">>> Pre-fit calibration"
+    tfs-prefit-calibration tfs_configure_config.yaml --seed "${SEED}" \
+        --k_scale_ceiling 0.005
+fi
+
+case "${INFERENCE}" in
+    component) INFERENCE_ARGS=() ;;
+    low_rank)  INFERENCE_ARGS=(--guide_type auto_low_rank_multivariate_normal)
+               if [[ -n "${GUIDE_RANK}" ]]; then
+                   INFERENCE_ARGS+=(--guide_rank "${GUIDE_RANK}")
+               fi ;;
+    # a converged MAP took ~130,000 epochs on run 1's run 0002; Laplace
+    # needs it converged (a MAP stopped at a fixed step size had 9 negative
+    # Hessian eigenvalues and gave 95% intervals ~1e10 wide)
+    map)       INFERENCE_ARGS=(--analysis_method map --max_num_epochs 200000) ;;
+    # MAP as for map, then two_stage.py: SVI refits at Laplace draws of k, m
+    # with the component guide (two_stage) or the low-rank autoguide
+    # (two_stage_low_rank)
+    two_stage|two_stage_low_rank)
+               INFERENCE_ARGS=(--analysis_method map --max_num_epochs 200000) ;;
+    *) echo "unknown inference '${INFERENCE}'" >&2; exit 1 ;;
+esac
+
+echo ">>> Fit model (${INFERENCE})"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" "${INFERENCE_ARGS[@]}"
+
+if [[ "${INFERENCE}" != "map" && "${INFERENCE}" != two_stage* ]]; then
+    echo ">>> Guide diagnostics"
+    # diagnostics only; never stops the run
+    python "{{ diagnostics_script }}" tfs_fit_model_checkpoint.pkl \
+        --num_draws 2000 --out_prefix tfs_guide_diagnostics \
+        || echo "guide diagnostics failed" >&2
+fi
+
+if [[ "${INFERENCE}" == two_stage* ]]; then
+    if [[ "${INFERENCE}" == "two_stage_low_rank" ]]; then
+        GUIDE=auto_low_rank_multivariate_normal
+    else
+        GUIDE=component
+    fi
+    echo ">>> Laplace posterior (stage 1)"
+    tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+        --out_prefix tfs_laplace
+    echo ">>> Conditional fits at Laplace draws of k, m (stage 2)"
+    python "{{ two_stage_script | default('two_stage.py') }}" \
+        --laplace_file tfs_laplace.h5 --num_draws 10 --samples_per_draw 1000 \
+        --seed "${SEED}" --guide_type "${GUIDE}" --out_prefix tfs_posterior
+else
+    echo ">>> Sample posterior"
+    tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl
+fi
+
+# ---------------------------------------------------------------------------
+# 4. Extract, predict, summarize
+# ---------------------------------------------------------------------------
+echo ">>> Extract parameters"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict theta"
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict growth"
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+    --num_marginal_samples=500
+
+echo ">>> Summarize fit"
+tfs-summarize-fit .
+
+echo ">>> Done"
diff --git a/planning/studies/relative-m/README.md b/planning/studies/relative-m/README.md
new file mode 100644
index 00000000..ebea06b4
--- /dev/null
+++ b/planning/studies/relative-m/README.md
@@ -0,0 +1,366 @@
+# m from relative information only
+
+**Question.** With the per-tube level offsets free, so that the count
+likelihood keeps only relative abundances within each tube, does stage 1's
+m land on the within-tube values on the real data and on the truth on the
+simulations? (`planning/relative-m.md`, step 1.)
+
+**Decision it feeds.** Whether the two-stage fit should take m (and X)
+from relative information only, dropping the tube totals and the k shift
+(`planning/relative-m.md`, steps 2-6).
+
+## Design
+
+`stage1_relative.py` reruns the two-stage fit's stage 1 (wt, spikes and
+singles with the congression mixture) with:
+- `sigma_fixed=10` for the level offsets (nearly free; they absorb each
+  tube's total and any growth shift shared by a tube);
+- k pinned per condition at the run's current stage 1 (it drops out of the
+  likelihood, so its value does not matter);
+- m free under Normal(0, 0.05), wider than the absolute stage 1's 0.01 so
+  the prior cannot hold pheS's m near 0.
+
+Runs: real, seed 1, seed 2.
+
+## How to run
+
+From the cluster's two-stage validation directory (each needs its
+`run_*/stage1/km.csv`):
+
+```bash
+for r in real seed1 seed2; do sbatch --export=ALL,RUN=$r /gpfs/home/harms/tfscreen/planning/studies/relative-m/stage1_relative.srun; done
+```
+
+Writes `rel_{run}/stage1/`.
+
+## Pass
+
+- Seeds: m within about 5% of the truth (the absolute stage 1's m / truth
+  was 1.04-1.06).
+- Real: m near the within-tube values (kanR+kan about -0.005, pheS+4CP
+  about +0.012 to +0.018; ratio about -2.3).
+- The within-tube posterior-predictive check
+  (`../within-tube-m/ppc_within_tube.py`) reproduces the observed
+  contrast.
+- No held direction in the Laplace beyond what stage 1 has now.
+
+## Commit
+
+The commit that adds this study.
+
+## Results
+
+### Run 1 (2026-10-15)
+
+`analyze.py` and `swing_ppc.py` (the fit's per-genotype within-tube swings
+against the observed, after `tfs-predict-growth` at the MAP, locally).
+
+**The seeds pass; the real data do not move.**
+
+| | relative m | absolute m | within tube | pheS/kanR (relative, absolute) |
+|---|---|---|---|---|
+| real kanR+kan | -0.0177 | -0.0148 | -0.0049 | |
+| real pheS+4CP | +0.0086 | +0.0070 | +0.0178 | -0.49, -0.47 |
+| seed 1, m / truth | 1.06, 1.05 | 1.06, 1.04 | | -1.00, -1.00 |
+| seed 2, m / truth | 1.05, 1.03 | 1.05, 1.02 | | -0.99, -0.99 |
+
+On the simulations the relative fit recovers the truth as well as the
+absolute one. On the real data freeing the offsets left the ratio at
+-0.49, so the tube totals are not what sets the fit's m, and the OD600
+explanation of `planning/studies/within-tube-m/` does not hold. The real
+run had not converged at its 100,000-step cap (still descending, not by
+enough to close a 5x gap); the seeds converged.
+
+**The fit discards pheS's IPTG response.** Across 903 deep singles, the
+fit's predicted swing against the observed: kanR+kan slope 0.63 (r 0.64,
+median 0.0037 predicted, 0.0045 observed); pheS+4CP slope 0.29 (r 0.59,
+median 0.0020 predicted, 0.0165 observed). The data's pheS/kanR ratio is
+-2.2, the fit's -0.42; wt 0.004 predicted against 0.018 observed.
+
+**Reading.** The constraint is the X shared by both markers: one Hill
+curve per genotype. In the library, wt's within-tube pheS response starts
+at about 0.1-1 uM IPTG and kanR's at 10-100 uM
+(`../within-tube-m/`, run 1), so one curve cannot fit both; the fit
+follows kanR and shrinks pheS's m. That would make pheS growth a strongly
+nonlinear function of occupancy (a little derepression is already toxic
+in 4CP). wt's monoculture pheS curve does not drop early, so if it holds
+it is specific to the library.
+
+**Next.** Model-free: an IPTG midpoint for each deep single's within-tube
+curve in each marker. pheS midpoints systematically below kanR's would
+mean the markers read occupancy differently (a per-marker map from X to
+growth). Steps 2-6 of `planning/relative-m.md` wait on it.
+
+### Run 2: per-marker midpoints (2026-10-15)
+
+`midpoints.py` fits each deep genotype's within-tube curve (against
+M42I/H74A/K84L) in each marker with y = A (1 - s(c)), s a Hill in IPTG
+(n bounded to 0.3-4), and compares the 136 singles whose swing is beyond
+3 SE in both (`out/midpoints.pdf`).
+
+**Midpoints differ modestly.** pheS's K is a median 1.5x lower than
+kanR's (ln K difference -0.42, IQR -0.66 to +0.34, 71% below 0; median K
+18 uM against 29 uM).
+
+**Steepness differs a lot.** kanR's curves are switch-like (median n at
+the bound of 4; the drop between about 30 and 100 uM). pheS's are broad
+(median n 0.93; a gradual decline over about three decades). wt: kanR K
+44 uM, n 4; pheS K 18 uM, n 0.90.
+
+**Reading.** One Hill curve per genotype cannot be steep in one marker
+and broad in the other, so the shared-X fit follows kanR and flattens
+pheS (run 1's 8x shortfall in pheS swings). The markers seem to turn
+occupancy into growth through differently shaped maps: kanR like a
+threshold in expression, 4CP toxicity graded. The model assumes both are
+linear in X.
+
+**An anomaly at 0 IPTG in pheS.** For the singles and for wt the pheS
+curve at 0 IPTG is well below its value at 0.1 uM (median single: the
+0.1 uM value is about 1.5x the 0 IPTG value). Binding does not change
+between 0 and 0.1 uM, so the 0-IPTG pheS+4CP tubes may be off
+systematically. They are the gauge point, so this needs settling before
+modeling pheS.
+
+**Next.** (1) The 0-IPTG pheS tubes: their t_sel, totals and reads
+against the 0.1 uM tubes, and whether the anomaly holds against other
+flat references. (2) If it is a tube problem, a per-marker monotone map
+from X to growth (for example a Hill or logistic warp per selection
+condition, gauged on wt) in the relative fit, tested on the simulations
+(which need a nonlinear marker map added) and against binding.
+
+### Run 3: the 0-IPTG pheS anomaly is one row of tubes (2026-10-15)
+
+Tube totals cancel in a within-tube ratio, so the anomaly had to be a
+difference in relative growth. Against both flat pure spikes the 421
+singles with >= 1e6 reads grew 0.010 per minute faster at 0.1 uM than at 0
+in pheS+4CP (the two spikes agreed with each other; against wt the singles
+moved -0.002), and not in kanR+kan. Per replicate, one row drove it:
+pheS+4CP replicate 2, t_sel 82.
+
+`tube_qc.py` scores every tube's apparent IPTG from its genotype
+composition (0 like the 0-IPTG tubes, 1 like the 1 mM tubes). Every
+replicate x time row rises smoothly from about 0 to 1.1-1.3 in both
+markers except that one:
+
+| pheS+4CP rep 2, t_sel 82 | 0 | 0.1 uM | 1 uM | 10 uM | 30 uM | 1 mM |
+|---|---|---|---|---|---|---|
+| apparent IPTG | 0.01 | 0.91 | 0.48 | 0.19 | 0.22 | 0.06 |
+
+The tube labeled 0.1 uM looks like a 1 mM tube and the one labeled 1 mM
+like a low-IPTG tube; 1 uM is also off. The rows with missing
+concentrations (replicate 1 t 200, replicate 2 t 185 and 199,
+replicate 3) are shifted by the centering, not swapped. A physical swap
+or a sample-sheet label swap would both do this; the lab's records can
+say which (the user: tube mixups were cross-checked and unlikely).
+
+Without that row the 0 vs 0.1 uM difference is +0.0010 (was +0.0097).
+The rest stands: kanR switch-like (median n at the bound of 4, K 35 uM)
+and pheS broad (median n 1.08, K 19 uM) over 69 singles responsive in
+both; wt's within-tube swing 0.0125 in pheS (near the monoculture's
+0.0145, was 0.018) and -0.0046 in kanR.
+
+**Next.** Check the records for pheS+4CP replicate 2, t_sel 82
+(0.1 uM, 1 uM, 1 mM) and exclude or relabel those tubes. Then a
+per-marker monotone map from X to growth (`planning/relative-m.md`).
+
+### Run 4 setup: a warp for one marker (2026-10-15)
+
+`linear` growth now takes `m_warp` per condition: that condition's growth
+follows f(X) = (s(X) - s(0)) / (s(1) - s(0)), s = sigmoid(a (X - x0)),
+with log a ~ Normal(1, 1.5) and x0 ~ Normal(0.5, 0.5) per warped
+condition; f(0) = 0 and f(1) = 1 keep the wt gauge, and f is linear as
+a -> 0.
+
+Which marker to warp: a monotone map of X changes values, not where in
+IPTG they change, so the broad marker (pheS) must carry X itself and the
+switch-like one (kanR) a steep map of it. A threshold x0 below 1 also
+predicts kanR responding at higher IPTG than pheS, as measured (35 against
+19 uM). The reverse is run as a check, and seed 1 (linear maps) as a null.
+
+`stage1_warp.py` is the relative stage 1 (offsets free, k pinned, m free)
+with the warp, leaving out the pheS+4CP replicate 2, t_sel 82 row on the
+real data until the lab's records settle it (run 3). Runs:
+
+| run | warped | tubes left out |
+|---|---|---|
+| warp_real_kanR | kanR+kan | pheS+4CP rep 2, t_sel 82 |
+| warp_real_pheS | pheS+4CP | the same |
+| warp_real_none | none | the same (the reference without a warp) |
+| warp_seed1_kanR | kanR+kan | none |
+
+Pass: on the real data, the kanR warp lowers the loss against both the
+no-warp and the pheS-warp runs, its fit follows the within-tube swings in
+both markers (`swing_ppc.py`), and m's pheS/kanR ratio comes near the
+within-tube one; on seed 1 the warp stays close to linear (small a or x0
+near the middle) and m stays on the truth.
+
+### Run 4 results (2026-10-16)
+
+The three real runs first failed (sbatch `--export` split the
+`--drop_tubes` triple on its commas; now `:`-separated, and the stage 1
+table is moved into place only when complete). `compare_warp.py`:
+
+| real (rep 2, t 82 left out) | kanR warped | pheS warped | no warp |
+|---|---|---|---|
+| final exact loss | 806,900 | 816,700 | 814,900 |
+| m kanR+kan / pheS+4CP | -0.0120 / +0.0088 | -0.0102 / +0.0041 | -0.0174 / +0.0095 |
+| pheS/kanR (within tube -2.7) | -0.74 | -0.41 | -0.55 |
+| wt's pheS swing predicted (observed 0.0125) | 0.0060 | 0.0031 | 0.0047 |
+| singles' pheS swing, predicted / observed (median) | 0.36 | 0.28 | 0.22 |
+| warp a, x0 | 2.96, 1.35 | 3264, 0.39 | |
+
+The kanR warp lowers the loss by about 8,000 nats for two parameters; the
+pheS warp collapsed to a step at X = 0.39 and fits worse than none. The
+fitted kanR map, f(0.2, 0.4, 0.6, 0.8) = 0.06, 0.16, 0.33, 0.60, holds
+kanamycin growth down only near full repression: a little derepression
+already gives much of the resistance. On seed 1 the warp is nearly linear
+(a 0.84, x0 1.07; f(0.2..0.8) = 0.18, 0.38, 0.58, 0.79) and m stays near the
+truth (1.07-1.09 of it, against 1.05-1.06 without), so the null passes.
+None of the real runs converged by the 100,000-step cap.
+
+**Reading.** The kanR warp is real and worth keeping, but it closes little
+of the pheS gap: pheS's swings are still predicted about 3x too small and
+the ratio reaches -0.74. Something else in the likelihood holds pheS's m
+down.
+
+**Next.** Likelihood attribution: refit stage 1 with the kanR warp and m
+pinned at the within-tube values (kanR -0.0046, pheS +0.0125), both runs
+to convergence, and compare the per-observation log likelihood with the
+free fit by marker, genotype class and tube.
+
+### Run 5 setup: likelihood attribution (2026-10-16)
+
+`attribute.py` traces a stage 1 at its MAP and keeps the count log
+likelihood of every growth observation (the `growth_obs` site, mapped back
+to its row through the growth table's index columns) and the log prior of
+every latent site, then sums the difference between two fits by marker,
+genotype class, IPTG and tube. Checked on run 4 (kanR warp against no
+warp): the warp's 8,100 nats come from the pheS+4CP singles (+9,700), with
+the kanR conditions losing about 1,900; wt's 40 pheS+4CP rows move by 15
+nats, so the gauge genotype carries almost no weight in the likelihood.
+
+Two runs, both with the kanR warp, without the replicate 2, t_sel 82 row
+and with the step cap raised to 500,000:
+
+| run | m |
+|---|---|
+| warp_real_attrfree | free |
+| warp_real_attrpinned | pinned at the within-tube values: kanR+kan -0.0046, pheS+4CP +0.0125 |
+
+`attribute.py` on the pair says what loses likelihood when the selection
+markers' m equal wt's own within-tube swings.
+
+### Run 5 results (2026-10-16)
+
+Both runs converged: free at 50,000 steps (loss 806,876; m kanR+kan
+-0.0119, pheS+4CP +0.0088, as run 4), pinned at 204,000 (810,725).
+
+Pinning m at the within-tube values costs 3,689 nats of count log
+likelihood and 155 of log prior (mostly the log K population SD and
+offsets), so the data, not the priors, prefer the free m. By group
+(pinned minus free):
+
+| | kanR+kan | kanR-kan | pheS+4CP | pheS-4CP |
+|---|---|---|---|---|
+| wt | -833 | -180 | -610 | -105 |
+| singles >= 1e6 reads | -678 | -8 | -1,297 | -235 |
+| singles 1e5-1e6 | -18 | -11 | +254 | -137 |
+| spikes | +42 | +7 | +23 | +7 |
+| total | -1,459 | -180 | -1,593 | -457 |
+
+wt alone loses 1,729 nats over its rows, in every condition including
+both no-selection controls, and in most series the loss grows with time
+(pheS+4CP replicate 1: -99, -120, -175 over t_sel 94, 111, 125). The
+pinned fit's kanR warp went step-like (a 11, x0 -0.05) and its X
+population locations moved (X_low 0.87 -> 0.53).
+
+Against the geometric mean of all deep singles instead of the flat spikes,
+the arithmetic is consistent (wt - spike = (wt - library) - (spike -
+library)), so the spikes are not the odd ones; the library on average
+swings about 0.83 of wt in kanR and 0.55 in pheS, which one shared X
+cannot do.
+
+**Reading.** The model can match wt's swing against the flat spikes or
+wt's levels against the rest of the library, not both. A wt whose growth
+relative to the library is not constant over the selection (a lag, a
+transient, a dk that changes with time) would do this; wt losing
+likelihood in the no-selection tubes, where m hardly matters, points the
+same way.
+
+**Next.** wt's observed and predicted ln(wt / library) against t_sel per
+condition and IPTG under both fits: a time trend or a level.
+
+### Run 6: wt against the library over time (2026-10-16)
+
+`wt_trajectory.py` plots ln(wt / library) per tube (the library: the
+geometric mean of the 881 deep singles in every tube) against t_sel for
+each condition and IPTG, observed and predicted by run 5's free and
+pinned fits (`out/*_wt-trajectory.pdf`, `out/wt_trajectory_residuals.csv`).
+
+**Not a wt transient.** The free fit follows wt against the library in
+every condition: mean residuals within +-0.06, trends within +-0.0025 per
+minute. The pinned fit misses by 0.3-0.8 in level and about -0.01 per
+minute in trend everywhere, the no-selection controls included: pinning m
+at the spike-based values makes the fit move the whole library's growth
+against wt, which the controls reject.
+
+**Not bulk origin.** wt's within-tube swing against weak binders that are
+pure spikes and against ones that come 69% from the bulk is the same
+(kanR+kan: -0.0051, -0.0046 pure; -0.0052 K84L; pheS+4CP +0.0093, +0.0125
+pure; +0.0087 K84L, +0.0074 H74A), so congression is not the difference.
+
+**The scale of X.** A within-tube swing is m (wt's X range - the
+reference's X range); turning it into m needs a reference whose X does not
+change. Each m so far rests on a different anchor:
+
+| anchor | kanR+kan | pheS+4CP |
+|---|---|---|
+| tube totals (absolute stage 1) | -0.0148 | +0.0070 |
+| priors and Hill shape (relative fit, kanR warp) | -0.0119 | +0.0088 |
+| weak binders flat (binding and expression) | -0.0046 to -0.0052 | +0.0087 to +0.0125 |
+
+pheS roughly agrees once the swapped row is out; kanR differs about 2.5x.
+The free fit reconciles kanR by giving the weak binders a partial kanR
+response (X range about 0.4-0.6 of wt's), against their flat binding. But
+kanR-alone X at the absolute m matched binding theta at 2 uM with no
+correction (`planning/studies/x-vs-binding/`), which a 2.5x smaller m
+would undo. So binding supports the larger kanR m and the weak binders'
+flatness the smaller one.
+
+**Decision needed** (user): which external fact anchors kanR's scale:
+binding (calibrate X to the spikes' 2 uM curves), flat weak binders (hold
+the pure weak-binder spikes' X constant), or neither for now (report
+kanR's scale as uncertain over that range). Knowing whether weak binders'
+kanR growth can respond to IPTG at flat occupancy would settle it.
+
+
+### Run 7: can the data order the replicate 2, t_sel 82 tubes? (2026-10-19)
+
+The lab found no mechanism for a three-way swap; the one candidate is a
+rack turned around. `row_order.py` scores each tube's IPTG signature (793
+genotypes with > 2e5 pheS reads, projection on the other rows' mean 1 mM
+direction, relative to the row's 0 tube) and fits all 720 assignments of
+the row's six labels to its six tubes against the other rows' profile
+(fraction of the 1 mM shift: 0, 0, 0.02, 0.21, 0.49, 1.00 at 0, 0.1, 1,
+10, 30 uM and 1 mM).
+
+| tube labeled | 0 | 0.1 uM | 1 uM | 10 uM | 30 uM | 1 mM |
+|---|---|---|---|---|---|---|
+| observed (other rows' 1 mM at t 94-127: 7.7-11.5) | 0 | 6.12 | 3.37 | 0.04 | -0.55 | -0.14 |
+| reversed, 0 in place: true concentration | 0 | 1 mM | 30 uM | 10 uM | 1 uM | 0.1 uM |
+
+- As labeled: rank 188 of 720 (residual SS 25.8, negative scale).
+- The five IPTG tubes reversed with the 0 tube in place: rank 10 (SS
+  1.48, scale 6.6, in line with the 1 mM shift at the other rows' times).
+  The best assignment (SS 1.30) differs only among the low tubes, which
+  the data cannot tell apart (0, 0.1 and 1 uM all sit near 0).
+- Reversing all six, the 0 tube included: rank 478.
+
+So the data do support a reversal of the IPTG tubes: the tube labeled
+0.1 uM is a 1 mM tube and the one labeled 1 uM a 30 uM tube, beyond
+doubt. Two things keep it short of a relabel: the 10 uM slot reads like
+a low tube (0.04 against 1.4 expected), and the low tubes' order is not
+identifiable. Natalie rules out a flip of the IPTG tubes with the 0
+tube in place, so the fit's support for it is a coincidence of the
+signature or an unknown mix-up. Decision (user, 2026-10-19): drop the row; record it as an
+exclusion in a corrections file with this as the evidence.
diff --git a/planning/studies/relative-m/analyze.py b/planning/studies/relative-m/analyze.py
new file mode 100644
index 00000000..11be2174
--- /dev/null
+++ b/planning/studies/relative-m/analyze.py
@@ -0,0 +1,60 @@
+"""
+Relative stage 1 against the absolute stage 1, the simulations' truth and
+the real data's within-tube m (README).
+
+    python analyze.py runs ../two-stage-validation
+
+RUNS_DIR holds rel_{run}/stage1/ (km.csv, tfs_configure_config.yaml);
+VALIDATION_DIR holds run_{run}/stage1/km.csv. Truth on the X gauge comes
+from the m-bias study's local copies of each simulation's parameters.
+"""
+
+import argparse
+import os
+import sys
+
+import pandas as pd
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, os.path.join(HERE, "..", "two-stage-validation"))
+from validate import truth_km  # noqa: E402
+
+SIM_DIRS = {"seed1": "../m-bias-factorial/m_subsets/no_doubles_mix",
+            "seed2": "../m-bias-factorial/sim_realistic_seed2"}
+WITHIN_TUBE = {"kanR+kan": -0.0049, "pheS+4CP": 0.0178}
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("runs_dir")
+    ap.add_argument("validation_dir")
+    args = ap.parse_args()
+    for run in ("real", "seed1", "seed2"):
+        rel = os.path.join(args.runs_dir, f"rel_{run}", "stage1")
+        if not os.path.exists(os.path.join(rel, "km.csv")):
+            print(f"{rel}: not there yet")
+            continue
+        t = pd.read_csv(os.path.join(rel, "km.csv"), index_col="condition_rep")
+        t = t.rename(columns={"k": "k_pinned", "m": "m_relative"})
+        a = pd.read_csv(os.path.join(args.validation_dir, f"run_{run}", "stage1",
+                                     "km.csv"), index_col="condition_rep")
+        t["m_absolute"] = a["m"]
+        if run in SIM_DIRS:
+            tr = truth_km(os.path.join(HERE, SIM_DIRS[run]),
+                          os.path.join(rel, "tfs_configure_config.yaml"))
+            t["m_truth"] = tr["m"]
+            t["relative/truth"] = t["m_relative"] / t["m_truth"]
+            t["absolute/truth"] = t["m_absolute"] / t["m_truth"]
+        else:
+            t["m_within_tube"] = pd.Series(WITHIN_TUBE)
+        sel = [c for c in t.index if "+" in c]
+        if len(sel) == 2:
+            k, p = sorted(sel)
+            print(f"\n== {run}: pheS/kanR m ratio, relative "
+                  f"{t.loc[p, 'm_relative'] / t.loc[k, 'm_relative']:+.2f}, absolute "
+                  f"{t.loc[p, 'm_absolute'] / t.loc[k, 'm_absolute']:+.2f}")
+        print(t.to_string(float_format="%+.5f"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/relative-m/attribute.py b/planning/studies/relative-m/attribute.py
new file mode 100644
index 00000000..ee820ad9
--- /dev/null
+++ b/planning/studies/relative-m/attribute.py
@@ -0,0 +1,121 @@
+"""
+Where two stage 1 fits differ in likelihood (README, run 5): the count
+log likelihood of every growth observation and the log prior of every
+latent site, at each fit's MAP, and their differences summed by marker,
+genotype class, IPTG and tube.
+
+    python attribute.py runs/attr_free/stage1 runs/attr_pinned/stage1 --out out/attr
+
+Each folder is a stage 1 (tfs_configure_config.yaml or config_local.yaml,
+tfs_fit_model_params.npz, tfs_growth.csv). The difference is the second
+minus the first: negative where the second fit explains the data worse.
+"""
+
+import argparse
+import os
+import warnings
+
+warnings.filterwarnings("ignore")
+
+import jax  # noqa: E402
+import jax.numpy as jnp  # noqa: E402
+import numpy as np  # noqa: E402
+import pandas as pd  # noqa: E402
+from numpyro import handlers  # noqa: E402
+
+from tfscreen.tfmodel.configuration_io import read_configuration  # noqa: E402
+from tfscreen.tfmodel.inference.run_inference import RunInference  # noqa: E402
+
+CLUSTER = "/gpfs/projects/harmslab/harms/studies/two-stage-validation"
+IDX = ["replicate_idx", "time_idx", "condition_pre_idx", "condition_sel_idx",
+       "titrant_name_idx", "titrant_conc_idx", "genotype_idx"]
+KEY = ["genotype", "replicate", "condition_sel", "titrant_conc", "t_sel"]
+SPIKES = {"M42I", "H74A", "K84L", "D88A", "M42I/H74A", "M42I/K84L",
+          "H74A/K84L", "M42I/H74A/K84L"}
+
+
+def config_path(stage1):
+    local = os.path.join(stage1, "config_local.yaml")
+    if not os.path.exists(local):
+        text = open(os.path.join(stage1, "tfs_configure_config.yaml")).read()
+        run = os.path.basename(os.path.dirname(os.path.abspath(stage1)))
+        open(local, "w").write(text.replace(f"{CLUSTER}/{run}/stage1/", ""))
+    return "config_local.yaml"
+
+
+def score(stage1):
+    """Per-row count log likelihood and per-site log prior at the MAP."""
+    cwd = os.getcwd()
+    os.chdir(stage1)
+    try:
+        orch, _ = read_configuration(config_path("."))
+        ri = RunInference(orch, 0)
+        with np.load("tfs_fit_model_params.npz") as p:
+            values = {k[:-len("_auto_loc")]: jnp.asarray(p[k]) for k in p.files
+                      if k.endswith("_auto_loc")}
+        G = orch.data.num_genotype
+        batch = ri._unscaled_batch(jax.device_put(orch.data), jnp.arange(G))
+        model = handlers.substitute(orch.jax_model, data=values)
+        tr = handlers.trace(handlers.seed(model, 0)).get_trace(
+            data=batch, priors=orch.priors)
+        site = tr["growth_obs"]
+        lp = np.asarray(site["fn"].log_prob(site["value"]))
+        df = orch.growth_df
+        ix = tuple(df[c].to_numpy() for c in IDX)
+        rows = df[KEY].copy()
+        rows["ll"] = lp[ix]
+        priors = {n: float(s["fn"].log_prob(s["value"]).sum())
+                  for n, s in tr.items() if s["type"] == "sample" and not s["is_observed"]}
+        return rows, pd.Series(priors), float(lp[np.asarray(orch.data.growth.good_mask)].sum())
+    finally:
+        os.chdir(cwd)
+
+
+def genotype_class(g, reads):
+    if g == "wt":
+        return "wt"
+    if g in SPIKES:
+        return "spike"
+    r = reads.get(g, 0)
+    for edge, name in ((1e4, "single <1e4"), (1e5, "single 1e4-1e5"),
+                       (1e6, "single 1e5-1e6")):
+        if r < edge:
+            return name
+    return "single >=1e6"
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("first")
+    ap.add_argument("second")
+    ap.add_argument("--out", default="out/attr")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+    a, pa, tot_a = score(args.first)
+    b, pb, tot_b = score(args.second)
+    m = a.merge(b, on=KEY, suffixes=("_1", "_2"))
+    m["d"] = m["ll_2"] - m["ll_1"]
+    reads = pd.read_csv(os.path.join(args.first, "tfs_growth.csv"),
+                        usecols=["genotype", "counts"]).groupby("genotype")["counts"].sum()
+    m["class"] = m["genotype"].map(lambda g: genotype_class(g, reads))
+    m.to_csv(os.path.join(args.out, "per_row.csv"), index=False)
+
+    print(f"count log likelihood: first {tot_a:.1f}, second {tot_b:.1f}, "
+          f"second - first {tot_b - tot_a:+.1f} ({len(m)} rows matched)")
+    dp = (pb - pa).dropna()
+    print(f"log prior: second - first {dp.sum():+.1f}; largest sites:")
+    print(dp[dp.abs().sort_values(ascending=False).index].head(8).round(1).to_string())
+    for by in (["condition_sel"], ["condition_sel", "class"],
+               ["condition_sel", "titrant_conc"],
+               ["condition_sel", "replicate", "t_sel"]):
+        t = m.groupby(by, observed=True)["d"].agg(["sum", "size"]).round(1)
+        t = t[t["size"] > 0]
+        print(f"\n== second - first, by {', '.join(by)}")
+        print(t.to_string())
+    g = m.groupby("genotype")["d"].sum().sort_values()
+    print("\n== genotypes losing most under the second fit")
+    print(g.head(10).round(1).to_string())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/relative-m/compare_warp.py b/planning/studies/relative-m/compare_warp.py
new file mode 100644
index 00000000..ef8a45e8
--- /dev/null
+++ b/planning/studies/relative-m/compare_warp.py
@@ -0,0 +1,82 @@
+"""
+Compare the warp runs (README, run 4): m and the pheS/kanR ratio, the
+fitted warp (a, x0), the final exact loss, and each deep single's
+predicted within-tube swing against the observed (after
+tfs-predict-growth at the MAP, run here if missing).
+
+    python compare_warp.py runs warp_real_kanR warp_real_pheS warp_real_none warp_seed1_kanR
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+
+import numpy as np
+import pandas as pd
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+CLUSTER = "/gpfs/projects/harmslab/harms/studies/two-stage-validation"
+WITHIN_TUBE = {"kanR+kan": -0.0046, "pheS+4CP": 0.0125}   # without rep 2, t 82
+
+
+def localize(stage1, run):
+    cfg = os.path.join(stage1, "config_local.yaml")
+    if not os.path.exists(cfg):
+        text = open(os.path.join(stage1, "tfs_configure_config.yaml")).read()
+        open(cfg, "w").write(text.replace(f"{CLUSTER}/{run}/stage1/", ""))
+    return cfg
+
+
+def predict(stage1, run):
+    if os.path.exists(os.path.join(stage1, "pred_growth.csv")):
+        return
+    localize(stage1, run)
+    code = ("from tfscreen.tfmodel.scripts.predict_growth_cli import predict_growth;"
+            "predict_growth('config_local.yaml','tfs_fit_model_checkpoint.pkl',"
+            "out_prefix='pred_growth')")
+    subprocess.run([sys.executable, "-c", code], cwd=stage1, check=True,
+                   stdout=subprocess.DEVNULL)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("runs_dir")
+    ap.add_argument("runs", nargs="+")
+    args = ap.parse_args()
+    rows = []
+    for run in args.runs:
+        st = os.path.join(args.runs_dir, run, "stage1")
+        if not os.path.exists(os.path.join(st, "km.csv")):
+            print(f"{run}: not there yet")
+            continue
+        km = pd.read_csv(os.path.join(st, "km.csv"), index_col="condition_rep")
+        z = np.load(os.path.join(st, "tfs_fit_model_params.npz"))
+        conv = pd.read_csv(os.path.join(st, "tfs_fit_model_convergence.csv"))
+        rec = dict(run=run, m_kanR=km.loc["kanR+kan", "m"], m_pheS=km.loc["pheS+4CP", "m"],
+                   loss=conv["loss_exact"].iloc[-1], steps=int(conv.iloc[-1, 0]),
+                   decision=conv["decision"].iloc[-1])
+        rec["ratio"] = rec["m_pheS"] / rec["m_kanR"]
+        if "condition_growth_warp_log_a_auto_loc" in z.files:
+            rec["warp_a"] = float(np.exp(np.asarray(z["condition_growth_warp_log_a_auto_loc"]).ravel()[0]))
+            rec["warp_x0"] = float(np.asarray(z["condition_growth_warp_x0_auto_loc"]).ravel()[0])
+        predict(st, run)
+        out = os.path.join(args.runs_dir, run, "swing")
+        subprocess.run([sys.executable, os.path.join(HERE, "swing_ppc.py"), st, "--out", out],
+                       check=True, stdout=subprocess.DEVNULL)
+        s = pd.read_csv(os.path.join(out, "swing_ppc.csv"))
+        sg = s[~s["genotype"].str.contains("/") & (s["genotype"] != "wt")]
+        for cond, tag in (("kanR+kan", "kanR"), ("pheS+4CP", "pheS")):
+            o, p = sg[f"obs_{cond}"], sg[f"pred_{cond}"]
+            rec[f"ppc_slope_{tag}"] = np.polyfit(o, p, 1)[0]
+            rec[f"ppc_med_ratio_{tag}"] = p.abs().median() / o.abs().median()
+        w = s[s["genotype"] == "wt"].iloc[0]
+        rec["wt_pred_pheS"], rec["wt_obs_pheS"] = w["pred_pheS+4CP"], w["obs_pheS+4CP"]
+        rows.append(rec)
+    t = pd.DataFrame(rows).set_index("run")
+    print(t.T.to_string(float_format=lambda v: f"{v:+.4g}"))
+    print("within tube (no rep 2 t82): kanR -0.0046, pheS +0.0125, ratio -2.7")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/relative-m/midpoints.py b/planning/studies/relative-m/midpoints.py
new file mode 100644
index 00000000..a3d3c45c
--- /dev/null
+++ b/planning/studies/relative-m/midpoints.py
@@ -0,0 +1,139 @@
+"""
+Does each selection marker respond at the same IPTG? (README, run 2.)
+
+For every deep genotype and marker, the within-tube curve y(c) = b(c) -
+b(1 mM), b(c) the slope over t_sel of ln(genotype / M42I/H74A/K84L) at
+IPTG c, is fit with y = A (1 - s(c)), s = sigmoid(n (ln c - ln K)), s(0) =
+0. Genotypes whose swing is beyond 3 SE in both markers are compared on
+ln K. Also the mean normalized curve (y / y(0)) per marker.
+
+    python midpoints.py ../within-tube-m/out/marker_rows.csv --out out
+"""
+
+import argparse
+import os
+import sys
+import warnings
+
+import numpy as np
+import pandas as pd
+from scipy.optimize import curve_fit
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, os.path.join(HERE, "..", "within-tube-m"))
+from within_tube_m import slopes_by_conc, ZERO_X  # noqa: E402
+
+plt.rc("pdf", fonttype=42)
+plt.rc("font", size=14)
+plt.rc("axes", titlesize=14, labelsize=16)
+
+REF = "M42I/H74A/K84L"
+KEY = ["replicate", "condition_sel", "titrant_conc", "t_sel"]
+COND = {"kanR+kan": "#0072B2", "pheS+4CP": "#D55E00"}
+
+
+def hill_drop(c, A, lnK, n):
+    s = np.where(c > 0, 1 / (1 + np.exp(-n * (np.log(np.maximum(c, 1e-12)) - lnK))), 0.0)
+    return A * (1 - s)
+
+
+def curve(t):
+    c, b, cov, _ = slopes_by_conc(t, "lr", "v")
+    j = list(c).index(1.0)
+    se = np.sqrt(np.clip(np.diag(cov) + cov[j, j] - 2 * cov[:, j], 0, None))
+    return c, b - b[j], se
+
+
+def fit(c, y, se):
+    se = np.where(se > 0, se, np.median(se[se > 0]))
+    p0 = [y[0], np.log(0.01), 1.0]
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        p, cov = curve_fit(hill_drop, c, y, p0=p0, sigma=se, absolute_sigma=True,
+                           bounds=([-1, np.log(1e-5), 0.3], [1, np.log(10), 4]),
+                           maxfev=20000)
+    return p, np.sqrt(np.diag(cov))
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("rows_csv")
+    ap.add_argument("--z", type=float, default=3.0)
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+    rows = pd.read_csv(args.rows_csv)
+    ref = rows[rows["genotype"] == REF].set_index(KEY)["counts"].rename("c_ref")
+    out, curves = [], []
+    for g, d in rows[rows["genotype"] != REF].groupby("genotype"):
+        rec = {"genotype": g}
+        for cond in COND:
+            t = d[d["condition_sel"] == cond].set_index(KEY)[["counts"]].join(
+                ref, how="inner").reset_index()
+            t = t[(t["counts"] > 0) & (t["c_ref"] > 0)]
+            if t["titrant_conc"].nunique() < 6:
+                continue
+            t["lr"] = np.log(t["counts"] / t["c_ref"])
+            t["v"] = 1 / t["counts"] + 1 / t["c_ref"]
+            try:
+                c, y, se = curve(t)
+                p, pse = fit(c, y, se)
+            except (ValueError, RuntimeError, np.linalg.LinAlgError):
+                continue
+            rec.update({f"y0_{cond}": y[0], f"y0_se_{cond}": se[0],
+                        f"lnK_{cond}": p[1], f"lnK_se_{cond}": pse[1],
+                        f"n_{cond}": p[2]})
+            curves.append(pd.DataFrame(dict(genotype=g, cond=cond, c=c, y=y, se=se)))
+        out.append(rec)
+    s = pd.DataFrame(out)
+    s.to_csv(os.path.join(args.out, "midpoints.csv"), index=False)
+    cv = pd.concat(curves, ignore_index=True)
+
+    ok = s.dropna(subset=["lnK_kanR+kan", "lnK_pheS+4CP"]).copy()
+    for cond in COND:
+        ok = ok[(ok[f"y0_{cond}"] / ok[f"y0_se_{cond}"]).abs() > args.z]
+    singles = ok[~ok["genotype"].str.contains("/") & (ok["genotype"] != "wt")]
+    d = singles["lnK_pheS+4CP"] - singles["lnK_kanR+kan"]
+    print(f"{len(singles)} singles with a swing beyond {args.z} SE in both markers")
+    print(f"ln K, pheS minus kanR: median {d.median():+.2f} (x{np.exp(d.median()):.2f} "
+          f"in IPTG), IQR {d.quantile(.25):+.2f} to {d.quantile(.75):+.2f}, "
+          f"{(d < 0).mean():.0%} below 0")
+    for cond in COND:
+        k = np.exp(singles[f"lnK_{cond}"]) * 1e3
+        print(f"  {cond}: median K {k.median():.1f} uM, median n {singles[f'n_{cond}'].median():.2f}")
+    show = s[s["genotype"].isin(["wt", "M42I/H74A", "M42I/K84L", "M42I", "H74A", "K84L", "D88A"])]
+    cols = ["genotype"] + [f"{v}_{c}" for c in COND for v in ("y0", "lnK", "n")]
+    print(show[cols].assign(**{f"K_uM_{c}": np.exp(show[f"lnK_{c}"]) * 1e3 for c in COND})
+          .to_string(index=False, float_format="%+.4f"))
+
+    fig, ax = plt.subplots(1, 2, figsize=(13, 5))
+    sel = cv[cv["genotype"].isin(singles["genotype"])]
+    for cond, col in COND.items():
+        x = sel[sel["cond"] == cond].copy()
+        y0 = x[x["c"] == 0.0].set_index("genotype")["y"]
+        x["yn"] = x["y"] / x["genotype"].map(y0)
+        m = x.groupby("c")["yn"].median()
+        ax[0].plot(np.where(m.index > 0, m.index, ZERO_X), m.values, "o-", color=col,
+                   label=f"{cond} (median of {len(y0)} singles)")
+        w = cv[(cv["genotype"] == "wt") & (cv["cond"] == cond)]
+        ax[0].plot(np.where(w.c > 0, w.c, ZERO_X), w.y / w.y.iloc[0], "s--", color=col,
+                   mfc="white", label=f"{cond}, wt")
+    ax[0].set_xscale("log")
+    ax[0].axhline(0, color="0.85", lw=0.8)
+    ax[0].axhline(1, color="0.85", lw=0.8)
+    ax[0].set_xlabel("IPTG (mM; 0 at left)")
+    ax[0].set_ylabel("within-tube curve / its value at 0")
+    ax[0].legend(fontsize=10)
+    ax[1].hist(d, bins=40, color="#999999")
+    ax[1].axvline(0, color="k", lw=1)
+    ax[1].set_xlabel("ln K, pheS minus kanR")
+    ax[1].set_ylabel("singles")
+    fig.tight_layout()
+    fig.savefig(os.path.join(args.out, "midpoints.pdf"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/relative-m/out/2026-10-16_wt-trajectory.pdf b/planning/studies/relative-m/out/2026-10-16_wt-trajectory.pdf
new file mode 100644
index 00000000..e86c16a3
Binary files /dev/null and b/planning/studies/relative-m/out/2026-10-16_wt-trajectory.pdf differ
diff --git a/planning/studies/relative-m/out/attr/per_row.csv b/planning/studies/relative-m/out/attr/per_row.csv
new file mode 100644
index 00000000..fa648cd1
--- /dev/null
+++ b/planning/studies/relative-m/out/attr/per_row.csv
@@ -0,0 +1,106961 @@
+genotype,replicate,condition_sel,titrant_conc,t_sel,ll_1,ll_2,d,class
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+M42I,1,pheS+4CP,0.0,111.0,-8.321146,-8.313929,0.007217407,spike
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+M42I,1,pheS+4CP,0.0001,111.0,-8.032528,-8.168312,-0.13578415,spike
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+M42I,1,pheS+4CP,0.001,111.0,-8.455678,-8.513451,-0.057772636,spike
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+M42I,1,pheS+4CP,0.01,111.0,-7.687441,-7.708956,-0.021514893,spike
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+M42I,1,pheS+4CP,0.03,111.0,-8.6679535,-8.5279455,0.14000797,spike
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+I64S,1,pheS-4CP,1.0,111.0,-7.3511763,-7.4227676,-0.07159138,single >=1e6
+I64S,2,pheS-4CP,1.0,116.0,-7.4649754,-7.508819,-0.043843746,single >=1e6
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+I64S,2,pheS+4CP,0.03,127.0,-7.3230925,-7.362045,-0.03895235,single >=1e6
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+I64S,1,pheS+4CP,0.1,111.0,-7.27911,-7.29678,-0.017670155,single >=1e6
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+I64S,1,pheS+4CP,1.0,111.0,-7.7757688,-7.737515,0.038253784,single >=1e6
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+I64S,2,pheS+4CP,1.0,127.0,-7.6568356,-7.5176535,0.13918209,single >=1e6
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+I64T,1,kanR-kan,0.0,105.0,-6.4124393,-6.518731,-0.10629177,single 1e5-1e6
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+I64T,1,kanR-kan,1.0,92.0,-6.3046346,-6.322182,-0.017547607,single 1e5-1e6
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+I64T,1,kanR-kan,1.0,105.0,-6.740573,-6.8377194,-0.09714651,single 1e5-1e6
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+I64T,1,kanR-kan,1.0,122.0,-6.502762,-6.626335,-0.1235733,single 1e5-1e6
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+I64T,1,kanR+kan,0.0,175.0,-6.326394,-6.245596,0.08079815,single 1e5-1e6
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+I64T,1,kanR+kan,0.003,200.0,-6.069943,-6.0891905,-0.019247532,single 1e5-1e6
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+I64T,1,kanR+kan,0.01,158.0,-8.44769,-8.559011,-0.11132145,single 1e5-1e6
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+I64T,1,kanR+kan,0.01,175.0,-9.469819,-9.5854435,-0.11562443,single 1e5-1e6
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+V95D,2,pheS+4CP,0.0001,116.0,-2.0317469,-1.9832808,0.048466086,single 1e5-1e6
+V95D,1,pheS+4CP,0.0001,125.0,-2.6893454,-2.6443431,0.045002222,single 1e5-1e6
+V95D,2,pheS+4CP,0.0001,127.0,-4.158983,-4.110032,0.04895115,single 1e5-1e6
+V95D,1,pheS+4CP,0.001,94.0,-5.2230186,-5.3078594,-0.084840775,single 1e5-1e6
+V95D,1,pheS+4CP,0.001,111.0,-3.5011399,-3.4229317,0.07820821,single 1e5-1e6
+V95D,2,pheS+4CP,0.001,116.0,-3.6349792,-3.5675511,0.06742811,single 1e5-1e6
+V95D,1,pheS+4CP,0.001,125.0,-2.9558885,-2.882729,0.073159456,single 1e5-1e6
+V95D,2,pheS+4CP,0.001,127.0,-3.6988966,-3.6190476,0.079849005,single 1e5-1e6
+V95D,1,pheS+4CP,0.003,94.0,-2.5175471,-2.4429445,0.074602604,single 1e5-1e6
+V95D,1,pheS+4CP,0.003,111.0,-1.4552904,-1.537286,-0.08199561,single 1e5-1e6
+V95D,2,pheS+4CP,0.003,116.0,-2.6105928,-2.5822806,0.028312206,single 1e5-1e6
+V95D,1,pheS+4CP,0.003,125.0,-2.9598975,-2.8862283,0.073669195,single 1e5-1e6
+V95D,2,pheS+4CP,0.003,127.0,-3.1426718,-3.0675864,0.0750854,single 1e5-1e6
+V95D,1,pheS+4CP,0.01,94.0,-2.9435694,-2.8843694,0.05920005,single 1e5-1e6
+V95D,1,pheS+4CP,0.01,111.0,-1.6474696,-1.6687334,-0.021263719,single 1e5-1e6
+V95D,2,pheS+4CP,0.01,116.0,-2.0320227,-1.9787157,0.053307056,single 1e5-1e6
+V95D,1,pheS+4CP,0.01,125.0,-2.5352745,-2.4743676,0.060906887,single 1e5-1e6
+V95D,2,pheS+4CP,0.01,127.0,-2.7282138,-2.6568522,0.07136154,single 1e5-1e6
+V95D,1,pheS+4CP,0.03,94.0,-2.4779437,-2.4017315,0.07621217,single 1e5-1e6
+V95D,1,pheS+4CP,0.03,111.0,-2.1533055,-2.07689,0.07641554,single 1e5-1e6
+V95D,2,pheS+4CP,0.03,116.0,-3.3887153,-3.3662124,0.0225029,single 1e5-1e6
+V95D,1,pheS+4CP,0.03,125.0,-2.9629056,-2.883016,0.079889536,single 1e5-1e6
+V95D,2,pheS+4CP,0.03,127.0,-1.8716501,-1.7736075,0.09804261,single 1e5-1e6
+V95D,1,pheS+4CP,0.1,94.0,-0.66010803,-0.63597447,0.024133563,single 1e5-1e6
+V95D,1,pheS+4CP,0.1,111.0,-3.272017,-3.2441068,0.027910233,single 1e5-1e6
+V95D,2,pheS+4CP,0.1,116.0,-3.2454257,-3.2105122,0.03491354,single 1e5-1e6
+V95D,1,pheS+4CP,0.1,125.0,-2.7814054,-2.6743195,0.10708594,single 1e5-1e6
+V95D,2,pheS+4CP,0.1,127.0,-2.7332847,-2.6363027,0.096982,single 1e5-1e6
+V95D,1,pheS+4CP,1.0,94.0,-3.3888912,-3.305806,0.0830853,single 1e5-1e6
+V95D,1,pheS+4CP,1.0,111.0,-6.420948,-6.536834,-0.115885735,single 1e5-1e6
+V95D,2,pheS+4CP,1.0,116.0,-3.0125499,-2.908267,0.104282856,single 1e5-1e6
+V95D,1,pheS+4CP,1.0,125.0,-3.3308768,-3.207587,0.12328982,single 1e5-1e6
+V95D,2,pheS+4CP,1.0,127.0,-3.0341766,-2.9202857,0.11389089,single 1e5-1e6
+V95F,1,kanR-kan,0.0,92.0,-4.142542,-4.059031,0.083510876,single <1e4
+V95F,2,kanR-kan,0.0,83.0,-4.3741508,-4.2962565,0.07789421,single <1e4
+V95F,1,kanR-kan,0.0,105.0,-4.564148,-4.5320673,0.03208065,single <1e4
+V95F,2,kanR-kan,0.0,97.0,-4.202631,-4.1231346,0.07949638,single <1e4
+V95F,1,kanR-kan,0.0,122.0,-4.6044607,-4.684199,-0.07973814,single <1e4
+V95F,2,kanR-kan,0.0,116.0,-4.1771955,-4.1070004,0.0701952,single <1e4
+V95F,1,kanR-kan,1.0,92.0,-4.5087423,-4.5815735,-0.072831154,single <1e4
+V95F,2,kanR-kan,1.0,83.0,-4.16902,-4.1009126,0.068107605,single <1e4
+V95F,1,kanR-kan,1.0,105.0,-4.561857,-4.479763,0.08209419,single <1e4
+V95F,2,kanR-kan,1.0,97.0,-4.990347,-4.9601116,0.03023529,single <1e4
+V95F,1,kanR-kan,1.0,122.0,-4.178916,-4.0855055,0.09341049,single <1e4
+V95F,2,kanR-kan,1.0,116.0,-4.157691,-4.0595856,0.09810543,single <1e4
+V95F,1,kanR+kan,0.0,158.0,-3.862174,-3.773548,0.08862615,single <1e4
+V95F,2,kanR+kan,0.0,155.0,-4.0921903,-4.0463285,0.04586172,single <1e4
+V95F,1,kanR+kan,0.0,175.0,-3.6333723,-3.5758922,0.057480097,single <1e4
+V95F,2,kanR+kan,0.0,176.0,-4.6485443,-4.696849,-0.048304558,single <1e4
+V95F,1,kanR+kan,0.0,200.0,-3.5827465,-3.5031314,0.079615116,single <1e4
+V95F,2,kanR+kan,0.0,199.0,-3.848473,-3.7967381,0.051734924,single <1e4
+V95F,1,kanR+kan,0.0001,158.0,-3.955708,-3.8957973,0.059910774,single <1e4
+V95F,2,kanR+kan,0.0001,155.0,-3.4802346,-3.405127,0.075107574,single <1e4
+V95F,1,kanR+kan,0.0001,175.0,-4.3097143,-4.3001685,0.009545803,single <1e4
+V95F,2,kanR+kan,0.0001,176.0,-4.2678947,-4.2532053,0.0146894455,single <1e4
+V95F,1,kanR+kan,0.0001,200.0,-3.525364,-3.447159,0.07820487,single <1e4
+V95F,2,kanR+kan,0.0001,199.0,-3.3900135,-3.3136988,0.07631469,single <1e4
+V95F,1,kanR+kan,0.001,158.0,-3.433847,-3.382619,0.051228046,single <1e4
+V95F,2,kanR+kan,0.001,155.0,-4.275487,-4.294601,-0.019114017,single <1e4
+V95F,1,kanR+kan,0.001,175.0,-3.8029766,-3.7232995,0.079677105,single <1e4
+V95F,2,kanR+kan,0.001,176.0,-3.6049948,-3.5339482,0.07104659,single <1e4
+V95F,1,kanR+kan,0.001,200.0,-3.3411775,-3.3313468,0.009830713,single <1e4
+V95F,1,kanR+kan,0.003,158.0,-3.9543693,-3.8914676,0.062901735,single <1e4
+V95F,2,kanR+kan,0.003,155.0,-3.8963857,-3.8143682,0.08201742,single <1e4
+V95F,1,kanR+kan,0.003,175.0,-4.0184374,-3.960134,0.058303356,single <1e4
+V95F,2,kanR+kan,0.003,176.0,-3.7165766,-3.637741,0.07883549,single <1e4
+V95F,1,kanR+kan,0.003,200.0,-3.3884563,-3.3160946,0.07236171,single <1e4
+V95F,2,kanR+kan,0.003,199.0,-3.7340322,-3.6547976,0.0792346,single <1e4
+V95F,1,kanR+kan,0.01,146.0,-4.761033,-4.8174095,-0.056376457,single <1e4
+V95F,2,kanR+kan,0.01,155.0,-3.825657,-3.7426724,0.08298445,single <1e4
+V95F,1,kanR+kan,0.01,158.0,-3.7810607,-3.6986198,0.08244085,single <1e4
+V95F,2,kanR+kan,0.01,176.0,-3.7178502,-3.6656396,0.05221057,single <1e4
+V95F,1,kanR+kan,0.01,175.0,-3.801021,-3.7237387,0.07728243,single <1e4
+V95F,2,kanR+kan,0.01,185.0,-3.8042958,-3.7268357,0.07746005,single <1e4
+V95F,1,kanR+kan,0.03,146.0,-4.3187127,-4.316806,0.0019068718,single <1e4
+V95F,2,kanR+kan,0.03,155.0,-4.0168576,-3.9245386,0.09231901,single <1e4
+V95F,1,kanR+kan,0.03,158.0,-4.329,-4.3191943,0.009805679,single <1e4
+V95F,2,kanR+kan,0.03,176.0,-4.004691,-3.922184,0.08250713,single <1e4
+V95F,1,kanR+kan,0.03,175.0,-3.8466368,-3.7566435,0.08999324,single <1e4
+V95F,2,kanR+kan,0.03,185.0,-4.010319,-3.9159234,0.094395876,single <1e4
+V95F,1,kanR+kan,0.1,146.0,-4.420415,-4.350952,0.069462776,single <1e4
+V95F,2,kanR+kan,0.1,155.0,-4.3843327,-4.323859,0.060473442,single <1e4
+V95F,1,kanR+kan,0.1,158.0,-4.315211,-4.235403,0.07980776,single <1e4
+V95F,2,kanR+kan,0.1,176.0,-4.220569,-4.1666055,0.05396366,single <1e4
+V95F,1,kanR+kan,0.1,175.0,-4.5310745,-4.4497385,0.08133602,single <1e4
+V95F,2,kanR+kan,0.1,185.0,-4.4828815,-4.3956842,0.087197304,single <1e4
+V95F,1,kanR+kan,1.0,146.0,-4.873225,-4.8192587,0.053966522,single <1e4
+V95F,2,kanR+kan,1.0,155.0,-5.3658876,-5.335707,0.030180454,single <1e4
+V95F,3,kanR+kan,1.0,168.0,-4.3741827,-4.294532,0.07965088,single <1e4
+V95F,1,kanR+kan,1.0,158.0,-4.53246,-4.471784,0.060676098,single <1e4
+V95F,2,kanR+kan,1.0,176.0,-4.478211,-4.3887033,0.08950758,single <1e4
+V95F,1,kanR+kan,1.0,175.0,-4.5645,-4.483201,0.08129883,single <1e4
+V95F,2,kanR+kan,1.0,185.0,-4.5950356,-4.6073117,-0.012276173,single <1e4
+V95F,1,pheS-4CP,0.0,81.0,-3.3137612,-3.226212,0.08754921,single <1e4
+V95F,2,pheS-4CP,0.0,82.0,-3.2195115,-3.1396444,0.079867125,single <1e4
+V95F,1,pheS-4CP,0.0,94.0,-3.3856091,-3.3118234,0.07378578,single <1e4
+V95F,2,pheS-4CP,0.0,99.0,-3.2760997,-3.1995485,0.0765512,single <1e4
+V95F,1,pheS-4CP,0.0,111.0,-3.369657,-3.3091877,0.06046939,single <1e4
+V95F,2,pheS-4CP,0.0,116.0,-3.1423671,-3.0668163,0.075550795,single <1e4
+V95F,1,pheS-4CP,1.0,81.0,-3.144968,-3.0821986,0.06276941,single <1e4
+V95F,2,pheS-4CP,1.0,82.0,-2.680029,-2.618781,0.061247826,single <1e4
+V95F,1,pheS-4CP,1.0,94.0,-3.336916,-3.2605188,0.07639718,single <1e4
+V95F,2,pheS-4CP,1.0,99.0,-3.2882571,-3.212103,0.07615423,single <1e4
+V95F,1,pheS-4CP,1.0,111.0,-3.2141943,-3.1411629,0.073031425,single <1e4
+V95F,2,pheS-4CP,1.0,116.0,-2.8434577,-2.7840378,0.05941987,single <1e4
+V95F,1,pheS+4CP,0.0,94.0,-2.9648707,-2.8974953,0.06737542,single <1e4
+V95F,3,pheS+4CP,0.0,104.0,-3.383605,-3.3058076,0.07779741,single <1e4
+V95F,1,pheS+4CP,0.0,111.0,-3.613511,-3.535419,0.0780921,single <1e4
+V95F,1,pheS+4CP,0.0,125.0,-3.826478,-3.7841747,0.042303324,single <1e4
+V95F,2,pheS+4CP,0.0,127.0,-3.3691294,-3.2983646,0.07076478,single <1e4
+V95F,1,pheS+4CP,0.0001,94.0,-2.6671748,-2.601068,0.066106796,single <1e4
+V95F,1,pheS+4CP,0.0001,111.0,-3.6158438,-3.551926,0.063917875,single <1e4
+V95F,2,pheS+4CP,0.0001,116.0,-2.692609,-2.6232908,0.069318295,single <1e4
+V95F,1,pheS+4CP,0.0001,125.0,-3.2795913,-3.2026367,0.0769546,single <1e4
+V95F,2,pheS+4CP,0.0001,127.0,-3.3163872,-3.2508914,0.06549573,single <1e4
+V95F,1,pheS+4CP,0.001,94.0,-2.9637113,-2.8910775,0.07263374,single <1e4
+V95F,1,pheS+4CP,0.001,111.0,-3.6750622,-3.5918865,0.08317566,single <1e4
+V95F,2,pheS+4CP,0.001,116.0,-3.8067758,-3.7815642,0.025211573,single <1e4
+V95F,1,pheS+4CP,0.001,125.0,-3.4916196,-3.4213417,0.07027793,single <1e4
+V95F,2,pheS+4CP,0.001,127.0,-3.8187861,-3.733986,0.08480024,single <1e4
+V95F,1,pheS+4CP,0.003,94.0,-3.0270262,-2.9632535,0.06377268,single <1e4
+V95F,1,pheS+4CP,0.003,111.0,-3.1422076,-3.0694623,0.07274532,single <1e4
+V95F,2,pheS+4CP,0.003,116.0,-3.277969,-3.2050633,0.07290554,single <1e4
+V95F,1,pheS+4CP,0.003,125.0,-3.2789245,-3.2008162,0.07810831,single <1e4
+V95F,2,pheS+4CP,0.003,127.0,-3.0371518,-2.9732656,0.063886166,single <1e4
+V95F,1,pheS+4CP,0.01,94.0,-2.5287995,-2.4667597,0.062039852,single <1e4
+V95F,1,pheS+4CP,0.01,111.0,-3.1412153,-3.065133,0.07608223,single <1e4
+V95F,2,pheS+4CP,0.01,116.0,-3.2832038,-3.2408478,0.042356014,single <1e4
+V95F,1,pheS+4CP,0.01,125.0,-3.1004915,-3.021025,0.07946658,single <1e4
+V95F,2,pheS+4CP,0.01,127.0,-3.0734375,-2.9941213,0.07931614,single <1e4
+V95F,1,pheS+4CP,0.03,94.0,-3.141586,-3.0651224,0.0764637,single <1e4
+V95F,1,pheS+4CP,0.03,111.0,-2.9647276,-2.889472,0.07525563,single <1e4
+V95F,2,pheS+4CP,0.03,116.0,-3.0288382,-2.9455266,0.08331156,single <1e4
+V95F,1,pheS+4CP,0.03,125.0,-3.816824,-3.796833,0.019990921,single <1e4
+V95F,2,pheS+4CP,0.03,127.0,-2.58385,-2.524057,0.059792995,single <1e4
+V95F,1,pheS+4CP,0.1,94.0,-3.2901046,-3.2084312,0.08167338,single <1e4
+V95F,1,pheS+4CP,0.1,111.0,-2.9534483,-2.9266465,0.026801825,single <1e4
+V95F,2,pheS+4CP,0.1,116.0,-2.9602458,-2.895973,0.06427288,single <1e4
+V95F,1,pheS+4CP,0.1,125.0,-3.1223366,-3.0832288,0.0391078,single <1e4
+V95F,2,pheS+4CP,0.1,127.0,-3.2533464,-3.1673803,0.08596611,single <1e4
+V95F,1,pheS+4CP,1.0,94.0,-3.8867736,-3.7902937,0.09647989,single <1e4
+V95F,1,pheS+4CP,1.0,111.0,-3.3530598,-3.3185196,0.034540176,single <1e4
+V95F,2,pheS+4CP,1.0,116.0,-3.181003,-3.0793128,0.10169029,single <1e4
+V95F,1,pheS+4CP,1.0,125.0,-3.3964655,-3.3057432,0.09072232,single <1e4
+V95F,2,pheS+4CP,1.0,127.0,-2.829751,-2.7767117,0.053039312,single <1e4
+V95G,1,kanR-kan,0.0,92.0,-7.3524284,-7.435253,-0.08282471,single 1e5-1e6
+V95G,2,kanR-kan,0.0,83.0,-8.539096,-8.55788,-0.018784523,single 1e5-1e6
+V95G,1,kanR-kan,0.0,105.0,-7.513715,-7.636777,-0.123062134,single 1e5-1e6
+V95G,2,kanR-kan,0.0,97.0,-7.3742876,-7.4079103,-0.03362274,single 1e5-1e6
+V95G,1,kanR-kan,0.0,122.0,-7.385165,-7.3348475,0.050317764,single 1e5-1e6
+V95G,2,kanR-kan,0.0,116.0,-7.611903,-7.512203,0.099699974,single 1e5-1e6
+V95G,1,kanR-kan,1.0,92.0,-7.5323367,-7.5836186,-0.05128193,single 1e5-1e6
+V95G,2,kanR-kan,1.0,83.0,-7.4955845,-7.527741,-0.032156467,single 1e5-1e6
+V95G,1,kanR-kan,1.0,105.0,-7.5656548,-7.64438,-0.07872534,single 1e5-1e6
+V95G,2,kanR-kan,1.0,97.0,-7.3658314,-7.4220295,-0.05619812,single 1e5-1e6
+V95G,1,kanR-kan,1.0,122.0,-7.283221,-7.3189507,-0.035729885,single 1e5-1e6
+V95G,2,kanR-kan,1.0,116.0,-7.3796015,-7.338127,0.041474342,single 1e5-1e6
+V95G,1,kanR+kan,0.0,158.0,-8.153065,-8.177067,-0.024002075,single 1e5-1e6
+V95G,2,kanR+kan,0.0,155.0,-7.851599,-7.826157,0.025442123,single 1e5-1e6
+V95G,1,kanR+kan,0.0,175.0,-9.279524,-8.987144,0.29237938,single 1e5-1e6
+V95G,2,kanR+kan,0.0,176.0,-7.8783073,-7.810445,0.06786251,single 1e5-1e6
+V95G,1,kanR+kan,0.0,200.0,-7.211878,-7.249667,-0.037789345,single 1e5-1e6
+V95G,2,kanR+kan,0.0,199.0,-7.931835,-7.7395415,0.19229364,single 1e5-1e6
+V95G,1,kanR+kan,0.0001,158.0,-7.283551,-7.340707,-0.05715561,single 1e5-1e6
+V95G,2,kanR+kan,0.0001,155.0,-8.055374,-7.982308,0.073066235,single 1e5-1e6
+V95G,1,kanR+kan,0.0001,175.0,-7.4581814,-7.531017,-0.072835445,single 1e5-1e6
+V95G,2,kanR+kan,0.0001,176.0,-7.2917547,-7.352145,-0.060390472,single 1e5-1e6
+V95G,1,kanR+kan,0.0001,200.0,-7.079172,-7.0848274,-0.0056552887,single 1e5-1e6
+V95G,2,kanR+kan,0.0001,199.0,-7.1435366,-7.107817,0.035719395,single 1e5-1e6
+V95G,1,kanR+kan,0.001,158.0,-7.0620203,-7.088935,-0.026914597,single 1e5-1e6
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+V95H,2,pheS+4CP,0.0001,127.0,-3.1943052,-3.1320448,0.06226039,single 1e4-1e5
+V95H,1,pheS+4CP,0.001,94.0,-2.1535048,-2.0869486,0.066556215,single 1e4-1e5
+V95H,1,pheS+4CP,0.001,111.0,-2.4370332,-2.3753672,0.06166601,single 1e4-1e5
+V95H,2,pheS+4CP,0.001,116.0,-3.2426357,-3.1737852,0.06885052,single 1e4-1e5
+V95H,1,pheS+4CP,0.001,125.0,-2.492263,-2.4364386,0.05582452,single 1e4-1e5
+V95H,2,pheS+4CP,0.001,127.0,-3.6645684,-3.6018121,0.0627563,single 1e4-1e5
+V95H,1,pheS+4CP,0.003,94.0,-2.414567,-2.340941,0.07362604,single 1e4-1e5
+V95H,1,pheS+4CP,0.003,111.0,-2.1230607,-2.0652933,0.05776739,single 1e4-1e5
+V95H,2,pheS+4CP,0.003,116.0,-3.0736403,-3.0007517,0.07288861,single 1e4-1e5
+V95H,1,pheS+4CP,0.003,125.0,-0.88383424,-0.8803828,0.0034514666,single 1e4-1e5
+V95H,2,pheS+4CP,0.003,127.0,-2.8537698,-2.7853177,0.06845212,single 1e4-1e5
+V95H,1,pheS+4CP,0.01,94.0,-2.2566621,-2.18984,0.06682205,single 1e4-1e5
+V95H,1,pheS+4CP,0.01,111.0,-3.2621412,-3.2219255,0.04021573,single 1e4-1e5
+V95H,2,pheS+4CP,0.01,116.0,-2.865615,-2.7951083,0.07050657,single 1e4-1e5
+V95H,1,pheS+4CP,0.01,125.0,-2.5188508,-2.4671352,0.051715612,single 1e4-1e5
+V95H,2,pheS+4CP,0.01,127.0,-3.0760386,-3.007834,0.06820464,single 1e4-1e5
+V95H,1,pheS+4CP,0.03,94.0,-0.698911,-0.70304525,-0.0041342378,single 1e4-1e5
+V95H,1,pheS+4CP,0.03,111.0,-2.6450837,-2.602182,0.042901754,single 1e4-1e5
+V95H,2,pheS+4CP,0.03,116.0,-2.7234504,-2.6548326,0.06861782,single 1e4-1e5
+V95H,1,pheS+4CP,0.03,125.0,-2.4635339,-2.414782,0.04875183,single 1e4-1e5
+V95H,2,pheS+4CP,0.03,127.0,-2.4682014,-2.4031248,0.06507659,single 1e4-1e5
+V95H,1,pheS+4CP,0.1,94.0,-0.29130107,-0.289526,0.0017750859,single 1e4-1e5
+V95H,1,pheS+4CP,0.1,111.0,-2.0316765,-1.9790915,0.052585006,single 1e4-1e5
+V95H,2,pheS+4CP,0.1,116.0,-2.6137013,-2.5440793,0.06962204,single 1e4-1e5
+V95H,1,pheS+4CP,0.1,125.0,-2.4378047,-2.3841858,0.053618908,single 1e4-1e5
+V95H,2,pheS+4CP,0.1,127.0,-2.7510104,-2.6676798,0.08333063,single 1e4-1e5
+V95H,1,pheS+4CP,1.0,94.0,-2.954453,-2.8828242,0.07162881,single 1e4-1e5
+V95H,1,pheS+4CP,1.0,111.0,-2.6686268,-2.6009555,0.0676713,single 1e4-1e5
+V95H,2,pheS+4CP,1.0,116.0,-2.804844,-2.74918,0.055663824,single 1e4-1e5
+V95H,1,pheS+4CP,1.0,125.0,-2.153165,-2.0919135,0.06125164,single 1e4-1e5
+V95H,2,pheS+4CP,1.0,127.0,-3.1397,-3.065431,0.07426882,single 1e4-1e5
+V95I,1,kanR-kan,0.0,92.0,-2.701095,-2.637298,0.063797,single <1e4
+V95I,2,kanR-kan,0.0,83.0,-3.2200325,-3.1744673,0.04556513,single <1e4
+V95I,1,kanR-kan,0.0,105.0,-2.670259,-2.604748,0.06551099,single <1e4
+V95I,2,kanR-kan,0.0,97.0,-1.0329423,-1.0385326,-0.0055903196,single <1e4
+V95I,1,kanR-kan,0.0,122.0,-1.2250504,-1.2407726,-0.015722156,single <1e4
+V95I,2,kanR-kan,0.0,116.0,-2.4553146,-2.396094,0.059220552,single <1e4
+V95I,1,kanR-kan,1.0,92.0,-3.0631633,-3.0017526,0.061410666,single <1e4
+V95I,2,kanR-kan,1.0,83.0,-2.7695646,-2.7089355,0.06062913,single <1e4
+V95I,1,kanR-kan,1.0,105.0,-2.6667533,-2.6002753,0.066478014,single <1e4
+V95I,2,kanR-kan,1.0,97.0,-0.9963666,-1.0101209,-0.013754249,single <1e4
+V95I,1,kanR-kan,1.0,122.0,-2.9470878,-2.8846664,0.062421322,single <1e4
+V95I,2,kanR-kan,1.0,116.0,-4.32919,-4.34955,-0.020359993,single <1e4
+V95I,1,kanR+kan,0.0,158.0,-3.8569741,-3.84974,0.0072340965,single <1e4
+V95I,2,kanR+kan,0.0,155.0,-3.5636916,-3.539461,0.024230719,single <1e4
+V95I,1,kanR+kan,0.0,175.0,-1.1176187,-1.13898,-0.021361351,single <1e4
+V95I,2,kanR+kan,0.0,176.0,-2.7377443,-2.6788504,0.05889392,single <1e4
+V95I,1,kanR+kan,0.0,200.0,-1.0657054,-1.088716,-0.023010612,single <1e4
+V95I,2,kanR+kan,0.0,199.0,-2.9628427,-2.9090283,0.05381441,single <1e4
+V95I,1,kanR+kan,0.0001,158.0,-3.14328,-3.088369,0.054911137,single <1e4
+V95I,2,kanR+kan,0.0001,155.0,-0.6589728,-0.6643608,-0.0053880215,single <1e4
+V95I,1,kanR+kan,0.0001,175.0,-1.2942014,-1.3192683,-0.025066972,single <1e4
+V95I,2,kanR+kan,0.0001,176.0,-2.4961097,-2.4370127,0.05909705,single <1e4
+V95I,1,kanR+kan,0.0001,200.0,-3.3642385,-3.317135,0.047103405,single <1e4
+V95I,2,kanR+kan,0.0001,199.0,-0.66650444,-0.6699464,-0.0034419298,single <1e4
+V95I,1,kanR+kan,0.001,158.0,-2.8637226,-2.803711,0.060011625,single <1e4
+V95I,2,kanR+kan,0.001,155.0,-1.1243104,-1.1327748,-0.008464456,single <1e4
+V95I,1,kanR+kan,0.001,175.0,-3.0396876,-2.979972,0.059715748,single <1e4
+V95I,2,kanR+kan,0.001,176.0,-2.0415463,-1.987466,0.054080367,single <1e4
+V95I,1,kanR+kan,0.001,200.0,-2.8101013,-2.7484627,0.061638594,single <1e4
+V95I,1,kanR+kan,0.003,158.0,-2.477692,-2.4160469,0.06164503,single <1e4
+V95I,2,kanR+kan,0.003,155.0,-1.1380265,-1.1426522,-0.004625678,single <1e4
+V95I,1,kanR+kan,0.003,175.0,-2.4636567,-2.4023848,0.061271906,single <1e4
+V95I,2,kanR+kan,0.003,176.0,-3.2655044,-3.2257302,0.03977418,single <1e4
+V95I,1,kanR+kan,0.003,200.0,-2.5175366,-2.4565637,0.06097293,single <1e4
+V95I,2,kanR+kan,0.003,199.0,-2.1377,-2.0829825,0.05471754,single <1e4
+V95I,1,kanR+kan,0.01,146.0,-2.4339254,-2.372418,0.061507463,single <1e4
+V95I,2,kanR+kan,0.01,155.0,-2.0338514,-1.9815094,0.052341938,single <1e4
+V95I,1,kanR+kan,0.01,158.0,-1.2942728,-1.3216573,-0.02738452,single <1e4
+V95I,2,kanR+kan,0.01,176.0,-2.4654913,-2.404713,0.06077838,single <1e4
+V95I,1,kanR+kan,0.01,175.0,-2.057374,-2.0095491,0.04782486,single <1e4
+V95I,2,kanR+kan,0.01,185.0,-2.4750204,-2.4147947,0.060225725,single <1e4
+V95I,1,kanR+kan,0.03,146.0,-2.0520024,-2.0174794,0.03452301,single <1e4
+V95I,2,kanR+kan,0.03,155.0,-2.4352574,-2.3719041,0.0633533,single <1e4
+V95I,1,kanR+kan,0.03,158.0,-2.666904,-2.6009884,0.065915585,single <1e4
+V95I,2,kanR+kan,0.03,176.0,-2.4342446,-2.3763258,0.057918787,single <1e4
+V95I,1,kanR+kan,0.03,175.0,-2.6718779,-2.6011832,0.070694685,single <1e4
+V95I,2,kanR+kan,0.03,185.0,-3.4491425,-3.3735197,0.0756228,single <1e4
+V95I,1,kanR+kan,0.1,146.0,-3.139689,-3.0652142,0.07447481,single <1e4
+V95I,2,kanR+kan,0.1,155.0,-2.8302648,-2.7620747,0.0681901,single <1e4
+V95I,1,kanR+kan,0.1,158.0,-3.5487418,-3.479789,0.0689528,single <1e4
+V95I,2,kanR+kan,0.1,176.0,-2.8474479,-2.7728467,0.07460117,single <1e4
+V95I,1,kanR+kan,0.1,175.0,-2.9092402,-2.8644514,0.044788837,single <1e4
+V95I,2,kanR+kan,0.1,185.0,-2.3314009,-2.3436494,-0.012248516,single <1e4
+V95I,1,kanR+kan,1.0,146.0,-3.416685,-3.3546395,0.062045574,single <1e4
+V95I,2,kanR+kan,1.0,155.0,-3.1608293,-3.0756392,0.08519006,single <1e4
+V95I,3,kanR+kan,1.0,168.0,-2.0322833,-1.9789124,0.053370953,single <1e4
+V95I,1,kanR+kan,1.0,158.0,-2.9865131,-2.916905,0.06960821,single <1e4
+V95I,2,kanR+kan,1.0,176.0,-4.125912,-4.1532907,-0.02737856,single <1e4
+V95I,1,kanR+kan,1.0,175.0,-4.1394496,-4.1645365,-0.02508688,single <1e4
+V95I,2,kanR+kan,1.0,185.0,-2.8563356,-2.7825441,0.073791504,single <1e4
+V95I,1,pheS-4CP,0.0,81.0,-3.698257,-3.622858,0.07539892,single <1e4
+V95I,2,pheS-4CP,0.0,82.0,-3.633431,-3.5585272,0.07490373,single <1e4
+V95I,1,pheS-4CP,0.0,94.0,-3.6015942,-3.526418,0.07517624,single <1e4
+V95I,2,pheS-4CP,0.0,99.0,-3.8406744,-3.764705,0.07596946,single <1e4
+V95I,1,pheS-4CP,0.0,111.0,-3.4366658,-3.3550453,0.081620455,single <1e4
+V95I,2,pheS-4CP,0.0,116.0,-3.553086,-3.498109,0.05497694,single <1e4
+V95I,1,pheS-4CP,1.0,81.0,-3.6718898,-3.5920696,0.079820156,single <1e4
+V95I,2,pheS-4CP,1.0,82.0,-3.284854,-3.2129703,0.07188368,single <1e4
+V95I,1,pheS-4CP,1.0,94.0,-3.6470342,-3.5685587,0.078475475,single <1e4
+V95I,2,pheS-4CP,1.0,99.0,-3.6157193,-3.5357585,0.07996082,single <1e4
+V95I,1,pheS-4CP,1.0,111.0,-3.5316396,-3.4584732,0.07316637,single <1e4
+V95I,2,pheS-4CP,1.0,116.0,-3.549056,-3.4703064,0.07874966,single <1e4
+V95I,1,pheS+4CP,0.0,94.0,-3.255272,-3.1727662,0.0825057,single <1e4
+V95I,3,pheS+4CP,0.0,104.0,-3.671821,-3.5917356,0.080085516,single <1e4
+V95I,1,pheS+4CP,0.0,111.0,-3.8439944,-3.7614632,0.082531214,single <1e4
+V95I,1,pheS+4CP,0.0,125.0,-3.6169868,-3.538003,0.07898378,single <1e4
+V95I,2,pheS+4CP,0.0,127.0,-3.5535862,-3.47539,0.07819629,single <1e4
+V95I,1,pheS+4CP,0.0001,94.0,-3.2243266,-3.1454408,0.07888579,single <1e4
+V95I,1,pheS+4CP,0.0001,111.0,-3.6483712,-3.574417,0.073954105,single <1e4
+V95I,2,pheS+4CP,0.0001,116.0,-3.5103345,-3.4353316,0.07500291,single <1e4
+V95I,1,pheS+4CP,0.0001,125.0,-3.5560565,-3.481022,0.07503462,single <1e4
+V95I,2,pheS+4CP,0.0001,127.0,-3.665583,-3.5955062,0.070076704,single <1e4
+V95I,1,pheS+4CP,0.001,94.0,-3.9362888,-3.8816721,0.05461669,single <1e4
+V95I,1,pheS+4CP,0.001,111.0,-3.9187946,-3.8343058,0.08448887,single <1e4
+V95I,2,pheS+4CP,0.001,116.0,-3.855729,-3.7926228,0.0631063,single <1e4
+V95I,1,pheS+4CP,0.001,125.0,-3.7304287,-3.6530118,0.0774169,single <1e4
+V95I,2,pheS+4CP,0.001,127.0,-4.155971,-4.0657682,0.09020281,single <1e4
+V95I,1,pheS+4CP,0.003,94.0,-3.2556353,-3.17374,0.08189535,single <1e4
+V95I,1,pheS+4CP,0.003,111.0,-3.2756324,-3.2166016,0.05903077,single <1e4
+V95I,2,pheS+4CP,0.003,116.0,-3.7400355,-3.6632285,0.07680702,single <1e4
+V95I,1,pheS+4CP,0.003,125.0,-3.7807345,-3.7080772,0.07265735,single <1e4
+V95I,2,pheS+4CP,0.003,127.0,-3.6987014,-3.618391,0.080310345,single <1e4
+V95I,1,pheS+4CP,0.01,94.0,-3.4178653,-3.3366516,0.08121371,single <1e4
+V95I,1,pheS+4CP,0.01,111.0,-3.7463071,-3.6742368,0.07207036,single <1e4
+V95I,2,pheS+4CP,0.01,116.0,-3.4728813,-3.3938985,0.07898283,single <1e4
+V95I,1,pheS+4CP,0.01,125.0,-3.5185843,-3.4488206,0.06976366,single <1e4
+V95I,2,pheS+4CP,0.01,127.0,-3.529122,-3.4544754,0.07464671,single <1e4
+V95I,1,pheS+4CP,0.03,94.0,-3.4307227,-3.3535376,0.077185154,single <1e4
+V95I,1,pheS+4CP,0.03,111.0,-3.4893155,-3.4105146,0.07880092,single <1e4
+V95I,2,pheS+4CP,0.03,116.0,-3.6815963,-3.603269,0.07832718,single <1e4
+V95I,1,pheS+4CP,0.03,125.0,-3.672285,-3.592705,0.07958007,single <1e4
+V95I,2,pheS+4CP,0.03,127.0,-3.753541,-3.6856785,0.06786251,single <1e4
+V95I,1,pheS+4CP,0.1,94.0,-2.8949366,-2.8144567,0.08047986,single <1e4
+V95I,1,pheS+4CP,0.1,111.0,-3.4429574,-3.3657324,0.07722497,single <1e4
+V95I,2,pheS+4CP,0.1,116.0,-3.3407621,-3.273696,0.06706619,single <1e4
+V95I,1,pheS+4CP,0.1,125.0,-3.7592552,-3.6905785,0.06867671,single <1e4
+V95I,2,pheS+4CP,0.1,127.0,-3.2573276,-3.2326546,0.024672985,single <1e4
+V95I,1,pheS+4CP,1.0,94.0,-3.9477148,-3.861905,0.08580971,single <1e4
+V95I,1,pheS+4CP,1.0,111.0,-3.7934356,-3.7136178,0.07981777,single <1e4
+V95I,2,pheS+4CP,1.0,116.0,-3.472611,-3.3939607,0.078650236,single <1e4
+V95I,1,pheS+4CP,1.0,125.0,-3.732028,-3.655263,0.07676506,single <1e4
+V95I,2,pheS+4CP,1.0,127.0,-3.5746055,-3.4980192,0.07658625,single <1e4
+V95L,1,kanR-kan,0.0,92.0,-4.983479,-4.5894794,0.39399958,single <1e4
+V95L,2,kanR-kan,0.0,83.0,-4.647565,-5.5285487,-0.8809838,single <1e4
+V95L,1,kanR-kan,0.0,105.0,-4.7684193,-4.5993114,0.16910791,single <1e4
+V95L,2,kanR-kan,0.0,97.0,-4.489244,-4.4725394,0.01670456,single <1e4
+V95L,1,kanR-kan,0.0,122.0,-4.952302,-5.071167,-0.11886501,single <1e4
+V95L,2,kanR-kan,0.0,116.0,-5.0422873,-5.2194934,-0.17720604,single <1e4
+V95L,1,kanR-kan,1.0,92.0,-4.9145584,-5.6680317,-0.7534733,single <1e4
+V95L,2,kanR-kan,1.0,83.0,-4.479926,-4.6905146,-0.21058846,single <1e4
+V95L,1,kanR-kan,1.0,105.0,-4.702982,-5.0171623,-0.31418037,single <1e4
+V95L,2,kanR-kan,1.0,97.0,-4.7226295,-5.5010805,-0.77845097,single <1e4
+V95L,1,kanR-kan,1.0,122.0,-5.5550623,-4.4392123,1.11585,single <1e4
+V95L,2,kanR-kan,1.0,116.0,-4.604083,-5.5717473,-0.96766424,single <1e4
+V95L,1,kanR+kan,0.0,158.0,-4.0782185,-4.2569923,-0.17877388,single <1e4
+V95L,2,kanR+kan,0.0,155.0,-3.976956,-4.429184,-0.45222807,single <1e4
+V95L,1,kanR+kan,0.0,175.0,-4.4540405,-4.113549,0.3404913,single <1e4
+V95L,2,kanR+kan,0.0,176.0,-4.138009,-4.7648697,-0.6268606,single <1e4
+V95L,1,kanR+kan,0.0,200.0,-4.0701847,-4.2436237,-0.17343903,single <1e4
+V95L,2,kanR+kan,0.0,199.0,-3.5509415,-3.9329424,-0.38200092,single <1e4
+V95L,1,kanR+kan,0.0001,158.0,-4.6585293,-4.1733217,0.48520756,single <1e4
+V95L,2,kanR+kan,0.0001,155.0,-4.247685,-3.9292426,0.31844234,single <1e4
+V95L,1,kanR+kan,0.0001,175.0,-4.7968397,-4.3008103,0.49602938,single <1e4
+V95L,2,kanR+kan,0.0001,176.0,-4.3908234,-4.1530476,0.2377758,single <1e4
+V95L,1,kanR+kan,0.0001,200.0,-3.8726583,-4.176941,-0.30428267,single <1e4
+V95L,2,kanR+kan,0.0001,199.0,-4.0287275,-4.717265,-0.6885376,single <1e4
+V95L,1,kanR+kan,0.001,158.0,-3.9127808,-3.8161376,0.09664321,single <1e4
+V95L,2,kanR+kan,0.001,155.0,-4.733177,-4.33665,0.3965273,single <1e4
+V95L,1,kanR+kan,0.001,175.0,-4.3410635,-4.2060227,0.13504076,single <1e4
+V95L,2,kanR+kan,0.001,176.0,-4.419119,-4.18023,0.23888874,single <1e4
+V95L,1,kanR+kan,0.001,200.0,-4.0164223,-4.136044,-0.11962175,single <1e4
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+V95Y,1,kanR+kan,0.001,175.0,-3.5114374,-3.4329588,0.078478575,single <1e4
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+V95Y,1,kanR+kan,0.001,200.0,-3.0559852,-2.9825068,0.07347846,single <1e4
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+S97N,1,kanR-kan,1.0,122.0,-5.3281217,-5.2706842,0.05743742,single 1e4-1e5
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+S97N,1,pheS+4CP,0.03,111.0,-0.2425868,-0.24071722,0.0018695891,single 1e4-1e5
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+S97N,1,pheS+4CP,0.03,125.0,-2.4409165,-2.3854182,0.05549836,single 1e4-1e5
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+S97N,1,pheS+4CP,0.1,111.0,-0.3421211,-0.3505333,-0.008412212,single 1e4-1e5
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+S97N,1,pheS+4CP,0.1,125.0,-2.3836658,-2.3062277,0.077438116,single 1e4-1e5
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+S97N,1,pheS+4CP,1.0,94.0,-0.8791406,-0.896342,-0.017201364,single 1e4-1e5
+S97N,1,pheS+4CP,1.0,111.0,-0.62506825,-0.649463,-0.02439475,single 1e4-1e5
+S97N,2,pheS+4CP,1.0,116.0,-0.61120224,-0.63836557,-0.027163327,single 1e4-1e5
+S97N,1,pheS+4CP,1.0,125.0,-0.5184594,-0.5479281,-0.029468715,single 1e4-1e5
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+S97P,1,kanR-kan,0.0,92.0,-3.9210873,-3.8122945,0.10879278,single <1e4
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+S97P,1,kanR-kan,0.0,105.0,-4.2642746,-4.461755,-0.1974802,single <1e4
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+S97P,1,kanR-kan,0.0,122.0,-4.4537563,-3.714027,0.7397294,single <1e4
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+S97P,1,kanR-kan,1.0,92.0,-3.835939,-3.927607,-0.09166813,single <1e4
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+S97P,1,kanR-kan,1.0,105.0,-4.333027,-4.5606813,-0.22765446,single <1e4
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diff --git a/planning/studies/relative-m/out/attr_test/per_row.csv b/planning/studies/relative-m/out/attr_test/per_row.csv
new file mode 100644
index 00000000..8c355b5d
--- /dev/null
+++ b/planning/studies/relative-m/out/attr_test/per_row.csv
@@ -0,0 +1,106961 @@
+genotype,replicate,condition_sel,titrant_conc,t_sel,ll_1,ll_2,d,class
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+I64D,1,pheS-4CP,1.0,81.0,-5.5169683,-5.691531,-0.17456293,single 1e5-1e6
+I64D,2,pheS-4CP,1.0,82.0,-4.893447,-4.983423,-0.08997631,single 1e5-1e6
+I64D,1,pheS-4CP,1.0,94.0,-5.155843,-5.3229427,-0.16709995,single 1e5-1e6
+I64D,2,pheS-4CP,1.0,99.0,-6.0625277,-6.2502446,-0.18771696,single 1e5-1e6
+I64D,1,pheS-4CP,1.0,111.0,-5.04059,-5.169121,-0.12853098,single 1e5-1e6
+I64D,2,pheS-4CP,1.0,116.0,-5.074329,-5.223873,-0.14954424,single 1e5-1e6
+I64D,1,pheS+4CP,0.0,94.0,-4.1540136,-4.2516174,-0.0976038,single 1e5-1e6
+I64D,3,pheS+4CP,0.0,104.0,-4.9951134,-5.1002903,-0.105176926,single 1e5-1e6
+I64D,1,pheS+4CP,0.0,111.0,-4.9583273,-5.0466127,-0.088285446,single 1e5-1e6
+I64D,1,pheS+4CP,0.0,125.0,-5.6442494,-5.711302,-0.067052364,single 1e5-1e6
+I64D,2,pheS+4CP,0.0,127.0,-4.8285704,-4.943993,-0.115422726,single 1e5-1e6
+I64D,1,pheS+4CP,0.0001,94.0,-5.459241,-5.419685,0.039556026,single 1e5-1e6
+I64D,1,pheS+4CP,0.0001,111.0,-4.8201275,-4.922584,-0.10245657,single 1e5-1e6
+I64D,2,pheS+4CP,0.0001,116.0,-4.9348392,-5.027481,-0.09264183,single 1e5-1e6
+I64D,1,pheS+4CP,0.0001,125.0,-4.983289,-5.1243925,-0.14110374,single 1e5-1e6
+I64D,2,pheS+4CP,0.0001,127.0,-5.2525167,-5.3430953,-0.090578556,single 1e5-1e6
+I64D,1,pheS+4CP,0.001,94.0,-7.7751646,-7.3265753,0.44858932,single 1e5-1e6
+I64D,1,pheS+4CP,0.001,111.0,-5.650305,-5.514495,0.1358099,single 1e5-1e6
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+I64D,1,pheS+4CP,0.001,125.0,-5.4661226,-5.5787334,-0.11261082,single 1e5-1e6
+I64D,2,pheS+4CP,0.001,127.0,-6.345805,-6.4739485,-0.12814331,single 1e5-1e6
+I64D,1,pheS+4CP,0.003,94.0,-4.3038006,-4.427492,-0.12369156,single 1e5-1e6
+I64D,1,pheS+4CP,0.003,111.0,-6.6578155,-6.541752,0.116063595,single 1e5-1e6
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+I64D,1,pheS+4CP,0.01,125.0,-4.8718014,-5.056719,-0.18491745,single 1e5-1e6
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+I64D,1,pheS+4CP,0.03,111.0,-5.184323,-5.0580444,0.1262784,single 1e5-1e6
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+I64D,1,pheS+4CP,0.1,111.0,-4.8833055,-4.9758825,-0.09257698,single 1e5-1e6
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+I64D,1,pheS+4CP,1.0,94.0,-8.704163,-7.9016314,0.80253124,single 1e5-1e6
+I64D,1,pheS+4CP,1.0,111.0,-10.37924,-9.829892,0.5493479,single 1e5-1e6
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+I64D,1,pheS+4CP,1.0,125.0,-5.0653343,-5.158059,-0.0927248,single 1e5-1e6
+I64D,2,pheS+4CP,1.0,127.0,-4.966179,-5.0818415,-0.115662575,single 1e5-1e6
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+I64F,2,kanR-kan,0.0,97.0,-7.435452,-7.102041,0.33341122,single >=1e6
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+I64F,2,kanR-kan,0.0,116.0,-7.710336,-7.171423,0.53891325,single >=1e6
+I64F,1,kanR-kan,1.0,92.0,-6.8804636,-6.9303846,-0.049921036,single >=1e6
+I64F,2,kanR-kan,1.0,83.0,-7.5397477,-7.9143424,-0.3745947,single >=1e6
+I64F,1,kanR-kan,1.0,105.0,-7.001715,-7.0429626,-0.041247368,single >=1e6
+I64F,2,kanR-kan,1.0,97.0,-7.1310015,-7.4595327,-0.32853127,single >=1e6
+I64F,1,kanR-kan,1.0,122.0,-6.928996,-6.6868324,0.24216366,single >=1e6
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+I64F,2,kanR+kan,0.0,176.0,-6.5909004,-6.514221,0.07667923,single >=1e6
+I64F,1,kanR+kan,0.0,200.0,-6.2310724,-6.261325,-0.030252457,single >=1e6
+I64F,2,kanR+kan,0.0,199.0,-6.2321873,-6.294499,-0.06231165,single >=1e6
+I64F,1,kanR+kan,0.0001,158.0,-6.3773823,-6.3205624,0.056819916,single >=1e6
+I64F,2,kanR+kan,0.0001,155.0,-6.3823524,-6.28375,0.098602295,single >=1e6
+I64F,1,kanR+kan,0.0001,175.0,-6.532958,-6.453207,0.079751015,single >=1e6
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+I64F,1,kanR+kan,0.0001,200.0,-6.233279,-6.286916,-0.05363655,single >=1e6
+I64F,2,kanR+kan,0.0001,199.0,-6.778228,-6.7760177,0.0022101402,single >=1e6
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+I64F,1,kanR+kan,0.001,200.0,-6.392089,-6.351501,0.040587902,single >=1e6
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+I64F,1,kanR+kan,0.003,175.0,-6.7619843,-6.9914417,-0.22945738,single >=1e6
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+I64F,1,kanR+kan,0.003,200.0,-6.3609457,-6.226173,0.13477278,single >=1e6
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+I64H,1,pheS-4CP,1.0,111.0,-6.725671,-6.6032524,0.1224184,single 1e5-1e6
+I64H,2,pheS-4CP,1.0,116.0,-7.233286,-7.6649528,-0.43166685,single 1e5-1e6
+I64H,1,pheS+4CP,0.0,94.0,-5.941828,-5.9830494,-0.04122162,single 1e5-1e6
+I64H,3,pheS+4CP,0.0,104.0,-6.8212776,-6.74809,0.07318783,single 1e5-1e6
+I64H,1,pheS+4CP,0.0,111.0,-7.0292788,-6.8434005,0.18587828,single 1e5-1e6
+I64H,1,pheS+4CP,0.0,125.0,-6.671227,-7.4435787,-0.77235174,single 1e5-1e6
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+I64H,1,pheS+4CP,0.0001,94.0,-6.545119,-6.513134,0.031984806,single 1e5-1e6
+I64H,1,pheS+4CP,0.0001,111.0,-6.7147646,-6.6805754,0.034189224,single 1e5-1e6
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+I64H,1,pheS+4CP,0.0001,125.0,-8.08406,-9.844686,-1.7606258,single 1e5-1e6
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+I64H,1,pheS+4CP,0.001,94.0,-6.6203084,-6.591325,0.028983593,single 1e5-1e6
+I64H,1,pheS+4CP,0.001,111.0,-7.12088,-6.9303594,0.19052076,single 1e5-1e6
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+I64H,1,pheS+4CP,0.001,125.0,-6.7467046,-6.537347,0.20935774,single 1e5-1e6
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+I79F,1,pheS-4CP,0.0,111.0,-6.9447227,-6.88233,0.06239271,single 1e5-1e6
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+I79R,1,pheS-4CP,1.0,94.0,-6.5864286,-6.5465536,0.03987503,single 1e5-1e6
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+I79R,1,pheS-4CP,1.0,111.0,-6.5542064,-6.8557987,-0.30159235,single 1e5-1e6
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+I79R,1,pheS+4CP,0.0,94.0,-6.160905,-6.1035514,0.057353497,single 1e5-1e6
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+V95H,1,kanR+kan,0.0001,200.0,-4.522048,-4.625715,-0.10366678,single 1e4-1e5
+V95H,2,kanR+kan,0.0001,199.0,-4.467184,-4.5960546,-0.12887049,single 1e4-1e5
+V95H,1,kanR+kan,0.001,158.0,-4.5216,-4.650887,-0.12928724,single 1e4-1e5
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+V95H,1,kanR+kan,0.001,175.0,-4.883031,-5.025718,-0.14268732,single 1e4-1e5
+V95H,2,kanR+kan,0.001,176.0,-4.8915157,-4.9403553,-0.04883957,single 1e4-1e5
+V95H,1,kanR+kan,0.001,200.0,-4.5888844,-4.724988,-0.13610363,single 1e4-1e5
+V95H,1,kanR+kan,0.003,158.0,-4.8811607,-4.977091,-0.0959301,single 1e4-1e5
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+V95H,1,kanR+kan,0.003,175.0,-4.734131,-4.850362,-0.116230965,single 1e4-1e5
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+V95H,1,kanR+kan,0.003,200.0,-4.712084,-4.827379,-0.11529541,single 1e4-1e5
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+V95H,1,kanR+kan,0.01,158.0,-4.8589,-4.9283824,-0.06948233,single 1e4-1e5
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+V95H,1,kanR+kan,0.01,175.0,-4.9038177,-4.9457693,-0.041951656,single 1e4-1e5
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+V95H,1,kanR+kan,0.03,146.0,-4.7417083,-4.870081,-0.12837267,single 1e4-1e5
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+V95H,1,kanR+kan,0.03,158.0,-4.83838,-4.9517283,-0.113348484,single 1e4-1e5
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+V95H,1,kanR+kan,0.03,175.0,-4.810214,-4.930461,-0.12024689,single 1e4-1e5
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+V95H,1,kanR+kan,0.1,175.0,-5.2757425,-5.336608,-0.060865402,single 1e4-1e5
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+V95H,1,kanR+kan,1.0,158.0,-5.9605064,-5.7712297,0.1892767,single 1e4-1e5
+V95H,2,kanR+kan,1.0,176.0,-6.5795913,-6.5620394,0.017551899,single 1e4-1e5
+V95H,1,kanR+kan,1.0,175.0,-5.6787796,-5.574994,0.103785515,single 1e4-1e5
+V95H,2,kanR+kan,1.0,185.0,-5.211293,-5.2822847,-0.070991516,single 1e4-1e5
+V95H,1,pheS-4CP,0.0,81.0,-2.361301,-2.4897106,-0.12840962,single 1e4-1e5
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+V95H,1,pheS-4CP,0.0,94.0,-2.3986251,-2.5328395,-0.1342144,single 1e4-1e5
+V95H,2,pheS-4CP,0.0,99.0,-3.3130584,-3.4599006,-0.14684224,single 1e4-1e5
+V95H,1,pheS-4CP,0.0,111.0,-2.8650343,-3.0249674,-0.15993309,single 1e4-1e5
+V95H,2,pheS-4CP,0.0,116.0,-3.0381618,-3.1814275,-0.14326572,single 1e4-1e5
+V95H,1,pheS-4CP,1.0,81.0,-0.93376267,-0.9235381,0.010224581,single 1e4-1e5
+V95H,2,pheS-4CP,1.0,82.0,-2.0327506,-2.0956376,-0.06288695,single 1e4-1e5
+V95H,1,pheS-4CP,1.0,94.0,-1.9429923,-2.032877,-0.08988464,single 1e4-1e5
+V95H,2,pheS-4CP,1.0,99.0,-3.6859698,-3.7184777,-0.032507896,single 1e4-1e5
+V95H,1,pheS-4CP,1.0,111.0,-2.3972564,-2.4846747,-0.08741832,single 1e4-1e5
+V95H,2,pheS-4CP,1.0,116.0,-3.3483543,-3.413474,-0.06511974,single 1e4-1e5
+V95H,1,pheS+4CP,0.0,94.0,-0.33157972,-0.29006004,0.04151967,single 1e4-1e5
+V95H,3,pheS+4CP,0.0,104.0,-0.0550507,-0.0591133,-0.0040626004,single 1e4-1e5
+V95H,1,pheS+4CP,0.0,111.0,-1.4809504,-1.2972065,0.18374383,single 1e4-1e5
+V95H,1,pheS+4CP,0.0,125.0,-0.95275927,-0.8378068,0.114952445,single 1e4-1e5
+V95H,2,pheS+4CP,0.0,127.0,-3.2301915,-3.390242,-0.16005063,single 1e4-1e5
+V95H,1,pheS+4CP,0.0001,94.0,-0.4853305,-0.42523435,0.060096145,single 1e4-1e5
+V95H,1,pheS+4CP,0.0001,111.0,-1.955086,-2.0318398,-0.076753855,single 1e4-1e5
+V95H,2,pheS+4CP,0.0001,116.0,-2.3467762,-2.4897594,-0.1429832,single 1e4-1e5
+V95H,1,pheS+4CP,0.0001,125.0,-2.3649247,-2.498294,-0.13336945,single 1e4-1e5
+V95H,2,pheS+4CP,0.0001,127.0,-3.030881,-3.1953826,-0.16450167,single 1e4-1e5
+V95H,1,pheS+4CP,0.001,94.0,-2.011749,-2.1456146,-0.1338656,single 1e4-1e5
+V95H,1,pheS+4CP,0.001,111.0,-2.3474903,-2.4372022,-0.089711905,single 1e4-1e5
+V95H,2,pheS+4CP,0.001,116.0,-3.0857487,-3.239359,-0.15361023,single 1e4-1e5
+V95H,1,pheS+4CP,0.001,125.0,-2.3666055,-2.4942462,-0.12764072,single 1e4-1e5
+V95H,2,pheS+4CP,0.001,127.0,-3.7768402,-3.6595898,0.11725044,single 1e4-1e5
+V95H,1,pheS+4CP,0.003,94.0,-2.2507322,-2.4026504,-0.15191817,single 1e4-1e5
+V95H,1,pheS+4CP,0.003,111.0,-2.0010865,-2.1213,-0.12021351,single 1e4-1e5
+V95H,2,pheS+4CP,0.003,116.0,-2.9387307,-3.0720751,-0.13334441,single 1e4-1e5
+V95H,1,pheS+4CP,0.003,125.0,-0.9512706,-0.8773342,0.0739364,single 1e4-1e5
+V95H,2,pheS+4CP,0.003,127.0,-2.7647653,-2.852696,-0.08793068,single 1e4-1e5
+V95H,1,pheS+4CP,0.01,94.0,-2.1208768,-2.2474551,-0.12657833,single 1e4-1e5
+V95H,1,pheS+4CP,0.01,111.0,-3.1574478,-3.2599874,-0.10253954,single 1e4-1e5
+V95H,2,pheS+4CP,0.01,116.0,-2.7217293,-2.862826,-0.14109683,single 1e4-1e5
+V95H,1,pheS+4CP,0.01,125.0,-2.4097967,-2.5219915,-0.11219478,single 1e4-1e5
+V95H,2,pheS+4CP,0.01,127.0,-2.9494913,-3.0767875,-0.12729621,single 1e4-1e5
+V95H,1,pheS+4CP,0.03,94.0,-0.7201675,-0.7092524,0.010915101,single 1e4-1e5
+V95H,1,pheS+4CP,0.03,111.0,-2.5595436,-2.644844,-0.085300446,single 1e4-1e5
+V95H,2,pheS+4CP,0.03,116.0,-2.6148243,-2.723943,-0.1091187,single 1e4-1e5
+V95H,1,pheS+4CP,0.03,125.0,-2.3746743,-2.46619,-0.09151578,single 1e4-1e5
+V95H,2,pheS+4CP,0.03,127.0,-2.364486,-2.4656901,-0.10120416,single 1e4-1e5
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+V95H,1,pheS+4CP,0.1,111.0,-1.9494853,-2.0314953,-0.08201003,single 1e4-1e5
+V95H,2,pheS+4CP,0.1,116.0,-2.4414167,-2.6098804,-0.1684637,single 1e4-1e5
+V95H,1,pheS+4CP,0.1,125.0,-2.3677886,-2.439498,-0.071709394,single 1e4-1e5
+V95H,2,pheS+4CP,0.1,127.0,-2.58145,-2.7433357,-0.16188574,single 1e4-1e5
+V95H,1,pheS+4CP,1.0,94.0,-2.8646283,-2.9541602,-0.0895319,single 1e4-1e5
+V95H,1,pheS+4CP,1.0,111.0,-2.5777934,-2.6671894,-0.089396,single 1e4-1e5
+V95H,2,pheS+4CP,1.0,116.0,-2.5878637,-2.7911472,-0.20328355,single 1e4-1e5
+V95H,1,pheS+4CP,1.0,125.0,-1.9618747,-2.1336243,-0.17174959,single 1e4-1e5
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+V95I,1,kanR-kan,0.0,92.0,-2.5546734,-2.697284,-0.14261055,single <1e4
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+V95I,1,kanR-kan,0.0,105.0,-2.6716754,-2.6690555,0.0026199818,single <1e4
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+V95I,1,kanR-kan,0.0,122.0,-2.7572322,-1.2347312,1.522501,single <1e4
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+V95I,1,kanR-kan,1.0,92.0,-2.8859367,-3.0570374,-0.17110062,single <1e4
+V95I,2,kanR-kan,1.0,83.0,-2.668785,-2.7626867,-0.093901634,single <1e4
+V95I,1,kanR-kan,1.0,105.0,-2.6670337,-2.666493,0.00054073334,single <1e4
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+V95I,1,kanR-kan,1.0,122.0,-2.7413063,-2.9437273,-0.20242095,single <1e4
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+V95I,1,kanR+kan,0.0,158.0,-4.4422355,-3.8557076,0.5865278,single <1e4
+V95I,2,kanR+kan,0.0,155.0,-4.0895014,-3.5613089,0.5281925,single <1e4
+V95I,1,kanR+kan,0.0,175.0,-0.7088737,-1.1156752,-0.40680152,single <1e4
+V95I,2,kanR+kan,0.0,176.0,-2.920076,-2.7378688,0.18220711,single <1e4
+V95I,1,kanR+kan,0.0,200.0,-0.77161336,-1.0588751,-0.28726172,single <1e4
+V95I,2,kanR+kan,0.0,199.0,-3.182604,-2.966504,0.21609998,single <1e4
+V95I,1,kanR+kan,0.0001,158.0,-3.47681,-3.1422389,0.33457112,single <1e4
+V95I,2,kanR+kan,0.0001,155.0,-0.35911685,-0.66099304,-0.3018762,single <1e4
+V95I,1,kanR+kan,0.0001,175.0,-0.8191062,-1.2920051,-0.47289884,single <1e4
+V95I,2,kanR+kan,0.0001,176.0,-2.6533456,-2.496193,0.15715265,single <1e4
+V95I,1,kanR+kan,0.0001,200.0,-3.5839438,-3.368704,0.21523976,single <1e4
+V95I,2,kanR+kan,0.0001,199.0,-0.45174682,-0.66299933,-0.21125251,single <1e4
+V95I,1,kanR+kan,0.001,158.0,-3.1749156,-2.8626084,0.31230712,single <1e4
+V95I,2,kanR+kan,0.001,155.0,-0.59865916,-1.1279398,-0.52928066,single <1e4
+V95I,1,kanR+kan,0.001,175.0,-3.263199,-3.0400994,0.22309971,single <1e4
+V95I,2,kanR+kan,0.001,176.0,-2.1242177,-2.0414352,0.08278251,single <1e4
+V95I,1,kanR+kan,0.001,200.0,-2.9419475,-2.812852,0.12909555,single <1e4
+V95I,1,kanR+kan,0.003,158.0,-2.6874723,-2.4769163,0.21055603,single <1e4
+V95I,2,kanR+kan,0.003,155.0,-0.57876873,-1.1419591,-0.56319034,single <1e4
+V95I,1,kanR+kan,0.003,175.0,-2.605986,-2.4637244,0.14226174,single <1e4
+V95I,2,kanR+kan,0.003,176.0,-3.6866944,-3.2657232,0.42097116,single <1e4
+V95I,1,kanR+kan,0.003,200.0,-2.640257,-2.5195258,0.120731115,single <1e4
+V95I,2,kanR+kan,0.003,199.0,-2.2898057,-2.1393774,0.1504283,single <1e4
+V95I,1,kanR+kan,0.01,146.0,-2.5532122,-2.4337616,0.11945057,single <1e4
+V95I,2,kanR+kan,0.01,155.0,-2.1165962,-2.0338056,0.08279061,single <1e4
+V95I,1,kanR+kan,0.01,158.0,-0.66802925,-1.2954407,-0.6274114,single <1e4
+V95I,2,kanR+kan,0.01,176.0,-2.666062,-2.4656737,0.20038843,single <1e4
+V95I,1,kanR+kan,0.01,175.0,-2.002172,-2.056557,-0.054384947,single <1e4
+V95I,2,kanR+kan,0.01,185.0,-2.665664,-2.4757485,0.18991542,single <1e4
+V95I,1,kanR+kan,0.03,146.0,-1.9808861,-2.0516727,-0.070786595,single <1e4
+V95I,2,kanR+kan,0.03,155.0,-2.474922,-2.4351058,0.03981614,single <1e4
+V95I,1,kanR+kan,0.03,158.0,-2.6714664,-2.666708,0.004758358,single <1e4
+V95I,2,kanR+kan,0.03,176.0,-2.3910263,-2.4337757,-0.042749405,single <1e4
+V95I,1,kanR+kan,0.03,175.0,-2.6620607,-2.6724224,-0.010361671,single <1e4
+V95I,2,kanR+kan,0.03,185.0,-3.661406,-3.4545672,0.20683885,single <1e4
+V95I,1,kanR+kan,0.1,146.0,-3.0174844,-3.1393838,-0.12189937,single <1e4
+V95I,2,kanR+kan,0.1,155.0,-2.7134047,-2.8299065,-0.11650181,single <1e4
+V95I,1,kanR+kan,0.1,158.0,-3.4207819,-3.5507746,-0.12999272,single <1e4
+V95I,2,kanR+kan,0.1,176.0,-2.7133613,-2.848555,-0.13519382,single <1e4
+V95I,1,kanR+kan,0.1,175.0,-2.994667,-2.9050534,0.089613676,single <1e4
+V95I,2,kanR+kan,0.1,185.0,-2.7133412,-2.3225582,0.39078307,single <1e4
+V95I,1,kanR+kan,1.0,146.0,-3.2532592,-3.4157777,-0.1625185,single <1e4
+V95I,2,kanR+kan,1.0,155.0,-3.4935677,-3.161573,0.33199477,single <1e4
+V95I,3,kanR+kan,1.0,168.0,-1.9431494,-2.0323987,-0.08924925,single <1e4
+V95I,1,kanR+kan,1.0,158.0,-3.4077384,-2.9858723,0.42186618,single <1e4
+V95I,2,kanR+kan,1.0,176.0,-3.6135998,-4.1291127,-0.51551294,single <1e4
+V95I,1,kanR+kan,1.0,175.0,-3.6481256,-4.143515,-0.49538946,single <1e4
+V95I,2,kanR+kan,1.0,185.0,-3.433541,-2.854602,0.57893896,single <1e4
+V95I,1,pheS-4CP,0.0,81.0,-4.0713425,-3.7002895,0.37105298,single <1e4
+V95I,2,pheS-4CP,0.0,82.0,-4.128307,-3.6355305,0.4927764,single <1e4
+V95I,1,pheS-4CP,0.0,94.0,-3.7184184,-3.6023114,0.11610699,single <1e4
+V95I,2,pheS-4CP,0.0,99.0,-4.489174,-3.8390532,0.65012074,single <1e4
+V95I,1,pheS-4CP,0.0,111.0,-3.555666,-3.4364824,0.11918354,single <1e4
+V95I,2,pheS-4CP,0.0,116.0,-3.3038955,-3.553037,-0.24914145,single <1e4
+V95I,1,pheS-4CP,1.0,81.0,-4.35102,-3.6714249,0.679595,single <1e4
+V95I,2,pheS-4CP,1.0,82.0,-3.686872,-3.2857666,0.4011054,single <1e4
+V95I,1,pheS-4CP,1.0,94.0,-3.8080916,-3.6476822,0.16040945,single <1e4
+V95I,2,pheS-4CP,1.0,99.0,-3.990784,-3.6156836,0.37510037,single <1e4
+V95I,1,pheS-4CP,1.0,111.0,-3.3878808,-3.5306125,-0.14273167,single <1e4
+V95I,2,pheS-4CP,1.0,116.0,-3.690724,-3.5487933,0.14193058,single <1e4
+V95I,1,pheS+4CP,0.0,94.0,-3.2748623,-3.2531676,0.02169466,single <1e4
+V95I,3,pheS+4CP,0.0,104.0,-3.538412,-3.6715763,-0.13316417,single <1e4
+V95I,1,pheS+4CP,0.0,111.0,-3.7503011,-3.843719,-0.09341788,single <1e4
+V95I,1,pheS+4CP,0.0,125.0,-3.6704435,-3.6169493,0.053494215,single <1e4
+V95I,2,pheS+4CP,0.0,127.0,-3.6414926,-3.5530846,0.08840799,single <1e4
+V95I,1,pheS+4CP,0.0001,94.0,-3.1781487,-3.2233512,-0.045202494,single <1e4
+V95I,1,pheS+4CP,0.0001,111.0,-3.6226106,-3.6482067,-0.025596142,single <1e4
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+V95I,1,pheS+4CP,0.0001,125.0,-3.9890747,-3.554638,0.4344368,single <1e4
+V95I,2,pheS+4CP,0.0001,127.0,-3.448972,-3.6658833,-0.21691132,single <1e4
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+V95I,1,pheS+4CP,0.001,125.0,-3.7900558,-3.730578,0.059477806,single <1e4
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+V95I,1,pheS+4CP,0.003,111.0,-3.2883654,-3.2751927,0.0131726265,single <1e4
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+V95I,1,pheS+4CP,0.003,125.0,-3.7314506,-3.781599,-0.050148487,single <1e4
+V95I,2,pheS+4CP,0.003,127.0,-3.7763865,-3.698308,0.07807851,single <1e4
+V95I,1,pheS+4CP,0.01,94.0,-3.3997753,-3.4166894,-0.01691413,single <1e4
+V95I,1,pheS+4CP,0.01,111.0,-3.5642676,-3.7464147,-0.18214703,single <1e4
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+V95I,1,pheS+4CP,0.01,125.0,-4.0350266,-3.5166306,0.5183959,single <1e4
+V95I,2,pheS+4CP,0.01,127.0,-3.7314415,-3.5281496,0.2032919,single <1e4
+V95I,1,pheS+4CP,0.03,94.0,-3.3808532,-3.4305768,-0.049723625,single <1e4
+V95I,1,pheS+4CP,0.03,111.0,-3.3555002,-3.489161,-0.1336608,single <1e4
+V95I,2,pheS+4CP,0.03,116.0,-3.5595374,-3.6812963,-0.12175894,single <1e4
+V95I,1,pheS+4CP,0.03,125.0,-3.656778,-3.6721535,-0.015375376,single <1e4
+V95I,2,pheS+4CP,0.03,127.0,-3.5340023,-3.7545815,-0.22057915,single <1e4
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+V95I,1,pheS+4CP,0.1,111.0,-3.301551,-3.4423914,-0.14084029,single <1e4
+V95I,2,pheS+4CP,0.1,116.0,-3.1520033,-3.3402495,-0.18824625,single <1e4
+V95I,1,pheS+4CP,0.1,125.0,-3.602724,-3.7604063,-0.15768218,single <1e4
+V95I,2,pheS+4CP,0.1,127.0,-3.2305741,-3.2542377,-0.02366352,single <1e4
+V95I,1,pheS+4CP,1.0,94.0,-4.0955715,-3.9480596,0.14751196,single <1e4
+V95I,1,pheS+4CP,1.0,111.0,-3.7361126,-3.7931464,-0.057033777,single <1e4
+V95I,2,pheS+4CP,1.0,116.0,-3.4067721,-3.4723253,-0.06555319,single <1e4
+V95I,1,pheS+4CP,1.0,125.0,-3.591042,-3.7325313,-0.14148927,single <1e4
+V95I,2,pheS+4CP,1.0,127.0,-3.4581423,-3.5751987,-0.11705637,single <1e4
+V95L,1,kanR-kan,0.0,92.0,-4.6726174,-4.917609,-0.24499178,single <1e4
+V95L,2,kanR-kan,0.0,83.0,-5.501933,-4.8359995,0.6659336,single <1e4
+V95L,1,kanR-kan,0.0,105.0,-4.708741,-4.811552,-0.10281086,single <1e4
+V95L,2,kanR-kan,0.0,97.0,-4.35894,-4.488618,-0.12967777,single <1e4
+V95L,1,kanR-kan,0.0,122.0,-4.4404945,-4.680329,-0.23983431,single <1e4
+V95L,2,kanR-kan,0.0,116.0,-4.545738,-4.7899947,-0.2442565,single <1e4
+V95L,1,kanR-kan,1.0,92.0,-4.8543744,-4.840454,0.013920307,single <1e4
+V95L,2,kanR-kan,1.0,83.0,-4.305978,-4.4398146,-0.13383675,single <1e4
+V95L,1,kanR-kan,1.0,105.0,-4.57045,-4.6851482,-0.11469841,single <1e4
+V95L,2,kanR-kan,1.0,97.0,-4.783278,-4.7102246,0.07305336,single <1e4
+V95L,1,kanR-kan,1.0,122.0,-4.426032,-5.0966606,-0.67062855,single <1e4
+V95L,2,kanR-kan,1.0,116.0,-4.987445,-4.646,0.34144497,single <1e4
+V95L,1,kanR+kan,0.0,158.0,-3.9590883,-4.0761642,-0.11707592,single <1e4
+V95L,2,kanR+kan,0.0,155.0,-3.987762,-4.0546875,-0.066925526,single <1e4
+V95L,1,kanR+kan,0.0,175.0,-4.311364,-4.3567553,-0.045391083,single <1e4
+V95L,2,kanR+kan,0.0,176.0,-4.045842,-4.220281,-0.17443895,single <1e4
+V95L,1,kanR+kan,0.0,200.0,-3.9547715,-4.072079,-0.11730766,single <1e4
+V95L,2,kanR+kan,0.0,199.0,-3.3816142,-3.5711327,-0.18951845,single <1e4
+V95L,1,kanR+kan,0.0001,158.0,-4.369288,-4.484459,-0.115170956,single <1e4
+V95L,2,kanR+kan,0.0001,155.0,-3.997513,-4.133609,-0.13609576,single <1e4
+V95L,1,kanR+kan,0.0001,175.0,-4.6984863,-4.6572266,0.041259766,single <1e4
+V95L,2,kanR+kan,0.0001,176.0,-4.2843614,-4.31847,-0.03410864,single <1e4
+V95L,1,kanR+kan,0.0001,200.0,-3.7090764,-3.8877501,-0.17867374,single <1e4
+V95L,2,kanR+kan,0.0001,199.0,-3.7829342,-4.0690084,-0.28607416,single <1e4
+V95L,1,kanR+kan,0.001,158.0,-3.7389023,-3.8777742,-0.13887191,single <1e4
+V95L,2,kanR+kan,0.001,155.0,-4.458791,-4.559761,-0.10097027,single <1e4
+V95L,1,kanR+kan,0.001,175.0,-4.2003736,-4.2957006,-0.0953269,single <1e4
+V95L,2,kanR+kan,0.001,176.0,-4.3324113,-4.3510227,-0.018611431,single <1e4
+V95L,1,kanR+kan,0.001,200.0,-3.8425808,-4.026416,-0.18383503,single <1e4
+V95L,1,kanR+kan,0.003,158.0,-4.9796643,-5.026232,-0.04656744,single <1e4
+V95L,2,kanR+kan,0.003,155.0,-4.1034994,-4.2004747,-0.09697533,single <1e4
+V95L,1,kanR+kan,0.003,175.0,-3.9952302,-4.122055,-0.12682486,single <1e4
+V95L,2,kanR+kan,0.003,176.0,-5.2820406,-5.0830617,0.1989789,single <1e4
+V95L,1,kanR+kan,0.003,200.0,-3.9914622,-4.0943155,-0.1028533,single <1e4
+V95L,2,kanR+kan,0.003,199.0,-4.8163013,-4.5880775,0.2282238,single <1e4
+V95L,1,kanR+kan,0.01,146.0,-4.0837507,-4.1848836,-0.10113287,single <1e4
+V95L,2,kanR+kan,0.01,155.0,-5.599605,-4.9494705,0.65013456,single <1e4
+V95L,1,kanR+kan,0.01,158.0,-4.078746,-4.1927686,-0.11402273,single <1e4
+V95L,2,kanR+kan,0.01,176.0,-4.7897625,-4.835376,-0.04561329,single <1e4
+V95L,1,kanR+kan,0.01,175.0,-4.251985,-4.260581,-0.008595943,single <1e4
+V95L,2,kanR+kan,0.01,185.0,-4.225088,-4.3544683,-0.12938023,single <1e4
+V95L,1,kanR+kan,0.03,146.0,-4.7384186,-5.1012034,-0.36278486,single <1e4
+V95L,2,kanR+kan,0.03,155.0,-4.2180986,-4.290941,-0.0728426,single <1e4
+V95L,1,kanR+kan,0.03,158.0,-4.3697686,-4.236829,0.13293982,single <1e4
+V95L,2,kanR+kan,0.03,176.0,-4.348728,-4.405302,-0.056573868,single <1e4
+V95L,1,kanR+kan,0.03,175.0,-4.1981125,-4.252537,-0.054424286,single <1e4
+V95L,2,kanR+kan,0.03,185.0,-4.5994673,-4.7187634,-0.119296074,single <1e4
+V95L,1,kanR+kan,0.1,146.0,-4.579856,-4.9154177,-0.33556175,single <1e4
+V95L,2,kanR+kan,0.1,155.0,-4.4118867,-4.5790143,-0.16712761,single <1e4
+V95L,1,kanR+kan,0.1,158.0,-4.8871994,-5.16405,-0.2768507,single <1e4
+V95L,2,kanR+kan,0.1,176.0,-4.5782533,-4.712633,-0.13437986,single <1e4
+V95L,1,kanR+kan,0.1,175.0,-5.1635933,-4.911421,0.25217247,single <1e4
+V95L,2,kanR+kan,0.1,185.0,-4.4718432,-4.5932817,-0.1214385,single <1e4
+V95L,1,kanR+kan,1.0,146.0,-4.446247,-4.503104,-0.05685711,single <1e4
+V95L,2,kanR+kan,1.0,155.0,-4.748605,-4.897964,-0.14935923,single <1e4
+V95L,3,kanR+kan,1.0,168.0,-4.213875,-4.3453007,-0.13142586,single <1e4
+V95L,1,kanR+kan,1.0,158.0,-4.5580873,-4.564293,-0.006205559,single <1e4
+V95L,2,kanR+kan,1.0,176.0,-4.582882,-4.662201,-0.079319,single <1e4
+V95L,1,kanR+kan,1.0,175.0,-5.239442,-5.153626,0.08581591,single <1e4
+V95L,2,kanR+kan,1.0,185.0,-4.591338,-4.7004642,-0.10912609,single <1e4
+V95L,1,pheS-4CP,0.0,81.0,-0.15512045,-0.11009145,0.045029,single <1e4
+V95L,2,pheS-4CP,0.0,82.0,-2.854313,-3.2320719,-0.37775898,single <1e4
+V95L,1,pheS-4CP,0.0,94.0,-0.15406713,-0.11927441,0.03479272,single <1e4
+V95L,2,pheS-4CP,0.0,99.0,-0.5239111,-0.39763886,0.12627226,single <1e4
+V95L,1,pheS-4CP,0.0,111.0,-0.079512246,-0.06849399,0.011018254,single <1e4
+V95L,2,pheS-4CP,0.0,116.0,-0.5787422,-0.49032238,0.088419825,single <1e4
+V95L,1,pheS-4CP,1.0,81.0,-3.0831506,-3.3186252,-0.23547459,single <1e4
+V95L,2,pheS-4CP,1.0,82.0,-3.2208729,-3.4721575,-0.2512846,single <1e4
+V95L,1,pheS-4CP,1.0,94.0,-2.8857288,-3.0240989,-0.13837004,single <1e4
+V95L,2,pheS-4CP,1.0,99.0,-3.51441,-3.6161017,-0.10169172,single <1e4
+V95L,1,pheS-4CP,1.0,111.0,-0.17879318,-0.19365722,-0.014864042,single <1e4
+V95L,2,pheS-4CP,1.0,116.0,-0.4770844,-0.5114595,-0.03437513,single <1e4
+V95L,1,pheS+4CP,0.0,94.0,-0.064911775,-0.04664214,0.018269636,single <1e4
+V95L,3,pheS+4CP,0.0,104.0,-0.061307125,-0.06970018,-0.008393057,single <1e4
+V95L,1,pheS+4CP,0.0,111.0,-0.3507127,-0.2781679,0.07254478,single <1e4
+V95L,1,pheS+4CP,0.0,125.0,-2.5385675,-2.739192,-0.20062447,single <1e4
+V95L,2,pheS+4CP,0.0,127.0,-1.9560425,-2.0901463,-0.13410378,single <1e4
+V95L,1,pheS+4CP,0.0001,94.0,-0.09548142,-0.06868053,0.026800886,single <1e4
+V95L,1,pheS+4CP,0.0001,111.0,-0.27867988,-0.2213841,0.057295784,single <1e4
+V95L,2,pheS+4CP,0.0001,116.0,-2.3331716,-2.5967448,-0.26357317,single <1e4
+V95L,1,pheS+4CP,0.0001,125.0,-0.29356888,-0.25410447,0.039464414,single <1e4
+V95L,2,pheS+4CP,0.0001,127.0,-0.6016419,-0.49985522,0.10178667,single <1e4
+V95L,1,pheS+4CP,0.001,94.0,-0.1332695,-0.096999146,0.036270358,single <1e4
+V95L,1,pheS+4CP,0.001,111.0,-2.2692795,-2.4976919,-0.22841239,single <1e4
+V95L,2,pheS+4CP,0.001,116.0,-2.5801177,-2.8107662,-0.23064852,single <1e4
+V95L,1,pheS+4CP,0.001,125.0,-2.4562306,-2.6369505,-0.18071985,single <1e4
+V95L,2,pheS+4CP,0.001,127.0,-2.3015194,-2.2340736,0.067445755,single <1e4
+V95L,1,pheS+4CP,0.003,94.0,-0.07985661,-0.059976723,0.019879889,single <1e4
+V95L,1,pheS+4CP,0.003,111.0,-0.1656257,-0.13887697,0.026748732,single <1e4
+V95L,2,pheS+4CP,0.003,116.0,-2.8839624,-3.0906684,-0.20670605,single <1e4
+V95L,1,pheS+4CP,0.003,125.0,-0.26578134,-0.24432501,0.021456331,single <1e4
+V95L,2,pheS+4CP,0.003,127.0,-0.906344,-0.7989421,0.10740191,single <1e4
+V95L,1,pheS+4CP,0.01,94.0,-0.09699518,-0.07879702,0.018198162,single <1e4
+V95L,1,pheS+4CP,0.01,111.0,-0.16690017,-0.15346847,0.013431698,single <1e4
+V95L,2,pheS+4CP,0.01,116.0,-2.4999962,-2.664886,-0.16488981,single <1e4
+V95L,1,pheS+4CP,0.01,125.0,-0.24048871,-0.24414292,-0.0036542118,single <1e4
+V95L,2,pheS+4CP,0.01,127.0,-2.883262,-2.975471,-0.0922091,single <1e4
+V95L,1,pheS+4CP,0.03,94.0,-0.12957115,-0.11904238,0.010528773,single <1e4
+V95L,1,pheS+4CP,0.03,111.0,-0.14219756,-0.15029603,-0.008098468,single <1e4
+V95L,2,pheS+4CP,0.03,116.0,-2.0356755,-2.109744,-0.07406855,single <1e4
+V95L,1,pheS+4CP,0.03,125.0,-0.2510952,-0.29809517,-0.04699996,single <1e4
+V95L,2,pheS+4CP,0.03,127.0,-2.0989354,-2.1322713,-0.033335924,single <1e4
+V95L,1,pheS+4CP,0.1,94.0,-0.04869869,-0.05182322,-0.0031245314,single <1e4
+V95L,1,pheS+4CP,0.1,111.0,-0.18856335,-0.23631513,-0.047751784,single <1e4
+V95L,2,pheS+4CP,0.1,116.0,-0.57228136,-0.7134557,-0.14117432,single <1e4
+V95L,1,pheS+4CP,0.1,125.0,-2.556263,-2.3986514,0.15761161,single <1e4
+V95L,2,pheS+4CP,0.1,127.0,-0.6376395,-0.8806023,-0.24296278,single <1e4
+V95L,1,pheS+4CP,1.0,94.0,-0.3468543,-0.41164336,-0.06478906,single <1e4
+V95L,1,pheS+4CP,1.0,111.0,-0.31446043,-0.44733757,-0.13287714,single <1e4
+V95L,2,pheS+4CP,1.0,116.0,-0.6210666,-0.8841724,-0.2631058,single <1e4
+V95L,1,pheS+4CP,1.0,125.0,-0.3196464,-0.5264813,-0.20683488,single <1e4
+V95L,2,pheS+4CP,1.0,127.0,-0.6964245,-1.1105744,-0.41414988,single <1e4
+V95N,1,kanR-kan,0.0,92.0,-6.124629,-6.083278,0.04135084,single 1e5-1e6
+V95N,2,kanR-kan,0.0,83.0,-6.1743536,-6.574153,-0.39979935,single 1e5-1e6
+V95N,1,kanR-kan,0.0,105.0,-6.207263,-6.3514843,-0.1442213,single 1e5-1e6
+V95N,2,kanR-kan,0.0,97.0,-6.1239367,-5.998377,0.1255598,single 1e5-1e6
+V95N,1,kanR-kan,0.0,122.0,-6.149598,-8.016593,-1.8669949,single 1e5-1e6
+V95N,2,kanR-kan,0.0,116.0,-6.1607256,-6.1068726,0.053853035,single 1e5-1e6
+V95N,1,kanR-kan,1.0,92.0,-6.476207,-7.4114733,-0.9352665,single 1e5-1e6
+V95N,2,kanR-kan,1.0,83.0,-6.292514,-6.9192657,-0.6267519,single 1e5-1e6
+V95N,1,kanR-kan,1.0,105.0,-6.4556828,-7.5991583,-1.1434755,single 1e5-1e6
+V95N,2,kanR-kan,1.0,97.0,-6.2136145,-7.022914,-0.80929947,single 1e5-1e6
+V95N,1,kanR-kan,1.0,122.0,-6.1236076,-7.2986565,-1.1750488,single 1e5-1e6
+V95N,2,kanR-kan,1.0,116.0,-5.907136,-5.6802926,0.22684336,single 1e5-1e6
+V95N,1,kanR+kan,0.0,158.0,-5.539099,-5.3428345,0.19626474,single 1e5-1e6
+V95N,2,kanR+kan,0.0,155.0,-5.6586113,-6.55316,-0.8945489,single 1e5-1e6
+V95N,1,kanR+kan,0.0,175.0,-5.561408,-6.5435104,-0.9821024,single 1e5-1e6
+V95N,2,kanR+kan,0.0,176.0,-5.2931967,-6.033779,-0.74058247,single 1e5-1e6
+V95N,1,kanR+kan,0.0,200.0,-5.37185,-6.378275,-1.0064249,single 1e5-1e6
+V95N,2,kanR+kan,0.0,199.0,-4.801152,-5.3702765,-0.5691242,single 1e5-1e6
+V95N,1,kanR+kan,0.0001,158.0,-5.516103,-6.3759584,-0.85985565,single 1e5-1e6
+V95N,2,kanR+kan,0.0001,155.0,-6.9872046,-7.9560432,-0.9688387,single 1e5-1e6
+V95N,1,kanR+kan,0.0001,175.0,-5.166382,-5.5592394,-0.39285755,single 1e5-1e6
+V95N,2,kanR+kan,0.0001,176.0,-6.4496145,-5.2859306,1.1636839,single 1e5-1e6
+V95N,1,kanR+kan,0.0001,200.0,-5.254991,-6.1278,-0.87280893,single 1e5-1e6
+V95N,2,kanR+kan,0.0001,199.0,-4.7326803,-5.0356493,-0.30296898,single 1e5-1e6
+V95N,1,kanR+kan,0.001,158.0,-4.985551,-5.2210083,-0.23545742,single 1e5-1e6
+V95N,2,kanR+kan,0.001,155.0,-5.736333,-5.2662153,0.47011757,single 1e5-1e6
+V95N,1,kanR+kan,0.001,175.0,-5.568816,-6.5275126,-0.95869637,single 1e5-1e6
+V95N,2,kanR+kan,0.001,176.0,-5.495904,-5.007156,0.48874807,single 1e5-1e6
+V95N,1,kanR+kan,0.001,200.0,-5.0482802,-5.7596607,-0.7113805,single 1e5-1e6
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+V95T,1,pheS+4CP,0.001,111.0,-2.1463008,-2.1524246,-0.006123781,single 1e5-1e6
+V95T,2,pheS+4CP,0.001,116.0,-2.7423983,-2.8324275,-0.09002924,single 1e5-1e6
+V95T,1,pheS+4CP,0.001,125.0,-1.083844,-0.9748557,0.108988225,single 1e5-1e6
+V95T,2,pheS+4CP,0.001,127.0,-3.5734634,-3.6281545,-0.054691076,single 1e5-1e6
+V95T,1,pheS+4CP,0.003,94.0,-2.1312346,-2.3016596,-0.17042494,single 1e5-1e6
+V95T,1,pheS+4CP,0.003,111.0,-1.9612265,-2.0829327,-0.12170625,single 1e5-1e6
+V95T,2,pheS+4CP,0.003,116.0,-3.1610613,-3.3143277,-0.15326643,single 1e5-1e6
+V95T,1,pheS+4CP,0.003,125.0,-1.0665492,-0.94574803,0.12080115,single 1e5-1e6
+V95T,2,pheS+4CP,0.003,127.0,-2.8815017,-2.837472,0.044029713,single 1e5-1e6
+V95T,1,pheS+4CP,0.01,94.0,-2.0102468,-2.1496441,-0.13939738,single 1e5-1e6
+V95T,1,pheS+4CP,0.01,111.0,-2.4075594,-2.545862,-0.13830256,single 1e5-1e6
+V95T,2,pheS+4CP,0.01,116.0,-1.285786,-1.0734818,0.21230423,single 1e5-1e6
+V95T,1,pheS+4CP,0.01,125.0,-1.0954679,-0.98966897,0.10579896,single 1e5-1e6
+V95T,2,pheS+4CP,0.01,127.0,-2.9434662,-3.0553343,-0.11186814,single 1e5-1e6
+V95T,1,pheS+4CP,0.03,94.0,-0.7798627,-0.8156745,-0.035811782,single 1e5-1e6
+V95T,1,pheS+4CP,0.03,111.0,-2.0153298,-2.0774853,-0.062155485,single 1e5-1e6
+V95T,2,pheS+4CP,0.03,116.0,-2.6525993,-2.8072445,-0.1546452,single 1e5-1e6
+V95T,1,pheS+4CP,0.03,125.0,-0.9491026,-1.079572,-0.13046938,single 1e5-1e6
+V95T,2,pheS+4CP,0.03,127.0,-3.0162914,-3.0868251,-0.07053375,single 1e5-1e6
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+V95T,1,pheS+4CP,0.1,111.0,-3.76931,-3.5924945,0.17681551,single 1e5-1e6
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+V95T,1,pheS+4CP,0.1,125.0,-2.552762,-2.5123458,0.04041624,single 1e5-1e6
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+V95T,1,pheS+4CP,1.0,94.0,-3.0946882,-3.0914423,0.0032458305,single 1e5-1e6
+V95T,1,pheS+4CP,1.0,111.0,-6.142526,-5.3894854,0.7530408,single 1e5-1e6
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+V95T,1,pheS+4CP,1.0,125.0,-2.816568,-2.7408552,0.07571268,single 1e5-1e6
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+V95Y,1,kanR-kan,0.0,122.0,-3.8621757,-3.9345994,-0.0724237,single <1e4
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+V95Y,1,kanR-kan,1.0,92.0,-4.0066323,-4.0477448,-0.041112423,single <1e4
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+V95Y,1,kanR-kan,1.0,105.0,-3.99548,-4.0623064,-0.06682634,single <1e4
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+V95Y,1,kanR-kan,1.0,122.0,-3.8595164,-3.9114003,-0.051883936,single <1e4
+V95Y,2,kanR-kan,1.0,116.0,-3.3221567,-3.485733,-0.16357636,single <1e4
+V95Y,1,kanR+kan,0.0,158.0,-3.3779426,-3.4483578,-0.07041526,single <1e4
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+V95Y,1,kanR+kan,0.0,175.0,-3.1431684,-3.2311366,-0.08796811,single <1e4
+V95Y,2,kanR+kan,0.0,176.0,-3.2849793,-3.231041,0.05393839,single <1e4
+V95Y,1,kanR+kan,0.0,200.0,-2.9615164,-2.8887372,0.07277918,single <1e4
+V95Y,2,kanR+kan,0.0,199.0,-3.5691652,-3.7314994,-0.1623342,single <1e4
+V95Y,1,kanR+kan,0.0001,158.0,-3.585365,-3.732408,-0.14704299,single <1e4
+V95Y,2,kanR+kan,0.0001,155.0,-2.7403858,-2.671663,0.068722725,single <1e4
+V95Y,1,kanR+kan,0.0001,175.0,-3.2485814,-3.3198752,-0.07129383,single <1e4
+V95Y,2,kanR+kan,0.0001,176.0,-3.3098927,-3.4552326,-0.14533997,single <1e4
+V95Y,1,kanR+kan,0.0001,200.0,-2.9327931,-3.060001,-0.12720776,single <1e4
+V95Y,2,kanR+kan,0.0001,199.0,-2.934928,-3.074018,-0.13909006,single <1e4
+V95Y,1,kanR+kan,0.001,158.0,-3.2904382,-3.4433742,-0.15293598,single <1e4
+V95Y,2,kanR+kan,0.001,155.0,-3.6498296,-3.4080102,0.24181938,single <1e4
+V95Y,1,kanR+kan,0.001,175.0,-3.3857024,-3.511035,-0.1253326,single <1e4
+V95Y,2,kanR+kan,0.001,176.0,-3.702548,-3.8480434,-0.14549541,single <1e4
+V95Y,1,kanR+kan,0.001,200.0,-2.9494646,-3.0553312,-0.10586667,single <1e4
+V95Y,1,kanR+kan,0.003,158.0,-3.258618,-3.4032328,-0.1446147,single <1e4
+V95Y,2,kanR+kan,0.003,155.0,-3.4169164,-3.5571904,-0.14027405,single <1e4
+V95Y,1,kanR+kan,0.003,175.0,-3.002924,-3.0289934,-0.026069403,single <1e4
+V95Y,2,kanR+kan,0.003,176.0,-3.3804226,-3.5462215,-0.1657989,single <1e4
+V95Y,1,kanR+kan,0.003,200.0,-3.1938677,-3.3592858,-0.16541815,single <1e4
+V95Y,2,kanR+kan,0.003,199.0,-3.0802937,-3.2512083,-0.17091465,single <1e4
+V95Y,1,kanR+kan,0.01,146.0,-4.3556447,-4.4302816,-0.074636936,single <1e4
+V95Y,2,kanR+kan,0.01,155.0,-3.2359114,-3.0916436,0.1442678,single <1e4
+V95Y,1,kanR+kan,0.01,158.0,-3.6739185,-3.8229306,-0.14901209,single <1e4
+V95Y,2,kanR+kan,0.01,176.0,-3.00241,-2.976088,0.026321888,single <1e4
+V95Y,1,kanR+kan,0.01,175.0,-3.2039888,-3.3355947,-0.13160586,single <1e4
+V95Y,2,kanR+kan,0.01,185.0,-3.992522,-4.121728,-0.12920594,single <1e4
+V95Y,1,kanR+kan,0.03,146.0,-3.212384,-3.333148,-0.12076402,single <1e4
+V95Y,2,kanR+kan,0.03,155.0,-3.3660955,-3.2687907,0.09730482,single <1e4
+V95Y,1,kanR+kan,0.03,158.0,-3.3410804,-3.345402,-0.004321575,single <1e4
+V95Y,2,kanR+kan,0.03,176.0,-3.8417623,-3.9628997,-0.12113738,single <1e4
+V95Y,1,kanR+kan,0.03,175.0,-3.154714,-3.181243,-0.026528835,single <1e4
+V95Y,2,kanR+kan,0.03,185.0,-3.7537847,-3.8858838,-0.13209915,single <1e4
+V95Y,1,kanR+kan,0.1,146.0,-3.8372884,-3.9816847,-0.1443963,single <1e4
+V95Y,2,kanR+kan,0.1,155.0,-3.7401927,-3.8782616,-0.13806891,single <1e4
+V95Y,1,kanR+kan,0.1,158.0,-4.7426257,-4.663599,0.0790267,single <1e4
+V95Y,2,kanR+kan,0.1,176.0,-3.6831272,-3.8068528,-0.12372565,single <1e4
+V95Y,1,kanR+kan,0.1,175.0,-3.7658525,-3.9047503,-0.1388979,single <1e4
+V95Y,2,kanR+kan,0.1,185.0,-3.653782,-3.7918,-0.13801813,single <1e4
+V95Y,1,kanR+kan,1.0,146.0,-4.2226954,-4.323303,-0.10060787,single <1e4
+V95Y,2,kanR+kan,1.0,155.0,-4.234375,-4.3777037,-0.14332867,single <1e4
+V95Y,3,kanR+kan,1.0,168.0,-4.1987147,-4.3303146,-0.1315999,single <1e4
+V95Y,1,kanR+kan,1.0,158.0,-4.2600384,-4.354001,-0.09396267,single <1e4
+V95Y,2,kanR+kan,1.0,176.0,-4.460511,-4.4868875,-0.026376247,single <1e4
+V95Y,1,kanR+kan,1.0,175.0,-4.406417,-4.4503827,-0.043965816,single <1e4
+V95Y,2,kanR+kan,1.0,185.0,-4.134698,-4.2428813,-0.108183384,single <1e4
+V95Y,1,pheS-4CP,0.0,81.0,-4.0815005,-4.0224953,0.05900526,single <1e4
+V95Y,2,pheS-4CP,0.0,82.0,-3.88139,-3.9770558,-0.09566569,single <1e4
+V95Y,1,pheS-4CP,0.0,94.0,-3.8540697,-3.996573,-0.14250326,single <1e4
+V95Y,2,pheS-4CP,0.0,99.0,-4.022574,-3.979135,0.04343891,single <1e4
+V95Y,1,pheS-4CP,0.0,111.0,-3.2990165,-3.4381106,-0.13909411,single <1e4
+V95Y,2,pheS-4CP,0.0,116.0,-3.9111354,-4.066986,-0.15585065,single <1e4
+V95Y,1,pheS-4CP,1.0,81.0,-3.8545256,-3.9900448,-0.13551927,single <1e4
+V95Y,2,pheS-4CP,1.0,82.0,-3.6370597,-3.777676,-0.14061642,single <1e4
+V95Y,1,pheS-4CP,1.0,94.0,-4.034929,-4.173437,-0.13850832,single <1e4
+V95Y,2,pheS-4CP,1.0,99.0,-3.8716965,-4.011144,-0.13944769,single <1e4
+V95Y,1,pheS-4CP,1.0,111.0,-4.236993,-4.331869,-0.09487629,single <1e4
+V95Y,2,pheS-4CP,1.0,116.0,-3.8776684,-4.0109935,-0.1333251,single <1e4
+V95Y,1,pheS+4CP,0.0,94.0,-3.267582,-3.4269736,-0.15939164,single <1e4
+V95Y,3,pheS+4CP,0.0,104.0,-3.5382326,-3.6715722,-0.13333964,single <1e4
+V95Y,1,pheS+4CP,0.0,111.0,-4.030167,-4.1463737,-0.116206646,single <1e4
+V95Y,1,pheS+4CP,0.0,125.0,-3.7342544,-3.8576257,-0.12337136,single <1e4
+V95Y,2,pheS+4CP,0.0,127.0,-4.028499,-4.1686482,-0.14014912,single <1e4
+V95Y,1,pheS+4CP,0.0001,94.0,-3.416278,-3.5519757,-0.13569784,single <1e4
+V95Y,1,pheS+4CP,0.0001,111.0,-3.8981497,-4.019332,-0.1211822,single <1e4
+V95Y,2,pheS+4CP,0.0001,116.0,-3.5055094,-3.6615539,-0.15604448,single <1e4
+V95Y,1,pheS+4CP,0.0001,125.0,-3.8131914,-3.9425707,-0.12937927,single <1e4
+V95Y,2,pheS+4CP,0.0001,127.0,-3.6965067,-3.842362,-0.14585519,single <1e4
+V95Y,1,pheS+4CP,0.001,94.0,-3.6387258,-3.6499224,-0.011196613,single <1e4
+V95Y,1,pheS+4CP,0.001,111.0,-4.1183176,-4.2411995,-0.12288189,single <1e4
+V95Y,2,pheS+4CP,0.001,116.0,-3.774376,-3.9126463,-0.13827038,single <1e4
+V95Y,1,pheS+4CP,0.001,125.0,-3.8367944,-3.921987,-0.08519268,single <1e4
+V95Y,2,pheS+4CP,0.001,127.0,-4.3992567,-4.4816475,-0.082390785,single <1e4
+V95Y,1,pheS+4CP,0.003,94.0,-3.5176039,-3.6814175,-0.16381359,single <1e4
+V95Y,1,pheS+4CP,0.003,111.0,-3.7038636,-3.8470645,-0.14320087,single <1e4
+V95Y,2,pheS+4CP,0.003,116.0,-3.8610592,-3.9285145,-0.06745529,single <1e4
+V95Y,1,pheS+4CP,0.003,125.0,-4.003453,-4.1355953,-0.13214254,single <1e4
+V95Y,2,pheS+4CP,0.003,127.0,-4.1228666,-4.252964,-0.13009739,single <1e4
+V95Y,1,pheS+4CP,0.01,94.0,-3.6326716,-3.7818832,-0.14921165,single <1e4
+V95Y,1,pheS+4CP,0.01,111.0,-3.7063725,-3.8359785,-0.12960601,single <1e4
+V95Y,2,pheS+4CP,0.01,116.0,-3.5820303,-3.726186,-0.14415574,single <1e4
+V95Y,1,pheS+4CP,0.01,125.0,-4.1658726,-4.2582746,-0.09240198,single <1e4
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+V95Y,1,pheS+4CP,0.03,94.0,-3.7864223,-3.9118292,-0.12540698,single <1e4
+V95Y,1,pheS+4CP,0.03,111.0,-3.7453713,-3.8591928,-0.11382151,single <1e4
+V95Y,2,pheS+4CP,0.03,116.0,-3.8198025,-3.9894567,-0.16965413,single <1e4
+V95Y,1,pheS+4CP,0.03,125.0,-3.8558168,-4.020326,-0.1645093,single <1e4
+V95Y,2,pheS+4CP,0.03,127.0,-4.191674,-4.1775823,0.014091969,single <1e4
+V95Y,1,pheS+4CP,0.1,94.0,-3.361404,-3.4454064,-0.084002495,single <1e4
+V95Y,1,pheS+4CP,0.1,111.0,-3.706279,-3.9119585,-0.20567942,single <1e4
+V95Y,2,pheS+4CP,0.1,116.0,-3.679385,-3.9111602,-0.23177528,single <1e4
+V95Y,1,pheS+4CP,0.1,125.0,-4.17012,-4.1213503,0.048769474,single <1e4
+V95Y,2,pheS+4CP,0.1,127.0,-3.7006688,-3.8872123,-0.18654346,single <1e4
+V95Y,1,pheS+4CP,1.0,94.0,-4.212332,-4.3304605,-0.11812878,single <1e4
+V95Y,1,pheS+4CP,1.0,111.0,-3.9269285,-4.1159654,-0.18903685,single <1e4
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+V95Y,1,pheS+4CP,1.0,125.0,-3.8083405,-3.9980989,-0.1897583,single <1e4
+V95Y,2,pheS+4CP,1.0,127.0,-3.7071495,-3.8363845,-0.12923503,single <1e4
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+V96A,2,kanR-kan,0.0,83.0,-8.066658,-7.546054,0.52060413,single 1e5-1e6
+V96A,1,kanR-kan,0.0,105.0,-7.3208513,-8.521677,-1.2008257,single 1e5-1e6
+V96A,2,kanR-kan,0.0,97.0,-7.5985374,-7.083123,0.51541424,single 1e5-1e6
+V96A,1,kanR-kan,0.0,122.0,-7.654776,-13.396339,-5.7415633,single 1e5-1e6
+V96A,2,kanR-kan,0.0,116.0,-7.2003098,-9.623299,-2.422989,single 1e5-1e6
+V96A,1,kanR-kan,1.0,92.0,-7.3185725,-7.1742773,0.14429522,single 1e5-1e6
+V96A,2,kanR-kan,1.0,83.0,-7.9184403,-7.686689,0.23175144,single 1e5-1e6
+V96A,1,kanR-kan,1.0,105.0,-7.9073677,-8.971459,-1.0640917,single 1e5-1e6
+V96A,2,kanR-kan,1.0,97.0,-7.5924973,-7.912033,-0.31953573,single 1e5-1e6
+V96A,1,kanR-kan,1.0,122.0,-8.148198,-11.318159,-3.169961,single 1e5-1e6
+V96A,2,kanR-kan,1.0,116.0,-7.833998,-9.911991,-2.077993,single 1e5-1e6
+V96A,1,kanR+kan,0.0,158.0,-6.189897,-6.505047,-0.31514978,single 1e5-1e6
+V96A,2,kanR+kan,0.0,155.0,-5.991536,-6.079187,-0.087650776,single 1e5-1e6
+V96A,1,kanR+kan,0.0,175.0,-6.038739,-6.689228,-0.65048885,single 1e5-1e6
+V96A,2,kanR+kan,0.0,176.0,-6.6897693,-7.5826845,-0.89291525,single 1e5-1e6
+V96A,1,kanR+kan,0.0,200.0,-8.833245,-10.829842,-1.9965963,single 1e5-1e6
+V96A,2,kanR+kan,0.0,199.0,-6.1287556,-6.9327564,-0.80400085,single 1e5-1e6
+V96A,1,kanR+kan,0.0001,158.0,-6.386525,-6.035538,0.35098696,single 1e5-1e6
+V96A,2,kanR+kan,0.0001,155.0,-5.707885,-5.6927433,0.015141487,single 1e5-1e6
+V96A,1,kanR+kan,0.0001,175.0,-5.7973695,-6.0834684,-0.28609896,single 1e5-1e6
+V96A,2,kanR+kan,0.0001,176.0,-6.4914136,-5.8927817,0.59863186,single 1e5-1e6
+V96A,1,kanR+kan,0.0001,200.0,-7.7243977,-9.204916,-1.4805183,single 1e5-1e6
+V96A,2,kanR+kan,0.0001,199.0,-8.215416,-6.427023,1.788393,single 1e5-1e6
+V96A,1,kanR+kan,0.001,158.0,-10.705014,-11.122245,-0.4172306,single 1e5-1e6
+V96A,2,kanR+kan,0.001,155.0,-6.0629797,-6.152428,-0.08944845,single 1e5-1e6
+V96A,1,kanR+kan,0.001,175.0,-5.8482223,-6.004023,-0.15580082,single 1e5-1e6
+V96A,2,kanR+kan,0.001,176.0,-7.2530584,-6.3665276,0.8865309,single 1e5-1e6
+V96A,1,kanR+kan,0.001,200.0,-7.735458,-9.133341,-1.3978829,single 1e5-1e6
+V96A,1,kanR+kan,0.003,158.0,-5.978636,-5.878132,0.10050392,single 1e5-1e6
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+S97N,1,pheS-4CP,1.0,111.0,-0.37380832,-0.38014138,-0.006333053,single 1e4-1e5
+S97N,2,pheS-4CP,1.0,116.0,-0.4908956,-0.5039621,-0.0130665,single 1e4-1e5
+S97N,1,pheS+4CP,0.0,94.0,-3.1447167,-3.283587,-0.13887024,single 1e4-1e5
+S97N,3,pheS+4CP,0.0,104.0,-0.05183687,-0.05493889,-0.0031020194,single 1e4-1e5
+S97N,1,pheS+4CP,0.0,111.0,-2.127566,-2.2535667,-0.12600064,single 1e4-1e5
+S97N,1,pheS+4CP,0.0,125.0,-0.30056417,-0.2775925,0.02297166,single 1e4-1e5
+S97N,2,pheS+4CP,0.0,127.0,-2.146245,-2.275269,-0.12902403,single 1e4-1e5
+S97N,1,pheS+4CP,0.0001,94.0,-0.18467511,-0.17460744,0.010067672,single 1e4-1e5
+S97N,1,pheS+4CP,0.0001,111.0,-2.8872302,-3.0190728,-0.13184261,single 1e4-1e5
+S97N,2,pheS+4CP,0.0001,116.0,-0.24929392,-0.23343967,0.015854254,single 1e4-1e5
+S97N,1,pheS+4CP,0.0001,125.0,-0.33631557,-0.31083187,0.025483698,single 1e4-1e5
+S97N,2,pheS+4CP,0.0001,127.0,-2.9949198,-3.127774,-0.13285422,single 1e4-1e5
+S97N,1,pheS+4CP,0.001,94.0,-2.534154,-2.6686823,-0.1345284,single 1e4-1e5
+S97N,1,pheS+4CP,0.001,111.0,-0.5885846,-0.5466862,0.04189843,single 1e4-1e5
+S97N,2,pheS+4CP,0.001,116.0,-0.4665746,-0.4358152,0.030759424,single 1e4-1e5
+S97N,1,pheS+4CP,0.001,125.0,-0.35093537,-0.32361734,0.02731803,single 1e4-1e5
+S97N,2,pheS+4CP,0.001,127.0,-2.5987296,-2.7195337,-0.12080407,single 1e4-1e5
+S97N,1,pheS+4CP,0.003,94.0,-0.16661082,-0.1565806,0.010030225,single 1e4-1e5
+S97N,1,pheS+4CP,0.003,111.0,-0.26262447,-0.24381723,0.018807247,single 1e4-1e5
+S97N,2,pheS+4CP,0.003,116.0,-2.6696792,-2.797652,-0.12797284,single 1e4-1e5
+S97N,1,pheS+4CP,0.003,125.0,-3.0302582,-3.172021,-0.14176273,single 1e4-1e5
+S97N,2,pheS+4CP,0.003,127.0,-0.5847922,-0.5414039,0.043388307,single 1e4-1e5
+S97N,1,pheS+4CP,0.01,94.0,-2.6903927,-2.8179967,-0.12760401,single 1e4-1e5
+S97N,1,pheS+4CP,0.01,111.0,-0.2852134,-0.26947144,0.015741974,single 1e4-1e5
+S97N,2,pheS+4CP,0.01,116.0,-2.6358829,-2.766012,-0.1301291,single 1e4-1e5
+S97N,1,pheS+4CP,0.01,125.0,-0.3303412,-0.30910957,0.021231622,single 1e4-1e5
+S97N,2,pheS+4CP,0.01,127.0,-2.1303928,-2.2505748,-0.12018204,single 1e4-1e5
+S97N,1,pheS+4CP,0.03,94.0,-0.28794256,-0.28953287,-0.0015903115,single 1e4-1e5
+S97N,1,pheS+4CP,0.03,111.0,-0.24262573,-0.2429929,-0.0003671646,single 1e4-1e5
+S97N,2,pheS+4CP,0.03,116.0,-2.114187,-2.1995547,-0.08536768,single 1e4-1e5
+S97N,1,pheS+4CP,0.03,125.0,-2.3519962,-2.4419208,-0.089924574,single 1e4-1e5
+S97N,2,pheS+4CP,0.03,127.0,-2.2933097,-2.3792145,-0.08590484,single 1e4-1e5
+S97N,1,pheS+4CP,0.1,94.0,-0.10647993,-0.11396701,-0.007487081,single 1e4-1e5
+S97N,1,pheS+4CP,0.1,111.0,-0.31562737,-0.33976844,-0.024141073,single 1e4-1e5
+S97N,2,pheS+4CP,0.1,116.0,-0.46680853,-0.5077153,-0.040906757,single 1e4-1e5
+S97N,1,pheS+4CP,0.1,125.0,-2.3501227,-2.3905053,-0.040382624,single 1e4-1e5
+S97N,2,pheS+4CP,0.1,127.0,-2.206031,-2.2496188,-0.043587685,single 1e4-1e5
+S97N,1,pheS+4CP,1.0,94.0,-0.77521294,-0.8732671,-0.09805417,single 1e4-1e5
+S97N,1,pheS+4CP,1.0,111.0,-0.5402678,-0.61702144,-0.07675362,single 1e4-1e5
+S97N,2,pheS+4CP,1.0,116.0,-0.52063435,-0.60266715,-0.0820328,single 1e4-1e5
+S97N,1,pheS+4CP,1.0,125.0,-0.44171908,-0.5092499,-0.06753084,single 1e4-1e5
+S97N,2,pheS+4CP,1.0,127.0,-0.4919306,-0.572822,-0.08089137,single 1e4-1e5
+S97P,1,kanR-kan,0.0,92.0,-3.7591405,-3.9316106,-0.1724701,single <1e4
+S97P,2,kanR-kan,0.0,83.0,-4.265155,-4.6242313,-0.3590765,single <1e4
+S97P,1,kanR-kan,0.0,105.0,-4.237771,-4.320411,-0.08264017,single <1e4
+S97P,2,kanR-kan,0.0,97.0,-4.803672,-4.911722,-0.10805035,single <1e4
+S97P,1,kanR-kan,0.0,122.0,-3.8495793,-4.1929016,-0.34332228,single <1e4
+S97P,2,kanR-kan,0.0,116.0,-4.100337,-4.389796,-0.28945875,single <1e4
+S97P,1,kanR-kan,1.0,92.0,-3.7305102,-3.859068,-0.12855768,single <1e4
+S97P,2,kanR-kan,1.0,83.0,-4.0375376,-4.1943455,-0.1568079,single <1e4
+S97P,1,kanR-kan,1.0,105.0,-4.5299063,-4.341414,0.1884923,single <1e4
+S97P,2,kanR-kan,1.0,97.0,-3.791594,-3.9458623,-0.15426826,single <1e4
+S97P,1,kanR-kan,1.0,122.0,-3.744102,-4.475896,-0.7317939,single <1e4
+S97P,2,kanR-kan,1.0,116.0,-4.5290413,-4.2015705,0.32747078,single <1e4
+S97P,1,kanR+kan,0.0,158.0,-2.0095272,-2.035396,-0.025868893,single <1e4
+S97P,2,kanR+kan,0.0,155.0,-2.3350472,-2.445595,-0.11054778,single <1e4
+S97P,1,kanR+kan,0.0,175.0,-3.1905484,-3.3575172,-0.16696882,single <1e4
+S97P,2,kanR+kan,0.0,176.0,-2.9733436,-3.1271603,-0.1538167,single <1e4
+S97P,1,kanR+kan,0.0,200.0,-1.9714853,-2.0639143,-0.09242904,single <1e4
+S97P,2,kanR+kan,0.0,199.0,-2.8514798,-2.9390328,-0.087553024,single <1e4
+S97P,1,kanR+kan,0.0001,158.0,-3.068408,-3.2962375,-0.22782946,single <1e4
+S97P,2,kanR+kan,0.0001,155.0,-3.4045925,-3.6493878,-0.24479532,single <1e4
+S97P,1,kanR+kan,0.0001,175.0,-3.2775688,-3.4317293,-0.1541605,single <1e4
+S97P,2,kanR+kan,0.0001,176.0,-1.1746403,-1.0231408,0.15149951,single <1e4
+S97P,1,kanR+kan,0.0001,200.0,-2.0466044,-2.1368303,-0.090225935,single <1e4
+S97P,2,kanR+kan,0.0001,199.0,-0.6981464,-0.7025397,-0.0043932796,single <1e4
+S97P,1,kanR+kan,0.001,158.0,-2.748997,-2.9466107,-0.19761372,single <1e4
+S97P,2,kanR+kan,0.001,155.0,-2.5552263,-2.678738,-0.12351179,single <1e4
+S97P,1,kanR+kan,0.001,175.0,-1.3486718,-1.23163,0.117041826,single <1e4
+S97P,2,kanR+kan,0.001,176.0,-1.9521099,-2.033092,-0.08098209,single <1e4
+S97P,1,kanR+kan,0.001,200.0,-1.9837447,-2.0580273,-0.07428253,single <1e4
+S97P,1,kanR+kan,0.003,158.0,-2.8817866,-3.0292592,-0.14747262,single <1e4
+S97P,2,kanR+kan,0.003,155.0,-3.2513037,-3.3935432,-0.14223957,single <1e4
+S97P,1,kanR+kan,0.003,175.0,-1.943645,-2.0328288,-0.08918381,single <1e4
+S97P,2,kanR+kan,0.003,176.0,-0.99295,-0.9924773,0.00047272444,single <1e4
+S97P,1,kanR+kan,0.003,200.0,-2.4942558,-2.5162396,-0.021983862,single <1e4
+S97P,2,kanR+kan,0.003,199.0,-2.497941,-2.520716,-0.022774935,single <1e4
+S97P,1,kanR+kan,0.01,146.0,-3.0005786,-3.1320941,-0.1315155,single <1e4
+S97P,2,kanR+kan,0.01,155.0,-2.407917,-2.5040696,-0.096152544,single <1e4
+S97P,1,kanR+kan,0.01,158.0,-3.2092772,-3.283814,-0.0745368,single <1e4
+S97P,2,kanR+kan,0.01,176.0,-3.4647253,-3.4271123,0.037612915,single <1e4
+S97P,1,kanR+kan,0.01,175.0,-3.1427698,-3.156984,-0.014214277,single <1e4
+S97P,2,kanR+kan,0.01,185.0,-3.0937836,-3.0955374,-0.0017538071,single <1e4
+S97P,1,kanR+kan,0.03,146.0,-2.3670883,-2.445375,-0.07828665,single <1e4
+S97P,2,kanR+kan,0.03,155.0,-3.2166739,-3.3235955,-0.10692167,single <1e4
+S97P,1,kanR+kan,0.03,158.0,-3.2918756,-3.3770452,-0.085169554,single <1e4
+S97P,2,kanR+kan,0.03,176.0,-2.6452577,-2.84563,-0.20037222,single <1e4
+S97P,1,kanR+kan,0.03,175.0,-2.3768516,-2.5290935,-0.15224195,single <1e4
+S97P,2,kanR+kan,0.03,185.0,-2.5783784,-2.8902297,-0.31185126,single <1e4
+S97P,1,kanR+kan,0.1,146.0,-3.3033974,-3.4383874,-0.13498998,single <1e4
+S97P,2,kanR+kan,0.1,155.0,-3.2728484,-3.22049,0.05235839,single <1e4
+S97P,1,kanR+kan,0.1,158.0,-2.9917517,-2.9010494,0.090702295,single <1e4
+S97P,2,kanR+kan,0.1,176.0,-3.7079616,-3.7826853,-0.07472372,single <1e4
+S97P,1,kanR+kan,0.1,175.0,-3.5346146,-3.6422753,-0.10766077,single <1e4
+S97P,2,kanR+kan,0.1,185.0,-3.1474848,-3.2990994,-0.15161467,single <1e4
+S97P,1,kanR+kan,1.0,146.0,-3.4913716,-3.5549812,-0.0636096,single <1e4
+S97P,2,kanR+kan,1.0,155.0,-3.854602,-3.9132037,-0.058601618,single <1e4
+S97P,3,kanR+kan,1.0,168.0,-2.8341866,-2.9548767,-0.12069011,single <1e4
+S97P,1,kanR+kan,1.0,158.0,-3.5204716,-3.447501,0.07297063,single <1e4
+S97P,2,kanR+kan,1.0,176.0,-3.3204064,-3.2706184,0.049788,single <1e4
+S97P,1,kanR+kan,1.0,175.0,-3.5008602,-3.4710603,0.029799938,single <1e4
+S97P,2,kanR+kan,1.0,185.0,-3.5218813,-3.61555,-0.0936687,single <1e4
+S97P,1,pheS-4CP,0.0,81.0,-6.6153717,-6.788278,-0.1729064,single <1e4
+S97P,2,pheS-4CP,0.0,82.0,-3.2139134,-3.2958465,-0.08193302,single <1e4
+S97P,1,pheS-4CP,0.0,94.0,-6.0364237,-6.0606313,-0.024207592,single <1e4
+S97P,2,pheS-4CP,0.0,99.0,-4.546929,-4.807395,-0.2604661,single <1e4
+S97P,1,pheS-4CP,0.0,111.0,-4.930361,-4.9040523,0.026308537,single <1e4
+S97P,2,pheS-4CP,0.0,116.0,-3.398592,-3.5185044,-0.119912386,single <1e4
+S97P,1,pheS-4CP,1.0,81.0,-6.971943,-6.6793785,0.2925644,single <1e4
+S97P,2,pheS-4CP,1.0,82.0,-4.481784,-4.659169,-0.17738533,single <1e4
+S97P,1,pheS-4CP,1.0,94.0,-5.0252523,-4.845332,0.1799202,single <1e4
+S97P,2,pheS-4CP,1.0,99.0,-3.1865559,-3.3139782,-0.12742233,single <1e4
+S97P,1,pheS-4CP,1.0,111.0,-5.6235175,-4.9913483,0.63216925,single <1e4
+S97P,2,pheS-4CP,1.0,116.0,-3.854117,-3.7710452,0.08307171,single <1e4
+S97P,1,pheS+4CP,0.0,94.0,-3.4032202,-3.817986,-0.41476583,single <1e4
+S97P,3,pheS+4CP,0.0,104.0,-3.2536347,-3.383757,-0.13012242,single <1e4
+S97P,1,pheS+4CP,0.0,111.0,-3.9582675,-3.641866,0.31640148,single <1e4
+S97P,1,pheS+4CP,0.0,125.0,-3.6004236,-3.755991,-0.15556741,single <1e4
+S97P,2,pheS+4CP,0.0,127.0,-5.0246863,-4.3709307,0.65375566,single <1e4
+S97P,1,pheS+4CP,0.0001,94.0,-2.2126856,-1.3538548,0.8588308,single <1e4
+S97P,1,pheS+4CP,0.0001,111.0,-3.51586,-3.6167712,-0.10091114,single <1e4
+S97P,2,pheS+4CP,0.0001,116.0,-3.5555615,-3.8200846,-0.26452303,single <1e4
+S97P,1,pheS+4CP,0.0001,125.0,-3.7475705,-3.6149056,0.13266492,single <1e4
+S97P,2,pheS+4CP,0.0001,127.0,-3.5788484,-3.6514926,-0.07264423,single <1e4
+S97P,1,pheS+4CP,0.001,94.0,-3.0967343,-1.9004359,1.1962984,single <1e4
+S97P,1,pheS+4CP,0.001,111.0,-4.507332,-3.8888016,0.6185303,single <1e4
+S97P,2,pheS+4CP,0.001,116.0,-3.6373515,-3.672254,-0.034902573,single <1e4
+S97P,1,pheS+4CP,0.001,125.0,-3.5581272,-3.673008,-0.1148808,single <1e4
+S97P,2,pheS+4CP,0.001,127.0,-4.394085,-4.4261494,-0.032064438,single <1e4
+S97P,1,pheS+4CP,0.003,94.0,-1.8777868,-1.1656722,0.7121146,single <1e4
+S97P,1,pheS+4CP,0.003,111.0,-3.359264,-3.5675263,-0.20826244,single <1e4
+S97P,2,pheS+4CP,0.003,116.0,-3.7311902,-3.871579,-0.14038873,single <1e4
+S97P,1,pheS+4CP,0.003,125.0,-3.5651472,-3.7052474,-0.14010024,single <1e4
+S97P,2,pheS+4CP,0.003,127.0,-3.9329534,-3.9305458,0.0024075508,single <1e4
+S97P,1,pheS+4CP,0.01,94.0,-2.518234,-2.4415834,0.07665062,single <1e4
+S97P,1,pheS+4CP,0.01,111.0,-3.172308,-3.2756038,-0.1032958,single <1e4
+S97P,2,pheS+4CP,0.01,116.0,-3.004386,-3.0605016,-0.056115627,single <1e4
+S97P,1,pheS+4CP,0.01,125.0,-4.018468,-4.088838,-0.0703702,single <1e4
+S97P,2,pheS+4CP,0.01,127.0,-3.7436447,-3.8742418,-0.13059711,single <1e4
+S97P,1,pheS+4CP,0.03,94.0,-2.7870605,-2.6175938,0.16946673,single <1e4
+S97P,1,pheS+4CP,0.03,111.0,-4.9992037,-4.835005,0.16419888,single <1e4
+S97P,2,pheS+4CP,0.03,116.0,-3.6030326,-3.7506275,-0.14759493,single <1e4
+S97P,1,pheS+4CP,0.03,125.0,-3.4204216,-3.7430155,-0.32259393,single <1e4
+S97P,2,pheS+4CP,0.03,127.0,-3.5028143,-3.715415,-0.21260071,single <1e4
+S97P,1,pheS+4CP,0.1,94.0,-2.3782315,-2.4836185,-0.10538697,single <1e4
+S97P,1,pheS+4CP,0.1,111.0,-4.47285,-4.1071415,0.36570835,single <1e4
+S97P,2,pheS+4CP,0.1,116.0,-4.373505,-4.0696125,0.3038926,single <1e4
+S97P,1,pheS+4CP,0.1,125.0,-3.490235,-3.8536048,-0.3633697,single <1e4
+S97P,2,pheS+4CP,0.1,127.0,-4.308814,-4.016886,0.2919278,single <1e4
+S97P,1,pheS+4CP,1.0,94.0,-4.157795,-5.0758424,-0.9180474,single <1e4
+S97P,1,pheS+4CP,1.0,111.0,-8.981494,-6.2257032,2.7557907,single <1e4
+S97P,2,pheS+4CP,1.0,116.0,-5.663494,-4.4225473,1.2409468,single <1e4
+S97P,1,pheS+4CP,1.0,125.0,-4.504636,-4.052736,0.4519,single <1e4
+S97P,2,pheS+4CP,1.0,127.0,-6.3078866,-4.4632864,1.8446002,single <1e4
+S97R,1,kanR-kan,0.0,92.0,-7.298207,-7.482914,-0.18470716,single 1e5-1e6
+S97R,2,kanR-kan,0.0,83.0,-7.617348,-7.3744817,0.24286652,single 1e5-1e6
+S97R,1,kanR-kan,0.0,105.0,-7.448896,-7.2374315,0.2114644,single 1e5-1e6
+S97R,2,kanR-kan,0.0,97.0,-7.371219,-7.1150455,0.2561736,single 1e5-1e6
+S97R,1,kanR-kan,0.0,122.0,-7.252699,-7.539589,-0.28689003,single 1e5-1e6
+S97R,2,kanR-kan,0.0,116.0,-7.7421684,-7.2886214,0.453547,single 1e5-1e6
+S97R,1,kanR-kan,1.0,92.0,-7.453055,-7.2609186,0.19213629,single 1e5-1e6
+S97R,2,kanR-kan,1.0,83.0,-7.9583273,-7.535306,0.42302132,single 1e5-1e6
+S97R,1,kanR-kan,1.0,105.0,-7.509642,-7.450575,0.05906725,single 1e5-1e6
+S97R,2,kanR-kan,1.0,97.0,-7.6014047,-7.246067,0.35533762,single 1e5-1e6
+S97R,1,kanR-kan,1.0,122.0,-7.1779547,-7.285882,-0.10792732,single 1e5-1e6
+S97R,2,kanR-kan,1.0,116.0,-6.9650993,-7.1031723,-0.13807297,single 1e5-1e6
+S97R,1,kanR+kan,0.0,158.0,-6.9646263,-7.0033493,-0.038722992,single 1e5-1e6
+S97R,2,kanR+kan,0.0,155.0,-8.384041,-8.924425,-0.5403843,single 1e5-1e6
+S97R,1,kanR+kan,0.0,175.0,-7.901134,-7.7464294,0.15470457,single 1e5-1e6
+S97R,2,kanR+kan,0.0,176.0,-6.821969,-6.897563,-0.07559395,single 1e5-1e6
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+M98I,1,kanR+kan,1.0,175.0,-7.6402245,-7.9483786,-0.3081541,single 1e5-1e6
+M98I,2,kanR+kan,1.0,185.0,-6.544015,-6.5143604,0.029654503,single 1e5-1e6
+M98I,1,pheS-4CP,0.0,81.0,-6.068573,-7.831679,-1.7631059,single 1e5-1e6
+M98I,2,pheS-4CP,0.0,82.0,-5.847085,-6.592493,-0.74540806,single 1e5-1e6
+M98I,1,pheS-4CP,0.0,94.0,-5.6379614,-5.6922855,-0.05432415,single 1e5-1e6
+M98I,2,pheS-4CP,0.0,99.0,-5.906657,-6.7451916,-0.83853436,single 1e5-1e6
+M98I,1,pheS-4CP,0.0,111.0,-6.5787563,-6.190174,0.38858223,single 1e5-1e6
+M98I,2,pheS-4CP,0.0,116.0,-6.4926662,-6.9597106,-0.46704435,single 1e5-1e6
+M98I,1,pheS-4CP,1.0,81.0,-7.061743,-8.599356,-1.5376129,single 1e5-1e6
+M98I,2,pheS-4CP,1.0,82.0,-5.7871537,-6.8994346,-1.1122808,single 1e5-1e6
+M98I,1,pheS-4CP,1.0,94.0,-6.1199017,-6.6500173,-0.5301156,single 1e5-1e6
+M98I,2,pheS-4CP,1.0,99.0,-5.9230347,-6.354776,-0.43174124,single 1e5-1e6
+M98I,1,pheS-4CP,1.0,111.0,-5.9948816,-5.9230986,0.071783066,single 1e5-1e6
+M98I,2,pheS-4CP,1.0,116.0,-7.642539,-7.370891,0.27164793,single 1e5-1e6
+M98I,1,pheS+4CP,0.0,94.0,-5.200781,-5.1128926,0.08788824,single 1e5-1e6
+M98I,3,pheS+4CP,0.0,104.0,-5.538226,-5.6019793,-0.06375313,single 1e5-1e6
+M98I,1,pheS+4CP,0.0,111.0,-6.5841303,-6.5056095,0.078520775,single 1e5-1e6
+M98I,1,pheS+4CP,0.0,125.0,-6.7069044,-7.0577936,-0.3508892,single 1e5-1e6
+M98I,2,pheS+4CP,0.0,127.0,-5.7512064,-5.888981,-0.13777447,single 1e5-1e6
+M98I,1,pheS+4CP,0.0001,94.0,-5.165741,-5.482173,-0.316432,single 1e5-1e6
+M98I,1,pheS+4CP,0.0001,111.0,-5.5940866,-5.6588516,-0.06476498,single 1e5-1e6
+M98I,2,pheS+4CP,0.0001,116.0,-6.169866,-6.0842943,0.085571766,single 1e5-1e6
+M98I,1,pheS+4CP,0.0001,125.0,-6.222792,-6.561074,-0.33828163,single 1e5-1e6
+M98I,2,pheS+4CP,0.0001,127.0,-6.6061554,-6.8373766,-0.2312212,single 1e5-1e6
+M98I,1,pheS+4CP,0.001,94.0,-7.204957,-6.3968277,0.8081293,single 1e5-1e6
+M98I,1,pheS+4CP,0.001,111.0,-5.843499,-5.8733125,-0.02981329,single 1e5-1e6
+M98I,2,pheS+4CP,0.001,116.0,-18.498037,-16.935982,1.5620556,single 1e5-1e6
+M98I,1,pheS+4CP,0.001,125.0,-5.508363,-5.5895133,-0.08115053,single 1e5-1e6
+M98I,2,pheS+4CP,0.001,127.0,-7.1934023,-7.663087,-0.4696846,single 1e5-1e6
+M98I,1,pheS+4CP,0.003,94.0,-5.690487,-5.4099755,0.28051138,single 1e5-1e6
+M98I,1,pheS+4CP,0.003,111.0,-5.312517,-5.4133654,-0.1008482,single 1e5-1e6
+M98I,2,pheS+4CP,0.003,116.0,-7.2100954,-6.9539957,0.2560997,single 1e5-1e6
+M98I,1,pheS+4CP,0.003,125.0,-5.495412,-5.6367226,-0.14131069,single 1e5-1e6
+M98I,2,pheS+4CP,0.003,127.0,-6.8047295,-7.049282,-0.24455261,single 1e5-1e6
+M98I,1,pheS+4CP,0.01,94.0,-5.770261,-5.4744225,0.29583836,single 1e5-1e6
+M98I,1,pheS+4CP,0.01,111.0,-5.933755,-5.8525543,0.0812006,single 1e5-1e6
+M98I,2,pheS+4CP,0.01,116.0,-5.1424847,-5.2509155,-0.10843086,single 1e5-1e6
+M98I,1,pheS+4CP,0.01,125.0,-5.887832,-6.1228666,-0.23503447,single 1e5-1e6
+M98I,2,pheS+4CP,0.01,127.0,-6.08749,-6.2588763,-0.17138624,single 1e5-1e6
+M98I,1,pheS+4CP,0.03,94.0,-5.350917,-5.457204,-0.106287,single 1e5-1e6
+M98I,1,pheS+4CP,0.03,111.0,-5.245229,-5.3340206,-0.08879185,single 1e5-1e6
+M98I,2,pheS+4CP,0.03,116.0,-6.6971197,-6.698097,-0.0009775162,single 1e5-1e6
+M98I,1,pheS+4CP,0.03,125.0,-5.4903035,-5.596898,-0.10659456,single 1e5-1e6
+M98I,2,pheS+4CP,0.03,127.0,-5.772502,-6.0032334,-0.23073149,single 1e5-1e6
+M98I,1,pheS+4CP,0.1,94.0,-4.9076757,-4.960041,-0.052365303,single 1e5-1e6
+M98I,1,pheS+4CP,0.1,111.0,-5.798641,-5.659983,0.13865805,single 1e5-1e6
+M98I,2,pheS+4CP,0.1,116.0,-8.169624,-7.480694,0.6889305,single 1e5-1e6
+M98I,1,pheS+4CP,0.1,125.0,-5.7363653,-5.529662,0.20670319,single 1e5-1e6
+M98I,2,pheS+4CP,0.1,127.0,-6.287966,-5.7610316,0.52693415,single 1e5-1e6
+M98I,1,pheS+4CP,1.0,94.0,-5.983512,-6.087056,-0.103544235,single 1e5-1e6
+M98I,1,pheS+4CP,1.0,111.0,-6.3926234,-5.7153378,0.6772857,single 1e5-1e6
+M98I,2,pheS+4CP,1.0,116.0,-7.4226913,-5.8532014,1.56949,single 1e5-1e6
+M98I,1,pheS+4CP,1.0,125.0,-7.162841,-5.5755234,1.5873175,single 1e5-1e6
+M98I,2,pheS+4CP,1.0,127.0,-6.5212765,-5.6386,0.8826766,single 1e5-1e6
+M98L,1,kanR-kan,0.0,92.0,-4.6484203,-4.793621,-0.14520073,single <1e4
+M98L,2,kanR-kan,0.0,83.0,-4.696228,-4.7681246,-0.07189655,single <1e4
+M98L,1,kanR-kan,0.0,105.0,-5.153281,-4.844,0.30928135,single <1e4
+M98L,2,kanR-kan,0.0,97.0,-4.474326,-4.628168,-0.15384197,single <1e4
+M98L,1,kanR-kan,0.0,122.0,-4.5432253,-4.4525747,0.09065056,single <1e4
+M98L,2,kanR-kan,0.0,116.0,-4.524107,-4.753893,-0.22978592,single <1e4
+M98L,1,kanR-kan,1.0,92.0,-5.0525618,-5.0035515,0.049010277,single <1e4
+M98L,2,kanR-kan,1.0,83.0,-4.831848,-5.0047264,-0.17287827,single <1e4
+M98L,1,kanR-kan,1.0,105.0,-4.623044,-4.7432704,-0.12022638,single <1e4
+M98L,2,kanR-kan,1.0,97.0,-4.451977,-4.5812006,-0.12922382,single <1e4
+M98L,1,kanR-kan,1.0,122.0,-5.2576227,-5.385621,-0.12799835,single <1e4
+M98L,2,kanR-kan,1.0,116.0,-4.958539,-5.0854683,-0.12692928,single <1e4
+M98L,1,kanR+kan,0.0,158.0,-4.5174885,-4.4643106,0.053177834,single <1e4
+M98L,2,kanR+kan,0.0,155.0,-4.4533415,-4.5492096,-0.09586811,single <1e4
+M98L,1,kanR+kan,0.0,175.0,-4.2402496,-4.384533,-0.1442833,single <1e4
+M98L,2,kanR+kan,0.0,176.0,-4.403882,-4.5490804,-0.14519835,single <1e4
+M98L,1,kanR+kan,0.0,200.0,-4.099553,-4.2936172,-0.19406414,single <1e4
+M98L,2,kanR+kan,0.0,199.0,-3.3177583,-3.3241668,-0.006408453,single <1e4
+M98L,1,kanR+kan,0.0001,158.0,-4.127337,-4.1521983,-0.024861336,single <1e4
+M98L,2,kanR+kan,0.0001,155.0,-3.7861884,-3.903027,-0.11683869,single <1e4
+M98L,1,kanR+kan,0.0001,175.0,-4.018569,-4.0908465,-0.072277546,single <1e4
+M98L,2,kanR+kan,0.0001,176.0,-3.951046,-4.061187,-0.1101408,single <1e4
+M98L,1,kanR+kan,0.0001,200.0,-4.0032606,-4.1993937,-0.19613314,single <1e4
+M98L,2,kanR+kan,0.0001,199.0,-3.6916218,-3.815226,-0.1236043,single <1e4
+M98L,1,kanR+kan,0.001,158.0,-4.0557656,-4.195514,-0.13974857,single <1e4
+M98L,2,kanR+kan,0.001,155.0,-4.129352,-4.243293,-0.113940716,single <1e4
+M98L,1,kanR+kan,0.001,175.0,-4.1067643,-4.245593,-0.13882875,single <1e4
+M98L,2,kanR+kan,0.001,176.0,-3.9883199,-4.14202,-0.15370035,single <1e4
+M98L,1,kanR+kan,0.001,200.0,-3.7757769,-3.8287072,-0.052930355,single <1e4
+M98L,1,kanR+kan,0.003,158.0,-3.974698,-4.1155033,-0.14080524,single <1e4
+M98L,2,kanR+kan,0.003,155.0,-6.6571107,-5.916946,0.74016476,single <1e4
+M98L,1,kanR+kan,0.003,175.0,-3.9611363,-4.107926,-0.14678955,single <1e4
+M98L,2,kanR+kan,0.003,176.0,-4.4067245,-4.5769753,-0.1702509,single <1e4
+M98L,1,kanR+kan,0.003,200.0,-3.812042,-3.7856612,0.026380777,single <1e4
+M98L,2,kanR+kan,0.003,199.0,-3.837772,-4.0370903,-0.19931841,single <1e4
+M98L,1,kanR+kan,0.01,146.0,-4.187539,-4.2962294,-0.10869026,single <1e4
+M98L,2,kanR+kan,0.01,155.0,-4.3841424,-4.4931803,-0.109037876,single <1e4
+M98L,1,kanR+kan,0.01,158.0,-4.0803027,-4.211961,-0.13165808,single <1e4
+M98L,2,kanR+kan,0.01,176.0,-4.078644,-4.218043,-0.13939905,single <1e4
+M98L,1,kanR+kan,0.01,175.0,-4.020279,-4.1512475,-0.13096857,single <1e4
+M98L,2,kanR+kan,0.01,185.0,-4.9665127,-5.074347,-0.10783434,single <1e4
+M98L,1,kanR+kan,0.03,146.0,-4.2613764,-4.311845,-0.050468445,single <1e4
+M98L,2,kanR+kan,0.03,155.0,-4.268281,-4.3596797,-0.091398716,single <1e4
+M98L,1,kanR+kan,0.03,158.0,-5.05013,-4.9161377,0.1339922,single <1e4
+M98L,2,kanR+kan,0.03,176.0,-4.2342505,-4.3273926,-0.09314203,single <1e4
+M98L,1,kanR+kan,0.03,175.0,-3.9914343,-4.128835,-0.13740087,single <1e4
+M98L,2,kanR+kan,0.03,185.0,-4.1862826,-4.314456,-0.12817335,single <1e4
+M98L,1,kanR+kan,0.1,146.0,-4.328041,-4.5151167,-0.18707561,single <1e4
+M98L,2,kanR+kan,0.1,155.0,-4.265009,-4.464263,-0.19925404,single <1e4
+M98L,1,kanR+kan,0.1,158.0,-4.4651346,-4.4678793,-0.0027446747,single <1e4
+M98L,2,kanR+kan,0.1,176.0,-4.791026,-4.6976175,0.093408585,single <1e4
+M98L,1,kanR+kan,0.1,175.0,-4.7229843,-4.9467335,-0.22374916,single <1e4
+M98L,2,kanR+kan,0.1,185.0,-4.1844225,-4.342101,-0.1576786,single <1e4
+M98L,1,kanR+kan,1.0,146.0,-4.533079,-4.5786557,-0.045576572,single <1e4
+M98L,2,kanR+kan,1.0,155.0,-5.1408396,-4.9407854,0.20005417,single <1e4
+M98L,3,kanR+kan,1.0,168.0,-4.4614263,-4.588011,-0.12658453,single <1e4
+M98L,1,kanR+kan,1.0,158.0,-4.4357367,-4.5397625,-0.10402584,single <1e4
+M98L,2,kanR+kan,1.0,176.0,-4.3123145,-4.3766294,-0.06431484,single <1e4
+M98L,1,kanR+kan,1.0,175.0,-4.487199,-4.5360913,-0.048892498,single <1e4
+M98L,2,kanR+kan,1.0,185.0,-4.2843924,-4.4827733,-0.19838095,single <1e4
+M98L,1,pheS-4CP,0.0,81.0,-3.6283946,-3.738,-0.10960531,single <1e4
+M98L,2,pheS-4CP,0.0,82.0,-3.8105173,-3.8938918,-0.0833745,single <1e4
+M98L,1,pheS-4CP,0.0,94.0,-3.7635107,-3.8616967,-0.098186016,single <1e4
+M98L,2,pheS-4CP,0.0,99.0,-3.616639,-3.7533813,-0.13674235,single <1e4
+M98L,1,pheS-4CP,0.0,111.0,-4.101064,-4.204758,-0.10369396,single <1e4
+M98L,2,pheS-4CP,0.0,116.0,-3.6438246,-3.7828822,-0.13905764,single <1e4
+M98L,1,pheS-4CP,1.0,81.0,-3.630321,-3.7029629,-0.07264185,single <1e4
+M98L,2,pheS-4CP,1.0,82.0,-3.8332987,-3.8115482,0.02175045,single <1e4
+M98L,1,pheS-4CP,1.0,94.0,-3.400586,-3.6114612,-0.21087527,single <1e4
+M98L,2,pheS-4CP,1.0,99.0,-3.556415,-3.7602615,-0.20384645,single <1e4
+M98L,1,pheS-4CP,1.0,111.0,-3.9714708,-3.9740129,-0.002542019,single <1e4
+M98L,2,pheS-4CP,1.0,116.0,-3.6944084,-3.7806787,-0.08627033,single <1e4
+M98L,1,pheS+4CP,0.0,94.0,-3.0208144,-3.191499,-0.17068458,single <1e4
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+M98L,1,pheS+4CP,0.0,111.0,-3.7006786,-3.848152,-0.14747334,single <1e4
+M98L,1,pheS+4CP,0.0,125.0,-3.4158015,-3.5739672,-0.1581657,single <1e4
+M98L,2,pheS+4CP,0.0,127.0,-3.59686,-3.6051185,-0.008258581,single <1e4
+M98L,1,pheS+4CP,0.0001,94.0,-2.690339,-2.718062,-0.027722836,single <1e4
+M98L,1,pheS+4CP,0.0001,111.0,-3.5030475,-3.5182636,-0.015216112,single <1e4
+M98L,2,pheS+4CP,0.0001,116.0,-3.4770875,-3.673742,-0.19665456,single <1e4
+M98L,1,pheS+4CP,0.0001,125.0,-3.6604733,-3.8790689,-0.2185955,single <1e4
+M98L,2,pheS+4CP,0.0001,127.0,-3.5696256,-3.7718995,-0.20227385,single <1e4
+M98L,1,pheS+4CP,0.001,94.0,-3.506216,-3.6589744,-0.15275836,single <1e4
+M98L,1,pheS+4CP,0.001,111.0,-3.748704,-3.8739233,-0.12521935,single <1e4
+M98L,2,pheS+4CP,0.001,116.0,-3.9117775,-4.096469,-0.18469143,single <1e4
+M98L,1,pheS+4CP,0.001,125.0,-3.5321796,-3.3726468,0.15953279,single <1e4
+M98L,2,pheS+4CP,0.001,127.0,-4.174369,-4.241227,-0.06685829,single <1e4
+M98L,1,pheS+4CP,0.003,94.0,-3.2705307,-3.426875,-0.15634441,single <1e4
+M98L,1,pheS+4CP,0.003,111.0,-3.8198419,-3.9958615,-0.17601967,single <1e4
+M98L,2,pheS+4CP,0.003,116.0,-3.5957215,-3.701158,-0.10543656,single <1e4
+M98L,1,pheS+4CP,0.003,125.0,-3.3812456,-3.4721808,-0.09093523,single <1e4
+M98L,2,pheS+4CP,0.003,127.0,-3.6702085,-3.7173548,-0.04714632,single <1e4
+M98L,1,pheS+4CP,0.01,94.0,-3.249435,-3.3915057,-0.14207077,single <1e4
+M98L,1,pheS+4CP,0.01,111.0,-3.1577682,-3.1582391,-0.0004708767,single <1e4
+M98L,2,pheS+4CP,0.01,116.0,-3.6554048,-3.816935,-0.16153026,single <1e4
+M98L,1,pheS+4CP,0.01,125.0,-3.342948,-3.368342,-0.025393963,single <1e4
+M98L,2,pheS+4CP,0.01,127.0,-3.5563443,-3.6483648,-0.09202051,single <1e4
+M98L,1,pheS+4CP,0.03,94.0,-3.5176265,-3.6302097,-0.11258316,single <1e4
+M98L,1,pheS+4CP,0.03,111.0,-3.0575864,-3.090652,-0.033065557,single <1e4
+M98L,2,pheS+4CP,0.03,116.0,-3.6030264,-3.7326035,-0.12957716,single <1e4
+M98L,1,pheS+4CP,0.03,125.0,-3.7860756,-3.9340534,-0.14797783,single <1e4
+M98L,2,pheS+4CP,0.03,127.0,-3.6296747,-3.7745872,-0.14491248,single <1e4
+M98L,1,pheS+4CP,0.1,94.0,-2.9249663,-3.016216,-0.091249704,single <1e4
+M98L,1,pheS+4CP,0.1,111.0,-3.4869819,-3.613428,-0.12644625,single <1e4
+M98L,2,pheS+4CP,0.1,116.0,-3.687056,-3.8954825,-0.20842648,single <1e4
+M98L,1,pheS+4CP,0.1,125.0,-3.5381722,-3.677604,-0.13943172,single <1e4
+M98L,2,pheS+4CP,0.1,127.0,-3.5389042,-3.7147613,-0.17585707,single <1e4
+M98L,1,pheS+4CP,1.0,94.0,-3.8677075,-4.2427535,-0.375046,single <1e4
+M98L,1,pheS+4CP,1.0,111.0,-3.7212553,-4.1105137,-0.38925838,single <1e4
+M98L,2,pheS+4CP,1.0,116.0,-4.7725353,-4.405428,0.3671074,single <1e4
+M98L,1,pheS+4CP,1.0,125.0,-3.7888432,-3.8969078,-0.10806465,single <1e4
+M98L,2,pheS+4CP,1.0,127.0,-3.6590633,-3.8559628,-0.19689941,single <1e4
+M98N,1,kanR-kan,0.0,92.0,-4.199012,-4.3487234,-0.14971161,single <1e4
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+M98N,2,kanR-kan,0.0,97.0,-4.161581,-4.2772117,-0.11563063,single <1e4
+M98N,1,kanR-kan,0.0,122.0,-4.218597,-4.527266,-0.3086691,single <1e4
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diff --git a/planning/studies/relative-m/out/marker_rows_no_r2t82.csv b/planning/studies/relative-m/out/marker_rows_no_r2t82.csv
new file mode 100644
index 00000000..bbe4be8d
--- /dev/null
+++ b/planning/studies/relative-m/out/marker_rows_no_r2t82.csv
@@ -0,0 +1,79905 @@
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diff --git a/planning/studies/relative-m/out/midpoints.csv b/planning/studies/relative-m/out/midpoints.csv
new file mode 100644
index 00000000..edf1d41a
--- /dev/null
+++ b/planning/studies/relative-m/out/midpoints.csv
@@ -0,0 +1,908 @@
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diff --git a/planning/studies/relative-m/out/midpoints.pdf b/planning/studies/relative-m/out/midpoints.pdf
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diff --git a/planning/studies/relative-m/out/no_r2t82/midpoints.csv b/planning/studies/relative-m/out/no_r2t82/midpoints.csv
new file mode 100644
index 00000000..ff3dd3f0
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diff --git a/planning/studies/relative-m/out/no_r2t82/midpoints.pdf b/planning/studies/relative-m/out/no_r2t82/midpoints.pdf
new file mode 100644
index 00000000..71a7a399
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diff --git a/planning/studies/relative-m/out/swing_ppc.csv b/planning/studies/relative-m/out/swing_ppc.csv
new file mode 100644
index 00000000..f5aff0ef
--- /dev/null
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diff --git a/planning/studies/relative-m/out/tube_qc.csv b/planning/studies/relative-m/out/tube_qc.csv
new file mode 100644
index 00000000..9d9430e9
--- /dev/null
+++ b/planning/studies/relative-m/out/tube_qc.csv
@@ -0,0 +1,95 @@
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+2|0.0001|82,2,0.0001,82.0,4.217657563335947,0.9078064092072675,pheS+4CP
+2|0.001|116,2,0.001,116.0,-2.967017151336091,-0.11363939377630763,pheS+4CP
+2|0.001|127,2,0.001,127.0,-3.17503026727516,-0.14321263580598476,pheS+4CP
+2|0.001|82,2,0.001,82.0,1.1811820901436285,0.47611043811187487,pheS+4CP
+2|0.003|116,2,0.003,116.0,-3.0030950176221336,-0.11876858703306359,pheS+4CP
+2|0.003|127,2,0.003,127.0,-2.6571127622937647,-0.06958026095752656,pheS+4CP
+2|0.01|116,2,0.01,116.0,-2.26204802094812,-0.013413873754188756,pheS+4CP
+2|0.01|127,2,0.01,127.0,-0.8287439047569983,0.19035909190182615,pheS+4CP
+2|0.01|82,2,0.01,82.0,-0.8074842804122739,0.19338157446677517,pheS+4CP
+2|0.03|116,2,0.03,116.0,1.2169913233871639,0.48120143978658864,pheS+4CP
+2|0.03|127,2,0.03,127.0,1.484898872399437,0.5192898783581175,pheS+4CP
+2|0.03|82,2,0.03,82.0,-0.6261420866153853,0.21916300927264049,pheS+4CP
+2|0.0|127,2,0.0,127.0,-2.9204220515345676,-0.10701496455410875,pheS+4CP
+2|0.0|82,2,0.0,82.0,-2.0881875948960587,0.011303877507834938,pheS+4CP
+2|0.1|116,2,0.1,116.0,4.092562598232535,0.890021647592616,pheS+4CP
+2|0.1|127,2,0.1,127.0,4.955916259050693,1.0127647095063754,pheS+4CP
+2|1.0|116,2,1.0,116.0,5.837230311472605,1.1380612017913294,pheS+4CP
+2|1.0|127,2,1.0,127.0,6.946126261287092,1.2957130313961436,pheS+4CP
+2|1.0|82,2,1.0,82.0,-1.7389184199594527,0.06095950521801964,pheS+4CP
+3|0.0|104,3,0.0,104.0,0.0,0.30818168191185935,pheS+4CP
diff --git a/planning/studies/relative-m/out/wt_trajectory_residuals.csv b/planning/studies/relative-m/out/wt_trajectory_residuals.csv
new file mode 100644
index 00000000..85b726e0
--- /dev/null
+++ b/planning/studies/relative-m/out/wt_trajectory_residuals.csv
@@ -0,0 +1,41 @@
+fit,condition_sel,titrant_conc,mean_resid,resid_trend,rms_resid
+free,kanR+kan,0.0,0.007287208394068341,0.0022888935879069464,0.046087471133300716
+free,kanR+kan,0.0001,-0.01250604788800036,0.0005114415042241922,0.04858943311641547
+free,kanR+kan,0.001,0.008617217241348386,0.0021781918877297564,0.03539878165041782
+free,kanR+kan,0.003,0.026531640738950795,0.001674848685362488,0.04957266405268518
+free,kanR+kan,0.01,-0.04465962327020723,-0.001900653572302479,0.08339678418842968
+free,kanR+kan,0.03,0.0369997609739956,-0.0024563143506508437,0.06786338819203977
+free,kanR+kan,0.1,-0.038550767217147154,-0.0017097084036349775,0.05037877252353303
+free,kanR+kan,1.0,-0.05926940172488011,-0.0020696977961420526,0.07208242802213576
+free,kanR-kan,0.0,0.049762427829883826,-0.0025072315488248622,0.06790569405646991
+free,kanR-kan,1.0,0.04940717010351984,-0.00039558478555720266,0.05393587165876026
+free,pheS+4CP,0.0,0.016336638149157777,0.0002011689031091051,0.02053257687316348
+free,pheS+4CP,0.0001,0.02678288803107165,0.00042938974528171245,0.030416937806397676
+free,pheS+4CP,0.001,-0.0017789209770324276,0.0017501262129867453,0.022496332130739823
+free,pheS+4CP,0.003,-0.007740934208773887,0.0013252419838592087,0.019492491651470996
+free,pheS+4CP,0.01,-0.022622651635543,-0.0007366510692391656,0.030793966894380374
+free,pheS+4CP,0.03,0.003792245281915996,-0.0001196943342426274,0.018308490770298273
+free,pheS+4CP,0.1,0.024666304946783412,-0.000525075787486599,0.02965964487508423
+free,pheS+4CP,1.0,0.003496985415488396,0.000486472625845421,0.009062307073812713
+free,pheS-4CP,0.0,-0.020497064505872704,-0.0013915547663237325,0.04114567482816935
+free,pheS-4CP,1.0,0.0350882182981754,-0.0001094454032909131,0.03786696004973472
+pinned,kanR+kan,0.0,-0.35712361754236915,-0.002695713524172202,0.36107633448856563
+pinned,kanR+kan,0.0001,-0.3519079293484581,-0.004476471553901574,0.3639782060459884
+pinned,kanR+kan,0.001,-0.3172459620549091,-0.002979996042454113,0.321399915528944
+pinned,kanR+kan,0.003,-0.3490555104528636,-0.00365996995003222,0.35598438167363416
+pinned,kanR+kan,0.01,-0.16807117728079154,-0.00605650065740309,0.18781116841298476
+pinned,kanR+kan,0.03,0.3988224743678564,-0.0036038294160605643,0.40406114341501825
+pinned,kanR+kan,0.1,0.561720996971277,-0.001335750414837605,0.5626903518408016
+pinned,kanR+kan,1.0,0.4359840141175036,-0.0017994294511863886,0.471623483885458
+pinned,kanR-kan,0.0,-0.43197275580612643,-0.012168638179295527,0.46374065704858997
+pinned,kanR-kan,1.0,-0.32116395231418693,-0.009217165332153717,0.34283903821389144
+pinned,pheS+4CP,0.0,-0.8241734751085037,-0.01928982321307418,0.9474490165616117
+pinned,pheS+4CP,0.0001,-0.38030898484067244,-0.014029018133281336,0.4110873301922843
+pinned,pheS+4CP,0.001,0.09796269284817072,-0.008697173859719678,0.13634914421990213
+pinned,pheS+4CP,0.003,0.23973227464480135,-0.007949577940923158,0.25543317961968404
+pinned,pheS+4CP,0.01,0.24550220198534323,-0.00985653833559492,0.2677436686275403
+pinned,pheS+4CP,0.03,0.10670663486194272,-0.010553647461345001,0.1602801318533885
+pinned,pheS+4CP,0.1,-0.1380122296956671,-0.013041254635988693,0.20195021510807168
+pinned,pheS+4CP,1.0,-0.3295515569454569,-0.013366187989081946,0.36202869516065433
+pinned,pheS-4CP,0.0,-0.01109033217518333,-0.01362881209625031,0.18254401989194963
+pinned,pheS-4CP,1.0,0.3247608689224541,-0.01014325175045324,0.3516770339610424
diff --git a/planning/studies/relative-m/row_order.py b/planning/studies/relative-m/row_order.py
new file mode 100644
index 00000000..d6020602
--- /dev/null
+++ b/planning/studies/relative-m/row_order.py
@@ -0,0 +1,64 @@
+"""
+Can the data order the tubes of pheS+4CP replicate 2, t_sel 82? (README, run 7)
+
+    python row_order.py GROWTH_PHES_CSV
+
+Genotypes with > 2e5 pheS reads (wt left out), centered log counts per tube.
+Each tube's IPTG signature is its projection, relative to its row's 0-IPTG
+tube, on the mean 1 mM - 0 direction of the other complete rows. The
+expected profile is the median, over those rows, of each concentration's
+projection as a fraction of the row's 1 mM one. Every assignment of the
+suspect row's six labels to its six tubes is then fitted as observed =
+a + b * expected; the script prints the best, the labels as written and
+two rack flips.
+"""
+import pandas as pd, numpy as np, itertools, sys
+g = pd.read_csv(sys.argv[1], usecols=["sample","genotype","counts","replicate","condition_sel","t_sel","titrant_conc"])
+g = g[g.condition_sel == "pheS+4CP"]
+tot = g.groupby("genotype").counts.sum()
+keep = tot[tot > 2e5].index.drop("wt", errors="ignore")
+g = g[g.genotype.isin(keep)]
+g["lf"] = np.log(g.counts + 0.5)
+g["lf"] -= g.groupby("sample").lf.transform("mean")          # centered log ratio
+w = g.pivot_table(index="genotype", columns="sample", values="lf")
+meta = g.drop_duplicates("sample").set_index("sample")[["replicate","t_sel","titrant_conc"]]
+print(len(keep), "genotypes")
+def tubes(rep, t):
+    m = meta[(meta.replicate == rep) & (meta.t_sel == t)].sort_values("titrant_conc")
+    return list(m.index), list(m.titrant_conc)
+# per-row IPTG profile, each tube relative to its row's 0 tube
+rows = meta.groupby(["replicate","t_sel"]).size().index
+prof = {}
+for rep, t in rows:
+    s, c = tubes(rep, t)
+    if 0.0 not in c: continue
+    base = w[s[c.index(0.0)]]
+    prof[(rep, t)] = {cc: w[ss] - base for ss, cc in zip(s, c)}
+# reference direction: 1 mM minus 0 averaged over the good full rows
+good = [k for k in prof if k != (2, 82) and 1.0 in prof[k]]
+v = pd.concat([prof[k][1.0] for k in good], axis=1).mean(axis=1)
+v = v / np.sqrt((v**2).sum())
+def score(x): return float((x * v).sum())
+print("projection on the 1 mM direction, each tube minus its row's 0 tube:")
+tab = {k: {c: round(score(x), 2) for c, x in prof[k].items()} for k in sorted(prof)}
+print(pd.DataFrame(tab).T.to_string())
+# expected per-concentration profile (median over good rows, as fraction of that row's 1 mM)
+frac = pd.DataFrame({k: {c: score(x) / score(prof[k][1.0]) for c, x in prof[k].items()} for k in good}).T
+expf = frac.median()
+print("expected fraction of the 1 mM shift by concentration:"); print(expf.round(2).to_string())
+s, c = tubes(2, 82)
+obs = np.array([score(w[ss] - w[s[c.index(0.0)]]) for ss in s])
+# fit obs = a + b * exp[label]; labels permuted (0 tube included)
+e = expf.loc[c].values
+out = []
+for perm in itertools.permutations(range(len(c))):
+    q = e[list(perm)]
+    A = np.c_[np.ones_like(q), q]
+    coef, *_ = np.linalg.lstsq(A, obs, rcond=None)
+    out.append((((obs - A @ coef)**2).sum(), [c[i] for i in perm], coef))
+out.sort(key=lambda r: r[0])
+print("row 2/82 tubes as labeled:", c); print("observed:", obs.round(2))
+for r in out[:6]: print(round(r[0], 4), "true conc per labeled tube:", r[1], "scale", round(r[2][1], 2))
+for name, perm in [("as labeled", c), ("flip all", c[::-1]), ("flip all but 0", [0.0] + c[:0:-1])]:
+    r = [x for x in out if x[1] == perm][0]
+    print(f"{name}: SS {r[0]:.4f}, rank {out.index(r)+1}/{len(out)}, scale {r[2][1]:.2f}")
diff --git a/planning/studies/relative-m/stage1_relative.py b/planning/studies/relative-m/stage1_relative.py
new file mode 100644
index 00000000..8b0b2724
--- /dev/null
+++ b/planning/studies/relative-m/stage1_relative.py
@@ -0,0 +1,61 @@
+"""
+Stage 1 of the two-stage fit with only relative information
+(planning/relative-m.md, step 1): the per-tube level offsets nearly free
+(sigma_fixed 10, so they absorb each tube's total), k pinned per condition
+at a reference stage 1's values (it drops out), m free under a loose
+prior (SD 0.05).
+
+    python stage1_relative.py ../two-stage-validation/two_stage_real.yaml \
+        ../two-stage-validation/run_real/stage1/km.csv rel_real
+
+Writes OUT_DIR/stage1/ (km.csv, the MAP and its checkpoint) and
+OUT_DIR/growth_priors_relative.csv.
+"""
+
+import argparse
+import os
+
+import pandas as pd
+
+from tfscreen.tfmodel.scripts import fit_two_stage_cli as cli
+
+SIGMA_FIXED = 10.0
+M_SCALE = 0.05
+
+
+def relative_priors(loose_csv, km_csv):
+    """The loose stage-1 table with each k pinned at km.csv's and m free."""
+    t = pd.read_csv(loose_csv).set_index("condition_rep")
+    km = pd.read_csv(km_csv, index_col="condition_rep")
+    missing = set(t.index) - set(km.index)
+    if missing:
+        raise ValueError(f"{km_csv} lacks conditions {sorted(missing)}")
+    t["k_loc"] = km.loc[t.index, "k"]
+    t["m_scale"] = M_SCALE
+    t["k_pinned"] = 1
+    t["m_pinned"] = 0
+    return t.reset_index()
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("config_file")
+    ap.add_argument("km_csv", help="a stage 1 km.csv; its k values are pinned")
+    ap.add_argument("out_dir")
+    args = ap.parse_args()
+    cfg, settings = cli.read_two_stage_config(args.config_file)
+    out = os.path.abspath(args.out_dir)
+    os.makedirs(out, exist_ok=True)
+    pri = os.path.join(out, "growth_priors_relative.csv")
+    if not os.path.exists(pri):
+        relative_priors(cfg["growth_priors"], args.km_csv).to_csv(pri, index=False)
+    cfg["growth_priors"] = pri
+    cfg["set_priors"] = [p for p in (cfg.get("set_priors") or [])
+                         if not p.split("=")[0].strip().endswith("sigma_fixed")]
+    cfg["set_priors"].append(f"sigma_fixed={SIGMA_FIXED}")
+    cli.run_stage1(out, cfg, settings)
+    cli._say("done")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/relative-m/stage1_relative.srun b/planning/studies/relative-m/stage1_relative.srun
new file mode 100644
index 00000000..a80f866e
--- /dev/null
+++ b/planning/studies/relative-m/stage1_relative.srun
@@ -0,0 +1,25 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --partition=gpulong
+#SBATCH --time=1-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+#SBATCH --job-name=ts_relative
+
+# Relative stage 1 (stage1_relative.py). Run from the two-stage validation
+# directory on the cluster, after its run_${RUN}/stage1 exists:
+#   sbatch --export=ALL,RUN=real .../stage1_relative.srun
+# writes rel_${RUN}/stage1/.
+
+module load cuda/12.4.1
+set -e
+RUN="${RUN:?set RUN (seed1, seed2 or real)}"
+HERE=/gpfs/home/harms/tfscreen/planning/studies
+echo ">>> relative stage 1 ${RUN} $(date)"
+python "${HERE}/relative-m/stage1_relative.py" \
+    "${HERE}/two-stage-validation/two_stage_${RUN}.yaml" \
+    "run_${RUN}/stage1/km.csv" "rel_${RUN}"
+echo ">>> Done relative stage 1 ${RUN} $(date)"
diff --git a/planning/studies/relative-m/stage1_warp.py b/planning/studies/relative-m/stage1_warp.py
new file mode 100644
index 00000000..589cff52
--- /dev/null
+++ b/planning/studies/relative-m/stage1_warp.py
@@ -0,0 +1,95 @@
+"""
+Relative stage 1 (stage1_relative.py) with one selection marker's growth
+following a monotone warp of X (linear growth's m_warp; README, run 4),
+optionally without some tubes.
+
+    python stage1_warp.py ../two-stage-validation/two_stage_real.yaml \
+        ../two-stage-validation/run_real/stage1/km.csv warp_real_kanR \
+        --warp kanR+kan --drop_tubes "pheS+4CP:2:82"
+
+--warp names the warped condition_rep (none: no warp). --drop_tubes takes
+condition_sel:replicate:t_sel triples (repeatable; ":" because sbatch
+--export splits on commas): every IPTG of that
+row is left out (the pheS+4CP replicate 2, t_sel 82 row whose contents
+disagree with its labels, run 3). --pin_m condition_rep:value pins that
+condition's m (repeatable; run 5); --max_num_epochs raises the step cap.
+"""
+
+import argparse
+import os
+import re
+
+import pandas as pd
+
+from tfscreen.genetics import library_composition_table
+from tfscreen.tfmodel import two_stage as ts
+from tfscreen.tfmodel.scripts import fit_two_stage_cli as cli
+
+from stage1_relative import SIGMA_FIXED, relative_priors
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("config_file")
+    ap.add_argument("km_csv")
+    ap.add_argument("out_dir")
+    ap.add_argument("--warp", default="none")
+    ap.add_argument("--drop_tubes", action="append", default=[])
+    ap.add_argument("--pin_m", action="append", default=[],
+                    help="condition_rep:value; pin that condition's m there")
+    ap.add_argument("--max_num_epochs", type=int, default=None)
+    args = ap.parse_args()
+    cfg, settings = cli.read_two_stage_config(args.config_file)
+    out = os.path.abspath(args.out_dir)
+    os.makedirs(os.path.join(out, "stage1"), exist_ok=True)
+
+    pri = os.path.join(out, "growth_priors_relative.csv")
+    if not os.path.exists(pri):
+        t = relative_priors(cfg["growth_priors"], args.km_csv)
+        if args.warp != "none":
+            if args.warp not in set(t["condition_rep"]):
+                raise ValueError(f"--warp {args.warp} is not a condition_rep")
+        t["m_warp"] = (t["condition_rep"] == args.warp).astype(int)
+        for spec in args.pin_m:
+            cond, value = spec.rsplit(":", 1)
+            if cond not in set(t["condition_rep"]):
+                raise ValueError(f"--pin_m {spec}: {cond} is not a condition_rep")
+            t.loc[t["condition_rep"] == cond, ["m_loc", "m_pinned"]] = [float(value), 1]
+        t.to_csv(pri, index=False)
+    cfg["growth_priors"] = pri
+    cfg["set_priors"] = [p for p in (cfg.get("set_priors") or [])
+                         if not p.split("=")[0].strip().endswith("sigma_fixed")]
+    cfg["set_priors"].append(f"sigma_fixed={SIGMA_FIXED}")
+
+    growth = os.path.join(out, "stage1", "tfs_growth.csv")
+    if not os.path.exists(growth):
+        lib = library_composition_table(cfg["library_config"])
+        keep = ts.stage1_genotypes(lib, ts.growth_genotypes(cfg["growth_df"]))
+        tmp = growth + ".partial"
+        ts.filter_growth_csv(cfg["growth_df"], tmp, genotypes=keep)
+        if args.drop_tubes:
+            g = pd.read_csv(tmp)
+            drop = pd.Series(False, index=g.index)
+            for spec in args.drop_tubes:
+                parts = re.split(r"[,:]", spec)
+                if len(parts) != 3:
+                    raise ValueError(f"--drop_tubes {spec!r}: expected "
+                                     "condition_sel:replicate:t_sel")
+                cond, rep, t_sel = parts
+                drop |= ((g["condition_sel"].astype(str) == cond)
+                         & (pd.to_numeric(g["replicate"]) == float(rep))
+                         & (g["t_sel"].astype(float) == float(t_sel)))
+            print(f"dropping {int(drop.sum())} rows ({', '.join(args.drop_tubes)})",
+                  flush=True)
+            g[~drop].to_csv(tmp, index=False)
+        # in place only when complete: run_stage1 reuses an existing table
+        os.replace(tmp, growth)
+    if args.max_num_epochs is not None:
+        settings["fit"] = dict(settings.get("fit") or {},
+                               max_num_epochs=args.max_num_epochs)
+    cli.run_stage1(out, cfg, settings)
+    cli._say("done")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/relative-m/stage1_warp.srun b/planning/studies/relative-m/stage1_warp.srun
new file mode 100644
index 00000000..cc03f6ed
--- /dev/null
+++ b/planning/studies/relative-m/stage1_warp.srun
@@ -0,0 +1,29 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --partition=gpulong
+#SBATCH --time=1-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+#SBATCH --job-name=ts_warp
+
+# Relative stage 1 with a warped marker (stage1_warp.py). Run from the
+# two-stage validation directory on the cluster:
+#   sbatch --export=ALL,RUN=real,WARP=kanR+kan,TAG=kanR,EXTRA="--drop_tubes pheS+4CP:2:82" .../stage1_warp.srun
+# writes warp_${RUN}_${TAG}/stage1/.
+
+module load cuda/12.4.1
+set -e
+RUN="${RUN:?set RUN (seed1, seed2 or real)}"
+WARP="${WARP:?set WARP (a condition_rep, or none)}"
+TAG="${TAG:?set TAG (output name)}"
+HERE=/gpfs/home/harms/tfscreen/planning/studies
+cd_out=$(pwd)
+echo ">>> warp stage 1 ${RUN} ${WARP} $(date)"
+cd "${HERE}/relative-m"
+python stage1_warp.py "${HERE}/two-stage-validation/two_stage_${RUN}.yaml" \
+    "${cd_out}/run_${RUN}/stage1/km.csv" "${cd_out}/warp_${RUN}_${TAG}" \
+    --warp "${WARP}" ${EXTRA:-}
+echo ">>> Done warp stage 1 ${RUN} ${WARP} $(date)"
diff --git a/planning/studies/relative-m/swing_ppc.py b/planning/studies/relative-m/swing_ppc.py
new file mode 100644
index 00000000..5dc6f7f3
--- /dev/null
+++ b/planning/studies/relative-m/swing_ppc.py
@@ -0,0 +1,80 @@
+"""
+Each genotype's within-tube swing (change from 0 to 1 mM IPTG of the
+slope of ln(genotype / M42I/H74A/K84L)), observed from counts and
+predicted by a stage 1 fit, in both selection markers (README).
+
+    python swing_ppc.py runs/rel_real/stage1 --out out
+"""
+
+import argparse
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, os.path.join(HERE, "..", "within-tube-m"))
+from within_tube_m import slopes_by_conc, swing  # noqa: E402
+
+REF = "M42I/H74A/K84L"
+KEY = ["replicate", "condition_sel", "titrant_conc", "t_sel"]
+COND = ["kanR+kan", "pheS+4CP"]
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("stage1_dir")
+    ap.add_argument("--min_reads", type=float, default=1e5)
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+    obs = pd.read_csv(os.path.join(args.stage1_dir, "tfs_growth.csv"),
+                      usecols=KEY + ["genotype", "counts"])
+    pred = pd.read_csv(os.path.join(args.stage1_dir, "pred_growth.csv"),
+                       usecols=KEY + ["genotype", "q0.5"])
+    m = obs.merge(pred, on=KEY + ["genotype"])
+    m = m[m["condition_sel"].isin(COND)]
+    reads = obs.groupby("genotype")["counts"].sum()
+    deep = set(reads[reads >= args.min_reads].index)
+    ref = m[m["genotype"] == REF].set_index(KEY)[["counts", "q0.5"]]
+    rows = []
+    for g, d in m[(m["genotype"] != REF) & m["genotype"].isin(deep)].groupby("genotype"):
+        rec = {"genotype": g}
+        for cond in COND:
+            t = d[d["condition_sel"] == cond].set_index(KEY)[["counts", "q0.5"]].join(
+                ref, rsuffix="_ref", how="inner").reset_index()
+            t = t[(t["counts"] > 0) & (t["counts_ref"] > 0)]
+            if t["titrant_conc"].isin([0.0, 1.0]).sum() < 4:
+                continue
+            t["lr"] = np.log(t["counts"] / t["counts_ref"])
+            t["v"] = 1 / t["counts"] + 1 / t["counts_ref"]
+            t["lp"] = t["q0.5"] - t["q0.5_ref"]
+            try:
+                c, b, cov, _ = slopes_by_conc(t, "lr", "v")
+                rec[f"obs_{cond}"], rec[f"obs_{cond}_se"] = swing(c, b, cov)
+                c, b, cov, _ = slopes_by_conc(t, "lp")
+                rec[f"pred_{cond}"] = swing(c, b, cov)[0]
+            except (ValueError, np.linalg.LinAlgError):
+                pass
+        rows.append(rec)
+    s = pd.DataFrame(rows).dropna()
+    s.to_csv(os.path.join(args.out, "swing_ppc.csv"), index=False)
+    singles = s[~s["genotype"].str.contains("/") & (s["genotype"] != "wt")]
+    print(f"{len(singles)} deep singles")
+    for cond in COND:
+        o, p = singles[f"obs_{cond}"], singles[f"pred_{cond}"]
+        slope = np.polyfit(o, p, 1)[0]
+        print(f"{cond}: predicted on observed swing slope {slope:+.3f}, r "
+              f"{np.corrcoef(o, p)[0, 1]:.3f}, median |obs| {o.abs().median():.4f}, "
+              f"median |pred| {p.abs().median():.4f}")
+    for kind in ("obs", "pred"):
+        a, b = singles[f"{kind}_kanR+kan"], singles[f"{kind}_pheS+4CP"]
+        print(f"{kind}: pheS on kanR swing, line through 0: "
+              f"{np.sum(a * b) / np.sum(a * a):+.3f}")
+    print(s[s["genotype"].isin(["wt", "H74A/K84L", "M42I/H74A", "M42I/K84L"])].to_string(
+        index=False, float_format="%+.4f"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/relative-m/tube_qc.py b/planning/studies/relative-m/tube_qc.py
new file mode 100644
index 00000000..810df718
--- /dev/null
+++ b/planning/studies/relative-m/tube_qc.py
@@ -0,0 +1,66 @@
+"""
+Apparent IPTG of every selection tube from its genotype composition
+(README, run 3): a tube whose label and contents disagree stands out.
+
+Within each replicate x time row, each tube's log frequencies are
+centered on the row and projected on the axis from the 0-IPTG tubes' mean
+composition to the 1 mM tubes' (computed without the tube itself), then
+scaled so 0-IPTG tubes average 0 and 1 mM tubes 1. Rows missing some
+concentrations are shifted by the centering.
+
+    python tube_qc.py ../within-tube-m/out/marker_rows.csv --out out
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+
+
+def apparent_iptg(rows, cond):
+    d = rows[(rows["condition_sel"] == cond) & (rows["counts"] > 0)].copy()
+    d["tube"] = (d["replicate"].astype(str) + "|" + d["titrant_conc"].astype(str)
+                 + "|" + d["t_sel"].astype(str))
+    P = d.pivot_table(index="genotype", columns="tube", values="counts").dropna()
+    L = np.log(P / P.sum())
+    meta = pd.DataFrame([(k, int(k.split("|")[0]), float(k.split("|")[1]),
+                          float(k.split("|")[2])) for k in L.columns],
+                        columns=["tube", "replicate", "titrant_conc", "t_sel"]).set_index("tube")
+    for _, g in meta.groupby(["replicate", "t_sel"]):
+        L[g.index] = L[g.index].sub(L[g.index].mean(axis=1), axis=0)
+    score = {}
+    for k in L.columns:
+        o = meta.drop(k)
+        axis = (L[o.index[o["titrant_conc"] == 1.0]].mean(axis=1)
+                - L[o.index[o["titrant_conc"] == 0.0]].mean(axis=1))
+        score[k] = float((L[k] * axis).sum() / np.sqrt((axis ** 2).sum()))
+    meta["score"] = pd.Series(score)
+    z0 = meta.loc[meta["titrant_conc"] == 0.0, "score"].mean()
+    z1 = meta.loc[meta["titrant_conc"] == 1.0, "score"].mean()
+    meta["apparent"] = (meta["score"] - z0) / (z1 - z0)
+    meta["condition_sel"] = cond
+    return meta.reset_index(), len(P)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("rows_csv")
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+    rows = pd.read_csv(args.rows_csv)
+    parts = []
+    for cond in sorted(rows["condition_sel"].unique()):
+        t, n = apparent_iptg(rows, cond)
+        parts.append(t)
+        tab = t.pivot_table(index=["replicate", "t_sel"], columns="titrant_conc",
+                            values="apparent")
+        tab.columns = [f"{c * 1e3:g}uM" for c in tab.columns]
+        print(f"\n== {cond} ({n} genotypes in every tube): apparent IPTG index")
+        print(tab.round(2).to_string())
+    pd.concat(parts).to_csv(os.path.join(args.out, "tube_qc.csv"), index=False)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/relative-m/wt_trajectory.py b/planning/studies/relative-m/wt_trajectory.py
new file mode 100644
index 00000000..8f4771f1
--- /dev/null
+++ b/planning/studies/relative-m/wt_trajectory.py
@@ -0,0 +1,119 @@
+"""
+wt against the library over the selection, observed and predicted by two
+stage 1 fits (README, run 6): ln(wt / library) per tube, the library the
+geometric mean of the deep singles present in every tube, against t_sel
+for each condition and IPTG. Tube totals and offsets cancel.
+
+    python wt_trajectory.py runs/warp_real_attrfree/stage1 runs/warp_real_attrpinned/stage1 \
+        --labels free pinned --out out
+
+Each folder needs tfs_growth.csv (observed counts) and pred_growth.csv
+(tfs-predict-growth at the MAP; q0.5 = predicted ln_cfu).
+"""
+
+import argparse
+import datetime
+import os
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.backends.backend_pdf import PdfPages  # noqa: E402
+
+plt.rc("pdf", fonttype=42)
+plt.rc("font", size=12)
+
+KEY = ["replicate", "condition_sel", "titrant_conc", "t_sel"]
+CONDS = ["kanR+kan", "kanR-kan", "pheS+4CP", "pheS-4CP"]
+REP_COLOR = {1: "#0072B2", 2: "#D55E00", 3: "#009E73"}
+FIT_STYLE = ["-", "--", ":"]
+
+
+def wt_vs_library(stage1, min_reads):
+    counts = pd.read_csv(os.path.join(stage1, "tfs_growth.csv"),
+                         usecols=KEY + ["genotype", "counts"])
+    reads = counts.groupby("genotype")["counts"].sum()
+    lib = [g for g, r in reads.items() if r >= min_reads and "/" not in g and g != "wt"]
+    obs_w = counts.pivot_table(index=KEY, columns="genotype", values="counts")
+    pred = pd.read_csv(os.path.join(stage1, "pred_growth.csv"),
+                       usecols=KEY + ["genotype", "q0.5"])
+    P = pred.pivot_table(index=KEY, columns="genotype", values="q0.5").reindex(obs_w.index)
+    keep = [g for g in lib if g in obs_w.columns and (obs_w[g] > 0).all() and g in P.columns]
+    out = pd.DataFrame(index=obs_w.index)
+    out["obs"] = np.log(obs_w["wt"]) - np.log(obs_w[keep]).mean(axis=1)
+    out["obs_se"] = np.sqrt(1 / obs_w["wt"])
+    out["pred"] = P["wt"] - P[keep].mean(axis=1)
+    return out.reset_index(), len(keep)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("stage1_dirs", nargs="+")
+    ap.add_argument("--labels", nargs="+", default=None)
+    ap.add_argument("--min_reads", type=float, default=1e5)
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    labels = args.labels or [os.path.basename(os.path.dirname(os.path.abspath(d)))
+                             for d in args.stage1_dirs]
+    fits = []
+    for d, lab in zip(args.stage1_dirs, labels):
+        t, n = wt_vs_library(d, args.min_reads)
+        print(f"{lab}: library = geometric mean of {n} deep singles")
+        fits.append(t.assign(fit=lab))
+    obs = fits[0][KEY + ["obs", "obs_se"]]
+    concs = sorted(obs["titrant_conc"].unique())
+
+    rows = []
+    for t in fits:
+        r = t.assign(resid=t["obs"] - t["pred"])
+        for (cond, c), g in r.groupby(["condition_sel", "titrant_conc"]):
+            tt = g["t_sel"] - g.groupby("replicate")["t_sel"].transform("mean")
+            rr = g["resid"] - g.groupby("replicate")["resid"].transform("mean")
+            slope = np.polyfit(tt, rr, 1)[0] if tt.abs().sum() > 0 else np.nan
+            rows.append(dict(fit=t["fit"].iloc[0], condition_sel=cond, titrant_conc=c,
+                             mean_resid=g["resid"].mean(), resid_trend=slope,
+                             rms_resid=np.sqrt((g["resid"] ** 2).mean())))
+    s = pd.DataFrame(rows)
+    s.to_csv(os.path.join(args.out, "wt_trajectory_residuals.csv"), index=False)
+    for what in ("mean_resid", "resid_trend"):
+        print(f"\n== observed - predicted ln(wt / library): {what}"
+              f"{' (per minute, within replicate)' if what == 'resid_trend' else ''}")
+        tab = s.pivot_table(index=["condition_sel", "fit"], columns="titrant_conc", values=what)
+        tab.columns = [f"{c * 1e3:g}uM" for c in tab.columns]
+        print(tab.to_string(float_format=lambda v: f"{v:+.4f}"))
+
+    stamp = datetime.date.today().isoformat()
+    with PdfPages(os.path.join(args.out, f"{stamp}_wt-trajectory.pdf")) as pdf:
+        fig, axes = plt.subplots(len(CONDS), len(concs), figsize=(2.6 * len(concs), 2.6 * len(CONDS)),
+                                 sharex="row", squeeze=False)
+        for i, cond in enumerate(CONDS):
+            for j, c in enumerate(concs):
+                ax = axes[i, j]
+                o = obs[(obs.condition_sel == cond) & (obs.titrant_conc == c)]
+                if o.empty:
+                    ax.axis("off")
+                    continue
+                for rep, g in o.groupby("replicate"):
+                    g = g.sort_values("t_sel")
+                    ax.errorbar(g.t_sel, g.obs, yerr=1.96 * g.obs_se, fmt="o",
+                                color=REP_COLOR.get(rep, "k"), ms=4)
+                    for k, t in enumerate(fits):
+                        p = t[(t.condition_sel == cond) & (t.titrant_conc == c)
+                              & (t.replicate == rep)].sort_values("t_sel")
+                        ax.plot(p.t_sel, p.pred, FIT_STYLE[k % 3], color=REP_COLOR.get(rep, "k"),
+                                lw=1.2, label=f"{t['fit'].iloc[0]}" if rep == 1 else None)
+                ax.set_title(f"{cond}, {c * 1e3:g} uM", fontsize=9)
+                if j == 0:
+                    ax.set_ylabel("ln(wt / library)")
+                if i == len(CONDS) - 1:
+                    ax.set_xlabel("t_sel (min)")
+        axes[0, 0].legend(fontsize=8)
+        fig.tight_layout()
+        pdf.savefig(fig)
+        plt.close(fig)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/staged-map/README.md b/planning/studies/staged-map/README.md
new file mode 100644
index 00000000..775782c4
--- /dev/null
+++ b/planning/studies/staged-map/README.md
@@ -0,0 +1,201 @@
+# Staged level-offset MAP
+
+**Question.** Does `tfs-fit-model`'s staged MAP (`--stage_offsets auto`)
+reach the hand-chained real-data fit from one command? A level-offset MAP
+started cold settled with the tube offsets near ±2.8. The real fit avoided
+that by hand: a no-offset MAP, then each tube's best offset with everything
+else held (`small_offsets.py`), then the joint MAP from there at step size
+1e-4 (`make_warm_start.py`, `make_init_npz.py`, `check_warm.py`, six runs;
+`../real-data-fit/README.md`).
+
+**Decision it feeds.** Pipeline plan step 3
+(`planning/experiment-pipeline.md`): whether the staged MAP replaces the
+hand chain and those scripts retire. If it does, the shortened recipe for the
+real fit is `run_staged.srun`.
+
+## Design
+
+The staged MAP (`tfmodel/inference/staged_map.py`) runs three MAPs on the
+same model. Stage 1 holds the tube offsets at 0. Stage 2 holds every other
+site at stage 1's MAP and fits the offsets alone. Stage 3 is the joint MAP
+from stage 1's point plus stage 2's offsets, at `--staged_step_size` 1e-4.
+
+Two arms on the dev data, both with `rel_off_n05`'s model: the growth-only
+relative fit, level offsets with SD held at 0.17, log(n) population SD held
+at 0.5, loose k/m priors (`growth_priors_loose.csv`, the old
+`set_growth_priors.py loose`). Every setting is a `tfs-configure-model`
+flag; nothing is edited by hand.
+
+| arm | fit |
+|---|---|
+| `staged_off` | `--stage_offsets off`: the cold MAP. Expected to reproduce the ±2.8 offset mode. |
+| `staged_auto` | `--stage_offsets auto`: the three stages. |
+
+Both then run the arrowhead Laplace, extract parameters and score the count
+likelihood exactly (`score_map.py`).
+
+**Pass criteria** (the plan's done criterion for the real fit):
+`staged_auto`'s count log-likelihood within a few thousand nats of
+`rel_off_n05`'s, and every spike's log K and n median inside
+`rel_off_n05_alap`'s 95% intervals. `staged_off` should fail them; if it
+does not, the trap was not reproduced and the comparison says nothing about
+staging.
+
+A local check on the 483-genotype simulate-and-analyze library, cold against
+staged, found no trap there (Results), as the relative-fit study's MAPs
+suggested.
+
+## How to run
+
+On the cluster, in `planning/dev-data/real_fit/` (gitignored, not pulled),
+with this repository at the commit that has `--stage_offsets`:
+
+```bash
+cp <repo>/planning/studies/staged-map/{run_staged.srun,growth_priors_loose.csv,score_map.py,compare.py} .
+```
+
+```bash
+grep -c stage_offsets run_staged.srun
+```
+
+```bash
+mkdir staged_auto && cd staged_auto && sbatch --export=ALL,STAGE=auto ../run_staged.srun
+```
+
+```bash
+mkdir staged_off && cd staged_off && sbatch --export=ALL,STAGE=off ../run_staged.srun
+```
+
+When both are done, from `real_fit/`, score the optima exactly (full
+batch, no mini-batch noise; the final losses cannot rank them) and compare
+the spike curves:
+
+```bash
+python score_map.py rel_off_n05 staged_auto staged_off
+```
+
+```bash
+python compare.py rel_off_n05_alap staged_auto staged_off
+```
+
+## Inputs
+
+`real_fit/inputs/growth.csv.gz` and `real_fit/inputs/library_config.yaml`
+(the nine-spike config), as for `rel_off_n05`.
+
+## Commit
+
+The commit that adds `--stage_offsets`. Every run writes its provenance
+(`*_provenance.json`, and the config's `provenance:` block).
+
+## Results
+
+Local check (2026-10-03, uncommitted working tree after 0.5.0; CPU). The
+simulate-and-analyze library (483 genotypes, `examples/simulate-and-analyze/
+simulate_config.yaml`, seed 7), configured as in `run_staged.srun` without
+the growth-priors table, MAP with default settings, `--stage_offsets off`
+against `auto`:
+
+| arm | final loss | tube offsets (nonzero) | wall time |
+|---|---|---|---|
+| off (cold) | 161,083 | SD 0.32, -1.18 to +1.02 | 7.8 min |
+| auto (staged) | 160,617 | SD 0.37, -1.15 to +1.07 | 16.8 min |
+
+Stage 1 ran to the 100,000-epoch cap (loss 213,522 with no offsets), stage
+2 converged in 28,000 steps (197,602; offsets SD 0.28), stage 3 ran to the
+cap. The cold MAP did not fall into the offset trap on this library, so this
+only shows that staging costs no fit (466 nats better) at about twice the
+time. Every run hit the epoch cap. Whether staging avoids the trap is the
+cluster arm's question.
+
+Cluster runs (2026-10-04, commit 8a7abbb470 plus the uncommitted staged
+MAP). Exact full-batch scores (`score_map.py`, run locally on the pulled
+runs):
+
+| run | log joint | vs rel_off_n05 | count ll | vs rel_off_n05 |
+|---|---|---|---|---|
+| rel_off_n05 (hand chain) | -61,477,486 | 0 | -58,985,729 | 0 |
+| staged_auto | -61,471,244 | +6,242 | -58,982,633 | +3,096 |
+| staged_off (cold) | -62,060,536 | -583,050 | -59,369,433 | -383,704 |
+
+The cold MAP reproduced the trap (5.8e5 nats worse; hill_n 4-9 for every
+spike, offsets to ±1.5) and the staged MAP avoided it, ending 6.2e3 nats
+better than the six-run hand chain from one command. The likelihood
+criterion passes. The final losses (`compare.py`) had said the opposite
+(staged 5.8e4 worse): they are block medians of mini-batch losses, whose
+per-window SE is about 5e4 here.
+
+The spike criterion failed (log K 3 of 9, n 1 of 9 inside
+rel_off_n05_alap's intervals; staged n about 1.2-1.4 against 0.5-1.1, wt n
+3.0). Both MAPs stopped on noise: the convergence monitor judges a window
+by its mini-batch losses, so at SE 5e4 per window a descent of about 1e5
+nats per window reads as a stall, and the step size was cut from 1e-3 to
+1e-6 by step 42,000 (stage 1), 20,000 (rel_off_n05) and 12,000 (stage 3)
+while log(n) and log K hyperparameters were still the most-moving
+parameters. Two points left on the flat n-K direction at different places
+differ there, and a Laplace at a point short of the optimum gives
+intervals too narrow to judge the other. The criterion can only be applied
+once both fits converge. Next: judge MAP windows by the exact full-batch
+loss (done, see "Run 2"), rerun both arms, and retest.
+
+### Run 2: exact-loss convergence
+
+`tfs-fit-model` now judges every MAP window by the exact full-batch loss
+(`RunInference.full_batch_loss`), so a fit stops only when it stops
+improving, not when mini-batch noise hides the descent. Three arms, in new
+directories (the run-1 directories hold run 1):
+
+```bash
+mkdir staged_auto2 && cd staged_auto2 && sbatch --export=ALL,STAGE=auto ../run_staged.srun
+```
+
+```bash
+mkdir staged_off2 && cd staged_off2 && sbatch --export=ALL,STAGE=off ../run_staged.srun
+```
+
+```bash
+mkdir ref_refit && cd ref_refit && sbatch --export=ALL,STAGE=off,INIT_FROM=../rel_off_n05/tfs_fit_model_params.npz ../run_staged.srun
+```
+
+`ref_refit` continues rel_off_n05's point under the current code at step
+size 1e-4: where it ends is the reference's optimum, against which
+`staged_auto2` is judged (and if it moves far, code changes since
+beacad0e are part of the difference). Compare with `score_map.py ref_refit
+staged_auto2 staged_off2 rel_off_n05` and, once both have converged,
+`compare.py` against `ref_refit`'s Laplace instead of rel_off_n05_alap's.
+The convergence CSVs now carry `loss_exact`, so the window-by-window paths
+can be compared too.
+
+Results (2026-10-05, commit 6671763d28 plus the uncommitted exact-loss
+change; every arm converged on the exact-loss rule, 4.0-4.4 h of fitting
+each). Exact scores (`score_map.py`, run in each job):
+
+| run | log joint | vs ref_refit | count ll | vs ref_refit | tube offsets |
+|---|---|---|---|---|---|
+| ref_refit | -61,024,460 | 0 | -58,755,810 | 0 | SD 1.56, -4.20 to +2.98 |
+| staged_auto2 | -61,103,315 | -78,855 | -58,846,646 | -90,835 | SD 0.21, -0.80 to +0.77 |
+| staged_off2 | -61,507,099 | -482,639 | -59,250,907 | -495,097 | SD 0.37, -1.93 to +1.96 |
+| rel_off_n05 (run 1) | -61,477,486 | -453,026 | -58,985,729 | -229,919 | |
+
+Run to convergence, the reference gained 4.5e5 nats, and it did so by
+moving into an offset mode: its tube offsets became a smooth function of
+IPTG in the two selection conditions (kanR selection +2 at 0 mM down to -3
+to -4 at 1 mM, pheS selection the reverse, -1 up to +1.9), nearly the same
+at all three time points, and near 0 in the no-selection conditions; growth
+k and m went unphysical (kanR+kan k 0.036 and m -0.050 against about 0.01
+elsewhere). Those offsets are 10-25 prior SDs each at sigma_fixed 0.17,
+about 4e4 nats of prior, which the likelihood repays several times over.
+The staged MAP's offsets are small and patternless and its k uniform (about
+0.027); the cold MAP sits in a third mode (log(n) population mean 4.6, k
+negative). Every run stopped on the loss_rtol floor while still descending
+about 40 nats per window at step size 1e-6.
+
+So the staged MAP does what it was built for (it avoids the offset mode),
+but the offset mode is the better optimum of this model's objective, not an
+optimizer trap: staging works only by stopping in a local optimum, which a
+longer or better fit would leave. A tube offset that is constant over time
+and structured by IPTG and selection is a population-level shift set before
+the first sampled time point, which the instant growth transition cannot
+produce; the offsets supply it. The spike criterion is moot until that is
+settled. Next decision: the model (roadmap step 7b, the growth transition;
+the offsets' freedom), not the optimizer.
diff --git a/planning/studies/staged-map/compare.py b/planning/studies/staged-map/compare.py
new file mode 100644
index 00000000..4559a2dc
--- /dev/null
+++ b/planning/studies/staged-map/compare.py
@@ -0,0 +1,103 @@
+"""
+Compare staged-MAP runs with the hand-chained real-data fit.
+
+    python compare.py <reference_run> <run> [<run> ...]
+
+Run from planning/dev-data/real_fit. The reference is the hand-chained
+level-offset MAP and its arrowhead Laplace (rel_off_n05 with
+rel_off_n05_alap for the intervals: pass rel_off_n05_alap, whose
+directory holds the extracted parameters, and the script reads the MAP's
+score from rel_off_n05). For each run it prints:
+
+- the final loss (last row of tfs_fit_model_losses.txt) and the count
+  log-likelihood (score_tubes.csv, from score_counts.py), and the
+  difference from the reference;
+- the tube offsets' SD and range (tfs_fit_model_params.npz);
+- for every spiked genotype, whether its log K and n medians fall inside
+  the reference's 95% intervals (the pipeline plan's done criterion).
+"""
+
+import glob
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+
+OFF = "sample_offset_offset_auto_loc"
+
+
+def _loss(run):
+    path = os.path.join(run, "tfs_fit_model_losses.txt")
+    if not os.path.exists(path):
+        return np.nan
+    df = pd.read_csv(path)
+    return float(df.iloc[-1]["loss"])
+
+
+def _score(run):
+    path = os.path.join(run, "score_tubes.csv")
+    return float(pd.read_csv(path)["ll"].sum()) if os.path.exists(path) else np.nan
+
+
+def _offsets(run):
+    path = os.path.join(run, "tfs_fit_model_params.npz")
+    if not os.path.exists(path):
+        return None
+    with np.load(path) as z:
+        if OFF not in z.files:
+            return None
+        off = np.asarray(z[OFF])
+    return off[off != 0]
+
+
+def _param(run, name):
+    hits = [p for p in glob.glob(os.path.join(run, f"*_{name}.csv"))
+            if "hyper" not in os.path.basename(p)]
+    return pd.read_csv(hits[0]) if hits else None
+
+
+def _spikes(run):
+    path = os.path.join(run, "tfs_configure_library.csv")
+    lib = pd.read_csv(path)
+    return sorted(lib.loc[lib["in_spiked_origin"].astype(bool), "genotype"])
+
+
+def main():
+    ref, runs = sys.argv[1], sys.argv[2:]
+    ref_map = ref[:-len("_alap")] if ref.endswith("_alap") else ref
+    ref_loss, ref_score = _loss(ref_map), _score(ref_map)
+    spikes = _spikes(ref_map)
+    print(f"reference {ref_map}: loss {ref_loss:.6g}, count ll {ref_score:.6g}")
+
+    for run in runs:
+        print(f"\n{run}")
+        loss, score = _loss(run), _score(run)
+        print(f"  loss {loss:.6g} (ref {loss - ref_loss:+.4g}); "
+              f"count ll {score:.6g} (ref {score - ref_score:+.4g})")
+        off = _offsets(run)
+        if off is not None and off.size:
+            print(f"  tube offsets: {off.size} nonzero, SD {off.std():.3f}, "
+                  f"range {off.min():+.2f} to {off.max():+.2f}")
+        for name in ("log_hill_K", "hill_n"):
+            mine, theirs = _param(run, name), _param(ref, name)
+            if mine is None or theirs is None:
+                print(f"  {name}: missing extracted parameters")
+                continue
+            m = mine.set_index("genotype")
+            t = theirs.set_index("genotype")
+            inside = []
+            for g in spikes:
+                if g not in m.index or g not in t.index:
+                    continue
+                v = float(m.loc[g, "q0.5"].mean())
+                lo, hi = float(t.loc[g, "q0.025"].mean()), float(t.loc[g, "q0.975"].mean())
+                inside.append(lo <= v <= hi)
+                if not inside[-1]:
+                    print(f"    {name} {g}: {v:.3g} outside [{lo:.3g}, {hi:.3g}]")
+            print(f"  {name}: {sum(inside)} of {len(inside)} spikes inside the "
+                  f"reference's 95% interval")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/staged-map/growth_priors_loose.csv b/planning/studies/staged-map/growth_priors_loose.csv
new file mode 100644
index 00000000..4cfb4e48
--- /dev/null
+++ b/planning/studies/staged-map/growth_priors_loose.csv
@@ -0,0 +1,5 @@
+condition_rep,k_loc,k_scale,m_loc,m_scale
+kanR+kan,0.015,0.01,0.0,0.01
+kanR-kan,0.015,0.01,0.0,0.01
+pheS+4CP,0.015,0.01,0.0,0.01
+pheS-4CP,0.015,0.01,0.0,0.01
diff --git a/planning/studies/staged-map/run_staged.srun b/planning/studies/staged-map/run_staged.srun
new file mode 100644
index 00000000..32e941fc
--- /dev/null
+++ b/planning/studies/staged-map/run_staged.srun
@@ -0,0 +1,70 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=tfs_staged
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpulong
+#SBATCH --time=01-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# The staged level-offset MAP on the dev-data screen (planning/studies/staged-map).
+# rel_off_n05's model (growth-only relative fit, level offsets at SD 0.17,
+# log(n) population SD held at 0.5, loose k/m priors), configured with flags
+# only, fit cold (STAGE=off) or staged (STAGE=auto), then the arrowhead
+# Laplace. Submit from a new run directory next to inputs/ in
+# planning/dev-data/real_fit:
+#
+#   mkdir staged_auto && cd staged_auto && sbatch --export=ALL,STAGE=auto ../run_staged.srun
+#   mkdir staged_off  && cd staged_off  && sbatch --export=ALL,STAGE=off  ../run_staged.srun
+#
+# Run 2 (exact-loss convergence): also the reference's point refit under the
+# current code, a MAP started at rel_off_n05's MAP (not staged):
+#   mkdir ref_refit && cd ref_refit && sbatch --export=ALL,STAGE=off,INIT_FROM=../rel_off_n05/tfs_fit_model_params.npz ../run_staged.srun
+#
+# Copy run_staged.srun, growth_priors_loose.csv, score_map.py and compare.py
+# into real_fit/ first
+# (planning/dev-data is gitignored and not pulled); check with
+#   grep -c stage_offsets run_staged.srun
+
+module load cuda/12.4.1
+
+set -e
+
+STAGE="${STAGE:?set STAGE=auto or STAGE=off}"
+GROWTH="${GROWTH:-../inputs/growth.csv.gz}"
+SEED="${SEED:-20260930}"
+INIT_FROM="${INIT_FROM:-}"
+FIT_ARGS=()
+if [[ -n "${INIT_FROM}" ]]; then
+    FIT_ARGS=(--init_from "${INIT_FROM}" --adam_step_size 1e-4)
+fi
+
+echo ">>> Configure $(date)"
+tfs-configure-model \
+    --growth_df "${GROWTH}" \
+    --library_config ../inputs/library_config.yaml \
+    --theta_model hill_relative \
+    --growth_shares_replicates \
+    --batch_size 4096 \
+    --growth_priors ../growth_priors_loose.csv \
+    --set_priors sigma_fixed=0.17 theta_log_hill_n_hyper_scale_fixed=0.5
+
+echo ">>> Fit (stage_offsets ${STAGE}) $(date)"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" \
+    --analysis_method map --stage_offsets "${STAGE}" "${FIT_ARGS[@]}"
+
+echo ">>> Arrowhead Laplace $(date)"
+tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+    --laplace arrowhead --num_posterior_samples 500 --skip_growth_observations
+
+echo ">>> Extract parameters $(date)"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Score (exact, full batch) $(date)"
+python ../score_map.py . --params tfs_fit_model_params.npz
+
+echo ">>> Done $(date)"
diff --git a/planning/studies/staged-map/score_map.py b/planning/studies/staged-map/score_map.py
new file mode 100644
index 00000000..61361de2
--- /dev/null
+++ b/planning/studies/staged-map/score_map.py
@@ -0,0 +1,129 @@
+"""
+Exact full-batch scores of MAP runs, for comparing their optima.
+
+    python score_map.py <run_dir> [<run_dir> ...] [--params <name>] [--chunk N]
+
+Run from planning/dev-data/real_fit. For each run directory, reads its
+tfs_configure_config.yaml and a MAP params file (default
+tfs_fit_model_params.npz; --params tfs_fit_model_stage1_params.npz, say)
+and evaluates, with every genotype once and no mini-batch scale:
+
+- count_ll: the log-likelihood of the growth observations;
+- other_obs_ll: any other observed sites (traced once);
+- log_prior: the log density of every latent at its MAP value (constrained
+  space, no Jacobian; latents are library-sized, so traced once);
+- log_joint: their sum.
+
+The losses in tfs_fit_model_losses.txt are block medians of mini-batch
+losses, scaled up from a batch: noisy at the 1e4-nat level on the dev data,
+so two runs' losses cannot rank optima that close. These numbers can.
+
+A latent the params file does not name (a site held fixed in that stage)
+is drawn from the prior, and the run is flagged; score a stage's params
+under its own held values only through tfs-fit-model's stage files.
+
+Prints one line per run and writes score_map.csv in the current directory.
+"""
+
+import argparse
+import os
+import warnings
+
+warnings.filterwarnings("ignore")
+
+import jax  # noqa: E402
+import jax.numpy as jnp  # noqa: E402
+import numpy as np  # noqa: E402
+import pandas as pd  # noqa: E402
+from numpyro import handlers  # noqa: E402
+
+from tfscreen.tfmodel.configuration_io import read_configuration  # noqa: E402
+from tfscreen.tfmodel.inference.run_inference import RunInference  # noqa: E402
+
+
+def _masked_sum(site):
+    lp = site["fn"].log_prob(site["value"])
+    mask = site.get("mask")
+    if mask is not None:
+        lp = jnp.where(jnp.broadcast_to(mask, lp.shape), lp, 0.0)
+    return lp.sum()
+
+
+def score_run(run, params_name, chunk):
+    cwd = os.getcwd()
+    os.chdir(run)  # config paths are relative to the run directory
+    try:
+        orch, _ = read_configuration("tfs_configure_config.yaml")
+        ri = RunInference(orch, 0)
+        with np.load(params_name) as p:
+            values = {k[:-len("_auto_loc")]: jnp.asarray(p[k]) for k in p.files
+                      if k.endswith("_auto_loc")}
+        data = jax.device_put(orch.data)
+        G = orch.data.num_genotype
+
+        def trace_at(batch):
+            model = handlers.substitute(orch.jax_model, data=values)
+            return handlers.trace(handlers.seed(model, 0)).get_trace(
+                data=batch, priors=orch.priors)
+
+        @jax.jit
+        def per_chunk(batch):
+            tr = trace_at(batch)
+            return sum((_masked_sum(s) for n, s in tr.items()
+                        if s["type"] == "sample" and s["is_observed"]
+                        and n.startswith("growth")), jnp.array(0.0))
+
+        count_ll = 0.0
+        for start in range(0, G, chunk):
+            idx = jnp.arange(start, min(start + chunk, G))
+            count_ll += float(per_chunk(ri._unscaled_batch(data, idx)))
+
+        # latents (library-sized) and any non-growth observations, once
+        tr = trace_at(ri._unscaled_batch(data, jnp.arange(min(chunk, G))))
+        log_prior, other_obs, missing = 0.0, 0.0, []
+        for name, s in tr.items():
+            if s["type"] != "sample":
+                continue
+            if s["is_observed"]:
+                if not name.startswith("growth"):
+                    other_obs += float(_masked_sum(s))
+            else:
+                if name not in values:
+                    missing.append(name)
+                log_prior += float(_masked_sum(s))
+        if G > chunk and other_obs != 0.0:
+            print(f"  {run}: non-growth observations scored on the first "
+                  f"{chunk} genotypes only")
+        return dict(run=run, params=params_name, count_ll=count_ll,
+                    other_obs_ll=other_obs, log_prior=log_prior,
+                    log_joint=count_ll + other_obs + log_prior,
+                    unset_latents=";".join(missing))
+    finally:
+        os.chdir(cwd)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("runs", nargs="+")
+    ap.add_argument("--params", default="tfs_fit_model_params.npz")
+    ap.add_argument("--chunk", type=int, default=20000)
+    args = ap.parse_args()
+
+    rows = []
+    for run in args.runs:
+        r = score_run(run, args.params, args.chunk)
+        flag = f"  UNSET {r['unset_latents']}" if r["unset_latents"] else ""
+        print(f"{run}: log joint {r['log_joint']:.8g}  count ll "
+              f"{r['count_ll']:.8g}  log prior {r['log_prior']:.6g}{flag}",
+              flush=True)
+        rows.append(r)
+    df = pd.DataFrame(rows)
+    if len(df) > 1:
+        df["log_joint_vs_first"] = df["log_joint"] - df["log_joint"].iloc[0]
+        df["count_ll_vs_first"] = df["count_ll"] - df["count_ll"].iloc[0]
+    df.to_csv("score_map.csv", index=False)
+    print(df.drop(columns=["params"]).to_string(index=False))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/step0-data/README.md b/planning/studies/step0-data/README.md
new file mode 100644
index 00000000..3f78c40e
--- /dev/null
+++ b/planning/studies/step0-data/README.md
@@ -0,0 +1,46 @@
+# Step 0 data: compact extract of the 2026-07-23 snapshot
+
+Support for the roadmap's step 0 studies (`planning/analysis-roadmap.md`),
+not a study itself.
+
+## What it makes
+
+`growth.csv` in `~/Desktop/2026-07-23_data-snapshot.tar.gz` is 8.2 GB: one
+row per sequenced tube x genotype, with every tube-level column repeated on
+every row. `extract_snapshot.py` streams it out of the tarball without
+unpacking (the disk had 17 GB free) and writes:
+
+| File | Content |
+|---|---|
+| `counts.parquet` | `sample, genotype, counts`: 20,904,551 rows, 113 tubes, 215,402 genotypes |
+| `samples.parquet` | one row per tube: design columns, per-tube `od600`, the processed `cfu_per_mL`/`sample_cfu` (from that tube's own OD600, per mL), total reads over listed genotypes, and `freq_denominator` = `adjusted_counts / frequency` (includes `__unknown__` reads and pseudocounts) |
+| `binding.csv`, `base_growth.csv`, `presplit.csv` | copied as-is (the snapshot's `presplit.csv` has the off-by-one residue numbering; the corrected one is `dev/presplit.csv` in the main clone) |
+
+Condition names are stripped of trailing spaces (`'pheS-4CP '`).
+
+## How to run
+
+```bash
+python extract_snapshot.py ~/Desktop/2026-07-23_data-snapshot.tar.gz \
+    ~/work/programming/git-clones/tfscreen/dev/real-2026-07-23
+```
+
+About 15 minutes. The output goes to the main clone's untracked `dev/` so
+it survives worktrees; nothing from it is committed.
+
+## Other inputs the studies use
+
+- `~/Desktop/2026-07-23_onward/combined_fit_sample_df.csv`: one row per
+  sequenced tube (118, of which 113 appear in `growth.csv`) with per-tube
+  `od600`, `cfu_est` and `fit_lncfu`/`fit_lncfu_sem`, the global smoothed
+  total (a polynomial in time per condition from repeated OD600 runs,
+  through the calibration). Sample names differ in format from
+  `growth.csv` (`0.0001` vs `0o0001`), so join on
+  `replicate, condition_sel, titrant_conc, t_sel`.
+- `~/Desktop/tfscreen-notebooks/od600-to-cfu/od600_to_cfu.yaml`: the lab's
+  OD600-to-CFU calibration.
+
+Units differ between files: `growth.csv`'s `sample_cfu` is cfu/mL from the
+tube's own OD600; `combined_fit_sample_df.csv`'s `cfu_est` is 10x cfu/mL.
+The studies use only within-file differences and slopes, which do not
+depend on the scale.
diff --git a/planning/studies/step0-data/extract_snapshot.py b/planning/studies/step0-data/extract_snapshot.py
new file mode 100644
index 00000000..f1349830
--- /dev/null
+++ b/planning/studies/step0-data/extract_snapshot.py
@@ -0,0 +1,99 @@
+"""
+Extract a compact, analysis-ready copy of the 2026-07-23 real-data snapshot
+for the roadmap's step 0 studies (planning/analysis-roadmap.md).
+
+growth.csv in the snapshot is 8.2 GB (one row per sample x genotype, with
+every sample-level column repeated on every row). This streams it straight
+out of the tarball, without unpacking, and writes:
+
+    counts.parquet   sample, genotype, counts   (one row per sample x genotype)
+    samples.parquet  one row per sample: design columns, per-tube OD600 and
+                     CFU columns as processed, total reads over the listed
+                     genotypes, and the frequency denominator implied by the
+                     processed columns (adjusted_counts / frequency), which
+                     includes the __unknown__ reads and pseudocounts
+    binding.csv, base_growth.csv, presplit.csv   copied as-is
+
+Usage:
+    python extract_snapshot.py SNAPSHOT_TAR OUT_DIR
+"""
+
+import sys
+import tarfile
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+PREFIX = "2026-07-23_data-snapshot/data/"
+
+SAMPLE_COLS = ["sample", "replicate", "condition_pre", "t_pre",
+               "condition_sel", "t_sel", "titrant_name", "titrant_conc",
+               "seq_run", "od600", "library", "cfu_per_mL", "cfu_per_mL_std",
+               "detectable", "sample_cfu", "sample_cfu_std"]
+ROW_COLS = ["sample", "genotype", "counts", "adjusted_counts", "frequency"]
+
+
+def _extract_growth(tar, out_dir, chunksize=2_000_000):
+
+    member = tar.getmember(PREFIX + "growth.csv")
+    fh = tar.extractfile(member)
+
+    usecols = sorted(set(SAMPLE_COLS + ROW_COLS))
+    count_parts = []
+    sample_rows = {}
+    denom = {}
+    total = {}
+
+    reader = pd.read_csv(fh, usecols=usecols, chunksize=chunksize,
+                         dtype={"sample": "string", "genotype": "string"})
+    n = 0
+    for chunk in reader:
+        n += len(chunk)
+        chunk["condition_pre"] = chunk["condition_pre"].str.strip()
+        chunk["condition_sel"] = chunk["condition_sel"].str.strip()
+
+        first = chunk.drop_duplicates("sample")
+        for _, r in first[SAMPLE_COLS].iterrows():
+            sample_rows.setdefault(r["sample"], r.to_dict())
+
+        d = (chunk["adjusted_counts"] / chunk["frequency"]).groupby(chunk["sample"]).median()
+        for s, v in d.items():
+            denom.setdefault(s, v)
+        t = chunk.groupby("sample")["counts"].sum()
+        for s, v in t.items():
+            total[s] = total.get(s, 0) + int(v)
+
+        count_parts.append(pd.DataFrame({
+            "sample": chunk["sample"].astype("category"),
+            "genotype": chunk["genotype"].astype("category"),
+            "counts": chunk["counts"].astype(np.int64),
+        }))
+        print(f"  {n:,} rows", flush=True)
+
+    counts = pd.concat(count_parts, ignore_index=True)
+    counts["sample"] = counts["sample"].astype(str).astype("category")
+    counts["genotype"] = counts["genotype"].astype(str).astype("category")
+    counts.to_parquet(out_dir / "counts.parquet", index=False)
+
+    samples = pd.DataFrame(list(sample_rows.values()))
+    samples["reads_listed"] = samples["sample"].map(total)
+    samples["freq_denominator"] = samples["sample"].map(denom)
+    samples.to_parquet(out_dir / "samples.parquet", index=False)
+
+    print(f"{len(counts):,} count rows, {len(samples)} samples, "
+          f"{counts['genotype'].nunique():,} genotypes")
+
+
+def main(tar_path, out_dir):
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    with tarfile.open(tar_path, "r:gz") as tar:
+        for name in ["binding.csv", "base_growth.csv", "presplit.csv"]:
+            data = tar.extractfile(tar.getmember(PREFIX + name)).read()
+            (out_dir / name).write_bytes(data)
+        _extract_growth(tar, out_dir)
+
+
+if __name__ == "__main__":
+    main(sys.argv[1], sys.argv[2])
diff --git a/planning/studies/svi-overconfidence/.gitignore b/planning/studies/svi-overconfidence/.gitignore
new file mode 100644
index 00000000..7cedb76e
--- /dev/null
+++ b/planning/studies/svi-overconfidence/.gitignore
@@ -0,0 +1,6 @@
+calib
+svi_overconfidence
+svi_overconfidence_v2
+svi_overconfidence_two_stage
+svi_overconfidence_fix
+svi_overconfidence_noclip
diff --git a/planning/studies/svi-overconfidence/README.md b/planning/studies/svi-overconfidence/README.md
new file mode 100644
index 00000000..ada39b42
--- /dev/null
+++ b/planning/studies/svi-overconfidence/README.md
@@ -0,0 +1,639 @@
+# SVI overconfidence study
+
+**Question.** Every SVI fit in the relative-fit grid (`../relative-fit/`,
+run 2) undercovered theta, and the gap grew with read depth. At fewer
+than 5 reads per tube, 95% coverage was 0.85-0.94. Above 1000 reads it
+was 0.24-0.50. `growth_m` sat 10-300 of its own SDs off truth, in the joint
+fit and in every relative arm. The MAP's Laplace intervals on `m` did cover.
+Is the gap the variational approximation, meaning the exact posterior
+covers and the guides do not? Or is it the model, meaning the exact
+posterior undercovers too?
+
+**Decision it feeds.** What to fix: the guide (richer guides, or a
+correction to the ELBO) or the model and simulation. Every calibration
+result since the count-likelihood study depends on it.
+
+## What we knew going in
+
+From `../relative-fit/relative_fit_v2` (Poisson arm, run 0001 joint and
+0003 relative low-rank):
+- **The error is a shared scale.** Above 1000 reads, the joint fit's theta
+  error is proportional to theta: -0.034 near theta = 1, about 0 near 0.
+  The median |z| there is 8-9. The joint fit's `m` is 3-7% too large in
+  every run.
+- **SVI collapses `m`'s uncertainty.** Its 68% intervals on `m` are about
+  0.05% wide. The Laplace intervals are a few percent wide and cover.
+- So each genotype's interval reflects its own counts but not the shared
+  scale's uncertainty. Once a genotype's counts are precise, the shared
+  error dominates.
+
+That points at the variational approximation, but the Laplace arm's 50%
+coverage at high depth was only 0.04, so its centers were off too. A
+reference posterior settles it.
+
+## Design
+
+[`simulate_config.yaml`](simulate_config.yaml) is the congression
+config, cut to singles and spikes: 50 genotypes, 120 tubes, median 1000
+reads per genotype per tube. Doubles are dropped, reads and cfu0 are
+scaled so singles keep their full-library depth, and 5 in-library
+binding anchors are used. The relative fit has 575 latents and 6,140
+observations. Poisson noise only.
+
+The first design used NUTS as the reference posterior, but NUTS did not mix
+on this model (Results, NUTS pilot). The design now calibrates each arm
+against truth over many simulations. MAP + Laplace is the arm with a
+full covariance.
+
+[`grid.yaml`](grid.yaml): 10 seeds x 2 fits x 3 inference arms = 60 runs,
+on the count likelihood with `sample_offset: level`, as in
+`../relative-fit/`.
+
+| axis | levels |
+|---|---|
+| fit | `joint` (`hill_geno`, binding at weight 1, prefit); `relative` (`hill_relative`, growth only) |
+| inference | `component`; `low_rank` (`auto_low_rank_multivariate_normal`); `map` (MAP with `--max_num_epochs 200000`, then a Laplace posterior) |
+| seed | 1-10 |
+
+Each run ([`run.srun`](run.srun)) simulates, configures, prefits (joint
+only), fits, samples the posterior and summarizes. SVI runs also run
+[`guide_diagnostics.py`](guide_diagnostics.py): the Pareto k-hat and
+log-weight SD of importance weights `p(theta, y) / q(theta)` over 2000
+guide draws, and growth_k/growth_m under the guide and reweighted. Where
+k-hat is small, the reweighted numbers are a reference. For mean-field
+guides in ~600 dimensions it is not expected to be small, so it mainly
+ranks the guides.
+
+NUTS changes made for the pilot, kept (2026-09-28):
+- **Start.** `run_nuts` used to start every chain at a uniform draw in
+  [-2, 2] on the unconstrained scale. It handed `initialize_model`'s
+  `ParamInfo` to `init_to_value`, and no site matched. It now starts at the
+  pre-MAP point.
+- **Batch.** It passes the full batch through `get_batch`.
+- **Diagnostics.** It prints the worst split R-hat and the smallest n_eff.
+- **Mass matrix.** `--nuts_dense_mass` is new.
+
+## How to run
+
+On the cluster, from this directory:
+
+```bash
+tfs-setup-sim-grid grid.yaml --out_prefix svi_overconfidence_v2
+for d in svi_overconfidence_v2/run_*/; do (cd "$d" && sbatch run.srun); done
+```
+
+When the runs finish:
+
+```bash
+tfs-summarize-calibration svi_overconfidence_v2 --out_prefix calib/svi_overconfidence_v2 --baseline inference=map
+python coverage_by_depth.py svi_overconfidence_v2 --out_prefix calib/svi_overconfidence_v2
+```
+
+## What to look at
+
+[`coverage_by_depth.py`](coverage_by_depth.py) reports theta/X coverage by
+each genotype's median reads per tube, k and m error in the arm's own SDs,
+and the guide diagnostics.
+- **Coverage by depth.** In `../relative-fit/relative_fit_v2`, 95%
+  coverage fell with depth in every arm:
+  - joint component: 0.75 at 5 reads or fewer, 0.37 above 1000
+  - relative low-rank: 0.94 to 0.50
+  - relative component: 0.85 to 0.05
+
+  If MAP + Laplace holds its coverage across depth here while the guides
+  do not, the gap is the approximation. If it falls too, the model is
+  (part of) the problem.
+- **k and m.** In the same grid, the SVI arms sat a median 10-90 of their
+  own SDs off truth (95% coverage 0-0.29). MAP + Laplace sat at 0.2 SDs and
+  covered (0.96-1.0).
+- **Guide diagnostics.** k-hat and log-weight SD, low-rank against
+  component, joint against relative.
+- **Laplace robustness.** In the large-library grid Laplace blew up in 5 of
+  6 runs. It needs to hold on this library before it can be the reference.
+
+## Inputs
+
+`simulate_config.yaml`, `hill_params.csv` (copied from
+`../congression-calibration/`), `grid.yaml`, `run.srun`,
+`guide_diagnostics.py`, `coverage_by_depth.py`. Seeds fixed in the grid.
+
+## Commit
+
+Grid runs do not log a commit. Where the README did not record one, the
+commit below is inferred: the last commit before the grid's runs finished,
+from the file times of the downloaded `run.out` files (2026-10-03).
+
+- Run 1 (local, `run_local.sh`, 2026-09-27/28): factored ln_cfu0, before
+  722393cf. The current `run.srun` uses `hierarchical`, so rerunning it
+  needs `--ln_cfu0_model hierarchical_factored` and an older commit.
+- Run 2 (`svi_overconfidence_v2`, 2026-09-28): 722393cf (inferred).
+- Two-stage grid (`svi_overconfidence_two_stage`, 2026-09-28): beaab7bb
+  (inferred).
+- Fix grid (`svi_overconfidence_fix`, 2026-09-29): 9db28286 (inferred).
+- Unclipped-binding grid (`svi_overconfidence_noclip`, 2026-09-29):
+  6e2903d5 (inferred).
+- The NUTS pilots, the reference MAP and full-covariance pilots, the
+  continuation runs and the high-plateau diagnosis (Student-t population,
+  flat-in-theta prior, pinned noise, precise binding, truth-pinned fit) were
+  local one-offs with scratch scripts or temporary code edits that were not
+  committed. Their numbers here are a record, not a reproducible result.
+
+None of the result directories are committed (gitignored).
+
+## Results
+
+**Run 2 (`svi_overconfidence_v2`, cluster, 2026-09-28):
+`ln_cfu0: hierarchical`.** All 60 runs finished. 95% theta/X coverage by
+median reads per tube, mean over 10 seeds (run 1 in brackets):
+
+| arm | <=5 | 21-100 | 101-1000 | >1000 | RMSE |
+|---|---|---|---|---|---|
+| joint component | 0.84 (0.78) | 0.81 (0.61) | 0.63 (0.47) | 0.36 (0.30) | 0.029 (0.052) |
+| joint low_rank | 0.88 (0.80) | 0.87 (0.65) | 0.75 (0.49) | 0.31 (0.27) | 0.027 (0.053) |
+| relative component | 0.86 (0.84) | 0.81 (0.62) | 0.75 (0.43) | 0.55 (0.30) | 0.060 (0.080) |
+| relative low_rank | 0.90 (0.88) | 0.81 (0.64) | 0.77 (0.43) | 0.63 (0.29) | 0.057 (0.078) |
+
+- **The ln_cfu0 fix halved the joint fit's RMSE** and lifted coverage at
+  every depth. It was less than the seed-1 refit suggested (0.80 above 1000
+  reads there, 0.31-0.63 across 10 seeds here).
+- **The depth gradient remains, and it tracks k and m.** SVI arms sit a
+  median 5-125 of their own SDs off truth on k and m (95% coverage
+  0-0.25). MAP + Laplace sits at 0.3-0.5 SDs and covers (0.95-1.0). With
+  the model mismatch gone, what remains is the guides dropping the shared
+  parameters' uncertainty. It matters most for genotypes whose own counts
+  are precise. The low-rank guide is closer to its posterior (log-weight SD
+  21-22 against 750-780 for the component guide) but still collapses k and
+  m.
+- **MAP + Laplace on theta is still unusable in the relative fit**
+  (Laplace blowups). In the joint fit it covers 0.53 above 1000 reads, the
+  best of any arm there.
+
+Next: carry k and m uncertainty into the per-genotype posteriors. Options:
+a guide that pairs the per-condition k and m with the genotype parameters
+(block structure), or a two-stage approach that samples k and m from their
+Laplace posterior and fits the rest conditionally.
+
+**NUTS pilot (2026-09-28, local, seed 1, relative fit): no usable
+reference yet.** Every attempt ran at the maximum tree depth (1023
+leapfrog steps) with a step size of 1e-6 to 1e-3. Over 500 warmup and 500
+draws, split R-hat was 3.5-3.7 and n_eff 3 on essentially every latent,
+with no divergences:
+
+| attempt | outcome |
+|---|---|
+| diagonal mass matrix | tree-depth ceiling through warmup (stopped at 41%) |
+| dense mass matrix | R-hat 3.6, n_eff 3 |
+| dense, `ln_cfu0_tube_scale` lifted off its collapsed MAP value (5e-5 against a prior median of 0.34) | R-hat 3.7, n_eff 3 |
+| dense, every hierarchical scale held at its value (removes the non-centered funnels) | R-hat 3.5, n_eff 3 |
+| the same in float64 | the same step sizes at the ceiling (stopped at 30% of warmup) |
+
+So the cause is not the funnels, the collapsed scale or float32 precision.
+It is the posterior's own curvature at this depth. Candidates are the
+curved m·X ridge and Hill parameters that are sharp for some genotypes and
+flat for others. A dense mass matrix cannot remove curvature, and this is
+the same geometry that defeats the mean-field guide.
+
+Options: reparameterize (centered per-genotype latents for well-measured
+genotypes) or try another sampler, with open-ended cost. Or drop the exact
+reference: simulation-based calibration of Laplace at a converged MAP over
+many seeds, plus diagnostics of how well each SVI guide matches the
+posterior. The relative-fit grid already hints at the answer without a
+reference: Laplace intervals on `m` covered in every run, where SVI's missed
+by 10-300 SDs.
+
+**Reference pilots (2026-09-28, local, seed 1, relative fit).** The small
+library reproduces the gap with the component guide. 95% X coverage is 1.0
+at 5 reads or fewer and 0.12 above 1000. growth_k and growth_m sit 20-100
+of their own SDs off truth. Both candidate references failed on it:
+
+- **MAP + Laplace.** The MAP hit its 200,000-epoch cap at step size 1e-5.
+  The Hessian had 5 negative eigenvalues, down to -66. The Laplace X
+  intervals came out about 4e10 wide, with 50% coverage 0-0.04 at every
+  depth. Its k/m intervals were sensible: |z| at most 0.6 for the
+  selective m, but up to 6.8 for the control conditions.
+- **`auto_multivariate_normal` (full-covariance guide).** The loss
+  diverged to about -1e23 by epoch 5,000 and ended near -8e23. The
+  convergence monitor then called it converged, which is a bug in the
+  monitor. X intervals were about 0.01 wide, with 95% coverage 0.05-0.25.
+
+**The common cause is a model bug, not the references.** `hierarchical_factored`
+draws `ln_cfu0_tube_offset ~ Normal(0, ln_cfu0_tube_scale)`, centered, over
+only 4 offsets (replicate x pre-condition). The offsets trade against the
+per-genotype baselines, so they can sit near 0. There the joint density
+grows like `tube_scale^-4` as the scale goes to 0, and the HalfNormal prior
+does not stop it. The posterior density is unbounded:
+- no MAP exists (the pre-MAP puts the scale at 5e-5, against a prior median
+  of 0.34);
+- a full-covariance guide chases it to -inf loss;
+- NUTS started there sits in the funnel.
+
+The mean-field guide survives only because it is too rigid to follow.
+
+Next: a non-centered tube offset (`offset = tube_scale * z`, `z ~ N(0, 1)`)
+removes the singularity. Then retry the full-covariance guide and
+MAP + Laplace as the reference on this library. Separately, the convergence
+monitor should refuse to call a run converged when its loss has run off to
+about -1e23.
+
+**Fixes (2026-09-28), then the grid.**
+
+1. **Tube offset.** The `hierarchical_factored` tube offset is now
+   non-centered. On seed 1 the MAP's `ln_cfu0_tube_scale` is now 0.26 (was
+   5e-5). A new test checks that the density stays bounded as the scale
+   goes to 0.
+2. **Runaway loss.** The convergence monitor ends a run whose loss falls
+   below -1000 times its starting magnitude as `diverged`.
+3. **Retried references (seed 1, relative), neither usable yet:**
+   - **Full-covariance guide.** Still ran away, to -2.8e22, and was stopped
+     at step 2000 as diverged. The model's log density at its draws is
+     finite (about -5e4). The runaway is the guide's own `log q` (-4.2e22):
+     its `scale_tril` is badly conditioned (all entries at most 6e-4),
+     and float32 triangular solves recover wrong standard-normal values
+     that the optimizer exploits. A numerical failure of the dense guide,
+     not a model singularity.
+   - **MAP + Laplace.** The MAP still ends at the 200,000-epoch cap,
+     improving (t = 17 at step size 1e-6), with 5 negative Hessian
+     eigenvalues. Laplace X is useless: widths about 2e10, mean error 5.7.
+     Its k and m intervals are sensible for the selective conditions
+     (|z| at most 0.4) but not the controls (|z| up to 9).
+
+The grid ran anyway (user, 2026-09-28): 60 runs locally, 3 at a time
+([`run_local.sh`](run_local.sh)), on the fixed model. It measures the
+component and low-rank guides' coverage by depth across 10 seeds. The MAP
+arm is expected to fail on theta but give k and m.
+
+**Grid results (2026-09-28): 60 runs, all finished locally.** No divergences.
+Pooled in `calib/` (not committed). 95% theta/X coverage fell with depth in
+every arm:
+
+| arm | <=5 reads | 21-100 | 101-1000 | >1000 |
+|---|---|---|---|---|
+| joint component | 0.78 | 0.61 | 0.47 | 0.30 |
+| joint low_rank | 0.80 | 0.66 | 0.49 | 0.27 |
+| joint map | 0.80 | 0.73 | 0.54 | 0.43 |
+| relative component | 0.84 | 0.62 | 0.43 | 0.30 |
+| relative low_rank | 0.88 | 0.64 | 0.43 | 0.29 |
+| relative map | 0.72 | 0.20 | 0.13 | 0.17 (Laplace blew up, as expected) |
+
+- **k and m.** SVI arms sat a median 13-62 of their own SDs off truth
+  (95% coverage 0-0.05). MAP + Laplace sat at 0.5-2.4 SDs (coverage
+  0.48-0.78).
+- **Guide diagnostics.** The low-rank guide is far closer to its posterior
+  than the component guide (log-weight SD 15 against 290; k-hat 4.4-4.5
+  against 7.4-7.6) and covers no better.
+- **The misses are per-genotype, not a shared scale.** Above 1000 reads the
+  relative fits are unbiased on average (slope 1.00, mean error +0.003).
+  Their RMSE of 0.042 is about 5 times the posterior SD, and the error is
+  nearly the same at every concentration of a genotype: a per-genotype
+  offset in X.
+
+**Cause: the ln_cfu0 model does not match the simulation.** Every study
+grid (congression-calibration, count-likelihood, relative-fit, this one),
+and `examples/simulate-and-analyze/run.sh`, fit with
+`ln_cfu0: hierarchical_factored`. That model gives each genotype one
+starting abundance shared by every pre-condition, plus one offset per
+tube. The simulator transforms kanR and pheS as separate libraries, so each
+genotype's starting abundance differs between them. Here the per-genotype
+kanR minus pheS difference has SD 0.17-0.29 ln units, up to 0.9. The fit
+has to put that difference into the growth rates, and so into a
+per-genotype X offset it is confident about.
+
+Refitting seed 1 (relative, low-rank) with `ln_cfu0: hierarchical`, one
+starting abundance per replicate x pre-condition x genotype and the
+configure default, on the same data:
+
+| | factored | hierarchical |
+|---|---|---|
+| X RMSE | 0.101 | 0.027 |
+| mean X error | -0.079 | -0.002 |
+| 95% coverage, 101-1000 reads | 0.19 | 0.81 |
+| 95% coverage, >1000 reads | 0.09 | 0.80 |
+| growth_m median abs z | ~80 | ~9 |
+
+**Conclusions.**
+1. **Most of the theta undercoverage in these grids is the ln_cfu0
+   mismatch.** It is not the variational approximation. Whether real
+   data need `hierarchical` or `hierarchical_factored` depends on whether
+   kanR and pheS come from one stock (the factored model's assumption) or
+   from separate transformations (the simulator's). That is a protocol
+   question, and the choice matters a great deal.
+2. **The guides do still collapse k and m.** On the `hierarchical` refit,
+   growth_k and growth_m remain 1-200 of their own SDs off truth with SDs
+   near 1e-5, and 95% theta coverage stays about 0.80 rather than 0.95.
+   That part is the approximation, and it is now small enough to study on
+   its own.
+
+Next: rerun this grid (or seed subsets) with `ln_cfu0: hierarchical` to
+measure what remains, once the ln_cfu0 question is settled for the real
+protocol. The earlier grids' calibration numbers carry the same mismatch.
+
+**Two-stage fit (2026-09-28).** The SVI guides collapse the uncertainty on
+the per-condition k and m, and Laplace at the MAP covers them. So the
+`two_stage` arm ([`two_stage.py`](two_stage.py)) works in two stages:
+- **Stage 1.** Fit the MAP and take a Laplace posterior.
+- **Stage 2.** Take 10 draws of (k, m) from it. For each, refit everything
+  else by SVI with k and m pinned at the draw (`linear`'s new `k_pinned`
+  and the existing `m_pinned`).
+- **Pool.** Combine the 10 conditional posteriors with equal weight. With
+  the draws from p(k, m | y), the pool approximates the marginal posterior
+  of the genotype parameters, and their intervals carry the k/m
+  uncertainty.
+
+Local test, seed 1, relative fit, only 2 draws (same data as v2):
+
+| | two-stage | v2 component | v2 low_rank |
+|---|---|---|---|
+| X RMSE | 0.027 | 0.030 | 0.027 |
+| 95% coverage, 101-1000 reads | 0.88 | 0.70 | 0.81 |
+| 95% coverage, >1000 reads | 0.91 | 0.60 | 0.79 |
+| growth_m abs z | 0.2-1.1 | 0.8-7.4 | 0.9-29 |
+
+With 2 draws growth_k is still 2-19 SDs off; the grid uses 10. Each
+conditional fit took about 5 minutes locally. Each conditional SVI fit
+ended at its 100,000-epoch cap, because the parameter test reads an
+infinite drift on `dk_geno_hyper_loc_scale`. That is a monitor quirk to fix
+separately.
+
+[`grid_two_stage.yaml`](grid_two_stage.yaml): the same simulations as
+`grid.yaml` (same seeds), `two_stage` only, 20 runs. On the cluster:
+
+```bash
+tfs-setup-sim-grid grid_two_stage.yaml --out_prefix svi_overconfidence_two_stage
+for d in svi_overconfidence_two_stage/run_*/; do (cd "$d" && sbatch run.srun); done
+```
+
+When they finish, pool and compare with `svi_overconfidence_v2`:
+
+```bash
+tfs-summarize-calibration svi_overconfidence_two_stage --out_prefix calib/svi_overconfidence_two_stage
+python coverage_by_depth.py svi_overconfidence_two_stage --out_prefix calib/svi_overconfidence_two_stage
+```
+
+### Two-stage grid (2026-09-28)
+
+`svi_overconfidence_two_stage`: 20 runs, the v2 simulations, 10 Laplace
+draws per run. All 20 finished. Pooled with the commands above; compared
+with `svi_overconfidence_v2`.
+
+Theta (joint) or X (relative), mean over 10 seeds:
+
+| fit | inference | 95% coverage | >1000 reads | 95% width | RMSE |
+|---|---|---|---|---|---|
+| joint | component | 0.57 | 0.36 | 0.062 | 0.037 |
+| joint | low_rank | 0.59 | 0.31 | 0.057 | 0.034 |
+| joint | two_stage | 0.86 | 0.81 | 0.136 | 0.046 |
+| relative | component | 0.68 | 0.55 | 0.10 | 0.071 |
+| relative | low_rank | 0.74 | 0.63 | 0.11 | 0.072 |
+| relative | two_stage | 0.94 | 0.97 | 0.52 | 0.071 |
+
+growth_k and growth_m 95% coverage rose from 0-0.25 under the guides to
+0.78-1.0, median |z| 0.4-0.7. dk_geno coverage rose from 0.85-0.86 to
+0.92-0.94 and log_hill_K from 0.76-0.88 to 0.91-0.93.
+
+The averages hide a split by seed. Every MAP hit its 200,000-epoch cap,
+and every Laplace Hessian had 2-7 negative eigenvalues, clamped to 1e-3.
+Where the clamped directions touch m, the Laplace draws of m spread widely
+(seed 8 joint: m for kanR+kan from -0.049 to +0.017, SD 0.018, truth
+-0.010), and the conditional fits' final losses differ by thousands of
+nats. So the loss range across a run's draws flags a broken Laplace:
+
+- Joint seeds with a sound Laplace (loss range under 170 nats; seeds 1, 6,
+  7, 10): k and m are covered (|z| at most 1.9), but theta above 1000
+  reads still covers only 0.44-0.63, and widths stay at 0.04-0.10.
+- Joint seeds with a broken Laplace (range 440-7,600 nats): coverage
+  0.93-1.0, bought with widths of 0.10-0.30. Seed 8's RMSE tripled
+  (0.033 to 0.113).
+- Relative: coverage above 1000 reads is 0.83-1.0 in every seed, sound
+  Laplace or not. Seeds 3 and 5 have broken Laplaces and X widths of 2.7
+  and 1.2. Seed 2's RMSE of 0.22 matches every other method on that seed.
+
+Reading: for the relative fit, carrying k and m uncertainty closes the
+coverage gap. For the joint fit it does not: with k and m honestly
+covered, the conditional fit still undercovers theta at high depth, so
+something inside the conditional component-guide fit collapses too. The
+Laplace stage is fragile everywhere, because the MAP never converges.
+
+Each conditional fit again ran to its 100,000-epoch cap on the
+`dk_geno_hyper_loc_scale` infinite-drift quirk.
+
+### Fixes after the two-stage grid, and the fix grid (2026-09-29)
+
+Two bugs, both fixed in the code:
+
+- **Frozen hyperparameter scales.** The component guide's hyperparameter
+  scales are constrained `greater_than(1e-4)`, and the guide start capped
+  every scale at 1e-4, so they started on the bound, `-inf` unconstrained,
+  and never moved. Every component-guide SVI fit since 2026-09-27 kept each
+  hyperparameter's guide SD at 1e-4, v2 and the two-stage grid included.
+  The conditional fits' "infinite drift" on `dk_geno_hyper_loc_scale` was
+  this, not a monitor quirk. It may explain part of the joint fit's
+  undercoverage.
+- **Laplace floor.** The 1e-3 eigenvalue floor is now the prior's
+  curvature along each eigenvector. Rerun locally on the grid's own MAPs,
+  the Laplace SD of m fell from 0.018 to 0.0005 on seed 8 joint, and m
+  and k now sit within about 3 SD of truth on seeds 1, 3 and 8 joint and
+  3 and 5 relative. The MAPs still stop at the cap with 4-7 negative
+  eigenvalues.
+
+`two_stage.py` now records each conditional fit's final loss in
+`draws.csv` and prints their span, and takes `--max_num_epochs`.
+`run.srun` has a `two_stage_low_rank` arm: the same pipeline with the
+low-rank autoguide in the conditional fits.
+
+[`grid_fix.yaml`](grid_fix.yaml): joint fit, seeds 1 and 7 (sound
+Laplace, still undercovered) and 3 and 8 (broken Laplace), each with
+`component`, `two_stage` and `two_stage_low_rank`. 12 runs.
+
+```bash
+tfs-setup-sim-grid grid_fix.yaml --out_prefix svi_overconfidence_fix
+for d in svi_overconfidence_fix/run_*/; do (cd "$d" && sbatch run.srun); done
+```
+
+Then:
+
+```bash
+tfs-summarize-calibration svi_overconfidence_fix --out_prefix calib/svi_overconfidence_fix
+python coverage_by_depth.py svi_overconfidence_fix --out_prefix calib/svi_overconfidence_fix
+```
+
+### Fix grid results, and clipped binding (2026-09-29)
+
+`svi_overconfidence_fix`, 12 runs, all finished. Joint theta, mean over
+seeds 1, 3, 7 and 8:
+
+| grid | inference | 95% coverage | >1000 reads | 95% width | RMSE |
+|---|---|---|---|---|---|
+| v2 | component | 0.57 | 0.34 | 0.060 | 0.030 |
+| v2 | low_rank | 0.61 | 0.33 | 0.055 | 0.029 |
+| two-stage (old Laplace) | two_stage | 0.84 | 0.76 | 0.177 | 0.051 |
+| fix | component | 0.61 | 0.37 | 0.061 | 0.029 |
+| fix | two_stage | 0.67 | 0.46 | 0.060 | 0.028 |
+| fix | two_stage_low_rank | 0.67 | 0.46 | 0.055 | 0.031 |
+
+- The unfrozen hyperparameter scales barely moved plain SVI (0.34 to 0.37
+  above 1000 reads).
+- With a sound Laplace, the two-stage fit helps only modestly (0.46). The
+  old grid's 0.76 came from the broken Laplace's inflated k/m draws.
+- The low-rank conditional guide adds nothing.
+- growth_k and growth_m cover 0.63-0.69 in two_stage (median |z| 1.7 and
+  0.8): the floored Laplace is somewhat narrow, since the MAP still stops
+  short of its optimum.
+
+Where the high-depth misses are: in the two_stage runs, every genotype
+above 1000 reads, wt included, has theta_low (about 0.99) and theta_high
+(about 0.01) about 0.01 low, median z of -3.3 and -3.2, in all four seeds.
+log_hill_K, hill_n and dk_geno cover 0.90-0.96. Theta misses are worst at
+the plateaus (coverage 0.2 at 0-0.001 and 1 mM) and fine at the
+transition (0.82-0.92 at 0.01-0.03 mM). So the remaining undercoverage is
+a shared offset in the absolute theta scale, which the joint fit takes
+from the binding data. The relative fit, with no binding, covers.
+
+One candidate: the simulator clipped noisy binding observations to [0, 1]
+(8-14 of 81 per run), while the fit's binding likelihood is an unclipped
+Normal. Clipping near 1 pulls the high plateau down, which fits
+theta_low's bias, but clipping near 0 would pull theta_high up, not down,
+so it may not be the whole story. `tfs-simulate` no longer clips
+(`binding_data.clip_theta_obs`, default false); regenerated with it,
+seed 1's binding table differs in exactly its 8 clipped rows and the
+growth data match to round-off.
+
+[`grid_noclip.yaml`](grid_noclip.yaml): the same 4 seeds, component and
+two_stage, 8 runs.
+
+```bash
+tfs-setup-sim-grid grid_noclip.yaml --out_prefix svi_overconfidence_noclip
+for d in svi_overconfidence_noclip/run_*/; do (cd "$d" && sbatch run.srun); done
+```
+
+### Unclipped-binding grid (2026-09-29)
+
+`svi_overconfidence_noclip`, seeds 1, 3 and 7 scored (seed 8 still
+copying). Clipping is not the cause. Median error of the two plateaus
+for genotypes above 1000 reads, clipped (fix grid) against unclipped:
+
+| seed | inference | high-IPTG plateau | low-IPTG plateau |
+|---|---|---|---|
+| 1 | component | -0.0102 / -0.0102 | -0.0106 / -0.0100 |
+| 1 | two_stage | -0.0095 / -0.0093 | -0.0089 / -0.0109 |
+| 3 | component | -0.0066 / -0.0068 | -0.0170 / -0.0132 |
+| 3 | two_stage | -0.0055 / -0.0059 | -0.0119 / -0.0089 |
+| 7 | component | -0.0087 / -0.0088 | -0.0106 / -0.0097 |
+| 7 | two_stage | -0.0076 / -0.0076 | -0.0095 / -0.0090 |
+
+Unclipped binding stays the simulator default: it matches the fit's
+likelihood and real anisotropy-derived theta.
+
+**Truth-pinned fit** (seed 1, joint, local, k and m pinned at their true
+values, component guide, 40,000 steps). The simulator's growth follows
+k + dk + m A theta exactly (to 2e-16) and its theta is the Hill truth
+(to 1.4e-5), so this is not a simulator-model mismatch. Above 1000
+reads:
+
+- low-IPTG plateau (theta about 0.99): error -0.003, z -1.1, 95%
+  coverage 0.85. Its bias came mostly from k and m.
+- high-IPTG plateau (theta about 0.01): error -0.010, SD 0.002, z -4.9,
+  coverage 0.04. It stays biased with k and m exact.
+- log_hill_K covers 0.96.
+
+So the high plateau is a separate problem. Near theta = 0 growth barely
+sees theta: with |m| about 0.01 per min, 0.01 of theta is about 0.02 ln
+units over a whole selection. Yet the fit reports an SD of 0.002, which
+must come from the logit-scale hierarchy (theta_high = sigmoid(logit_low +
+logit_delta), pooled across genotypes), not from the data. This is the
+saturated regime `in_regime` already treats as model-conditional.
+
+**Full noclip grid (8 of 8).** Joint theta coverage above 1000 reads:
+component 0.39 (fix grid 0.37), two_stage 0.57 (0.46). k and m in
+two_stage cover 0.63 and 0.81.
+
+**Split by true theta** (points above 1000 reads, seeds 1, 3, 7, 8;
+saturated = true theta below 0.02 or above 0.98, 46% of the points):
+
+| regime | grid | inference | 95% coverage | RMSE |
+|---|---|---|---|---|
+| resolvable | v2 | component | 0.56 | 0.012 |
+| resolvable | fix | component | 0.59 | 0.012 |
+| resolvable | fix | two_stage | 0.68 | 0.010 |
+| resolvable | noclip | component | 0.64 | 0.011 |
+| resolvable | noclip | two_stage | 0.80 | 0.009 |
+| saturated | v2 | component | 0.09 | 0.014 |
+| saturated | fix | two_stage | 0.21 | 0.011 |
+| saturated | noclip | component | 0.11 | 0.011 |
+| saturated | noclip | two_stage | 0.31 | 0.010 |
+
+Where growth can see theta, the two-stage fit with the fixes covers
+0.80. The saturated plateaus, which growth barely sees, carry the
+remaining overconfidence: the logit hierarchy in `hill_geno` gives them
+intervals the data do not support. That is the next thing to fix.
+
+### High-plateau diagnosis (2026-09-29, local, seed 1 joint, k and m pinned at truth)
+
+All fits component-guide SVI from the same pre-MAP, 40,000 steps, scored
+above 1000 reads. The warm-up MAP puts every deep genotype's high-IPTG
+plateau near truth (H74S logit -4.19 against a true -4.53); every SVI
+variant slides it to about 0.0005 (logit -7 to -8) with too-narrow
+intervals:
+
+| variant | high plateau error | coverage | width |
+|---|---|---|---|
+| baseline | -0.0102 | 0.31 | 0.0076 |
+| Student-t population (df 3) on the plateau offsets | -0.0102 | 0.31 | 0.0077 |
+| no per-tube offsets (misspecified: loss 11,000 worse) | -0.0102 | 1.00 | 0.029 |
+| count noise held near the MAP by a tight prior | -0.0096 | 0.15 | 0.0065 |
+
+The fix grid's low-rank conditional guide is also low (-0.0070).
+
+Profiles of the full log density over one genotype's high plateau,
+everything else held fixed:
+
+- at the MAP: H74S's data pin it near 0.015; pushing it to 0.0001 costs
+  61 nats (K84D 31, M42Y 6).
+- at the SVI posterior median: flat from 0 to 0.003 and almost the same
+  shape for all three genotypes, so something shared has changed.
+
+SVI's shared parameters differ from the MAP's: count overdispersion
+`growth_inv_r` 5x larger (0.0005 against 0.0001; about 0 is right for
+this Poisson-only simulation), and the per-tube offsets of one condition
+shifted by about +0.006 at low IPTG and -0.005 at high IPTG, the size
+that absorbs a common shift of about 0.01 in theta. Pinning either one
+alone did not remove the slide.
+
+Ruled out: binding clipping, k and m, the population's tail, the guide
+family. Not yet tested: the plateau parameterization itself. On the
+logit scale a plateau at 0.0005 has as much prior room as one at 0.01,
+and growth, linear in theta, separates them only weakly once the shared
+parameters loosen. A plateau parameterized on the theta scale, or
+bounded away from 0, would test that. The Student-t option was reverted.
+
+Further tests, same setup (all SVI, 100,000 steps at a constant step
+size unless noted; high plateau error above 1000 reads):
+
+| variant | high plateau error | coverage |
+|---|---|---|
+| prior flat in theta for both plateaus (log-Jacobian factor) | +0.0105 | 0.23 |
+| constant step size 1e-3, no cuts | -0.0106 | 0.08 |
+| + count noise hard-pinned at the MAP values | -0.0102 | 0.04 |
+| + binding data 5x more precise (noise 0.005) | -0.0103 | 0.27 |
+
+- Flat-in-theta prior: the plateaus follow whatever prior they get
+  (the low plateau went to -0.027), so in the SVI solution they are
+  prior-dominated; the parameterization is not the cause.
+- Constant step size: the loss levels off, so this is SVI's optimum, not
+  a transient frozen by early step-size cuts. `growth_inv_r` rises to
+  0.00098 there, 10x the MAP.
+- Precise binding pins the shared level (wt's high plateau 0.0092 against
+  0.0100), yet the non-binding genotypes still collapse: H74S, 65,000
+  reads, 0.0002 against 0.0107, with its low plateau, K, n and dk_geno all
+  on truth. So it is per genotype, not a shared shift.
+
+Found along the way: `tfs-extract-params` mislabeled `ln_cfu0` rows
+whenever a genotype was missing from a block (fixed; see CHANGELOG). The
+model's own `ln_cfu0` matches the presplit data at 0.999; only the
+extraction was wrong.
+
+Open: SVI's optimum puts the high plateau of well-measured genotypes near
+0 while the MAP puts it near truth, and none of prior shape, population
+tail, tube offsets, count noise, step size or binding precision changes
+that. Next candidates: pin the tube offsets and the noise together at the
+MAP, or compare SVI with a reference that needs no guide (importance
+reweighting of the Laplace, or NUTS on a smaller library).
diff --git a/planning/studies/svi-overconfidence/coverage_by_depth.py b/planning/studies/svi-overconfidence/coverage_by_depth.py
new file mode 100644
index 00000000..02c2e352
--- /dev/null
+++ b/planning/studies/svi-overconfidence/coverage_by_depth.py
@@ -0,0 +1,129 @@
+"""
+Per-arm calibration against truth where the relative-fit grid showed the gap.
+
+For every run of the grid:
+
+- theta (X for the relative fit) test coverage at 50% and 95%, by the
+  genotype's median reads per tube (<= 5, 6-20, 21-100, 101-1000, > 1000).
+  A guide that leaves out shared uncertainty covers the low-read genotypes
+  and misses the high-read ones.
+- growth_m and growth_k: error in the arm's own SDs (z) and the arm's SD,
+  per condition.
+- the guide diagnostics (Pareto k-hat, log-weight SD), where present.
+
+Writes {out_prefix}_depth.csv, {out_prefix}_growth.csv and
+{out_prefix}_guides.csv, and prints per-arm means.
+
+Usage (from the study directory):
+    python coverage_by_depth.py svi_overconfidence --out_prefix calib/svi_overconfidence
+"""
+
+import argparse
+import json
+import os
+
+import numpy as np
+import pandas as pd
+
+DEPTH_BINS = [-1, 5, 20, 100, 1000, np.inf]
+DEPTH_LABELS = ["<=5", "6-20", "21-100", "101-1000", ">1000"]
+
+
+def _runs(grid_dir):
+    with open(os.path.join(grid_dir, "grid_summary.json")) as f:
+        summary = json.load(f)
+    return [dict(run=r["run"], path=os.path.join(grid_dir, r["run"]),
+                 seed=r["simulate"]["seed"], fit=r["template"]["fit"],
+                 inference=r["template"]["inference"])
+            for r in summary["runs"]]
+
+
+def _depth(run):
+    theta = os.path.join(run["path"], "summary",
+                         "tfs_summarize_theta_corr_test.csv")
+    growth = os.path.join(run["path"], "tfs_sim_growth.csv")
+    if not (os.path.exists(theta) and os.path.exists(growth)):
+        return None
+    d = pd.read_csv(theta)
+    reads = pd.read_csv(growth).groupby("genotype")["counts"].median()
+    d["reads"] = d["genotype"].map(reads)
+    d["depth"] = pd.cut(d["reads"], DEPTH_BINS, labels=DEPTH_LABELS)
+    d["c50"] = (d["q0.25"] <= d["ref"]) & (d["ref"] <= d["q0.75"])
+    d["c95"] = (d["q0.025"] <= d["ref"]) & (d["ref"] <= d["q0.975"])
+    out = (d.groupby("depth", observed=True)
+           .agg(n=("c95", "size"), coverage_50=("c50", "mean"),
+                coverage_95=("c95", "mean"))
+           .reset_index())
+    return out.assign(**{k: run[k] for k in ("run", "seed", "fit", "inference")})
+
+
+def _growth(run):
+    rows = []
+    for name in ("growth_k", "growth_m"):
+        f = os.path.join(run["path"], "summary",
+                         f"tfs_summarize_params_{name}.csv")
+        if not os.path.exists(f):
+            continue
+        d = pd.read_csv(f)
+        sd = (d["q0.841"] - d["q0.159"]) / 2
+        rows.append(pd.DataFrame(dict(
+            parameter=name, condition_rep=d["condition_rep"],
+            error=d["q0.5"] - d["ref"], sd=sd,
+            z=(d["q0.5"] - d["ref"]) / sd,
+            covered_95=(d["q0.025"] <= d["ref"]) & (d["ref"] <= d["q0.975"]))))
+    if not rows:
+        return None
+    return pd.concat(rows).assign(
+        **{k: run[k] for k in ("run", "seed", "fit", "inference")})
+
+
+def _guide(run):
+    f = os.path.join(run["path"], "tfs_guide_diagnostics.json")
+    if not os.path.exists(f):
+        return None
+    with open(f) as fh:
+        d = json.load(fh)
+    return dict(d, **{k: run[k] for k in ("run", "seed", "fit", "inference")})
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+    parser.add_argument("grid_dir")
+    parser.add_argument("--out_prefix", default="coverage_by_depth")
+    args = parser.parse_args()
+
+    runs = _runs(args.grid_dir)
+    depth = [x for x in map(_depth, runs) if x is not None]
+    growth = [x for x in map(_growth, runs) if x is not None]
+    guides = [x for x in map(_guide, runs) if x is not None]
+    if not depth:
+        raise SystemExit("No run has a theta test summary yet.")
+
+    os.makedirs(os.path.dirname(os.path.abspath(args.out_prefix)), exist_ok=True)
+    depth = pd.concat(depth)
+    depth.to_csv(f"{args.out_prefix}_depth.csv", index=False)
+    arm = ["fit", "inference"]
+    with pd.option_context("display.width", 200, "display.max_rows", 200):
+        print("theta/X coverage by median reads per tube (mean over seeds):")
+        print(depth.groupby(arm + ["depth"], observed=True)
+              [["n", "coverage_50", "coverage_95"]].mean().round(3).to_string())
+        if growth:
+            growth = pd.concat(growth)
+            growth.to_csv(f"{args.out_prefix}_growth.csv", index=False)
+            print("\ngrowth_k/growth_m: median |z| and 95% coverage over "
+                  "seeds and conditions:")
+            print(growth.assign(abs_z=growth.z.abs())
+                  .groupby(arm + ["parameter"])
+                  .agg(median_abs_z=("abs_z", "median"),
+                       coverage_95=("covered_95", "mean"),
+                       median_sd=("sd", "median")).round(4).to_string())
+        if guides:
+            guides = pd.DataFrame(guides)
+            guides.to_csv(f"{args.out_prefix}_guides.csv", index=False)
+            print("\nguide diagnostics (median over seeds):")
+            print(guides.groupby(arm)[["khat", "log_w_sd", "ess"]]
+                  .median().round(2).to_string())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/svi-overconfidence/grid.yaml b/planning/studies/svi-overconfidence/grid.yaml
new file mode 100644
index 00000000..cfa3b7d1
--- /dev/null
+++ b/planning/studies/svi-overconfidence/grid.yaml
@@ -0,0 +1,77 @@
+# ---------------------------------------------------------------------------
+# SVI overconfidence: repeated simulations on a small library
+# (planning/studies/svi-overconfidence/README.md)
+#
+#   tfs-setup-sim-grid grid.yaml --out_prefix svi_overconfidence
+#   for d in svi_overconfidence/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Every SVI arm of the relative-fit grid undercovered, worst for the
+# best-measured genotypes, with growth_m 10-300 of its own SDs off truth.
+# Is that the variational approximation or the model? NUTS gave no reference
+# here (tree-depth ceiling, R-hat 3.5; README), so the reference is
+# calibration against truth over many simulations: a small library
+# (singles and spikes, ~50 genotypes, ~600 latents) fit three ways on the
+# same data, in the joint and the relative fit, with a Laplace posterior at
+# a converged MAP as the full-covariance comparison. Each SVI run also
+# reports how far its guide is from the posterior (guide_diagnostics.py).
+#
+# Poisson noise only (no founder sampling): the overconfidence was worst
+# there, and it keeps the question about the fit.
+#
+# 10 seeds x 2 fits x 3 inference arms = 60 runs.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "seed{{ seed }}__{{ fit }}__{{ inference }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: noise
+    variants:
+      - founder_sampling: false
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+      - seed: 3
+      - seed: 4
+      - seed: 5
+      - seed: 6
+      - seed: 7
+      - seed: 8
+      - seed: 9
+      - seed: 10
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: joint
+      - fit: relative
+
+  - name: inference
+    variants:
+      - inference: component
+      - inference: low_rank
+      - inference: map
+
+  # copied into the grid's inputs/ and named from each run
+  - name: scripts
+    variants:
+      - diagnostics_script: guide_diagnostics.py
diff --git a/planning/studies/svi-overconfidence/grid_fix.yaml b/planning/studies/svi-overconfidence/grid_fix.yaml
new file mode 100644
index 00000000..c13b9f1f
--- /dev/null
+++ b/planning/studies/svi-overconfidence/grid_fix.yaml
@@ -0,0 +1,75 @@
+# ---------------------------------------------------------------------------
+# SVI overconfidence, fix grid (planning/studies/svi-overconfidence/)
+#
+#   tfs-setup-sim-grid grid_fix.yaml --out_prefix svi_overconfidence_fix
+#   for d in svi_overconfidence_fix/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# After the two-stage grid (2026-09-28), with two fixes in the code:
+#
+# - Laplace eigenvalues are floored at the prior's curvature along each
+#   eigenvector, not at 1e-3. The flat floor let the MAP's negative
+#   eigenvalues spread the Laplace draws of m 50x (seeds 3 and 8).
+# - Component-guide scales with a greater_than(1e-4) constraint start
+#   above the bound. They had started on it, -inf unconstrained, and every
+#   hyperparameter scale stayed frozen at 1e-4 in every SVI fit since
+#   2026-09-27 (the conditional fits' "infinite drift").
+#
+# Joint fit only, four seeds of grid.yaml: 1 and 7 (sound Laplace, joint
+# still undercovered at high depth), 3 and 8 (broken Laplace).
+#
+# - component: plain SVI, to see what the scale fix alone does
+# - two_stage: MAP, floored Laplace, conditional component-guide fits
+# - two_stage_low_rank: the same, conditional low-rank autoguide fits
+#
+# 4 seeds x 3 = 12 runs. Compare with svi_overconfidence_v2 and
+# svi_overconfidence_two_stage on the same seeds.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "seed{{ seed }}__{{ fit }}__{{ inference }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: noise
+    variants:
+      - founder_sampling: false
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 3
+      - seed: 7
+      - seed: 8
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: joint
+
+  - name: inference
+    variants:
+      - inference: component
+      - inference: two_stage
+      - inference: two_stage_low_rank
+
+  # copied into the grid's inputs/ and named from each run
+  - name: scripts
+    variants:
+      - diagnostics_script: guide_diagnostics.py
+        two_stage_script: two_stage.py
diff --git a/planning/studies/svi-overconfidence/grid_noclip.yaml b/planning/studies/svi-overconfidence/grid_noclip.yaml
new file mode 100644
index 00000000..13cbda04
--- /dev/null
+++ b/planning/studies/svi-overconfidence/grid_noclip.yaml
@@ -0,0 +1,65 @@
+# ---------------------------------------------------------------------------
+# SVI overconfidence, unclipped-binding grid (planning/studies/svi-overconfidence/)
+#
+#   tfs-setup-sim-grid grid_noclip.yaml --out_prefix svi_overconfidence_noclip
+#   for d in svi_overconfidence_noclip/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# The fix grid (2026-09-29) left the joint fit undercovering at high depth
+# (0.37-0.46 above 1000 reads) with k and m covered. Every well-measured
+# genotype, wt included, had theta_low (about 0.99) and theta_high (about
+# 0.01) about 0.01 low, 3 posterior SDs. The simulator clipped noisy binding
+# observations to [0, 1] (10-17% of them), while the fit's binding
+# likelihood is an unclipped Normal. tfs-simulate no longer clips
+# (binding_data.clip_theta_obs, default false). Same seeds and random draws
+# as grid_fix.yaml: only the clipped binding values change.
+#
+# 4 seeds x 2 = 8 runs. Compare with svi_overconfidence_fix, run for run.
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "seed{{ seed }}__{{ fit }}__{{ inference }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: noise
+    variants:
+      - founder_sampling: false
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 3
+      - seed: 7
+      - seed: 8
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: joint
+
+  - name: inference
+    variants:
+      - inference: component
+      - inference: two_stage
+
+  # copied into the grid's inputs/ and named from each run
+  - name: scripts
+    variants:
+      - diagnostics_script: guide_diagnostics.py
+        two_stage_script: two_stage.py
diff --git a/planning/studies/svi-overconfidence/grid_two_stage.yaml b/planning/studies/svi-overconfidence/grid_two_stage.yaml
new file mode 100644
index 00000000..bafc915f
--- /dev/null
+++ b/planning/studies/svi-overconfidence/grid_two_stage.yaml
@@ -0,0 +1,88 @@
+# ---------------------------------------------------------------------------
+# SVI overconfidence, two-stage arm (planning/studies/svi-overconfidence/)
+#
+#   tfs-setup-sim-grid grid_two_stage.yaml --out_prefix svi_overconfidence_two_stage
+#   for d in svi_overconfidence_two_stage/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# The same simulations as grid.yaml (same seeds, noise, library) with one
+# inference arm, two_stage: MAP + Laplace, then SVI refits at 10 Laplace
+# draws of the per-condition k and m (two_stage.py), pooled. Compare with
+# svi_overconfidence_v2, which fit the same data with component, low_rank
+# and map.
+#
+# 10 seeds x 2 fits = 20 runs.
+#
+# Original grid.yaml header follows.
+#
+#
+#   tfs-setup-sim-grid grid.yaml --out_prefix svi_overconfidence
+#   for d in svi_overconfidence/run_*/; do (cd "$d" && sbatch run.srun); done
+#
+# Every SVI arm of the relative-fit grid undercovered, worst for the
+# best-measured genotypes, with growth_m 10-300 of its own SDs off truth.
+# Is that the variational approximation or the model? NUTS gave no reference
+# here (tree-depth ceiling, R-hat 3.5; README), so the reference is
+# calibration against truth over many simulations: a small library
+# (singles and spikes, ~50 genotypes, ~600 latents) fit three ways on the
+# same data, in the joint and the relative fit, with a Laplace posterior at
+# a converged MAP as the full-covariance comparison. Each SVI run also
+# reports how far its guide is from the posterior (guide_diagnostics.py).
+#
+# Poisson noise only (no founder sampling): the overconfidence was worst
+# there, and it keeps the question about the fit.
+#
+# ---------------------------------------------------------------------------
+
+base_config: simulate_config.yaml
+
+run_name: "seed{{ seed }}__{{ fit }}__{{ inference }}"
+
+output_file: run.srun
+
+
+simulate:
+
+  - name: noise
+    variants:
+      - founder_sampling: false
+
+  - name: lam
+    variants:
+      - transformation_poisson_lambda: 0.0
+
+  - name: dk
+    variants:
+      - dk_geno_hyper_loc: -3.5
+        dk_geno_hyper_scale: 0.5
+        dk_geno_hyper_shift: 0.02
+
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+      - seed: 3
+      - seed: 4
+      - seed: 5
+      - seed: 6
+      - seed: 7
+      - seed: 8
+      - seed: 9
+      - seed: 10
+
+
+template:
+
+  - name: fit
+    variants:
+      - fit: joint
+      - fit: relative
+
+  - name: inference
+    variants:
+      - inference: two_stage
+
+  # copied into the grid's inputs/ and named from each run
+  - name: scripts
+    variants:
+      - diagnostics_script: guide_diagnostics.py
+        two_stage_script: two_stage.py
diff --git a/planning/studies/svi-overconfidence/guide_diagnostics.py b/planning/studies/svi-overconfidence/guide_diagnostics.py
new file mode 100644
index 00000000..e2067bc5
--- /dev/null
+++ b/planning/studies/svi-overconfidence/guide_diagnostics.py
@@ -0,0 +1,188 @@
+"""
+How far an SVI guide is from the posterior it approximates (PSIS).
+
+Draws S samples from the fitted guide, computes the importance weights
+w = p(theta, y) / q(theta) on the full data set, and reports:
+
+- khat: the Pareto shape of the largest weights (Vehtari et al. 2024,
+  "Pareto smoothed importance sampling"). Below 0.5 the guide is close to
+  the posterior; 0.5-0.7 importance sampling still works with more draws;
+  above 0.7 the guide misses mass the posterior has, and reweighted
+  estimates are unreliable.
+- ess: the effective sample size of the smoothed weights.
+- per condition, growth_k and growth_m under the guide and reweighted by
+  the smoothed weights (mean and SD), plus the simulated truth when the run
+  directory has it. Where khat is small the reweighted numbers are a
+  reference for the guide's own.
+
+Writes {out_prefix}.json and {out_prefix}_growth.csv.
+
+Usage, from a run directory holding tfs_configure_config.yaml and an SVI
+checkpoint:
+    python guide_diagnostics.py tfs_fit_model_checkpoint.pkl --num_draws 2000
+"""
+
+import argparse
+import json
+import os
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+from numpyro.handlers import seed, substitute, trace
+from numpyro.infer.util import log_density
+
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.inference.run_inference import RunInference
+
+
+# ---------------------------------------------------------------------------
+# PSIS (after Vehtari, Simpson, Gelman, Yao and Gabry, the reference code)
+# ---------------------------------------------------------------------------
+
+def _gpd_fit(x):
+    """Generalized Pareto (k, sigma) of exceedances x > 0 (Zhang and Stephens 2009)."""
+    x = np.sort(np.asarray(x, dtype=float))
+    n = x.size
+    prior_bs, prior_k = 3, 10
+    m = 30 + int(np.sqrt(n))
+    bs = 1 - np.sqrt(m / (np.arange(1, m + 1) - 0.5))
+    bs /= prior_bs * x[int(n / 4 + 0.5) - 1]
+    bs += 1 / x[-1]
+    ks = np.mean(np.log1p(-bs[:, None] * x), axis=1)
+    L = n * (np.log(-bs / ks) - ks - 1)
+    w = 1 / np.sum(np.exp(L[None, :] - L[:, None]), axis=1)
+    keep = w >= 10 * np.finfo(float).eps
+    w, bs = w[keep], bs[keep]
+    w /= w.sum()
+    b = np.sum(bs * w)
+    k = np.mean(np.log1p(-b * x))
+    sigma = -k / b
+    k = (n * k + prior_k * 0.5) / (n + prior_k)   # weak prior toward 0.5
+    return k, sigma
+
+
+def psis(log_w):
+    """Smoothed log weights, Pareto k-hat and ESS for one set of log weights."""
+    log_w = np.asarray(log_w, dtype=float)
+    log_w = log_w - log_w.max()
+    S = log_w.size
+    M = int(min(0.2 * S, 3 * np.sqrt(S)))
+    order = np.argsort(log_w)
+    tail_idx = order[-M:]
+    cutoff = log_w[order[-M - 1]]
+    exceed = np.exp(log_w[tail_idx]) - np.exp(cutoff)
+    if np.all(exceed <= 0):
+        k = -np.inf
+        smoothed = log_w.copy()
+    else:
+        k, sigma = _gpd_fit(exceed)
+        p = (np.arange(1, M + 1) - 0.5) / M
+        # generalized Pareto quantiles, in units of sigma
+        if abs(k) < 1e-8:
+            q = -np.log1p(-p)
+        else:
+            q = ((1 - p) ** (-k) - 1) / k
+        smoothed_tail = np.log(np.exp(cutoff) + sigma * q)
+        smoothed = log_w.copy()
+        smoothed[tail_idx] = np.minimum(smoothed_tail, 0.0)
+    w = np.exp(smoothed - smoothed.max())
+    w /= w.sum()
+    ess = 1.0 / np.sum(w ** 2)
+    return np.log(w), float(k), float(ess)
+
+
+# ---------------------------------------------------------------------------
+
+def _weighted_mean_sd(x, w):
+    mean = np.sum(w[:, None] * x, axis=0)
+    sd = np.sqrt(np.sum(w[:, None] * (x - mean) ** 2, axis=0))
+    return mean, sd
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+    parser.add_argument("checkpoint_file")
+    parser.add_argument("--config_file", default="tfs_configure_config.yaml")
+    parser.add_argument("--num_draws", type=int, default=2000)
+    parser.add_argument("--chunk", type=int, default=100)
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--out_prefix", default="tfs_guide_diagnostics")
+    args = parser.parse_args()
+
+    orchestrator, init_params = read_configuration(args.config_file)
+    ri = RunInference(orchestrator, args.seed)
+    svi, state = ri.restore_svi_from_checkpoint(args.checkpoint_file,
+                                                init_params=init_params)
+    params = jax.tree_util.tree_map(jnp.asarray, svi.get_params(state))
+    data = orchestrator.get_batch(orchestrator.data,
+                                  jnp.arange(orchestrator.data.num_genotype))
+    kwargs = dict(priors=orchestrator.priors, data=data)
+    guide = svi.guide
+    model = orchestrator.jax_model
+
+    def one(key):
+        g = substitute(seed(guide, key), data=params)
+        log_q, g_trace = log_density(g, (), kwargs, {})
+        latent = {n: s["value"] for n, s in g_trace.items()
+                  if s["type"] == "sample" and not s.get("is_observed", False)
+                  and not n.startswith("_")}
+        log_p, m_trace = log_density(model, (), kwargs, latent)
+        return (log_p - log_q,
+                m_trace["condition_growth_k"]["value"],
+                m_trace["condition_growth_m"]["value"])
+
+    one = jax.jit(jax.vmap(one))
+    keys = jax.random.split(jax.random.PRNGKey(args.seed), args.num_draws)
+    log_w, ks, ms = [], [], []
+    for i in range(0, args.num_draws, args.chunk):
+        lw, k, m = one(keys[i:i + args.chunk])
+        log_w.append(np.asarray(lw)); ks.append(np.asarray(k)); ms.append(np.asarray(m))
+    log_w = np.concatenate(log_w)
+    ks = np.concatenate(ks).reshape(args.num_draws, -1)
+    ms = np.concatenate(ms).reshape(args.num_draws, -1)
+
+    finite = np.isfinite(log_w)
+    smoothed, khat, ess = psis(log_w[finite])
+    w = np.exp(smoothed)
+
+    # growth_k/growth_m are indexed in map_condition_rep order
+    cond_df = orchestrator.growth_tm.map_groups["condition_rep"]
+    conditions = list(cond_df.sort_values("map_condition_rep")["condition_rep"])
+    rows = []
+    for name, draws in (("growth_k", ks), ("growth_m", ms)):
+        d = draws[finite]
+        g_mean, g_sd = d.mean(0), d.std(0)
+        w_mean, w_sd = _weighted_mean_sd(d, w)
+        for j in range(d.shape[1]):
+            rows.append(dict(parameter=name, index=j,
+                             condition_rep=(conditions[j]
+                                            if j < len(conditions) else None),
+                             guide_mean=g_mean[j], guide_sd=g_sd[j],
+                             reweighted_mean=w_mean[j], reweighted_sd=w_sd[j]))
+    table = pd.DataFrame(rows)
+
+    summary_files = {"growth_k": "summary/tfs_summarize_params_growth_k.csv",
+                     "growth_m": "summary/tfs_summarize_params_growth_m.csv"}
+    for name, f in summary_files.items():
+        # the summary's ref column is truth on the fit's scale (X for relative)
+        if os.path.exists(f):
+            ref = pd.read_csv(f)[["condition_rep", "ref"]]
+            sel = table.parameter == name
+            table.loc[sel, "ref"] = table.loc[sel, "condition_rep"].map(
+                ref.set_index("condition_rep")["ref"])
+
+    out = dict(checkpoint=args.checkpoint_file, num_draws=args.num_draws,
+               num_finite=int(finite.sum()), khat=khat, ess=ess,
+               log_w_sd=float(np.std(log_w[finite])))
+    with open(f"{args.out_prefix}.json", "w") as f:
+        json.dump(out, f, indent=2)
+    table.to_csv(f"{args.out_prefix}_growth.csv", index=False)
+    print(json.dumps(out, indent=2))
+    with pd.option_context("display.width", 200):
+        print(table.round(6).to_string(index=False))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/svi-overconfidence/hill_params.csv b/planning/studies/svi-overconfidence/hill_params.csv
new file mode 100644
index 00000000..a3568471
--- /dev/null
+++ b/planning/studies/svi-overconfidence/hill_params.csv
@@ -0,0 +1,5 @@
+genotype,log_hill_K,hill_n,theta_high,theta_low
+wt,-4.1,2,0.01,0.99
+M42I,-2.2104779610300698,1.5156149571654287,0.5054105302329879,0.9538933267956679
+H74A,-5.872705439673401,1.2001278450518955,0.05,0.7115380202706272
+K84L,-5.755115159475512,1.0593068151944411,0.1,0.856776050040223
diff --git a/planning/studies/svi-overconfidence/run.srun b/planning/studies/svi-overconfidence/run.srun
new file mode 100644
index 00000000..1af059ce
--- /dev/null
+++ b/planning/studies/svi-overconfidence/run.srun
@@ -0,0 +1,148 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --job-name=svi_overconf
+#SBATCH --output=run.out
+#SBATCH --error=run.err
+#SBATCH --partition=gpu
+#SBATCH --time=00-12:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --ntasks-per-node=1
+
+# SVI overconfidence study, one run (rendered by tfs-setup-sim-grid from
+# planning/studies/svi-overconfidence/run.srun). Submit from the run directory:
+# sbatch run.srun
+
+# Swap lines if on cluster (module load) or local (XLA_FLAGS to use CPU)
+module load cuda/12.4.1
+#export XLA_FLAGS="--xla_force_host_platform_device_count=8"
+
+set -e
+
+# joint (hill_geno + binding + prefit) or relative (hill_relative, growth only)
+FIT="{{ fit }}"
+# Growth likelihood: counts (default; with a per-tube level offset, and no
+# extra growth noise, which it refuses) or lncfu (with normal_kt growth
+# noise). grid.yaml runs counts only.
+LIKELIHOOD="{{ likelihood | default('counts') }}"
+# component (mean-field component guide), low_rank (low-rank multivariate
+# normal autoguide), map (MAP, then a Laplace posterior), or two_stage /
+# two_stage_low_rank (MAP, Laplace, then conditional SVI refits)
+INFERENCE="{{ inference | default('component') }}"
+
+SEED=$(python -c "import yaml; print(yaml.safe_load(open('tfs_sim_config.yaml'))['seed'])")
+
+# ---------------------------------------------------------------------------
+# 1. Simulate
+# ---------------------------------------------------------------------------
+echo ">>> Simulate (seed=${SEED})"
+tfs-simulate tfs_sim_config.yaml . --seed "${SEED}"
+
+# ---------------------------------------------------------------------------
+# 2. Configure
+# ---------------------------------------------------------------------------
+if [[ "${LIKELIHOOD}" == "counts" ]]; then
+    LIKELIHOOD_ARGS=(--growth_likelihood counts --growth_noise_model zero
+                     --sample_offset_model level)
+else
+    LIKELIHOOD_ARGS=(--growth_likelihood lncfu --growth_noise_model normal_kt
+                     --sample_offset_model zero)
+fi
+
+if [[ "${FIT}" == "relative" ]]; then
+    FIT_ARGS=(--theta_model hill_relative)
+else
+    FIT_ARGS=(--theta_model hill_geno
+              --binding_df tfs_sim_binding.csv
+              --binding_weight 1.0)
+fi
+
+echo ">>> Configure (${FIT}, ${LIKELIHOOD})"
+tfs-configure-model \
+    --growth_df tfs_sim_growth.csv \
+    --presplit_df tfs_sim_presplit.csv \
+    --condition_growth_model linear \
+    --growth_transition_model instant \
+    --ln_cfu0_model hierarchical \
+    --dk_geno_model hierarchical_geno \
+    --activity_model fixed \
+    --theta_rescale_model passthrough \
+    --theta_growth_noise_model zero \
+    --theta_binding_noise_model zero \
+    --transformation_model single \
+    --library_config tfs_sim_config.yaml \
+    --growth_shares_replicates \
+    "${FIT_ARGS[@]}" \
+    "${LIKELIHOOD_ARGS[@]}"
+
+# ---------------------------------------------------------------------------
+# 3. Pre-fit calibration (joint only: it needs binding), fit, posterior
+# ---------------------------------------------------------------------------
+if [[ "${FIT}" == "joint" ]]; then
+    echo ">>> Pre-fit calibration"
+    tfs-prefit-calibration tfs_configure_config.yaml --seed "${SEED}" \
+        --k_scale_ceiling 0.005
+fi
+
+case "${INFERENCE}" in
+    component) INFERENCE_ARGS=() ;;
+    low_rank)  INFERENCE_ARGS=(--guide_type auto_low_rank_multivariate_normal) ;;
+    # a converged MAP took ~130,000 epochs on run 1's run 0002; Laplace
+    # needs it converged (a MAP stopped at a fixed step size had 9 negative
+    # Hessian eigenvalues and gave 95% intervals ~1e10 wide)
+    map)       INFERENCE_ARGS=(--analysis_method map --max_num_epochs 200000) ;;
+    # MAP as for map, then two_stage.py: SVI refits at Laplace draws of k, m
+    # with the component guide (two_stage) or the low-rank autoguide
+    # (two_stage_low_rank)
+    two_stage|two_stage_low_rank)
+               INFERENCE_ARGS=(--analysis_method map --max_num_epochs 200000) ;;
+    *) echo "unknown inference '${INFERENCE}'" >&2; exit 1 ;;
+esac
+
+echo ">>> Fit model (${INFERENCE})"
+tfs-fit-model tfs_configure_config.yaml --seed "${SEED}" "${INFERENCE_ARGS[@]}"
+
+if [[ "${INFERENCE}" != "map" && "${INFERENCE}" != two_stage* ]]; then
+    echo ">>> Guide diagnostics"
+    # diagnostics only; never stops the run
+    python "{{ diagnostics_script }}" tfs_fit_model_checkpoint.pkl \
+        --num_draws 2000 --out_prefix tfs_guide_diagnostics \
+        || echo "guide diagnostics failed" >&2
+fi
+
+if [[ "${INFERENCE}" == two_stage* ]]; then
+    if [[ "${INFERENCE}" == "two_stage_low_rank" ]]; then
+        GUIDE=auto_low_rank_multivariate_normal
+    else
+        GUIDE=component
+    fi
+    echo ">>> Laplace posterior (stage 1)"
+    tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl \
+        --out_prefix tfs_laplace
+    echo ">>> Conditional fits at Laplace draws of k, m (stage 2)"
+    python "{{ two_stage_script | default('two_stage.py') }}" \
+        --laplace_file tfs_laplace.h5 --num_draws 10 --samples_per_draw 1000 \
+        --seed "${SEED}" --guide_type "${GUIDE}" --out_prefix tfs_posterior
+else
+    echo ">>> Sample posterior"
+    tfs-sample-posterior tfs_configure_config.yaml tfs_fit_model_checkpoint.pkl
+fi
+
+# ---------------------------------------------------------------------------
+# 4. Extract, predict, summarize
+# ---------------------------------------------------------------------------
+echo ">>> Extract parameters"
+tfs-extract-params tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict theta"
+tfs-predict-theta tfs_configure_config.yaml tfs_posterior.h5
+
+echo ">>> Predict growth"
+tfs-predict-growth tfs_configure_config.yaml tfs_posterior.h5 \
+    --num_marginal_samples=500
+
+echo ">>> Summarize fit"
+tfs-summarize-fit .
+
+echo ">>> Done"
diff --git a/planning/studies/svi-overconfidence/run_local.sh b/planning/studies/svi-overconfidence/run_local.sh
new file mode 100755
index 00000000..d85f95d6
--- /dev/null
+++ b/planning/studies/svi-overconfidence/run_local.sh
@@ -0,0 +1,37 @@
+#!/bin/bash
+# Run a rendered grid locally instead of through SLURM: each run's run.srun
+# with the cluster's `module load` line dropped, JOBS runs at a time.
+#
+#   bash run_local.sh svi_overconfidence [JOBS] [PYTHON_BIN_DIR] [TFSCREEN_SRC]
+#
+# PYTHON_BIN_DIR holds python and the tfs-* entry points (default
+# ~/miniconda3/bin); TFSCREEN_SRC, if given, goes first on PYTHONPATH so the
+# runs use that checkout. Each run logs to its run.out/run.err; a run that
+# already finished (run.out ends with ">>> Done") is skipped, so the script
+# can be restarted.
+
+set -u
+GRID="$1"
+JOBS="${2:-3}"
+BIN="${3:-$HOME/miniconda3/bin}"
+SRC="${4:-}"
+
+export PATH="${BIN}:${PATH}"
+if [[ -n "${SRC}" ]]; then
+    export PYTHONPATH="${SRC}${PYTHONPATH:+:${PYTHONPATH}}"
+fi
+run_one() {
+    d="$1"
+    if [[ -f "$d/run.out" ]] && tail -1 "$d/run.out" | grep -q ">>> Done"; then
+        echo "skip $d (done)"
+        return 0
+    fi
+    rm -rf "$d/checkpoints" "$d"/tfs_fit_model_* "$d"/tfs_prefit_* 2>/dev/null
+    echo "start $d $(date +%H:%M:%S)"
+    ( cd "$d" && sed '/^module load/d' run.srun | bash > run.out 2> run.err )
+    status=$?
+    echo "end   $d $(date +%H:%M:%S) status ${status}"
+}
+export -f run_one
+
+ls -d "${GRID}"/run_*/ | sed 's#/$##' | xargs -P "${JOBS}" -I{} bash -c 'run_one "$@"' _ {}
diff --git a/planning/studies/svi-overconfidence/simulate_config.yaml b/planning/studies/svi-overconfidence/simulate_config.yaml
new file mode 100644
index 00000000..cc00136d
--- /dev/null
+++ b/planning/studies/svi-overconfidence/simulate_config.yaml
@@ -0,0 +1,285 @@
+# Small-library simulate config for the SVI overconfidence study
+# (planning/studies/svi-overconfidence/). Small enough for NUTS. Copied from
+# ../congression-calibration/simulate_config.yaml with these changes:
+#   - singles and spikes only (tile_combos single-1, single-2; about 45
+#     singles + 9 spikes); the doubles are dropped from tile_combos,
+#     transform_sizes and library_mixture;
+#   - total_num_reads scaled by 201/1201 so each single keeps its reads per
+#     tube from the full library (the doubles took 1000/1201 of the bulk);
+#   - cfu0 scaled the same way;
+#   - library_binding.num 5 (was 20; about the same fraction of the
+#     library as in the congression config's library).
+# The congression config's notes follow.
+#
+#   - library_mixture is the real design (single-1 100, single-2 100,
+#     double-1-2 1000, spiked 1), as in planning/studies/congression-estimator;
+#   - library_binding adds 20 in-library (bulk) binding anchors;
+#   - transformation_poisson_lambda and the dk_geno hyperparameters are set
+#     per run by grid.yaml;
+#   - from 2026-09-26, binding noise 0.025 (the experimental theta error; was
+#     0.001) and a Hill coefficient limit of 4 (theta_sim_priors.max_hill_n).
+# binding_data.spiked_binding.choose_by names hill_params.csv relative to this
+# file; tfs-setup-sim-grid copies it into the grid's inputs/ directory.
+
+
+# ---------------------------------------------------------------------------
+# Library genetic information
+# ---------------------------------------------------------------------------
+
+# Reading frame 0, 1, 2. No reverse (negative) strand allowed.
+reading_frame: 0
+
+# Amino acid residue number for the first in-frame residue of the amplicon.
+first_amplicon_residue: 27
+
+# Wildtype DNA sequence of the amplicon.
+wt_seq: gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacattcccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctgcacgcgccgtcgcaaattgtcgcggcgattaaatctcgcgccgatcaactgggtgccagcgtggtggtgtcgatg
+
+# Degenerate codon pattern for library positions.  '.' = wildtype; 'nnt' = NNT
+# codon (encodes all 20 amino acids + stop with reduced synonymous redundancy).
+# Must be the same length as wt_seq.
+degen_sites: |
+  .............................................nnt.............................................................................................nnt...........................nnt..........................................
+# ......nntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnnt......nntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnntnnt
+
+
+# tiles groups contiguous degenerate positions that were cloned together.
+# '.' = wildtype; any other character defines a tile block.  Blocks must
+# be contiguous (......111... ok; ....11.1... not ok).
+tiles: |
+  ......111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111......222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222
+
+# Flanking sequences immediately upstream and downstream of the amplicon.
+expected_5p: cgcgtggtgaaccag
+expected_3p: gtagaacgaagcggc
+
+# Which genotype classes to include in the library.  'single-x' = all
+# single-mutation variants in tile x.  'double-x-y' = all pairwise
+# combinations between tiles x and y.  'double-x-x' = internal
+# doubles within tile x.
+tile_combos: ["single-1", "single-2"]
+
+# Control sequences (e.g. known variants) added as spikes.  Must be the same
+# length as wt_seq.
+spiked_seqs:
+- ........................................................................................................................................................................................................................  # wt
+- .............................................ata........................................................................................................................................................................  # m42i
+- .............................................................................................................................................gcg........................................................................  # h74a
+- ...........................................................................................................................................................................ctg..........................................  # k84l
+- .............................................ata.............................................................................................gcg........................................................................  # m42i/h74a
+- .............................................ata...........................................................................................................................ctg..........................................  # m42i/k84l
+- .............................................................................................................................................gcg...........................ctg..........................................  # h74a/k84l
+- .............................................ata.............................................................................................gcg...........................ctg..........................................  # m42i/h74a/k84l
+- ........................................................................................................................................................................................cc..............................  # d88a
+
+# ---------------------------------------------------------------------------
+# Phenotype calculation
+# ---------------------------------------------------------------------------
+
+# Registered theta component name.  Any key in model_registry["theta"] is
+# valid except "_simple" (calibration-only).  Examples: "hill_geno", "thermo.O2_C12_K5_U0_a.PK".
+theta_component: "hill_mut"
+
+# Pairwise epistasis (regularized horseshoe; used when library has doubles):
+theta_sim_priors: 
+   epi_tau_scale:        0.1    # HalfCauchy scale for global sparsity τ
+   max_hill_n:           4.0    # physical upper limit on the Hill coefficient
+
+# Optional: path to structural/thermodynamic data file required by PnnC and
+# PddG theta components.  Omit (or set to null) for all other models.
+#   .h5 / .hdf5  → HDF5 structural ensemble (PnnC)
+#   .csv         → per-mutation per-structure ddG prior means (PddG)
+# thermo_data: null
+
+# Optional: overrides for the theta component's default hyperparameters.
+# Any key returned by the component's get_hyperparameters() can be overridden.
+# Omit this block to use all component defaults.
+# theta_priors:
+#   theta_tf_total_M: 3.3e-7
+
+# ---------------------------------------------------------------------------
+# Conditions
+# ---------------------------------------------------------------------------
+
+# Each block defines one set of growth conditions.  The Cartesian product of
+# titrant_conc × t_sel gives one sample per entry.  condition_pre and
+# condition_sel must also appear as keys in the 'growth' section below.
+condition_blocks:
+  - library: "kanR"
+    titrant_name: "iptg"
+    titrant_conc: [0, 0.0001, 0.001, 0.003]
+    condition_pre: "kanR-kan"
+    t_pre: 30
+    condition_sel: "kanR+kan"
+    t_sel: [160, 180, 200]
+
+  - library: "kanR"
+    titrant_name: "iptg"
+    titrant_conc: [0.01, 0.03, 0.1, 1.0]
+    condition_pre: "kanR-kan"
+    t_pre: 30
+    condition_sel: "kanR+kan"
+    t_sel: [145, 165, 180]
+
+  - library: "kanR"
+    titrant_name: "iptg"
+    titrant_conc: [0, 1.0]
+    condition_pre: "kanR-kan"
+    t_pre: 30
+    condition_sel: "kanR-kan"
+    t_sel: [160, 180, 200]
+
+  - library: "pheS"
+    titrant_name: "iptg"
+    titrant_conc: [0, 0.0001, 0.001, 0.003, 0.01, 0.03, 0.1, 1.0]
+    condition_pre: "pheS-4CP"
+    t_pre: 30
+    condition_sel: "pheS+4CP"
+    t_sel: [95, 110, 125]
+
+  - library: "pheS"
+    titrant_name: "iptg"
+    titrant_conc: [0, 1.0]
+    condition_pre: "pheS-4CP"
+    t_pre: 30
+    condition_sel: "pheS-4CP"
+    t_sel: [95, 110, 125]
+
+# ---------------------------------------------------------------------------
+# Growth rate parameters
+# ---------------------------------------------------------------------------
+
+# Linear mapping from operator occupancy (theta) to growth rate k for each
+# condition:  k = m * theta + b.  Every condition_pre and condition_sel that
+# appears in condition_blocks must have an entry here, and vice versa.
+growth:
+  kanR+kan: {m: -0.009933, b: 0.010696}
+  kanR-kan: {m:  0.000808, b: 0.015366}
+  pheS+4CP: {m:  0.006226, b: 0.021437}
+  pheS-4CP: {m: -0.000344, b: 0.028558}
+
+# Per-genotype pleiotropic growth cost is sampled from a shifted lognormal:
+#   dk_geno = hyper_shift - exp(Normal(hyper_loc, hyper_scale))
+# Most variants are mildly deleterious; a short right tail (at most hyper_shift)
+# allows beneficial mutations.  Matches the hierarchical tfmodel inference prior.
+dk_geno_hyper_loc: -3.5
+dk_geno_hyper_scale: 0.5
+dk_geno_hyper_shift: 0.02
+dk_geno_zero: false
+
+# Per-genotype TF activity (A) — a scalar that multiplies the theta contribution
+# to growth: k = b + A * m * theta.  The wild-type is fixed at activity_wt=1.0
+# by inference convention.  Set activity_mut_scale > 0 to draw each mutant
+# genotype's log(A) independently from Normal(log(activity_wt), activity_mut_scale).
+# activity_mut_scale=0 (default) gives all genotypes identical activity, matching
+# the 'fixed' activity inference component.
+activity_wt: 1.0
+activity_mut_scale: 0.0
+
+# ---------------------------------------------------------------------------
+# Experimental simulation parameters
+# ---------------------------------------------------------------------------
+
+# Number of transformants per tile combo.  Keys must match the entries in
+# tile_combos (e.g. 'single-1', 'double-1-2') plus 'spiked'.
+transform_sizes:
+  single-1: 10_000
+  single-2: 10_000
+  spiked: 1000
+
+# Mixing ratio of tile combos in the pooled library.  Only the ratio matters.
+library_mixture:
+  single-1: 100
+  single-2: 100
+  spiked: 1
+
+# Sigma of the log-normal used to draw initial genotype frequencies.  0 = flat
+# distribution; 1–2 is realistic for a typical transformation.
+lib_assembly_skew_sigma: 1.25
+
+# Each bacterium receives a number of plasmids drawn from a zero-truncated
+# Poisson with this lambda.  Set to 0 or null for exactly one plasmid per cell.
+transformation_poisson_lambda: 0
+
+# How a cell carrying several plasmids gets its growth rate. The cell's theta
+# and TF activity come from congression_theta_rule ('max': the highest-theta
+# plasmid sets both, at each condition); its dk_geno from congression_dk_rule
+# ('dilution': share-weighted mean; also 'softmin' with congression_dk_alpha,
+# or 'min'). Growth is then computed once from these cell-level values. The
+# study keeps 'max' so its recorded grids stay reproducible (the default is
+# now 'homodimer'); grids set the rules they test.
+congression_theta_rule: max
+congression_dk_rule: dilution
+
+# Colony-forming units per mL at the start of pre-growth.
+cfu0: 1.09e7
+
+# Per-tube environmental growth-rate noise (hr⁻¹).  Each physical tube gets its
+# own delta_k ~ Normal(0, tube_noise_sigma); the kt contribution per tube is
+# delta_k * (t_pre + t_sel), shared by all genotypes in that tube.  A value of
+# ~0.001–0.002 hr⁻¹ is typical for well-controlled liquid cultures.
+# Set to 0 or null to disable.
+tube_noise_sigma: 0.002
+
+# ---------------------------------------------------------------------------
+# Growth transition model (optional)
+# ---------------------------------------------------------------------------
+
+# Models the lag phase when bacteria switch from pre-selection to selection medium.
+# Every condition_pre in condition_blocks must have an entry.  Use model: "instant"
+# for conditions with no detectable lag.  Omit this block to use instant everywhere.
+#
+# "memory" model:  tau = tau0 + k1 / (theta + k2)
+#   tau0 = baseline lag (minutes)
+#   k1   = occupancy-dependence of lag (higher = more effect)
+#   k2   = curvature constant (> 0; prevents division by zero)
+growth_transition:
+  - condition_pre: "pheS-4CP"
+    model: "instant"
+
+  - condition_pre: "kanR-kan"
+    model: "instant"
+
+# ---------------------------------------------------------------------------
+# Data collection parameters
+# ---------------------------------------------------------------------------
+
+# Total sequencing reads across all samples.
+total_num_reads: 16_736_053
+
+# Fraction of reads re-assigned to a randomly chosen genotype (index hopping).
+# Set to 0 or null to disable.
+prob_index_hop: 0.00
+
+# Random seed for reproducibility.  Set to null for a random seed each run.
+seed: 12345
+
+# ---------------------------------------------------------------------------
+# Binding data simulation (optional)
+# ---------------------------------------------------------------------------
+
+# If present, tfs-simulate also writes a binding CSV with per-genotype theta
+# curves.  
+
+binding_data:
+  # The binding assay (shared by both genotype sets).
+  titrant_name: iptg
+  titrant_conc: [0, 0.0001, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1.0]
+  noise: 0.025    # experimental theta error (user, 2026-09-26)
+
+  # Clean, monoclonal (congression-free) controls. Pool = spiked_seqs.
+  #   choose_by: <params file> | stratified | random
+  #   num: optional [1, num_spiked], default all (forbidden with a file)
+  spiked_binding:
+    choose_by: hill_params.csv
+
+  # In-library controls, drawn from the bulk (congression-affected growth).
+  # Pool = library genotypes NOT in spiked_seqs. Omit to disable.
+  #   choose_by: stratified | random | <params file>
+  #   num: required for stratified/random (forbidden with a file)
+  library_binding:
+    num: 5
+    choose_by: stratified
+
+presplit_data: 
+  noise: 0.00
diff --git a/planning/studies/svi-overconfidence/two_stage.py b/planning/studies/svi-overconfidence/two_stage.py
new file mode 100644
index 00000000..4c425351
--- /dev/null
+++ b/planning/studies/svi-overconfidence/two_stage.py
@@ -0,0 +1,169 @@
+"""
+Two-stage fit: carry the per-condition k and m uncertainty into every
+genotype's posterior.
+
+The SVI guides collapse the uncertainty on the shared growth parameters
+(k, m sit 5-125 of their own SDs off truth), and genotypes whose own counts
+are precise then undercover. A Laplace posterior at the MAP covers k and m.
+So:
+
+1. Stage 1 (done before this script): MAP, then a Laplace posterior
+   (`tfs-sample-posterior` on the MAP checkpoint), written to
+   ``--laplace_file``.
+2. Stage 2 (this script): take ``--num_draws`` draws of (k, m) from it; for
+   each, refit everything else by SVI with k and m pinned at the draw
+   (`linear`'s ``k_pinned``/``m_pinned``), and sample that conditional
+   posterior.
+3. Pool the conditional posteriors with equal weight into one posterior
+   file (``--out_prefix``.h5, the layout `tfs-sample-posterior` writes), so
+   `tfs-extract-params`, `tfs-predict-theta` and `tfs-summarize-fit` run on
+   it unchanged. With the draws from p(k, m | y), the pool approximates the
+   marginal posterior of everything else.
+
+Run from a run directory holding ``tfs_configure_config.yaml``:
+
+    python two_stage.py --laplace_file tfs_laplace.h5 --num_draws 10
+"""
+
+import argparse
+import os
+import shutil
+
+import h5py
+import numpy as np
+import pandas as pd
+import yaml
+
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.scripts.fit_model_cli import fit_model
+from tfscreen.tfmodel.scripts.prefit_calibration_cli import (
+    _apply_priors_updates,
+    _condition_rep_labels,
+    _csv_row_name,
+)
+from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+
+
+def draw_config(config_file, draw_dir, k, m, cond_labels):
+    """Config + priors CSV with k and m pinned at one draw; returns the config path."""
+    os.makedirs(draw_dir, exist_ok=True)
+    with open(config_file) as f:
+        config = yaml.safe_load(f)
+    base = os.path.dirname(os.path.abspath(config_file))
+
+    priors_src = os.path.join(base, config["priors_file"])
+    priors_dst = os.path.join(draw_dir, "priors.csv")
+    shutil.copy(priors_src, priors_dst)
+    _apply_priors_updates(priors_dst, {
+        _csv_row_name("condition_growth", "k_loc"): np.asarray(k, dtype=float),
+        _csv_row_name("condition_growth", "m_loc"): np.asarray(m, dtype=float),
+        _csv_row_name("condition_growth", "k_pinned"): 1.0,
+        _csv_row_name("condition_growth", "m_pinned"): 1.0,
+    }, cond_rep_labels=cond_labels)
+
+    # the other sibling files stay where they are
+    config["priors_file"] = os.path.relpath(priors_dst, draw_dir)
+    for key in ("guesses_file", "library_file"):
+        if config.get(key):
+            config[key] = os.path.relpath(os.path.join(base, config[key]), draw_dir)
+    out = os.path.join(draw_dir, "config.yaml")
+    with open(out, "w") as f:
+        yaml.safe_dump(config, f, sort_keys=False)
+    return out
+
+
+def final_loss(prefix):
+    """Last logged loss (block median) of a fit, or NaN."""
+    try:
+        lines = open(f"{prefix}_losses.txt").read().strip().splitlines()
+        return float(lines[-1].split(",")[1])
+    except (OSError, IndexError, ValueError):
+        return float("nan")
+
+
+def pool(files, out_file):
+    """Concatenate posterior files draw-wise (same keys and trailing shapes)."""
+    handles = [h5py.File(f, "r") for f in files]
+    try:
+        keys = set(handles[0].keys())
+        for h in handles[1:]:
+            keys &= set(h.keys())
+        dropped = sorted(set().union(*[set(h.keys()) for h in handles]) - keys)
+        if dropped:
+            print(f"pool: sites missing from some draws, left out: {dropped}")
+        with h5py.File(out_file, "w") as out:
+            total = 0
+            for k in sorted(keys):
+                arr = np.concatenate([np.asarray(h[k]) for h in handles], axis=0)
+                out.create_dataset(k, data=arr)
+                total = arr.shape[0]
+            out.attrs["num_samples"] = total
+            out.attrs["two_stage_draws"] = len(files)
+    finally:
+        for h in handles:
+            h.close()
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+    parser.add_argument("--config_file", default="tfs_configure_config.yaml")
+    parser.add_argument("--laplace_file", default="tfs_laplace.h5")
+    parser.add_argument("--num_draws", type=int, default=10)
+    parser.add_argument("--samples_per_draw", type=int, default=1000)
+    parser.add_argument("--guide_type", default="component")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--work_dir", default="two_stage")
+    parser.add_argument("--out_prefix", default="tfs_posterior")
+    parser.add_argument("--max_num_epochs", type=int, default=None,
+                        help="cap on each conditional fit (default: "
+                             "tfs-fit-model's)")
+    args = parser.parse_args()
+
+    with h5py.File(args.laplace_file, "r") as f:
+        k_all = np.asarray(f["condition_growth_k"])
+        m_all = np.asarray(f["condition_growth_m"])
+    rng = np.random.default_rng(args.seed)
+    idx = rng.choice(len(k_all), size=args.num_draws, replace=False)
+
+    orchestrator, _ = read_configuration(args.config_file)
+    cond_labels = _condition_rep_labels(orchestrator)
+    del orchestrator
+
+    records, posteriors = [], []
+    for d, i in enumerate(idx):
+        draw_dir = os.path.join(args.work_dir, f"draw{d:02d}")
+        k, m = k_all[i], m_all[i]
+        cfg = draw_config(args.config_file, draw_dir, k, m, cond_labels)
+        prefix = os.path.join(draw_dir, "fit")
+        print(f">>> two-stage draw {d + 1}/{args.num_draws}: k={np.round(k, 5)} "
+              f"m={np.round(m, 5)}", flush=True)
+        fit_kwargs = {}
+        if args.max_num_epochs is not None:
+            fit_kwargs["max_num_epochs"] = args.max_num_epochs
+        fit_model(cfg, seed=args.seed + 1 + d, out_prefix=prefix,
+                  guide_type=args.guide_type, epoch_checkpoint_interval=None,
+                  **fit_kwargs)
+        post = os.path.join(draw_dir, "posterior")
+        sample_posterior(cfg, f"{prefix}_checkpoint.pkl", out_prefix=post,
+                         seed=args.seed + 1 + d,
+                         num_posterior_samples=args.samples_per_draw)
+        posteriors.append(f"{post}.h5")
+        records.append(dict(draw=d, laplace_index=int(i),
+                            final_loss=final_loss(prefix),
+                            **{f"k_{j}": float(v) for j, v in enumerate(k)},
+                            **{f"m_{j}": float(v) for j, v in enumerate(m)}))
+
+    draws = pd.DataFrame(records)
+    draws.to_csv(os.path.join(args.work_dir, "draws.csv"), index=False)
+    spread = draws["final_loss"].max() - draws["final_loss"].min()
+    print(f"Conditional fits' final losses span {spread:.0f} nats "
+          f"(min {draws['final_loss'].min():.0f}). A span of hundreds to "
+          f"thousands of nats flags Laplace draws of k, m far outside their "
+          f"posterior (two-stage grid, 2026-09-28).", flush=True)
+    pool(posteriors, f"{args.out_prefix}.h5")
+    print(f"Pooled {len(posteriors)} conditional posteriors into "
+          f"{args.out_prefix}.h5", flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/two-stage-validation/README.md b/planning/studies/two-stage-validation/README.md
new file mode 100644
index 00000000..7c4e435a
--- /dev/null
+++ b/planning/studies/two-stage-validation/README.md
@@ -0,0 +1,340 @@
+# Validating tfs-fit-two-stage against the m-bias study
+
+**Question.** Does `tfs-fit-two-stage` reproduce what the m-bias study
+(`planning/studies/m-bias-factorial/`) found with study scripts, on both
+full-size simulation seeds and on the real data? And where should the
+consistency step's flag sit?
+
+**Decision it feeds.** `planning/two-stage-fit.md` step 4: whether the
+command is the recipe the students and the NSF preliminary data use.
+
+## Runs
+
+| run | growth table | library config | lambda prior |
+|---|---|---|---|
+| `run_seed1` | `sim_realistic_s2` (seed 1) | its simulate config (wt mostly spiked, as simulated) | 0.357 +- 0.15 |
+| `run_seed2` | `sim_realistic_seed2` | its simulate config | 0.357 +- 0.15 |
+| `run_real` | the recipe's growth table | the real config as written (wt mostly bulk) | 0.38 +- 0.15 (the lab's 0.357, 95% CI 0.156-0.674) |
+
+Every run uses the study's model (growth-only relative fit, level offsets
+at SD 0.17, log(n) population SD 0.5, loose k/m priors in stage 1), 30
+jackknife subsamples dropping 20% of tubes, 500 posterior draws, and the
+consistency step on kanR alone. The YAMLs (`two_stage_*.yaml`) hold
+cluster paths.
+
+## What each should reproduce
+
+| | study | from |
+|---|---|---|
+| seed 1 stage 1 m / truth | kanR 1.056, pheS 1.037 | `m_subsets/no_doubles_mix` |
+| seed 2 stage 1 m / truth | kanR 1.047, pheS 1.023 | `sim_realistic_seed2/stage1/no_doubles_mix` |
+| seed 1 jackknife k SE | kanR+kan 0.0048, pheS+4CP 0.0028 | `jk_seed1` |
+| seed 1 X coverage with the shift, 1e4-1e5 / > 1e5 reads | 0.94 / 0.91 | `jk_seed1/x_kband.csv` |
+| seed 1 X without the shift (stage 2) | 0.90 / 0.76 | `stage2_single_seed1` |
+| seed 2 X without the shift (stage 2) | 0.88 / 0.67 | `stage2_single_seed2` |
+| real stage 1 | kanR+kan k 0.0167, m -0.0147 | `m_subsets_real/no_doubles_mix_cfg` |
+| real X | r about 1 with `held_mix_real` | `held_mix_real` |
+
+Differences to expect: the command picks the stage-1 genotypes from the
+library config (`in_spiked_origin` or one mutation) where the study used
+every non-double; the real data's three spiked doubles with growth data
+now join stage 1. The jackknife's k step is 1.7% smaller (covariance over
+N, not N - 1). The k-shift refits predict X at their MAP, where the study
+used their Laplace medians. Seed 2's coverage with the shift is new.
+
+For the consistency flag: the m-bias study found 2.5% of deep simulated
+doubles' rows and 8.2% of real ones beyond 2 SD between the kanR-only and
+both-marker fits (`kanR_only_seed1`, `kanR_only_real`). The command's
+statistic is per genotype (level offset over the median combined SD), so
+its seed 1 and seed 2 rates are the no-signal baseline for the real
+rate.
+
+## How to run
+
+On the cluster, after pulling the commit that adds this study (the job
+calls the command as a module, so no reinstall is needed):
+
+```bash
+mkdir -p /gpfs/projects/harmslab/harms/studies/two-stage-validation && cd /gpfs/projects/harmslab/harms/studies/two-stage-validation
+```
+
+```bash
+for c in seed1 seed2 real; do bash /gpfs/home/harms/tfscreen/planning/studies/two-stage-validation/submit.sh /gpfs/home/harms/tfscreen/planning/studies/two-stage-validation/two_stage_${c}.yaml run_${c}; done
+```
+
+Each `submit.sh` prints its five job ids (stage1; the jackknife array and
+stage2 after it; kshift + predict after both; consistency after stage2).
+Logs are in `run_*/logs/`. Each step skips finished work, so resubmitting
+a chain resumes it. Expect about a day of wall time per run with 10
+jackknife tasks at once, longer if GPUs are short.
+
+Then, per run (locally after downloading, or on the cluster),
+`validate.py` with the matching study outputs:
+
+```bash
+python validate.py run_seed1 --depth <seed-1 depth.csv> --truth <stage2_single_seed1>/summary/tfs_summarize_theta_corr_test.csv --sim_dir <sim_realistic_s2> --reference <stage2_single_seed1>/tfs_pred_theta.csv --study_stage1 <m_subsets/no_doubles_mix>
+```
+
+The follow-ups, from the same directory once a run is done:
+
+```bash
+sbatch --export=ALL,RUN=real /gpfs/home/harms/tfscreen/planning/studies/two-stage-validation/control_refit.srun
+```
+
+```bash
+sbatch --export=ALL,RUN=real,LIB=pheS /gpfs/home/harms/tfscreen/planning/studies/two-stage-validation/one_library.srun
+```
+
+`control_refit.py` refits stage 2 as the k-shift step does but with k
+unchanged (`kshift/zero/`) and extracts the tube offsets of all three
+refits. `one_library.py` fits stage 2 on one library's tubes alone
+(`consistency_{library}/`).
+
+## Commit
+
+The three runs: 3ce04cc5. The control: 4f52e68f. The pheS-only fit:
+9477a578.
+
+## Results
+
+### Run 1 (2026-10-13 to 2026-10-15)
+
+All three runs finished without errors.
+
+**The command reproduces the study.** Stage 1's k and m match the study's
+to about 1%, except the real pheS+4CP k (0.0132 against 0.0120), which we
+put down to the three spiked doubles now in stage 1. Seed 1's jackknife k
+SEs match (kanR+kan 0.0048, pheS+4CP 0.0028). Seed 1's stage 2 covers the
+deep doubles exactly as the study's did:
+
+| | 1e4-1e5 reads | > 1e5 reads |
+|---|---|---|
+| seed 1, study, no shift | 0.90 | 0.76 |
+| seed 1, run, no shift | 0.90 | 0.76 |
+| seed 1, run, with shift | 0.915 | 0.858 |
+| seed 1, study, with shift (`x_kband.csv`) | 0.94 | 0.91 |
+| seed 2, run, no shift | 0.907 | 0.814 |
+| seed 2, run, with shift | 0.921 | 0.867 |
+
+Stage 2 against the study's matching fit, deep genotypes: r 0.9997 (seed
+2) and 0.9999 (seed 1, real), slope 0.99-1.02, widths within 2%, and a
+uniform offset of -0.024, +0.020 and -0.017. The whole-library slopes
+(0.91 and 1.17 in `validate.py`'s line) come from genotypes below 100
+reads, whose X is mostly prior.
+
+**The marker-consistency flag.** It flagged 1.9% of the deep (>= 1e4
+reads) genotypes on both simulations and 10.6% on the real data (kanR
+alone). The real tail is signal, about 5.5x the no-signal rate.
+
+**The k shift does not work as built.** The shift came out 25% below the
+study's k-band SD on seed 1 (and about 3x below it at high IPTG), so the
+coverage with the shift fell short of the study's. The two refits did not
+bracket stage 2: on seed 1 both landed 0.06-0.11 above stage 2 in X for
+the deep doubles, and on the real data both landed 0.25-0.32 above it,
+1.4-1.6e4 nats lower in loss.
+
+The control (k left at stage 1's values) moved X by +0.32 as well,
+uniformly over depth, IPTG, singles and doubles, and ended 2.3e3 nats
+above stage 2. So the refit procedure moves X, not k:
+
+| fit (real) | steps | final exact loss | first window's loss |
+|---|---|---|---|
+| stage 2 (staged MAP) | 54,000 | 61,106,742 | 61,162,781 |
+| control | 188,000 | 61,109,081 | 61,773,654 |
+| +k | 410,000 | 61,092,275 | 61,796,625 |
+| -k | 540,000 | 61,090,397 | 61,771,936 |
+
+The refits start at stage 2's MAP but at Adam's default step size 1e-3,
+where stage 2 ended at 1e-6, and the first 2,000 steps knocked them about
+6.7e5 nats off it. Each re-descended and stopped where its convergence
+rule fired. Where it stopped set the X level.
+
+**What slides: the dk_geno population against wt.** Stage 2's arrowhead
+Laplace held one negative direction (eigenvalue -9.5e3), the dk_geno
+population location (loading 0.89) and scale (-0.45). In the refits every
+genotype's dk_geno moved by nearly the same amount, about 5 jackknife k
+SEs:
+
+| fit (real) | dk_geno median | change from stage 2 (q10 to q90) | X_delta population location |
+|---|---|---|---|
+| stage 2 | 0.0034 | | -0.53 |
+| control | 0.0138 | +0.0106 (+0.0081 to +0.0108) | -0.27 |
+| +k | 0.0097 | +0.0063 (+0.0055 to +0.0066) | -0.30 |
+| -k | 0.0149 | +0.0116 (+0.0108 to +0.0119) | -0.28 |
+
+This is the k/dk_geno slide reopened through the population: pinning k
+fixes wt's growth, but every other genotype can move together against wt
+through their shared dk_geno location, and only wt's own data and the
+tube offsets resist. In the refits the offsets moved most in the pheS-4CP
+tubes (+0.4 to +0.5, both IPTG). The tube-offset mode of
+`planning/offset-mode-growth-transition.md` may be the same direction seen
+in a fit with k free.
+
+**pheS alone compresses X** (confirmed with wt's curve held, below). Deep
+genotypes, X from one library's tubes
+against the both-library stage 2:
+
+| | r | slope | intercept | rms |
+|---|---|---|---|---|
+| kanR alone vs both | 0.941 | 1.04 | -0.03 | 0.12 |
+| pheS alone vs both | 0.689 | 0.71 | +0.15 | 0.24 |
+| pheS alone vs kanR alone | 0.559 | 0.52 | +0.24 | 0.31 |
+| control vs kanR alone | 0.941 | 0.86 | +0.40 | 0.35 |
+
+The both-library fit follows kanR (median SD 0.09 against pheS's 0.14).
+pheS alone sits 0.14 below it at 0 IPTG and 0.12 above it at 1 mM: the
+mutants' pheS response spans less than wt's. A mutant in wt's repressed
+or derepressed state should grow as wt does there whatever the shape of
+the theta-to-growth map, so the compression says wt and the library
+differ in pheS. Since X is gauged on wt, an anomaly in wt's pheS growth
+gives exactly such an affine map. Neither library alone supports the
+control's level (+0.40 and +0.46).
+
+The deep genotypes flagged by the two one-library checks barely overlap:
+10.6% (kanR alone), 8.7% (pheS alone), 1.8% both, and their level
+offsets do not correlate (r -0.14).
+
+**The compression is real (2026-10-15, `planning/studies/x-vs-binding/`).**
+The pheS-only fit is degenerate at wt (its wt X falls from 1 to 0 below
+1 uM IPTG, where wt's library trajectories fall at 10-30 uM), but
+refitting with wt's curve held at stage 2's (`consistency_pheS_wtheld/`)
+changed no mutant (r 0.996 with the first pheS fit) and kept the
+compression (slope 0.52, intercept +0.24 against kanR). Binding theta at
+2 uM agrees with kanR's X (mean -0.03) and not pheS's: pheS puts the
+weak binders 0.25-0.5 below their theta and below X = 0. Binding also
+supports stage 2's X level over the refits' (+0.3).
+
+**Proposed next steps** (not yet decided).
+
+- Drop the k-shift refits and take k's (and m's) uncertainty from the
+  Laplace instead: stage 2 with k and m free under a prior equal to the
+  jackknife covariance. The
+  real jackknife puts m's SE at 0.001 (7% of kanR's m; 0.5% on the
+  simulations), which the shift never carried.
+- That needs a real optimum. Candidate: hold the dk_geno population
+  location at stage 1's estimate, as the k and m pins do. Its scale
+  probably cannot be held: stage 1 (singles) gave 0.53, stage 2 1.53.
+- Find why wt does better in 4CP, derepressed, than mutants at the same
+  or lower occupancy (`planning/studies/x-vs-binding/`).
+
+### Run 2 setup: the k-shift repair (2026-10-19)
+
+Decided (user, 2026-10-19; `planning/release-0.5.0.md` decision 1):
+repair the shift, and if one round fails, ship without it.
+`tfs-fit-two-stage`'s kshift step now:
+
+- starts each refit from stage 2's MAP at `two_stage.kshift_step_size`
+  (default 1e-5, final 1e-6), not Adam's 1e-3, which knocked run 1's
+  refits about 6.7e5 nats off the MAP in the first window;
+- holds the dk_geno population (`hyper_loc`, `hyper_scale`,
+  `hyper_shift`) at stage 2's MAP in every refit, the direction stage 2's
+  Laplace held and run 1's refits slid along. Holding it at stage 1's
+  values, as first proposed, would change stage 2 itself (stage 1's
+  singles give a scale of 0.53 against stage 2's 1.53) and need stage 2
+  revalidated;
+- runs a third refit with k unchanged (`kshift/zero/`, on unless
+  `kshift_control: false`) and writes `kshift/drift.csv`: each refit's
+  median change in X from stage 2's MAP (`stage2/tfs_pred_theta_map.csv`)
+  over non-wt rows, with q10 and q90. It warns when the control moves
+  more than a quarter of the median |shift|.
+
+A risk that remains: the data preferred run 1's slid state by about
+1.5e4 nats, and with the population held, every genotype can still move
+its own dk_geno offset together, at a prior cost of a few hundred nats.
+The step size slows that slide but does not forbid it; the control
+refit is there to show whether it happens.
+
+Stage 1, the jackknife and stage 2 are unchanged, so only kshift and
+predict rerun. On the cluster, after pulling, in
+`/gpfs/projects/harmslab/harms/studies/two-stage-validation`, set run 1's
+outputs aside:
+
+```bash
+for c in seed1 seed2 real; do mv run_${c}/kshift run_${c}/kshift_run1; mv run_${c}/predict run_${c}/predict_run1; done
+```
+
+```bash
+ls -d run_*/kshift_run1 run_*/predict_run1
+```
+
+Then one job per run. Every path is absolute: an earlier form built `OUT`
+from `$PWD`, and jobs submitted from another directory made empty run
+directories there and failed (2026-10-19). The partition and time on the
+command line override the script's `#SBATCH` lines:
+
+```bash
+D=/gpfs/projects/harmslab/harms/studies/two-stage-validation; S=/gpfs/home/harms/tfscreen/planning/studies/two-stage-validation; for c in seed1 seed2 real; do sbatch --partition=gpu --time=00-06:00:00 --job-name=ts_kshift_${c} --output=${D}/run_${c}/logs/kshift2_%j.out --error=${D}/run_${c}/logs/kshift2_%j.err --export=ALL,CFG=${S}/two_stage_${c}.yaml,OUT=${D}/run_${c},STEP=kshift_predict ${S}/run_step.srun; done
+```
+
+Three refits per run, each probably 1-3
+hours at the small step size (run 1's took 188,000-540,000 steps at
+1e-3); these ask for `gpu`, 6 hours (`gpulong` is congested).
+
+Once a run's kshift job is done, after pulling the commit that adds the
+summary step, its results table (`run_*/tfs_x.csv`, a few minutes on
+CPU; the GPU partition is not needed but `run_step.srun` asks for it):
+
+```bash
+D=/gpfs/projects/harmslab/harms/studies/two-stage-validation; S=/gpfs/home/harms/tfscreen/planning/studies/two-stage-validation; for c in seed1 seed2 real; do sbatch --time=00-01:00:00 --job-name=ts_summary_${c} --output=${D}/run_${c}/logs/summary_%j.out --error=${D}/run_${c}/logs/summary_%j.err --export=ALL,CFG=${S}/two_stage_${c}.yaml,OUT=${D}/run_${c},STEP=summary ${S}/run_step.srun; done
+```
+
+Pass:
+
+- `kshift/drift.csv`: the control (`zero`) moves X by less than a quarter
+  of the median |shift|, and plus and minus move by about the same
+  amount in opposite directions;
+- seed 1: the shift within about 15% of the study's k-band SD
+  (`jk_seed1/x_kband.csv`; run 1 was 25% low), and deep X coverage with
+  the shift near the study's 0.94 / 0.91 (`validate.py`, as run 1);
+- seed 2: coverage with the shift above run 1's 0.921 / 0.867;
+- real: the control near 0 (run 1: +0.32).
+
+### Run 2 results (2026-10-20)
+
+All three ran on commit ccd5544c (after a first submission from the
+wrong directory and one on code before the repair; both set aside as
+`kshift_oldcode/`, `predict_oldcode/` on seed 1). Every refit converged
+at 1e-6. `kshift/drift.csv`, median change in X from stage 2's MAP over
+non-wt rows:
+
+| run | plus | minus | control (k unchanged) | median shift | a quarter of it |
+|---|---|---|---|---|---|
+| seed 1 | +0.002 | -0.024 | -0.004 | 0.013 | 0.003 |
+| seed 2 | -0.007 | +0.000 | -0.001 | 0.004 | 0.001 |
+| real | +0.022 | -0.026 | -0.008 | 0.024 | 0.006 |
+
+The drift is mostly gone (the real control moved -0.008, against +0.32
+in run 1), but the repair fails its pass criteria:
+
+- On the simulations the shift is a third (seed 1) and a tenth (seed 2)
+  of the study's k-band SD (one SD of k moved X by 0.032-0.038), and the
+  plus and minus refits are lopsided.
+- The control moved more than a quarter of the shift on seed 1 and the
+  real data, and as much on seed 2.
+
+Reading: holding the dk_geno population blocks the slide but also part
+of X's real response to k, in which the population moves with k. The
+real data's symmetric refits cannot be checked against a truth.
+
+Seed 1, `validate.py` (the doubles' X against the truth by read depth;
+the shift column for the record):
+
+| reads | genotypes | slope | RMSE | 95% coverage, no shift | with run 2's shift | median width |
+|---|---|---|---|---|---|---|
+| <= 1e2 | 37,707 | 0.04 | 2.60 | 0.977 | 0.977 | 5.87 |
+| 1e2-1e3 | 89,022 | 0.39 | 0.54 | 0.896 | 0.896 | 1.18 |
+| 1e3-1e4 | 64,362 | 0.88 | 0.17 | 0.883 | 0.885 | 0.48 |
+| 1e4-1e5 | 15,283 | 1.02 | 0.07 | 0.899 | 0.906 | 0.20 |
+| > 1e5 | 1,225 | 1.02 | 0.05 | 0.759 | 0.830 | 0.07 |
+
+Run as `python validate.py run_seed1 --depth $F/depth.csv --truth
+$F/summary/tfs_summarize_theta_corr_test.csv` with
+`F=/gpfs/projects/harmslab/harms/studies/full-sized-sims-3/sim_realistic_s2`
+(seed 2's simulation has no summary to take the truth from).
+
+**Decision (user, 2026-10-20):** ship without the shift (decision 1's
+fallback in `planning/release-0.5.0.md`); undercoverage of 0.76-0.90 is
+acceptable for now. `tfs-fit-two-stage` now has `two_stage.kshift`
+(default false): the jackknife and kshift steps are skipped and predict
+runs without `--shared_shift`. The usable-genotype threshold is 1,000
+reads (decision 5), where the slope reaches 0.88.
diff --git a/planning/studies/two-stage-validation/control_refit.py b/planning/studies/two-stage-validation/control_refit.py
new file mode 100644
index 00000000..b15d32db
--- /dev/null
+++ b/planning/studies/two-stage-validation/control_refit.py
@@ -0,0 +1,52 @@
+"""
+The k-shift control: refit stage 2 exactly as the k-shift step does
+(start at stage 2's MAP, same settings) but with k left at stage 1's values.
+If X drifts as far as in kshift/plus and kshift/minus, the refit procedure
+moves X, not k. Also extracts the parameters (tube offsets included) of all
+three refits, which the k-shift step does not.
+
+    python control_refit.py two_stage_real.yaml run_real
+
+Writes run_dir/kshift/zero/ (fit, tfs_pred_theta.csv at the MAP,
+tfs_params_*) and tfs_params_* in kshift/plus and kshift/minus.
+"""
+
+import argparse
+import os
+
+import pandas as pd
+
+from tfscreen.tfmodel import two_stage as ts
+from tfscreen.tfmodel.scripts import fit_two_stage_cli as cli
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("config_file")
+    ap.add_argument("run_dir")
+    args = ap.parse_args()
+    cfg, settings = cli.read_two_stage_config(args.config_file)
+    run = os.path.abspath(args.run_dir)
+    init = os.path.join(run, "stage2", cli.PARAMS)
+    km = pd.read_csv(os.path.join(run, "stage1", "km.csv"),
+                     index_col="condition_rep")
+
+    with cli._in_dir(os.path.join(run, "kshift", "zero")):
+        cli._say("control refit: k at stage 1")
+        ts.pinned_growth_priors(km).to_csv("growth_priors_pinned.csv", index=False)
+        cli._configure(cfg, cfg["growth_df"], "growth_priors_pinned.csv",
+                       drop_wt_rates=True)
+        cli._fit(settings, init_from=init)
+        if not os.path.exists(cli.PRED):
+            cli.predict_theta(cli.CONFIG, cli.CHECKPOINT, out_prefix="tfs_pred_theta")
+
+    for name in ("zero", "plus", "minus"):
+        with cli._in_dir(os.path.join(run, "kshift", name)):
+            if not os.path.exists("tfs_params_sample_offset_offset.csv"):
+                cli._say(f"extract {name}")
+                cli.extract_params(cli.CONFIG, cli.CHECKPOINT, out_prefix="tfs_params")
+    cli._say("done")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/two-stage-validation/control_refit.srun b/planning/studies/two-stage-validation/control_refit.srun
new file mode 100644
index 00000000..5221cc66
--- /dev/null
+++ b/planning/studies/two-stage-validation/control_refit.srun
@@ -0,0 +1,22 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --partition=gpulong
+#SBATCH --time=02-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+#SBATCH --job-name=ts_control
+
+# The k-shift control refit (control_refit.py). Run from the validation
+# directory:
+#   sbatch --export=ALL,RUN=real .../control_refit.srun
+
+module load cuda/12.4.1
+set -e
+RUN="${RUN:?set RUN (seed1, seed2 or real)}"
+HERE=/gpfs/home/harms/tfscreen/planning/studies/two-stage-validation
+echo ">>> control ${RUN} $(date)"
+python "${HERE}/control_refit.py" "${HERE}/two_stage_${RUN}.yaml" "run_${RUN}"
+echo ">>> Done control ${RUN} $(date)"
diff --git a/planning/studies/two-stage-validation/one_library.py b/planning/studies/two-stage-validation/one_library.py
new file mode 100644
index 00000000..22e1cd40
--- /dev/null
+++ b/planning/studies/two-stage-validation/one_library.py
@@ -0,0 +1,44 @@
+"""
+Stage 2 on one library's tubes alone, k and m pinned at stage 1: the
+consistency step with another library. The run's consistency/ is kanR
+alone; this gives pheS alone for the X-vs-binding comparison (does each
+marker's X follow binding theta the same way?).
+
+    python one_library.py two_stage_real.yaml run_real pheS
+    python one_library.py two_stage_real.yaml run_real pheS --wt_hill 0.0238 0.753
+
+Writes run_dir/consistency_{library}/ (tfs_pred_theta.csv, and
+consistency.csv against the full stage 2). --wt_hill K N holds wt's Hill K
+(titrant units) and n there (theta_wt_hill_K_fixed / _n_fixed) and writes
+consistency_{library}_wtheld/ instead: the pheS-only fit collapsed wt's
+curve below 1 uM IPTG (planning/studies/x-vs-binding/).
+"""
+
+import argparse
+import os
+
+from tfscreen.tfmodel.scripts import fit_two_stage_cli as cli
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("config_file")
+    ap.add_argument("run_dir")
+    ap.add_argument("library")
+    ap.add_argument("--wt_hill", nargs=2, type=float, metavar=("K", "N"),
+                    default=None, help="hold wt's Hill K and n")
+    args = ap.parse_args()
+    cfg, settings = cli.read_two_stage_config(args.config_file)
+    settings["consistency_library"] = args.library
+    name = f"consistency_{args.library}"
+    if args.wt_hill is not None:
+        k, n = args.wt_hill
+        cfg["set_priors"] = list(cfg.get("set_priors") or []) + [
+            f"theta_wt_hill_K_fixed={k}", f"theta_wt_hill_n_fixed={n}"]
+        name += "_wtheld"
+    cli.run_consistency(os.path.abspath(args.run_dir), cfg, settings, name=name)
+    cli._say("done")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/two-stage-validation/one_library.srun b/planning/studies/two-stage-validation/one_library.srun
new file mode 100644
index 00000000..9f6727a9
--- /dev/null
+++ b/planning/studies/two-stage-validation/one_library.srun
@@ -0,0 +1,24 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --partition=gpulong
+#SBATCH --time=02-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+#SBATCH --job-name=ts_one_library
+
+# Stage 2 on one library alone (one_library.py). Run from the validation
+# directory:
+#   sbatch --export=ALL,RUN=real,LIB=pheS .../one_library.srun
+#   sbatch --export=ALL,RUN=real,LIB=pheS,EXTRA="--wt_hill 0.0238 0.753" .../one_library.srun
+
+module load cuda/12.4.1
+set -e
+RUN="${RUN:?set RUN (seed1, seed2 or real)}"
+LIB="${LIB:?set LIB (kanR or pheS)}"
+HERE=/gpfs/home/harms/tfscreen/planning/studies/two-stage-validation
+echo ">>> one library ${RUN} ${LIB} $(date)"
+python "${HERE}/one_library.py" "${HERE}/two_stage_${RUN}.yaml" "run_${RUN}" "${LIB}" ${EXTRA:-}
+echo ">>> Done one library ${RUN} ${LIB} $(date)"
diff --git a/planning/studies/two-stage-validation/run_step.srun b/planning/studies/two-stage-validation/run_step.srun
new file mode 100644
index 00000000..499fe3c9
--- /dev/null
+++ b/planning/studies/two-stage-validation/run_step.srun
@@ -0,0 +1,37 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --partition=gpulong
+#SBATCH --time=02-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+
+# One step of tfs-fit-two-stage (planning/studies/two-stage-validation),
+# called as a module, so it needs no reinstall for the new console script,
+# submitted by submit.sh, which sets the job name, log file, dependencies
+# and (for the jackknife) the array.
+#
+#   CFG   the two-stage YAML
+#   OUT   the output directory
+#   STEP  stage1 | jackknife | stage2 | kshift_predict | consistency | summary
+#         (jackknife runs task SLURM_ARRAY_TASK_ID)
+
+module load cuda/12.4.1
+set -e
+CFG="${CFG:?set CFG}"
+OUT="${OUT:?set OUT}"
+STEP="${STEP:?set STEP}"
+echo ">>> ${STEP} ${SLURM_ARRAY_TASK_ID:-} $(date)"
+case "${STEP}" in
+    jackknife)
+        python -m tfscreen.tfmodel.scripts.fit_two_stage_cli "${CFG}" --out_dir "${OUT}" \
+            --jackknife_task "${SLURM_ARRAY_TASK_ID:?run as an array}" ;;
+    kshift_predict)
+        python -m tfscreen.tfmodel.scripts.fit_two_stage_cli "${CFG}" --out_dir "${OUT}" --step kshift
+        python -m tfscreen.tfmodel.scripts.fit_two_stage_cli "${CFG}" --out_dir "${OUT}" --step predict ;;
+    *)
+        python -m tfscreen.tfmodel.scripts.fit_two_stage_cli "${CFG}" --out_dir "${OUT}" --step "${STEP}" ;;
+esac
+echo ">>> Done ${STEP} ${SLURM_ARRAY_TASK_ID:-} $(date)"
diff --git a/planning/studies/two-stage-validation/submit.sh b/planning/studies/two-stage-validation/submit.sh
new file mode 100644
index 00000000..8ac767a6
--- /dev/null
+++ b/planning/studies/two-stage-validation/submit.sh
@@ -0,0 +1,32 @@
+#!/bin/bash
+# Submit one tfs-fit-two-stage run as chained SLURM jobs
+# (planning/studies/two-stage-validation):
+#
+#   bash submit.sh two_stage.yaml run_dir
+#
+# stage1; then the jackknife array and stage2 in parallel; then kshift +
+# predict (after both) and consistency (after stage2) in parallel; then the
+# summary table (after both). Logs go
+# to run_dir/logs/. Each step skips finished work, so resubmitting the
+# whole chain resumes it.
+set -e
+CFG="$(cd "$(dirname "$1")" && pwd)/$(basename "$1")"
+OUT="$(mkdir -p "$2" && cd "$2" && pwd)"
+HERE="$(cd "$(dirname "$0")" && pwd)"
+SRUN="${HERE}/run_step.srun"
+N="$(python -c "import yaml,sys; c=yaml.safe_load(open(sys.argv[1])); print((c.get('two_stage') or {}).get('jackknife_subsamples', 30))" "${CFG}")"
+mkdir -p "${OUT}/logs"
+sub() {  # name dependency extra-args...
+    local name=$1 dep=$2; shift 2
+    sbatch --parsable --job-name="ts_${name}" \
+        --output="${OUT}/logs/${name}_%A_%a.out" --error="${OUT}/logs/${name}_%A_%a.err" \
+        ${dep:+--dependency=afterok:${dep}} \
+        --export=ALL,CFG="${CFG}",OUT="${OUT}",STEP="${name}" "$@" "${SRUN}"
+}
+a=$(sub stage1 "")
+b=$(sub jackknife "${a}" --array=1-${N}%10)
+c=$(sub stage2 "${a}")
+d=$(sub kshift_predict "${b}:${c}")
+e=$(sub consistency "${c}")
+f=$(sub summary "${d}:${e}")
+echo "stage1 ${a}; jackknife ${b} (1-${N}); stage2 ${c}; kshift_predict ${d}; consistency ${e}; summary ${f}"
diff --git a/planning/studies/two-stage-validation/two_stage_real.yaml b/planning/studies/two-stage-validation/two_stage_real.yaml
new file mode 100644
index 00000000..2baead88
--- /dev/null
+++ b/planning/studies/two-stage-validation/two_stage_real.yaml
@@ -0,0 +1,23 @@
+# tfs-fit-two-stage validation, real (planning/studies/two-stage-validation).
+# The dev-data screen (the recipe's growth table). Its library config as written (wt mostly bulk, which the data favored); lambda prior from the lab's measurement (0.357, 95% CI 0.156-0.674).
+# Cluster paths: run from the cluster with submit.sh.
+
+growth_df: /gpfs/projects/harmslab/harms/studies/dev-data/real_fit/recipe/tfs_growth.csv
+library_config: /gpfs/projects/harmslab/harms/studies/dev-data/processed/library_config.yaml
+theta_model: hill_relative
+growth_shares_replicates: true
+batch_size: 4096
+allow_missing_spikes: true
+growth_priors: /gpfs/projects/harmslab/harms/studies/dev-data/real_fit/growth_priors_loose.csv
+set_priors: [sigma_fixed=0.17, theta_log_hill_n_hyper_scale_fixed=0.5]
+skip_model_stats: true
+
+two_stage:
+  transformation_lambda: [0.38, 0.15]
+  seed: 1
+  jackknife_subsamples: 30
+  jackknife_drop_frac: 0.2
+  num_posterior_samples: 500
+  consistency: true
+  consistency_library: kanR
+  predict_epistasis: false
diff --git a/planning/studies/two-stage-validation/two_stage_seed1.yaml b/planning/studies/two-stage-validation/two_stage_seed1.yaml
new file mode 100644
index 00000000..313d8906
--- /dev/null
+++ b/planning/studies/two-stage-validation/two_stage_seed1.yaml
@@ -0,0 +1,23 @@
+# tfs-fit-two-stage validation, seed1 (planning/studies/two-stage-validation).
+# Full-size simulation, seed 1 (sim_realistic_s2: the simulator's second calibration). Its own simulate config is the library config, as in the m-bias study (wt mostly spiked, as simulated); lambda prior around the simulated 0.357.
+# Cluster paths: run from the cluster with submit.sh.
+
+growth_df: /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/sim_realistic_s2/tfs_growth.csv
+library_config: /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/sim_realistic_s2/simulate_config.yaml
+theta_model: hill_relative
+growth_shares_replicates: true
+batch_size: 4096
+allow_missing_spikes: true
+growth_priors: /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/growth_priors_loose.csv
+set_priors: [sigma_fixed=0.17, theta_log_hill_n_hyper_scale_fixed=0.5]
+skip_model_stats: true
+
+two_stage:
+  transformation_lambda: [0.357, 0.15]
+  seed: 1
+  jackknife_subsamples: 30
+  jackknife_drop_frac: 0.2
+  num_posterior_samples: 500
+  consistency: true
+  consistency_library: kanR
+  predict_epistasis: false
diff --git a/planning/studies/two-stage-validation/two_stage_seed2.yaml b/planning/studies/two-stage-validation/two_stage_seed2.yaml
new file mode 100644
index 00000000..578c094b
--- /dev/null
+++ b/planning/studies/two-stage-validation/two_stage_seed2.yaml
@@ -0,0 +1,23 @@
+# tfs-fit-two-stage validation, seed2 (planning/studies/two-stage-validation).
+# Full-size simulation, seed 2. As seed 1.
+# Cluster paths: run from the cluster with submit.sh.
+
+growth_df: /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/sim_realistic_seed2/tfs_growth.csv
+library_config: /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/sim_realistic_seed2/simulate_config.yaml
+theta_model: hill_relative
+growth_shares_replicates: true
+batch_size: 4096
+allow_missing_spikes: true
+growth_priors: /gpfs/projects/harmslab/harms/studies/full-sized-sims-3/growth_priors_loose.csv
+set_priors: [sigma_fixed=0.17, theta_log_hill_n_hyper_scale_fixed=0.5]
+skip_model_stats: true
+
+two_stage:
+  transformation_lambda: [0.357, 0.15]
+  seed: 1
+  jackknife_subsamples: 30
+  jackknife_drop_frac: 0.2
+  num_posterior_samples: 500
+  consistency: true
+  consistency_library: kanR
+  predict_epistasis: false
diff --git a/planning/studies/two-stage-validation/validate.py b/planning/studies/two-stage-validation/validate.py
new file mode 100644
index 00000000..72922707
--- /dev/null
+++ b/planning/studies/two-stage-validation/validate.py
@@ -0,0 +1,145 @@
+"""
+Check a tfs-fit-two-stage run against the m-bias study's numbers
+(planning/studies/two-stage-validation/README.md).
+
+    python validate.py run_seed1 --depth depth.csv \
+        --truth ../m-bias-factorial/stage2_single_seed1/summary/tfs_summarize_theta_corr_test.csv \
+        --sim_dir ../m-bias-factorial/m_subsets/no_doubles_mix \
+        --reference ../m-bias-factorial/stage2_single_seed1/tfs_pred_theta.csv \
+        --study_stage1 ../m-bias-factorial/m_subsets/no_doubles_mix
+
+Prints stage 1's k and m (against the truth on the X scale when --sim_dir
+holds the simulation's tfs_sim_parameters.csv and
+tfs_sim_growth_parameters.csv, and against the study's stage 1 with
+--study_stage1), the jackknife summary, the doubles' X by read depth
+without the shift (stage2/) and with it (predict/) when --truth is given,
+agreement with --reference (the study's matching fit), and the share of
+genotypes the consistency step flags, overall and above 1e4 reads.
+"""
+
+import argparse
+import json
+import os
+
+import numpy as np
+import pandas as pd
+import yaml
+
+BINS = [0, 1e2, 1e3, 1e4, 1e5, 1e13]
+LABELS = ["<=1e2", "1e2-1e3", "1e3-1e4", "1e4-1e5", ">1e5"]
+KEY = ["genotype", "titrant_conc"]
+COLS = KEY + ["q0.025", "q0.5", "q0.975"]
+
+
+def km(d):
+    k = pd.read_csv(os.path.join(d, "tfs_params_growth_k.csv"))
+    m = pd.read_csv(os.path.join(d, "tfs_params_growth_m.csv"))
+    return pd.DataFrame({"k": k.groupby("condition_rep")["q0.5"].mean(),
+                         "m": m.groupby("condition_rep")["q0.5"].mean()})
+
+
+def truth_km(sim_dir, config):
+    cfg = yaml.safe_load(open(config))
+    c_lo, c_hi = (float(x) for x in cfg["components"]["theta_gauge_conc"])
+    par = pd.read_csv(os.path.join(sim_dir, "tfs_sim_parameters.csv"))
+    wt = par[par["genotype"] == "wt"].iloc[0]
+
+    def th(c):
+        if c <= 0:
+            return wt["theta_low"]
+        occ = 1 / (1 + np.exp(-wt["hill_n"] * (np.log(c) - wt["log_hill_K"])))
+        return wt["theta_low"] + (wt["theta_high"] - wt["theta_low"]) * occ
+
+    tr = pd.read_csv(os.path.join(sim_dir, "tfs_sim_growth_parameters.csv"))
+    tr = tr.set_index("condition_rep")
+    return pd.DataFrame({"k": tr["growth_k"] + tr["growth_m"] * th(c_hi),
+                         "m": tr["growth_m"] * (th(c_lo) - th(c_hi))})
+
+
+def score(pred, truth, depth):
+    df = pred[COLS].merge(truth, on=KEY)
+    df = df[df["genotype"].astype(str).str.count("/") == 1]
+    df = df.assign(bin=pd.cut(df["genotype"].map(depth), BINS, labels=LABELS))
+    rows = []
+    for b, d in df.groupby("bin", observed=True):
+        x, y = d["q0.5"].to_numpy(), d["ref"].to_numpy()
+        rows.append(dict(bin=b, n_geno=d["genotype"].nunique(),
+                         slope=np.polyfit(x, y, 1)[0],
+                         rmse=np.sqrt(np.mean((x - y) ** 2)),
+                         cover95=np.mean((d["q0.025"] <= y) & (y <= d["q0.975"])),
+                         width95=np.median(d["q0.975"] - d["q0.025"])))
+    return pd.DataFrame(rows)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("run_dir")
+    ap.add_argument("--depth", default=None, help="genotype,reads CSV")
+    ap.add_argument("--truth", default=None,
+                    help="a simulation fit's summary/tfs_summarize_theta_corr_test.csv")
+    ap.add_argument("--sim_dir", default=None,
+                    help="directory with tfs_sim_parameters.csv and "
+                         "tfs_sim_growth_parameters.csv")
+    ap.add_argument("--reference", default=None,
+                    help="the study's matching tfs_pred_theta.csv")
+    ap.add_argument("--study_stage1", default=None,
+                    help="the study's stage 1 directory (tfs_params_growth_*)")
+    args = ap.parse_args()
+    run = args.run_dir
+    depth = (pd.read_csv(args.depth).set_index("genotype")["reads"]
+             if args.depth else None)
+
+    s1 = km(os.path.join(run, "stage1"))
+    table = s1.add_prefix("run_")
+    if args.study_stage1:
+        table = table.join(km(args.study_stage1).add_prefix("study_"))
+    if args.sim_dir:
+        t = truth_km(args.sim_dir, os.path.join(run, "stage1",
+                                                "tfs_configure_config.yaml"))
+        table = table.join(t.add_prefix("truth_"))
+        table["m_ratio"] = table["run_m"] / table["truth_m"]
+    print("== stage 1 k and m")
+    print(table.to_string(float_format="%.5f"))
+
+    jk = os.path.join(run, "jackknife")
+    if os.path.exists(os.path.join(jk, "jackknife.json")):
+        print("\n== jackknife", json.load(open(os.path.join(jk, "jackknife.json"))))
+        print(pd.read_csv(os.path.join(jk, "jackknife_se.csv"),
+                          index_col=0).to_string(float_format="%.5f"))
+
+    preds = {}
+    for name, f in (("stage2 (no shift)", "stage2/tfs_pred_theta.csv"),
+                    ("predict (shift)", "predict/tfs_pred_theta.csv")):
+        p = os.path.join(run, f)
+        if os.path.exists(p):
+            preds[name] = pd.read_csv(p, usecols=COLS)
+    if args.truth and depth is not None:
+        truth = pd.read_csv(args.truth, usecols=KEY + ["ref"])
+        for name, p in preds.items():
+            print(f"\n== doubles' X against the truth: {name}")
+            print(score(p, truth, depth).round(3).to_string(index=False))
+
+    if args.reference and preds:
+        ref = pd.read_csv(args.reference, usecols=COLS)
+        p = preds.get("predict (shift)", next(iter(preds.values())))
+        m = p.merge(ref, on=KEY, suffixes=("_run", "_ref"))
+        m = m[m["genotype"] != "wt"]
+        r = np.corrcoef(m["q0.5_run"], m["q0.5_ref"])[0, 1]
+        slope = np.polyfit(m["q0.5_ref"], m["q0.5_run"], 1)[0]
+        print(f"\n== against the reference: r {r:.4f}, slope {slope:.3f}, "
+              f"mean shift {(m['q0.5_run'] - m['q0.5_ref']).mean():+.4f}")
+
+    c = os.path.join(run, "consistency", "consistency.csv")
+    if os.path.exists(c):
+        cons = pd.read_csv(c, index_col="genotype")
+        line = (f"\n== consistency: {cons['inconsistent'].mean():.3f} of "
+                f"{len(cons)} genotypes flagged")
+        if depth is not None:
+            deep = cons[cons.index.map(depth) >= 1e4]
+            line += (f"; {deep['inconsistent'].mean():.3f} of {len(deep)} "
+                     "above 1e4 reads")
+        print(line)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/within-tube-m/README.md b/planning/studies/within-tube-m/README.md
new file mode 100644
index 00000000..d9cfd30c
--- /dev/null
+++ b/planning/studies/within-tube-m/README.md
@@ -0,0 +1,209 @@
+# Each marker's growth slope m, within tube and absolute
+
+**Question.** The two-stage fit pins each condition's slope m at stage
+1's. In the library, does wt's growth against IPTG span that m when
+measured against genotypes in the same tube, where tube totals cancel?
+And does the absolute growth (through the tube totals) agree?
+
+**Decision it feeds.** Whether stage 1's m, and the linear growth model's
+single k per condition, can be the pins for stage 2
+(`planning/two-stage-fit.md`), and what the pheS/kanR disagreement in
+`planning/studies/x-vs-binding/` is.
+
+## Method
+
+With growth = k + dk + m X and wt gauged to X = 1 at 0 IPTG and 0 at
+1 mM, the slope over the selection of ln(counts_wt / counts_ref) is
+(dk_wt - dk_ref) + m (X_wt - X_ref). For a reference whose X does not
+change with IPTG, the change of that slope from 0 to 1 mM is m: dk, the
+reference's X, tube totals and tube offsets all cancel. The references
+are the pure spikes H74A/K84L and M42I/H74A/K84L, flat in binding and
+expression (user, 2026-10-15; the second with a slightly different lacI
+construct).
+
+The slopes come from one weighted fit per condition and reference,
+ln ratio = a[IPTG, replicate] + b[IPTG] t_sel, weights from Poisson
+counting, covariance scaled by the reduced chi-square (64-740:
+overdispersed). The absolute versions fit wt's and the reference's
+ln_cfu the same way, unweighted. Early and late m use the first and last
+interval of each replicate's three time points.
+
+## How to run
+
+```bash
+python within_tube_m.py ../../dev-data/processed --stage1_km ../two-stage-validation/run_real/stage1/km.csv --out out
+```
+
+Outputs in `out/` (gitignored): `m_estimates.csv`, `wt_curves.csv`
+(wt's within-tube growth against IPTG, minus its value at 1 mM) and
+`YYYY-MM-DD_within-tube-m.pdf`.
+
+## Commit
+
+The commit that adds this study.
+
+## Results
+
+### Run 1 (2026-10-15)
+
+m per minute, +- SE:
+
+| | within tube, vs H74A/K84L | within tube, vs M42I/H74A/K84L | wt absolute | wt monoculture | stage 1 (pinned) |
+|---|---|---|---|---|---|
+| kanR+kan | -0.0051 +- 0.0030 | -0.0046 +- 0.0020 | -0.0152 +- 0.0070 | -0.0141 +- 0.0014 | -0.0148 |
+| pheS+4CP | +0.0175 +- 0.0041 | +0.0181 +- 0.0042 | +0.0124 +- 0.0061 | +0.0144 +- 0.0022 | +0.0070 |
+
+**The markers disagree with stage 1 in opposite directions.** Within
+tube, kanR's m is a third of the monoculture's and of stage 1's (2.7
+and 3.9 SE below the monoculture). pheS's m matches
+the monoculture and is 2.5x stage 1's (about 2.7 SE above it). The two
+references agree with each other in both markers.
+
+**The flat references' own absolute growth moves with IPTG in the
+selection conditions.** Their absolute slope at 1 mM minus at 0 IPTG:
+
+| | H74A/K84L | M42I/H74A/K84L |
+|---|---|---|
+| kanR+kan | +0.0094 +- 0.0064 | +0.0105 +- 0.0072 |
+| pheS+4CP | +0.0082 +- 0.0070 | +0.0096 +- 0.0064 |
+| kanR-kan | +0.0037 +- 0.0067 | +0.0003 +- 0.0055 |
+| pheS-4CP | -0.0035 +- 0.0058 | -0.0002 +- 0.0050 |
+
+Under the model it is 0 (a flat X, one k per condition). Each is about
+1.5 SE, but all four selection entries point the same way: every
+genotype, references included, grows about 0.01 per minute faster at
+1 mM than at 0 IPTG under kanamycin and under 4CP, and not without
+selection. wt's absolute swing is then its within-tube m plus that
+shared term, which is why the absolute swing sits near the monoculture
+in kanR while the within-tube m does not.
+
+**Reading.** The linear model has one k per condition, so it has no
+place for a growth shift that depends on IPTG and is shared by every
+genotype in the selection conditions. Fitting the absolute and the
+within-tube information with one m forces a compromise that differs by
+marker, and since X is gauged on wt, the misfit falls between wt and the
+library. The shared term's sign is the same under kanamycin (derepressed
+cells grow) and 4CP (derepressed cells die), so a public-good effect of
+the resistance enzyme alone does not explain it; an IPTG effect on the
+tube totals in selection (OD600 to CFU under stress), or on growth under
+selection, would.
+
+**wt's curve.** Within tube, wt's pheS response keeps falling from
+0.1 uM to 1 mM and spans about 0.03 per minute from 0.1 uM, where the
+monoculture's is flat to 10 uM and drops between 10 and 30 uM. In kanR
+the within-tube curve is compressed but has the monoculture's shape.
+Early and late intervals agree within their noise.
+
+**Next.** Test a per-(condition, IPTG) k in selection conditions (a
+growth shift shared by every genotype in those tubes) against the
+current model on the dev data, and see whether stage 1's m then matches
+the within-tube m.
+
+### Run 2 setup: stage 1 with a titrant-dependent k (2026-10-15)
+
+`linear` growth now takes `k_titrant_free_scale` (a k shift per
+selection condition and nonzero IPTG, prior SD 0.02) and
+`k_titrant_loglinear_scale` (a slope in log IPTG per selection
+condition, from a tenth of the lowest nonzero concentration, prior SD
+0.005), both 0 at no IPTG. The old monoculture model, delta_k = s log(IPTG)
++ b (user, 2026-10-15), is the log-linear form. `stage1_titrant.py`
+reruns the two-stage fit's stage 1 with either, four runs: real and
+seed 1 (a null check: the simulation has no such term, so the shift
+should come out near 0 and m unchanged), each free and log-linear. From
+the cluster's two-stage validation directory:
+
+```bash
+for r in real seed1; do for m in free loglinear; do sbatch --export=ALL,RUN=$r,MODE=$m /gpfs/home/harms/tfscreen/planning/studies/within-tube-m/stage1_titrant.srun; done; done
+```
+
+Compare each `ktitrant_*/stage1/km.csv` with `run_*/stage1/km.csv`
+(no term) and with the within-tube m above (kanR+kan about -0.005,
+pheS+4CP about +0.018); the shift itself is
+`condition_growth_k_titrant[_slope]_auto_loc` in the stage's
+`tfs_fit_model_params.npz`.
+
+### Run 2 results (2026-10-15)
+
+The free form was removed from the code after this run.
+
+The four runs failed first (the shift used numpy on the traced
+titrant_conc; fixed) and then ran, about 30 minutes each.
+
+**The free form is not identified.** On seed 1, which has no such term,
+it put the shift at -0.021 (kanR+kan) and +0.023 (pheS+4CP) per minute at
+1 mM and made both m's 2.5x too steep (-0.0365, +0.0369 against
+-0.0146, +0.0146 without it). Its shift took wt's own IPTG curve: a k
+free at every concentration can stand in for m X_wt(c), so k(c) trades
+with m along the gauge. On the real data its numbers mean nothing.
+
+**The log-linear form passes the null and moves kanR, not pheS.**
+
+| | seed 1 m, none -> log-linear | seed 1 shift at 1 mM | real m, none -> log-linear | real shift at 1 mM | real within-tube m |
+|---|---|---|---|---|---|
+| kanR+kan | -0.0146 -> -0.0146 | +0.0002 | -0.0148 -> -0.0089 | +0.0030 | -0.0049 |
+| pheS+4CP | +0.0146 -> +0.0146 | +0.0013 | +0.0070 -> +0.0041 | +0.0021 | +0.0178 |
+
+On the real data it finds a small shift of the expected sign, about a
+quarter of the 0.01 the flat spikes' absolute growth suggested, and it
+moves kanR's m most of the way to the within-tube value. pheS's m moves
+away from it.
+
+**Reading.** A shared IPTG shift is probably real but small and accounts
+for part of the kanR discrepancy. It does not account for pheS: within
+tube, wt against the flat spikes swings like the monoculture (0.015-0.018
+per minute); every fit puts pheS's m at 0.004-0.011.
+
+**Next.** A posterior-predictive check of the within-tube statistic:
+stage 1's predicted ln_cfu for wt and the two flat spikes in pheS+4CP,
+the same ratio slopes, against the observed. Whether the fit reproduces
+the 0.018 swing says whether something in its likelihood outweighs it or
+wt's X in stage 1 does not span 1 to 0 as assumed.
+
+### Run 3: posterior-predictive check and the marker ratio (2026-10-15)
+
+`ppc_within_tube.py` (stage 1 folders downloaded to `runs/ppc/`, gitignored;
+`tfs-predict-growth` at the MAP, locally) computes the within-tube swing
+from the fit's predicted ln_cfu on the same tubes. `marker_ratio.py`
+computes each deep genotype's swing in each marker against
+M42I/H74A/K84L, model-free.
+
+**Stage 1 does not reproduce the within-tube contrast.** pheS+4CP:
+predicted 0.004, observed 0.018 (0.018-0.03 at low IPTG, 3-4 SE).
+kanR+kan: predicted -0.009, observed -0.005 (1-2 SE). The log-linear
+titrant k changes neither. On seed 1 the fit reproduces the statistic
+(both near 0, since the simulation's spikes are not flat), so the check
+works. The real fits' count overdispersion floor (inv_r 0.0065, about 8%
+per tube at high counts) is 6x the simulation's.
+
+**The pheS-only fit's curves follow from the pinned m.** Read with
+m = 0.018, H74A/K84L sits near X 0.4 at both IPTG ends, as its binding and
+kanR X say.
+
+**The singles' marker ratio matches wt, not stage 1.** With one X shared
+by both markers, each genotype's pheS swing over its kanR swing is
+m_pheS / m_kanR:
+
+| | pheS / kanR |
+|---|---|
+| 903 singles with >= 1e5 reads, weighted line through 0 | -2.2 |
+| median over the 372 with a kanR swing > 2 SE | -2.5 |
+| M42I/H74A | -2.4 |
+| wt | -3.9 (0.018 +- 0.004 over -0.005 +- 0.002) |
+| stage 1's pinned m | -0.47 |
+| wt monoculture | -1.03 |
+
+wt sits with the library. Every within-tube measure agrees, and stage 1's
+m's are about 5x off in ratio: kanR's m about 3x too steep, pheS's about
+2x too shallow.
+
+**Reading.** The within-tube ratio is the information free of tube totals.
+The fit's absolute scale comes from the OD600-derived totals, which tie
+each condition's k and m to the population's growth; the per-tube level
+offsets cannot absorb a rate difference across IPTG, so m does. The
+monoculture is OD-based too. An OD-to-CFU conversion that drifts with
+IPTG or cell state under selection (filamentation under kanamycin, say)
+would do this, as the staged-MAP study already listed. Under the wt gauge,
+m is identified by wt's contrast with the library without the totals, so
+a fit that takes m from relative information alone is possible in
+principle; the IPTG-structured offset mode is a warning that freeing the
+totals has not gone cleanly before.
diff --git a/planning/studies/within-tube-m/compare_stage1.py b/planning/studies/within-tube-m/compare_stage1.py
new file mode 100644
index 00000000..62eff3e5
--- /dev/null
+++ b/planning/studies/within-tube-m/compare_stage1.py
@@ -0,0 +1,74 @@
+"""
+Stage 1's k and m with and without a titrant-dependent k, and the fitted
+shift (README, run 2).
+
+    python compare_stage1.py runs ../two-stage-validation
+
+RUNS_DIR holds ktitrant_{run}_{mode}/stage1/ (km.csv, tfs_configure_config.yaml,
+tfs_fit_model_params.npz); VALIDATION_DIR holds run_{run}/stage1/km.csv,
+the same stage 1 without the term.
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+
+WITHIN_TUBE = {"kanR+kan": -0.0049, "pheS+4CP": 0.0178}
+
+
+def shift_table(stage1_dir):
+    """
+    The fitted shift from the stage's MAP: a (selection condition x nonzero
+    IPTG) table for the free form, a slope per selection condition for the
+    log-linear one. Selection conditions in condition_rep order (the '+'
+    rows of km.csv), IPTG ascending.
+    """
+    z = np.load(os.path.join(stage1_dir, "tfs_fit_model_params.npz"))
+    km = pd.read_csv(os.path.join(stage1_dir, "km.csv"), index_col="condition_rep")
+    sel_conds = [c for c in km.index if "+" in c]
+    if "condition_growth_k_titrant_auto_loc" in z.files:
+        v = np.asarray(z["condition_growth_k_titrant_auto_loc"])
+        return pd.DataFrame(v, index=sel_conds), "free"
+    if "condition_growth_k_titrant_slope_auto_loc" in z.files:
+        v = np.asarray(z["condition_growth_k_titrant_slope_auto_loc"])
+        return pd.Series(v, index=sel_conds), "loglinear"
+    return None, None
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("runs_dir")
+    ap.add_argument("validation_dir")
+    args = ap.parse_args()
+    concs = np.array([0.0, 1e-4, 1e-3, 3e-3, 0.01, 0.03, 0.1, 1.0])
+    L = np.log(np.maximum(concs, 1e-5) / 1e-5)
+    for run in ("real", "seed1"):
+        base = pd.read_csv(os.path.join(args.validation_dir, f"run_{run}", "stage1",
+                                        "km.csv"), index_col="condition_rep")
+        table = base.add_prefix("none_")
+        for mode in ("free", "loglinear"):
+            d = os.path.join(args.runs_dir, f"ktitrant_{run}_{mode}", "stage1")
+            if not os.path.exists(os.path.join(d, "km.csv")):
+                print(f"{d}: not there yet")
+                continue
+            table = table.join(pd.read_csv(os.path.join(d, "km.csv"),
+                                           index_col="condition_rep").add_prefix(f"{mode}_"))
+            sh, kind = shift_table(d)
+            if kind == "free":
+                print(f"\n{run} {mode}: k shift per IPTG (1/min), 0 at no IPTG")
+                sh.columns = concs[:sh.shape[1]]
+                print(sh.to_string(float_format="%+.4f"))
+            elif kind == "loglinear":
+                print(f"\n{run} {mode}: slope per log IPTG; shift at 1 mM")
+                print(pd.DataFrame({"slope": sh, "shift_1mM": sh * L[-1]}).to_string(
+                    float_format="%+.5f"))
+        if run == "real":
+            table["within_tube_m"] = pd.Series(WITHIN_TUBE)
+        print(f"\n== {run}: stage 1 k and m")
+        print(table.to_string(float_format="%+.5f"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/within-tube-m/marker_ratio.py b/planning/studies/within-tube-m/marker_ratio.py
new file mode 100644
index 00000000..ce678201
--- /dev/null
+++ b/planning/studies/within-tube-m/marker_ratio.py
@@ -0,0 +1,115 @@
+"""
+Each genotype's within-tube swing in each selection marker: the change,
+from 0 to 1 mM IPTG, of the slope of ln(genotype / M42I/H74A/K84L) over
+the selection (README, run 3). With one X per genotype shared by both
+markers, swing = m_marker (X(0) - X(1 mM)), so across genotypes the pheS
+swing against the kanR swing is a line through 0 with slope m_pheS /
+m_kanR. wt is one point on it.
+
+    python marker_ratio.py ../../dev-data/processed ../m-bias-factorial/real_depth/depth.csv --out out
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+
+from within_tube_m import slopes_by_conc, swing  # noqa: E402
+
+plt.rc("pdf", fonttype=42)
+plt.rc("font", size=14)
+plt.rc("axes", titlesize=14, labelsize=16)
+
+REF = "M42I/H74A/K84L"
+COND = {"kanR": "kanR+kan", "pheS": "pheS+4CP"}
+COLS = ["genotype", "counts", "replicate", "condition_sel", "t_sel", "titrant_conc"]
+
+
+def read(processed_dir, genotypes, cache):
+    if os.path.exists(cache):
+        return pd.read_csv(cache)
+    parts = []
+    for lib, cond in COND.items():
+        for ch in pd.read_csv(os.path.join(processed_dir, f"growth_{lib}.csv.gz"),
+                              usecols=COLS, chunksize=2_000_000):
+            parts.append(ch[(ch["condition_sel"] == cond) & ch["genotype"].isin(genotypes)])
+    out = pd.concat(parts, ignore_index=True)
+    out.to_csv(cache, index=False)
+    return out
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("processed_dir")
+    ap.add_argument("depth_csv")
+    ap.add_argument("--min_reads", type=float, default=1e5)
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    depth = pd.read_csv(args.depth_csv).set_index("genotype")["reads"]
+    singles = [g for g, r in depth.items()
+               if r >= args.min_reads and isinstance(g, str) and "/" not in g
+               and g not in ("wt", "__unknown__")]
+    keep = set(singles) | {"wt", REF, "H74A/K84L", "M42I/H74A", "M42I", "H74A",
+                           "K84L", "D88A", "M42I/K84L"}
+    rows = read(args.processed_dir, keep, os.path.join(args.out, "marker_rows.csv"))
+    key = ["replicate", "condition_sel", "titrant_conc", "t_sel"]
+    ref = rows[rows["genotype"] == REF].set_index(key)["counts"].rename("c_ref")
+    out = []
+    for g, d in rows[rows["genotype"] != REF].groupby("genotype"):
+        rec = {"genotype": g, "reads": depth.get(g, np.nan)}
+        for lib, cond in COND.items():
+            t = d[d["condition_sel"] == cond].set_index(key)[["counts"]].join(ref, how="inner")
+            t = t[(t["counts"] > 0) & (t["c_ref"] > 0)].reset_index()
+            if t["titrant_conc"].isin([0.0, 1.0]).sum() < 4:
+                continue
+            t["lr"] = np.log(t["counts"] / t["c_ref"])
+            t["v"] = 1.0 / t["counts"] + 1.0 / t["c_ref"]
+            try:
+                c, b, cov, _ = slopes_by_conc(t, "lr", "v")
+                rec[f"swing_{lib}"], rec[f"swing_{lib}_se"] = swing(c, b, cov)
+            except (ValueError, np.linalg.LinAlgError):
+                pass
+        out.append(rec)
+    s = pd.DataFrame(out).dropna(subset=["swing_kanR", "swing_pheS"])
+    s.to_csv(os.path.join(args.out, "marker_swings.csv"), index=False)
+
+    lib = s[~s["genotype"].isin(["wt"]) & ~s["genotype"].str.contains("/")]
+    w = 1 / (lib["swing_pheS_se"] ** 2 + 1e-12)
+    # weighted line through 0 (pheS on kanR), and a robust median ratio
+    big = lib[lib["swing_kanR"].abs() > 2 * lib["swing_kanR_se"]]
+    slope0 = np.sum(w * lib.swing_kanR * lib.swing_pheS) / np.sum(w * lib.swing_kanR ** 2)
+    print(f"{len(lib)} singles with >= {args.min_reads:g} reads")
+    print(f"pheS swing on kanR swing, weighted line through 0: slope {slope0:+.3f}")
+    print(f"median pheS/kanR ratio, singles whose kanR swing is > 2 SE ({len(big)}): "
+          f"{np.median(big.swing_pheS / big.swing_kanR):+.3f}")
+    print("stage 1 m_pheS / m_kanR: 0.0070 / -0.0148 = -0.47")
+    print(s[~s["genotype"].isin(lib["genotype"])].to_string(index=False, float_format="%+.4f"))
+
+    fig, ax = plt.subplots(figsize=(7, 6))
+    ax.errorbar(lib.swing_kanR, lib.swing_pheS, xerr=lib.swing_kanR_se, yerr=lib.swing_pheS_se,
+                fmt="o", ms=3, color="#56B4E9", alpha=0.4, lw=0.5, label="singles")
+    sp = s[~s["genotype"].isin(lib["genotype"])]
+    for _, r in sp.iterrows():
+        col = "#000000" if r.genotype == "wt" else "#D55E00"
+        ax.errorbar(r.swing_kanR, r.swing_pheS, xerr=r.swing_kanR_se, yerr=r.swing_pheS_se,
+                    fmt="s", color=col, ms=7)
+        ax.annotate(r.genotype, (r.swing_kanR, r.swing_pheS), fontsize=9,
+                    xytext=(4, 4), textcoords="offset points")
+    x = np.array([-0.03, 0.01])
+    ax.plot(x, slope0 * x, color="#0072B2", lw=1, label=f"singles, slope {slope0:+.2f}")
+    ax.plot(x, -0.47 * x, color="0.5", lw=1, ls=":", label="stage 1 m ratio -0.47")
+    ax.axhline(0, color="0.85", lw=0.8, zorder=0)
+    ax.axvline(0, color="0.85", lw=0.8, zorder=0)
+    ax.set_xlabel("kanR+kan swing vs M42I/H74A/K84L")
+    ax.set_ylabel("pheS+4CP swing vs M42I/H74A/K84L")
+    ax.legend(fontsize=10)
+    fig.tight_layout()
+    fig.savefig(os.path.join(args.out, "marker_ratio.pdf"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/within-tube-m/ppc_within_tube.py b/planning/studies/within-tube-m/ppc_within_tube.py
new file mode 100644
index 00000000..727e2d5b
--- /dev/null
+++ b/planning/studies/within-tube-m/ppc_within_tube.py
@@ -0,0 +1,142 @@
+"""
+Posterior-predictive check of the within-tube statistic (README, run 3):
+does a fit reproduce the change, between 0 and 1 mM IPTG, of the slope of
+ln(wt / reference) for the flat pure spikes?
+
+    python ppc_within_tube.py runs/ppc/run_real runs/ppc/ktitrant_real_loglinear runs/ppc/run_seed1 --out out
+
+Each RUN_DIR is a stage 1 folder with tfs_growth.csv (the observed counts
+and ln_cfu) and pred_growth.csv (tfs-predict-growth at the MAP; q0.5 is
+the predicted ln_cfu). The observed ratio comes from counts, the
+predicted one from the difference of predicted ln_cfu in the same tube
+(tube totals and offsets cancel in both). Also the absolute ln_cfu slopes
+of wt and each reference, observed and predicted.
+"""
+
+import argparse
+import datetime
+import os
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.backends.backend_pdf import PdfPages  # noqa: E402
+
+from within_tube_m import REFS, ZERO_X, slopes_by_conc, swing  # noqa: E402
+
+plt.rc("pdf", fonttype=42)
+plt.rc("font", size=14)
+plt.rc("axes", titlesize=14, labelsize=16)
+plt.rc("legend", fontsize=11)
+
+KEY = ["replicate", "condition_sel", "titrant_conc", "t_sel"]
+SELECTION = ["kanR+kan", "pheS+4CP"]
+
+
+def load(run_dir):
+    keep = ["wt"] + REFS
+    obs = pd.read_csv(os.path.join(run_dir, "tfs_growth.csv"),
+                      usecols=KEY + ["genotype", "counts", "ln_cfu"])
+    obs = obs[obs["genotype"].isin(keep)]
+    pred = pd.read_csv(os.path.join(run_dir, "pred_growth.csv"),
+                       usecols=KEY + ["genotype", "q0.5"])
+    pred = pred[pred["genotype"].isin(keep)]
+    m = obs.merge(pred, on=KEY + ["genotype"], how="inner")
+    return m.rename(columns={"q0.5": "ln_cfu_pred"})
+
+
+def pairs(m, cond, ref):
+    d = m[m["condition_sel"] == cond]
+    a = d[d["genotype"] == "wt"].set_index(KEY)
+    b = d[d["genotype"] == ref].set_index(KEY)
+    t = a.join(b, lsuffix="_wt", rsuffix="_ref", how="inner").reset_index()
+    t = t[(t["counts_wt"] > 0) & (t["counts_ref"] > 0)].copy()
+    t["lr_obs"] = np.log(t["counts_wt"] / t["counts_ref"])
+    t["lr_var"] = 1.0 / t["counts_wt"] + 1.0 / t["counts_ref"]
+    t["lr_pred"] = t["ln_cfu_pred_wt"] - t["ln_cfu_pred_ref"]
+    return t
+
+
+def curve(t, col, var=None):
+    c, b, cov, _ = slopes_by_conc(t, col, var)
+    j = list(c).index(1.0)
+    se = np.sqrt(np.clip(np.diag(cov) + cov[j, j] - 2 * cov[:, j], 0, None))
+    return c, b - b[j], se
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("run_dirs", nargs="+")
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+
+    rows, curves = [], []
+    for run_dir in args.run_dirs:
+        name = os.path.basename(os.path.normpath(run_dir))
+        m = load(run_dir)
+        for cond in SELECTION:
+            for ref in REFS:
+                t = pairs(m, cond, ref)
+                if t.empty:
+                    continue
+                c, b, cov, chi2 = slopes_by_conc(t, "lr_obs", "lr_var")
+                obs, obs_se = swing(c, b, cov)
+                c, b, cov, _ = slopes_by_conc(t, "lr_pred")
+                pred, _ = swing(c, b, cov)
+                absolute = {}
+                for g, col in (("wt", "wt"), ("ref", "ref")):
+                    for kind, y in (("obs", f"ln_cfu_{col}"), ("pred", f"ln_cfu_pred_{col}")):
+                        c2, b2, cov2, _ = slopes_by_conc(t, y)
+                        absolute[f"{g}_abs_{kind}"] = swing(c2, b2, cov2)[0]
+                rows.append(dict(run=name, condition_sel=cond, reference=ref,
+                                 within_obs=obs, within_obs_se=obs_se,
+                                 within_pred=pred, **absolute))
+                for kind, col, var in (("observed", "lr_obs", "lr_var"),
+                                       ("predicted", "lr_pred", None)):
+                    cc, yy, ss = curve(t, col, var)
+                    curves.append(pd.DataFrame(dict(run=name, condition_sel=cond,
+                                                    reference=ref, kind=kind,
+                                                    titrant_conc=cc, curve=yy, se=ss)))
+    s = pd.DataFrame(rows)
+    s.to_csv(os.path.join(args.out, "ppc_within_tube.csv"), index=False)
+    print("Change from 0 to 1 mM of each slope (1/min). within: ln(wt/ref);"
+          " *_abs: the genotype's own ln_cfu.")
+    print(s.to_string(index=False, float_format="%+.4f"))
+    cv = pd.concat(curves, ignore_index=True)
+    cv.to_csv(os.path.join(args.out, "ppc_curves.csv"), index=False)
+
+    stamp = datetime.date.today().isoformat()
+    runs = list(dict.fromkeys(cv["run"]))
+    with PdfPages(os.path.join(args.out, f"{stamp}_ppc-within-tube.pdf")) as pdf:
+        fig, axes = plt.subplots(len(runs), len(SELECTION), squeeze=False,
+                                 figsize=(13, 4.5 * len(runs)))
+        for i, run in enumerate(runs):
+            for j, cond in enumerate(SELECTION):
+                ax = axes[i, j]
+                for ref, col in zip(REFS, ("#0072B2", "#009E73")):
+                    for kind, mk, ls in (("observed", "o", "-"), ("predicted", "s", "--")):
+                        d = cv[(cv.run == run) & (cv.condition_sel == cond)
+                               & (cv.reference == ref) & (cv.kind == kind)]
+                        if d.empty:
+                            continue
+                        x = np.where(d.titrant_conc > 0, d.titrant_conc, ZERO_X)
+                        ax.errorbar(x, d.curve, yerr=1.96 * d.se if kind == "observed" else None,
+                                    fmt=mk + ls, color=col, ms=5, capsize=2,
+                                    mfc="white" if kind == "predicted" else col,
+                                    label=f"{kind}, vs {ref}")
+                ax.axhline(0, color="0.8", lw=0.8, zorder=0)
+                ax.set_xscale("log")
+                ax.set_title(f"{run}: {cond}", fontsize=12)
+                ax.set_xlabel("IPTG (mM; 0 at left)")
+                ax.set_ylabel("wt/ref slope minus at 1 mM")
+        axes[0, 0].legend(fontsize=9)
+        fig.tight_layout()
+        pdf.savefig(fig)
+        plt.close(fig)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/within-tube-m/stage1_titrant.py b/planning/studies/within-tube-m/stage1_titrant.py
new file mode 100644
index 00000000..e3f867b3
--- /dev/null
+++ b/planning/studies/within-tube-m/stage1_titrant.py
@@ -0,0 +1,36 @@
+"""
+Stage 1 of the two-stage fit with a titrant-dependent k in the selection
+conditions (linear growth's k_titrant_loglinear_scale), to see whether
+stage 1's m moves to the within-tube m (README).
+
+    python stage1_titrant.py ../two-stage-validation/two_stage_real.yaml ktitrant_real_loglinear loglinear
+
+MODE loglinear: one slope in log IPTG per selection condition, prior SD
+0.005. Run 2 also had a free form (one k shift per IPTG, prior SD 0.02);
+it was not identified and was removed from the code afterwards. Writes
+OUT_DIR/stage1/ (km.csv and the MAP's tfs_fit_model_params.npz, which
+holds the slope as condition_growth_k_titrant_slope_auto_loc).
+"""
+
+import argparse
+import os
+
+from tfscreen.tfmodel.scripts import fit_two_stage_cli as cli
+
+PRIORS = {"loglinear": "k_titrant_loglinear_scale=0.005"}
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("config_file")
+    ap.add_argument("out_dir")
+    ap.add_argument("mode", choices=sorted(PRIORS))
+    args = ap.parse_args()
+    cfg, settings = cli.read_two_stage_config(args.config_file)
+    cfg["set_priors"] = list(cfg.get("set_priors") or []) + [PRIORS[args.mode]]
+    cli.run_stage1(os.path.abspath(args.out_dir), cfg, settings)
+    cli._say("done")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/within-tube-m/stage1_titrant.srun b/planning/studies/within-tube-m/stage1_titrant.srun
new file mode 100644
index 00000000..e9326c64
--- /dev/null
+++ b/planning/studies/within-tube-m/stage1_titrant.srun
@@ -0,0 +1,25 @@
+#!/bin/bash -l
+#SBATCH --account=harmslab
+#SBATCH --partition=gpulong
+#SBATCH --time=1-00:00:00
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --gpus=1
+#SBATCH --mem=0
+#SBATCH --ntasks-per-node=1
+#SBATCH --job-name=ts_ktitrant
+
+# Stage 1 with a titrant-dependent k (stage1_titrant.py). Run from the
+# two-stage validation directory on the cluster:
+#   sbatch --export=ALL,RUN=real,MODE=free .../stage1_titrant.srun
+# writes ktitrant_${RUN}_${MODE}/stage1/.
+
+module load cuda/12.4.1
+set -e
+RUN="${RUN:?set RUN (seed1, seed2 or real)}"
+MODE="${MODE:-loglinear}"
+HERE=/gpfs/home/harms/tfscreen/planning/studies
+echo ">>> stage 1 k_titrant ${RUN} ${MODE} $(date)"
+python "${HERE}/within-tube-m/stage1_titrant.py" \
+    "${HERE}/two-stage-validation/two_stage_${RUN}.yaml" "ktitrant_${RUN}_${MODE}" "${MODE}"
+echo ">>> Done stage 1 k_titrant ${RUN} ${MODE} $(date)"
diff --git a/planning/studies/within-tube-m/within_tube_m.py b/planning/studies/within-tube-m/within_tube_m.py
new file mode 100644
index 00000000..f57e646c
--- /dev/null
+++ b/planning/studies/within-tube-m/within_tube_m.py
@@ -0,0 +1,225 @@
+"""
+Each selection condition's growth slope m, measured two ways from the
+same data (planning/studies/within-tube-m/README.md):
+
+- within tube: the slope over t_sel of ln(counts_wt / counts_ref), for a
+  reference genotype whose X is flat in IPTG (pure spikes H74A/K84L and
+  M42I/H74A/K84L). With growth = k + dk + m X and wt gauged to X = 1 at
+  0 IPTG and 0 at 1 mM, that slope is (dk_wt - dk_ref) + m (X_wt - X_ref),
+  so its change from 0 to 1 mM is m. Tube totals, offsets and dk cancel.
+- absolute: the same change in wt's own ln_cfu slope, which goes through
+  the tube totals (OD600).
+
+Also the reference's own absolute slope change (0 under the model, since
+its X is flat), wt's within-tube curve against IPTG, and m from the early
+and late time intervals. Compared with the wt monoculture and a
+two-stage run's stage 1.
+
+    python within_tube_m.py PROCESSED_DIR --stage1_km RUN/stage1/km.csv --out out
+"""
+
+import argparse
+import datetime
+import os
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.backends.backend_pdf import PdfPages  # noqa: E402
+
+plt.rc("pdf", fonttype=42)
+plt.rc("font", size=14)
+plt.rc("axes", titlesize=14, labelsize=16)
+plt.rc("legend", fontsize=12)
+
+REFS = ["H74A/K84L", "M42I/H74A/K84L"]
+REF_COLOR = {"H74A/K84L": "#0072B2", "M42I/H74A/K84L": "#009E73"}
+SELECTION = ["kanR+kan", "pheS+4CP"]
+ZERO_X = 3e-5
+COLS = ["sample", "genotype", "counts", "replicate", "condition_sel", "t_sel",
+        "titrant_conc", "ln_cfu"]
+
+
+def read_rows(processed_dir, genotypes, cache):
+    if os.path.exists(cache):
+        return pd.read_csv(cache)
+    parts = []
+    for lib in ("kanR", "pheS"):
+        path = os.path.join(processed_dir, f"growth_{lib}.csv.gz")
+        for ch in pd.read_csv(path, usecols=COLS, chunksize=2_000_000):
+            parts.append(ch[ch["genotype"].isin(genotypes)])
+    out = pd.concat(parts, ignore_index=True)
+    out.to_csv(cache, index=False)
+    return out
+
+
+def slopes_by_conc(df, y, var=None):
+    """
+    Fit y = a[conc, replicate] + b[conc] * t_sel by weighted least squares
+    (weights 1/var, or unweighted), scaling the covariance by the reduced
+    chi-square when it exceeds 1. Returns (concs, b, cov, chi2/dof).
+    """
+    df = df.dropna(subset=[y]).copy()
+    concs = np.sort(df["titrant_conc"].unique())
+    groups = df.groupby(["titrant_conc", "replicate"]).ngroup().to_numpy()
+    n_g = groups.max() + 1
+    ci = np.searchsorted(concs, df["titrant_conc"].to_numpy())
+    X = np.zeros((len(df), n_g + len(concs)))
+    X[np.arange(len(df)), groups] = 1.0
+    X[np.arange(len(df)), n_g + ci] = df["t_sel"].to_numpy()
+    w = np.ones(len(df)) if var is None else 1.0 / df[var].to_numpy()
+    sw = np.sqrt(w)
+    A, yy = X * sw[:, None], df[y].to_numpy() * sw
+    beta, *_ = np.linalg.lstsq(A, yy, rcond=None)
+    resid = yy - A @ beta
+    dof = max(len(df) - X.shape[1], 1)
+    chi2 = float(resid @ resid) / dof
+    cov = np.linalg.pinv(A.T @ A)
+    scale = max(chi2, 1.0) if var is not None else chi2
+    cov = cov * scale
+    k = slice(n_g, n_g + len(concs))
+    return concs, beta[k], cov[k, k], chi2
+
+
+def swing(concs, b, cov, lo=0.0, hi=1.0):
+    """b(lo) - b(hi) and its SE."""
+    i, j = list(concs).index(lo), list(concs).index(hi)
+    v = cov[i, i] + cov[j, j] - 2 * cov[i, j]
+    return b[i] - b[j], np.sqrt(max(v, 0.0))
+
+
+def pair_table(rows, ref):
+    """One row per tube with wt's and ref's counts and ln_cfu."""
+    key = ["sample", "replicate", "condition_sel", "t_sel", "titrant_conc"]
+    a = rows[rows["genotype"] == "wt"].set_index(key)[["counts", "ln_cfu"]]
+    b = rows[rows["genotype"] == ref].set_index(key)[["counts", "ln_cfu"]]
+    t = a.join(b, lsuffix="_wt", rsuffix="_ref", how="inner").reset_index()
+    t = t[(t["counts_wt"] > 0) & (t["counts_ref"] > 0)]
+    t["lr"] = np.log(t["counts_wt"] / t["counts_ref"])
+    t["lr_var"] = 1.0 / t["counts_wt"] + 1.0 / t["counts_ref"]
+    return t
+
+
+def interval_swings(t):
+    """m from the early and the late interval of each (conc, replicate)
+    series: per-interval slopes of lr, averaged over replicates."""
+    out = {}
+    rows = []
+    for (c, r), d in t.groupby(["titrant_conc", "replicate"]):
+        d = d.sort_values("t_sel")
+        if len(d) < 3:
+            continue
+        tt, yy = d["t_sel"].to_numpy(), d["lr"].to_numpy()
+        for name, (i, j) in (("early", (0, 1)), ("late", (len(d) - 2, len(d) - 1))):
+            dt = tt[j] - tt[i]
+            if dt <= 0:
+                continue
+            rows.append(dict(titrant_conc=c, replicate=r, interval=name,
+                             slope=(yy[j] - yy[i]) / dt))
+    s = pd.DataFrame(rows)
+    if s.empty:
+        return out
+    g = s.groupby(["interval", "titrant_conc"])["slope"].agg(["mean", "std", "count"])
+    for name in ("early", "late"):
+        if (name, 0.0) in g.index and (name, 1.0) in g.index:
+            a, b = g.loc[(name, 0.0)], g.loc[(name, 1.0)]
+            se = np.sqrt((a["std"] ** 2) / a["count"] + (b["std"] ** 2) / b["count"])
+            out[name] = (a["mean"] - b["mean"], se)
+    return out
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("processed_dir")
+    ap.add_argument("--stage1_km", default=None, help="a two-stage run's stage1/km.csv")
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+
+    rows = read_rows(args.processed_dir, ["wt"] + REFS,
+                     os.path.join(args.out, "rows.csv"))
+    mono = pd.read_csv(os.path.join(args.processed_dir, "wt_control_rate_summary.csv"))
+    km = (pd.read_csv(args.stage1_km, index_col="condition_rep")
+          if args.stage1_km else None)
+
+    summary, curves = [], []
+    for cond in sorted(rows["condition_sel"].unique()):
+        r = rows[rows["condition_sel"] == cond]
+        mo = mono[mono["condition_sel"] == cond].set_index("titrant_conc")
+        m_mono = (mo.loc[0.0, "rate_mean"] - mo.loc[1.0, "rate_mean"]
+                  if {0.0, 1.0} <= set(mo.index) else np.nan)
+        m_mono_se = (np.hypot(mo.loc[0.0, "rate_sd"] / np.sqrt(mo.loc[0.0, "num_replicates"]),
+                              mo.loc[1.0, "rate_sd"] / np.sqrt(mo.loc[1.0, "num_replicates"]))
+                     if {0.0, 1.0} <= set(mo.index) else np.nan)
+        # stage 1's m is the X-scale slope: growth = k + m X, so b(0) - b(1 mM) = m
+        m_s1 = km.loc[cond, "m"] if km is not None and cond in km.index else np.nan
+
+        wt = r[r["genotype"] == "wt"]
+        c, b, cov, chi2 = slopes_by_conc(wt, "ln_cfu")
+        m_abs, m_abs_se = swing(c, b, cov)
+        base = dict(condition_sel=cond, m_stage1=m_s1, m_mono=m_mono,
+                    m_mono_se=m_mono_se, m_abs_wt=m_abs, m_abs_wt_se=m_abs_se)
+        for ref in REFS:
+            t = pair_table(r, ref)
+            c, b, cov, chi2 = slopes_by_conc(t, "lr", "lr_var")
+            m_rel, m_rel_se = swing(c, b, cov)
+            rr = r[r["genotype"] == ref]
+            c2, b2, cov2, _ = slopes_by_conc(rr, "ln_cfu")
+            ref_abs, ref_abs_se = swing(c2, b2, cov2)
+            iv = interval_swings(t)
+            summary.append(dict(base, reference=ref, m_within=m_rel,
+                                m_within_se=m_rel_se, within_chi2=chi2,
+                                ref_abs_change=ref_abs, ref_abs_change_se=ref_abs_se,
+                                m_within_early=iv.get("early", (np.nan,))[0],
+                                m_within_early_se=iv.get("early", (np.nan, np.nan))[1],
+                                m_within_late=iv.get("late", (np.nan,))[0],
+                                m_within_late_se=iv.get("late", (np.nan, np.nan))[1]))
+            j = list(c).index(1.0)
+            se = np.sqrt(np.clip(np.diag(cov) + cov[j, j] - 2 * cov[:, j], 0, None))
+            curves.append(pd.DataFrame(dict(condition_sel=cond, reference=ref,
+                                            titrant_conc=c, curve=b - b[j],
+                                            curve_se=se)))
+    s = pd.DataFrame(summary)
+    s.to_csv(os.path.join(args.out, "m_estimates.csv"), index=False)
+    cv = pd.concat(curves, ignore_index=True)
+    cv.to_csv(os.path.join(args.out, "wt_curves.csv"), index=False)
+    show = ["condition_sel", "reference", "m_within", "m_within_se", "within_chi2",
+            "m_within_early", "m_within_late", "m_abs_wt", "m_abs_wt_se",
+            "ref_abs_change", "ref_abs_change_se", "m_mono", "m_mono_se", "m_stage1"]
+    print(s[show].to_string(index=False, float_format="%.4f"))
+
+    stamp = datetime.date.today().isoformat()
+    with PdfPages(os.path.join(args.out, f"{stamp}_within-tube-m.pdf")) as pdf:
+        fig, axes = plt.subplots(1, len(SELECTION), figsize=(13, 5), squeeze=False)
+        for ax, cond in zip(axes[0], SELECTION):
+            for ref in REFS:
+                d = cv[(cv.condition_sel == cond) & (cv.reference == ref)]
+                x = np.where(d.titrant_conc > 0, d.titrant_conc, ZERO_X)
+                ax.errorbar(x, d.curve, yerr=1.96 * d.curve_se, fmt="o-",
+                            color=REF_COLOR[ref], ms=6, capsize=2,
+                            label=f"wt vs {ref}, within tube")
+            mo = mono[mono["condition_sel"] == cond].sort_values("titrant_conc")
+            if len(mo):
+                hi = mo.set_index("titrant_conc").loc[1.0, "rate_mean"]
+                x = np.where(mo.titrant_conc > 0, mo.titrant_conc, ZERO_X)
+                ax.errorbar(x, mo.rate_mean - hi, yerr=mo.rate_sd, fmt="s--",
+                            color="#E69F00", mfc="white", ms=6, capsize=2,
+                            label="wt monoculture")
+            if km is not None and cond in km.index:
+                ax.axhline(km.loc[cond, "m"], color="#000000", lw=1, ls=":",
+                           label="stage 1 m")
+            ax.axhline(0, color="0.8", lw=0.8, zorder=0)
+            ax.set_xscale("log")
+            ax.set_title(cond)
+            ax.set_xlabel("IPTG (mM; 0 at left)")
+            ax.set_ylabel("wt's growth minus at 1 mM (1/min)")
+        axes[0, 0].legend(fontsize=10)
+        fig.tight_layout()
+        pdf.savefig(fig)
+        plt.close(fig)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/x-vs-binding/README.md b/planning/studies/x-vs-binding/README.md
new file mode 100644
index 00000000..7b1d2db4
--- /dev/null
+++ b/planning/studies/x-vs-binding/README.md
@@ -0,0 +1,163 @@
+# Growth X against in vitro binding theta
+
+**Question.** Does the two-stage fit's growth X follow in vitro binding
+theta for the genotypes we measured, and does it follow it the same way
+in each selection marker? Which X level does binding support when fits
+disagree (the k-shift refits moved every mutant by +0.3 against wt,
+`planning/studies/two-stage-validation/`)?
+
+**Decision it feeds.** How to replace the two-stage fit's k shift
+(`planning/two-stage-fit.md` step 4) and whether the pheS disagreement is
+growth physics or a fit problem (`planning/offset-mode-growth-transition.md`).
+
+## Inputs
+
+- Binding: `planning/dev-data/binding/2026-10-15_binding.csv` (gitignored,
+  C10). Nine genotypes, theta per well normalized to day-of internal
+  controls, mostly at 2 and 20 uM repressor dimer; wt at 0.02-120 uM. A
+  work in progress (more replicates coming).
+- Growth X: the real run of `planning/studies/two-stage-validation/`
+  (`run_real/`): stage 2 (both libraries), `consistency/` (kanR alone)
+  and `consistency_pheS/` (pheS alone), all with k and m pinned at
+  stage 1.
+- wt monoculture rates and the processed growth tables:
+  `planning/dev-data/processed/`.
+
+Spikes (user, 2026-10-15): wt, M42I, H74A, K84L, M42I/H74A, M42I/K84L,
+H74A/K84L, M42I/H74A/K84L and D88A, mixed evenly and spiked into the
+growth culture at single tile 1 : single tile 2 : doubles : spikes =
+100 : 100 : 1000 : 1, as `processed/library_config.yaml` has it. Of the
+binding genotypes, G65H and V95N are bulk only. All nine have at least
+1e5 reads.
+
+## How to run
+
+```bash
+python x_vs_binding.py ../../dev-data/binding/2026-10-15_binding.csv ../two-stage-validation/run_real ../../dev-data/processed --out out
+```
+
+Binding theta goes onto the X gauge through wt at the same protein
+concentration, `(theta - theta_wt(1 mM)) / (theta_wt(0) - theta_wt(1 mM))`.
+Outputs in `out/` (gitignored): `x_vs_binding.csv`, `summary.csv`,
+`wt_rates.csv` and `YYYY-MM-DD_x-vs-binding.pdf` (per-genotype curves, X
+against gauged theta, wt's rates).
+
+## Commit
+
+The commit that adds this study.
+
+## Results
+
+### Run 1 (2026-10-15)
+
+**At 2 uM protein, kanR's X follows binding with no correction.** Mutant
+rows (6 genotypes at 2 uM, 8 at 20 uM):
+
+| protein | fit | X - gauged theta, mean | rms | affine rms | r |
+|---|---|---|---|---|---|
+| 2 uM | kanR alone | -0.03 | 0.12 | 0.09 | 0.89 |
+| 2 uM | both libraries | -0.10 | 0.15 | 0.09 | 0.89 |
+| 2 uM | pheS alone | -0.20 | 0.32 | 0.24 | 0.67 |
+| 20 uM | kanR alone | -0.16 | 0.32 | 0.27 | 0.63 |
+| 20 uM | both libraries | -0.23 | 0.36 | 0.27 | 0.64 |
+| 20 uM | pheS alone | -0.33 | 0.44 | 0.30 | 0.70 |
+
+The 2026-10-01 comparison (`planning/dev-data/real_fit`, theta
+interpolated to 10 uM, the old full-library X) found no affine map (rms
+0.30). H74A, H74A/K84L, K84L, M42I/H74A and G65H now sit on their 2 uM
+curves. D88A and V95N, measured only at 20 uM, match that.
+
+**wt's curve has the 20 uM midpoint.** Its growth X falls at about
+30 uM IPTG, as its 20 uM binding does, where its 2 uM binding falls at
+about 3 uM. The mutants' levels match 2 uM. One protein concentration
+does not reproduce both.
+
+**M42I** grows as if less repressed than its 2 uM binding at 0-1 uM IPTG
+(X 0.3-0.4 below), the same direction as before.
+
+**Binding supports stage 2's level, not the refits'.** The k-shift
+control moved every mutant's X by about +0.32 over stage 2, which would
+take the 2 uM mean difference from -0.10 to about +0.2, against kanR
+alone's -0.03.
+
+**The pheS-only fit is degenerate at wt, but that is not why pheS
+disagrees.** Its wt X falls from 1 to 0 between 0 and 1 uM IPTG, where
+wt's own library trajectories fall between 10 and 30 uM. Refitting pheS
+alone with wt's curve held at stage 2's (K 0.0238 mM, n 0.753;
+`theta_wt_hill_K_fixed`/`_n_fixed`, `consistency_pheS_wtheld/`) fixed
+wt's curve and changed nothing else: mutants' X against the first pheS
+fit r 0.996, slope 0.99. Against kanR alone, deep genotypes, the held
+fit gives slope 0.52, intercept +0.24 (X 0.17 lower at 0 IPTG, 0.17
+higher at 1 mM; 18% of rows beyond 2 SD, median z -0.04). The pheS
+compression is real.
+
+**Binding sides with kanR.** At 2 uM, X minus gauged theta:
+
+| | kanR | pheS (wt held) |
+|---|---|---|
+| H74A | +0.07 | -0.33 |
+| H74A/K84L | -0.01 | -0.50 |
+| K84L | -0.03 | -0.25 |
+| M42I/H74A | 0.00 | -0.27 |
+| G65H | -0.06 | +0.10 |
+| M42I | -0.14 | -0.11 |
+
+pheS puts the weak binders below X = 0: they grow worse in 4CP than wt
+does at 1 mM IPTG, though their in vitro occupancy (and their kanR X)
+stays above wt's there. Relative to every mutant, wt does better in 4CP
+when derepressed than its occupancy says. Since X is gauged on wt, the
+pheS anomaly is wt's, or a pheS response that is not one function of
+occupancy for wt and the mutants alike.
+
+**wt's library rates are too noisy to settle the pheS question.** The
+slope of wt's ln_cfu over the selection, per replicate, scatters by
+0.006-0.015 per minute (tube totals from single OD600 readings). In
+pheS+4CP wt's library rate averages 0.024 at 0-3 uM IPTG and 0.015 at
+30 uM-1 mM, against the monoculture's 0.016 and 0.004: a smaller drop in
+the library, but at about one SE per IPTG level.
+
+**Next.** Find why wt does better in 4CP than the mutants at the same or
+lower occupancy (see the two-stage validation README). Repeat with the
+replicated binding data.
+
+### Run 2 (2026-10-18): does M42I's inverted order survive the two-stage X?
+
+The 2026-10-01 "no map" found M42I's growth K about half of wt's while its
+binding K is about 10x wt's, and growth curves broader than binding, both
+on X compressed 1.47x by the doubles and shared by both markers
+(`planning/findings-ledger.md`, Open item 2). `hill_compare.py` fits a
+four-parameter Hill curve to each genotype's predicted X (median, eight
+IPTG concentrations) from this study's two-stage run (stage 2, both
+libraries; `consistency/`, kanR alone) and to its mean binding theta at 2
+and 20 uM. The pheS-alone fit is left out: it is degenerate at wt (run 1).
+K is trusted only for curves with amplitude >= 0.35 in every source: wt,
+M42I, M42I/H74A, D88A, V95N (G65H, H74A, K84L and H74A/K84L are nearly flat
+in growth or binding).
+
+K over wt's K:
+
+| genotype | X, kanR alone | X, both | binding 2 uM | binding 20 uM |
+|---|---|---|---|---|
+| wt (K) | 21 uM | 23 uM | 2.6 uM | 21 uM |
+| M42I | 0.18 | 0.22 | 3.6 | 5.3 |
+| M42I/H74A | 0.056 | 0.056 | 0.52 | 0.55 |
+| D88A | 4.3 | 4.7 | - | 9.2 |
+| V95N | 2.2 | 2.0 | - | 3.2 |
+
+- **M42I's inversion survives and is larger:** in growth it responds at
+  about a fifth of wt's IPTG, in binding at 4-5 times wt's (a factor of
+  20-30 apart; 2026-10-01 had about 20).
+- **M42I/H74A** keeps the binding order but sits 10x lower in growth.
+  D88A and V95N keep it within 2x.
+- **wt's growth midpoint equals its 20 uM binding midpoint** (21 uM); its
+  2 uM binding midpoint is 8x lower.
+- **Growth curves stay broader:** n 0.7-0.95 on the kanR-only X for most
+  genotypes (V95N 1.9, M42I/H74A 1.2), against 1.9-3.2 in binding at 20 uM
+  and 1.2-1.9 at 2 uM.
+
+So the two facts the "no map" rested on are not artifacts of the
+compressed, shared X. Both genotypes carrying M42I respond at lower IPTG in
+vivo than in vitro, relative to wt. One reading (a hypothesis, not tested):
+a genotype-specific in vivo factor such as repressor level, since binding K
+rises with protein (wt 2.6 to 21 uM from 2 to 20 uM protein).
+
diff --git a/planning/studies/x-vs-binding/hill_compare.py b/planning/studies/x-vs-binding/hill_compare.py
new file mode 100644
index 00000000..69d37b9d
--- /dev/null
+++ b/planning/studies/x-vs-binding/hill_compare.py
@@ -0,0 +1,93 @@
+"""
+Hill midpoints and steepness of growth X against binding, per genotype
+(README, run 2): does M42I's inverted order survive on the two-stage,
+kanR-only X?
+
+    python hill_compare.py ../../dev-data/binding/2026-10-15_binding.csv ../two-stage-validation/run_real --out out
+
+Fits a four-parameter Hill curve (top, bottom, K, n; n in [0.2, 8]) to
+each genotype's predicted X (median, 8 IPTG concentrations) from the
+two-stage run's stage 2 (both libraries) and consistency/ (kanR alone),
+and to its mean binding theta at 2 and 20 uM protein. Writes out/hill.csv
+and prints K (with its ratio to wt's), n and amplitude. K is trustworthy
+only where the curve has amplitude (here >= 0.35).
+"""
+
+import argparse
+import os
+
+import numpy as np
+import pandas as pd
+from scipy.optimize import least_squares
+
+GENOTYPES = ["wt", "M42I", "H74A", "K84L", "M42I/H74A", "H74A/K84L", "M42I/K84L",
+             "M42I/H74A/K84L", "D88A", "G65H", "V95N"]
+FITS = {"X both": "stage2", "X kanR": "consistency"}
+MIN_AMPLITUDE = 0.35
+
+
+def fit_hill(c, y):
+    c, y = np.asarray(c, float), np.asarray(y, float)
+    lc = np.log(np.where(c == 0, 1e-9, c))
+
+    def resid(p):
+        top, bottom, log_k, log_n = p
+        return top + (bottom - top) / (1 + np.exp(-np.exp(log_n) * (lc - log_k))) - y
+
+    best = None
+    for k0 in np.log([1e-4, 1e-3, 1e-2, 1e-1]):
+        r = least_squares(resid, [y[0], y[-1], k0, 0.0],
+                          bounds=([-2, -2, np.log(1e-6), np.log(0.2)],
+                                  [3, 3, np.log(10), np.log(8)]))
+        if best is None or r.cost < best.cost:
+            best = r
+    top, bottom, log_k, log_n = best.x
+    return dict(K_uM=1e3 * np.exp(log_k), n=np.exp(log_n), amplitude=top - bottom,
+                rms=float(np.sqrt(np.mean(best.fun ** 2))))
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("binding_csv")
+    ap.add_argument("run_dir")
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+    rows = []
+    for name, sub in FITS.items():
+        p = pd.read_csv(os.path.join(args.run_dir, sub, "tfs_pred_theta.csv"),
+                        usecols=["genotype", "titrant_conc", "q0.5"])
+        p["genotype"] = p["genotype"].astype(str)
+        for g in GENOTYPES:
+            x = p[p["genotype"] == g].sort_values("titrant_conc")
+            if len(x) >= 5:
+                rows.append(dict(source=name, genotype=g,
+                                 **fit_hill(x["titrant_conc"], x["q0.5"])))
+    b = pd.read_csv(args.binding_csv).dropna(subset=["theta"])
+    b["protein_uM"] = (b["protein_dimer_conc_molar"] * 1e6).round(3)
+    b["c"] = (b["iptg_conc_molar"] * 1e3).round(9)
+    for protein in (2.0, 20.0):
+        for g in GENOTYPES:
+            x = b[(b["genotype"] == g) & np.isclose(b["protein_uM"], protein)]
+            if x["c"].nunique() >= 5:
+                m = x.groupby("c")["theta"].mean().reset_index()
+                rows.append(dict(source=f"binding {protein:g} uM", genotype=g,
+                                 **fit_hill(m["c"], m["theta"])))
+    r = pd.DataFrame(rows)
+    wt = r[r["genotype"] == "wt"].set_index("source")["K_uM"]
+    r["K_over_wt"] = r["K_uM"] / r["source"].map(wt)
+    r["trusted"] = r["amplitude"] >= MIN_AMPLITUDE
+    r.to_csv(os.path.join(args.out, "hill.csv"), index=False)
+    pd.set_option("display.width", 200)
+    for col, fmt in (("K_over_wt", "{:.2g}"), ("K_uM", "{:.3g}"), ("n", "{:.2f}"),
+                     ("amplitude", "{:.2f}")):
+        t = r.pivot(index="genotype", columns="source", values=col)
+        print(f"\n{col}")
+        print(t.map(lambda v: "" if pd.isna(v) else fmt.format(v)).to_string())
+    print(f"\ntrusted (amplitude >= {MIN_AMPLITUDE} in every source with data):")
+    ok = r.groupby("genotype")["trusted"].all()
+    print(", ".join(ok[ok].index))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/studies/x-vs-binding/x_vs_binding.py b/planning/studies/x-vs-binding/x_vs_binding.py
new file mode 100644
index 00000000..270bf913
--- /dev/null
+++ b/planning/studies/x-vs-binding/x_vs_binding.py
@@ -0,0 +1,254 @@
+"""
+Growth X against in vitro binding theta for the binding genotypes
+(planning/studies/x-vs-binding/README.md).
+
+    python x_vs_binding.py BINDING_CSV RUN_DIR PROCESSED_DIR --out out
+
+BINDING_CSV   wells: genotype, biorep, date, protein_dimer_conc_molar,
+              iptg_conc_molar, theta (dev-data/binding/2026-10-15_binding.csv)
+RUN_DIR       a tfs-fit-two-stage run with stage2/, consistency/ (kanR
+              alone), consistency_pheS/ (pheS alone) and, when present,
+              consistency_pheS_wtheld/ (pheS alone, wt's curve held)
+PROCESSED_DIR growth_{kanR,pheS}.csv.gz and wt_control_rate_summary.csv
+
+Binding theta goes onto the X gauge through wt at the same protein
+concentration: (theta - theta_wt(1 mM)) / (theta_wt(0) - theta_wt(1 mM)).
+Writes out/x_vs_binding.csv (one row per genotype, protein, IPTG and fit),
+out/summary.csv (gauged and affine agreement per protein and fit),
+out/wt_rates.csv (wt's growth rate in the library, model-free, against the
+monoculture) and out/YYYY-MM-DD_x-vs-binding.pdf.
+"""
+
+import argparse
+import datetime
+import os
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.backends.backend_pdf import PdfPages  # noqa: E402
+
+plt.rc("pdf", fonttype=42)
+plt.rc("font", size=14)
+plt.rc("axes", titlesize=14, labelsize=16)
+plt.rc("legend", fontsize=12)
+
+FITS = {"both": "stage2", "kanR": "consistency", "pheS": "consistency_pheS",
+        "pheS_wtheld": "consistency_pheS_wtheld"}
+FIT_COLOR = {"both": "#000000", "kanR": "#0072B2", "pheS": "#D55E00",
+             "pheS_wtheld": "#E69F00"}
+PROTEIN_MARK = {2.0: ("o", "#009E73"), 20.0: ("s", "#CC79A7")}
+SCREEN_IPTG = [0.0, 1e-4, 1e-3, 3e-3, 0.01, 0.03, 0.1, 1.0]   # mM
+ZERO_X = 3e-5   # where 0 IPTG is drawn on the log axis (mM)
+
+
+def read_binding(path):
+    """Mean theta per genotype, protein (uM dimer) and IPTG (mM)."""
+    b = pd.read_csv(path).dropna(subset=["theta"])
+    b["protein_uM"] = (b["protein_dimer_conc_molar"] * 1e6).round(3)
+    b["titrant_conc"] = (b["iptg_conc_molar"] * 1e3).round(7)
+    b = b[b["titrant_conc"].isin(SCREEN_IPTG)]
+    out = b.groupby(["genotype", "protein_uM", "titrant_conc"])["theta"].agg(
+        theta="mean", theta_sd="std", n_wells="size").reset_index()
+    out["theta_se"] = out["theta_sd"] / np.sqrt(out["n_wells"])
+    return out
+
+
+def gauge(b):
+    """Put theta on the X gauge through wt at each protein concentration."""
+    rows = []
+    for p, d in b.groupby("protein_uM"):
+        w = d[d["genotype"] == "wt"].set_index("titrant_conc")["theta"]
+        if 0.0 not in w.index or 1.0 not in w.index:
+            continue
+        span = w[0.0] - w[1.0]
+        rows.append(d.assign(theta_gauged=(d["theta"] - w[1.0]) / span,
+                             theta_gauged_se=d["theta_se"] / abs(span)))
+    return pd.concat(rows, ignore_index=True)
+
+
+def read_x(run_dir, genotypes):
+    rows = []
+    for fit, sub in FITS.items():
+        p = os.path.join(run_dir, sub, "tfs_pred_theta.csv")
+        if not os.path.exists(p):
+            print(f"no {p}; skipping {fit}")
+            continue
+        t = pd.read_csv(p, usecols=["genotype", "titrant_conc", "q0.025",
+                                    "q0.5", "q0.975"])
+        t = t[t["genotype"].isin(genotypes)].assign(fit=fit)
+        t["titrant_conc"] = t["titrant_conc"].round(7)
+        rows.append(t)
+    return pd.concat(rows, ignore_index=True)
+
+
+def agreement(m):
+    rows = []
+    for (p, fit), d in m[m["genotype"] != "wt"].groupby(["protein_uM", "fit"]):
+        x, t = d["q0.5"].to_numpy(), d["theta_gauged"].to_numpy()
+        slope, icpt = np.polyfit(t, x, 1)
+        rows.append(dict(protein_uM=p, fit=fit, n=len(d),
+                         n_genotypes=d["genotype"].nunique(),
+                         gauged_mean=np.mean(x - t),
+                         gauged_rms=np.sqrt(np.mean((x - t) ** 2)),
+                         affine_intercept=icpt, affine_slope=slope,
+                         affine_rms=np.sqrt(np.mean((x - icpt - slope * t) ** 2)),
+                         r=np.corrcoef(t, x)[0, 1]))
+    return pd.DataFrame(rows)
+
+
+def wt_rates(processed_dir, cache):
+    """wt's growth rate per condition and IPTG in the library (slope of its
+    ln_cfu over t_sel, averaged over replicates), with the monoculture's."""
+    if os.path.exists(cache):
+        return pd.read_csv(cache)
+    keep = ["genotype", "replicate", "condition_sel", "t_sel", "titrant_conc",
+            "ln_cfu", "sample_ln_cfu"]
+    parts = []
+    for lib in ("kanR", "pheS"):
+        for ch in pd.read_csv(os.path.join(processed_dir, f"growth_{lib}.csv.gz"),
+                              usecols=keep, chunksize=2_000_000):
+            parts.append(ch[ch["genotype"] == "wt"])
+    w = pd.concat(parts)
+
+    def slopes(g):
+        if g["t_sel"].nunique() < 2:
+            return pd.Series({"library_rate": np.nan, "population_rate": np.nan})
+        return pd.Series({
+            "library_rate": np.polyfit(g["t_sel"], g["ln_cfu"], 1)[0],
+            "population_rate": np.polyfit(g["t_sel"], g["sample_ln_cfu"], 1)[0]})
+
+    r = (w.groupby(["condition_sel", "titrant_conc", "replicate"])
+         .apply(slopes, include_groups=False)
+         .groupby(["condition_sel", "titrant_conc"])
+         .agg(["mean", "std"]))
+    r.columns = [f"{a}_{'sd' if b == 'std' else b}" for a, b in r.columns]
+    mono = pd.read_csv(os.path.join(processed_dir, "wt_control_rate_summary.csv"))
+    mono = mono.set_index(["condition_sel", "titrant_conc"])[["rate_mean", "rate_sd"]]
+    mono.columns = ["mono_rate_mean", "mono_rate_sd"]
+    out = r.join(mono, how="outer").reset_index()
+    out.to_csv(cache, index=False)
+    return out
+
+
+def xpos(c):
+    return np.where(c > 0, c, ZERO_X)
+
+
+def plot_curves(pdf, m, b, spiked):
+    genos = ["wt"] + sorted(g for g in m["genotype"].unique() if g != "wt")
+    n = len(genos)
+    ncol = 3
+    fig, axes = plt.subplots(int(np.ceil(n / ncol)), ncol, figsize=(15, 4.2 * np.ceil(n / ncol)),
+                             sharex=True, sharey=True)
+    for ax, g in zip(axes.flat, genos):
+        x = m[m["genotype"] == g].drop_duplicates(["fit", "titrant_conc"])
+        for fit, d in x.groupby("fit"):
+            d = d.sort_values("titrant_conc")
+            ax.fill_between(xpos(d["titrant_conc"]), d["q0.025"], d["q0.975"],
+                            color=FIT_COLOR[fit], alpha=0.15, lw=0)
+            ax.plot(xpos(d["titrant_conc"]), d["q0.5"], "-", color=FIT_COLOR[fit],
+                    label=f"X, {fit}")
+        bb = b[b["genotype"] == g]
+        for p, (mk, col) in PROTEIN_MARK.items():
+            d = bb[bb["protein_uM"] == p].sort_values("titrant_conc")
+            if len(d):
+                ax.errorbar(xpos(d["titrant_conc"]), d["theta_gauged"],
+                            yerr=1.96 * d["theta_gauged_se"].fillna(0), fmt=mk + "--",
+                            color=col, mfc="white", ms=7, lw=1,
+                            label=f"theta, {p:g} uM")
+        ax.set_xscale("log")
+        ax.axhline(0, color="0.8", lw=0.8, zorder=0)
+        ax.axhline(1, color="0.8", lw=0.8, zorder=0)
+        ax.set_title(f"{g} ({'spike' if g in spiked else 'bulk only'})")
+    for ax in axes.flat[n:]:
+        ax.axis("off")
+    for ax in axes[-1]:
+        ax.set_xlabel("IPTG (mM; 0 at left)")
+    for ax in axes[:, 0]:
+        ax.set_ylabel("X, gauged theta")
+    axes.flat[0].legend(loc="lower left", fontsize=10)
+    fig.tight_layout()
+    pdf.savefig(fig)
+    plt.close(fig)
+
+
+def plot_scatter(pdf, m):
+    ps = sorted(m["protein_uM"].unique())
+    fits = [f for f in FITS if f in set(m["fit"])]
+    fig, axes = plt.subplots(len(ps), len(fits), figsize=(5 * len(fits), 5 * len(ps)),
+                             squeeze=False, sharex=True, sharey=True)
+    for i, p in enumerate(ps):
+        for j, fit in enumerate(fits):
+            ax = axes[i, j]
+            d = m[(m["protein_uM"] == p) & (m["fit"] == fit) & (m["genotype"] != "wt")]
+            ax.plot([-0.5, 1.5], [-0.5, 1.5], color="0.7", lw=1)
+            ax.errorbar(d["theta_gauged"], d["q0.5"],
+                        yerr=[d["q0.5"] - d["q0.025"], d["q0.975"] - d["q0.5"]],
+                        fmt="o", color=FIT_COLOR[fit], ms=5, alpha=0.7, lw=0.8)
+            ax.set_title(f"{fit}, {p:g} uM protein")
+            if i == len(ps) - 1:
+                ax.set_xlabel("gauged binding theta")
+            if j == 0:
+                ax.set_ylabel("growth X")
+    fig.tight_layout()
+    pdf.savefig(fig)
+    plt.close(fig)
+
+
+def plot_wt_rates(pdf, r):
+    conds = [c for c in ("kanR+kan", "pheS+4CP") if c in set(r["condition_sel"])]
+    fig, axes = plt.subplots(1, len(conds), figsize=(6.5 * len(conds), 5), squeeze=False)
+    for ax, c in zip(axes[0], conds):
+        d = r[r["condition_sel"] == c].sort_values("titrant_conc")
+        for col, lab, colr in (("library_rate", "wt in the library", "#0072B2"),
+                               ("mono_rate", "wt monoculture", "#E69F00"),
+                               ("population_rate", "library population", "#000000")):
+            dd = d.dropna(subset=[f"{col}_mean"])
+            ax.errorbar(xpos(dd["titrant_conc"]), dd[f"{col}_mean"], yerr=dd[f"{col}_sd"],
+                        fmt="o-", color=colr, label=lab, ms=6, lw=1.2, capsize=2)
+        ax.set_xscale("log")
+        ax.set_title(c)
+        ax.set_xlabel("IPTG (mM; 0 at left)")
+        ax.set_ylabel("growth rate (1/min)")
+    axes[0, 0].legend(fontsize=11)
+    fig.tight_layout()
+    pdf.savefig(fig)
+    plt.close(fig)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("binding_csv")
+    ap.add_argument("run_dir")
+    ap.add_argument("processed_dir")
+    ap.add_argument("--out", default="out")
+    args = ap.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+
+    b = gauge(read_binding(args.binding_csv))
+    lib = pd.read_csv(os.path.join(args.run_dir, "stage2", "tfs_configure_library.csv"))
+    spiked = set(lib.loc[lib["in_spiked_origin"].astype(bool), "genotype"])
+    x = read_x(args.run_dir, set(b["genotype"]))
+    m = b.merge(x, on=["genotype", "titrant_conc"])
+    m["spiked"] = m["genotype"].isin(spiked)
+    m.to_csv(os.path.join(args.out, "x_vs_binding.csv"), index=False)
+
+    s = agreement(m)
+    s.to_csv(os.path.join(args.out, "summary.csv"), index=False)
+    print(s.round(3).to_string(index=False))
+
+    r = wt_rates(args.processed_dir, os.path.join(args.out, "wt_rates.csv"))
+    print(r.round(4).to_string(index=False))
+
+    stamp = datetime.date.today().isoformat()
+    with PdfPages(os.path.join(args.out, f"{stamp}_x-vs-binding.pdf")) as pdf:
+        plot_curves(pdf, m, b, spiked)
+        plot_scatter(pdf, m)
+        plot_wt_rates(pdf, r)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/planning/two-stage-fit.md b/planning/two-stage-fit.md
new file mode 100644
index 00000000..c917e6e9
--- /dev/null
+++ b/planning/two-stage-fit.md
@@ -0,0 +1,242 @@
+---
+title: The two-stage fit as one command, with honest intervals
+status: active
+filed: 2026-10-13
+area: tfmodel
+revisit_when: >-
+  Active (decisions settled by the user, 2026-10-13). The real-data route
+  shipped in 0.5.0 (2026-10-24); steps 5 and 7, for the students' design
+  simulations, are left. Keep the step list current.
+related:
+  - planning/deep-coverage.md
+  - planning/experiment-pipeline.md
+  - planning/design-noise-sweep.md
+  - planning/studies/m-bias-factorial/README.md
+  - planning/studies/two-stage-validation/README.md
+  - planning/studies/full-size-sim/make_sim_config.py
+  - src/tfscreen/tfmodel/scripts/configure_model_cli.py
+  - src/tfscreen/tfmodel/scripts/fit_model_cli.py
+  - src/tfscreen/tfmodel/scripts/predict_theta_cli.py
+  - src/tfscreen/tfmodel/analysis/prediction.py
+---
+
+## Why
+
+The m-bias study (`planning/studies/m-bias-factorial/`) found the fit
+that gives honest X for this screen, but it runs as a dozen study
+scripts and hand-copied cluster commands:
+
+- The full-library MAP compresses X about 1.45-fold: each double pulls m
+  steeper, and the pulls add. A stage 1 without the doubles gets k and m
+  right; stage 2 fits the full library with them pinned.
+- Congression flattens pheS's m about 10% in any stage 1 large enough to
+  pin X's scale; stage 1 must fit the congression mixture (`max` rule,
+  lambda fitted with the lab's measurement as its prior). Stage 2 does not
+  need it.
+- Stage 1's Laplace understates k's error about fivefold. A delete-d
+  jackknife of stage 1 measures it. Its effect on X is a shift shared by
+  every non-wt genotype (one SD moves X by -0.032 to -0.038 on the
+  simulation; genotype-to-genotype SD 0.005).
+- With that shift in, the simulation's 95% X coverage is 0.94 at 1e4-1e5
+  reads and 0.91 above 1e5 (0.90 and 0.76 without), the rest being stage
+  1's accepted steep m bias (2-6%).
+
+Students running design simulations, and the NSF preliminary data, need
+this as one documented, tested command that reproduces those numbers.
+
+## The fit, step by step
+
+1. **Stage 1 genotypes.** wt, every spiked genotype and every single
+   (from the library config: `in_spiked_origin`, or one mutation), taken
+   from the growth table in memory. About 950 genotypes here.
+2. **Stage 1 fit.** The configured model plus `transformation: mixture`,
+   `congression_theta_rule: max` and the lambda prior; staged MAP. Keeps
+   every condition's k and m (and the fitted lambda, for the record).
+3. **Jackknife.** N subsamples (default 30), each dropping a share of the
+   tubes (default 20%) at random, stratified by condition and IPTG level,
+   keeping one tube per stratum; each refits stage 1. Tubes cannot be
+   duplicated (a duplicate lands on its original's tensor cell), hence no
+   classic bootstrap. Output: k and m SEs (variance factor (n - d) / d),
+   k's covariance and its main direction.
+4. **Stage 2.** The full library, `single`, every condition's k and m
+   pinned at stage 1 (`--growth_priors` pin columns); staged MAP; arrowhead
+   Laplace.
+5. **The k shift.** Two stage 2 refits with k at plus and minus one SD
+   along k's main direction (m at stage 1's), started from stage 2's MAP
+   (`--init_from`); X predicted at each. The shift is half their
+   difference, per genotype and concentration.
+6. **Posterior draws with the shift.** Each posterior draw gets one shared
+   standard normal z, and every non-wt genotype's X in that draw moves by
+   z times its shift; then quantiles. Rankings and differences between
+   genotypes mostly cancel it; epistasis on X keeps the one copy it
+   should (wt does not shift).
+7. **Marker-consistency flag** (on by default; `--no_consistency` skips
+   it). Stage 2 refit on one library's
+   tubes (kanR), against the full stage 2; per genotype, the z-score of
+   the level offset between them. About 8% of the real deep doubles
+   disagree beyond 2 SD against 2.5% simulated (cause unknown; not a
+   library-specific dk_geno).
+
+## Interface
+
+One new command, `tfs-fit-two-stage`, that chains the existing CLI
+functions (it does not reimplement them). Each step writes its own
+subdirectory (`stage1/`, `jackknife/jk_NN/`, `stage2/`, `kshift/plus/`,
+`kshift/minus/`, `consistency/`) and is skipped when its outputs exist, so
+a run resumes and a step reruns by deleting its directory.
+
+- `tfs-fit-two-stage config.yaml` runs every step in order on one machine.
+- `--step NAME` runs one step (`stage1`, `jackknife`, `stage2`, `kshift`,
+  `predict`, `consistency`), and `--jackknife_task i` one jackknife
+  subsample, so a SLURM array can run the jackknife in parallel. The
+  command never writes cluster settings; the docs show an example array
+  script.
+- The YAML holds what `tfs-configure-model` takes now (data, library
+  config, model choices, held priors) plus the two-stage settings: the
+  lambda prior (required with congression), the jackknife N and drop
+  share. The consistency step runs unless `--no_consistency` (or the
+  YAML) turns it off.
+- Prediction takes the shift as an input: `tfs-predict-theta` and
+  `tfs-predict-epistasis` get `--shared_shift kshift.csv`, applied to the
+  draws before quantiles (`prediction.predict` already works from joint
+  draws). The two-stage command writes `kshift.csv`.
+
+This is a stage of the deferred orchestrator (`planning/experiment-
+pipeline.md` step 5, `tfs-run-experiment`): built so the orchestrator can
+call it later, without waiting for it.
+
+**Cost per experiment** (one GPU, full-size library): stage 1 15-30
+minutes; jackknife 30 x 15-30 minutes (about 10 hours serial, about one
+hour as an array of 10); stage 2 2-5 hours plus the Laplace; the two
+shift refits 1-2 hours each from the MAP; consistency about 5 hours.
+About a day serial.
+
+**Design simulations do not need the intervals.** A design sweep compares
+points (slope, RMSE, r by depth) across noise settings; `--step stage1`,
+`--step stage2` and `predict` without the shift cost about 3-6 hours per
+simulation. Intervals matter for the final analysis.
+
+## Steps
+
+1. [x] **Library functions, with tests.** Port from the study scripts:
+   stage 1 genotype selection; stratified tube subsampling
+   (`resample_growth.py`); the jackknife summary, k's main direction and
+   the plus/minus pin tables (`jackknife_summary.py`, `held_priors.py`);
+   the shift table (`k_band.py`). Done 2026-10-13: `tfmodel/two_stage.py`,
+   `tests/tfscreen/tfmodel/test_two_stage.py`; on the seed-1 jackknife and
+   k-band refits it reproduces the study's k and m SEs and the shift
+   exactly (k's step is 1.7% smaller: the covariance now divides by N, as
+   the jackknife variance does, where the study script divided by N - 1).
+2. [x] **The shared shift in prediction.** `--shared_shift` for
+   `tfs-predict-theta` and `tfs-predict-epistasis`. Tests: one z per draw
+   shared across genotypes; wt unshifted; quantiles widen by the shift
+   in quadrature when the posterior is otherwise narrow; differences
+   between genotypes unchanged. Done 2026-10-13:
+   `tfmodel/analysis/shared_shift.py`, wired into `extract_theta_curves`
+   and `extract_theta_epistasis` and both CLIs (refused for out-of-training
+   genotypes and for a single MAP draw);
+   `tests/tfscreen/tfmodel/analysis/test_shared_shift.py`.
+3. [x] **`tfs-fit-two-stage`**, with step skipping, `--step` and
+   `--jackknife_task`; the YAML schema; `docs/source/cli.rst`. Done
+   2026-10-13 (`tfmodel/scripts/fit_two_stage_cli.py`,
+   `examples/tfmodel/two_stage.yaml`). The YAML's top-level keys are
+   `tfs-configure-model`'s arguments (paths relative to the YAML); the
+   `two_stage` block holds transformation_lambda (required), seed,
+   jackknife_subsamples, jackknife_drop_frac, num_posterior_samples,
+   consistency, consistency_library, predict_epistasis, fit and posterior
+   (extra arguments). It refuses a non-relative or non-linear model,
+   unshared replicates, transformation keys at the top level and pins in
+   set_priors. Each step runs in its own directory with absolute input
+   paths; the stage-1 and one-library tables are written by chunked
+   filters (a full-size table is 8 GB). Tests mock the chained functions
+   (`test_fit_two_stage_cli.py`); a real end-to-end run on round 1's small
+   table (6,500 genotypes, fits cut to 40 steps) finished every step in
+   4.7 minutes, stage 2's k equal to stage 1's, wt's intervals untouched,
+   and a rerun skipped everything.
+4. [x] **Validate by reproduction.** Rerun the seed-1 and seed-2
+   simulations through the command and match the study's numbers
+   (stage 1 m ratios; coverage 0.94 / 0.91 on seed 1); rerun the real
+   data and match `held_mix_real`. Set up 2026-10-13 as
+   `planning/studies/two-stage-validation/` (three chained SLURM runs via
+   `submit.sh`; `validate.py` compares each with the study's outputs and
+   gives the consistency flag's no-signal rate on the simulations).
+   Run 1 (2026-10-15, README "Results"): stage 1, the jackknife, stage 2
+   and the consistency flag reproduce the study (flag 1.9% of deep
+   genotypes on both simulations, 10.6% real). The k shift does not:
+   its refits restart at step size 1e-3, leave stage 2's MAP and
+   re-descend along the dk_geno population against wt (every genotype's
+   dk_geno +0.006 to +0.012, X +0.25 to +0.32 on the real data, the
+   same with k unchanged), so the shift measures where the optimizer
+   stopped. Proposed replacement: k and m free in stage 2 under the
+   jackknife covariance as a prior, uncertainty from the Laplace, with
+   the dk_geno population location held at stage 1. Open until that is
+   built and validated.
+   Decided 2026-10-19 (`planning/release-0.5.0.md` decision 1): repair
+   the shift first, and if one validation round fails, ship without it.
+   Built 2026-10-19: the refits start at `kshift_step_size` (1e-5) with
+   the dk_geno population held at stage 2's MAP (not stage 1's, which
+   would change the validated stage 2), plus a k-unchanged control refit
+   and `kshift/drift.csv`. Run 2 of the validation study reruns kshift
+   and predict on the three runs.
+   Run 2 (2026-10-20): the drift is mostly gone but the shift is a third
+   or less of the true one on simulations and the control still moves
+   too much, so the fallback applies (user): `two_stage.kshift` off by
+   default, with the jackknife; intervals without k's uncertainty cover
+   0.88-0.91 up to 1e5 reads and 0.76-0.81 above.
+5. [ ] **Scoring for simulations.** X against the truth by read depth
+   (`score_x.py`) as a `tfs-summarize-fit` option or its own CLI, so the
+   students score design sweeps without study scripts.
+6. [x] **Consistency flag** step (on by default, `--no_consistency`) and
+   its output column. Built with step 3: `consistency/consistency.csv`
+   (per genotype: level offset after the affine map, its SD, z,
+   `inconsistent` beyond |z| 2; `two_stage.marker_consistency`). Its
+   threshold should be calibrated against the simulation in step 4.
+7. [ ] **A simulation config from a real experiment**, for the students'
+   design sweeps: `tfs-build-sim-config`, the port of
+   `planning/studies/full-size-sim/make_sim_config.py`. Inputs: the tube
+   table (the simulation follows it as its `design`), the per-tube counts
+   (total reads and each library's unassigned share), the initial (pre-
+   split) composition (the realized `library_mixture` via
+   `estimate_library_mixture`, and wt's share of the pool), the library
+   config, the OD600 calibration, the wt monoculture rates (k and m per
+   selection condition at the gauge concentrations), and optionally a
+   pilot run to calibrate cfu0 per library from real against simulated
+   tube totals. The values the study fitted to this screen by hand become
+   explicit arguments, not constants: pool skew by origin (doubles 2.0,
+   singles 0.8, spikes 0.5 here), clones per double (300), the dk_geno
+   spread (0.15), the pre-split OD600 and volumes, lambda. A documented
+   example (with this screen's values, no lab data) shows them. Validate:
+   the built config reproduces `sim_realistic_s2`'s read depth (median
+   double about 470 reads) and composition.
+8. [x] **Docs.** A recipe page (what each step does and why, with the
+   study's numbers), an example SLURM array script, the students'
+   simulate-fit-score loop, CLAUDE.md, CHANGELOG.
+   Done for real data with 0.5.0 (`docs/source/screen-analysis.rst`,
+   `examples/tfmodel/two_stage_submit.sh`, `two_stage_step.srun`); the
+   students' simulate-fit-score loop waits on steps 5 and 7.
+
+After 0.5.0 (2026-10-24): steps 5 and 7, the students' design-sweep
+tools, belong to the experimental-design work that follows the release
+(`planning/release-0.5.0.md`, "Out of scope"); the plan stays active for
+them.
+
+## Decisions (user, 2026-10-13)
+
+1. A standalone command, `tfs-fit-two-stage`, callable later by the
+   deferred orchestrator (`tfs-run-experiment`).
+2. The shift is a prediction input (`--shared_shift`), not written into
+   the posterior file.
+3. Defaults: stage 1 = wt, spikes and singles; jackknife 30 subsamples,
+   20% of tubes dropped; the lambda prior required whenever congression is
+   fitted, with no default value.
+4. wt purity: the library config as written (wt mostly bulk), which the
+   real data favored.
+5. The consistency step is on by default; `--no_consistency` skips it.
+6. The students' simulation-config builder is part of this plan (step 7).
+
+## Out of scope
+
+The accepted limitations: stage 1's small steep m bias (quantified in the
+study; correctable from simulation if the methods need it) and the real
+pheS gap (`planning/deep-coverage.md` step 3). Marker-specific dk_geno
+(tested, not supported).
diff --git a/pyproject.toml b/pyproject.toml
index eef3791e..765dbe2b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,30 +14,32 @@ version = { attr = "tfscreen.__version__.__version__" }
 [project]
 name = "tfscreen"
 dynamic = ["version"]
+# Floors: the oldest versions the test suite has passed with (the
+# maintainer's environment, 2026-10-20); CI tests the newest releases.
 dependencies = [
-  "numpy",
-  "pandas",
-  "openpyxl",
-  "matplotlib",
-  "tqdm",
-  "statsmodels",
-  "PyYAML",
-  "corner",
-  "numba",
-"pybktree",
-  "pyfastx",
-  "optax",
-  "numpyro>=0.19.0",
-  "dill",
-  "flax",
-  "jax",
-  "jaxlib",
-  "h5py",
-  "scipy",
-  "jinja2",
+  "numpy>=2.3",
+  "pandas>=2.3",
+  "openpyxl>=3.1",
+  "matplotlib>=3.10",
+  "tqdm>=4.67",
+  "statsmodels>=0.14.6",
+  "PyYAML>=6.0",
+  "corner>=2.2",
+  "numba>=0.63",
+  "pybktree>=1.1",
+  "pyfastx>=2.2",
+  "optax>=0.2.6",
+  "numpyro>=0.22",
+  "dill>=0.4",
+  "flax>=0.12.2",
+  "jax>=0.11.2",
+  "jaxlib>=0.11.2",
+  "h5py>=3.15",
+  "scipy>=1.16",
+  "jinja2>=3.1.4",
 ]
 
-requires-python = ">=3.11"
+requires-python = ">=3.12"  # jax 0.11 and newer need 3.12
 authors = [{ name = "Mike Harms", email = "harms@uoregon.edu" }]
 maintainers = [{ name = "Mike Harms", email = "harms@uoregon.edu" }]
 description = "Simulate or analyze a high-throughput screen of a transcription factor library"
@@ -54,28 +56,47 @@ keywords = [
 classifiers = [
   "Development Status :: 5 - Production/Stable",
   "Programming Language :: Python",
+  "Programming Language :: Python :: 3.12",
+  "Programming Language :: Python :: 3.13",
+  "Programming Language :: Python :: 3.14",
 ]
 
 [project.urls]
 Repository = "https://github.com/harmslab/tfscreen.git"
 "Bug Tracker" = "https://github.com/harmslab/tfscreen/issues"
 
+[tool.coverage.run]
+branch = true
+source = ["tfscreen"]
+
+[tool.coverage.report]
+show_missing = true
+skip_covered = true
+
 [tool.pytest.ini_options]
 markers = [
     "slow: mark test as slow to run",
 ]
 
 [project.optional-dependencies]
-test = ["coverage", "flake8", "pytest", "genbadge", "pytest-mock"]
+test = ["coverage", "diff-cover", "flake8", "pytest", "genbadge", "pytest-mock",
+        "pytest-xdist", "pytest-cov"]
 docs = ["pydata-sphinx-theme"]
+# GPU jax on Linux (pip's plain jax is CPU-only there); match the CUDA the
+# cluster's driver supports.
+cuda12 = ["jax[cuda12]>=0.11.2"]
 
 [project.scripts]
 tfs-process-fastq = "tfscreen.process_raw.scripts.process_fastq_cli:main"
 tfs-process-counts = "tfscreen.process_raw.scripts.process_counts_cli:main"
-tfs-process-presplit = "tfscreen.process_raw.scripts.process_presplit_cli:main"
+tfs-calibrate-od600 = "tfscreen.process_raw.scripts.calibrate_od600_cli:main"
+tfs-check-tubes = "tfscreen.process_raw.scripts.check_tubes_cli:main"
+tfs-correct-tubes = "tfscreen.process_raw.scripts.correct_tubes_cli:main"
+tfs-process-binding = "tfscreen.process_raw.scripts.process_binding_cli:main"
 tfs-configure-model = "tfscreen.tfmodel.scripts.configure_model_cli:main"
 tfs-prefit-calibration = "tfscreen.tfmodel.scripts.prefit_calibration_cli:main"
 tfs-fit-model = "tfscreen.tfmodel.scripts.fit_model_cli:main"
+tfs-fit-two-stage = "tfscreen.tfmodel.scripts.fit_two_stage_cli:main"
 tfs-fit-genotypes = "tfscreen.tfmodel.scripts.fit_genotypes_cli:main"
 tfs-sample-posterior = "tfscreen.tfmodel.scripts.sample_posterior_cli:main"
 tfs-sample-prior = "tfscreen.tfmodel.scripts.sample_prior_cli:main"
@@ -86,13 +107,10 @@ tfs-predict-epistasis = "tfscreen.tfmodel.scripts.predict_epistasis_cli:main"
 tfs-cat-response = "tfscreen.analysis.scripts.cat_response_cli:main"
 tfs-extract-epistasis = "tfscreen.analysis.scripts.extract_epistasis_cli:main"
 tfs-compare-runs = "tfscreen.analysis.scripts.compare_runs_cli:main"
-tfs-diagnose-nan = "tfscreen.tfmodel.scripts.diagnose_nan_cli:main"
+tfs-summarize-calibration = "tfscreen.analysis.scripts.summarize_calibration_cli:main"
 tfs-simulate = "tfscreen.simulate.scripts.simulate_cli:main"
-tfs-report-cfu0 = "tfscreen.simulate.scripts.report_cfu0_cli:main"
 tfs-build-empirical = "tfscreen.simulate.scripts.build_empirical_cli:main"
 tfs-setup-sim-grid = "tfscreen.simulate.scripts.setup_sim_grid_cli:main"
 tfs-setup-grid = "tfscreen.tfmodel.scripts.setup_grid_cli:main"
 tfs-summarize-grid = "tfscreen.tfmodel.scripts.summarize_grid_cli:main"
-tfs-subset-genotypes = "tfscreen.tfmodel.scripts.subset_genotypes_cli:main"
 tfs-summarize-fit = "tfscreen.tfmodel.scripts.summarize_fit_cli:main"
-tfs-summarize-sbc = "tfscreen.tfmodel.scripts.summarize_sbc_cli:main"
diff --git a/reports/flake.txt b/reports/flake.txt
deleted file mode 100644
index 5d19625b..00000000
--- a/reports/flake.txt
+++ /dev/null
@@ -1,7483 +0,0 @@
-./scripts/generate_struct_ensemble.py:266:11: E221 multiple spaces before operator
-./scripts/generate_struct_ensemble.py:272:24: E221 multiple spaces before operator
-./scripts/generate_struct_ensemble.py:433:18: E221 multiple spaces before operator
-./src/tfscreen/__init__.py:19:1: W391 blank line at end of file
-./src/tfscreen/__version__.py:5:16: W291 trailing whitespace
-./src/tfscreen/analysis/__init__.py:13:1: W391 blank line at end of file
-./src/tfscreen/analysis/cat_response/__init__.py:2:77: W291 trailing whitespace
-./src/tfscreen/analysis/cat_response/__init__.py:3:55: W291 trailing whitespace
-./src/tfscreen/analysis/cat_response/cat_fit.py:148:1: E305 expected 2 blank lines after class or function definition, found 1
-./src/tfscreen/analysis/cat_response/cat_fit.py:188:1: C901 'cat_fit' is too complex (28)
-./src/tfscreen/analysis/cat_response/cat_fit.py:365:72: E231 missing whitespace after ','
-./src/tfscreen/analysis/cat_response/cat_fit.py:365:74: E231 missing whitespace after ','
-./src/tfscreen/analysis/cat_response/cat_fit.py:471:55: E712 comparison to True should be 'if cond is True:' or 'if cond:'
-./src/tfscreen/analysis/cat_response/cat_fit.py:473:57: E712 comparison to False should be 'if cond is False:' or 'if not cond:'
-./src/tfscreen/analysis/cat_response/cat_response.py:65:1: C901 'cat_response' is too complex (16)
-./src/tfscreen/analysis/compare_runs.py:626:1: C901 'compare_runs' is too complex (11)
-./src/tfscreen/analysis/extract_epistasis.py:9:1: C901 'mutant_cycle_pivot' is too complex (11)
-./src/tfscreen/analysis/extract_epistasis.py:9:1: E302 expected 2 blank lines, found 1
-./src/tfscreen/analysis/extract_epistasis.py:79:25: E231 missing whitespace after ','
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-./src/tfscreen/analysis/extract_epistasis.py:203:1: C901 'extract_epistasis' is too complex (16)
-./src/tfscreen/analysis/extract_epistasis.py:207:37: E252 missing whitespace around parameter equals
-./src/tfscreen/analysis/extract_epistasis.py:207:38: E252 missing whitespace around parameter equals
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-./src/tfscreen/genetics/build_mut_geno_matrix.py:5:1: C901 'build_mut_geno_matrix' is too complex (17)
-./src/tfscreen/genetics/combine_mutation_effects.py:8:1: E302 expected 2 blank lines, found 1
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-./src/tfscreen/genetics/genotype_sorting.py:31:1: C901 '_standardize_one' is too complex (11)
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-./src/tfscreen/plot/heatmap/aa_v_titrant_heatmap.py:27:25: E211 whitespace before '('
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-./src/tfscreen/plot/heatmap/aa_v_titrant_heatmap.py:39:43: E231 missing whitespace after ','
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-./src/tfscreen/plot/heatmap/epistasis_heatmap.py:6:1: E302 expected 2 blank lines, found 1
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-./src/tfscreen/plot/heatmap/epistasis_heatmap.py:16:53: E231 missing whitespace after ':'
-./src/tfscreen/plot/heatmap/epistasis_heatmap.py:19:21: E231 missing whitespace after ','
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-./src/tfscreen/plot/heatmap/epistasis_heatmap.py:50:43: E231 missing whitespace after ','
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-./src/tfscreen/plot/heatmap/heatmap_core.py:6:18: W291 trailing whitespace
-./src/tfscreen/plot/heatmap/heatmap_core.py:28:1: C901 '_build_heatmap_collection' is too complex (11)
-./src/tfscreen/plot/heatmap/heatmap_core.py:28:1: E302 expected 2 blank lines, found 1
-./src/tfscreen/plot/heatmap/heatmap_core.py:88:38: W291 trailing whitespace
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-./src/tfscreen/plot/heatmap/heatmap_core.py:172:1: C901 '_build_color_mapper' is too complex (13)
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-./src/tfscreen/plot/heatmap/heatmap_core.py:457:1: C901 'heatmap' is too complex (13)
-./src/tfscreen/plot/heatmap/heatmap_core.py:509:76: W291 trailing whitespace
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-./src/tfscreen/plot/heatmap/heatmap_core.py:570:5: F841 local variable 'e' is assigned to but never used
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-./src/tfscreen/plot/hill_fit.py:9:1: E302 expected 2 blank lines, found 1
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-./src/tfscreen/plot/plot_theta_fits.py:89:35: E231 missing whitespace after ','
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-./src/tfscreen/plot/uncertainty_calibration.py:12:1: E302 expected 2 blank lines, found 1
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-./src/tfscreen/plot/uncertainty_calibration.py:97:14: W292 no newline at end of file
-./src/tfscreen/plot/xy_corr.py:14:1: E302 expected 2 blank lines, found 1
-./src/tfscreen/plot/xy_corr.py:24:5: E125 continuation line with same indent as next logical line
-./src/tfscreen/plot/xy_corr.py:63:47: E231 missing whitespace after ','
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-./src/tfscreen/process_raw/_counts_io.py:110:16: F541 f-string is missing placeholders
-./src/tfscreen/process_raw/counts_to_lncfu.py:15:1: E302 expected 2 blank lines, found 1
-./src/tfscreen/process_raw/counts_to_lncfu.py:42:1: E302 expected 2 blank lines, found 1
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-./src/tfscreen/process_raw/counts_to_lncfu.py:68:1: E302 expected 2 blank lines, found 1
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-./src/tfscreen/process_raw/fastq_to_counts.py:21:14: E401 multiple imports on one line
-./src/tfscreen/process_raw/fastq_to_counts.py:23:1: E302 expected 2 blank lines, found 1
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-./src/tfscreen/simulate/binding_params.py:222:1: C901 'build_theta_gc_override_hill_mut' is too complex (15)
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-./src/tfscreen/simulate/library_prediction.py:76:1: C901 '_validate_binding_config' is too complex (13)
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-./src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PK.py:880:1: F401 'tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.thermo.compute_theta_samples' imported but unused
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-./src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PddG.py:67:1: F401 'tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.thermo.get_population_moments' imported but unused
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-./src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PnnC.py:781:1: F401 'tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.thermo.compute_theta_samples' imported but unused
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-./src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PnnC.py:483:5: F841 local variable 'titrant_names' is assigned to but never used
-./src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PnnC.py:533:1: F401 'tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.thermo.build_calc_df' imported but unused
-./src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PnnC.py:533:1: F401 'tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.thermo.compute_theta_samples' imported but unused
-./src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PnnC.py:533:1: E402 module level import not at top of file
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-./tests/tfscreen/genetics/test_genotype_sorting_set_categorical_genotype.py:42:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_genotype_sorting_set_categorical_genotype.py:48:38: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_set_categorical_genotype.py:53:1: E303 too many blank lines (4)
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-./tests/tfscreen/genetics/test_genotype_sorting_set_categorical_genotype.py:78:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/genetics/test_genotype_sorting_set_categorical_genotype.py:133:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:15:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:22:59: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:24:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:31:59: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:33:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:40:59: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:42:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:51:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:64:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:77:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:84:59: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:86:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:92:59: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:94:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:101:59: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:103:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:107:52: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:109:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:115:59: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:117:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:127:26: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:138:59: E231 missing whitespace after ','
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:144:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:157:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:163:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:167:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_genotype_sorting_standardize_genotypes.py:176:48: W292 no newline at end of file
-./tests/tfscreen/genetics/test_get_single_with_wt.py:4:1: F401 'pandas.testing.assert_frame_equal' imported but unused
-./tests/tfscreen/genetics/test_get_single_with_wt.py:13:1: E303 too many blank lines (3)
-./tests/tfscreen/genetics/test_get_single_with_wt.py:15:1: E302 expected 2 blank lines, found 3
-./tests/tfscreen/genetics/test_get_single_with_wt.py:32:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_get_single_with_wt.py:42:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:47:1: W293 blank line contains whitespace
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-./tests/tfscreen/genetics/test_get_single_with_wt.py:63:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:74:80: W291 trailing whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:76:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:79:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:88:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:91:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:94:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:106:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:109:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:124:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:129:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:132:35: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_get_single_with_wt.py:133:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:142:72: W291 trailing whitespace
-./tests/tfscreen/genetics/test_get_single_with_wt.py:145:31: W292 no newline at end of file
-./tests/tfscreen/genetics/test_library_manager.py:16:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:24:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:31:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:37:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:50:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:54:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:60:1: W293 blank line contains whitespace
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-./tests/tfscreen/genetics/test_library_manager.py:72:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:94:25: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:117:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:124:44: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:127:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:144:45: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:153:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:173:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:186:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:191:27: E221 multiple spaces before operator
-./tests/tfscreen/genetics/test_library_manager.py:193:26: E221 multiple spaces before operator
-./tests/tfscreen/genetics/test_library_manager.py:198:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:206:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:211:39: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:215:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:223:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:231:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:236:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:241:47: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:245:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:257:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:293:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:312:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:319:14: E201 whitespace after '('
-./tests/tfscreen/genetics/test_library_manager.py:319:39: E202 whitespace before ')'
-./tests/tfscreen/genetics/test_library_manager.py:320:14: E201 whitespace after '('
-./tests/tfscreen/genetics/test_library_manager.py:320:55: E202 whitespace before ')'
-./tests/tfscreen/genetics/test_library_manager.py:335:22: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:336:23: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:356:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:360:26: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:361:21: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:367:36: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:371:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:377:26: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:378:21: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:384:35: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:385:36: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:387:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:390:28: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:391:26: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:392:21: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:400:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:403:30: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:404:36: E114 indentation is not a multiple of 4 (comment)
-./tests/tfscreen/genetics/test_library_manager.py:404:36: E116 unexpected indentation (comment)
-./tests/tfscreen/genetics/test_library_manager.py:405:36: E114 indentation is not a multiple of 4 (comment)
-./tests/tfscreen/genetics/test_library_manager.py:405:36: E116 unexpected indentation (comment)
-./tests/tfscreen/genetics/test_library_manager.py:406:36: E114 indentation is not a multiple of 4 (comment)
-./tests/tfscreen/genetics/test_library_manager.py:406:36: E116 unexpected indentation (comment)
-./tests/tfscreen/genetics/test_library_manager.py:407:31: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:408:26: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:409:21: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:417:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:421:26: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:421:37: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:422:21: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:475:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:491:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:493:32: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:526:19: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:527:58: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:528:18: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:534:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:539:51: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:540:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:551:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:556:19: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:557:67: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:558:18: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:562:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:566:36: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:579:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:586:19: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:588:35: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:597:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:599:24: E201 whitespace after '{'
-./tests/tfscreen/genetics/test_library_manager.py:599:75: E202 whitespace before '}'
-./tests/tfscreen/genetics/test_library_manager.py:601:24: E201 whitespace after '{'
-./tests/tfscreen/genetics/test_library_manager.py:601:75: E202 whitespace before '}'
-./tests/tfscreen/genetics/test_library_manager.py:603:24: E201 whitespace after '{'
-./tests/tfscreen/genetics/test_library_manager.py:603:75: E202 whitespace before '}'
-./tests/tfscreen/genetics/test_library_manager.py:610:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:625:19: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:627:18: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:648:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:654:19: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:656:18: E222 multiple spaces after operator
-./tests/tfscreen/genetics/test_library_manager.py:656:44: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:665:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:680:1: E303 too many blank lines (3)
-./tests/tfscreen/genetics/test_library_manager.py:684:1: E302 expected 2 blank lines, found 3
-./tests/tfscreen/genetics/test_library_manager.py:694:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:702:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:704:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:707:48: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:708:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:720:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:734:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:747:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:751:42: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:759:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:764:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:768:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/genetics/test_library_manager.py:774:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:779:1: W293 blank line contains whitespace
-./tests/tfscreen/genetics/test_library_manager.py:811:22: E261 at least two spaces before inline comment
-./tests/tfscreen/genetics/test_library_manager.py:815:35: E231 missing whitespace after ':'
-./tests/tfscreen/genetics/test_library_manager.py:815:49: W291 trailing whitespace
-./tests/tfscreen/genetics/test_library_manager.py:840:34: W292 no newline at end of file
-./tests/tfscreen/mle/curve_models/test_guesses.py:14:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_guesses.py:27:44: E261 at least two spaces before inline comment
-./tests/tfscreen/mle/curve_models/test_guesses.py:78:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_guesses.py:81:32: E261 at least two spaces before inline comment
-./tests/tfscreen/mle/curve_models/test_guesses.py:88:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_guesses.py:96:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_guesses.py:101:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_guesses.py:106:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_guesses.py:112:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_guesses.py:114:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_guesses.py:120:45: E261 at least two spaces before inline comment
-./tests/tfscreen/mle/curve_models/test_guesses.py:121:45: E261 at least two spaces before inline comment
-./tests/tfscreen/mle/curve_models/test_guesses.py:122:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_guesses.py:127:45: E261 at least two spaces before inline comment
-./tests/tfscreen/mle/curve_models/test_guesses.py:128:46: E261 at least two spaces before inline comment
-./tests/tfscreen/mle/curve_models/test_guesses.py:134:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:2:1: F401 'pytest' imported but unused
-./tests/tfscreen/mle/curve_models/test_models.py:18:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_models.py:23:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_models.py:29:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_models.py:33:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:36:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:40:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:44:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_models.py:52:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_models.py:57:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:58:52: W291 trailing whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:62:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:67:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_models.py:72:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:74:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:77:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_models.py:82:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:85:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:87:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:91:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/curve_models/test_models.py:95:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:98:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:101:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/curve_models/test_models.py:103:1: W293 blank line contains whitespace
-./tests/tfscreen/mle/fitters/test_least_squares.py:3:1: F401 'pytest' imported but unused
-./tests/tfscreen/mle/fitters/test_least_squares.py:6:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/mle/fitters/test_least_squares.py:10:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/mle/fitters/test_matrix_nls.py:3:1: F401 'pytest' imported but unused
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-./tests/tfscreen/mle/fitters/test_util.py:3:1: F401 'pytest' imported but unused
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-./tests/tfscreen/mle/test_fit_manager.py:6:1: F401 'scipy.special.expit' imported but unused
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-./tests/tfscreen/mle/test_predict_with_error.py:2:1: F401 'pytest' imported but unused
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-./tests/tfscreen/plot/heatmap/test_heatmap_core.py:25:1: F401 'tfscreen.plot.default_styles.DEFAULT_HMAP_PATCH_KWARGS' imported but unused
-./tests/tfscreen/plot/heatmap/test_heatmap_core.py:31:1: F401 'tfscreen.plot.helper.get_ax_limits' imported but unused
-./tests/tfscreen/plot/heatmap/test_heatmap_core.py:31:47: W291 trailing whitespace
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-./tests/tfscreen/plot/heatmap/test_heatmap_core.py:274:9: E303 too many blank lines (2)
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-./tests/tfscreen/plot/heatmap/test_heatmap_core.py:512:1: E303 too many blank lines (3)
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-./tests/tfscreen/plot/test_est_v_real_summary.py:2:1: F401 'pytest' imported but unused
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-./tests/tfscreen/plot/test_hill_fit.py:2:1: F401 'pytest' imported but unused
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-./tests/tfscreen/plot/test_hill_fit.py:118:34: E741 ambiguous variable name 'l'
-./tests/tfscreen/plot/test_plot_theta_fits.py:2:1: F401 'pytest' imported but unused
-./tests/tfscreen/plot/test_plot_theta_fits.py:301:26: E741 ambiguous variable name 'l'
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-./tests/tfscreen/process_raw/test_fastq_to_counts.py:9:1: F401 'tfscreen.process_raw.fastq_to_counts._bktree_find' imported but unused
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-./tests/tfscreen/process_raw/test_fastq_to_counts.py:381:35: E261 at least two spaces before inline comment
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-./tests/tfscreen/process_raw/test_process_presplit_cli.py:5:1: F401 'unittest.mock.MagicMock' imported but unused
-./tests/tfscreen/process_raw/test_process_presplit_cli.py:80:41: E127 continuation line over-indented for visual indent
-./tests/tfscreen/process_raw/test_process_presplit_cli.py:88:1: E303 too many blank lines (3)
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-./tests/tfscreen/simulate/empirical/test_build_empirical_cli.py:33:1: E305 expected 2 blank lines after class or function definition, found 1
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-./tests/tfscreen/simulate/empirical/test_library_prediction_empirical.py:9:1: F401 'tfscreen.util' imported but unused
-./tests/tfscreen/simulate/empirical/test_population.py:4:1: F401 'pandas as pd' imported but unused
-./tests/tfscreen/simulate/growth/test_growth_linkage.py:50:16: E221 multiple spaces before operator
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-./tests/tfscreen/simulate/growth/test_transition_linkage.py:564:14: E221 multiple spaces before operator
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-./tests/tfscreen/simulate/growth/test_transition_linkage.py:568:14: E221 multiple spaces before operator
-./tests/tfscreen/simulate/test_binding_params.py:5:1: F401 'io' imported but unused
-./tests/tfscreen/simulate/test_binding_params.py:8:1: F401 'pandas as pd' imported but unused
-./tests/tfscreen/simulate/test_binding_params.py:11:1: F401 'tfscreen.simulate.binding_params._logit' imported but unused
-./tests/tfscreen/simulate/test_binding_params.py:83:20: E221 multiple spaces before operator
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-./tests/tfscreen/simulate/test_binding_params.py:232:35: E221 multiple spaces before operator
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-./tests/tfscreen/simulate/test_binding_params.py:305:38: E221 multiple spaces before operator
-./tests/tfscreen/simulate/test_binding_params.py:307:37: E221 multiple spaces before operator
-./tests/tfscreen/simulate/test_binding_params.py:316:5: F811 redefinition of unused 'pd' from line 8
-./tests/tfscreen/simulate/test_binding_params.py:319:15: E221 multiple spaces before operator
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-./tests/tfscreen/simulate/test_binding_params.py:508:45: E221 multiple spaces before operator
-./tests/tfscreen/simulate/test_binding_params.py:587:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_build_sample_dataframes.py:9:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_build_sample_dataframes.py:33:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_build_sample_dataframes.py:52:1: W293 blank line contains whitespace
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-./tests/tfscreen/simulate/test_build_sample_dataframes.py:74:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/simulate/test_build_sample_dataframes.py:94:18: E261 at least two spaces before inline comment
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-./tests/tfscreen/simulate/test_build_sample_dataframes.py:104:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/simulate/test_build_sample_dataframes.py:128:55: W292 no newline at end of file
-./tests/tfscreen/simulate/test_growth_parameters_output.py:4:1: F401 'pandas as pd' imported but unused
-./tests/tfscreen/simulate/test_library_prediction.py:47:41: E127 continuation line over-indented for visual indent
-./tests/tfscreen/simulate/test_library_prediction.py:293:5: F401 'numpy as np' imported but unused
-./tests/tfscreen/simulate/test_library_prediction.py:441:16: E272 multiple spaces before keyword
-./tests/tfscreen/simulate/test_presplit_data.py:71:36: E127 continuation line over-indented for visual indent
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-./tests/tfscreen/simulate/test_presplit_data.py:121:14: E221 multiple spaces before operator
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-./tests/tfscreen/simulate/test_report_cfu0_cli.py:5:1: F401 'pytest' imported but unused
-./tests/tfscreen/simulate/test_report_cfu0_cli.py:131:26: E741 ambiguous variable name 'l'
-./tests/tfscreen/simulate/test_run_simulation.py:3:1: F401 'os' imported but unused
-./tests/tfscreen/simulate/test_run_simulation.py:5:1: F401 'unittest.mock.MagicMock' imported but unused
-./tests/tfscreen/simulate/test_run_simulation.py:7:1: F401 'tfscreen.util' imported but unused
-./tests/tfscreen/simulate/test_run_simulation.py:9:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:13:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:27:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:30:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:34:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:41:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:47:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:51:1: W293 blank line contains whitespace
-./tests/tfscreen/simulate/test_run_simulation.py:55:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:65:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:70:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:82:128: E501 line too long (150 > 127 characters)
-./tests/tfscreen/simulate/test_run_simulation.py:101:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/simulate/test_run_simulation.py:110:1: W293 blank line contains whitespace
-./tests/tfscreen/simulate/test_run_simulation.py:113:1: W293 blank line contains whitespace
-./tests/tfscreen/simulate/test_run_simulation.py:116:5: F841 local variable 'results' is assigned to but never used
-./tests/tfscreen/simulate/test_run_simulation.py:117:1: W293 blank line contains whitespace
-./tests/tfscreen/simulate/test_sample_theta.py:260:21: E221 multiple spaces before operator
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-./tests/tfscreen/simulate/test_selection_experiment.py:4:1: F401 'numpy.ma' imported but unused
-./tests/tfscreen/simulate/test_selection_experiment.py:6:1: F401 'scipy.stats.gmean' imported but unused
-./tests/tfscreen/simulate/test_selection_experiment.py:9:1: F401 'unittest.mock.MagicMock' imported but unused
-./tests/tfscreen/simulate/test_selection_experiment.py:34:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/simulate/test_selection_experiment.py:68:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/simulate/test_selection_experiment.py:116:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/simulate/test_selection_experiment.py:185:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/simulate/test_selection_experiment.py:203:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/analysis/test_extract_samples.py:373:9: F841 local variable 'rng' is assigned to but never used
-./tests/tfscreen/tfmodel/analysis/test_extract_theta_curves.py:4:1: F401 'unittest.mock.call' imported but unused
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-./tests/tfscreen/tfmodel/generative/components/dk_geno/test_hierarchical_geno.py:4:1: F401 'numpyro' imported but unused
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-./tests/tfscreen/tfmodel/generative/components/growth/test_linear.py:7:1: F401 'tfscreen.tfmodel.generative.components.growth.linear.LinearParams' imported but unused
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-./tests/tfscreen/tfmodel/generative/components/growth/test_power.py:4:1: F401 'numpyro.handlers.substitute' imported but unused
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-./tests/tfscreen/tfmodel/generative/components/noise/test_zero.py:75:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/noise/test_zero.py:82:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/generative/components/noise/test_zero.py:93:46: W292 no newline at end of file
-./tests/tfscreen/tfmodel/generative/components/sample_offset/test_normal.py:1:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/generative/components/sample_offset/test_normal.py:5:1: F401 'numpyro' imported but unused
-./tests/tfscreen/tfmodel/generative/components/sample_offset/test_normal.py:6:1: F401 'numpyro.distributions as dist' imported but unused
-./tests/tfscreen/tfmodel/generative/components/sample_offset/test_zero.py:1:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/generative/components/sample_offset/test_zero.py:2:1: F401 'jax.numpy as jnp' imported but unused
-./tests/tfscreen/tfmodel/generative/components/test_pinning.py:4:1: F401 'numpyro as pyro' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/test__simple.py:6:1: F401 'tfscreen.tfmodel.generative.components.theta._simple._THETA_EPS' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/test__simple.py:447:34: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:5:1: F401 'numpyro.distributions as dist' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:6:1: F401 'numpyro.handlers.trace' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:10:1: F401 'tfscreen.tfmodel.generative.components.theta.categorical_geno.ThetaParam' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:38:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:46:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:69:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:96:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:102:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:107:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:119:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:125:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:146:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:158:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:167:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:172:21: W291 trailing whitespace
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:174:34: W291 trailing whitespace
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-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:180:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:186:37: E261 at least two spaces before inline comment
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:188:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:200:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:220:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py:379:44: W292 no newline at end of file
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:4:1: F401 'numpyro.distributions as dist' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:5:1: F401 'numpyro.handlers.trace' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:41:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:176:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:179:36: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:180:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:205:32: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:248:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:252:32: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:253:32: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:278:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:279:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:285:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:285:64: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:304:20: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:320:20: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:329:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:331:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:355:38: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:357:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:358:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:477:51: E222 multiple spaces after operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:492:18: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:537:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:540:36: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py:541:26: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:12:1: F401 'tfscreen.tfmodel.generative.components.theta.hill_mut._population_moments' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:55:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:61:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:68:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:125:15: F841 local variable 'G' is assigned to but never used
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:136:32: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:140:15: F841 local variable 'G' is assigned to but never used
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:140:18: F841 local variable 'P' is assigned to but never used
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:246:9: F841 local variable 'M' is assigned to but never used
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:254:62: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:453:56: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:480:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:482:33: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:492:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:494:26: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:509:43: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:512:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:513:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:528:21: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:530:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:530:52: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:719:34: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:722:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:730:36: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py:730:70: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:25:11: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:30:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:155:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:156:33: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:157:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:158:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:159:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:160:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:161:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:162:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:164:36: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:166:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:167:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:204:16: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:205:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/test_predict_unmeasured.py:301:19: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:41:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:58:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:198:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:251:6: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:283:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:318:27: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:335:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:336:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:349:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:350:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:427:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:428:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:429:45: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:430:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:431:34: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:432:47: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:433:47: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:446:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:488:27: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:599:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:618:17: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:618:28: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:618:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:619:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:620:18: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:620:28: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:620:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:621:19: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:621:28: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:621:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:688:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py:696:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:43:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:97:45: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:138:23: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:195:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:197:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:223:38: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:245:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:266:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:277:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:308:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:327:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:391:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:473:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:478:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:509:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:528:17: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:528:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:528:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:529:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:530:18: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:530:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:530:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:531:19: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:531:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:531:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:588:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:591:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:595:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:599:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:602:15: E222 multiple spaces after operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:754:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:755:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:756:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:757:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py:759:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:42:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:113:45: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:165:23: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:249:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:251:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:299:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:305:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:306:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:338:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:349:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:380:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:418:26: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:426:25: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:457:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:463:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:464:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:543:39: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:545:39: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:571:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:590:17: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:590:28: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:590:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:591:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:592:18: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:592:28: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:592:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:593:19: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:593:28: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:593:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py:647:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_predict_unmeasured.py:5:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_predict_unmeasured.py:21:11: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_predict_unmeasured.py:32:14: E222 multiple spaces after operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_predict_unmeasured.py:258:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_thermo.py:26:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_thermo.py:35:6: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_thermo.py:219:32: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_thermo.py:219:33: E201 whitespace after '['
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_thermo.py:221:32: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:49:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:66:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:206:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:278:6: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:279:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:280:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:281:33: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:295:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:302:45: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:309:46: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:324:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:363:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:364:27: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:381:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:382:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:397:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:398:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:424:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:425:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:512:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:523:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:524:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:526:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:527:34: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:528:47: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:529:47: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:543:39: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:544:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:545:45: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:546:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:547:34: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:548:47: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:549:47: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:562:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:609:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:610:27: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:620:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:712:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:748:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:767:17: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:767:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:767:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:768:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:769:18: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:769:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:769:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:770:19: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:770:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:770:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:798:24: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:806:24: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:815:26: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:823:26: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:833:28: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:847:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py:855:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:53:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:107:45: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:147:23: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:204:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:206:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:234:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:239:38: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:272:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:285:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:294:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:305:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:351:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:380:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:399:18: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:433:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:438:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:531:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:536:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:567:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:586:17: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:586:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:586:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:587:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:588:18: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:588:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:588:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:589:19: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:589:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:589:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:616:24: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:648:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:651:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:655:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:658:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:659:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:662:15: E222 multiple spaces after operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:667:11: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:694:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:695:29: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:696:29: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:697:29: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:698:29: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:699:29: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py:820:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:49:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:121:45: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:163:23: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:230:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:232:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:263:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:287:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:288:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:298:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:299:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:320:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:329:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:340:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:386:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:427:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:430:26: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:447:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:453:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:454:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:544:39: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:546:39: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:572:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:591:17: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:591:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:591:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:592:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:593:18: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:593:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:593:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:594:19: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:594:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:594:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:621:24: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py:646:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:28:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:39:6: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:43:8: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:312:32: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:312:33: E201 whitespace after '['
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:314:32: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:512:18: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:533:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:561:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py:563:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:48:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:49:25: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:55:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:62:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:69:1: E305 expected 2 blank lines after class or function definition, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:69:6: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:285:38: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:286:43: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:287:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:346:38: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:347:43: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:348:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:349:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:350:34: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:351:46: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:352:46: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:364:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:365:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:366:42: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:367:42: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:368:42: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:369:41: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py:379:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:47:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:98:45: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:123:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:134:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:265:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:274:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:292:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:308:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:383:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:448:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:467:17: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:467:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:467:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:468:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:469:18: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:469:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:469:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:470:19: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:470:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:470:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py:504:26: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:9:1: F401 'functools.partial' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:40:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:211:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:212:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:213:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:214:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:220:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:221:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:237:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:270:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:271:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:272:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:273:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:278:27: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:279:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:280:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:281:32: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:282:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:283:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:284:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:318:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:325:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:326:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:343:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:381:38: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:383:39: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:491:26: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:503:28: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py:513:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:5:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:21:11: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:33:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:51:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:52:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:53:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:54:27: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:58:33: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:71:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:72:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:73:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:74:27: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:78:33: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:125:16: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py:244:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py:68:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py:75:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py:84:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py:91:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py:100:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py:103:19: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py:261:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py:267:16: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py:284:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:48:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:49:25: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:55:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:62:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:69:1: E305 expected 2 blank lines after class or function definition, found 1
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:69:6: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:316:38: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:317:43: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:318:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:329:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:330:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:331:43: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:351:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:352:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:353:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:354:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:356:39: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:357:39: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:403:38: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:404:43: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:405:44: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:406:35: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:407:34: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:408:46: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:409:46: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:420:37: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:421:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:422:43: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:434:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:435:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:436:42: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:437:42: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:438:42: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:439:41: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:441:41: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:451:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:453:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:509:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:552:46: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:553:45: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:554:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:557:48: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:558:47: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:559:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:561:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py:567:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:47:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:100:45: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:215:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:220:38: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:229:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:253:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:265:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:266:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:275:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:286:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:310:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:311:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:323:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:340:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:365:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:432:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:433:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:461:37: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:494:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:495:23: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:496:23: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:499:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:530:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:549:17: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:549:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:549:40: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:550:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:551:18: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:551:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:551:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:552:19: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:552:29: E701 multiple statements on one line (colon)
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py:552:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:9:1: F401 'functools.partial' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:39:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:208:41: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:218:42: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:229:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:230:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:231:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:232:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:233:34: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:244:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:245:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:261:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:272:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:324:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:326:33: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:343:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:350:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:351:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:400:28: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:448:38: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:449:40: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:450:38: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:451:40: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:452:39: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:460:37: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:488:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:489:32: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:510:30: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:582:26: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:594:28: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py:604:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:69:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:77:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:86:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:93:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:102:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:105:19: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:111:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:167:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:173:12: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:218:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:312:12: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:321:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:327:16: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:364:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py:365:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_features.py:62:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_features.py:63:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_features.py:150:19: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_features.py:168:9: F841 local variable 'sdata' is assigned to but never used
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_features.py:177:33: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_features.py:178:33: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_features.py:179:33: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:6:1: F401 'numpyro.distributions as dist' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:7:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:30:18: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:37:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:38:31: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:39:30: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:43:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:49:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:61:18: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:71:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:76:15: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:82:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_horseshoe.py:89:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:39:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:41:21: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:130:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:147:21: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:166:11: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:167:11: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:172:31: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:229:49: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:236:49: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:241:10: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:244:55: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:272:13: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py:377:49: E272 multiple spaces before keyword
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:3:1: F401 'jax' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:7:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:9:1: F401 'tfscreen.tfmodel.generative.components.theta.thermo.nn._DEFAULT_HIDDEN_SIZE' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:24:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:26:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:39:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:41:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:54:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:72:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:74:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:77:9: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:95:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py:97:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_prior.py:6:1: F401 'numpyro.distributions as dist' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_prior.py:7:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/generative/components/theta/thermo/test_prior.py:29:17: E221 multiple spaces before operator
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-./tests/tfscreen/tfmodel/generative/test_model.py:139:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/test_model.py:152:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/generative/test_model.py:221:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/generative/test_model.py:224:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/generative/test_model.py:233:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/generative/test_model.py:332:61: W292 no newline at end of file
-./tests/tfscreen/tfmodel/generative/test_model_math.py:16:1: F401 'numpyro.handlers.seed' imported but unused
-./tests/tfscreen/tfmodel/generative/test_model_math.py:130:27: E128 continuation line under-indented for visual indent
-./tests/tfscreen/tfmodel/generative/test_model_math.py:131:27: E128 continuation line under-indented for visual indent
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:29:11: E221 multiple spaces before operator
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-./tests/tfscreen/tfmodel/generative/test_reference_math.py:148:22: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:148:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:148:31: E251 unexpected spaces around keyword / parameter equals
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-./tests/tfscreen/tfmodel/generative/test_reference_math.py:149:27: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:149:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:150:29: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:150:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:151:26: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:151:26: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:151:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:160:22: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:160:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:160:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:161:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:161:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:162:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:162:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:163:19: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:163:19: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:163:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:175:12: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:179:22: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:179:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:179:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:180:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:180:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:181:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:181:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:182:19: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:182:19: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:182:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:186:38: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:196:22: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:196:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:196:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:197:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:197:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:198:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:198:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:199:19: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:199:19: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:199:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:218:22: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:218:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:218:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:219:27: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:219:27: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:219:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:220:29: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:220:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:221:26: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:221:26: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:221:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:225:22: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:225:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:225:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:226:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:226:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:227:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:227:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:228:19: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:228:19: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:228:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:232:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:248:22: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:248:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:248:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:249:27: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:249:27: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:249:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:250:29: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:250:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:251:26: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:251:26: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:251:31: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:254:22: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:254:22: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:254:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:255:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:255:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:256:23: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:256:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:257:19: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:257:19: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:257:25: E251 unexpected spaces around keyword / parameter equals
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:320:29: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:354:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:410:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:411:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:412:13: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:422:13: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:429:11: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/generative/test_reference_math.py:432:13: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:5:1: F401 'numpyro.handlers' imported but unused
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:7:1: F401 'numpyro.infer.SVI' imported but unused
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:7:1: F401 'numpyro.infer.Trace_ELBO' imported but unused
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:7:1: F401 'numpyro.infer.Predictive' imported but unused
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:11:1: F401 'jax' imported but unused
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:14:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:20:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:26:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:30:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:37:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:61:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:65:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:74:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:78:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:81:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:85:50: W291 trailing whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:86:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:86:49: W291 trailing whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:87:46: W291 trailing whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:88:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:89:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:92:48: E261 at least two spaces before inline comment
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:106:63: W291 trailing whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:107:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:107:49: W291 trailing whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:108:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:108:46: W291 trailing whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:109:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:110:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:118:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:126:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:176:5: F841 local variable 'num_titrants' is assigned to but never used
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:179:5: E306 expected 1 blank line before a nested definition, found 0
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:184:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:187:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:193:50: W291 trailing whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:194:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:194:48: W291 trailing whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:195:46: W291 trailing whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:196:24: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:197:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:199:54: E261 at least two spaces before inline comment
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:201:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:205:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:210:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:225:5: F841 local variable 'svi' is assigned to but never used
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:226:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:230:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:239:59: W291 trailing whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:240:28: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:240:52: W291 trailing whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:241:50: W291 trailing whitespace
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-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:243:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_get_posteriors_mapping.py:247:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:6:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:16:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:20:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:22:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:27:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:32:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:37:14: E261 at least two spaces before inline comment
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-./tests/tfscreen/tfmodel/inference/test_posteriors.py:45:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:54:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:65:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:72:1: W293 blank line contains whitespace
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:75:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/inference/test_posteriors.py:86:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:95:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:101:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/inference/test_posteriors.py:108:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/inference/test_run_inference.py:13:1: F401 'numpyro.infer.autoguide.AutoLaplaceApproximation' imported but unused
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-./tests/tfscreen/tfmodel/inference/test_run_inference.py:597:5: F841 local variable 'mock_seed' is assigned to but never used
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-./tests/tfscreen/tfmodel/scripts/test_checkpoint_protection.py:2:1: F401 'os' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_checkpoint_protection.py:6:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/scripts/test_configure_and_run.py:21:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/scripts/test_configure_and_run.py:562:9: F841 local variable 'mock_ri_class' is assigned to but never used
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-./tests/tfscreen/tfmodel/scripts/test_diagnose_nan_cli.py:77:64: E203 whitespace before ','
-./tests/tfscreen/tfmodel/scripts/test_extract_params_cli.py:14:1: F401 'numpyro.infer.svi.SVIState' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_fit_model_cli.py:5:1: F401 'dill' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_fit_model_cli.py:6:1: F401 'jax.numpy as jnp' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_fit_model_helpers.py:16:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/scripts/test_predict_growth_cli.py:4:1: F401 'os' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_predict_growth_cli.py:11:1: F401 'tfscreen.tfmodel.inference.checkpoint_io.resolve_param_file' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_predict_growth_cli.py:299:35: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/scripts/test_predict_theta_cli.py:4:1: F401 'os' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_predict_theta_cli.py:7:1: F401 'numpy as np' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_predict_theta_cli.py:8:1: F401 'unittest.mock.call' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:11:1: F401 'dataclasses' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:13:1: F401 'shutil' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:14:1: F401 'sys' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:22:1: F401 'jax' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:1034:29: E128 continuation line under-indented for visual indent
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-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:1800:128: E501 line too long (136 > 127 characters)
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-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:1814:128: E501 line too long (136 > 127 characters)
-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:1828:128: E501 line too long (136 > 127 characters)
-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:1842:128: E501 line too long (136 > 127 characters)
-./tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py:1854:128: E501 line too long (136 > 127 characters)
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-./tests/tfscreen/tfmodel/scripts/test_sample_posterior_cli.py:5:1: F401 'unittest.mock.call' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_sample_prior_cli.py:3:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/scripts/test_subset_genotypes_cli.py:84:31: E741 ambiguous variable name 'l'
-./tests/tfscreen/tfmodel/scripts/test_subset_genotypes_cli.py:598:28: E127 continuation line over-indented for visual indent
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:16:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:17:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:18:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:19:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:21:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:31:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:969:18: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:972:17: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:1237:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:1419:14: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:1681:1: E402 module level import not at top of file
-./tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py:2061:21: E221 multiple spaces before operator
-./tests/tfscreen/tfmodel/scripts/test_summarize_grid_cli.py:78:9: F841 local variable 'data' is assigned to but never used
-./tests/tfscreen/tfmodel/scripts/test_summarize_grid_cli.py:182:91: E712 comparison to True should be 'if cond is True:' or 'if cond:'
-./tests/tfscreen/tfmodel/tensors/test_batch.py:3:1: F401 'numpy as np' imported but unused
-./tests/tfscreen/tfmodel/tensors/test_batch.py:4:1: F401 'collections.namedtuple' imported but unused
-./tests/tfscreen/tfmodel/tensors/test_batch.py:14:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/tensors/test_batch.py:140:5: F841 local variable 'full_array' is assigned to but never used
-./tests/tfscreen/tfmodel/tensors/test_batch.py:140:52: E261 at least two spaces before inline comment
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-./tests/tfscreen/tfmodel/tensors/test_batch.py:142:46: W292 no newline at end of file
-./tests/tfscreen/tfmodel/tensors/test_batch_consistency.py:1:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/tensors/test_batch_consistency.py:3:1: F401 'numpy as np' imported but unused
-./tests/tfscreen/tfmodel/tensors/test_batch_consistency.py:5:1: F401 'numpyro.distributions as dist' imported but unused
-./tests/tfscreen/tfmodel/tensors/test_batch_consistency.py:6:1: F401 'tfscreen.tfmodel.data_class.BindingData' imported but unused
-./tests/tfscreen/tfmodel/tensors/test_batch_consistency.py:10:1: F401 'tfscreen.tfmodel.model_orchestrator.ModelOrchestrator' imported but unused
-./tests/tfscreen/tfmodel/tensors/test_batch_consistency.py:12:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/tfmodel/tensors/test_batch_consistency.py:17:5: F841 local variable 'batch_size' is assigned to but never used
-./tests/tfscreen/tfmodel/tensors/test_batch_consistency.py:19:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/tensors/test_batch_fix.py:1:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/tensors/test_batch_fix.py:5:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/tensors/test_batch_fix.py:14:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/tensors/test_batch_fix.py:57:56: E261 at least two spaces before inline comment
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-./tests/tfscreen/tfmodel/tensors/test_populate_dataclass.py:11:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/tensors/test_populate_dataclass.py:32:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/tensors/test_populate_dataclass.py:133:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/tensors/test_tensor_manager.py:3:1: F401 'numpy as np' imported but unused
-./tests/tfscreen/tfmodel/tensors/test_tensor_manager.py:7:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/test_base_growth.py:18:1: F401 'jax.numpy as jnp' imported but unused
-./tests/tfscreen/tfmodel/test_base_growth.py:19:1: F401 'numpyro' imported but unused
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-./tests/tfscreen/tfmodel/test_data_class.py:11:1: F401 'numpy as np' imported but unused
-./tests/tfscreen/tfmodel/test_data_class_jit.py:5:1: F401 'numpy as np' imported but unused
-./tests/tfscreen/tfmodel/test_data_class_jit.py:8:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/test_model_orchestrator.py:6:1: F401 'yaml' imported but unused
-./tests/tfscreen/tfmodel/test_model_orchestrator.py:8:1: F401 'unittest.mock.call' imported but unused
-./tests/tfscreen/tfmodel/test_model_orchestrator.py:8:1: F401 'unittest.mock.ANY' imported but unused
-./tests/tfscreen/tfmodel/test_model_orchestrator.py:34:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/test_model_orchestrator.py:955:79: E712 comparison to False should be 'if cond is False:' or 'if not cond:'
-./tests/tfscreen/tfmodel/test_model_orchestrator.py:956:64: E712 comparison to True should be 'if cond is True:' or 'if cond:'
-./tests/tfscreen/tfmodel/test_model_orchestrator.py:960:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/test_model_orchestrator.py:974:77: E231 missing whitespace after ':'
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-./tests/tfscreen/tfmodel/test_model_orchestrator.py:974:128: E501 line too long (131 > 127 characters)
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-./tests/tfscreen/tfmodel/test_model_orchestrator.py:991:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/test_model_orchestrator.py:1105:43: E231 missing whitespace after ','
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-./tests/tfscreen/tfmodel/test_model_orchestrator.py:1119:1: W293 blank line contains whitespace
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-./tests/tfscreen/tfmodel/test_presplit.py:12:1: F401 'numpy as np' imported but unused
-./tests/tfscreen/tfmodel/test_presplit.py:15:1: F401 'numpyro' imported but unused
-./tests/tfscreen/tfmodel/test_presplit.py:24:1: F401 'tfscreen.tfmodel.data_class.DataClass' imported but unused
-./tests/tfscreen/tfmodel/test_presplit.py:25:1: F401 'tfscreen.tfmodel.tensors.populate_dataclass.populate_dataclass' imported but unused
-./tests/tfscreen/tfmodel/test_presplit.py:95:52: E127 continuation line over-indented for visual indent
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-./tests/tfscreen/tfmodel/test_share_replicates.py:1:1: F401 'pytest' imported but unused
-./tests/tfscreen/tfmodel/test_share_replicates.py:3:1: F401 'numpy as np' imported but unused
-./tests/tfscreen/tfmodel/test_share_replicates.py:7:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/test_spiked_genotypes.py:5:1: F401 'jax.numpy as jnp' imported but unused
-./tests/tfscreen/tfmodel/test_spiked_genotypes.py:7:1: F401 'unittest.mock.MagicMock' imported but unused
-./tests/tfscreen/tfmodel/test_spiked_genotypes.py:7:1: F401 'unittest.mock.patch' imported but unused
-./tests/tfscreen/tfmodel/test_spiked_genotypes.py:9:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/tfmodel/test_struct_wiring.py:226:9: F401 'jax.numpy as jnp' imported but unused
-./tests/tfscreen/tfmodel/test_struct_wiring.py:233:71: E272 multiple spaces before keyword
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-./tests/tfscreen/tfmodel/test_struct_wiring.py:283:5: F401 'os' imported but unused
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-./tests/tfscreen/util/cli/test_generalized_main.py:1:1: F401 'argparse' imported but unused
-./tests/tfscreen/util/cli/test_generalized_main.py:7:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/util/cli/test_generalized_main.py:10:9: F841 local variable 'mock_fcn' is assigned to but never used
-./tests/tfscreen/util/cli/test_generalized_main.py:13:1: W293 blank line contains whitespace
-./tests/tfscreen/util/cli/test_generalized_main.py:14:75: W291 trailing whitespace
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-./tests/tfscreen/util/cli/test_generalized_main.py:125:9: F401 'sys' imported but unused
-./tests/tfscreen/util/cli/test_generalized_main.py:126:1: W293 blank line contains whitespace
-./tests/tfscreen/util/cli/test_generalized_main.py:128:9: E306 expected 1 blank line before a nested definition, found 0
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-./tests/tfscreen/util/cli/test_generalized_main.py:140:9: E306 expected 1 blank line before a nested definition, found 0
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-./tests/tfscreen/util/cli/test_generalized_main.py:161:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/util/dataframe/test_add_group_columns.py:3:1: F401 'numpy as np' imported but unused
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-./tests/tfscreen/util/dataframe/test_check_columns.py:5:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/util/dataframe/test_df_to_arrays.py:83:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/util/dataframe/test_get_group_mean_std.py:9:1: E302 expected 2 blank lines, found 1
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-./tests/tfscreen/util/io/test_read_dataframe.py:192:1: E302 expected 2 blank lines, found 1
-./tests/tfscreen/util/io/test_read_dataframe.py:200:1: E302 expected 2 blank lines, found 1
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-55    C901 'cat_fit' is too complex (28)
-10    E111 indentation is not a multiple of 4
-6     E114 indentation is not a multiple of 4 (comment)
-6     E116 unexpected indentation (comment)
-10    E117 over-indented
-1     E122 continuation line missing indentation or outdented
-2     E124 closing bracket does not match visual indentation
-6     E125 continuation line with same indent as next logical line
-213   E127 continuation line over-indented for visual indent
-92    E128 continuation line under-indented for visual indent
-3     E131 continuation line unaligned for hanging indent
-46    E201 whitespace after '['
-28    E202 whitespace before ']'
-6     E203 whitespace before ','
-4     E211 whitespace before '('
-1839  E221 multiple spaces before operator
-26    E222 multiple spaces after operator
-7     E225 missing whitespace around operator
-1093  E231 missing whitespace after ','
-103   E251 unexpected spaces around keyword / parameter equals
-14    E252 missing whitespace around parameter equals
-207   E261 at least two spaces before inline comment
-2     E265 block comment should start with '# '
-2     E266 too many leading '#' for block comment
-149   E272 multiple spaces before keyword
-1     E301 expected 1 blank line, found 0
-771   E302 expected 2 blank lines, found 1
-47    E303 too many blank lines (2)
-20    E305 expected 2 blank lines after class or function definition, found 1
-29    E306 expected 1 blank line before a nested definition, found 0
-1     E401 multiple imports on one line
-38    E402 module level import not at top of file
-87    E501 line too long (153 > 127 characters)
-43    E701 multiple statements on one line (colon)
-5     E712 comparison to True should be 'if cond is True:' or 'if cond:'
-2     E713 test for membership should be 'not in'
-1     E722 do not use bare 'except'
-2     E731 do not assign a lambda expression, use a def
-21    E741 ambiguous variable name 'l'
-231   F401 'tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.thermo.run_model' imported but unused
-11    F541 f-string is missing placeholders
-4     F811 redefinition of unused 'np' from line 1
-40    F841 local variable 'e' is assigned to but never used
-556   W291 trailing whitespace
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-28    W391 blank line at end of file
-7435
diff --git a/reports/junit/junit.xml b/reports/junit/junit.xml
deleted file mode 100644
index af0ebd3f..00000000
--- a/reports/junit/junit.xml
+++ /dev/null
@@ -1 +0,0 @@
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classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_observed_pair_only_pair_in_data_gets_entry" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_triple_mutant_produces_three_pairs" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_triple_mutant_pair_matrix_correct_column" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_triple_mutant_singles_do_not_have_pairs" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_coo_indices_same_length" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_coo_nnz_equals_total_pair_occurrences" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_skip_pairs_returns_empty_coo" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_mut_labels_first_seen_order" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_wt_label_case_insensitive" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_only_doubles_no_singles" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_num_columns_M_equals_num_genotypes" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_num_rows_M_equals_num_unique_mutations" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_build_mut_geno_matrix.TestBuildMutGenoMatrix" name="test_num_rows_P_equals_num_unique_pairs" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_combine_mutation_effects" name="test_combine_effects_with_series_sum" time="0.004" /><testcase classname="tests.tfscreen.genetics.test_combine_mutation_effects" name="test_combine_effects_with_dataframe_mean" time="0.006" /><testcase classname="tests.tfscreen.genetics.test_combine_mutation_effects" name="test_combine_effects_with_callable" time="0.004" /><testcase classname="tests.tfscreen.genetics.test_combine_mutation_effects" name="test_combine_effects_raises_errors[&lt;lambda&gt;-TypeError-must be a pandas DataFrame or Series]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_combine_mutation_effects" name="test_combine_effects_raises_errors[&lt;lambda&gt;-TypeError-must be a string or a callable]" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_combine_mutation_effects" name="test_combine_effects_raises_errors[&lt;lambda&gt;-ValueError-Mutations are missing from the effects map]" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_combine_mutation_effects" name="test_combine_effects_raises_errors[&lt;lambda&gt;-ValueError-is not a recognized aggregator]" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_combine_mutation_effects" name="test_combine_effects_edge_cases[genotype_input0-0]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_combine_mutation_effects" name="test_combine_effects_edge_cases[genotype_input1-1]" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_docstring_example_from_list" time="0.004" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_docstring_example_from_dataframe" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_column_count_equals_max_mutations" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_only_singles_creates_one_column" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_only_wt_returns_no_new_columns" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_wt_rows_are_all_zeros" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_mutation_appearing_once_has_count_one" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_mutation_count_includes_both_singles_and_doubles" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_input_types[list]" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_input_types[Series]" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_input_types[array]" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_dataframe_input_missing_genotype_column_raises" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_new_columns_inserted_after_genotype" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_does_not_modify_original_dataframe" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_empty_input" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_count_mutation_backgrounds.TestCountMutationBackgrounds" name="test_triple_mutant" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_data.TestCodonToAa" name="test_has_64_entries" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_data.TestCodonToAa" name="test_all_keys_are_three_lowercase_nt" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestCodonToAa" name="test_all_values_are_single_char" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestCodonToAa" name="test_standard_start_codon" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestCodonToAa" name="test_stop_codons" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestCodonToAa" name="test_known_amino_acids" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_data.TestCodonToAa" name="test_all_standard_amino_acids_represented" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_data.TestDegenBaseSpecifier" name="test_canonical_bases_map_to_themselves" time="0.007" /><testcase classname="tests.tfscreen.genetics.test_data.TestDegenBaseSpecifier" name="test_n_maps_to_all_bases" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_data.TestDegenBaseSpecifier" name="test_r_is_purine" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_data.TestDegenBaseSpecifier" name="test_y_is_pyrimidine" time="0.004" /><testcase classname="tests.tfscreen.genetics.test_data.TestDegenBaseSpecifier" name="test_all_values_contain_only_acgt" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_data.TestDegenBaseSpecifier" name="test_all_expansions_are_sorted" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_data.TestDegenBaseSpecifier" name="test_has_all_iupac_codes" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestComplementDict" name="test_a_complements_t" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestComplementDict" name="test_t_complements_a" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestComplementDict" name="test_g_complements_c" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestComplementDict" name="test_c_complements_g" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestComplementDict" name="test_self_complementary_entries" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_data.TestComplementDict" name="test_complement_is_involution" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_expand_genotype_columns.TestExpandGenotypeColumns" name="test_input_types[list]" time="0.006" /><testcase classname="tests.tfscreen.genetics.test_expand_genotype_columns.TestExpandGenotypeColumns" name="test_input_types[Series]" time="0.005" /><testcase classname="tests.tfscreen.genetics.test_expand_genotype_columns.TestExpandGenotypeColumns" name="test_input_types[array]" time="0.004" /><testcase classname="tests.tfscreen.genetics.test_expand_genotype_columns.TestExpandGenotypeColumns" name="test_dataframe_input_preserves_columns_and_order" time="0.011" /><testcase classname="tests.tfscreen.genetics.test_expand_genotype_columns.TestExpandGenotypeColumns" name="test_parsing_correctness" time="0.039" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_argsort_genotypes" name="test_argsort_genotypes" time="0.074" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_set_categorical_genotype" name="test_no_standardize_no_sort" time="0.007" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_set_categorical_genotype" name="test_no_standardize_with_sort" time="0.010" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_set_categorical_genotype" name="test_standardize_no_sort" time="0.011" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_set_categorical_genotype" name="test_standardize_with_sort" time="0.007" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_set_categorical_genotype" name="test_no_genotype_column" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_set_categorical_genotype" name="test_empty_dataframe" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_simple_case" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_wildtype_variations" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_self_mutation_to_wildtype" time="0.004" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_na_like_to_wildtype" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_reserved_label_passthrough" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_non_integer_site_with_equal_ends_raises" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_self_mutation_is_dropped" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_duplicate_mutations_are_dropped" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_sorting_of_mutations" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_empty_input" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_large_site_numbers" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_all_features_combined" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_error_unparsable_mutation" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_error_unparsable_site_number" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_genotype_sorting_standardize_genotypes" name="test_error_multiple_mutations_at_same_site" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_get_single_with_wt.TestGetSingleWithWT" name="test_happy_path" time="0.032" /><testcase classname="tests.tfscreen.genetics.test_get_single_with_wt.TestGetSingleWithWT" name="test_calls_expand_genotype_columns_if_needed" time="0.014" /><testcase classname="tests.tfscreen.genetics.test_get_single_with_wt.TestGetSingleWithWT" name="test_does_not_call_expand_if_already_expanded" time="0.018" /><testcase classname="tests.tfscreen.genetics.test_get_single_with_wt.TestGetSingleWithWT" name="test_handles_no_mutants" time="0.023" /><testcase classname="tests.tfscreen.genetics.test_get_single_with_wt.TestGetSingleWithWT" name="test_handles_no_wt" time="0.010" /><testcase classname="tests.tfscreen.genetics.test_get_single_with_wt.TestGetSingleWithWT" name="test_handles_empty_input" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_char_success" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_char_empty_string" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_char_failure_single_unrecognized" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_char_failure_multiple_unrecognized" time="0.000" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_char_allowed_chars_types" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_success[..111..22..333...]" time="0.013" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_success[11111]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_success[.......]" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_success[]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_success[1..2..3..4]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_success[.1.2.3.4.]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_failure[..11.1..22..-1]" time="0.009" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_failure[1.1-1]" time="0.006" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_failure[121-1]" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_failure[..ab.a.c.c-a]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_contiguous_lib_blocks_failure[ab.c.b-b]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_success" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[triple-1-Invalid command 'triple']" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[sgle-1-Invalid command 'sgle']" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[-Invalid command '']" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[single-'single' keys must have one tile part]" time="0.019" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[single-1-2-'single' keys must have one tile part]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[double-1-'double' keys must have two tile parts]" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[double-1-2-3-'double' keys must have two tile parts]" time="0.004" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[single-3-Unrecognized tile '3']" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[double-1-Z-Unrecognized tile 'Z']" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_check_lib_key_failure[double-X-Y-Unrecognized tile 'X']" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_parse_success" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_parse_whitespace_stripped" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_parse_missing_key" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_parse_mismatched_lengths" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_parse_non_contiguous_sublibs" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_parse_misplaced_degen_base" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_parse_bad_lib_combo" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_parse_bad_reading_frame" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_prepare_blocks_simple_case" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_prepare_blocks_no_libraries" time="0.003" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_prepare_blocks_multiple_libraries" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_pib_aligned_no_flanks" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_pib_with_left_flank" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_pib_with_right_flank" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_pib_with_both_flanks" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_pib_fails_degen_in_left_flank" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_prepare_indexes" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_convert_to_aa" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_singles_one_position" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_singles_multiple_positions" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_intra_doubles" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_inter_doubles_simple" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_inter_doubles_multiple_sites" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_spiked_seqs_success" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_spiked_seqs_dot_expansion" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_spiked_seqs_whitespace_stripped" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_spiked_seqs_invalid_character" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_spiked_seqs_incorrect_length" time="0.001" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_spiked_seqs_empty_list" time="0.002" /><testcase classname="tests.tfscreen.genetics.test_library_manager" name="test_get_libraries_dispatcher" time="0.005" /><testcase classname="tests.tfscreen.mle.curve_models.test_guesses" name="test_polynomial_guesses[guess_flat-1]" time="0.001" /><testcase classname="tests.tfscreen.mle.curve_models.test_guesses" name="test_polynomial_guesses[guess_poly_2nd-3]" time="0.003" /><testcase classname="tests.tfscreen.mle.curve_models.test_guesses" name="test_polynomial_guesses[guess_poly_3rd-4]" time="0.002" /><testcase classname="tests.tfscreen.mle.curve_models.test_guesses" name="test_polynomial_guesses[guess_poly_4th-5]" time="0.002" /><testcase classname="tests.tfscreen.mle.curve_models.test_guesses" name="test_polynomial_guesses[guess_poly_5th-6]" time="0.001" /><testcase 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classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestGetTicks" name="test_downsampling_logic" time="0.001" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestGetTicks" name="test_downsampling_with_uneven_division" time="0.001" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestGetTicks" name="test_zero_or_negative_max_ticks[0]" time="0.001" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestGetTicks" name="test_zero_or_negative_max_ticks[-1]" time="0.001" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestGetTicks" name="test_empty_input" time="0.000" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestFormatAxis" name="test_formats_x_axis" time="0.629" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestFormatAxis" name="test_formats_y_axis" time="0.031" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestFormatAxis" 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classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestHeatmap" name="test_custom_kwargs_are_passed_to_helpers" time="0.048" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestHeatmap" name="test_validation_errors[not_a_df-plot_df-plot_df should be a pandas DataFrame]" time="0.007" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestHeatmap" name="test_validation_errors[bad_input1-plot_df-must all be numbers]" time="0.006" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestHeatmap" name="test_validation_errors[not_an_ax-ax-ax must be a matplotlib Axes instance]" time="0.006" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmap_core.TestHeatmap" name="test_validation_errors[not_a_type-x_axis_type-x_axis_type.*not recognized]" time="0.018" /><testcase classname="tests.tfscreen.plot.heatmap.test_heatmaps" name="test_aa_v_titrant_heatmap" time="0.007" /><testcase 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classname="tests.tfscreen.process_raw.test_fastq_to_counts" name="test_bktree_find_scratch_buffers_allocated_lazily" time="0.019" /><testcase classname="tests.tfscreen.process_raw.test_fastq_to_counts" name="test_check_numba_jit_reports_status" time="0.001" /><testcase classname="tests.tfscreen.process_raw.test_process_counts_cli" name="test_process_counts_orchestration" time="0.006" /><testcase classname="tests.tfscreen.process_raw.test_process_counts_cli" name="test_process_counts_missing_cfu_columns" time="0.009" /><testcase classname="tests.tfscreen.process_raw.test_process_fastq_cli" name="test_process_reads_chunk_happy_path" time="0.011" /><testcase classname="tests.tfscreen.process_raw.test_process_fastq_cli" name="test_process_reads_chunk_id_mismatch" time="0.012" /><testcase classname="tests.tfscreen.process_raw.test_process_fastq_cli" name="test_process_reads_chunk_call_failure" time="0.012" /><testcase classname="tests.tfscreen.process_raw.test_process_fastq_cli" 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name="test_known_models_returned[instant]" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestGetTransitionModel" name="test_known_models_returned[memory]" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestGetTransitionModel" name="test_known_models_returned[baranyi]" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestGetTransitionModel" name="test_known_models_returned[baranyi_k]" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestGetTransitionModel" name="test_known_models_returned[baranyi_tau]" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestGetTransitionModel" name="test_known_models_returned[two_pop]" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestGetTransitionModel" name="test_instant_returned" time="0.000" 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classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiAgreementWithTFModel" name="test_scalar_agreement[case2]" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiAgreementWithTFModel" name="test_vector_agreement[case0]" time="0.002" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiAgreementWithTFModel" name="test_returns_numpy_array" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestTwoPopAgreementWithTFModel" name="test_scalar_agreement[case0]" time="0.002" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestTwoPopAgreementWithTFModel" name="test_scalar_agreement[case1]" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestTwoPopAgreementWithTFModel" name="test_scalar_agreement[case2]" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestTwoPopAgreementWithTFModel" name="test_vector_agreement[case0]" time="0.002" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestTwoPopAgreementWithTFModel" name="test_returns_numpy_array" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiKTransition" name="test_zero_delta_g_equals_baranyi" time="0.086" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiKTransition" name="test_larger_delta_g_softer_sharpness" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiKTransition" name="test_effective_k_formula" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiKTransition" name="test_theta_ignored" time="0.001" /><testcase 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classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiTauTransition" name="test_theta_ignored" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiTauTransition" name="test_vectorized" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiTauTransition" name="test_returns_numpy_array" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestBaranyiTauTransition" name="test_matches_inference_formula" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestMemoryAgreementWithInference" name="test_scalar_agreement[50.0-1.0-1.0-0.5-100.0]" time="0.227" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestMemoryAgreementWithInference" name="test_scalar_agreement[45.0-10.0-2.0-0.1-200.0]" time="0.003" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestMemoryAgreementWithInference" name="test_scalar_agreement[30.0-5.0-0.5-0.9-60.0]" time="0.003" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestMemoryAgreementWithInference" name="test_scalar_agreement[80.0-0.0-1.0-0.0-150.0]" time="0.004" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestMemoryAgreementWithInference" name="test_vector_agreement" time="0.131" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestRegistryCompleteness" name="test_baranyi_k_registered" time="0.001" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestRegistryCompleteness" name="test_baranyi_tau_registered" time="0.000" /><testcase classname="tests.tfscreen.simulate.growth.test_transition_linkage.TestRegistryCompleteness" name="test_all_six_models_present" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_basic_construction_default_genotypes" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_multiple_genotypes_use_k_ref_plus_dk_geno" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_wt_force_included_when_omitted_from_genotypes" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_wt_case_insensitive_not_duplicated" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_rate_override_bypasses_dk_geno" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_missing_genotype_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_missing_genotype_in_rates_but_valid_elsewhere_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_missing_k_ref_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_noise_perturbs_rate_and_sets_rate_std" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_zero_noise_rate_equals_rate_true" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_output_columns" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_k_ref_df_single_row_with_configured_value" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_k_ref_df_columns" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_k_ref_df_missing_k_ref_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_base_growth_data" name="test_k_ref_df_coerces_to_float" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_config_validation" name="test_is_file_choice" time="0.000" /><testcase classname="tests.tfscreen.simulate.test_binding_config_validation" name="test_valid_configs_do_not_raise" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_config_validation" name="test_spiked_file_genotype_not_spiked_errors" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_config_validation" name="test_library_file_genotype_in_spiked_errors" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_binding_config_validation" name="test_num_with_file_errors" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_binding_config_validation" name="test_file_requires_hill_component" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_binding_config_validation" name="test_spiked_num_out_of_range_errors" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_config_validation" name="test_library_stratified_requires_num" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_config_validation" name="test_missing_choose_by_errors" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_data" name="test_generate_binding_data_uses_precomputed_theta" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_binding_data" name="test_generate_binding_data_missing_genotype_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_reads_all_params" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_reads_partial_columns" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_nan_preserved" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_raises_no_genotype_column" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_raises_no_param_columns" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_ignores_extra_columns" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_clips_theta_above_one" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_clips_theta_below_zero" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_no_warning_for_valid_values" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestReadBindingGenotypeParams" name="test_nan_theta_not_clipped" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestHillTheta" name="test_wt_curve_shape" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestHillTheta" name="test_decreasing_for_repressor" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestHillTheta" name="test_zero_concentration_handled" time="0.000" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestHillTheta" name="test_endpoints" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestFillParamsFromWt" name="test_fills_nan_from_wt" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestFillParamsFromWt" name="test_does_not_overwrite_present" time="0.000" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestFillParamsFromWt" name="test_missing_key_uses_wt" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestWtParamsFromSimPriors" name="test_extracts_correct_keys" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillGeno" name="test_returns_correct_genotypes" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillGeno" name="test_theta_shape" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillGeno" name="test_wt_uses_wt_params" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillGeno" name="test_mutant_uses_measured_params" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillGeno" name="test_nan_params_filled_from_wt" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillGeno" name="test_theta_in_unit_interval" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillGeno" name="test_effective_params_returned" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_returns_all_genotypes" time="0.190" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_theta_shape" time="0.011" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_theta_in_unit_interval" time="0.010" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_wt_receives_exact_wt_curve" time="0.008" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_single_mutant_uses_measured_params" time="0.010" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_double_differs_from_wt" time="0.010" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_unmeasured_mut_uses_prior" time="0.094" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_csv_wt_overrides_sim_priors" time="0.011" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_direct_double_in_csv_overrides_assembled" time="0.010" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_effective_params_all_genotypes" time="0.008" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_effective_params_measured_singles" time="0.010" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildThetaGcOverrideHillMut" name="test_effective_params_direct_double" time="0.009" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildBindingThetaFromParams" name="test_output_shape" time="0.004" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildBindingThetaFromParams" name="test_correct_titrant_name" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildBindingThetaFromParams" name="test_no_noise" time="0.004" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildBindingThetaFromParams" name="test_noise_changes_values" time="0.004" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildBindingThetaFromParams" name="test_theta_in_unit_interval" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params.TestBuildBindingThetaFromParams" name="test_nan_filled_from_wt" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_binding_params" name="test_supported_components" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_binding_params" name="test_hill_param_cols" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_build_sample_dataframes" name="test_happy_path" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_build_sample_dataframes" name="test_non_default_replicate" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_build_sample_dataframes" name="test_raises_error_on_empty_list" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_build_sample_dataframes" name="test_raises_error_on_invalid_container_type[invalid_input0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_build_sample_dataframes" name="test_raises_error_on_invalid_container_type[not_a_list]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_build_sample_dataframes" name="test_raises_error_on_invalid_container_type[123]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_build_sample_dataframes" name="test_raises_error_on_invalid_item_type" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_build_sample_dataframes" name="test_single_condition_block" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_linear_model_maps_b_and_m" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_linear_is_default_model_when_omitted" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_power_model_maps_b_a_n" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_saturation_model_maps_kmin_kmax" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_multiple_conditions_one_row_each" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_mixed_models_nan_fill_unused_columns" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_unknown_model_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_missing_required_key_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_empty_growth_cfg_returns_empty_df" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_growth_parameters_output" name="test_original_growth_cfg_not_mutated" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_library_binding_data" name="test_stratified_selects_num_nonspiked_nonwt" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_library_binding_data" name="test_random_selects_num_survivors" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_library_binding_data" name="test_only_survivors_are_eligible" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_library_binding_data" name="test_num_required_for_stratified" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_library_binding_data" name="test_theta_matches_hill_params_noise_free" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_library_binding_data" name="test_noise_sets_theta_std_and_perturbs" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_library_binding_data" name="test_file_path_uses_named_genotypes_and_warns_on_nonsurvivor" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_library_prediction_success" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_library_prediction_config_error" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_library_prediction_dk_geno_zero_passes_flag" time="0.004" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_library_prediction_dk_geno_zero_makes_hyper_params_optional" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_library_prediction_missing_hyper_params_raises_without_dk_geno_zero" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_jax_key_derived_from_seed" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_jax_key_defaults_to_zero_when_no_seed" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_numpy_rng_passed_to_thermo_to_growth" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_numpy_rng_seeded_with_seed" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_params_file_produces_binding_theta_df" time="0.015" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_params_file_theta_gc_override_passed_to_thermo" time="0.008" /><testcase classname="tests.tfscreen.simulate.test_library_prediction" name="test_unsupported_theta_component_raises" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_presplit_data" name="test_output_columns" time="0.007" /><testcase classname="tests.tfscreen.simulate.test_presplit_data" name="test_output_row_count" time="0.007" /><testcase classname="tests.tfscreen.simulate.test_presplit_data" name="test_ln_cfu_is_finite_for_present_genotypes" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_presplit_data" name="test_ln_cfu_std_is_positive" time="0.009" /><testcase classname="tests.tfscreen.simulate.test_presplit_data" name="test_absent_genotype_lower_lncfu" time="0.007" /><testcase classname="tests.tfscreen.simulate.test_presplit_data" name="test_extra_noise_inflates_ln_cfu_std" time="0.011" /><testcase classname="tests.tfscreen.simulate.test_presplit_data" name="test_reproducibility" time="0.012" /><testcase classname="tests.tfscreen.simulate.test_report_cfu0_cli" name="test_classify_genotypes_basic_classes" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_report_cfu0_cli" name="test_classify_genotypes_wt_beats_spiked" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_report_cfu0_cli" name="test_classify_genotypes_triple_mutant_is_other" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_report_cfu0_cli" name="test_report_cfu0_prints_expected_classes" time="0.012" /><testcase classname="tests.tfscreen.simulate.test_report_cfu0_cli" name="test_report_cfu0_excludes_infinite_ln_cfu0_from_mean" time="0.014" /><testcase classname="tests.tfscreen.simulate.test_report_cfu0_cli" name="test_report_cfu0_seed_overrides_config" time="0.013" /><testcase classname="tests.tfscreen.simulate.test_report_cfu0_cli" name="test_seed_cli_parsed_as_int" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_report_cfu0_cli" name="test_seed_cli_defaults_to_none" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_run_simulation" name="test_setup_file_output_none" time="0.000" /><testcase classname="tests.tfscreen.simulate.test_run_simulation" name="test_setup_file_output_bad_prefix" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_run_simulation" name="test_setup_file_output_create_dir" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_run_simulation" name="test_setup_file_output_dir_exists_is_file" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_run_simulation" name="test_setup_file_output_files_exist" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_run_simulation" name="test_setup_file_output_success_existing_dir" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_run_simulation" name="test_run_simulation_config_error" time="0.000" /><testcase classname="tests.tfscreen.simulate.test_run_simulation" name="test_run_simulation_success" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_run_simulation" name="test_run_simulation_no_output" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_raises_on_unknown_component" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_raises_on_excluded_component" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_returns_theta_gc_shape" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_returns_numpy_array" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_priors_overrides_applied" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_define_model_called_with_correct_args" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_dispatch_uses_simulate_when_present" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_dispatch_prior_path_when_no_simulate" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_sim_priors_overrides_applied" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_greedy_maximin_returns_correct_count" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_greedy_maximin_no_duplicates" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_greedy_maximin_selects_spread_curves" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_greedy_maximin_all_when_n_equals_pool" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_greedy_maximin_all_when_n_exceeds_pool" time="0.000" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_stratified_output_shapes" time="0.089" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_stratified_raises_when_n_select_exceeds_pool" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_stratified_pool_size_calls" time="0.007" /><testcase classname="tests.tfscreen.simulate.test_sample_theta" name="test_stratified_ignores_simulate_path" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_success[input_dict0-value-kwargs0-5.0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_success[input_dict1-value-kwargs1-5]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_success[input_dict2-value-kwargs2-0.0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_success[input_dict3-value-kwargs3-10.0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_success[input_dict4-value-kwargs4-0.1]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_success[input_dict5-value-kwargs5-9.9]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_success[input_dict6-value-kwargs6-None]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_success[input_dict7-value-kwargs7-None]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_failures[input_dict0-value-kwargs0-Required key 'value' not found]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_failures[input_dict1-value-kwargs1-Value must be a scalar]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_failures[input_dict2-value-kwargs2-Could not process key 'value']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_failures[input_dict3-value-kwargs3-must be &gt;= 0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_failures[input_dict4-value-kwargs4-must be &gt; 0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_failures[input_dict5-value-kwargs5-must be &lt;= 10]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_dict_number_failures[input_dict6-value-kwargs6-must be &lt; 10]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_success" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_loads_from_path" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_failures[cfu0-None-None-Required key 'cfu0' not found]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_failures[transform_sizes-None-None-Configuration key 'transform_sizes' is invalid]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_failures[transform_sizes-libA-bad_string-Could not process key 'libA']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_failures[condition_selector-None-not_a_list-condition_selector must be a list]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_lib_spec_success" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_lib_spec_sets_defaults" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_lib_spec_failures[&lt;lambda&gt;-missing from phenotype_df]" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_lib_spec_failures[&lt;lambda&gt;-missing required columns]" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_lib_spec_failures[&lt;lambda&gt;-must be identical]" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_lib_spec_failures[&lt;lambda&gt;-must be a subset]" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_lib_spec_failures[&lt;lambda&gt;-cannot be zero]" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_lib_spec_failures[&lt;lambda&gt;-not recognized]" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_plasmid_probabilities_no_skew" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_plasmid_probabilities_with_skew" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_plasmid_probabilities_failures[bad_frequencies0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_plasmid_probabilities_failures[bad_frequencies1]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_plasmid_probabilities_failures[bad_frequencies2]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_index_hop_no_hopping[None]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_index_hop_no_hopping[0.0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_index_hop_deterministic" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_index_hop_edge_cases[edge_case_counts0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_index_hop_edge_cases[edge_case_counts1]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_transform_single_plasmid" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_transform_multi_plasmid" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_transform_zero_transformants" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_transform_and_mix[library_origin]" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_transform_and_mix[groupby_key1]" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_transform_and_mix_invariant_to_transform_sizes" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_growth_single_plasmid" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_growth_multi_plasmid[gmean]" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_growth_multi_plasmid[mean]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_growth_multi_plasmid[min]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_growth_multi_plasmid[max]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_growth_multi_plasmid[sum]" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_growth_invalid_fcn" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_sequencing_deterministic" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_sequencing_zero_abundance_condition" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_sim_sequencing_single_survivor" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_calc_genotype_cfu0_single_plasmid" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_calc_genotype_cfu0_multi_plasmid" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_calc_genotype_cfu0_no_transformants" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_simulate_library_group_integration" time="0.496" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_simulate_library_group_handles_sparse_data" time="0.467" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_absent" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_none" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_instant" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_memory" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_multiple_conditions" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt0-must be a list]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt1-must be a dict]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt2-missing required key 'condition_pre']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt3-missing required key 'model']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt4-'condition_pre' must be a string]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt5-must be one of]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt6-duplicate condition_pre]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt7-requires 'tau0']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt8-requires 'k1']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt9-requires 'k2']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt10-'k2' must be &gt; 0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt11-'k2' must be &gt; 0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt12-'tau0' must be a number]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt13-requires 'tau_lag']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt14-requires 'k_sharp']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt15-'k_sharp' must be &gt; 0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt16-requires 'k_trans']" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt17-'k_trans' must be &gt; 0]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_failures[bad_gt18-'k_trans' must be a number]" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_baranyi" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_growth_transition_two_pop" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_baranyi" time="0.061" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_two_pop" time="0.061" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_none_gives_instant" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_instant_model" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_memory_lag_not_expired" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_memory_lag_expired" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_memory_tau_equals_t_sel" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_memory_higher_theta_shorter_tau" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_mixed_conditions" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_compute_kt_memory_less_than_instant" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_selection_experiment_missing_condition_pre_raises" time="0.016" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_selection_experiment_end_to_end" time="0.474" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_simulate_known_keys_is_frozenset" time="0.000" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_unknown_key_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_unknown_key_error_mentions_label" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_multiple_unknown_keys" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_all_known_keys_accepted" time="0.000" /><testcase classname="tests.tfscreen.simulate.test_selection_experiment" name="test_check_cf_accepts_base_growth_data" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_expand_block_variants" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_expand_block_no_variants_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_expand_block_auto_raises" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_resolve_paths_relative" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_resolve_paths_absolute_unchanged" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_resolve_paths_unknown_key_unchanged" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_creates_subdirs" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_writes_config" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_writes_combo_json" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_writes_summary" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_cartesian_product" time="0.010" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_renders_template" time="0.015" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_missing_grid_yaml" time="0.004" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_missing_base_config" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_no_base_config_key" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_setup_sim_grid_cli" name="test_setup_sim_grid_override_applied" time="0.007" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_returns_sim_data_instance" time="0.022" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_num_genotype" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_num_titrant_conc_unique" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_num_titrant_conc_deduplicates" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_num_titrant_name_always_one" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_scatter_theta_always_zero" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_batch_size_equals_num_genotype" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_concentrations_sorted" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_zero_concentration_replaced_by_sentinel_in_log" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_titrant_conc_preserves_zero" time="0.003" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_batch_idx_is_arange" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_geno_theta_idx_is_arange" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_num_mutation_positive" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_no_struct_by_default" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_sim_data_class.TestBuildSimData" name="test_skip_pairs_sets_num_pair_zero" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_seed_cli_parsed_as_int" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_seed_cli_defaults_to_none" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_seed_overrides_config" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_seed_none_preserves_config" time="0.007" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_writes_parameters_not_phenotype" time="0.007" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_output_file_names_include_expected_stems" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_growth_parameters_csv_always_written" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_growth_parameters_csv_written_for_real" time="0.008" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_transformation_lam_csv_always_written" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_transformation_lam_csv_written_for_real" time="0.008" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_transformation_lam_participates_in_existence_guard" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_base_growth_not_written_without_config" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_base_growth_written_when_configured" time="0.006" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" name="test_k_ref_not_written_without_base_growth_config" time="0.005" /><testcase classname="tests.tfscreen.simulate.test_simulate_cli" 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name="test_horseshoe_activities_appear_in_phenotype_df" time="0.013" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaNoise" name="test_zero_sigma_leaves_theta_unchanged" time="0.012" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaNoise" name="test_nonzero_sigma_perturbs_theta" time="0.012" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaNoise" name="test_noisy_theta_stays_in_unit_interval" time="0.011" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaNoise" name="test_reproducible_with_same_rng" time="0.022" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaNoise" name="test_different_seeds_give_different_theta" time="0.024" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaRescale" name="test_rescale_dict_has_passthrough_and_logit" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaRescale" name="test_passthrough_is_identity" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaRescale" name="test_logit_matches_manual_formula" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaRescale" name="test_logit_clips_boundary_values" time="0.001" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaRescale" name="test_unknown_theta_rescale_raises" time="0.002" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaRescale" name="test_passthrough_matches_default" time="0.025" /><testcase classname="tests.tfscreen.simulate.test_thermo_to_growth.TestThermo_to_growth_ThetaRescale" 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time="0.007" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_new_models.TestExtractThetaCurvesHillMut" name="test_different_indices_use_different_posteriors" time="0.007" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_new_models.TestExtractThetaCurvesHillMut" name="test_zero_concentration_handled" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_new_models.TestExtractThetaCurvesHillMut" name="test_broadcast_without_genotype" time="0.008" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_new_models.TestExtractThetaCurvesDispatcher" name="test_dispatches_to_hill" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_new_models.TestExtractThetaCurvesDispatcher" name="test_dispatches_to_hill_mut" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_new_models.TestExtractThetaCurvesDispatcher" name="test_raises_for_categorical" 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classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_theta_curves_broadcast" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_parameters_file_loading" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_parameters_condition_growth_shares_replicates[linear-posteriors_keys0]" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_parameters_condition_growth_shares_replicates[power-posteriors_keys1]" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_parameters_condition_growth_shares_replicates[saturation-posteriors_keys2]" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_parameters_growth_transition_shares_replicates[memory-posteriors_keys0]" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_parameters_growth_transition_shares_replicates[baranyi-posteriors_keys1]" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_parameters_errors" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_parameters_h5_file" time="0.020" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_theta_curves_hdf5_path" time="0.011" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_growth_predictions_hdf5_basic" time="0.027" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_extraction_robustness" name="test_extract_growth_predictions_hdf5_multiple_groups" 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name="test_draw_prior_returns_dicts" time="0.213" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_draw_prior_contains_growth_pred" time="0.147" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_draw_prior_growth_pred_shape" time="2.008" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_draw_prior_latent_params_shape" time="1.962" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_draw_prior_no_observed_in_latent" time="0.148" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_draw_prior_reproducible" time="0.247" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_draw_prior_different_seeds" time="0.259" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_draw_prior_multiple_independent_draws" time="1.969" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_growth_df_from_prior_returns_dataframe" time="0.293" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_growth_df_from_prior_same_rows" time="0.344" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_growth_df_from_prior_has_ln_cfu" time="0.299" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_growth_df_from_prior_preserves_metadata" time="0.273" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_growth_df_from_prior_noise_changes_ln_cfu" time="0.398" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_growth_df_from_prior_different_draws" time="2.441" /><testcase classname="tests.tfscreen.tfmodel.analysis.test_prior_predictive" name="test_latent_params_compatible_with_predict" time="2.180" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_fixed" name="test_get_hyperparameters" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_fixed" name="test_get_guesses" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_fixed" name="test_get_priors" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_fixed" name="test_define_model_logic_and_shapes" time="0.113" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_fixed" name="test_guide_logic_and_shapes" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_get_hyperparameters" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_get_priors" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_get_guesses" time="0.051" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_define_model_logic_and_shapes" time="0.847" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_guide_logic_and_shapes" time="0.198" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_pinnable_suffixes" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_model_priors_default_pinned_is_empty_dict" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_model_priors_accepts_pinned_dict" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_define_model_unpinned_uses_sample_sites" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_define_model_pinned_replaces_with_deterministic" time="0.030" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_define_model_all_pinned_has_only_offset_sample_site" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_guide_pinned_drops_variational_params" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" name="test_guide_all_pinned_keeps_only_offset_machinery" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.activity.test_hierarchical_geno" 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name="test_manual_titrant_df_unknown_conc_raises" time="0.007" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_categorical_geno.TestBuildCalcDf" name="test_manual_titrant_df_missing_columns_raises" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_categorical_geno.TestComputeThetaSamples" name="test_output_shape" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_categorical_geno.TestComputeThetaSamples" name="test_correct_values_indexed" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_categorical_geno.TestComputeThetaSamples" name="test_all_rows_finite" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_full_population_contract" name="test_hill_mut_subset_matches_full_population_indexed_down" time="1.315" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_full_population_contract" name="test_hill_geno_subset_matches_full_population_indexed_down" time="1.235" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_full_population_contract" name="test_thermo_subset_matches_full_population_indexed_down[O2_C4_K3_U0_a-d_ln_K_op_offset]" time="0.579" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_full_population_contract" name="test_thermo_subset_matches_full_population_indexed_down[O2_C4_K3_U1_a-d_ln_K_op_offset]" time="0.214" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_full_population_contract" name="test_thermo_subset_matches_full_population_indexed_down[O2_C12_K5_U0_a-d_ln_K_h_o_offset]" time="0.388" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_full_population_contract" name="test_thermo_subset_matches_full_population_indexed_down[O2_C12_K5_U1_a-d_ln_K_h_o_offset]" time="0.194" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_get_hyperparameters" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_get_priors" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_get_guesses" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_get_guesses_log_hill_K_from_median" time="0.038" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_get_guesses_excludes_neg_inf" time="0.036" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_get_guesses_fallback_all_neg_inf" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_get_guesses_offsets_are_zero" time="0.029" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_define_model_shapes_and_values" time="0.940" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_run_model_calculation" time="0.272" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_run_model_scatter" time="0.022" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_run_model_no_scatter" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_run_model_batch_idx_translation" time="0.376" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_guide_logic_and_shapes" time="0.141" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_guide_values_in_valid_range" time="0.063" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_population_moments_shape" time="0.061" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_population_moments_helper_shape" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno.TestHillPredictUnmeasured" name="test_output_shape_and_columns" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno.TestHillPredictUnmeasured" name="test_theta_in_unit_interval" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno.TestHillPredictUnmeasured" name="test_unknown_mutations_not_nan" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno.TestHillPredictUnmeasured" name="test_all_genotypes_get_same_prediction_per_titrant" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno.TestHillPredictUnmeasured" name="test_zero_conc_handled" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno.TestHillPredictUnmeasured" name="test_upper_ge_median_ge_lower" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno.TestHillPredictUnmeasured" name="test_different_titrants_get_different_predictions" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_get_sim_hyperparameters_returns_dict" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_sim_priors_constructs" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_simulate_output_shapes" time="0.111" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_simulate_theta_in_unit_interval" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_simulate_normal_genotypes_wide_range" time="0.053" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_simulate_stuck_bound_genotypes" time="0.027" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_simulate_mixture_probabilities_error" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_simulate_deterministic_with_same_key" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_simulate_different_keys_differ" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_geno" name="test_simulate_theta_param_compatible_with_run_model" time="0.028" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestAssemble" name="test_no_epistasis" time="0.118" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestAssemble" name="test_with_epistasis" time="0.110" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut" name="test_get_hyperparameters" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut" name="test_get_priors" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut" name="test_get_guesses_no_epi" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut" name="test_get_guesses_with_epi" time="0.025" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoEpi" name="test_output_is_theta_param" time="0.415" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoEpi" name="test_theta_low_shape" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoEpi" name="test_population_moments_shape" time="0.011" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoEpi" name="test_wt_column_equals_wt_param_when_deltas_zero" time="0.044" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoEpi" name="test_assembly_with_nonzero_deltas" time="0.058" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoEpi" name="test_theta_values_in_valid_range" time="0.080" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoEpi" name="test_sample_sites_in_trace" time="0.011" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelWithEpi" name="test_output_shapes" time="0.200" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelWithEpi" name="test_epistasis_shifts_only_double_column" time="0.144" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelWithEpi" name="test_epistasis_sample_sites_present" time="0.019" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestRunModel" name="test_scatter_theta_1_shape" time="0.051" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestRunModel" name="test_scatter_theta_0_shape" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestRunModel" name="test_hill_equation_midpoint" time="0.062" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestGuide" name="test_guide_no_epi_returns_theta_param" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestGuide" name="test_guide_no_epi_shapes" time="0.017" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestGuide" name="test_guide_with_epi_runs" time="0.213" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestGuide" name="test_guide_no_epi_no_epistasis_sample_sites" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestGuide" name="test_guide_values_in_valid_range" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut" name="test_get_population_moments_shape" time="0.010" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut" name="test_get_sim_hyperparameters_returns_dict" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut" name="test_get_sim_hyperparameters_horseshoe_defaults_positive" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut" name="test_sim_priors_constructs" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateNoEpi" name="test_output_shapes" time="0.019" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateNoEpi" name="test_theta_in_unit_interval" time="0.009" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateNoEpi" name="test_wt_genotype_exact_reference" time="0.010" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateNoEpi" name="test_mutant_differs_from_wt" time="0.008" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateNoEpi" name="test_deterministic_same_key" time="0.013" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateNoEpi" name="test_different_keys_differ" time="0.014" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateNoEpi" name="test_compatible_with_run_model" time="0.169" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateEpi" name="test_output_shapes" time="0.014" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateEpi" name="test_theta_in_unit_interval" time="0.013" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateEpi" name="test_epistasis_only_shifts_double_mutant" time="0.013" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateEpi" name="test_epi_tau_zero_produces_no_epistasis" time="0.008" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateEpi" name="test_horseshoe_larger_tau_produces_larger_effects" time="4.608" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestSimulateEpi" name="test_deterministic_same_key" time="0.026" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoMutation" name="test_does_not_raise" time="0.385" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoMutation" name="test_output_shapes" time="0.011" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoMutation" name="test_per_genotype_equals_wt" time="0.063" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoMutation" name="test_different_keys_give_different_curves" time="0.053" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoMutation" name="test_force_prior_predictive_path_does_not_raise" time="0.035" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_hill_mut.TestDefineModelNoMutation" name="test_pool_members_diverse_under_force_prior_predictive" time="0.131" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildGenotypeIndicators" name="test_wt_row_is_all_zeros" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildGenotypeIndicators" name="test_single_mutation" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildGenotypeIndicators" name="test_double_mutant_sets_pair" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildGenotypeIndicators" name="test_unknown_mutation_marks_invalid" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildGenotypeIndicators" name="test_novel_pair_not_in_pair_labels_is_zero" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildGenotypeIndicators" name="test_multiple_genotypes" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildGenotypeIndicators" name="test_output_dtypes" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildPredictionGrid" name="test_shape" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildPredictionGrid" name="test_index_values" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildPredictionGrid" name="test_raises_on_unknown_titrant_name" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestBuildPredictionGrid" name="test_columns_present" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestHillMutPredictUnmeasured" name="test_wt_returns_finite" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestHillMutPredictUnmeasured" name="test_unknown_mutation_is_nan" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestHillMutPredictUnmeasured" name="test_output_columns" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestHillMutPredictUnmeasured" name="test_zero_concentration_handled" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestHillMutPredictUnmeasured" name="test_additivity_double_mutant" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.test_predict_unmeasured.TestHillMutPredictUnmeasured" name="test_epistasis_shifts_double_mutant" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleScalar" name="test_zero_offsets_gives_wt_everywhere" time="0.027" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleScalar" name="test_mut0_shifts_correct_genotypes" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleScalar" name="test_sigma_scales_effect" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleScalar" name="test_both_mutations_additive" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleScalar" name="test_epistasis_shifts_only_double_mutant" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleScalar" name="test_additive_mut_plus_epistasis" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleTitrant" name="test_output_shape" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleTitrant" name="test_zero_offsets_all_equal_wt" time="0.040" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleTitrant" name="test_sigma_scales_per_titrant" time="0.026" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestAssembleTitrant" name="test_epistasis_shifts_only_double_mutant" time="0.059" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK" name="test_get_hyperparameters_has_required_keys" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK" name="test_get_priors_type" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK" name="test_get_priors_physical_constants" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK" name="test_get_priors_matches_hyperparameters" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK" name="test_get_guesses_shapes_no_epi" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK" name="test_get_guesses_shapes_with_epi" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestDefineModelNoEpi" name="test_returns_theta_param" time="0.579" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestDefineModelNoEpi" name="test_parameter_shapes" time="0.062" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestDefineModelNoEpi" name="test_all_genotypes_equal_when_deltas_zero" time="0.092" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestDefineModelNoEpi" name="test_assembly_with_known_deltas" time="0.107" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestDefineModelNoEpi" name="test_titrant_dim_assembly" time="0.061" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestDefineModelNoEpi" name="test_sigma_nonneg" time="0.062" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestDefineModelNoEpi" name="test_sample_sites_present" time="0.060" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestDefineModelNoEpi" name="test_no_epi_sample_sites_absent" time="0.063" /><testcase 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name="test_third_spec_has_scalar_delta_lnK" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestGetExtractSpecs" name="test_fourth_spec_has_titrant_delta_lnK" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestGetExtractSpecs" name="test_fifth_spec_has_scalar_epi_terms" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestGetExtractSpecs" name="test_sixth_spec_has_titrant_epi_terms" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestGetExtractSpecs" name="test_all_specs_use_theta_prefix" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PK.TestGetExtractSpecs" name="test_second_spec_covers_all_titrant_genotype_combos" time="0.003" /><testcase 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time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestProjectDdG" name="test_K_l_o_is_L_minus_LO" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestProjectDdG" name="test_K_l_e_is_L_minus_LE2_halved" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestProjectDdG" name="test_H_perturbation_does_not_affect_l_o_or_l_e" time="0.052" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestConfig" name="test_hyperparameters_has_required_keys" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestConfig" name="test_hyperparameters_has_no_nn_keys" time="0.000" /><testcase 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name="test_get_guesses_ddG_offset_shape" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestConfig" name="test_get_guesses_wt_K_shapes" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDefineModel" name="test_returns_theta_param" time="0.471" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDefineModel" name="test_K_shapes" time="0.125" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDefineModel" name="test_population_moment_shapes" time="0.127" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDefineModel" name="test_sample_sites_present" time="0.126" /><testcase 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name="test_permuted_features_give_same_result_as_canonical" time="0.128" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDefineModel" name="test_tf_op_totals_preserved" time="0.128" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestGuide" name="test_returns_theta_param" time="0.154" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestGuide" name="test_K_shapes" time="0.125" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestGuide" name="test_sample_sites_match_model" time="0.255" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestGuide" name="test_variational_params_registered" time="0.124" /><testcase 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name="test_second_spec_has_titrant_K_values" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestGetExtractSpecs" name="test_third_spec_has_all_projected_delta_lnK" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestGetExtractSpecs" name="test_all_specs_use_theta_prefix" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestGetExtractSpecs" name="test_second_spec_covers_all_titrant_genotype_combos" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestGetExtractSpecs" name="test_third_spec_has_one_row_per_mutation" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_output_shape_and_columns" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_wt_theta_in_unit_interval" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_unknown_mutation_is_nan" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_zero_conc_handled" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_upper_ge_median_ge_lower" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_epistasis_shifts_double_mutant" time="0.007" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_multiple_titrants_broadcast_correctly" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_scalar_d_he_le_matches_lnK_mut_when_replicated" time="0.009" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetStructPerm" name="test_canonical_order_gives_identity_perm" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetStructPerm" name="test_permuted_order_gives_correct_perm" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetStructPerm" name="test_canonical_perm_output_reindexes_correctly" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetStructPerm" name="test_wrong_structure_names_raises" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetStructPerm" name="test_missing_structure_raises" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetStructPerm" name="test_duplicate_structure_raises" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestProjectDdG" name="test_output_shape" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestProjectDdG" name="test_zero_ddG_gives_zero_delta_lnK" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestProjectDdG" name="test_K_h_l_equals_H_minus_L" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestProjectDdG" name="test_K_h_o_equals_H_minus_HO" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestProjectDdG" name="test_K_h_e_equals_H_minus_HE2_over_two" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestProjectDdG" name="test_K_l_o_equals_L_minus_LO" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestProjectDdG" name="test_K_l_e_equals_L_minus_LE2_over_two" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestProjectDdG" name="test_leading_batch_dims" time="0.041" /><testcase 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name="test_nn_params_use_canonical_names_with_shuffled_input" time="0.144" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGuide" name="test_no_epi_params_when_num_pair_zero" time="0.539" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGuide" name="test_epi_params_registered_when_num_pair_nonzero" time="0.161" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGuide" name="test_tf_op_totals_preserved" time="0.148" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC" name="test_registry_entry" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_returns_list" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_three_specs_without_epi" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_four_specs_with_epi" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_first_spec_has_scalar_K_values" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_second_spec_has_titrant_K_values" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_third_spec_has_projected_delta_lnK" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_fourth_spec_has_epi_terms" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_all_specs_use_theta_prefix" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_second_spec_covers_all_titrant_genotype_combos" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_PnnC.TestGetExtractSpecs" name="test_third_spec_has_one_row_per_mutation" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_predict_unmeasured.TestMwcLnKMutPredict" name="test_output_shape_and_columns" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_predict_unmeasured.TestMwcLnKMutPredict" name="test_wt_theta_in_unit_interval" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_predict_unmeasured.TestMwcLnKMutPredict" name="test_unknown_mutation_is_nan" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_predict_unmeasured.TestMwcLnKMutPredict" name="test_upper_ge_median_ge_lower" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_predict_unmeasured.TestMwcLnKMutPredict" name="test_zero_conc_handled" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_predict_unmeasured.TestMwcLnKMutPredict" name="test_epistasis_shifts_double_mutant" time="0.007" /><testcase 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classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_output_shape" time="0.708" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_values_in_0_1" time="0.710" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_all_finite" time="0.802" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_monotone_decreasing_in_effector" time="0.666" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_apo_matches_analytic_formula" time="0.480" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_higher_K_h_o_gives_higher_theta_at_zero_effector" time="0.329" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_higher_K_h_l_gives_lower_theta_at_zero_effector" time="0.115" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_titrant_dim_broadcasts_independently" time="0.634" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_wt_params_give_high_apo_theta" time="0.319" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestComputeTheta" name="test_e_free_bounded_by_e_total" time="0.149" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestPopulationMoments" name="test_output_shapes" time="0.194" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestPopulationMoments" name="test_sigma_zero_when_uniform" time="0.038" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestPopulationMoments" name="test_sigma_positive_when_varying" time="0.169" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestPopulationMoments" name="test_mu_finite" time="0.026" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestPopulationMoments" name="test_data_arg_unused" time="0.038" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestRunModel" name="test_scatter_theta_0_shape" time="0.119" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestRunModel" name="test_scatter_theta_1_shape" time="0.068" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestRunModel" name="test_values_in_0_1" time="0.131" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestRunModel" name="test_all_finite" time="0.082" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestRunModel" name="test_geno_theta_idx_selects_genotypes" time="0.720" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestRunModel" name="test_nontrivial_batch_idx_selects_correct_genotypes" time="0.872" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestRunModel" name="test_apo_matches_analytic_formula" time="0.319" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo.TestRunModel" name="test_higher_effector_lower_theta" time="0.116" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.test_thermo" name="test_get_population_moments_returns_stored_values" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleScalar" name="test_zero_offsets_gives_wt_everywhere" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleScalar" name="test_mut0_shifts_correct_genotypes" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleScalar" name="test_sigma_scales_effect" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleScalar" name="test_both_mutations_additive" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleScalar" name="test_epistasis_shifts_only_double_mutant" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleScalar" name="test_additive_mut_plus_epistasis" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleTitrant" name="test_output_shape" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleTitrant" name="test_zero_offsets_all_equal_wt" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleTitrant" name="test_sigma_scales_per_titrant" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK.TestAssembleTitrant" name="test_epistasis_shifts_only_double_mutant" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK" name="test_get_hyperparameters_has_required_keys" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK" name="test_get_hyperparameters_K_u_wt_defaults" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK" name="test_get_priors_type" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK" name="test_get_priors_physical_constants" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PK" name="test_get_priors_K_u_fields" time="0.001" /><testcase 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time="0.136" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_K_shapes" time="0.133" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_ln_K_u_shape" time="0.134" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_ln_K_u_is_homogeneous" time="0.131" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_conc_unit_scale_preserved" time="0.135" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_population_moment_shapes" time="0.133" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_sample_sites_present" time="0.143" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_deterministic_sites_present" time="0.141" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_theta_ln_K_u_det_site_is_homogeneous" time="0.146" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_no_epi_sites" time="0.144" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_wrong_struct_names_raises" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDefineModel" name="test_permuted_struct_names_accepted" time="0.144" /><testcase 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time="0.150" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGuide" name="test_ln_K_u_is_homogeneous" time="0.155" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGuide" name="test_sample_sites_match_model" time="0.307" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGuide" name="test_variational_params_registered" time="0.156" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGuide" name="test_no_nn_params_registered" time="0.152" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGuide" name="test_no_epi_params_registered" time="0.151" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGuide" name="test_ddG_sigma_s_shape" time="0.148" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGuide" name="test_ddG_offset_locs_shape" time="0.149" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGuide" name="test_tf_op_totals_preserved" time="0.153" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGuide" name="test_permuted_features_give_same_output_as_canonical" time="0.266" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG" name="test_registry_entry" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGetExtractSpecs" name="test_returns_list" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGetExtractSpecs" name="test_three_specs" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGetExtractSpecs" name="test_four_specs_when_pair_labels_present" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGetExtractSpecs" name="test_first_spec_has_scalar_K_values_including_ln_K_u" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGetExtractSpecs" name="test_second_spec_has_titrant_K_values" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGetExtractSpecs" name="test_third_spec_has_all_projected_delta_lnK" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGetExtractSpecs" name="test_all_specs_use_theta_prefix" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGetExtractSpecs" name="test_second_spec_covers_all_titrant_genotype_combos" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestGetExtractSpecs" name="test_third_spec_has_one_row_per_mutation" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_output_shape_and_columns" time="0.007" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_wt_theta_in_unit_interval" time="0.007" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_unknown_mutation_is_nan" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_zero_conc_handled" time="0.007" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_upper_ge_median_ge_lower" time="0.007" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_ln_K_u_is_homogeneous" time="0.009" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_epistasis_shifts_double_mutant" time="0.009" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PddG.TestDdGPriorPredictUnmeasured" name="test_scalar_d_he_le_matches_lnK_mut_when_replicated" time="0.010" /><testcase 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classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetStructPerm" name="test_duplicate_structure_raises" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestProjectDdG" name="test_output_shape" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestProjectDdG" name="test_zero_ddG_gives_zero_delta_lnK" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestProjectDdG" name="test_K_h_l_equals_H_minus_L" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestProjectDdG" name="test_K_h_o_equals_H_minus_HO" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestProjectDdG" name="test_K_h_e_equals_H_minus_HE2_over_two" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestProjectDdG" name="test_K_l_o_equals_L_minus_LO" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestProjectDdG" name="test_K_l_e_equals_L_minus_LE2_over_two" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestProjectDdG" name="test_leading_batch_dims" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestProjectDdG" name="test_H_perturbation_does_not_affect_l_o_or_l_e" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestConfig" name="test_hyperparameters_has_required_keys" time="0.001" /><testcase 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classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestConfig" name="test_get_guesses_with_epi_keys" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestConfig" name="test_get_guesses_ddG_offset_shape" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestConfig" name="test_get_guesses_epi_shapes_with_epi" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestConfig" name="test_get_guesses_wt_K_shapes" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_returns_theta_param" time="0.527" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_K_shapes" time="0.166" 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name="test_deterministic_sites_present" time="0.172" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_theta_ln_K_u_det_site_is_homogeneous" time="0.250" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_wrong_struct_names_raises" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_permuted_struct_names_accepted" time="0.197" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_permuted_features_give_same_K_as_canonical" time="0.193" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_no_epi_sites_when_num_pair_zero" time="0.161" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_epi_sites_present_when_num_pair_nonzero" time="0.192" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_epi_absent_when_contact_distances_none" time="0.165" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestDefineModel" name="test_tf_op_totals_preserved" time="0.166" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_returns_theta_param" time="0.162" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_K_shapes" time="0.165" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_ln_K_u_shape" time="0.168" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_ln_K_u_is_homogeneous" time="0.175" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_sample_sites_match_model_no_epi" time="0.330" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_sample_sites_match_model_with_epi" time="0.399" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_variational_params_registered" time="0.176" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_nn_params_registered_per_structure" time="0.170" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_nn_params_use_canonical_names_with_shuffled_input" time="0.172" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_no_epi_params_when_num_pair_zero" time="0.170" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_epi_params_registered_when_num_pair_nonzero" time="0.193" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGuide" name="test_tf_op_totals_preserved" time="0.166" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC" name="test_registry_entry" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_returns_list" time="0.007" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_three_specs_without_epi" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_four_specs_with_epi" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_first_spec_has_scalar_K_values_including_ln_K_u" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_second_spec_has_titrant_K_values" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_third_spec_has_projected_delta_lnK" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_fourth_spec_has_epi_terms" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_all_specs_use_theta_prefix" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_second_spec_covers_all_titrant_genotype_combos" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_PnnC.TestGetExtractSpecs" name="test_third_spec_has_one_row_per_mutation" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_output_shape" time="0.075" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_values_in_0_1" time="0.074" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_all_finite" time="0.112" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_monotone_decreasing_in_effector" time="0.075" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_apo_matches_analytic_formula" time="0.070" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_large_K_u_collapses_theta_to_zero" time="0.604" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_small_K_u_matches_mwc_dimer_behavior" time="0.061" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_K_u_reduces_theta_at_all_concentrations" time="0.636" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_higher_K_h_o_gives_higher_theta_at_zero_effector" time="0.068" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_higher_K_h_l_gives_lower_theta_at_zero_effector" time="0.091" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_wt_params_give_high_apo_theta" time="0.063" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_e_free_bounded_by_e_total" time="0.064" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestComputeTheta" name="test_titrant_dim_broadcasts_independently" time="0.071" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestPopulationMoments" name="test_output_shapes" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestPopulationMoments" name="test_sigma_zero_when_uniform" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestPopulationMoments" name="test_sigma_positive_when_varying" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestPopulationMoments" name="test_mu_finite" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestPopulationMoments" name="test_data_arg_unused" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_scatter_theta_0_shape" time="0.078" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_scatter_theta_1_shape" time="0.073" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_values_in_0_1" time="0.075" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_all_finite" time="0.076" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_geno_theta_idx_selects_genotypes" time="0.148" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_nontrivial_batch_idx_selects_correct_genotypes" time="0.150" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_apo_matches_analytic_formula" time="0.068" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_large_K_u_collapses_theta_via_run_model" time="0.159" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_higher_effector_lower_theta" time="0.066" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo.TestRunModel" name="test_conc_unit_scale_applied" time="0.235" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.test_thermo" name="test_get_population_moments_returns_stored_values" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestAssembleScalar" name="test_zero_offsets_all_genotypes_equal_wt" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestAssembleScalar" name="test_mutation_effect_shifts_correct_genotypes" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestAssembleScalar" name="test_sigma_scales_effect" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestAssembleScalar" name="test_epistasis_shifts_only_double_mutant" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestAssembleScalar" name="test_additive_mut_plus_epistasis" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestAssembleTitrant" name="test_output_shape" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestAssembleTitrant" name="test_zero_offsets_all_equal_wt" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestAssembleTitrant" name="test_sigma_scales_per_titrant" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestAssembleTitrant" name="test_epistasis_shifts_only_double_mutant" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK" name="test_get_hyperparameters_keys" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK" name="test_get_priors_values" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK" name="test_get_guesses_shapes_no_epi" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK" name="test_get_guesses_shapes_with_epi" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelNoEpi" name="test_returns_theta_param" time="0.154" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelNoEpi" name="test_parameter_shapes" time="0.088" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelNoEpi" name="test_all_genotypes_equal_when_deltas_zero" time="0.076" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelNoEpi" name="test_assembly_with_known_deltas" time="0.074" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelNoEpi" name="test_population_moments_sigma_nonneg" time="0.075" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelNoEpi" name="test_sample_sites_present" time="0.078" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelNoEpi" name="test_deterministic_sites_registered" time="0.074" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelWithEpi" name="test_parameter_shapes" time="0.075" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelWithEpi" name="test_epistasis_shifts_only_double_mutant" time="0.075" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelWithEpi" name="test_epistasis_sample_sites_present" time="0.073" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestDefineModelWithEpi" name="test_epistasis_deterministic_sites_registered" time="0.078" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestGuide" name="test_no_epi_returns_theta_param" time="0.078" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestGuide" name="test_no_epi_shapes" time="0.077" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestGuide" name="test_with_epi_runs" time="0.087" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestGuide" name="test_no_epi_no_epistasis_sample_sites" time="0.077" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK.TestGuide" name="test_tf_op_totals_preserved" time="0.079" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK" name="test_run_model_uses_assembled_K" time="0.134" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PK" name="test_get_population_moments_shape" time="0.069" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetStructPerm" name="test_canonical_order_gives_identity_perm" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetStructPerm" name="test_wrong_name_raises" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetStructPerm" name="test_too_few_names_raises" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetStructPerm" name="test_permuted_order_returns_correct_perm" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestProjectDdG" name="test_output_shape" time="0.051" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestProjectDdG" name="test_zero_ddG_gives_zero_delta_lnK" time="0.051" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestProjectDdG" name="test_K_op_is_H_minus_HD" time="0.048" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestProjectDdG" name="test_K_HL_is_H_minus_L" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestProjectDdG" name="test_K_E_is_L_minus_LE2" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestProjectDdG" name="test_batch_dim" time="0.044" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestConfig" name="test_hyperparameters_has_required_keys" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestConfig" name="test_hyperparameters_has_no_nn_keys" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestConfig" name="test_get_priors_returns_model_priors_instance" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestConfig" name="test_model_priors_has_no_nn_fields" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestConfig" name="test_physical_constants" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestConfig" name="test_get_guesses_required_keys" time="0.030" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestConfig" name="test_get_guesses_ddG_offset_shape" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestConfig" name="test_get_guesses_no_epi_keys" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestConfig" name="test_get_guesses_wt_K_shapes" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestDefineModel" name="test_returns_theta_param" time="0.483" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestDefineModel" name="test_K_shapes" time="0.145" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestDefineModel" name="test_sample_sites_present" time="0.152" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestDefineModel" name="test_deterministic_sites_present" time="0.152" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestDefineModel" name="test_no_epi_sites" time="0.150" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestDefineModel" name="test_wrong_struct_names_raises" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestDefineModel" name="test_permuted_struct_names_accepted" time="0.154" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestDefineModel" name="test_ddG_prior_shifts_delta_lnK" time="0.152" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestDefineModel" name="test_tf_op_totals_preserved" time="0.152" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGuide" name="test_returns_theta_param" time="0.173" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGuide" name="test_K_shapes" time="0.142" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGuide" name="test_sample_sites_match_model" time="0.361" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGuide" name="test_variational_params_registered" time="0.152" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGuide" name="test_no_nn_params_registered" time="0.236" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGuide" name="test_no_epi_params_registered" time="0.251" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGuide" name="test_ddG_sigma_s_shape" time="0.177" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGuide" name="test_ddG_offset_locs_shape" time="0.152" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGuide" name="test_tf_op_totals_preserved" time="0.140" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG" name="test_registry_entry" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetExtractSpecs" name="test_returns_list" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetExtractSpecs" name="test_three_specs" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetExtractSpecs" name="test_four_specs_when_pair_labels_present" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetExtractSpecs" name="test_first_spec_has_ln_K_op_and_ln_K_HL" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetExtractSpecs" name="test_second_spec_has_ln_K_E" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetExtractSpecs" name="test_third_spec_has_d_ln_K_op_HL_E" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetExtractSpecs" name="test_all_specs_use_theta_prefix" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetExtractSpecs" name="test_second_spec_covers_all_titrant_genotype_combos" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PddG.TestGetExtractSpecs" name="test_third_spec_has_one_row_per_mutation" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestCheckStructNames" name="test_correct_names_pass" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestCheckStructNames" name="test_wrong_order_raises" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestCheckStructNames" name="test_missing_structure_raises" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestProjectDdG" name="test_output_shape" time="0.047" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestProjectDdG" name="test_zero_ddG_gives_zero_delta_lnK" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestProjectDdG" name="test_K_op_equals_H_minus_HD" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestProjectDdG" name="test_K_HL_equals_H_minus_L" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestProjectDdG" name="test_K_E_equals_L_minus_LE2" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestProjectDdG" name="test_leading_batch_dim" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestConfig" name="test_hyperparameters_keys" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestConfig" name="test_get_priors_type" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestConfig" name="test_physical_constants" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestConfig" name="test_get_guesses_keys_no_epi" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestConfig" name="test_get_guesses_keys_with_epi" time="0.059" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestConfig" name="test_get_guesses_ddG_offset_shape" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_returns_theta_param" time="0.569" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_ln_K_op_shape" time="0.152" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_ln_K_HL_shape" time="0.146" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_ln_K_E_shape" time="0.154" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_sample_sites_present" time="0.139" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_deterministic_sites_present" time="0.143" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_wrong_struct_names_raises" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_zero_nn_weights_zero_offsets_all_genotypes_at_wt" time="0.072" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_no_epi_sites_when_num_pair_zero" time="0.157" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_epi_sites_present_when_num_pair_nonzero" time="0.746" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestDefineModel" name="test_epi_sites_absent_when_no_contact_distances" time="0.139" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestGuide" name="test_returns_theta_param" time="0.142" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestGuide" name="test_sample_sites_match_model" time="0.295" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_PnnC.TestGuide" name="test_sample_sites_match_model_with_epi" time="0.382" /><testcase 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name="test_scatter_theta_1_shape" time="0.074" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_thermo.TestRunModel" name="test_values_in_0_1" time="0.060" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_thermo.TestRunModel" name="test_all_finite" time="0.061" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_thermo.TestRunModel" name="test_zero_effector_matches_apo_formula" time="0.101" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_thermo.TestRunModel" name="test_uses_data_titrant_conc" time="0.384" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.test_thermo.TestRunModel" name="test_nontrivial_batch_idx_selects_correct_genotypes" time="0.643" /><testcase 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name="test_K_U_not_shifted_by_epistasis" time="0.071" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PK.TestDefineModelWithEpi" name="test_epistasis_sample_sites_present" time="0.072" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PK.TestDefineModelWithEpi" name="test_epistasis_deterministic_sites_registered" time="0.073" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PK.TestGuide" name="test_no_epi_returns_theta_param" time="0.071" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PK.TestGuide" name="test_no_epi_shapes" time="0.071" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PK.TestGuide" name="test_with_epi_runs" time="0.077" /><testcase 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name="test_output_shape" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PddG.TestProjectDdG" name="test_zero_ddG_gives_zero_delta_lnK" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PddG.TestProjectDdG" name="test_K_op_is_H_minus_HD" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PddG.TestProjectDdG" name="test_K_HL_is_H_minus_L" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PddG.TestProjectDdG" name="test_K_E_is_L_minus_LE2" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PddG.TestProjectDdG" name="test_H_perturbation_does_not_affect_K_E" time="0.043" /><testcase 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time="0.147" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestDefineModel" name="test_ln_K_op_shape" time="0.138" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestDefineModel" name="test_ln_K_HL_shape" time="0.142" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestDefineModel" name="test_ln_K_E_shape" time="0.142" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestDefineModel" name="test_ln_K_U_shape" time="0.139" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestDefineModel" name="test_ln_K_U_homogeneous" time="0.140" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestDefineModel" name="test_sample_sites_present" time="0.146" /><testcase 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name="test_ln_K_U_shape" time="0.139" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGuide" name="test_ln_K_U_homogeneous_in_guide" time="0.145" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGuide" name="test_sample_sites_match_model" time="0.289" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGuide" name="test_sample_sites_match_model_with_epi" time="0.344" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGuide" name="test_variational_params_registered" time="0.157" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGuide" name="test_ln_K_U_wt_variational_params_registered" time="0.171" /><testcase 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classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_has_three_specs_without_epi" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_has_four_specs_with_epi" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_first_spec_has_ln_K_op_and_ln_K_HL" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_second_spec_has_ln_K_E" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_third_spec_has_d_ln_K" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_fourth_spec_has_epi_terms" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_map_geno_covers_all_genotypes" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_map_theta_KE_covers_all_titrant_genotype_combos" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_in_run_prefix_is_theta" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_PnnC.TestGetExtractSpecs" name="test_third_spec_has_one_row_per_mutation" time="0.007" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_thermo.TestSolveFreeEffector" name="test_zero_effector_returns_zero" time="0.081" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_thermo.TestSolveFreeEffector" name="test_e_free_leq_e_total" time="0.061" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_thermo.TestSolveFreeEffector" name="test_output_shape" time="0.066" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_thermo.TestSolveFreeEffector" name="test_finite_values" time="0.061" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_thermo.TestSolveFreeEffector" name="test_high_affinity_depletes_effector" time="0.119" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_thermo.TestSolveFreeEffector" name="test_larger_Z0_reduces_depletion" time="0.149" /><testcase classname="tests.tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.test_thermo.TestComputeTheta" 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classname="tests.tfscreen.tfmodel.generative.components.transformation.test_logit_norm" name="test_needs_full_population_theta_flag" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_logit_norm" name="test_get_priors_mode" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_logit_norm" name="test_get_hyperparameters_forwards_lam_mean_std" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_logit_norm" name="test_get_priors_forwards_lam_mean_std" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_logit_norm" name="test_update_thetas_bound_with_logit_norm" time="0.020" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_logit_norm" name="test_update_thetas_logit_norm_distinguishable_from_empirical" time="0.152" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_single" name="test_get_hyperparameters" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_single" name="test_get_guesses" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_single" name="test_get_priors" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_single" name="test_define_model" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_single" name="test_guide" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_single" name="test_update_thetas" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.components.transformation.test_single" name="test_needs_full_population_theta_flag" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_observe_k_ref_site" time="0.206" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_observe_obs_site_structure" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_observe_uses_shared_genotype_plate" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_observe_batch_idx_slices_genotypes" time="0.155" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_observe_masking" time="0.246" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_guide_registers_k_ref" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_get_hyperparameters_default_scale" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_derive_k_ref_guess_returns_wt_rate" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_get_priors_uses_loc_and_default_scale" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_base_growth" name="test_get_guesses_keys_by_name" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_binding" name="test_observe_binding_site" time="0.565" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_binding" name="test_guide" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_growth" name="test_observe_structure_and_distribution" time="0.208" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_growth" name="test_observe_subsampling_scaling" time="0.540" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_growth" name="test_observe_masking_logic" time="0.052" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_growth" name="test_guide_structure" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_growth" name="test_sigma_k_zero_preserves_original_scale" time="0.043" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_growth" name="test_sigma_k_inflates_scale" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_growth" name="test_sigma_k_quadrature_formula" time="0.049" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_growth" name="test_get_hyperparameters_default_nu_prior" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_growth" name="test_get_priors_builds_growth_obs_priors_from_defaults" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_presplit" name="test_observe_site_structure" time="0.358" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_presplit" name="test_observe_uses_shared_genotype_plate" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_presplit" name="test_observe_batch_idx_slices_genotypes" time="0.270" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_presplit" name="test_observe_scaling" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_presplit" name="test_observe_masking" time="0.188" /><testcase classname="tests.tfscreen.tfmodel.generative.observe.test_observe_presplit" name="test_guide_is_noop" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestGrowthObserveLogLikelihood" name="test_perfect_prediction_beats_imperfect" time="0.712" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestGrowthObserveLogLikelihood" name="test_known_log_prob" time="0.114" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestGrowthObserveLogLikelihood" name="test_residual_reduces_log_prob" time="0.561" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestGrowthObserveLogLikelihood" name="test_mask_zeroes_log_prob" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestBindingObserveLogLikelihood" name="test_perfect_prediction_beats_imperfect" time="0.354" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestBindingObserveLogLikelihood" name="test_known_log_prob" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestBindingObserveLogLikelihood" name="test_residual_reduces_log_prob" time="0.322" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestDistributionLogProbs" name="test_student_t_at_loc" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestDistributionLogProbs" name="test_normal_at_loc" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestDistributionLogProbs" name="test_half_normal_at_zero" time="0.031" /><testcase classname="tests.tfscreen.tfmodel.generative.test_loglikelihood.TestDistributionLogProbs" name="test_gamma_known_value" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model" name="test_jax_model_execution_flow" time="0.102" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model" name="test_jax_model_guide_flow" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model" name="test_jax_model_population_not_needed_skips_extra_calc_theta" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model" name="test_jax_model_population_needed_computes_locally" time="0.086" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model" name="test_jax_model_population_needed_uses_external_override" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model_math" name="test_jax_model_runs_with_real_components" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model_math" name="test_jax_model_growth_pred_arithmetic" time="0.042" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model_math" name="test_jax_model_deterministic_sites_registered" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model_math" name="test_jax_model_guide_skips_prediction" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.test_model_math" name="test_jax_model_guide_still_calls_real_components" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestLinearGrowthMath" name="test_scalar_inputs" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestLinearGrowthMath" name="test_vector_inputs_and_mapping" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestLinearGrowthMath" name="test_zero_activity_zeroes_modulator" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestLinearGrowthMath" name="test_known_values" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestHillRunModel" name="test_output_shape" time="0.139" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestHillRunModel" name="test_bounds" time="0.281" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestHillRunModel" name="test_monotone_in_concentration" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestHillRunModel" name="test_at_log_K_equals_half_occupancy" time="0.063" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestHillRunModel" name="test_known_values" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_logit_normal_cdf_at_midpoint" time="0.499" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_logit_normal_cdf_monotone" time="0.250" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_logit_normal_cdf_known_value" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_empirical_cdf_shape" time="0.234" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_empirical_cdf_monotone" time="0.377" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_empirical_cdf_bounds" time="0.291" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_empirical_cdf_known_values" time="0.140" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_update_thetas_logit_norm_shape" time="0.217" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_update_thetas_empirical_shape" time="0.458" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestCongression" name="test_update_thetas_zero_lambda_is_identity" time="0.250" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestBetaNoiseMath" name="test_alpha_beta_values" time="0.040" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestBetaNoiseMath" name="test_alpha_plus_beta_equals_kappa" time="0.104" /><testcase classname="tests.tfscreen.tfmodel.generative.test_reference_math.TestBetaNoiseMath" name="test_clip_prevents_zero" time="0.061" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestRegistryStructure" name="test_all_component_categories_present" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestRegistryStructure" name="test_all_observe_keys_present" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestRegistryStructure" name="test_no_unexpected_top_level_keys" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestRegistryStructure" name="test_component_categories_are_dicts" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestRegistryStructure" name="test_observe_values_are_modules" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[condition_growth-3]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[ln_cfu0-2]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[dk_geno-2]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[activity-5]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[transformation-3]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[theta_rescale-2]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[theta-4]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[theta_growth_noise-3]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[theta_binding_noise-2]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[growth_noise-2]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[sample_offset-2]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_minimum_component_count[growth_transition-5]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentCounts" name="test_all_component_names_are_strings" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[linear-tfscreen.tfmodel.generative.components.growth.linear]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[power-tfscreen.tfmodel.generative.components.growth.power]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[saturation-tfscreen.tfmodel.generative.components.growth.saturation]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[hierarchical-tfscreen.tfmodel.generative.components.ln_cfu0.hierarchical]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[hierarchical_factored-tfscreen.tfmodel.generative.components.ln_cfu0.hierarchical_factored]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[fixed-tfscreen.tfmodel.generative.components.dk_geno.fixed]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[hierarchical_geno-tfscreen.tfmodel.generative.components.dk_geno.hierarchical_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[pinned-tfscreen.tfmodel.generative.components.dk_geno.pinned]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[fixed-tfscreen.tfmodel.generative.components.activity.fixed]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[hierarchical_geno-tfscreen.tfmodel.generative.components.activity.hierarchical_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[horseshoe_geno-tfscreen.tfmodel.generative.components.activity.horseshoe_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[hierarchical_mut-tfscreen.tfmodel.generative.components.activity.hierarchical_mut]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[horseshoe_mut-tfscreen.tfmodel.generative.components.activity.horseshoe_mut]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[empirical-tfscreen.tfmodel.generative.components.transformation.empirical]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[logit_norm-tfscreen.tfmodel.generative.components.transformation.logit_norm]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[single-tfscreen.tfmodel.generative.components.transformation.single]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[_simple-tfscreen.tfmodel.generative.components.theta._simple]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[categorical_geno-tfscreen.tfmodel.generative.components.theta.categorical_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[hill_geno-tfscreen.tfmodel.generative.components.theta.hill_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[hill_mut-tfscreen.tfmodel.generative.components.theta.hill_mut]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C4_K3_U0_a.PK-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.PK]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C4_K3_U0_a.PnnC-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.PnnC]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C4_K3_U0_a.PddG-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.PddG]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C4_K3_U1_a.PK-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.PK]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C4_K3_U1_a.PnnC-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.PnnC]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C4_K3_U1_a.PddG-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.PddG]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C12_K5_U0_a.PK-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.PK]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C12_K5_U0_a.PnnC-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.PnnC]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C12_K5_U0_a.PddG-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.PddG]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C12_K5_U1_a.PK-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.PK]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C12_K5_U1_a.PnnC-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.PnnC]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[thermo.O2_C12_K5_U1_a.PddG-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.PddG]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[zero-tfscreen.tfmodel.generative.components.noise.zero0]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[beta-tfscreen.tfmodel.generative.components.noise.beta0]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[logit_normal-tfscreen.tfmodel.generative.components.noise.logit_normal]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[zero-tfscreen.tfmodel.generative.components.noise.zero1]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[beta-tfscreen.tfmodel.generative.components.noise.beta1]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[zero-tfscreen.tfmodel.generative.components.growth_noise.zero]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[normal_kt-tfscreen.tfmodel.generative.components.growth_noise.normal_kt]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[zero-tfscreen.tfmodel.generative.components.sample_offset.zero]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[normal-tfscreen.tfmodel.generative.components.sample_offset.normal]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[instant-tfscreen.tfmodel.generative.components.growth_transition.instant]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[memory-tfscreen.tfmodel.generative.components.growth_transition.memory]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[baranyi-tfscreen.tfmodel.generative.components.growth_transition.baranyi]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[baranyi_k-tfscreen.tfmodel.generative.components.growth_transition.baranyi_k]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[baranyi_tau-tfscreen.tfmodel.generative.components.growth_transition.baranyi_tau]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_standard_components_have_define_model_and_get_guesses[two_pop-tfscreen.tfmodel.generative.components.growth_transition.two_pop]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_theta_rescale_has_rescale[passthrough-tfscreen.tfmodel.generative.components.theta_rescale.passthrough]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_theta_rescale_has_rescale[logit-tfscreen.tfmodel.generative.components.theta_rescale.logit]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[linear-tfscreen.tfmodel.generative.components.growth.linear]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[power-tfscreen.tfmodel.generative.components.growth.power]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[saturation-tfscreen.tfmodel.generative.components.growth.saturation]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[hierarchical-tfscreen.tfmodel.generative.components.ln_cfu0.hierarchical]" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[hierarchical_factored-tfscreen.tfmodel.generative.components.ln_cfu0.hierarchical_factored]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[fixed-tfscreen.tfmodel.generative.components.dk_geno.fixed]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[hierarchical_geno-tfscreen.tfmodel.generative.components.dk_geno.hierarchical_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[pinned-tfscreen.tfmodel.generative.components.dk_geno.pinned]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[fixed-tfscreen.tfmodel.generative.components.activity.fixed]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[hierarchical_geno-tfscreen.tfmodel.generative.components.activity.hierarchical_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[horseshoe_geno-tfscreen.tfmodel.generative.components.activity.horseshoe_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[hierarchical_mut-tfscreen.tfmodel.generative.components.activity.hierarchical_mut]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[horseshoe_mut-tfscreen.tfmodel.generative.components.activity.horseshoe_mut]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[empirical-tfscreen.tfmodel.generative.components.transformation.empirical]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[logit_norm-tfscreen.tfmodel.generative.components.transformation.logit_norm]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[single-tfscreen.tfmodel.generative.components.transformation.single]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[_simple-tfscreen.tfmodel.generative.components.theta._simple]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[categorical_geno-tfscreen.tfmodel.generative.components.theta.categorical_geno]" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[hill_geno-tfscreen.tfmodel.generative.components.theta.hill_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[hill_mut-tfscreen.tfmodel.generative.components.theta.hill_mut]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C4_K3_U0_a.PK-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.PK]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C4_K3_U0_a.PnnC-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.PnnC]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C4_K3_U0_a.PddG-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.PddG]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C4_K3_U1_a.PK-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.PK]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C4_K3_U1_a.PnnC-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.PnnC]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C4_K3_U1_a.PddG-tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.PddG]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C12_K5_U0_a.PK-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.PK]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C12_K5_U0_a.PnnC-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.PnnC]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C12_K5_U0_a.PddG-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.PddG]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C12_K5_U1_a.PK-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.PK]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C12_K5_U1_a.PnnC-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.PnnC]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[thermo.O2_C12_K5_U1_a.PddG-tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.PddG]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[zero-tfscreen.tfmodel.generative.components.noise.zero0]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[beta-tfscreen.tfmodel.generative.components.noise.beta0]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[logit_normal-tfscreen.tfmodel.generative.components.noise.logit_normal]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[zero-tfscreen.tfmodel.generative.components.noise.zero1]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[beta-tfscreen.tfmodel.generative.components.noise.beta1]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[zero-tfscreen.tfmodel.generative.components.growth_noise.zero]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[normal_kt-tfscreen.tfmodel.generative.components.growth_noise.normal_kt]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[zero-tfscreen.tfmodel.generative.components.sample_offset.zero]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[normal-tfscreen.tfmodel.generative.components.sample_offset.normal]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[instant-tfscreen.tfmodel.generative.components.growth_transition.instant]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[memory-tfscreen.tfmodel.generative.components.growth_transition.memory]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[baranyi-tfscreen.tfmodel.generative.components.growth_transition.baranyi]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[baranyi_k-tfscreen.tfmodel.generative.components.growth_transition.baranyi_k]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[baranyi_tau-tfscreen.tfmodel.generative.components.growth_transition.baranyi_tau]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_get_priors_returns_object[two_pop-tfscreen.tfmodel.generative.components.growth_transition.two_pop]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_transformation_has_update_thetas[empirical-tfscreen.tfmodel.generative.components.transformation.empirical]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_transformation_has_update_thetas[logit_norm-tfscreen.tfmodel.generative.components.transformation.logit_norm]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_transformation_has_update_thetas[single-tfscreen.tfmodel.generative.components.transformation.single]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_observe_modules_have_observe_and_guide[observe_binding]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_observe_modules_have_observe_and_guide[observe_base_growth]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_observe_modules_have_observe_and_guide[observe_presplit]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.generative.test_registry.TestComponentInterfaces" name="test_observe_modules_have_observe_and_guide[observe_growth]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_congression" name="test_correct_theta_matrix_history_matches_final" time="0.585" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_congression" name="test_correct_theta_matrix_history_lambda_zero" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_congression" name="test_deattenuate_returns_history_df" time="0.983" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_congression" name="test_deattenuate_history_noop_when_lambda_none" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_fit" name="test_fits_to_results_df_matches_fit_phenotypes" time="0.034" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_fit" name="test_fits_to_results_df_empty_warns" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_fit" name="test_predict_theta_shape_and_values" time="0.034" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_fit" name="test_predict_theta_nonfinite_fit_emits_nan" time="0.034" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_fit_genotypes_cli" name="test_raw_only_writes_params_and_theta" time="0.049" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_fit_genotypes_cli" name="test_congression_writes_deattenuated_and_history" time="1.275" /><testcase classname="tests.tfscreen.tfmodel.genotype_fit.test_fit_genotypes_cli" name="test_congression_without_history_skips_history_file" time="0.221" /><testcase classname="tests.tfscreen.tfmodel.inference.test_get_posteriors_mapping" name="test_get_genotype_dim_map" time="0.373" /><testcase classname="tests.tfscreen.tfmodel.inference.test_get_posteriors_mapping" name="test_get_posteriors_batching_mapping" time="3.811" /><testcase classname="tests.tfscreen.tfmodel.inference.test_get_posteriors_mapping" name="test_dim_map_excludes_non_genotype_plate_at_same_dim" time="0.063" /><testcase classname="tests.tfscreen.tfmodel.inference.test_get_posteriors_mapping" name="test_get_posteriors_shape_ambiguity" time="1.100" /><testcase classname="tests.tfscreen.tfmodel.inference.test_get_posteriors_mapping" name="test_get_posteriors_manual_guide_indexing" time="0.720" /><testcase classname="tests.tfscreen.tfmodel.inference.test_get_posteriors_mapping" name="test_get_posteriors_sites_to_save_filters_output" time="0.239" /><testcase classname="tests.tfscreen.tfmodel.inference.test_get_posteriors_mapping" name="test_get_posteriors_sites_to_save_none_saves_all" time="2.329" /><testcase classname="tests.tfscreen.tfmodel.inference.test_get_posteriors_mapping" name="test_get_posteriors_compression_enabled" time="0.192" /><testcase classname="tests.tfscreen.tfmodel.inference.test_get_posteriors_mapping" name="test_get_posteriors_sites_to_save_with_batched_forward" time="0.260" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_load_posteriors_dict" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_load_posteriors_npz" time="0.009" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_load_posteriors_h5" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_load_posteriors_custom_q" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_load_posteriors_errors" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_get_posterior_samples" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_get_posterior_samples_auto_loc_priority" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_get_posterior_samples_error_truncates_long_key_list" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_get_posterior_samples_error_no_truncation_short_key_list" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_load_posteriors_h5_oserror_retries" time="0.005" /><testcase classname="tests.tfscreen.tfmodel.inference.test_posteriors" name="test_load_posteriors_h5_oserror_exhausted" time="0.003" /><testcase 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classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_read_binding_df_preserves_canonical_genotype_order" time="0.022" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_extract_param_est" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_model_class_invalid_component" time="0.070" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_initialize_data_spiked" time="0.034" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_initialize_classes_logic" time="0.013" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_initialize_classes_forwards_presplit_to_declaring_component" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_dk_geno_pinned_requires_pins_file" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_dk_geno_pins_file_without_pinned_component_raises" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_dk_geno_pinned_builds_values_and_wires_into_priors" time="0.051" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_transformation_lambda_forbidden_with_single_transformation" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_transformation_lambda_wrong_length_raises" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_transformation_lambda_wired_into_component_priors_and_guesses" time="0.011" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_transformation_lambda_omitted_falls_back_to_placeholder" time="0.011" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_transformation_control_kwargs_carry_needs_population_flag[single-False]" time="0.014" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_transformation_control_kwargs_carry_needs_population_flag[logit_norm-False]" time="0.011" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_transformation_control_kwargs_carry_needs_population_flag[empirical-True]" time="0.011" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_check_theta_transformation_compatibility_rejects_categorical_geno_empirical" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_check_theta_transformation_compatibility_allows_categorical_geno_elsewhere[single]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_check_theta_transformation_compatibility_allows_categorical_geno_elsewhere[logit_norm]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_check_theta_transformation_compatibility_allows_other_theta_with_empirical[hill_geno]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_check_theta_transformation_compatibility_allows_other_theta_with_empirical[hill_mut]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_check_theta_transformation_compatibility_allows_other_theta_with_empirical[some_thermo_variant]" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_model_orchestrator_rejects_categorical_geno_with_empirical" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_check_theta_transformation_compatibility_binding_only_skips_check" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_model_class_properties" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_extract_parameters_full" time="0.043" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_extract_parameters_errors" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_extract_parameters_npz" time="0.019" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_extract_parameters_categorical" time="0.006" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_extract_theta_curves_full" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_extract_theta_curves_manual" time="0.024" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_extract_theta_curves_errors" time="0.002" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_extract_growth_predictions_full" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_get_random_idx_logic" time="0.062" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_binding_weight_auto_computed" time="0.004" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_binding_weight_explicit_preserved" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_binding_weight_explicit_in_settings" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_binding_weight_auto_in_settings" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator" name="test_binding_weight_applied_to_scale_vector" time="0.003" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestConditionLabelExtraction" name="test_labels_extracted_in_index_order" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestConditionLabelExtraction" name="test_labels_correct_when_already_ordered" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestConditionLabelExtraction" name="test_single_condition" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestConditionLabelExtraction" name="test_shares_replicates_false_includes_replicate_but_still_sorts" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestConditionLabelExtraction" name="test_linear_get_priors_signature_has_is_selection" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestConditionLabelExtraction" name="test_setup_model_extracts_is_selection_and_calls_get_priors" time="0.044" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestGenotypeOrderSentinel" name="test_read_growth_df_produces_genetic_categorical_order" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestGenotypeOrderSentinel" name="test_read_growth_df_genotype_stays_categorical" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestGenotypeOrderSentinel" name="test_read_growth_df_row_order_unchanged" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestGenotypeOrderSentinel" name="test_build_growth_tm_tensor_genotype_axis_genetic_order" time="0.062" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestGenotypeOrderSentinel" name="test_genotype_idx_consistent_with_tensor_labels" time="0.040" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestGenotypeOrderSentinel" name="test_condition_sel_reduced_does_not_reorder_rows" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestGenotypeOrderSentinel" name="test_genotype_categorical_survives_condition_sel_reduced" time="0.012" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestLibraryGroupingPhase1" name="test_single_library_regression" time="0.034" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestLibraryGroupingPhase1" name="test_two_libraries_genotype_order_preserved" time="0.058" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestLibraryGroupingPhase1" name="test_two_libraries_have_distinct_ln_cfu0_slots" time="0.036" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestLibraryGroupingPhase1" name="test_condition_sel_reduced_is_per_library" time="0.011" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestIsSelectionInference" name="test_standard_case" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestIsSelectionInference" name="test_control_arm_same_condition_in_pre_and_sel" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestIsSelectionInference" name="test_multiple_null_conditions_both_false" time="0.001" 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name="test_two_libraries_shared_condition_pre_labels_contain_library" time="0.033" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestCombinedConditionPreKey" name="test_two_libraries_shared_condition_pre_data_not_pooled" time="0.034" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestCombinedConditionPreKey" name="test_distinct_condition_pre_names_not_affected" time="0.032" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestCombinedConditionPreKey" name="test_slash_in_condition_name_is_safe" time="0.035" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestCombinedConditionPreKey" name="test_presplit_combined_key_matches_growth_tm_categories" time="0.059" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_basic_construction" time="0.018" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_zero_rate_std_raises" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_negative_rate_std_raises" time="0.018" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_missing_required_column_raises" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_genotype_not_in_growth_df_is_dropped" time="0.019" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_missing_wt_after_filtering_raises" time="0.016" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_wt_dropped_by_growth_df_filter_raises" time="0.017" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_multiple_rows_per_genotype_combined_by_inverse_variance" time="0.015" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_build_base_growth_arrays_shapes_and_values" time="0.041" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_derive_k_ref_guess_returns_wt_rate" time="0.018" /><testcase classname="tests.tfscreen.tfmodel.test_model_orchestrator.TestBaseGrowthDf" name="test_base_growth_df_wires_k_ref_into_growth_priors" time="0.072" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_site_table_shape" time="0.420" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_per_genotype_site_counts" time="0.209" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_condition_level_site_counts" time="0.203" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_hyperparameters_are_labelled" time="0.217" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_scale_like_detection" time="0.225" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_total_matches_site_sum" time="0.215" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_component_attribution_is_longest_prefix" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_scope_global_when_no_plates" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_scope_ignores_size_one_design_plates" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_scope_entity_priority" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_scope_per_datum_requires_entity_and_measurement_axis" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_scope_design_axes_without_entity" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_is_scale_like_handles_missing_support" time="0.000" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_observation_counts_match_applied_masks" time="1.998" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_observation_counts_match_applied_masks_all_channels" time="0.242" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_all_channels_counted" time="0.279" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_observation_counts_exclude_padding" time="0.218" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_ragged_growth_data_is_counted_not_padded" time="0.397" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_coverage_one_row_per_genotype" time="0.287" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_coverage_matches_direct_mask_reduction" time="0.225" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_coverage_distinct_levels" time="0.223" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_coverage_ragged_titration" time="0.200" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_coverage_binding_joined_by_label" time="0.205" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_theta_under_determined_flag" time="0.369" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_effective_parameter_bracket_is_ordered" time="0.199" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_bracket_excludes_per_datum_latents" time="0.208" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_no_per_datum_latents_by_default" time="0.209" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_mutation_level_model_scales_with_mutations" time="0.214" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_anchor_inventory" time="0.203" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_spiked_genotypes_split_binding_anchors" time="0.192" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_minibatching_does_not_change_counts" time="1.195" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_binding_only_model" time="0.177" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_trace_is_abstract" time="0.202" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_falls_back_to_concrete_trace" time="0.253" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_write_model_stats" time="0.240" /><testcase classname="tests.tfscreen.tfmodel.test_model_stats" name="test_format_model_stats_is_text" time="0.231" /><testcase classname="tests.tfscreen.tfmodel.test_needs_mut_wiring" name="test_needs_mut_growth_path[thermo.O2_C4_K3_U1_a.PK]" time="0.142" /><testcase classname="tests.tfscreen.tfmodel.test_needs_mut_wiring" name="test_needs_mut_growth_path[thermo.O2_C12_K5_U1_a.PK]" time="0.074" /><testcase classname="tests.tfscreen.tfmodel.test_needs_mut_wiring" name="test_needs_mut_binding_only_path[thermo.O2_C4_K3_U1_a.PK]" time="0.036" /><testcase classname="tests.tfscreen.tfmodel.test_needs_mut_wiring" name="test_needs_mut_binding_only_path[thermo.O2_C12_K5_U1_a.PK]" time="0.028" /><testcase classname="tests.tfscreen.tfmodel.test_needs_mut_wiring" name="test_offset_guesses_have_correct_shape_binding_only[thermo.O2_C4_K3_U1_a.PK]" time="0.029" /><testcase classname="tests.tfscreen.tfmodel.test_needs_mut_wiring" name="test_offset_guesses_have_correct_shape_binding_only[thermo.O2_C12_K5_U1_a.PK]" time="0.031" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_read_presplit_df_passes_through_valid" time="0.021" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_read_presplit_df_drops_unknown_genotypes" time="0.022" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_read_presplit_df_missing_column_raises" time="0.018" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_build_presplit_tm_shape" time="0.139" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_build_presplit_tm_genotype_alignment" time="0.085" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_build_presplit_tm_partial_coverage" time="0.102" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_model_orchestrator_without_presplit" time="0.077" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_model_orchestrator_with_presplit" time="0.118" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_jax_model_presplit_obs_site_present" time="1.220" /><testcase classname="tests.tfscreen.tfmodel.test_presplit" name="test_jax_model_no_presplit_obs_site_without_data" time="0.106" /><testcase classname="tests.tfscreen.tfmodel.test_share_replicates" name="test_growth_shares_replicates" time="0.176" /><testcase classname="tests.tfscreen.tfmodel.test_spiked_genotypes" name="test_spiked_genotypes_masking" time="0.066" /><testcase classname="tests.tfscreen.tfmodel.test_spiked_genotypes" name="test_spiked_genotypes_validation" time="0.038" /><testcase classname="tests.tfscreen.tfmodel.test_spiked_genotypes" name="test_setup_batching_zero_division" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_spiked_genotypes" name="test_spiked_genotypes_single_string" time="0.062" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_registry_entry" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_settings_includes_thermo_data" time="0.087" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_default_thermo_data_is_none" time="0.085" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_missing_thermo_data_raises" time="0.057" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsOnGrowthData" name="test_struct_names_is_correct_tuple" time="0.084" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsOnGrowthData" name="test_num_struct_is_four" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsOnGrowthData" name="test_struct_features_shape" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsOnGrowthData" name="test_struct_n_chains_shape" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsOnGrowthData" name="test_struct_n_chains_value" time="0.048" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsOnGrowthData" name="test_no_contact_distances_without_epistasis" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsOnGrowthData" name="test_mut_labels_populated" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsOnGrowthData" name="test_num_mutation_nonzero" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsWithEpistasis" name="test_contact_distances_present" time="0.103" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsWithEpistasis" name="test_contact_distances_shape" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestStructFieldsWithEpistasis" name="test_pair_labels_populated" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_svi_runs_without_error" time="15.887" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_svi_with_epistasis_runs_without_error" time="7.582" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_binding_only_struct_raises_without_thermo_data" time="0.022" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestBindingOnlyDdGStructFields" name="test_struct_names_is_correct_tuple" time="0.028" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestBindingOnlyDdGStructFields" name="test_num_struct_is_six" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestBindingOnlyDdGStructFields" name="test_struct_features_shape" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestBindingOnlyDdGStructFields" name="test_mut_labels_populated" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestBindingOnlyDdGStructFields" name="test_num_mutation_nonzero" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestBindingOnlyDdGStructFields" name="test_growth_data_is_none" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring.TestBindingOnlyDdGStructFields" name="test_struct_n_chains_is_none_for_ddG_csv" time="0.001" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_binding_only_ddG_svi_runs" time="3.978" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_binding_only_minibatch_get_random_idx_does_not_crash" time="0.042" /><testcase classname="tests.tfscreen.tfmodel.test_struct_wiring" name="test_binding_only_minibatch_svi_runs" time="3.104" /><testcase classname="tests.tfscreen.util.cli.test_generalized_main.TestGeneralizedMain" name="test_required_args" time="0.002" /><testcase classname="tests.tfscreen.util.cli.test_generalized_main.TestGeneralizedMain" name="test_defaults_and_types" time="0.001" /><testcase classname="tests.tfscreen.util.cli.test_generalized_main.TestGeneralizedMain" name="test_bool_flags" time="0.001" /><testcase classname="tests.tfscreen.util.cli.test_generalized_main.TestGeneralizedMain" name="test_manual_overrides" time="0.001" /><testcase classname="tests.tfscreen.util.cli.test_generalized_main.TestGeneralizedMain" name="test_manual_nargs" time="0.001" /><testcase classname="tests.tfscreen.util.cli.test_generalized_main.TestGeneralizedMain" name="test_missing_required_arg" time="0.001" /><testcase classname="tests.tfscreen.util.cli.test_generalized_main.TestGeneralizedMain" name="test_bool_int_default_behavior" time="0.001" /><testcase classname="tests.tfscreen.util.cli.test_generalized_main.TestGeneralizedMain" name="test_default_argv" time="0.001" /><testcase classname="tests.tfscreen.util.cli.test_generalized_main.TestGeneralizedMain" name="test_list_handling" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_add_group_columns.TestAddGroupColumns" name="test_new_group_creation" time="0.005" /><testcase classname="tests.tfscreen.util.dataframe.test_add_group_columns.TestAddGroupColumns" name="test_with_existing_df_merge_and_filter" time="0.004" /><testcase classname="tests.tfscreen.util.dataframe.test_add_group_columns.TestAddGroupColumns" name="test_existing_df_raises_missing_group_col" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_add_group_columns.TestAddGroupColumns" name="test_existing_df_raises_missing_group_name_col" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_check_columns" name="test_all_columns_present" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_check_columns" name="test_missing_columns_raises_error" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_check_columns" name="test_error_message_contents" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_check_columns" name="test_empty_required_list" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_check_columns" name="test_empty_dataframe" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_chunk_by_group" name="test_chunk_by_group" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_df_to_arrays" name="test_basic_functionality_single_pivot" time="0.006" /><testcase classname="tests.tfscreen.util.dataframe.test_df_to_arrays" name="test_multi_column_pivot" time="0.007" /><testcase classname="tests.tfscreen.util.dataframe.test_df_to_arrays" name="test_no_numeric_columns" time="0.004" /><testcase classname="tests.tfscreen.util.dataframe.test_df_to_arrays" name="test_empty_dataframe" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_df_to_arrays" name="test_no_side_effects" time="0.005" /><testcase classname="tests.tfscreen.util.dataframe.test_df_to_arrays" name="test_all_nan_column_is_handled" time="0.006" /><testcase classname="tests.tfscreen.util.dataframe.test_df_to_arrays" name="test_all_numeric_columns_are_nan" time="0.004" /><testcase classname="tests.tfscreen.util.dataframe.test_df_to_arrays" name="test_empty_dataframe_multi_pivot" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_df_to_arrays" name="test_pivot_mismatched_nan_patterns" time="0.005" /><testcase classname="tests.tfscreen.util.dataframe.test_expand_on_conditions" name="test_expand_on_conditions" time="0.016" /><testcase classname="tests.tfscreen.util.dataframe.test_get_group_mean_std" name="test_basic_functionality" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_group_mean_std" name="test_input_types_and_conversion" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_group_mean_std" name="test_edge_cases" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_group_mean_std" name="test_invalid_inputs[values0-groups0-values and groups must be arrays of the same shape.]" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_group_mean_std" name="test_invalid_inputs[values1-groups1-group numbers must be non-negative.]" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_group_mean_std" name="test_invalid_inputs[values2-groups2-groups array must contain integer values.]" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[A1: Linear std -&gt; var]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[A2: Linear -&gt; log value]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[A3: Linear std -&gt; log std]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[A4: Linear std -&gt; log var (multi-step)]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[A5: Linear var -&gt; std]" time="0.004" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[A6: Linear var -&gt; log std (multi-step)]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[A7: Linear var -&gt; log var (multi-step)]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[A8: Linear -&gt; All]" time="0.004" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[B1: Log std -&gt; var]" time="0.002" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[B2: Log -&gt; linear value]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[B3: Log std -&gt; linear std]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[B4: Log std -&gt; linear var (multi-step)]" time="0.002" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[B5: Log var -&gt; std]" time="0.002" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[B6: Log var -&gt; linear std (multi-step)]" time="0.002" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[B7: Log var -&gt; linear var (multi-step)]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_successful_calculations[B8: Log -&gt; All]" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_complex_cross_scale_calculation" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_impossible_calculations[Fail: Missing error source for std]" time="0.002" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_impossible_calculations[Fail: Missing value source (cfu)]" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_impossible_calculations[Fail: Missing error source for var]" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_impossible_calculations[Fail: Missing value source (ln_cfu)]" time="0.002" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_impossible_calculations[Fail: No value source to start from]" time="0.002" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_invalid_column_name_raises_error" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_no_needed_columns_returns_original_df" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_non_contiguous_index" time="0.003" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_already_present_columns_returns_original_df" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_prefix_infers_prefixed_columns" time="0.005" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_prefix_present_returns_original_df" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_get_scaled_cfu" name="test_prefix_insufficient_names_prefixed_columns" time="0.002" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_explicit_names_pass_through_unchanged" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_y_obs_defaults_to_median_quantile" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_sigma_is_symmetric_half_width" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_original_frame_not_mutated_when_sigma_added" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_explicit_y_obs_but_default_y_std" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_no_sigma_columns_leaves_y_std_none" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_only_one_sigma_column_leaves_y_std_none" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_missing_point_quantile_raises" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_custom_quantiles" time="0.001" /><testcase classname="tests.tfscreen.util.dataframe.test_resolve_obs_columns" name="test_custom_sigma_col_name" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_read_from_dataframe_source" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_read_from_invalid_source" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_read_from_csv" time="0.004" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_read_from_tsv" time="0.003" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_read_from_file_with_no_extension" time="0.006" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_read_from_excel" time="0.168" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_read_file_not_found" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_read_raises_ioerror_on_parse_error" time="0.004" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_drops_spurious_unnamed_col_by_default" time="0.002" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_keeps_non_spurious_unnamed_col" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_set_existing_column_as_index" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_rename_unnamed_col_to_index" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_raises_error_if_index_col_not_found" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_no_change_if_already_indexed" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_genotype_column_becomes_categorical" time="0.004" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_genotype_column_standardized" time="0.004" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_genotype_rows_not_reordered" time="0.004" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_invalid_genotype_raises_valueerror" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_no_genotype_column_unchanged" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_dataframe" name="test_genotype_as_index_not_categorized" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestNormalizeTypes" name="test_scientific_notation_string_to_float" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestNormalizeTypes" name="test_float_passthrough" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestNormalizeTypes" name="test_int_passthrough" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestNormalizeTypes" name="test_recursion" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestNormalizeTypes" name="test_other_types_remain" time="0.000" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestReadYaml" name="test_read_dict_returns_as_is" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestReadYaml" name="test_read_file_success" time="0.003" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestReadYaml" name="test_int_literal_stays_int" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestReadYaml" name="test_file_not_found_raises" time="0.001" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestReadYaml" name="test_invalid_yaml_raises" time="0.003" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestReadYaml" name="test_override_keys" time="0.002" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestReadYaml" name="test_override_keys_missing_raises" time="0.002" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestGrowthConditionBlocksValidation" name="test_valid_config_passes" time="0.004" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestGrowthConditionBlocksValidation" name="test_condition_missing_from_growth_raises" time="0.002" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestGrowthConditionBlocksValidation" name="test_extra_key_in_growth_raises" time="0.003" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestGrowthConditionBlocksValidation" name="test_both_errors_reported_together" time="0.003" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestGrowthConditionBlocksValidation" name="test_multiple_blocks_all_conditions_checked" time="0.004" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestGrowthConditionBlocksValidation" name="test_same_condition_pre_and_sel_allowed" time="0.003" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestGrowthConditionBlocksValidation" name="test_no_growth_key_skips_validation" time="0.002" /><testcase classname="tests.tfscreen.util.io.test_read_yaml.TestGrowthConditionBlocksValidation" name="test_no_condition_blocks_key_skips_validation" time="0.002" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_match_at_start" time="0.002" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_match_at_end" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_match_in_middle" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_multiple_matches" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_no_match" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_partial_match_no_full_match" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_identical_arrays" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_float_arrays" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_search_for_is_longer" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_empty_search_for" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_empty_search_in" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_both_arrays_empty" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_search_in_non_contiguous_array" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_found[search_in0-search_for0-0-0]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_found[search_in1-search_for1-0-2]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_found[search_in2-search_for2-1-0]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_found[search_in3-search_for3-2-0]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_found[search_in4-search_for4-3-0]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_found[search_in5-search_for5-0-0]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_guard_clause[search_in0-search_for0]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_guard_clause[search_in1-search_for1]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_guard_clause[search_in2-search_for2]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_array_search" name="test_fuzzy_array_search_guard_clause[search_in3-search_for3]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_broadcast_args" name="test_broadcast_args_standard_case" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_broadcast_args" name="test_broadcast_args_already_matching_length" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_broadcast_args" name="test_broadcast_args_all_scalars" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_broadcast_args" name="test_broadcast_args_single_array_input" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_broadcast_args" name="test_broadcast_args_no_args" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_broadcast_args" name="test_broadcast_args_raises_error_on_incompatible_length" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_broadcast_args" name="test_broadcast_args_raises_error_with_scalar_and_incompatible_array" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_broadcast_args" name="test_broadcast_args_handles_none" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_padding" name="test_vstack_padded_different_widths" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_padding" name="test_vstack_padded_same_widths" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_padding" name="test_vstack_padded_empty_list" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_padding" name="test_vstack_padded_with_1d_array" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_padding" name="test_vstack_padded_different_dtypes" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_padding" name="test_vstack_padded_with_nan_fill" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_padding" name="test_vstack_padded_raises_type_error" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_to_log_scalar" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_to_log_list" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_to_log_numpy_array" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_to_log_pandas_series" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_to_log_with_std" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_to_log_with_var" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_to_log_with_std_and_var" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_to_log_edge_cases" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_to_log_length_mismatch_raises_error" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_from_log_scalar" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_from_log_list" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_from_log_numpy_array" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_from_log_pandas_series" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_from_log_with_std" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_from_log_with_var" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_from_log_with_std_and_var" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_transform" name="test_from_log_length_mismatch_raises_error" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_linear_basic" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_log_basic" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_log_autodetection" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_linear_autodetection" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_raises_error_for_log_with_negative_x" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_empty_input" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_non_finite_input" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_single_value_input" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_all_zeros_input" time="0.000" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_zeros_and_positives_log_autodetect" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_actually_single_value" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_log_fallback_not_enough_points" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_min_value_clamps_lower_bound" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_xfill" name="test_xfill_min_value_noop_when_above_floor" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_happy_path" time="0.002" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_reproducibility" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_default_rng_creation" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_statistical_properties" time="0.074" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_num_samples[0]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_num_samples[-1]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_num_samples[1.5]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_num_samples[a]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_num_samples[None]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_poisson_lambda[0]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_poisson_lambda[-1.5]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_poisson_lambda[invalid_lambda2]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_poisson_lambda[a]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_invalid_poisson_lambda[None]" time="0.001" /><testcase classname="tests.tfscreen.util.numerical.test_zero_truncated_poisson" name="test_edge_case_low_lambda" time="0.001" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_plain_word_unchanged" time="0.001" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_replaces_dot_with_underscore" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_replaces_space_with_underscore" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_replaces_slash_with_underscore" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_collapses_consecutive_underscores" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_strips_leading_trailing_underscores" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_preserves_hyphens" time="0.001" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_preserves_digits" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_numeric_string" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_empty_string_gives_empty" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestSanitize" name="test_only_special_chars_gives_empty" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeJinjaEnv" name="test_returns_jinja_environment" time="0.001" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeJinjaEnv" name="test_basename_filter_registered" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeJinjaEnv" name="test_basename_filter_works" time="0.002" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeJinjaEnv" name="test_strict_mode_raises_on_missing_var" time="0.003" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeJinjaEnv" name="test_non_strict_mode_renders_empty_on_missing" time="0.001" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeRunName" name="test_prefix_always_present" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeRunName" name="test_index_zero_padded_to_4_digits" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeRunName" name="test_large_index" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeRunName" name="test_no_template_uses_var_values" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeRunName" name="test_no_template_sanitizes_values" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeRunName" name="test_with_template" time="0.001" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeRunName" name="test_template_output_sanitized" time="0.001" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeRunName" name="test_empty_suffix_returns_prefix_only" time="0.000" /><testcase classname="tests.tfscreen.util.test_grid_utils.TestMakeRunName" name="test_bad_template_raises_value_error" time="0.001" /><testcase classname="tests.tfscreen.util.test_parallel" name="test_none_is_serial" time="0.001" /><testcase classname="tests.tfscreen.util.test_parallel" name="test_one_is_serial" time="0.001" /><testcase classname="tests.tfscreen.util.test_parallel" name="test_explicit_count_passes_through" time="0.000" /><testcase classname="tests.tfscreen.util.test_parallel" name="test_negative_uses_cpu_count_minus_one" time="0.001" /><testcase classname="tests.tfscreen.util.test_parallel" name="test_negative_other_values_also_cpu_count_minus_one" time="0.001" /><testcase classname="tests.tfscreen.util.test_parallel" name="test_negative_floors_at_one" time="0.001" /><testcase classname="tests.tfscreen.util.test_parallel" name="test_cpu_count_none_falls_back" time="0.001" /><testcase classname="tests.tfscreen.util.test_parallel" name="test_reexported_from_util_package" time="0.000" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_success[5-kwargs0-5.0]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_success[5.5-kwargs1-5]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_success[None-kwargs2-None]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_success[0-kwargs3-0.0]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_success[10-kwargs4-10.0]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_success[5-kwargs5-5.0]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_success[0.1-kwargs6-0.1]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_success[9.9-kwargs7-9.9]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_failures[None-kwargs0-ValueError-cannot be None]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_failures[None-kwargs1-ValueError-test_param cannot be None]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_failures[value2-kwargs2-ValueError-Value must be a scalar]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_failures[abc-kwargs3-ValueError-could not convert string to float: 'abc']" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_failures[-1-kwargs4-ValueError-must be &gt;= 0]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_failures[0-kwargs5-ValueError-must be &gt; 0]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_failures[11-kwargs6-ValueError-must be &lt;= 10]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_number_failures[10-kwargs7-ValueError-must be &lt; 10]" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_unknown_keys_all_valid" time="0.000" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_unknown_keys_empty_config" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_unknown_keys_single_unknown" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_unknown_keys_multiple_unknown" time="0.001" /><testcase classname="tests.tfscreen.util.validation.test_check" name="test_check_unknown_keys_label_in_message" time="0.018" /></testsuite></testsuites>
\ No newline at end of file
diff --git a/run_all_tests.sh b/run_all_tests.sh
index 5099a1ed..bfd61606 100755
--- a/run_all_tests.sh
+++ b/run_all_tests.sh
@@ -1,17 +1,21 @@
 #!/bin/bash
+#
+# Run the full local test suite (lint, unit tests with --runslow, smoke tests)
+# and write every report into reports/, which is gitignored. CI badges come from
+# GitHub Actions, so nothing here is meant to be committed.
+
+set -e
 
 echo "Running flake8"
 flake_test=`flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics`
 if [[ "${flake_test}" != 0 ]]; then
+    echo "${flake_test}"
     echo "flake failed"
-    exit
+    exit 1
 fi
 
 rm -rf reports
-mkdir reports
-mkdir reports/junit
-mkdir reports/coverage
-mkdir reports/badges
+mkdir -p reports/junit reports/coverage reports/badges
 
 echo "Running flake8, aggressive"
 flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics > reports/flake.txt
@@ -19,21 +23,13 @@ flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statist
 echo "Running coverage.py"
 coverage erase
 NUMBA_DISABLE_JIT=1 coverage run --branch -m pytest tests/tfscreen --runslow --junit-xml=reports/junit/junit.xml
-NUMBA_DISABLE_JIT=1 pytest tests/smoke-tests --runslow 
+NUMBA_DISABLE_JIT=1 pytest tests/smoke-tests --runslow
 
 echo "Generating reports"
-coverage html
-mv htmlcov reports
-
-coverage xml
-mv coverage.xml reports/coverage/coverage.xml
-
-genbadge tests
-sleep 1
-genbadge coverage
-sleep 1
+coverage html -d reports/htmlcov
+coverage xml -o reports/coverage/coverage.xml
 
-#wget https://github.com/harmslab/dataprob/actions/workflows/python-app.yml/badge.svg -O ghwf.svg
-#wget https://readthedocs.org/projects/dataprob/badge/?version=latest -O rtd.svg
+genbadge tests -o reports/badges/tests-badge.svg
+genbadge coverage -o reports/badges/coverage-badge.svg
 
-mv *.svg docs/badges
+echo "Reports written to reports/"
diff --git a/scripts/generate_struct_ensemble.py b/scripts/generate_struct_ensemble.py
index 1bed94cd..b58da9d5 100644
--- a/scripts/generate_struct_ensemble.py
+++ b/scripts/generate_struct_ensemble.py
@@ -22,6 +22,9 @@
 {name}/dist_matrix           — float32 (L, L)  : min Cα-Cα distance in Å across
                                all chain-pair combinations
 {name}/n_chains_bearing_mut  — int32 scalar    : chains that carry a mutation
+{name}/wt_aa                 — str     (L,)    : one-letter wt residue at each
+                               residue number ('X' if unknown); the loader
+                               checks it against the library's wt residues
 
 Input YAML format
 -----------------
@@ -113,6 +116,35 @@ def _parse_pdb_ca(pdb_path):
     return chains
 
 
+_THREE_TO_ONE = {
+    'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F', 'GLY': 'G',
+    'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L', 'MET': 'M', 'ASN': 'N',
+    'PRO': 'P', 'GLN': 'Q', 'ARG': 'R', 'SER': 'S', 'THR': 'T', 'VAL': 'V',
+    'TRP': 'W', 'TYR': 'Y', 'MSE': 'M', 'HSD': 'H', 'HSE': 'H', 'HSP': 'H',
+    'HID': 'H', 'HIE': 'H', 'HIP': 'H',
+}
+
+
+def _parse_pdb_wt_aa(pdb_path, unique_resnums):
+    """
+    One-letter residue at each residue number, from the first Cα ATOM record
+    with that number in any chain ('X' when absent or non-canonical).
+    """
+    found = {}
+    with open(pdb_path) as fh:
+        for line in fh:
+            if not line.startswith('ATOM') or line[12:16].strip() != 'CA':
+                continue
+            try:
+                resnum = int(line[22:26])
+            except ValueError:
+                continue
+            if resnum not in found:
+                found[resnum] = _THREE_TO_ONE.get(line[17:20].strip(), 'X')
+    return np.array([found.get(int(r), 'X') for r in unique_resnums],
+                    dtype=object)
+
+
 def _build_min_dist_matrix(ca_chains, unique_resnums):
     """
     Build a (n, n) minimum Cα-Cα distance matrix over all chain pairs.
@@ -450,6 +482,8 @@ def main():
                 'residue_nums':         unique_resnums,
                 'dist_matrix':          dist_matrix,
                 'n_chains_bearing_mut': np.int32(n_chains),
+                'wt_aa':                _parse_pdb_wt_aa(pdb_path,
+                                                         unique_resnums.tolist()),
             }
 
     # ------------------------------------------------------------------
@@ -473,6 +507,8 @@ def main():
             grp.create_dataset('residue_nums',         data=arrays['residue_nums'])
             grp.create_dataset('dist_matrix',          data=arrays['dist_matrix'])
             grp.create_dataset('n_chains_bearing_mut', data=arrays['n_chains_bearing_mut'])
+            grp.create_dataset('wt_aa', data=arrays['wt_aa'].astype(str).tolist(),
+                               dtype=h5py.string_dtype())
 
     print(f"\nWrote HDF5:            {args.out}")
 
diff --git a/src/tfscreen/__version__.py b/src/tfscreen/__version__.py
index 99a34f32..57a1676c 100644
--- a/src/tfscreen/__version__.py
+++ b/src/tfscreen/__version__.py
@@ -5,5 +5,5 @@
 Version string. 
 """
 
-VERSION = (0, 4, 4)
+VERSION = (0, 5, 0)
 __version__ = '.'.join(map(str, VERSION))
diff --git a/src/tfscreen/analysis/calibration_grid.py b/src/tfscreen/analysis/calibration_grid.py
new file mode 100644
index 00000000..8b8baa05
--- /dev/null
+++ b/src/tfscreen/analysis/calibration_grid.py
@@ -0,0 +1,651 @@
+"""
+Calibration summary across a simulation grid (``tfs-summarize-calibration``).
+
+Every run directory of a ``tfs-setup-sim-grid`` grid holds a ``combo.json``
+(its grid variables) and, once the run finishes, the ``tfs-summarize-fit``
+outputs in ``summary/``: posterior quantile ladders (``q<level>`` columns)
+joined to the simulated truth (``ref``) for θ (``*_theta_corr_test.csv``) and
+for every fitted parameter with ground truth (``*_params_*.csv``).  This
+module turns those into calibration metrics per run, averages them into arms
+(runs sharing every grid variable except the replicate keys), and pairs every
+run with a baseline run fit to the same simulated data set.
+
+Metrics (per run x quantity x stratum)
+--------------------------------------
+- ``coverage_<a>``: fraction of true values inside the central ``a``
+  credible interval, for ``a`` in ``CURVE_LEVELS``.  Computed from PIT values
+  interpolated from the quantile ladder -- the same calculation as
+  ``tfs-summarize-fit`` (``error_calibration``), so numbers agree.
+- ``calibration_error``: mean |coverage - nominal| over ``CURVE_LEVELS``.
+- ``calibration_bias``: mean (coverage - nominal); negative = intervals too
+  narrow (overconfident), positive = too wide.
+- ``ks_stat``/``ks_pval``/``mean_pit``: PIT uniformity (one-sample KS).
+- ``width_<a>``: mean width of the central ``a`` interval, for ``a`` in
+  ``WIDTH_LEVELS`` -- so a guide cannot look calibrated just by being vague.
+- ``rmse``/``pearson_r``: posterior median vs truth.
+
+Strata
+------
+Genotype-indexed quantities are also broken out by ``has_binding`` (the
+genotype has binding data: ``*_sim_binding.csv``) and ``purity`` (from the
+sub-libraries encoding the genotype in ``*_sim_library.csv``), separately and
+crossed.  ``purity`` follows the genotype's ``bulk_fraction``, which sets its
+congressed fraction: ``spike`` (only a spiked sequence encodes it,
+``bulk_fraction`` 0, never congressed), ``bulk`` (only bulk sub-libraries,
+``bulk_fraction`` 1) or ``mixed`` (both, as for wt and the spiked single
+mutants, which the bulk sub-libraries also encode).  θ is additionally split
+by ``theta_regime``:
+``resolvable`` when the true θ lies in ``[regime_eps, 1 - regime_eps]``,
+``saturated`` otherwise; a relative fit's θ is the wt-relative X (its
+``theta_scale`` column is ``X``, and ``ref`` is the truth on the X gauge), where
+the band means nothing, so its ``theta_regime`` is ``X``.  The pooled row
+carries ``"all"`` in every stratum column.
+
+No thresholds, no grades: every value is a raw number; filter downstream.
+"""
+
+import glob
+import json
+import os
+import re
+import warnings
+
+import numpy as np
+import pandas as pd
+
+from tfscreen.tfmodel.analysis.error_calibration import (
+    calibration_curve,
+    pit_from_quantiles,
+    pit_uniformity_test,
+)
+
+CURVE_LEVELS = (0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99)
+WIDTH_LEVELS = (0.5, 0.8, 0.95)
+STRATUM_COLS = ("has_binding", "purity", "theta_regime")
+SPIKED_ORIGIN = "spiked"
+ALL = "all"
+
+# Metrics compared against the baseline in paired_differences.
+PAIRED_METRICS = ("calibration_error", "calibration_bias", "coverage_0.95",
+                  "width_0.95", "rmse", "ks_stat")
+
+_Q_COL_RE = re.compile(r"^q(\d*\.?\d+)$")
+_NONE_STRINGS = {"none", "null", "~", ""}
+
+
+# ---------------------------------------------------------------------------
+# Metrics
+# ---------------------------------------------------------------------------
+
+def quantile_columns(df):
+    """Return (sorted ``q<level>`` column names, their levels as an array)."""
+    cols = sorted((c for c in df.columns if _Q_COL_RE.match(c)),
+                  key=lambda c: float(c[1:]))
+    return cols, np.array([float(c[1:]) for c in cols])
+
+
+def _interp_quantile(qmat, levels, p):
+    """Per-row quantile at probability ``p``, linear between stored levels."""
+    j = int(np.searchsorted(levels, p))
+    if j < len(levels) and np.isclose(levels[j], p):
+        return qmat[:, j]
+    if j == 0 or j == len(levels):
+        return np.full(qmat.shape[0], np.nan)
+    w = (p - levels[j - 1]) / (levels[j] - levels[j - 1])
+    return qmat[:, j - 1] + w * (qmat[:, j] - qmat[:, j - 1])
+
+
+def metric_columns():
+    """Names of the metric columns ``calibration_metrics`` returns."""
+    return ([f"coverage_{a:g}" for a in CURVE_LEVELS]
+            + ["calibration_error", "calibration_bias",
+               "ks_stat", "ks_pval", "mean_pit"]
+            + [f"width_{a:g}" for a in WIDTH_LEVELS]
+            + ["rmse", "pearson_r"])
+
+
+def calibration_metrics(ref, qmat, levels):
+    """
+    Calibration, sharpness and accuracy of posterior quantiles vs truth.
+
+    Parameters
+    ----------
+    ref : array-like, shape (N,)
+        True values.
+    qmat : array-like, shape (N, Q)
+        Posterior quantiles, columns ordered as ``levels``.
+    levels : array-like, shape (Q,)
+        Ascending quantile levels.
+
+    Returns
+    -------
+    dict
+        ``n`` plus every name in ``metric_columns()``.  Rows with a
+        non-finite truth or quantile are dropped first; with no rows left
+        every metric is NaN.
+    """
+    ref = np.asarray(ref, dtype=float)
+    qmat = np.asarray(qmat, dtype=float)
+    levels = np.asarray(levels, dtype=float)
+
+    ok = np.isfinite(ref) & np.all(np.isfinite(qmat), axis=1)
+    ref, qmat = ref[ok], qmat[ok]
+
+    out = {"n": int(len(ref))}
+    out.update({k: np.nan for k in metric_columns()})
+    if len(ref) == 0:
+        return out
+
+    pit = pit_from_quantiles(ref, qmat, levels)
+    curve = calibration_curve(pit, CURVE_LEVELS)
+    for a in CURVE_LEVELS:
+        out[f"coverage_{a:g}"] = curve[float(a)]
+    diffs = np.array([curve[float(a)] - a for a in CURVE_LEVELS])
+    out["calibration_error"] = float(np.mean(np.abs(diffs)))
+    out["calibration_bias"] = float(np.mean(diffs))
+
+    ks = pit_uniformity_test(pit)
+    out["ks_stat"] = ks["ks_stat"]
+    out["ks_pval"] = ks["ks_pval"]
+    out["mean_pit"] = ks["mean_pit"]
+
+    for a in WIDTH_LEVELS:
+        lo = _interp_quantile(qmat, levels, (1.0 - a) / 2.0)
+        hi = _interp_quantile(qmat, levels, (1.0 + a) / 2.0)
+        out[f"width_{a:g}"] = float(np.nanmean(hi - lo))
+
+    median = _interp_quantile(qmat, levels, 0.5)
+    out["rmse"] = float(np.sqrt(np.nanmean((median - ref) ** 2)))
+    if len(ref) > 1 and np.nanstd(median) > 0 and np.nanstd(ref) > 0:
+        out["pearson_r"] = float(np.corrcoef(median, ref)[0, 1])
+
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Reading one run
+# ---------------------------------------------------------------------------
+
+def read_combo(run_dir):
+    """Flatten a run's ``combo.json`` (simulate + template variables)."""
+    with open(os.path.join(run_dir, "combo.json")) as fh:
+        combo = json.load(fh)
+    flat = dict(combo.get("simulate") or {})
+    for key, value in (combo.get("template") or {}).items():
+        if key in flat:
+            warnings.warn(f"combo.json in {run_dir}: '{key}' is both a simulate "
+                          f"and a template variable; using the template value.")
+        flat[key] = value
+    return flat
+
+
+def _sim_file(run_dir, name):
+    matches = sorted(glob.glob(os.path.join(run_dir, f"*_sim_{name}.csv")))
+    return matches[0] if matches else None
+
+
+def genotype_strata(run_dir):
+    """
+    Per-genotype strata from the simulation outputs, or None.
+
+    Returns a DataFrame with ``genotype``, ``has_binding`` ("yes"/"no") and
+    ``purity`` ("spike"/"mixed"/"bulk").  None when ``*_sim_library.csv`` is
+    absent.
+    """
+    lib_path = _sim_file(run_dir, "library")
+    if lib_path is None:
+        return None
+    lib_df = pd.read_csv(lib_path)
+    genotypes = pd.unique(lib_df["genotype"].astype(str))
+    is_spiked = lib_df["library_origin"].astype(str) == SPIKED_ORIGIN
+    by_genotype = (lib_df.assign(genotype=lib_df["genotype"].astype(str),
+                                 _spiked=is_spiked)
+                   .groupby("genotype")["_spiked"])
+    any_spiked, all_spiked = by_genotype.any(), by_genotype.all()
+
+    binding_path = _sim_file(run_dir, "binding")
+    binding = (set(pd.read_csv(binding_path)["genotype"].astype(str))
+               if binding_path else set())
+
+    return pd.DataFrame({
+        "genotype": genotypes,
+        "has_binding": ["yes" if g in binding else "no" for g in genotypes],
+        "purity": ["spike" if all_spiked[g] else
+                   "mixed" if any_spiked[g] else "bulk" for g in genotypes],
+    })
+
+
+def read_quantities(summary_dir):
+    """
+    Return [(quantity, DataFrame)] for every calibration-ready summary CSV.
+
+    ``theta_test`` comes from ``*_theta_corr_test.csv``; each
+    ``*_params_<name>.csv`` gives quantity ``<name>``.  A file qualifies when
+    it has a ``ref`` column and at least two ``q<level>`` columns.
+    """
+    candidates = [("theta_test", p) for p in
+                  sorted(glob.glob(os.path.join(summary_dir, "*_theta_corr_test.csv")))]
+    for path in sorted(glob.glob(os.path.join(summary_dir, "*_params_*.csv"))):
+        name = re.search(r"_params_(.+)\.csv$", os.path.basename(path)).group(1)
+        if name.endswith(("_calibration_curve", "_pit")):
+            continue
+        candidates.append((name, path))
+
+    out = []
+    for quantity, path in candidates:
+        df = pd.read_csv(path)
+        cols, _ = quantile_columns(df)
+        if "ref" in df.columns and len(cols) >= 2:
+            out.append((quantity, df))
+    return out
+
+
+def _groupings(df):
+    """Stratum groupings present in ``df`` (the pooled one first)."""
+    present = [c for c in STRATUM_COLS if c in df.columns and df[c].notna().any()]
+    groupings = [()] + [(c,) for c in present]
+    if "has_binding" in present and "purity" in present:
+        groupings.append(("has_binding", "purity"))
+    return groupings
+
+
+def summarize_run(run_dir, summary_subdir="summary", regime_eps=0.01):
+    """
+    Calibration metrics for one run.
+
+    Returns
+    -------
+    rows : list of dict
+        One per quantity x stratum: ``quantity``, the ``STRATUM_COLS``
+        (``"all"`` where pooled), ``n`` and the metric columns.
+    problem : str or None
+        Why the run produced no rows, else None.
+    """
+    summary_dir = os.path.join(run_dir, summary_subdir)
+    if not os.path.isdir(summary_dir):
+        return [], f"no {summary_subdir}/ directory"
+    quantities = read_quantities(summary_dir)
+    if not quantities:
+        return [], "no calibration outputs"
+
+    strata = genotype_strata(run_dir)
+    rows = []
+    for quantity, df in quantities:
+        cols, levels = quantile_columns(df)
+        df = df.copy()
+        if "genotype" in df.columns and strata is not None:
+            df["genotype"] = df["genotype"].astype(str)
+            df = df.merge(strata, on="genotype", how="left")
+            unmatched = df["purity"].isna().mean()
+            if unmatched > 0:
+                warnings.warn(f"{run_dir}: {unmatched:.1%} of '{quantity}' rows "
+                              f"have genotypes missing from the sim library; "
+                              f"they are pooled but not stratified.")
+        if quantity == "theta_test":
+            resolvable = df["ref"].between(regime_eps, 1.0 - regime_eps)
+            df["theta_regime"] = np.where(resolvable, "resolvable", "saturated")
+            if "theta_scale" in df.columns:
+                df.loc[df["theta_scale"] == "X", "theta_regime"] = "X"
+
+        for keys in _groupings(df):
+            groups = [((), df)] if not keys else df.groupby(list(keys), dropna=True)
+            for values, sub in groups:
+                values = values if isinstance(values, tuple) else (values,)
+                row = {"quantity": quantity, **{c: ALL for c in STRATUM_COLS}}
+                row.update(dict(zip(keys, values)))
+                row.update(calibration_metrics(sub["ref"], sub[cols], levels))
+                rows.append(row)
+    return rows, None
+
+
+# ---------------------------------------------------------------------------
+# Grid
+# ---------------------------------------------------------------------------
+
+def summarize_grid_runs(grid_dir, summary_subdir="summary", regime_eps=0.01):
+    """
+    Calibration metrics for every run under ``grid_dir``.
+
+    Returns
+    -------
+    runs_df : DataFrame
+        One row per run x quantity x stratum: ``run``, the grid variables,
+        ``quantity``, the strata, ``n`` and the metrics.
+    status_df : DataFrame
+        One row per run directory: ``run``, grid variables, ``status``
+        ("ok" or the problem) and ``n_rows``.
+    grid_vars : list of str
+        Grid variable names, in first-seen order.
+    """
+    run_dirs = sorted(d for d in glob.glob(os.path.join(grid_dir, "*"))
+                      if os.path.isfile(os.path.join(d, "combo.json")))
+    if not run_dirs:
+        raise ValueError(f"No run directories with combo.json found in {grid_dir}")
+
+    rows, status, grid_vars = [], [], []
+    for run_dir in run_dirs:
+        run = os.path.basename(run_dir)
+        combo = read_combo(run_dir)
+        grid_vars += [k for k in combo if k not in grid_vars]
+        run_rows, problem = summarize_run(run_dir, summary_subdir, regime_eps)
+        status.append({"run": run, **combo, "status": problem or "ok",
+                       "n_rows": len(run_rows)})
+        rows += [{"run": run, **combo, **r} for r in run_rows]
+
+    return pd.DataFrame(rows), pd.DataFrame(status), grid_vars
+
+
+def _metrics_present(df):
+    return [c for c in metric_columns() if c in df.columns]
+
+
+def summarize_arms(runs_df, grid_vars, replicate_keys):
+    """
+    Average metrics over replicates within each arm.
+
+    An arm is every run sharing all grid variables except ``replicate_keys``.
+    Returns one row per arm x quantity x stratum with ``n_runs`` and
+    ``<metric>_mean`` / ``<metric>_std`` columns.
+    """
+    arm_keys = [k for k in grid_vars if k not in replicate_keys]
+    group_cols = arm_keys + ["quantity", *STRATUM_COLS]
+    metrics = ["n"] + _metrics_present(runs_df)
+
+    grouped = runs_df.groupby(group_cols, dropna=False, sort=True)
+    stats = grouped[metrics].agg(["mean", "std"])
+    stats.columns = [f"{m}_{s}" for m, s in stats.columns]
+    stats.insert(0, "n_runs", grouped.size())
+    return stats.reset_index()
+
+
+def parse_baseline(baseline):
+    """
+    Parse ``["key=value", ...]`` into a dict.  "None"/"null"/"~" -> None;
+    other values are parsed as int, then float, else kept as strings.
+    """
+    out = {}
+    for item in baseline or []:
+        if "=" not in item:
+            raise ValueError(f"baseline entry '{item}' must be key=value.")
+        key, value = item.split("=", 1)
+        key, value = key.strip(), value.strip()
+        if value.lower() in _NONE_STRINGS:
+            out[key] = None
+            continue
+        for cast in (int, float):
+            try:
+                out[key] = cast(value)
+                break
+            except ValueError:
+                continue
+        else:
+            out[key] = value
+    return out
+
+
+def _matches(series, value):
+    if value is None:
+        return series.isna()
+    return series == value
+
+
+def paired_differences(runs_df, grid_vars, baseline):
+    """
+    Pair every non-baseline run with the baseline run of the same design.
+
+    ``baseline`` maps grid variables to their baseline values (e.g.
+    ``{"guide_type": "component", "guide_rank": None}``).  A run's partner
+    shares every other grid variable -- including the simulation and fit
+    seeds -- plus quantity and stratum.  Returns the run rows with
+    ``delta_<metric>`` = run - baseline for each of ``PAIRED_METRICS``.
+    """
+    unknown = [k for k in baseline if k not in grid_vars]
+    if unknown:
+        raise ValueError(f"baseline keys {unknown} are not grid variables "
+                         f"({grid_vars}).")
+
+    is_base = np.ones(len(runs_df), dtype=bool)
+    for key, value in baseline.items():
+        is_base &= _matches(runs_df[key], value).to_numpy()
+    if not is_base.any():
+        raise ValueError(f"No runs match baseline {baseline}.")
+
+    match_keys = [k for k in grid_vars if k not in baseline] + ["quantity", *STRATUM_COLS]
+    metrics = [m for m in PAIRED_METRICS if m in runs_df.columns]
+    base = runs_df.loc[is_base, match_keys + metrics].rename(
+        columns={m: f"baseline_{m}" for m in metrics})
+    paired = runs_df.loc[~is_base].merge(base, on=match_keys, how="inner")
+    for m in metrics:
+        paired[f"delta_{m}"] = paired[m] - paired[f"baseline_{m}"]
+
+    keep = ["run", *grid_vars, "quantity", *STRATUM_COLS]
+    keep += [f"delta_{m}" for m in metrics]
+    return paired[keep]
+
+
+def summarize_paired(paired_df, grid_vars, replicate_keys):
+    """Mean, std, count and standard error of each delta per arm x quantity x stratum."""
+    arm_keys = [k for k in grid_vars if k not in replicate_keys]
+    group_cols = arm_keys + ["quantity", *STRATUM_COLS]
+    deltas = [c for c in paired_df.columns if c.startswith("delta_")]
+
+    grouped = paired_df.groupby(group_cols, dropna=False, sort=True)
+    stats = grouped[deltas].agg(["mean", "std", "count"])
+    for d in deltas:
+        stats[(d, "se")] = stats[(d, "std")] / np.sqrt(stats[(d, "count")])
+    stats = stats.sort_index(axis=1)
+    stats.columns = [f"{d}_{s}" for d, s in stats.columns]
+    stats.insert(0, "n_pairs", grouped.size())
+    return stats.reset_index()
+
+
+# ---------------------------------------------------------------------------
+# Plot
+# ---------------------------------------------------------------------------
+
+def _label(row, keys):
+    parts = []
+    for k in keys:
+        v = row[k]
+        if v is None or (isinstance(v, float) and np.isnan(v)):
+            continue
+        if isinstance(v, float) and v.is_integer():
+            v = int(v)
+        parts.append(f"{k}={v}")
+    return ", ".join(parts) or "(all)"
+
+
+def plot_calibration_curves(arms_df, arm_keys, quantity, out_path, facet_by=None):
+    """
+    Pooled calibration curves (coverage vs nominal) per arm for one quantity.
+
+    Faceted by the ``facet_by`` grid variables; one line per remaining arm.
+    Returns the path written, or None when there is nothing to plot.
+    """
+    from matplotlib import pyplot as plt
+
+    facet_by = list(facet_by or [])
+    sub = arms_df[arms_df["quantity"] == quantity]
+    for c in STRATUM_COLS:
+        sub = sub[sub[c] == ALL]
+    if sub.empty:
+        return None
+
+    line_keys = [k for k in arm_keys if k not in facet_by]
+    facets = ([((), sub)] if not facet_by
+              else list(sub.groupby(facet_by, dropna=False, sort=True)))
+
+    fig, axes = plt.subplots(1, len(facets), figsize=(4.6 * len(facets), 4.4),
+                             squeeze=False)
+    nominal = np.array(CURVE_LEVELS)
+    for ax, (fvals, fdf) in zip(axes[0], facets):
+        ax.plot([0.45, 1.0], [0.45, 1.0], color="gray", lw=1, ls="--")
+        for _, row in fdf.iterrows():
+            coverage = [row[f"coverage_{a:g}_mean"] for a in CURVE_LEVELS]
+            ax.plot(nominal, coverage, marker="o", ms=3, lw=1.2,
+                    label=_label(row, line_keys))
+        fvals = fvals if isinstance(fvals, tuple) else (fvals,)
+        ax.set_title(", ".join(f"{k}={v}" for k, v in zip(facet_by, fvals))
+                     or quantity, fontsize=9)
+        ax.set_xlabel("nominal coverage")
+        ax.set_ylabel("empirical coverage")
+        ax.set_xlim(0.45, 1.0)
+        ax.set_ylim(0.0, 1.0)
+        ax.legend(fontsize=6, loc="upper left")
+    fig.suptitle(f"{quantity}: pooled calibration by arm", fontsize=10)
+    fig.tight_layout()
+    fig.savefig(out_path)
+    plt.close(fig)
+    return out_path
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+def summarize_calibration(grid_dir,
+                          out_prefix="tfs_calibration",
+                          summary_subdir="summary",
+                          replicate_keys=None,
+                          baseline=None,
+                          facet_by=None,
+                          plot_quantity="theta_test",
+                          regime_eps=0.01):
+    """
+    Summarize posterior calibration across a simulation grid.
+
+    Parameters
+    ----------
+    grid_dir : str
+        Directory of run subdirectories written by ``tfs-setup-sim-grid``
+        (each with ``combo.json`` and, when finished, ``summary/``).
+    out_prefix : str, optional
+        Prefix for all outputs (default ``tfs_calibration``).
+    summary_subdir : str, optional
+        Subdirectory holding the ``tfs-summarize-fit`` outputs (default
+        ``summary``).
+    replicate_keys : list of str, optional
+        Grid variables that index replicates rather than arms (default
+        ``seed`` and ``fit_seed``).  Keys absent from the grid are ignored.
+    baseline : list of str, optional
+        ``key=value`` pairs selecting the baseline arm, e.g.
+        ``guide_type=component guide_rank=None``.  When given, every other
+        run is paired with the baseline run sharing all remaining grid
+        variables (same simulated data, same fit seed).
+    facet_by : list of str, optional
+        Grid variables that split the calibration-curve plot into panels.
+    plot_quantity : str, optional
+        Quantity plotted (default ``theta_test``).
+    regime_eps : float, optional
+        θ within ``[regime_eps, 1 - regime_eps]`` is ``resolvable``
+        (default 0.01).
+
+    Outputs
+    -------
+    ``{out_prefix}_runs.csv``            per run x quantity x stratum
+    ``{out_prefix}_run_status.csv``      per run: ok / what is missing
+    ``{out_prefix}_arms.csv``            per arm: mean/std over replicates
+    ``{out_prefix}_paired.csv``          per run: deltas vs baseline (if given)
+    ``{out_prefix}_paired_summary.csv``  per arm: mean/std/se of deltas
+    ``{out_prefix}_{plot_quantity}_calibration_curves.pdf``
+    ``{out_prefix}_metadata.json``       resolved settings and counts
+    """
+    if replicate_keys is None:
+        replicate_keys = ["seed", "fit_seed"]
+    baseline_spec = parse_baseline(baseline)
+
+    runs_df, status_df, grid_vars = summarize_grid_runs(
+        grid_dir, summary_subdir=summary_subdir, regime_eps=regime_eps)
+
+    # A baseline must name real grid variables and match at least one run in
+    # the grid (finished or not) -- otherwise it is a typo.  Matching only
+    # unfinished runs is normal mid-grid and just skips the paired outputs.
+    if baseline_spec:
+        unknown = [k for k in baseline_spec if k not in grid_vars]
+        if unknown:
+            raise ValueError(f"baseline keys {unknown} are not grid variables "
+                             f"({grid_vars}).")
+        in_grid = np.ones(len(status_df), dtype=bool)
+        for key, value in baseline_spec.items():
+            in_grid &= _matches(status_df[key], value).to_numpy()
+        if not in_grid.any():
+            raise ValueError(f"No run in {grid_dir} matches baseline {baseline_spec}.")
+
+    ignored = [k for k in replicate_keys if k not in grid_vars]
+    if ignored:
+        warnings.warn(f"replicate keys {ignored} are not grid variables; ignored.")
+    replicate_keys = [k for k in replicate_keys if k in grid_vars]
+    arm_keys = [k for k in grid_vars if k not in replicate_keys]
+
+    facet_by = list(facet_by or [])
+    bad_facets = [k for k in facet_by if k not in arm_keys]
+    if bad_facets:
+        raise ValueError(f"facet_by keys {bad_facets} must be arm variables "
+                         f"({arm_keys}).")
+
+    out_dir = os.path.dirname(os.path.abspath(out_prefix))
+    os.makedirs(out_dir, exist_ok=True)
+
+    n_ok = int((status_df["status"] == "ok").sum())
+    print(f"{n_ok} of {len(status_df)} runs have calibration outputs.", flush=True)
+    status_df.to_csv(f"{out_prefix}_run_status.csv", index=False)
+    written = [f"{out_prefix}_run_status.csv"]
+
+    metadata = {
+        "grid_dir": os.path.abspath(grid_dir),
+        "summary_subdir": summary_subdir,
+        "grid_vars": grid_vars,
+        "replicate_keys": replicate_keys,
+        "arm_keys": arm_keys,
+        "baseline": baseline_spec,
+        "facet_by": facet_by,
+        "regime_eps": regime_eps,
+        "curve_levels": list(CURVE_LEVELS),
+        "width_levels": list(WIDTH_LEVELS),
+        "n_runs": int(len(status_df)),
+        "n_runs_ok": n_ok,
+    }
+
+    if runs_df.empty:
+        warnings.warn("No run has calibration outputs yet; only the run status "
+                      "table was written.")
+    else:
+        sort_cols = [c for c in arm_keys + replicate_keys + ["quantity"]
+                     if c in runs_df.columns]
+        runs_df = runs_df.sort_values(sort_cols, kind="stable")
+        runs_df.to_csv(f"{out_prefix}_runs.csv", index=False)
+        written.append(f"{out_prefix}_runs.csv")
+
+        arms_df = summarize_arms(runs_df, grid_vars, replicate_keys)
+        arms_df.to_csv(f"{out_prefix}_arms.csv", index=False)
+        written.append(f"{out_prefix}_arms.csv")
+
+        base_done = np.ones(len(runs_df), dtype=bool)
+        for key, value in baseline_spec.items():
+            base_done &= _matches(runs_df[key], value).to_numpy()
+        if baseline_spec and not base_done.any():
+            warnings.warn(f"No finished run matches baseline {baseline_spec} yet; "
+                          f"paired outputs skipped.")
+        elif baseline_spec:
+            paired = paired_differences(runs_df, grid_vars, baseline_spec)
+            paired.to_csv(f"{out_prefix}_paired.csv", index=False)
+            summarize_paired(paired, grid_vars, replicate_keys).to_csv(
+                f"{out_prefix}_paired_summary.csv", index=False)
+            written += [f"{out_prefix}_paired.csv",
+                        f"{out_prefix}_paired_summary.csv"]
+            metadata["n_pairs"] = int(len(paired))
+
+        pdf = plot_calibration_curves(
+            arms_df, arm_keys, plot_quantity,
+            f"{out_prefix}_{plot_quantity}_calibration_curves.pdf",
+            facet_by=facet_by)
+        if pdf is not None:
+            written.append(pdf)
+
+    with open(f"{out_prefix}_metadata.json", "w") as fh:
+        json.dump(metadata, fh, indent=2, default=str)
+    written.append(f"{out_prefix}_metadata.json")
+
+    for path in written:
+        print(f"Wrote {path}", flush=True)
+    return runs_df, status_df
diff --git a/src/tfscreen/analysis/scripts/cat_response_cli.py b/src/tfscreen/analysis/scripts/cat_response_cli.py
index 2b7ecb09..5bf85700 100644
--- a/src/tfscreen/analysis/scripts/cat_response_cli.py
+++ b/src/tfscreen/analysis/scripts/cat_response_cli.py
@@ -99,7 +99,7 @@ def cat_response(data_file,
     x_obs : str
         Name of the column holding the independent variable (e.g. 'titrant_conc').
     y_obs : str or None, optional
-        Name of the column holding the observable (e.g. 'q0.5', 'point_est'). If
+        Name of the column holding the observable (e.g. 'q0.5'). If
         None (default) and the input has a 'q0.5' column (as written by
         tfs-predict-theta), 'q0.5' is used.
     out_prefix : str, optional
diff --git a/src/tfscreen/analysis/scripts/extract_epistasis_cli.py b/src/tfscreen/analysis/scripts/extract_epistasis_cli.py
index 74fdd693..510a3c4a 100644
--- a/src/tfscreen/analysis/scripts/extract_epistasis_cli.py
+++ b/src/tfscreen/analysis/scripts/extract_epistasis_cli.py
@@ -156,6 +156,16 @@ def extract_epistasis(data_file,
     # estimate, (q0.841 - q0.159)/2 for the std) when not given explicitly.
     df, y_obs, y_std = resolve_obs_columns(df, y_obs=y_obs, y_std=y_std)
 
+    # tfs-predict-theta labels a relative fit's output as X (theta_scale),
+    # which is defined only up to an affine map: only additive epistasis
+    # applies to it (roadmap C8).
+    if "theta_scale" in df.columns and (df["theta_scale"] == "X").any() \
+            and scale != "add":
+        raise ValueError(
+            f"'{data_file}' holds the wt-relative growth variable X "
+            f"(theta_scale == 'X'), not an occupancy; only scale='add' "
+            f"applies to it (got scale={scale!r}).")
+
     # Fail fast on missing columns rather than deep inside the pivot.
     required = ["genotype", y_obs]
     if y_std is not None:
diff --git a/src/tfscreen/analysis/scripts/summarize_calibration_cli.py b/src/tfscreen/analysis/scripts/summarize_calibration_cli.py
new file mode 100644
index 00000000..5f9875d2
--- /dev/null
+++ b/src/tfscreen/analysis/scripts/summarize_calibration_cli.py
@@ -0,0 +1,37 @@
+"""
+tfs-summarize-calibration — posterior calibration across a simulation grid.
+
+Collects the ``tfs-summarize-fit`` calibration inputs from every run of a
+``tfs-setup-sim-grid`` grid, computes coverage / PIT / interval width /
+accuracy per run, quantity and stratum, averages them over replicates within
+each arm, and (with ``--baseline``) pairs each run with the baseline run fit
+to the same simulated data.  See ``tfscreen.analysis.calibration_grid``.
+
+Example (the guide-calibration grid)::
+
+    tfs-summarize-calibration guide_grid --out_prefix calib/guide \\
+        --baseline guide_type=component guide_rank=None \\
+        --facet_by batch_size theta_growth_noise_model
+"""
+
+from tfscreen.analysis.calibration_grid import summarize_calibration
+from tfscreen.util.cli import generalized_main
+
+
+def main():
+    generalized_main(summarize_calibration,
+                     manual_arg_types={"grid_dir": str,
+                                       "out_prefix": str,
+                                       "summary_subdir": str,
+                                       "replicate_keys": str,
+                                       "baseline": str,
+                                       "facet_by": str,
+                                       "plot_quantity": str,
+                                       "regime_eps": float},
+                     manual_arg_nargs={"replicate_keys": "+",
+                                       "baseline": "+",
+                                       "facet_by": "+"})
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tfscreen/analysis/stats_test_suite.py b/src/tfscreen/analysis/stats_test_suite.py
index 0b96f3f5..9449f421 100644
--- a/src/tfscreen/analysis/stats_test_suite.py
+++ b/src/tfscreen/analysis/stats_test_suite.py
@@ -5,12 +5,12 @@
 
 import warnings
 
-def stats_test_suite(param_est,param_real,param_std=None):
+def stats_test_suite(param_est,param_real):
     """
     Run a test suite comparing parameter estimates against true values.
 
-    This function takes arrays of estimated parameters, their standard errors,
-    and the corresponding true (known) values from a simulation. It computes
+    This function takes arrays of estimated parameters and the corresponding
+    true (known) values from a simulation. It computes
     a dictionary of key metrics to evaluate the performance, accuracy, and
     robustness of the estimation method.
 
@@ -19,9 +19,6 @@ def stats_test_suite(param_est,param_real,param_std=None):
     param_est : np.ndarray
         A 1D array of the parameter estimates from a model. May contain NaNs
         for failed fits.
-    param_std : np.ndarray, optional
-        A 1D array of the standard errors associated with each parameter
-        estimate. If not provided, ``coverage_prob`` is returned as NaN.
     param_real : np.ndarray
         A 1D array of the true, known parameter values used to generate the
         simulated data.
@@ -58,12 +55,6 @@ def stats_test_suite(param_est,param_real,param_std=None):
             The average difference between estimated and real values (bias).
             A value close to zero indicates the model is unbiased on
             average.
-        coverage_prob : float
-            The fraction of times the true parameter value falls within the
-            estimated 95% confidence interval. For a perfectly calibrated
-            model, this value should be close to 0.95. If this is higher than
-            0.95, the model *overestimates* error; if this value is lower than
-            0.95, the model *underestimates* error. 
         residual_corr : float
             The Pearson correlation coefficient between the estimation errors
             and the true parameter values. A value near zero is ideal,
@@ -86,8 +77,6 @@ def stats_test_suite(param_est,param_real,param_std=None):
     # Filter out bad fits for this analysis
     param_est = param_est[not_nan_mask]
     param_real = param_real[not_nan_mask]
-    if param_std is not None:
-        param_std = param_std[not_nan_mask]
     
     # Get RMSE and mean error
     diff = param_est - param_real
@@ -115,17 +104,6 @@ def stats_test_suite(param_est,param_real,param_std=None):
 
     mean_error = np.mean(diff)
 
-    # Get coverage probability (probability real values fall in the 95% CI).
-    # This will be 95% for perfectly calibrated error estimator.
-    if param_std is not None:
-        lower_ci = param_est - param_std*1.96
-        upper_ci = param_est + param_std*1.96
-        in_ci = np.logical_and(param_real >= lower_ci,
-                               param_real <= upper_ci)
-        coverage_prob = np.sum(in_ci)/param_est.shape[0]
-    else:
-        coverage_prob = np.nan
-
     # Look for correlation in residuals
     if np.var(diff) < 1e-12 or np.var(param_real) < 1e-12:
         residual_corr, residual_corr_p_value = np.nan, np.nan
@@ -153,7 +131,6 @@ def stats_test_suite(param_est,param_real,param_std=None):
         "spearman_r": spearman_r,
         "r_squared": r_squared,
         "mean_error":mean_error,
-        "coverage_prob":coverage_prob,
         "residual_corr":residual_corr,
         "residual_corr_p_value":residual_corr_p_value,
         "bp_p_value":bp_p_value
diff --git a/src/tfscreen/genetics/__init__.py b/src/tfscreen/genetics/__init__.py
index 139a3689..1861b22e 100644
--- a/src/tfscreen/genetics/__init__.py
+++ b/src/tfscreen/genetics/__init__.py
@@ -41,3 +41,9 @@
     scale_library_design,
     write_library_composition,
 )
+
+from .library_subsample import (  # noqa: F401
+    build_library,
+    subsample_design,
+    subsample_library_df,
+)
diff --git a/src/tfscreen/genetics/build_mut_geno_matrix.py b/src/tfscreen/genetics/build_mut_geno_matrix.py
index 526869f8..ee20c434 100644
--- a/src/tfscreen/genetics/build_mut_geno_matrix.py
+++ b/src/tfscreen/genetics/build_mut_geno_matrix.py
@@ -2,7 +2,8 @@
 import numpy as np
 
 
-def build_mut_geno_matrix(genotypes, skip_pairs=False):
+def build_mut_geno_matrix(genotypes, skip_pairs=False, mut_labels=None,
+                          pair_labels=None):
     """
     Build binary indicator matrices mapping mutations and mutation pairs to
     genotypes, from an ordered list of genotype strings.
@@ -20,6 +21,17 @@ def build_mut_geno_matrix(genotypes, skip_pairs=False):
         If True, skip building the pair index arrays entirely and return empty
         arrays of shape ``(0,)``. Use this when epistasis is disabled to avoid
         iterating over all multi-mutation genotypes. Default is False.
+    mut_labels : list of str, optional
+        Fixed mutation index space. When given, the output rows follow this
+        list (mutations absent from ``genotypes`` get all-zero rows) instead
+        of the first-seen order of ``genotypes``. A model rebuilt on a subset
+        of a fitted model's genotypes passes the fitted model's labels so
+        that per-mutation posterior arrays keep their meaning. A mutation in
+        ``genotypes`` that is not in this list raises ``ValueError``.
+    pair_labels : list of str, optional
+        Fixed pair index space, as for ``mut_labels`` (ignored when
+        ``skip_pairs=True``). A pair in ``genotypes`` that is not in this
+        list raises ``ValueError``.
 
     Returns
     -------
@@ -52,12 +64,23 @@ def _parse(g):
 
     geno_muts = [_parse(g) for g in genotypes]
 
-    # Collect unique single mutations in first-seen order.
-    mut_seen = {}
-    for muts in geno_muts:
-        for m in muts:
-            if m not in mut_seen:
-                mut_seen[m] = len(mut_seen)
+    # Collect unique single mutations in first-seen order, or use the fixed
+    # index space when one is given.
+    if mut_labels is not None:
+        mut_seen = {m: i for i, m in enumerate(mut_labels)}
+        unknown = sorted({m for muts in geno_muts for m in muts
+                          if m not in mut_seen})
+        if unknown:
+            raise ValueError(
+                f"Genotypes contain mutation(s) not in the fixed mutation "
+                f"labels: {unknown[:10]}"
+            )
+    else:
+        mut_seen = {}
+        for muts in geno_muts:
+            for m in muts:
+                if m not in mut_seen:
+                    mut_seen[m] = len(mut_seen)
 
     mut_labels = list(mut_seen.keys())
     num_mutation = len(mut_labels)
@@ -78,6 +101,9 @@ def _parse(g):
         return mut_labels, pair_labels, mut_geno_matrix, pair_nnz_pair_idx, pair_nnz_geno_idx
 
     pair_seen = {}
+    if pair_labels is not None:
+        pair_seen = {p: i for i, p in enumerate(pair_labels)}
+    unknown = set()
     for muts in geno_muts:
         if len(muts) < 2:
             continue
@@ -86,7 +112,15 @@ def _parse(g):
                 a, b = sorted([muts[i], muts[j]])
                 label = f"{a}/{b}"
                 if label not in pair_seen:
-                    pair_seen[label] = len(pair_seen)
+                    if pair_labels is not None:
+                        unknown.add(label)
+                    else:
+                        pair_seen[label] = len(pair_seen)
+    if unknown:
+        raise ValueError(
+            f"Genotypes contain mutation pair(s) not in the fixed pair "
+            f"labels: {sorted(unknown)[:10]}"
+        )
 
     pair_labels = list(pair_seen.keys())
 
diff --git a/src/tfscreen/genetics/library_design.py b/src/tfscreen/genetics/library_design.py
index 4b170649..30ef5517 100644
--- a/src/tfscreen/genetics/library_design.py
+++ b/src/tfscreen/genetics/library_design.py
@@ -359,7 +359,8 @@ def estimate_library_mixture(library_df,
 
 
 def library_composition_table(library_config,
-                              spiked_origin=SPIKED_ORIGIN):
+                              spiked_origin=SPIKED_ORIGIN,
+                              base_dir=None):
     """
     Build the per-genotype library composition table from a library config.
 
@@ -379,11 +380,16 @@ def library_composition_table(library_config,
         (a full ``tfs-simulate`` config, say) are ignored.
     spiked_origin : str, default "spiked"
         Library origin holding the monoclonal spiked controls.
+    base_dir : str, optional
+        Directory a relative ``library_genotypes`` path is read against.
+        Default: the YAML's directory when ``library_config`` is a path, else
+        the working directory.
 
     Returns
     -------
     pandas.DataFrame
-        One row per amino-acid genotype in the library, with columns:
+        One row per amino-acid genotype in the library (after any subsample
+        the config names, ``genetics.library_subsample``), with columns:
 
         - ``genotype``: standardized genotype name ("wt", "M42I", ...)
         - ``is_wt``: True for the wildtype genotype
@@ -412,9 +418,16 @@ def library_composition_table(library_config,
     should be the best available estimate of what actually went into the pool.
     """
 
+    import os
+
     from tfscreen.util import read_yaml
-    from tfscreen.genetics.library_manager import LibraryManager
+    from tfscreen.genetics.library_subsample import (
+        build_library,
+        subsample_design,
+    )
 
+    if isinstance(library_config, (str, os.PathLike)) and base_dir is None:
+        base_dir = os.path.dirname(os.path.abspath(library_config))
     config = read_yaml(library_config)
     if "library_mixture" not in config:
         raise ValueError(
@@ -423,9 +436,13 @@ def library_composition_table(library_config,
             "'tile_combos', plus 'spiked' if 'spiked_seqs' is defined)."
         )
 
-    library_df = LibraryManager(config).build_library_df()
+    # A subsampled library (library_genotypes / library_subsample) is the one
+    # tfs-simulate simulated, with the mixture it simulated it with.
+    library_df, summary = build_library(config, base_dir=base_dir)
+    mixture = subsample_design(config, summary).get(
+        "library_mixture", config["library_mixture"])
     composition = expected_library_composition(library_df,
-                                               config["library_mixture"],
+                                               mixture,
                                                spiked_origin=spiked_origin)
 
     library_df = library_df.copy()
diff --git a/src/tfscreen/genetics/library_manager.py b/src/tfscreen/genetics/library_manager.py
index 6d925563..cbddab48 100644
--- a/src/tfscreen/genetics/library_manager.py
+++ b/src/tfscreen/genetics/library_manager.py
@@ -20,6 +20,8 @@
 
 from typing import Iterable, Set, Dict, Union, Tuple, List
 
+import warnings
+
 
 def _check_char(some_str: str,
                 name: str,
@@ -508,18 +510,35 @@ def _prepare_indexes(self):
 
         self.indexers : dict[str, list[int]]
             A dictionary mapping each tile identifier (e.g., '1') to a
-            list of the integer indices where that tile's blocks appear in
-            the main block lists (`wt_blocks`, `mut_blocks`).
+            list of the integer indices of that tile's sites in the main
+            block lists (`wt_blocks`, `mut_blocks`). A site is a codon block
+            whose designed sequence differs from wt: a degenerate codon or
+            an explicit mutant codon. Non-degenerate codons and out-of-frame
+            flanks are part of the tile but are not sites. Counting them
+            would add one wt copy per such codon to every single-mutant
+            sub-library (and a full single-mutant expansion per pair to the
+            double-mutant ones), inflating wt's share of the pool.
         self.residues : list[str]
             A list of strings, where each string is the amino acid residue
             number corresponding to each block. This is offset by
             `self.first_amplicon_residue`.
+
+        Raises
+        ------
+        ValueError
+            If a tile has no sites (no codon differs from wt).
         """
 
         self.indexers = {}
         for lib in self.libraries_seen:
             self.indexers[lib] = [i for i in range(len(self.lib_lookup))
-                                  if self.lib_lookup[i] == lib]
+                                  if self.lib_lookup[i] == lib
+                                  and self.mut_blocks[i] != self.wt_blocks[i]]
+            if len(self.indexers[lib]) == 0:
+                raise ValueError(
+                    f"Tile '{lib}' has no library sites: none of its codons "
+                    f"differ from wt in degen_sites."
+                )
 
         self.residues = [f"{i + self.first_amplicon_residue}"
                          for i in range(len(self.wt_blocks))]
@@ -596,8 +615,9 @@ def _get_singles(self, target_lib: str) -> Tuple[List[str], List[str]]:
         This method relies on pre-computed attributes: `self.indexers`,
         `self.wt_blocks`, and `self.mut_blocks`. It calls the helper method
         `self._convert_to_aa` for the final translation step.
-        The wild-type sequence will be present in the output list once for
-        each mutable position in the tile.
+        The wild-type sequence is present in the output once for each site
+        whose expansion includes the wt codon (e.g. GCT at an NNT site).
+        Codons that are not sites (see `_prepare_indexes`) contribute nothing.
 
         """
 
@@ -806,6 +826,18 @@ def get_libraries(self) -> Tuple[Dict[str, List[str]], Dict[str, List[str]]]:
             all_lib_seqs["spiked"] = lib_seqs
             all_aa_muts["spiked"] = aa_muts
 
+        # wt is only in the library if some site's expansion encodes the wt
+        # amino acid or a spiked sequence is wt. A design can legitimately
+        # lack it, but it is more often a mistake, so say so.
+        if not any(aa == "" for muts in all_aa_muts.values() for aa in muts):
+            warnings.warn(
+                "The library design encodes no wildtype sequence: no library "
+                "site's degenerate codon encodes the wt amino acid and no "
+                "spiked sequence is wt. wt will not be an expected genotype.",
+                UserWarning,
+                stacklevel=2,
+            )
+
 
         return all_lib_seqs, all_aa_muts
     
diff --git a/src/tfscreen/genetics/library_subsample.py b/src/tfscreen/genetics/library_subsample.py
new file mode 100644
index 00000000..29cd21df
--- /dev/null
+++ b/src/tfscreen/genetics/library_subsample.py
@@ -0,0 +1,354 @@
+"""
+Restrict a described library to a subset of its genotypes.
+
+A realistic library is far too large for some studies (a full Laplace
+posterior needs a dense Hessian over every genotype's parameters), so the
+library YAML can name a subset to simulate and fit. Four optional keys, read
+here by ``tfs-simulate`` and ``tfs-configure-model --library_config`` (never
+by ``tfs-process-fastq``, which counts every designed genotype):
+
+``library_genotypes``
+    Path to a text file, one genotype per line (``#`` comments allowed),
+    relative to the YAML's own directory. Every library genotype not listed
+    is dropped from every origin that encodes it. A listed genotype the
+    library does not encode is an error.
+``library_subsample``
+    ``{origin: N}``: keep ``N`` genotypes of that origin, drawn at random
+    (after ``library_genotypes``, if both are given). Origins not named are
+    kept whole. The ``spiked`` origin cannot be subsampled.
+``library_subsample_seed``
+    Integer seed of the random draw; required with ``library_subsample``.
+    Each origin draws from its own stream (the seed and the origin name), as
+    a prefix of one fixed permutation, so N = 500 keeps a subset of the
+    genotypes N = 1000 keeps and subsampling a second origin does not change
+    the first's draw.
+``library_subsample_depth``
+    ``full_library`` (default) or ``as_written``; see
+    :func:`subsample_design`.
+
+wt and every genotype a spiked sequence encodes are always kept, in every
+origin that encodes them, and do not count toward ``N``. A kept genotype
+keeps all its codon-level sequences (its degeneracy), and ``weight`` is
+renormalized within each origin.
+"""
+
+import os
+import zlib
+
+import numpy as np
+
+from .genotype_sorting import standardize_genotypes
+
+SPIKED_ORIGIN = "spiked"
+
+SUBSAMPLE_KEYS = ("library_genotypes", "library_subsample",
+                  "library_subsample_seed", "library_subsample_depth")
+
+SUBSAMPLE_DEPTHS = ("full_library", "as_written")
+
+SUMMARY_COLUMNS = ["library_origin", "num_genotypes", "num_genotypes_kept",
+                   "num_sequences", "num_sequences_kept"]
+
+
+def has_subsample(config):
+    """True if ``config`` restricts the library (a list or a subsample)."""
+    return (config.get("library_genotypes") is not None
+            or bool(config.get("library_subsample")))
+
+
+def subsample_depth(config):
+    """The validated ``library_subsample_depth`` (default ``full_library``)."""
+    depth = config.get("library_subsample_depth")
+    if depth is None:
+        return "full_library"
+    if depth not in SUBSAMPLE_DEPTHS:
+        raise ValueError(f"library_subsample_depth must be one of "
+                         f"{list(SUBSAMPLE_DEPTHS)}, not {depth!r}.")
+    return depth
+
+
+def _resolve(path, base_dir):
+    path = str(path)
+    if os.path.isabs(path) or base_dir is None:
+        return path
+    return os.path.join(base_dir, path)
+
+
+def _read_genotype_list(path):
+    if not os.path.isfile(path):
+        raise FileNotFoundError(f"library_genotypes file not found: {path}")
+    with open(path) as fh:
+        lines = [ln.strip() for ln in fh]
+    lines = [ln for ln in lines if ln and not ln.startswith("#")]
+    if not lines:
+        raise ValueError(f"library_genotypes file {path} lists no genotypes.")
+    return set(str(g) for g in standardize_genotypes(lines))
+
+
+def _check_seed(seed):
+    if seed is None:
+        raise ValueError(
+            "library_subsample needs library_subsample_seed, an integer. It "
+            "is not taken from 'seed': tfs-configure-model reads the library "
+            "YAML without tfs-simulate's --seed, and both must draw the same "
+            "genotypes.")
+    if isinstance(seed, bool) or not isinstance(seed, (int, np.integer)) \
+            or seed < 0:
+        raise ValueError(f"library_subsample_seed must be a non-negative "
+                         f"integer, not {seed!r}.")
+    return int(seed)
+
+
+def _check_counts(spec, origins):
+    if not isinstance(spec, dict):
+        raise ValueError("library_subsample must map library origins to the "
+                         "number of genotypes to keep, e.g. "
+                         "{double-1-2: 1000}.")
+    out = {}
+    for origin, n in spec.items():
+        origin = str(origin)
+        if origin == SPIKED_ORIGIN:
+            raise ValueError("library_subsample cannot subsample the spiked "
+                             "origin; every spiked genotype is kept.")
+        if origin not in origins:
+            raise ValueError(f"library_subsample names origin '{origin}', "
+                             f"which is not in the library. Origins: "
+                             f"{sorted(origins)}.")
+        if isinstance(n, bool) or not isinstance(n, (int, np.integer)) \
+                or n < 1:
+            raise ValueError(f"library_subsample['{origin}'] must be an "
+                             f"integer >= 1, not {n!r}. To leave an origin "
+                             f"out, remove it from tile_combos.")
+        out[origin] = int(n)
+    return out
+
+
+def subsample_library_df(library_df, config, base_dir=None):
+    """
+    Apply the subsample keys of ``config`` to a library table.
+
+    Parameters
+    ----------
+    library_df : pandas.DataFrame
+        One row per (library_origin, genotype) with ``degeneracy`` and
+        ``weight``, as written by ``LibraryManager.build_library_df``.
+    config : dict
+        The library (or simulate) config; see the module docstring.
+    base_dir : str, optional
+        Directory a relative ``library_genotypes`` path is read against (the
+        YAML's own directory). None reads it against the working directory.
+
+    Returns
+    -------
+    library_df : pandas.DataFrame
+        The kept rows, ``weight`` renormalized within each origin. The input
+        unchanged (not a copy) when ``config`` has no subsample.
+    summary : pandas.DataFrame or None
+        One row per origin (``SUMMARY_COLUMNS``), or None with no subsample.
+    """
+    subsample_depth(config)  # validated even when unused
+    if not has_subsample(config):
+        return library_df, None
+
+    df = library_df.copy()
+    geno = df["genotype"].astype(str)
+    origin = df["library_origin"].astype(str)
+    origins = set(origin)
+
+    protected = set(geno[origin == SPIKED_ORIGIN]) | {"wt"}
+    keep = np.ones(len(df), dtype=bool)
+
+    list_path = config.get("library_genotypes")
+    if list_path is not None:
+        path = _resolve(list_path, base_dir)
+        listed = _read_genotype_list(path)
+        missing = sorted(listed - set(geno))
+        if missing:
+            raise ValueError(
+                f"{len(missing)} genotype(s) in library_genotypes ({path}) "
+                f"are not in the described library, e.g. {missing[:10]}. "
+                f"Check the residue numbering (first_amplicon_residue) and "
+                f"tile_combos.")
+        keep &= geno.isin(listed | protected).to_numpy()
+
+    counts = config.get("library_subsample")
+    if counts:
+        counts = _check_counts(counts, origins)
+        seed = _check_seed(config.get("library_subsample_seed"))
+        for o in sorted(counts):
+            in_o = (origin == o).to_numpy()
+            cand = sorted(set(geno[in_o & keep & ~geno.isin(protected)
+                                   .to_numpy()]))
+            n = counts[o]
+            if n > len(cand):
+                raise ValueError(
+                    f"library_subsample['{o}'] = {n}, but origin '{o}' has "
+                    f"only {len(cand)} genotypes to draw from (wt and spiked "
+                    f"genotypes are kept anyway and not counted).")
+            rng = np.random.default_rng([seed, zlib.crc32(o.encode())])
+            chosen = {cand[i] for i in rng.permutation(len(cand))[:n]}
+            keep[in_o] &= geno[in_o].isin(chosen | protected).to_numpy()
+
+    full = (df.assign(_o=origin)
+            .groupby("_o")
+            .agg(num_genotypes=("genotype", "size"),
+                 num_sequences=("degeneracy", "sum")))
+    out = df.loc[keep].copy()
+    kept = (out.assign(_o=out["library_origin"].astype(str))
+            .groupby("_o")
+            .agg(num_genotypes_kept=("genotype", "size"),
+                 num_sequences_kept=("degeneracy", "sum")))
+    summary = full.join(kept, how="left").fillna(0)
+    empty = sorted(summary.index[summary["num_genotypes_kept"] == 0])
+    if empty:
+        raise ValueError(
+            f"The library subsample leaves origin(s) {empty} with no "
+            f"genotypes. library_genotypes lists every genotype to keep (wt "
+            f"and spiked genotypes are added); list some from each origin in "
+            f"tile_combos, or remove the origin from tile_combos.")
+    summary = (summary.astype(int)
+               .rename_axis("library_origin")
+               .reset_index()[SUMMARY_COLUMNS])
+
+    out["weight"] = (out["degeneracy"]
+                     / out.groupby(out["library_origin"].astype(str),
+                                   observed=True)["degeneracy"]
+                     .transform("sum"))
+    return out.reset_index(drop=True), summary
+
+
+def build_library(config, base_dir=None):
+    """
+    The described library with the subsample of ``config`` applied.
+
+    Parameters
+    ----------
+    config : str or dict
+        Path to the library (or simulate) YAML, or its parsed contents.
+    base_dir : str, optional
+        Directory for a relative ``library_genotypes``. Default: the YAML's
+        directory when ``config`` is a path, else the working directory.
+
+    Returns
+    -------
+    library_df : pandas.DataFrame
+        As ``LibraryManager.build_library_df``, restricted to the subsample.
+    summary : pandas.DataFrame or None
+        Per-origin counts (see :func:`subsample_library_df`).
+    """
+    from tfscreen.util.io import read_yaml
+    from .library_manager import LibraryManager
+
+    if isinstance(config, (str, os.PathLike)) and base_dir is None:
+        base_dir = os.path.dirname(os.path.abspath(config))
+    config = read_yaml(config)
+    library_df = LibraryManager(config).build_library_df()
+    return subsample_library_df(library_df, config, base_dir=base_dir)
+
+
+def subsample_design(config, summary):
+    """
+    The experiment settings a subsampled library is simulated (and fit) with.
+
+    With ``library_subsample_depth: full_library`` (the default) the config's
+    ``library_mixture``, ``transform_sizes``, ``total_num_reads`` and
+    ``pcr_template_molecules`` describe the full described library, and each
+    kept sequence keeps the pool share, transformants, reads and PCR templates
+    it has there (the per-sequence rule of
+    ``library_design.scale_library_design``): each origin's mixture weight
+    and transformants are multiplied by its kept fraction of sequences, and
+    the reads and templates by the kept share of the pool. ``cfu0`` is not
+    changed, so tube totals (and OD600) stay those of the full experiment and
+    each kept genotype has ``1 / kept share`` times the cells.
+
+    With ``as_written`` the config's values apply to the subsampled library
+    as given: the subsample is the library.
+
+    Parameters
+    ----------
+    config : dict
+        The library or simulate config.
+    summary : pandas.DataFrame or None
+        From :func:`subsample_library_df`.
+
+    Returns
+    -------
+    dict
+        The keys to replace in ``config`` (only those present in it), plus
+        ``kept_pool_share``. Empty without a subsample or with ``as_written``.
+    """
+    if summary is None or subsample_depth(config) == "as_written":
+        return {}
+    frac = dict(zip(summary["library_origin"],
+                    summary["num_sequences_kept"] / summary["num_sequences"]))
+    out = {}
+    mixture = config.get("library_mixture")
+    share = None
+    if mixture is not None:
+        missing = sorted(set(frac) - set(map(str, mixture)))
+        if missing:
+            raise ValueError(f"library_mixture has no value for origin(s) "
+                             f"{missing}.")
+        new = {k: float(v) * frac.get(str(k), 1.0) for k, v in mixture.items()}
+        total = sum(float(v) for v in mixture.values())
+        share = sum(new.values()) / total if total > 0 else 1.0
+        out["library_mixture"] = new
+        out["kept_pool_share"] = share
+    sizes = config.get("transform_sizes")
+    if sizes is not None:
+        out["transform_sizes"] = {
+            k: max(1, int(round(float(v) * frac.get(str(k), 1.0))))
+            for k, v in sizes.items()}
+    if share is not None:
+        for key in ("total_num_reads", "pcr_template_molecules"):
+            if config.get(key) is not None:
+                out[key] = max(1, int(round(float(config[key]) * share)))
+    return out
+
+
+def format_subsample_summary(config, summary, design):
+    """Human-readable report of a subsample for the run log."""
+    lines = [f"Library subsample (library_subsample_depth: "
+             f"{subsample_depth(config)}"
+             + (f", seed {config['library_subsample_seed']}"
+                if config.get("library_subsample") else "")
+             + (f", list {config['library_genotypes']}"
+                if config.get("library_genotypes") is not None else "")
+             + "):"]
+    width = max(len("origin"), *(len(o) for o in summary["library_origin"]))
+    lines.append(f"  {'origin':<{width}}  {'genotypes':>10}  {'kept':>8}  "
+                 f"{'sequences':>10}  {'kept':>8}")
+    for row in summary.itertuples(index=False):
+        lines.append(f"  {row.library_origin:<{width}}  "
+                     f"{row.num_genotypes:>10}  {row.num_genotypes_kept:>8}  "
+                     f"{row.num_sequences:>10}  {row.num_sequences_kept:>8}")
+    if design:
+        share = design.get("kept_pool_share")
+        if share is not None:
+            lines.append(f"  kept share of the pool {share:.4g}: cells per "
+                         f"kept genotype x{1 / share:.3g} (cfu0 unchanged)")
+        for key in ("library_mixture", "transform_sizes", "total_num_reads",
+                    "pcr_template_molecules"):
+            if key in design:
+                val = design[key]
+                if isinstance(val, dict):
+                    val = {k: (float(f"{v:.6g}") if isinstance(v, float)
+                               else v) for k, v in val.items()}
+                lines.append(f"  {key}: {config.get(key)} -> {val}")
+    return "\n".join(lines)
+
+
+def subsample_record(config):
+    """The subsample keys of ``config`` for a provenance record (or None)."""
+    if not has_subsample(config):
+        return None
+    rec = {k: config.get(k) for k in SUBSAMPLE_KEYS if config.get(k) is not None}
+    if "library_genotypes" in rec:
+        rec["library_genotypes"] = os.path.basename(str(rec["library_genotypes"]))
+    rec["library_subsample_depth"] = subsample_depth(config)
+    return rec
+
+
+__all__ = ["SUBSAMPLE_KEYS", "SUBSAMPLE_DEPTHS", "build_library",
+           "subsample_library_df", "subsample_design",
+           "format_subsample_summary", "subsample_record", "has_subsample"]
diff --git a/src/tfscreen/process_raw/__init__.py b/src/tfscreen/process_raw/__init__.py
index 1c124ea8..c1b2d8f2 100644
--- a/src/tfscreen/process_raw/__init__.py
+++ b/src/tfscreen/process_raw/__init__.py
@@ -2,4 +2,3 @@
 from .counts_to_lncfu import counts_to_lncfu  # noqa: F401
 from .scripts.process_counts_cli import process_counts  # noqa: F401
 from .scripts.process_fastq_cli import process_fastq  # noqa: F401
-from .scripts.process_presplit_cli import process_presplit  # noqa: F401
diff --git a/src/tfscreen/process_raw/binding.py b/src/tfscreen/process_raw/binding.py
new file mode 100644
index 00000000..44b27c93
--- /dev/null
+++ b/src/tfscreen/process_raw/binding.py
@@ -0,0 +1,206 @@
+"""
+A lab binding table to a ``binding_df`` for tfs-configure-model.
+
+The lab's binding table has one row per measurement: a genotype, a protein
+concentration, a titrant (IPTG) concentration and the measured occupancy
+theta, plus the columns that say which biological replicate and which day
+the measurement belongs to. A ``binding_df`` has one row per genotype and
+titrant concentration at a single protein concentration: ``genotype``,
+``titrant_name``, ``titrant_conc`` (mM, the unit the growth table uses),
+``theta_obs`` and ``theta_std``.
+
+``binding_table`` makes one from the other:
+
+1. Drops rows with no theta (counted and reported).
+2. Converts both concentrations from the table's unit and rounds them to
+   ``sig_digits`` significant digits. The lab's concentrations carry
+   floating-point residue that differs between days (6.000000000000001e-08
+   against 6.000000000000002e-08), so without rounding the replicates of
+   one point would not line up.
+3. Keeps the rows at the requested protein concentration.
+4. Averages the measurements within each replicate (the ``replicate_cols``,
+   biological replicate and date by default), so technical repeats count
+   once.
+5. Averages the replicates of each (genotype, titrant concentration) for
+   ``theta_obs``.
+6. Sets ``theta_std`` to the standard error of that mean, from one replicate
+   SD pooled over every replicated point at this protein concentration
+   (``s_pooled / sqrt(n)``), or from a fixed per-replicate SD given as
+   ``theta_std``. Pooling is needed because most points have a single
+   replicate; it assumes the replicate scatter does not depend on genotype
+   or theta.
+
+Measurements of one replicate across titrant concentrations share that
+day's normalization, so their errors are correlated. The binding likelihood
+treats every row as independent; this is not corrected here.
+"""
+
+import numpy as np
+
+import tfscreen
+
+# Multiplier from each unit to molar.
+CONC_UNITS = {"M": 1.0, "mM": 1e-3, "uM": 1e-6, "nM": 1e-9}
+
+# The growth table's titrant unit.
+TITRANT_OUT_UNITS = "mM"
+
+BINDING_COLUMNS = ["genotype", "titrant_name", "titrant_conc",
+                   "theta_obs", "theta_std"]
+
+
+def _unit_factor(units, what):
+    if units not in CONC_UNITS:
+        raise ValueError(f"{what} '{units}' is not one of {sorted(CONC_UNITS)}")
+    return CONC_UNITS[units]
+
+
+def _round_sig(values, sig_digits):
+    """Round each value to sig_digits significant digits (0 stays 0)."""
+    values = np.asarray(values, dtype=float)
+    out = np.zeros_like(values)
+    nz = values != 0
+    mag = np.floor(np.log10(np.abs(values[nz])))
+    scale = 10.0 ** (sig_digits - 1 - mag)
+    out[nz] = np.round(values[nz] * scale) / scale
+    return out
+
+
+def binding_table(binding_wells,
+                  protein_conc,
+                  protein_conc_units="uM",
+                  genotype_col="genotype",
+                  protein_col="protein_dimer_conc_molar",
+                  titrant_col="iptg_conc_molar",
+                  theta_col="theta",
+                  conc_units="M",
+                  replicate_cols=("biorep", "date"),
+                  titrant_name="iptg",
+                  theta_std=None,
+                  sig_digits=6):
+    """
+    Average a lab binding table into a binding_df at one protein
+    concentration.
+
+    Parameters
+    ----------
+    binding_wells : pandas.DataFrame or str
+        The lab's table (or a path to it), one row per measurement.
+    protein_conc : float
+        The protein concentration to keep, in ``protein_conc_units``.
+    protein_conc_units : str
+        Unit of ``protein_conc``: 'M', 'mM', 'uM' or 'nM'.
+    genotype_col, protein_col, titrant_col, theta_col : str
+        Column names in ``binding_wells``.
+    conc_units : str
+        Unit of the protein and titrant columns.
+    replicate_cols : sequence of str
+        Columns that identify one replicate (measurements sharing them are
+        averaged first). Empty: every row is a replicate.
+    titrant_name : str
+        The ``titrant_name`` written to every row; must match the growth
+        table's.
+    theta_std : float, optional
+        A fixed per-replicate SD. None (default): pooled from the replicated
+        points.
+    sig_digits : int
+        Significant digits the concentrations are rounded to.
+
+    Returns
+    -------
+    binding_df : pandas.DataFrame
+        ``BINDING_COLUMNS``, sorted by genotype and titrant concentration.
+    summary : pandas.DataFrame
+        One row per binding_df row with ``n_replicates``, ``n_measurements``,
+        the point's own replicate SD (``replicate_sd``, NaN below two
+        replicates) and ``theta_std``.
+    info : dict
+        ``rows_in``, ``rows_no_theta``, ``rows_kept``, ``pooled_sd``,
+        ``pooled_dof``, ``replicate_sd_used`` and ``protein_conc_M``.
+    """
+
+    df = tfscreen.util.io.read_dataframe(binding_wells)
+    replicate_cols = list(replicate_cols)
+    required = [genotype_col, protein_col, titrant_col, theta_col] + replicate_cols
+    missing = [c for c in required if c not in df.columns]
+    if missing:
+        raise ValueError(f"binding table is missing column(s) {missing}; "
+                         f"it has {list(df.columns)}")
+    if sig_digits < 1:
+        raise ValueError("sig_digits must be at least 1")
+
+    to_M = _unit_factor(conc_units, "conc_units")
+    target_M = protein_conc * _unit_factor(protein_conc_units, "protein_conc_units")
+    if not np.isfinite(target_M) or target_M <= 0:
+        raise ValueError(f"protein_conc must be positive, got {protein_conc}")
+
+    info = {"rows_in": len(df)}
+    no_theta = df[theta_col].isna()
+    info["rows_no_theta"] = int(no_theta.sum())
+    df = df[~no_theta].copy()
+
+    df["_protein_M"] = _round_sig(df[protein_col].to_numpy() * to_M, sig_digits)
+    target_M = float(_round_sig([target_M], sig_digits)[0])
+    keep = np.isclose(df["_protein_M"], target_M, rtol=1e-9, atol=0)
+    if not keep.any():
+        have = sorted(set(df["_protein_M"]))
+        raise ValueError(
+            f"no measurements at protein {protein_conc} {protein_conc_units} "
+            f"({target_M:g} M); the table has (M) {have}")
+    df = df[keep].copy()
+    info["rows_kept"] = len(df)
+    info["protein_conc_M"] = target_M
+
+    titrant_M = _round_sig(df[titrant_col].to_numpy() * to_M, sig_digits)
+    df["titrant_conc"] = _round_sig(titrant_M / CONC_UNITS[TITRANT_OUT_UNITS],
+                                    sig_digits)
+    df["genotype"] = df[genotype_col].astype(str)
+
+    point = ["genotype", "titrant_conc"]
+    if not replicate_cols:
+        df["_row"] = np.arange(len(df))
+        replicate_cols = ["_row"]
+    reps = (df.groupby(point + replicate_cols, observed=True)[theta_col]
+            .agg(["mean", "size"]).reset_index())
+    g = reps.groupby(point, observed=True)
+    summary = g.agg(theta_obs=("mean", "mean"),
+                    n_replicates=("mean", "size"),
+                    n_measurements=("size", "sum"),
+                    replicate_sd=("mean", "std")).reset_index()
+
+    resid = reps["mean"] - g["mean"].transform("mean")
+    n = g["mean"].transform("size")
+    dof = int((summary["n_replicates"] - 1).sum())
+    info["pooled_dof"] = dof
+    info["pooled_sd"] = (float(np.sqrt((resid[n > 1] ** 2).sum() / dof))
+                         if dof > 0 else float("nan"))
+
+    if theta_std is not None:
+        if not np.isfinite(theta_std) or theta_std <= 0:
+            raise ValueError(f"theta_std must be positive, got {theta_std}")
+        sd = float(theta_std)
+    elif dof > 0 and info["pooled_sd"] > 0:
+        sd = info["pooled_sd"]
+    else:
+        raise ValueError(
+            "no replicated points at this protein concentration to pool a "
+            "replicate SD from; give theta_std")
+    info["replicate_sd_used"] = sd
+
+    summary["theta_std"] = sd / np.sqrt(summary["n_replicates"])
+    summary.insert(1, "titrant_name", titrant_name)
+    summary = summary.sort_values(point, kind="stable").reset_index(drop=True)
+    return summary[BINDING_COLUMNS].copy(), summary, info
+
+
+def format_info(info):
+    """A short printable report of binding_table's info dict."""
+    lines = [
+        f"binding rows: {info['rows_in']} in, {info['rows_no_theta']} without "
+        f"theta dropped, {info['rows_kept']} at protein "
+        f"{info['protein_conc_M']:g} M",
+        f"pooled replicate SD: {info['pooled_sd']:.4g} "
+        f"({info['pooled_dof']} dof)",
+        f"replicate SD used for theta_std: {info['replicate_sd_used']:.4g}",
+    ]
+    return "\n".join(lines)
diff --git a/src/tfscreen/process_raw/counts_to_lncfu.py b/src/tfscreen/process_raw/counts_to_lncfu.py
index 5d3b5040..9d9c9ffe 100644
--- a/src/tfscreen/process_raw/counts_to_lncfu.py
+++ b/src/tfscreen/process_raw/counts_to_lncfu.py
@@ -31,7 +31,7 @@ def get_sample_ln_cfu(sample_df: pd.DataFrame) -> pd.DataFrame:
                                    prefix="sample_")
     except ValueError as e:
         raise ValueError(
-            "sample_df must give each sample's cfu/mL and its uncertainty "
+            "sample_df must give each tube's total cells (cfu in the tube) and its uncertainty "
             "as 'sample_ln_cfu' with 'sample_ln_cfu_std' (or "
             "'sample_ln_cfu_var'), or as 'sample_cfu' with 'sample_cfu_std' "
             f"(or 'sample_cfu_var'). ({e})"
@@ -142,7 +142,7 @@ def _calculate_concentrations_and_variance(
         df: pd.DataFrame,
         total_counts_per_sample: pd.Series) -> pd.DataFrame:
     """
-    Calculate ln(cfu/mL) for each genotype and propagate the variance.
+    Calculate ln(cfu) (cells in the tube) for each genotype and propagate the variance.
 
     The calculation is done in log space: ln_cfu = ln(frequency) +
     sample_ln_cfu, with ln_cfu_var = var(frequency)/frequency**2 +
@@ -176,7 +176,7 @@ def _calculate_concentrations_and_variance(
     df['ln_cfu'] = ln_freq + df['sample_ln_cfu']
     df['ln_cfu_var'] = ln_freq_var + df['sample_ln_cfu_std']**2
 
-    # Linear-space genotype cfu/mL and its variance
+    # Linear-space genotype cfu and its variance
     df['cfu'] = df['frequency'] * np.exp(df['sample_ln_cfu'])
     df['cfu_var'] = (df['cfu']**2) * df['ln_cfu_var']
 
@@ -198,7 +198,7 @@ def counts_to_lncfu(
 
     This function takes sample metadata and genotype counts, merges them,
     filters out low-observation genotypes, and then calculates the frequency
-    and concentration (cfu/mL) for each genotype in each sample. It also
+    and number of cells (cfu in the tube) for each genotype in each sample. It also
     propagates experimental variance to estimate the variance of the
     genotype-specific concentrations.
 
@@ -207,7 +207,7 @@ def counts_to_lncfu(
     sample_df : pd.DataFrame
         DataFrame indexed by a unique 'sample' string. Must contain metadata
         for each sample. Requires a 'library' column plus the total
-        cfu/mL in each sample tube and its uncertainty, given either in log
+        cells (cfu) in each sample tube and its uncertainty, given either in log
         space ('sample_ln_cfu' with 'sample_ln_cfu_std' or
         'sample_ln_cfu_var') or in linear space ('sample_cfu' with
         'sample_cfu_std' or 'sample_cfu_var'). Log-space columns are used
@@ -227,7 +227,9 @@ def counts_to_lncfu(
     -------
     pd.DataFrame
         A new DataFrame containing the combined data, with calculated
-        frequencies, genotype-specific cfu/mL, and propagated variances.
+        frequencies, genotype-specific cfu, and propagated variances.
+        ``sample_reads`` is each sample's total reads (``__unknown__``
+        included, no pseudocounts), the depth the count likelihood uses.
         The DataFrame is sorted, and the 'genotype' column is cast as a
         categorical type.
 
@@ -275,6 +277,10 @@ def counts_to_lncfu(
                         .reindex(group['counts'].sum().index, fill_value=0))
     total_counts_per_sample = group['counts'].sum() + n_real_genotypes * pseudocount
 
+    # The tube's total reads, __unknown__ included and without pseudocounts:
+    # the depth the count likelihood (growth_likelihood='counts') uses.
+    sample_reads = group['counts'].sum()
+
     # Drop the unknown bucket now that it has contributed to the denominator;
     # everything downstream treats only real library genotypes.
     combined_df = combined_df[~is_unknown].copy()
@@ -294,10 +300,12 @@ def counts_to_lncfu(
                                      pseudocount,
                                      total_counts_per_sample)
 
-    # Calculate genotype cfu/mL and propagate variance
+    # Calculate genotype cfu and propagate variance
     final_df = _calculate_concentrations_and_variance(freq_df,
                                                       total_counts_per_sample)
 
+    final_df['sample_reads'] = final_df['sample'].map(sample_reads)
+
     # Define genotype as a categorical variable
     final_df = set_categorical_genotype(final_df,standardize=True,sort=False)
     
diff --git a/src/tfscreen/process_raw/od600.py b/src/tfscreen/process_raw/od600.py
new file mode 100644
index 00000000..e5613ed2
--- /dev/null
+++ b/src/tfscreen/process_raw/od600.py
@@ -0,0 +1,466 @@
+"""
+OD600-to-CFU calibration: fit, read, write and apply.
+
+Roadmap step 3 (planning/analysis-roadmap.md; constraints C10, C11). A tube's
+total population is estimated from its OD600 reading through a calibration
+that depends on the plate reader, plate, volume and strain, so every lab
+measures its own. This module holds the method; the calibration data stay
+with the lab. ``tfs-calibrate-od600`` (``scripts/calibrate_od600_cli.py``)
+runs it.
+
+Two experiments feed a calibration:
+
+- **Replicate readings of a dilution series** (``replicate_df``: columns
+  ``dilution`` and ``od600``, one row per reading). Each reading repeats the
+  whole handling step used in production (swirl, pipette into the plate,
+  read). The spread of repeated readings gives the reading noise, as a
+  fraction of the reading; the flattening of the series at high dilution
+  gives the detection threshold.
+- **Plate counts of cultures whose OD600 was read** (``plate_df``: columns
+  ``od600``, ``colonies``, ``dilution`` (total dilution factor before
+  plating), ``plated_volume_mL``, ``num_dilutions`` and ``plating_steps``).
+  CFU/mL is ``colonies * dilution / plated_volume_mL``; its variance
+  combines Poisson counting error (``1 / colonies``, relative) and one
+  pipetting error per dilution and plating step.
+
+A polynomial of CFU/mL in OD600 (the better-measured variable) is fit by
+weighted least squares. Its full parameter covariance is kept: the curve's
+error is one error shared by every tube calibrated with it (C11), so it
+cannot be treated as independent per tube, and callers get it separately
+from the reading noise (``cfu_per_mL_error_components``).
+
+The method follows the lab notebook it replaces, with one change: the
+notebook's CFU/mL omitted the division by the plated volume.
+"""
+
+import datetime
+
+import numpy as np
+import pandas as pd
+import yaml
+
+from tfscreen.util import read_yaml
+
+CALIBRATION_KIND = "od600_to_cfu_per_mL"
+
+REPLICATE_COLUMNS = ("dilution", "od600")
+PLATE_COLUMNS = ("od600", "colonies", "dilution", "plated_volume_mL",
+                 "num_dilutions", "plating_steps")
+
+# Keys of the lab notebook's format, refused with a pointer to the new one.
+_LEGACY_KEYS = ("A_CFU", "B_CFU", "C_CFU", "OD600_PCT_STD",
+                "OD600_MEAS_THRESHOLD")
+
+
+def _require_columns(df, columns, label):
+    missing = [c for c in columns if c not in df.columns]
+    if missing:
+        raise ValueError(f"{label} is missing column(s) {missing}; it needs "
+                         f"{list(columns)}.")
+
+
+# ---------------------------------------------------------------------------
+# The two experiments
+# ---------------------------------------------------------------------------
+
+def reading_noise(replicate_df):
+    """
+    Per-dilution summary of repeated OD600 readings.
+
+    Parameters
+    ----------
+    replicate_df : pandas.DataFrame
+        Columns ``dilution`` and ``od600``, one row per reading.
+
+    Returns
+    -------
+    pandas.DataFrame
+        One row per dilution, most concentrated first: ``dilution``, ``n``,
+        ``mean``, ``sd`` (population SD, as the lab notebook) and ``rel_sd``.
+    """
+    _require_columns(replicate_df, REPLICATE_COLUMNS, "replicate_df")
+    df = replicate_df[list(REPLICATE_COLUMNS)].dropna()
+    table = (df.groupby("dilution")["od600"]
+               .agg(n="size", mean="mean", sd=lambda x: np.std(x, ddof=0))
+               .reset_index()
+               .sort_values("dilution", ascending=False)
+               .reset_index(drop=True))
+    if len(table) < 2:
+        raise ValueError("replicate_df needs at least two dilutions.")
+    table["rel_sd"] = table["sd"] / table["mean"]
+    return table
+
+
+def detection_threshold(noise_table):
+    """
+    Midway between the mean readings of the two most dilute samples, where
+    the series has flattened onto the reader's floor (the lab notebook's
+    rule). ``noise_table`` is the output of :func:`reading_noise`.
+    """
+    lowest = noise_table.sort_values("dilution")["mean"].to_numpy()[:2]
+    return float(lowest.mean())
+
+
+def plate_counts_to_cfu(plate_df, pipette_rel_error=0.02):
+    """
+    CFU/mL and its SD for each plated culture.
+
+    ``cfu/mL = colonies * dilution / plated_volume_mL``, with relative
+    variance ``1 / colonies + (num_dilutions + plating_steps) *
+    pipette_rel_error^2`` (Poisson counting plus independent pipetting
+    errors of equal relative size).
+
+    Returns
+    -------
+    pandas.DataFrame
+        ``plate_df`` with ``cfu_per_mL`` and ``cfu_per_mL_std`` added.
+    """
+    _require_columns(plate_df, PLATE_COLUMNS, "plate_df")
+    df = plate_df.copy()
+    colonies = df["colonies"].to_numpy(dtype=float)
+    if np.any(colonies <= 0):
+        raise ValueError("plate_df has plates with no colonies; they carry "
+                         "no count and cannot be weighted.")
+    cfu = colonies * df["dilution"].to_numpy(dtype=float) \
+        / df["plated_volume_mL"].to_numpy(dtype=float)
+    steps = (df["num_dilutions"].to_numpy(dtype=float)
+             + df["plating_steps"].to_numpy(dtype=float))
+    rel_var = 1.0 / colonies + steps * pipette_rel_error ** 2
+    df["cfu_per_mL"] = cfu
+    df["cfu_per_mL_std"] = cfu * np.sqrt(rel_var)
+    return df
+
+
+def fit_polynomial(od600, cfu, cfu_std, degree=2):
+    """
+    Weighted least-squares polynomial of CFU/mL in OD600.
+
+    The covariance is scaled by the reduced chi-square, so misfit beyond the
+    stated errors widens it (the convention of ``scipy.optimize.curve_fit``
+    and of the lab notebook).
+
+    Returns
+    -------
+    coefficients : numpy.ndarray
+        ``c_0 .. c_degree``; ``cfu/mL = sum_i c_i OD600^i``.
+    covariance : numpy.ndarray
+    chi2_reduced : float
+    """
+    od = np.asarray(od600, dtype=float)
+    y = np.asarray(cfu, dtype=float)
+    w = 1.0 / np.asarray(cfu_std, dtype=float)
+    dof = len(y) - (degree + 1)
+    if dof < 1:
+        raise ValueError(f"A degree-{degree} polynomial needs at least "
+                         f"{degree + 2} plate counts; got {len(y)}.")
+    X = np.vander(od, degree + 1, increasing=True)
+    Xw = X * w[:, None]
+    yw = y * w
+    coef, *_ = np.linalg.lstsq(Xw, yw, rcond=None)
+    resid = yw - Xw @ coef
+    chi2_red = float(resid @ resid / dof)
+    covariance = np.linalg.inv(Xw.T @ Xw) * chi2_red
+    covariance = 0.5 * (covariance + covariance.T)
+    return coef, covariance, chi2_red
+
+
+def calibrate(replicate_df, plate_df, degree=2, pipette_rel_error=0.02,
+              threshold=None):
+    """
+    Build a calibration from the two experiments.
+
+    Parameters
+    ----------
+    replicate_df, plate_df : pandas.DataFrame
+        See the module docstring.
+    degree : int, optional
+        Polynomial degree (default 2).
+    pipette_rel_error : float, optional
+        Relative error of one pipetting step (default 0.02).
+    threshold : float, optional
+        Detection threshold; default from the dilution series
+        (:func:`detection_threshold`).
+
+    Returns
+    -------
+    calibration : dict
+        The calibration, as written by :func:`write_calibration`.
+    noise_table : pandas.DataFrame
+        :func:`reading_noise` output.
+    plate_table : pandas.DataFrame
+        The plate counts with CFU/mL, its SD, the fitted curve and the
+        standardized residual.
+    """
+    noise_table = reading_noise(replicate_df)
+    if threshold is None:
+        threshold = detection_threshold(noise_table)
+    plate_table = plate_counts_to_cfu(plate_df, pipette_rel_error)
+    od = plate_table["od600"].to_numpy(dtype=float)
+    coef, cov, chi2_red = fit_polynomial(od, plate_table["cfu_per_mL"],
+                                         plate_table["cfu_per_mL_std"],
+                                         degree)
+
+    calibration = {
+        "kind": CALIBRATION_KIND,
+        "degree": int(degree),
+        "coefficients": [float(c) for c in coef],
+        "covariance": [[float(v) for v in row] for row in cov],
+        "chi2_reduced": chi2_red,
+        "reading_rel_sd": float(noise_table["rel_sd"].max()),
+        "detection_threshold": float(threshold),
+        "calibrated_od600_range": [float(od.min()), float(od.max())],
+        "num_plate_counts": int(len(od)),
+        "pipette_rel_error": float(pipette_rel_error),
+    }
+    check_calibration(calibration)
+
+    fitted = np.vander(od, degree + 1, increasing=True) @ coef
+    plate_table["cfu_per_mL_fit"] = fitted
+    plate_table["z"] = (plate_table["cfu_per_mL"] - fitted) \
+        / plate_table["cfu_per_mL_std"]
+    return calibration, noise_table, plate_table
+
+
+# ---------------------------------------------------------------------------
+# The calibration file
+# ---------------------------------------------------------------------------
+
+def check_calibration(cal):
+    """
+    Validate a calibration dict; raise ValueError on a problem.
+
+    The curve must increase across ``[detection_threshold, top of the
+    calibrated range]`` so every reading maps to one total.
+    """
+    if any(k in cal for k in _LEGACY_KEYS) and "coefficients" not in cal:
+        raise ValueError(
+            "This is the lab notebook's calibration format (A_CFU, B_CFU, "
+            "...). Regenerate it with tfs-calibrate-od600, which keeps the "
+            "full parameter covariance and fixes the notebook's CFU/mL, "
+            "which did not divide by the plated volume.")
+    required = ("kind", "degree", "coefficients", "covariance",
+                "reading_rel_sd", "detection_threshold",
+                "calibrated_od600_range")
+    missing = [k for k in required if k not in cal]
+    if missing:
+        raise ValueError(f"OD600 calibration is missing {missing}.")
+    if cal["kind"] != CALIBRATION_KIND:
+        raise ValueError(f"Not an OD600 calibration (kind {cal['kind']!r}).")
+    n = int(cal["degree"]) + 1
+    coef = np.asarray(cal["coefficients"], dtype=float)
+    cov = np.asarray(cal["covariance"], dtype=float)
+    if coef.shape != (n,) or cov.shape != (n, n):
+        raise ValueError(f"A degree-{n - 1} calibration needs {n} "
+                         f"coefficients and an {n}x{n} covariance.")
+    lo, hi = (float(v) for v in cal["calibrated_od600_range"])
+    grid = np.linspace(min(float(cal["detection_threshold"]), lo), hi, 200)
+    if np.any(_poly_slope(coef, grid) <= 0):
+        raise ValueError("The calibration curve is not increasing between "
+                         "the detection threshold and the top of the "
+                         "calibrated range; it cannot be inverted.")
+
+
+def read_calibration(calibration):
+    """
+    Read and validate an OD600 calibration (a YAML path or the dict).
+
+    Returns
+    -------
+    dict
+        With ``coefficients`` and ``covariance`` as numpy arrays and the
+        other values as floats.
+    """
+    cal = dict(read_yaml(calibration))
+    check_calibration(cal)
+    cal["coefficients"] = np.asarray(cal["coefficients"], dtype=float)
+    cal["covariance"] = np.asarray(cal["covariance"], dtype=float)
+    cal["reading_rel_sd"] = float(cal["reading_rel_sd"])
+    cal["detection_threshold"] = float(cal["detection_threshold"])
+    cal["calibrated_od600_range"] = [float(v) for v in
+                                     cal["calibrated_od600_range"]]
+    return cal
+
+
+def write_calibration(calibration, path, source=None):
+    """
+    Write a calibration YAML, with a header saying what it is and how it was
+    made (``source``: optional dict recorded under ``source``).
+    """
+    cal = dict(calibration)
+    if source is not None:
+        cal["source"] = dict(source)
+    cal["written"] = datetime.date.today().isoformat()
+    header = (
+        "# OD600-to-CFU calibration, written by tfs-calibrate-od600.\n"
+        "# cfu/mL = sum_i coefficients[i] * OD600^i. covariance is the\n"
+        "# coefficients' (shared by every tube calibrated with this file);\n"
+        "# reading_rel_sd is one reading's SD as a fraction of the reading.\n"
+        "# Specific to one plate reader, plate, volume and strain.\n")
+    with open(path, "w") as fh:
+        fh.write(header)
+        yaml.safe_dump(cal, fh, sort_keys=False)
+
+
+# ---------------------------------------------------------------------------
+# Applying it
+# ---------------------------------------------------------------------------
+
+def as_calibration(cal):
+    """A calibration as read by :func:`read_calibration` (read it if not)."""
+    if isinstance(cal, dict) and isinstance(cal.get("coefficients"), np.ndarray):
+        return cal
+    return read_calibration(cal)
+
+
+def _poly(coef, od):
+    return np.vander(np.ravel(od), len(coef), increasing=True) @ coef
+
+
+def _poly_slope(coef, od):
+    powers = np.arange(1, len(coef))
+    od = np.ravel(od)
+    return (od[:, None] ** (powers - 1)) @ (coef[1:] * powers)
+
+
+def cfu_per_mL_error_components(od600, cal):
+    """
+    The two parts of the SD of a calibrated CFU/mL, kept apart.
+
+    Returns
+    -------
+    curve_sd : numpy.ndarray
+        From the coefficients' covariance, ``sqrt(J C J^T)`` with
+        ``J = [1, OD, OD^2, ...]``: one error shared by every tube
+        calibrated with this curve (C11).
+    reading_sd : numpy.ndarray
+        One reading's noise through the curve's slope, independent per
+        tube.
+    """
+    cal = as_calibration(cal)
+    od = np.atleast_1d(np.asarray(od600, dtype=float))
+    J = np.vander(od, len(cal["coefficients"]), increasing=True)
+    curve_var = np.einsum("ij,jk,ik->i", J, cal["covariance"], J)
+    reading_sd = np.abs(_poly_slope(cal["coefficients"], od)) \
+        * cal["reading_rel_sd"] * od
+    return np.sqrt(np.maximum(curve_var, 0.0)), reading_sd
+
+
+def od600_to_cfu_per_mL(od600, cal):
+    """
+    Forward calibration.
+
+    Readings below the detection threshold are evaluated at the threshold,
+    so they give an upper bound.
+
+    Returns
+    -------
+    cfu_per_mL, cfu_per_mL_std, detectable : numpy.ndarray
+        The SD combines the curve and reading parts
+        (:func:`cfu_per_mL_error_components`).
+    """
+    cal = as_calibration(cal)
+    od = np.atleast_1d(np.asarray(od600, dtype=float)).copy()
+    detectable = od >= cal["detection_threshold"]
+    od[~detectable] = cal["detection_threshold"]
+    cfu = _poly(cal["coefficients"], od)
+    curve_sd, reading_sd = cfu_per_mL_error_components(od, cal)
+    return cfu, np.sqrt(curve_sd ** 2 + reading_sd ** 2), detectable
+
+
+def in_calibrated_range(od600, cal):
+    """True where a reading is inside the plate counts' OD600 range."""
+    lo, hi = as_calibration(cal)["calibrated_od600_range"]
+    od = np.atleast_1d(np.asarray(od600, dtype=float))
+    return (od >= lo) & (od <= hi)
+
+
+def cfu_per_mL_to_od600(cfu_per_mL, cal, od_max=None):
+    """
+    Inverse calibration: the OD600 whose calibrated CFU/mL is the given one,
+    by bisection on ``[0, od_max]`` (default twice the top of the calibrated
+    range), where the curve must be increasing. Totals below the curve at
+    OD 0 map to 0; totals above it at ``od_max`` map to ``od_max``.
+    """
+    cal = as_calibration(cal)
+    coef = cal["coefficients"]
+    if od_max is None:
+        od_max = 2.0 * cal["calibrated_od600_range"][1]
+    y = np.atleast_1d(np.asarray(cfu_per_mL, dtype=float))
+    lo = np.zeros_like(y)
+    hi = np.full_like(y, float(od_max))
+    for _ in range(80):
+        mid = 0.5 * (lo + hi)
+        below = _poly(coef, mid) < y
+        lo = np.where(below, mid, lo)
+        hi = np.where(below, hi, mid)
+    od = 0.5 * (lo + hi)
+    od[y <= _poly(coef, np.zeros(1))[0]] = 0.0
+    return od
+
+
+TUBE_OD600_COLUMNS = ("sample", "od600")
+
+
+def tube_totals_from_od600(od600_df, cal, tube_volume_mL):
+    """
+    Each tube's total CFU from its OD600 reading.
+
+    Parameters
+    ----------
+    od600_df : pandas.DataFrame
+        One row per sequenced tube, with columns ``sample`` and ``od600``.
+    cal : dict or str
+        A ``tfs-calibrate-od600`` calibration, or the path to one.
+    tube_volume_mL : float
+        Culture volume of a tube in mL. The calibration gives CFU/mL; the
+        model's ``sample_cfu`` is the cells in the whole tube.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Indexed by ``sample``: ``od600``, ``sample_cfu``, ``sample_cfu_std``
+        (curve and reading errors combined), ``sample_cfu_curve_std`` (the
+        curve's error, shared by every tube read through this calibration;
+        C11), ``sample_cfu_reading_std`` (the reading's own noise, independent
+        per tube) and ``od600_in_calibrated_range``.
+
+    Raises
+    ------
+    ValueError
+        On missing columns, a duplicated or missing sample, a non-positive
+        volume, or a reading below the calibration's detection threshold.
+    """
+    _require_columns(od600_df, TUBE_OD600_COLUMNS, "OD600 table")
+    if not tube_volume_mL or tube_volume_mL <= 0:
+        raise ValueError("tube_volume_mL must be a positive volume in mL.")
+
+    df = od600_df[list(TUBE_OD600_COLUMNS)].copy()
+    df["sample"] = df["sample"].astype(str)
+    dups = df["sample"][df["sample"].duplicated()].unique().tolist()
+    if dups:
+        raise ValueError(f"OD600 table has more than one reading for: {dups}")
+    od = pd.to_numeric(df["od600"], errors="coerce").to_numpy(dtype=float)
+    if np.any(~np.isfinite(od)):
+        bad = df["sample"][~np.isfinite(od)].tolist()
+        raise ValueError(f"OD600 table has no numeric reading for: {bad}")
+
+    cal = as_calibration(cal)
+    cfu, cfu_sd, detectable = od600_to_cfu_per_mL(od, cal)
+    if not np.all(detectable):
+        bad = df["sample"][~detectable].tolist()
+        raise ValueError(
+            f"OD600 below the calibration's detection threshold "
+            f"({cal['detection_threshold']:g}) for {bad}. Their totals are "
+            "only upper bounds; drop these tubes from the tube table."
+        )
+    curve_sd, reading_sd = cfu_per_mL_error_components(od, cal)
+
+    out = pd.DataFrame({
+        "od600": od,
+        "sample_cfu": cfu * tube_volume_mL,
+        "sample_cfu_std": cfu_sd * tube_volume_mL,
+        "sample_cfu_curve_std": curve_sd * tube_volume_mL,
+        "sample_cfu_reading_std": reading_sd * tube_volume_mL,
+        "od600_in_calibrated_range": in_calibrated_range(od, cal),
+    }, index=pd.Index(df["sample"].to_numpy(), name="sample"))
+    return out
diff --git a/src/tfscreen/process_raw/scripts/calibrate_od600_cli.py b/src/tfscreen/process_raw/scripts/calibrate_od600_cli.py
new file mode 100644
index 00000000..8e49e06d
--- /dev/null
+++ b/src/tfscreen/process_raw/scripts/calibrate_od600_cli.py
@@ -0,0 +1,133 @@
+"""
+tfs-calibrate-od600: fit an OD600-to-CFU calibration from a lab's own data.
+
+See ``tfscreen.process_raw.od600`` for the method and the input formats.
+"""
+
+import os
+
+import numpy as np
+
+from tfscreen.process_raw import od600 as od600_cal
+from tfscreen.util.cli import generalized_main
+from tfscreen.util.io import read_dataframe
+
+
+def _plot(calibration, noise_table, plate_table, pdf_file):
+    """Dilution series, fitted curve, and the calibrated CFU's relative SD."""
+    from matplotlib import pyplot as plt
+    from tfscreen.plot import default_styles  # noqa: F401 (applies rcParams)
+
+    cal = od600_cal.read_calibration(calibration)
+    fig, ax = plt.subplots(1, 3, figsize=(15, 4.5))
+
+    ax[0].errorbar(noise_table["dilution"], noise_table["mean"],
+                   yerr=noise_table["sd"], fmt="o", mfc="none", capsize=3)
+    ax[0].axhline(cal["detection_threshold"], ls="--", color="gray")
+    ax[0].set_xscale("log")
+    ax[0].set_yscale("log")
+    ax[0].set_xlabel("dilution")
+    ax[0].set_ylabel("OD600 (mean +/- SD)")
+
+    lo, hi = cal["calibrated_od600_range"]
+    grid = np.linspace(cal["detection_threshold"], hi * 1.05, 200)
+    cfu, sd, _ = od600_cal.od600_to_cfu_per_mL(grid, cal)
+    ax[1].fill_between(grid, cfu - sd, cfu + sd, color="lightgray")
+    ax[1].plot(grid, cfu, color="black")
+    ax[1].errorbar(plate_table["od600"], plate_table["cfu_per_mL"],
+                   yerr=plate_table["cfu_per_mL_std"], fmt="o", mfc="none",
+                   capsize=3, color="tab:red")
+    ax[1].axvline(cal["detection_threshold"], ls="--", color="gray")
+    ax[1].set_xlabel("OD600")
+    ax[1].set_ylabel("CFU/mL")
+
+    curve_sd, reading_sd = od600_cal.cfu_per_mL_error_components(grid, cal)
+    ax[2].plot(grid, curve_sd / cfu, label="curve (shared by all tubes)")
+    ax[2].plot(grid, reading_sd / cfu, label="one reading")
+    ax[2].plot(grid, sd / cfu, color="black", label="total")
+    ax[2].axvspan(lo, hi, color="lightgray", alpha=0.4, lw=0)
+    ax[2].set_xlabel("OD600")
+    ax[2].set_ylabel("SD / CFU")
+    ax[2].legend(frameon=False)
+
+    fig.tight_layout()
+    fig.savefig(pdf_file)
+    plt.close(fig)
+
+
+def calibrate_od600(replicate_file,
+                    plate_count_file,
+                    out_prefix="tfs_od600",
+                    degree=2,
+                    pipette_rel_error=0.02,
+                    detection_threshold=None):
+    """
+    Fit an OD600-to-CFU/mL calibration and write it for the pipeline.
+
+    A calibration depends on the plate reader, plate, volume and strain, so
+    each lab makes its own from two small experiments done with the same
+    handling as production samples.
+
+    Parameters
+    ----------
+    replicate_file : str
+        CSV/TSV/Excel of repeated OD600 readings of a dilution series, one
+        row per reading: ``dilution`` (relative to the undiluted culture),
+        ``od600``. Gives the reading noise (the largest relative SD across
+        dilutions) and the detection threshold (midway between the mean
+        readings of the two most dilute samples).
+    plate_count_file : str
+        CSV/TSV/Excel of plate counts of cultures whose OD600 was read, one
+        row per plate: ``od600``, ``colonies``, ``dilution`` (total dilution
+        factor before plating), ``plated_volume_mL``, ``num_dilutions`` and
+        ``plating_steps`` (for the pipetting error).
+    out_prefix : str, optional
+        Writes ``{out_prefix}.yaml`` (the calibration, read by the rest of
+        the pipeline), ``{out_prefix}_replicates.csv`` (reading noise by
+        dilution), ``{out_prefix}_plate_counts.csv`` (CFU/mL, its SD, the
+        fitted curve and the standardized residual) and ``{out_prefix}.pdf``.
+    degree : int, optional
+        Degree of the polynomial of CFU/mL in OD600 (default 2).
+    pipette_rel_error : float, optional
+        Relative error of one pipetting step (default 0.02).
+    detection_threshold : float, optional
+        Override the detection threshold estimated from the dilution series.
+    """
+    replicate_df = read_dataframe(replicate_file)
+    plate_df = read_dataframe(plate_count_file)
+
+    calibration, noise_table, plate_table = od600_cal.calibrate(
+        replicate_df, plate_df, degree=degree,
+        pipette_rel_error=pipette_rel_error, threshold=detection_threshold)
+
+    out_dir = os.path.dirname(os.path.abspath(out_prefix))
+    os.makedirs(out_dir, exist_ok=True)
+
+    yaml_file = f"{out_prefix}.yaml"
+    od600_cal.write_calibration(
+        calibration, yaml_file,
+        source={"replicate_file": os.path.basename(replicate_file),
+                "plate_count_file": os.path.basename(plate_count_file)})
+    noise_table.to_csv(f"{out_prefix}_replicates.csv", index=False)
+    plate_table.to_csv(f"{out_prefix}_plate_counts.csv", index=False)
+    _plot(calibration, noise_table, plate_table, f"{out_prefix}.pdf")
+
+    coef = ", ".join(f"{c:.4g}" for c in calibration["coefficients"])
+    lo, hi = calibration["calibrated_od600_range"]
+    print(f"cfu/mL = polynomial in OD600 with coefficients [{coef}] "
+          f"(reduced chi2 {calibration['chi2_reduced']:.3g}, "
+          f"{calibration['num_plate_counts']} plates)")
+    print(f"reading noise {100 * calibration['reading_rel_sd']:.2g}% of the "
+          f"reading; detection threshold {calibration['detection_threshold']:.4g}; "
+          f"calibrated OD600 range {lo:.3g}-{hi:.3g}")
+    print(f"Wrote {yaml_file}")
+    return calibration
+
+
+def main():
+    generalized_main(calibrate_od600,
+                     manual_arg_types={"detection_threshold": float})
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tfscreen/process_raw/scripts/check_tubes_cli.py b/src/tfscreen/process_raw/scripts/check_tubes_cli.py
new file mode 100644
index 00000000..c585e0ee
--- /dev/null
+++ b/src/tfscreen/process_raw/scripts/check_tubes_cli.py
@@ -0,0 +1,99 @@
+"""
+``tfs-check-tubes``: flag selection tubes whose genotype composition
+disagrees with their titrant label (a likely tube or sample-sheet swap).
+
+See ``tfscreen.process_raw.tube_qc`` for the method.
+"""
+
+import pandas as pd
+
+from tfscreen.process_raw.tube_qc import (
+    PANEL_COLUMNS,
+    ROW_COLUMNS,
+    score_tubes,
+    score_table,
+)
+from tfscreen.util.cli import generalized_main
+from tfscreen.util.io import read_dataframe
+
+
+def check_tubes(growth_df,
+                out_prefix="tfs_tube_qc",
+                min_counts=1,
+                min_genotypes=10,
+                flag_z=4.0):
+    """
+    Score every tube's apparent titrant from its genotype composition and
+    flag tubes that are out of order within their row.
+
+    Within each library x condition_pre x condition_sel x titrant_name
+    panel, each tube's log-ratio composition has its row (replicate x t_pre
+    x t_sel) effect removed by a robust fit (medians, with a per-row scale
+    since the titrant's effect grows with time) and is projected on the
+    axis from the lowest concentration's composition (score 0) to the
+    highest's (score 1). Tube totals and read depth cancel, so OD600 and depth errors
+    do not enter. A tube is flagged when its score is out of order with the
+    rest of its row (leave-one-out isotonic fit) or far from its label's
+    expected score, by more than ``flag_z`` noise SDs; rows of fewer than
+    three tubes are not flagged. Rows missing some concentrations are
+    handled without the shift of centering a row on itself.
+
+    Writes ``{out_prefix}.csv``, one row per tube (``score``, ``expected``,
+    ``order_z``, ``deviation_z``, ``flagged`` and more), and prints each
+    panel's scores by row and concentration and every tube of the rows
+    with a flagged tube. Once a row is down to two unflagged tubes neither
+    is flagged, so read the whole row: a large ``order_z`` left there is
+    also suspect.
+
+    Parameters
+    ----------
+    growth_df : str
+        Growth table from tfs-process-counts (any long-form table with
+        ``genotype``, ``counts``, ``replicate`` and ``titrant_conc``).
+    out_prefix : str
+        Output prefix.
+    min_counts : int
+        Use a genotype only if it has at least this many reads in every tube
+        of its panel.
+    min_genotypes : int
+        Fewest usable genotypes a panel may have.
+    flag_z : float
+        Flag a tube whose order violation or deviation from its expected
+        score exceeds this many noise SDs.
+    """
+    df = read_dataframe(growth_df)
+    scores = score_tubes(df, min_counts=min_counts,
+                         min_genotypes=min_genotypes, flag_z=flag_z)
+
+    with pd.option_context("display.width", 200,
+                           "display.max_columns", 100):
+        print("Apparent titrant score (0 = lowest concentration, "
+              "1 = highest), by row and titrant_conc:")
+        print(score_table(scores).round(2).to_string())
+        n_flagged = int(scores["flagged"].sum())
+        if n_flagged:
+            # Show whole rows: a row's last two tubes are never flagged.
+            keys = [c for c in PANEL_COLUMNS + ROW_COLUMNS
+                    if c in scores.columns]
+            bad = scores.groupby(keys)["flagged"].transform("any")
+            rows = scores[bad]
+            cols = [c for c in rows.columns
+                    if c not in ("n_genotypes", "sigma", "row_scale",
+                                 "deviation_sd")]
+            stats = ["score", "expected", "deviation", "order_violation",
+                     "order_z", "deviation_z"]
+            print(f"\n{n_flagged} flagged tube(s); every tube of their rows:")
+            print(rows[cols].round({c: 3 for c in stats})
+                  .to_string(index=False))
+        else:
+            print("\nNo tubes flagged.")
+
+    scores.to_csv(f"{out_prefix}.csv", index=False)
+
+
+def main():
+    generalized_main(check_tubes)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tfscreen/process_raw/scripts/correct_tubes_cli.py b/src/tfscreen/process_raw/scripts/correct_tubes_cli.py
new file mode 100644
index 00000000..cf30773a
--- /dev/null
+++ b/src/tfscreen/process_raw/scripts/correct_tubes_cli.py
@@ -0,0 +1,64 @@
+"""
+tfs-correct-tubes: apply documented corrections to a tube table before
+tfs-process-counts.
+
+See ``tfscreen.process_raw.tube_corrections`` for the corrections file and
+why a swap moves only the design columns.
+"""
+
+from tfscreen.process_raw.tube_corrections import apply_corrections
+from tfscreen.util.cli import generalized_main
+
+
+def correct_tubes(tube_table_file,
+                  corrections_file,
+                  out_prefix="tfs_tubes_corrected"):
+    """
+    Apply documented corrections to a tube table, leaving the original as is.
+
+    A correction is an entry in a YAML corrections file kept with the
+    experiment's data: an ``id``, an ``action``, the ``tubes`` it names (by
+    the tube table's ``sample``), a ``reason``, who decided it (``by``) and
+    when (``date``), and optional ``evidence`` such as a QC table. Actions:
+    ``swap`` (two tubes of one library exchange their design columns),
+    ``relabel`` (one tube gets the design values in ``set``) and ``exclude``
+    (tubes left out). The design columns are ``replicate``,
+    ``condition_pre``, ``t_pre``, ``condition_sel``, ``t_sel``,
+    ``titrant_name`` and ``titrant_conc``. Every other column (the sample
+    name and so its count file, OD600, tube totals, barcodes) stays with the
+    physical sample it was measured on, which is right whether the tubes
+    were swapped at the bench or the sample sheet was. ``library`` never
+    changes, and a swap across libraries is refused.
+
+    Writes ``{out_prefix}.csv`` (the corrected tube table, with a
+    ``correction_id`` column, for tfs-process-counts),
+    ``{out_prefix}_changes.csv`` (one row per changed value or excluded
+    tube, with its original and corrected value) and
+    ``{out_prefix}_provenance.json`` (the corrections and the SHA-256 of
+    the inputs, the evidence files and the output).
+
+    Fails on an unknown tube, a tube named in two corrections, a swap
+    across libraries or one that changes nothing, a relabel of a column that
+    is not a design column, a correction that leaves two tubes with the same
+    library and design, a missing or empty reason, by or date, or an output
+    that would overwrite an input.
+
+    Parameters
+    ----------
+    tube_table_file : str
+        The original tube table (CSV, TSV or Excel), in tfs-process-counts
+        format.
+    corrections_file : str
+        YAML corrections file.
+    out_prefix : str
+        Output prefix.
+    """
+    apply_corrections(tube_table_file, corrections_file, out_prefix=out_prefix)
+
+
+def main():
+    return generalized_main(correct_tubes)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tfscreen/process_raw/scripts/process_binding_cli.py b/src/tfscreen/process_raw/scripts/process_binding_cli.py
new file mode 100644
index 00000000..a324b9e9
--- /dev/null
+++ b/src/tfscreen/process_raw/scripts/process_binding_cli.py
@@ -0,0 +1,102 @@
+"""
+tfs-process-binding: a lab binding table to a binding_df for
+tfs-configure-model.
+
+See ``tfscreen.process_raw.binding`` for the averaging and theta_std.
+"""
+
+import os
+
+from tfscreen.process_raw.binding import binding_table, format_info
+from tfscreen.util.cli import generalized_main
+
+
+def process_binding(binding_file,
+                    protein_conc=2.0,
+                    protein_conc_units="uM",
+                    genotype_col="genotype",
+                    protein_col="protein_dimer_conc_molar",
+                    titrant_col="iptg_conc_molar",
+                    theta_col="theta",
+                    conc_units="M",
+                    replicate_cols="biorep,date",
+                    titrant_name="iptg",
+                    theta_std=None,
+                    sig_digits=6,
+                    out_prefix="tfs_binding"):
+    """
+    Average a lab binding table into a binding_df at one protein
+    concentration.
+
+    The table has one row per measurement (genotype, protein and titrant
+    concentrations, theta, and the columns naming its replicate). Rows
+    without theta are dropped; concentrations are rounded to sig_digits
+    significant digits so replicates from different days line up;
+    measurements within a replicate are averaged, then replicates per
+    genotype and titrant concentration (theta_obs). theta_std is the
+    standard error of that mean from one replicate SD pooled over every
+    replicated point at this protein concentration, or from --theta_std.
+    The titrant is written in mM.
+
+    Writes {out_prefix}.csv (genotype, titrant_name, titrant_conc,
+    theta_obs, theta_std; for tfs-configure-model --binding_df) and
+    {out_prefix}_summary.csv (replicate and measurement counts and each
+    point's own replicate SD).
+
+    Parameters
+    ----------
+    binding_file : str
+        The lab's binding table (CSV, TSV or Excel).
+    protein_conc : float
+        Protein concentration to keep, in protein_conc_units.
+    protein_conc_units : str
+        Unit of protein_conc: M, mM, uM or nM.
+    genotype_col : str
+        Genotype column.
+    protein_col : str
+        Protein concentration column.
+    titrant_col : str
+        Titrant concentration column.
+    theta_col : str
+        Measured theta column.
+    conc_units : str
+        Unit of the protein and titrant columns: M, mM, uM or nM.
+    replicate_cols : str
+        Comma-separated columns that identify one replicate; empty for one
+        replicate per row.
+    titrant_name : str
+        titrant_name for every row; must match the growth table's.
+    theta_std : float, optional
+        Fixed per-replicate SD instead of the pooled one.
+    sig_digits : int
+        Significant digits the concentrations are rounded to.
+    out_prefix : str
+        Output prefix.
+    """
+    reps = [c.strip() for c in replicate_cols.split(",") if c.strip()]
+    df, summary, info = binding_table(
+        binding_file, protein_conc,
+        protein_conc_units=protein_conc_units,
+        genotype_col=genotype_col, protein_col=protein_col,
+        titrant_col=titrant_col, theta_col=theta_col,
+        conc_units=conc_units, replicate_cols=reps,
+        titrant_name=titrant_name, theta_std=theta_std,
+        sig_digits=sig_digits)
+
+    out = f"{out_prefix}.csv"
+    if os.path.abspath(out) == os.path.abspath(binding_file):
+        raise ValueError(f"{out} would overwrite the input")
+    print(format_info(info), flush=True)
+    print(f"{len(df)} points, {df['genotype'].nunique()} genotypes", flush=True)
+    df.to_csv(out, index=False)
+    summary.to_csv(f"{out_prefix}_summary.csv", index=False)
+    print(f"wrote {out} and {out_prefix}_summary.csv", flush=True)
+
+
+def main():
+    return generalized_main(process_binding,
+                            manual_arg_types={"theta_std": float})
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tfscreen/process_raw/scripts/process_counts_cli.py b/src/tfscreen/process_raw/scripts/process_counts_cli.py
index 74b962e6..a27bdec1 100644
--- a/src/tfscreen/process_raw/scripts/process_counts_cli.py
+++ b/src/tfscreen/process_raw/scripts/process_counts_cli.py
@@ -1,80 +1,206 @@
+"""
+``tfs-process-counts``: per-tube genotype counts to the growth (or presplit)
+table that ``tfs-configure-model`` reads.
+"""
+
+
 from tfscreen.process_raw import counts_to_lncfu
 from tfscreen.process_raw.counts_to_lncfu import get_sample_ln_cfu
 from tfscreen.process_raw._counts_io import _prep_sample_df, _aggregate_counts
+from tfscreen.process_raw.od600 import tube_totals_from_od600
 from tfscreen.util.cli import generalized_main
+from tfscreen.util.dataframe import check_columns, get_scaled_cfu
+from tfscreen.util.io import read_dataframe
+
+# Tube-total columns a tube table may supply instead of OD600.
+_TOTAL_COLUMNS = ("sample_cfu", "sample_cfu_std", "sample_cfu_var",
+                  "sample_ln_cfu", "sample_ln_cfu_std", "sample_ln_cfu_var")
+
+PRESPLIT_COLUMNS = ["library", "replicate", "condition_pre", "genotype",
+                    "ln_cfu", "ln_cfu_std"]
+
+
+def _read_tube_table(sample_file):
+    """The tube table as a DataFrame with a string ``sample`` column."""
+    df = read_dataframe(sample_file)
+    if df.index.name == "sample":
+        df = df.reset_index()
+    check_columns(df, required_columns=["sample"])
+    df = df.copy()
+    df["sample"] = df["sample"].astype(str)
+    return df
+
+
+def add_tube_totals(sample_df, od600_file=None, od600_calibration_file=None,
+                    tube_volume_mL=None):
+    """
+    Give every tube its total CFU, from OD600 or as supplied.
+
+    With an OD600 table (``od600_file``, or an ``od600`` column in the tube
+    table) and a calibration, each tube's ``sample_cfu`` and its SD come from
+    ``tube_totals_from_od600``. Otherwise the tube table must already carry
+    the totals (``sample_cfu`` + ``sample_cfu_std``, or the ``sample_ln_cfu``
+    forms; see ``counts_to_lncfu``).
+
+    Raises
+    ------
+    ValueError
+        If OD600 is given without a calibration or volume (or the reverse),
+        if the tube table supplies totals as well as OD600, or if a tube has
+        no OD600 reading.
+    """
+    sample_df = sample_df.copy()
+
+    od600_df = None
+    if od600_file is not None:
+        od600_df = read_dataframe(od600_file)
+        if od600_df.index.name == "sample":
+            od600_df = od600_df.reset_index()
+        if "od600" in sample_df.columns:
+            raise ValueError("OD600 is given twice: as od600_file and as an "
+                             "'od600' column of the tube table. Use one.")
+    elif "od600" in sample_df.columns:
+        od600_df = sample_df[["sample", "od600"]]
+
+    if od600_df is None:
+        if od600_calibration_file is not None:
+            raise ValueError("od600_calibration_file was given, but there is no "
+                             "OD600: pass od600_file or add an 'od600' column "
+                             "to the tube table.")
+        return sample_df
 
-from typing import Union
+    if od600_calibration_file is None:
+        raise ValueError("OD600 needs an OD600 calibration "
+                         "(od600_calibration_file, made by tfs-calibrate-od600).")
+    if tube_volume_mL is None:
+        raise ValueError("OD600 needs the culture volume of a tube "
+                         "(tube_volume_mL): the calibration gives CFU/mL and "
+                         "the model wants the cells in the tube.")
+    supplied = [c for c in _TOTAL_COLUMNS if c in sample_df.columns]
+    if supplied:
+        raise ValueError(f"The tube table supplies tube totals {supplied} and "
+                         "OD600 is also given. Use one or the other.")
 
-import pandas as pd
+    totals = tube_totals_from_od600(od600_df, od600_calibration_file, tube_volume_mL)
+    missing = sorted(set(sample_df["sample"]) - set(totals.index))
+    if missing:
+        raise ValueError(f"No OD600 reading for tube(s): {missing}")
+    if "od600" in sample_df.columns:
+        sample_df = sample_df.drop(columns="od600")
+    return sample_df.merge(totals, left_on="sample", right_index=True,
+                           how="left")
 
 
-def process_counts(
-    sample_df: Union[pd.DataFrame, str],
-    counts_csv_path: str,
-    output_file: str,
-    counts_glob_prefix: str="counts",
-    min_genotype_obs: int=10,
-    pseudocount: int=1,
-    verbose: bool = True):
+def process_counts(sample_file,
+                   counts_dir,
+                   out_prefix=None,
+                   od600_file=None,
+                   od600_calibration_file=None,
+                   tube_volume_mL=None,
+                   presplit=False,
+                   counts_glob_prefix="counts",
+                   min_genotype_obs=10,
+                   pseudocount=1,
+                   verbose=True):
     """
-    Convert per-sample genotype count CSVs into a single ln_cfu DataFrame.
+    Turn per-tube genotype counts into the table tfs-configure-model reads.
 
-    Reads sample metadata from sample_df, matches each sample to a counts
-    CSV file in counts_csv_path, aggregates all counts, converts to ln(CFU)
-    via counts_to_lncfu, and writes the result to output_file.
+    The tube table has one row per sequenced tube: a unique ``sample`` name,
+    its ``library``, and the design columns (``replicate``, ``condition_pre``,
+    ``t_pre``, ``condition_sel``, ``t_sel``, ``titrant_name``,
+    ``titrant_conc``), which are carried through to the output. Every library
+    is processed in one call; genotypes are filtered and filled in within
+    each library.
+
+    Each tube needs its total CFU. Give its OD600 (``--od600_file``, or an
+    ``od600`` column in the tube table) with ``--od600_calibration_file`` and
+    ``--tube_volume_mL``, and the totals and their SDs are computed here:
+    ``sample_cfu_curve_std`` is the calibration curve's error, shared by every
+    tube, and ``sample_cfu_reading_std`` is the reading's own. Or supply them
+    in the tube table as ``sample_cfu`` and ``sample_cfu_std``.
+
+    Writes ``{out_prefix}.csv``. Without ``--presplit`` it is the growth table
+    for ``tfs-configure-model --growth_df``: one row per genotype and tube
+    with ``counts``, ``sample_reads``, ``frequency``, ``ln_cfu`` and
+    ``ln_cfu_var``. With ``--presplit`` it is the presplit table for
+    ``--presplit_df`` (``library``, ``replicate``, ``condition_pre``,
+    ``genotype``, ``ln_cfu``, ``ln_cfu_std``).
 
     Parameters
     ----------
-    sample_df : str or pandas.DataFrame
-        Path to (or pre-loaded) sample metadata CSV.  Must contain a unique
-        'sample' column (used as the index) plus the total CFU in each
-        sample tube and its uncertainty, as 'sample_ln_cfu' and
-        'sample_ln_cfu_std' (or 'sample_ln_cfu_var'), or as 'sample_cfu'
-        and 'sample_cfu_std' (or 'sample_cfu_var'). See counts_to_lncfu.
-    counts_csv_path : str
-        Directory containing per-sample count CSV files.  Each file must
-        match the glob pattern ``{counts_glob_prefix}*{sample}*.csv``.
-    output_file : str
-        Path to write the output ln_cfu CSV (passed directly to
-        tfs-fit-model as the growth data).
-    counts_glob_prefix : str, optional
-        Prefix used when globbing for count files in counts_csv_path.
-        Default 'counts'.
-    min_genotype_obs : int, optional
-        Minimum total count across samples for a genotype to be retained.
-        Genotypes below this threshold are dropped before the ln_cfu
-        calculation.  Default 10.
-    pseudocount : int, optional
-        Pseudocount added to each genotype count before the log transform,
-        to avoid log(0).  Default 1.
-    verbose : bool, optional
-        If True, print a summary of matched samples and file paths.
-        Default True.
+    sample_file : str
+        Tube table (CSV, TSV or Excel), one row per sequenced tube.
+    counts_dir : str
+        Directory of tfs-process-fastq count files. Each tube's file must be
+        the only one matching ``{counts_glob_prefix}*{sample}*.csv``.
+    out_prefix : str, optional
+        Output prefix. Default tfs_growth, or tfs_presplit with --presplit.
+    od600_file : str, optional
+        Table of one OD600 reading per tube (columns ``sample``, ``od600``).
+    od600_calibration_file : str, optional
+        OD600-to-CFU/mL calibration written by tfs-calibrate-od600.
+    tube_volume_mL : float, optional
+        Culture volume of one tube in mL. Required with OD600.
+    presplit : bool
+        Write the presplit table instead of the growth table. The tube table
+        then needs ``replicate`` and ``condition_pre``.
+    counts_glob_prefix : str
+        File-name prefix of the count files.
+    min_genotype_obs : int
+        Drop genotypes with fewer total reads than this within a library.
+    pseudocount : int
+        Added to every genotype's count before taking frequencies.
+    verbose : bool
+        Print which count file was matched to each tube.
     """
 
-    # After this call, sample_df will be indexed by sample name and have
-    # a column 'obs_file' that points to the csv file to read.
+    if out_prefix is None:
+        out_prefix = "tfs_presplit" if presplit else "tfs_growth"
+
+    sample_df = _read_tube_table(sample_file)
+    if presplit:
+        check_columns(sample_df, required_columns=["replicate", "condition_pre"])
+    if "library" not in sample_df.columns:
+        sample_df["library"] = "default"
+
+    sample_df = add_tube_totals(sample_df,
+                                od600_file=od600_file,
+                                od600_calibration_file=od600_calibration_file,
+                                tube_volume_mL=tube_volume_mL)
+
+    # Indexed by sample, with an 'obs_file' column naming each counts file.
     sample_df = _prep_sample_df(sample_df,
-                                counts_csv_path,
+                                counts_dir,
                                 counts_glob_prefix,
                                 verbose)
 
-    # Check/infer sample_ln_cfu and sample_ln_cfu_std before reading counts.
     sample_df = get_sample_ln_cfu(sample_df)
 
-    # This will be a single dataframe holding all counts for all samples, with
-    # a 'sample' column that can be indexed back to to counts.
     counts_df = _aggregate_counts(sample_df)
 
-    # Infer cfu per genotype
     ln_cfu_df = counts_to_lncfu(sample_df,
                                 counts_df,
                                 min_genotype_obs=min_genotype_obs,
                                 pseudocount=pseudocount)
 
-    # Write outputs
-    ln_cfu_df.to_csv(output_file,index=False)
+    if presplit:
+        ln_cfu_df = get_scaled_cfu(ln_cfu_df, need_columns=["ln_cfu", "ln_cfu_std"])
+        ln_cfu_df = (ln_cfu_df[PRESPLIT_COLUMNS]
+                     .sort_values(by=["library", "replicate", "condition_pre",
+                                      "genotype"],
+                                  ignore_index=True))
+    else:
+        ln_cfu_df = ln_cfu_df.drop(columns=["obs_file"], errors="ignore")
+
+    out_file = f"{out_prefix}.csv"
+    ln_cfu_df.to_csv(out_file, index=False)
+    return out_file
 
 
 def main():
-    return generalized_main(process_counts)
\ No newline at end of file
+    return generalized_main(process_counts,
+                            manual_arg_types={"tube_volume_mL": float})
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tfscreen/process_raw/scripts/process_fastq_cli.py b/src/tfscreen/process_raw/scripts/process_fastq_cli.py
index 30bd907c..9272b62a 100644
--- a/src/tfscreen/process_raw/scripts/process_fastq_cli.py
+++ b/src/tfscreen/process_raw/scripts/process_fastq_cli.py
@@ -161,8 +161,8 @@ def _process_paired_fastq(f1_fastq: str,
     chunk_size : int
         break the reads into chunks of chunk_size reads for processing by their
         own worker
-    num_workers : int or None
-        number of workers to use. If not specified, set to os.cpu_count() - 1
+    num_workers : int
+        number of worker processes; -1 (or None) uses os.cpu_count() - 1
 
     Returns
     -------
@@ -371,10 +371,10 @@ def _create_counts_df(sequences: Counter,
     return df
 
 
-def process_fastq(f1_fastq: str,
+def process_fastq(library_config: Union[str, dict, LibraryManager],
+                  f1_fastq: str,
                   f2_fastq: str,
-                  out_dir: str,
-                  run_config: Union[str, dict, LibraryManager],
+                  out_dir: str = "counts",
                   phred_cutoff: int = 10,
                   min_read_length: int = 50,
                   allowed_num_flank_diffs: int = 1,
@@ -382,20 +382,26 @@ def process_fastq(f1_fastq: str,
                   print_raw_seq: bool = False,
                   max_num_reads: Optional[int] = None,
                   chunk_size: int = 10000,
-                  num_workers: int | None = None) -> None:
+                  num_workers: int = -1) -> None:
     """
-    Count the protein genotypes observed in a pair of fastq files. 
+    Count the protein genotypes observed in a pair of fastq files.
+
+    Writes ``counts_<f1 name>.csv`` and ``stats_<f1 name>.csv`` into
+    ``out_dir``. tfs-process-counts finds each tube's counts file by its
+    sample name, so name the fastq files after the tubes.
 
     Parameters
     ----------
-    f1_fastq, f2_fastq : str, str
-        fastq/fastq.gz with paired-end reads
+    library_config : str, dict, LibraryManager
+        The library YAML (the same file later passed to tfs-configure-model
+        --library_config), a dict of its keys, or a LibraryManager. Only the
+        genetics keys are read; see LibraryManager.
+    f1_fastq : str
+        forward-read fastq/fastq.gz file
+    f2_fastq : str
+        reverse-read fastq/fastq.gz file of the same tube
     out_dir : str
-        output directory
-    run_config : str, dict, LibraryManager
-        input file/dict to initialize a LibraryManager or a pre-initialized
-        library instance. This defines the expected library. See the docstring
-        for the `LibraryManager` class for more details on the inputs. 
+        output directory, created if absent
     phred_cutoff : int, default 10
         assign any base with phred < cutoff to "N"
     min_read_length : int, default=50
@@ -419,8 +425,8 @@ def process_fastq(f1_fastq: str,
     chunk_size : int
         break the reads into chunks of chunk_size reads for processing by their
         own worker
-    num_workers : int or None
-        number of workers to use. If not specified, set to os.cpu_count() - 1
+    num_workers : int
+        number of worker processes; -1 (or None) uses os.cpu_count() - 1
     
     Returns
     -------
@@ -440,11 +446,14 @@ def process_fastq(f1_fastq: str,
     else:
         os.makedirs(out_dir)
 
+    if num_workers is not None and num_workers < 0:
+        num_workers = None
+
     # Initialize library manager
-    if isinstance(run_config,LibraryManager):
-        lm = run_config
+    if isinstance(library_config,LibraryManager):
+        lm = library_config
     else:
-        lm = LibraryManager(run_config)
+        lm = LibraryManager(library_config)
 
     # Set up a FastqToCounts object for calling read pairs
     ftc_instance = FastqToCounts(lm,
@@ -473,4 +482,9 @@ def process_fastq(f1_fastq: str,
     counts_df.to_csv(counts_file,index=False)
 
 def main():
-    return generalized_main(process_fastq,manual_arg_types={"num_workers": int})
+    return generalized_main(process_fastq,
+                            manual_arg_types={"max_num_reads": int})
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tfscreen/process_raw/scripts/process_presplit_cli.py b/src/tfscreen/process_raw/scripts/process_presplit_cli.py
deleted file mode 100644
index 2beaf981..00000000
--- a/src/tfscreen/process_raw/scripts/process_presplit_cli.py
+++ /dev/null
@@ -1,111 +0,0 @@
-from tfscreen.process_raw import counts_to_lncfu
-from tfscreen.process_raw.counts_to_lncfu import get_sample_ln_cfu
-from tfscreen.process_raw._counts_io import _prep_sample_df, _aggregate_counts
-from tfscreen.util.cli import generalized_main
-from tfscreen.util.dataframe import check_columns, get_scaled_cfu
-
-from typing import Union
-
-import pandas as pd
-
-
-def process_presplit(
-    sample_df: Union[pd.DataFrame, str],
-    counts_csv_path: str,
-    output_file: str,
-    counts_glob_prefix: str = "counts",
-    min_genotype_obs: int = 10,
-    pseudocount: int = 1,
-    verbose: bool = True
-):
-    """
-    Convert per-sample genotype count CSVs into a presplit ln_cfu DataFrame.
-
-    Reads sample metadata from sample_df, matches each sample to a counts
-    CSV file in counts_csv_path, aggregates all counts, converts to ln(CFU)
-    via counts_to_lncfu, and writes a presplit CSV to output_file.  The
-    presplit CSV contains only the columns required by tfs-configure-model
-    (replicate, condition_pre, genotype, ln_cfu, ln_cfu_std).
-
-    Parameters
-    ----------
-    sample_df : str or pandas.DataFrame
-        Path to (or pre-loaded) sample metadata CSV.  Must contain a unique
-        'sample' column (used as the index) plus 'replicate' and
-        'condition_pre' columns and the total CFU in each tube with its
-        uncertainty ('sample_ln_cfu' + 'sample_ln_cfu_std', or 'sample_cfu'
-        + 'sample_cfu_std'; see counts_to_lncfu).  An optional 'library'
-        column groups genotypes for the minimum-observation filter; defaults
-        to 'default' when absent.
-    counts_csv_path : str
-        Directory containing per-sample count CSV files.  Each file must
-        match the glob pattern ``{counts_glob_prefix}*{sample}*.csv``.
-    output_file : str
-        Path to write the output presplit CSV (passed to tfs-configure-model
-        as the presplit_df argument).
-    counts_glob_prefix : str, optional
-        Prefix used when globbing for count files in counts_csv_path.
-        Default 'counts'.
-    min_genotype_obs : int, optional
-        Minimum total count across samples for a genotype to be retained.
-        Genotypes below this threshold are dropped before the ln_cfu
-        calculation.  Default 10.
-    pseudocount : int, optional
-        Pseudocount added to each genotype count before the log transform,
-        to avoid log(0).  Default 1.
-    verbose : bool, optional
-        If True, print a summary of matched samples and file paths.
-        Default True.
-    """
-
-    # After this call, sample_df will be indexed by sample name and have
-    # a column 'obs_file' that points to the csv file to read.
-    sample_df = _prep_sample_df(sample_df,
-                                counts_csv_path,
-                                counts_glob_prefix,
-                                verbose)
-
-    # Require the caller to supply the presplit-specific metadata columns.
-    check_columns(sample_df, required_columns=["replicate", "condition_pre"])
-
-    # Check/infer sample_ln_cfu and sample_ln_cfu_std before reading counts.
-    sample_df = get_sample_ln_cfu(sample_df)
-
-    # counts_to_lncfu requires a 'library' column for grouping; default when absent.
-    if "library" not in sample_df.columns:
-        sample_df = sample_df.copy()
-        sample_df["library"] = "default"
-
-    counts_df = _aggregate_counts(sample_df)
-
-    ln_cfu_df = counts_to_lncfu(sample_df,
-                                 counts_df,
-                                 min_genotype_obs=min_genotype_obs,
-                                 pseudocount=pseudocount)
-
-    # counts_to_lncfu outputs ln_cfu_var; compute ln_cfu_std from it.
-    ln_cfu_df = get_scaled_cfu(ln_cfu_df, need_columns=["ln_cfu", "ln_cfu_std"])
-
-    # Keep only the columns expected by _read_presplit_df.
-    presplit_df = ln_cfu_df[["replicate", "condition_pre", "genotype",
-                              "ln_cfu", "ln_cfu_std"]].copy()
-
-    presplit_df = presplit_df.sort_values(
-        by=["replicate", "condition_pre", "genotype"],
-        ignore_index=True
-    )
-
-    presplit_df.to_csv(output_file, index=False)
-
-
-def main():
-    return generalized_main(process_presplit,
-                            manual_arg_types={"sample_df": str,
-                                              "counts_csv_path": str,
-                                              "output_file": str,
-                                              "min_genotype_obs": int,
-                                              "pseudocount": int})
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/tfscreen/process_raw/tube_corrections.py b/src/tfscreen/process_raw/tube_corrections.py
new file mode 100644
index 00000000..24a3cbb4
--- /dev/null
+++ b/src/tfscreen/process_raw/tube_corrections.py
@@ -0,0 +1,485 @@
+"""
+Documented corrections to a tube table, applied before tfs-process-counts.
+
+A tube table is the record of what each sequenced tube is. When the data
+show that a tube's label is wrong (its genotype composition says 1 mM IPTG
+and its label says 0.1 uM, say), the fix is a correction, not an edit: an
+entry in a corrections file that names the tubes, says what changes and
+why, and who decided it when. ``apply_corrections`` applies the file to the
+original table and writes a new one; the original is never touched. The
+corrections file lives with the experiment's data, under version control.
+
+**Design and measurement columns.** A tube table mixes two kinds of column.
+The design columns (``DESIGN_COLUMNS``: replicate, conditions, times,
+titrant) say what was meant to be in the tube. Every other column is a
+measurement of, or a fact about, the physical sample: its ``sample`` name
+(which picks its count file), its OD600 and tube totals, its barcodes, its
+sequencing run. A correction moves only design columns. A swap exchanges
+two tubes' design columns and leaves every measurement with the sample it
+was made on. That is right for both ways a swap happens:
+
+- the tubes were physically swapped at the bench (the tube labeled 0.1 uM
+  held the 1 mM culture): the OD600, reads and counts were measured on the
+  culture now known to be 1 mM, so they belong with the 1 mM label;
+- the sample sheet mapped files or barcodes to the wrong tubes: the count
+  file and its reads came from the culture whose design is the other label,
+  and the OD600 reading was taken on that same culture when it was pulled.
+
+Either way the sample's measurements stay together and only its label
+changes. ``library`` is neither: kanR and pheS libraries are transformed
+and grown separately, so a swap between libraries is refused, and a
+relabel cannot change it.
+
+**Actions.**
+
+- ``swap``: exactly two tubes of one library exchange every design column
+  the table has.
+- ``relabel``: one tube gets new values for the design columns named in
+  ``set``. Three relabels express a rotation of three tubes.
+- ``exclude``: one or more tubes are left out of the corrected table.
+
+No tube may appear in two corrections, so the corrections do not depend on
+order: each one is applied to the original table.
+
+**Corrections file** (YAML)::
+
+    description: Corrections to the example screen's tube table.
+    corrections:
+      - id: pheS-t60-iptg-swap
+        action: swap
+        tubes: [pheS_pheSp4CP_0mM_t60, pheS_pheSp4CP_1mM_t60]
+        reason: >
+          Composition QC: the tube labeled 0 mM looks like a 1 mM tube
+          and the one labeled 1 mM like a 0 mM tube.
+        by: A. Researcher
+        date: 2026-10-15
+        evidence: qc/apparent_iptg.csv
+      - id: kanR-t120-time
+        action: relabel
+        tubes: [kanR_kanRpkan_0mM_t120]
+        set: {t_sel: 125}
+        reason: The bench log says this row was pulled at 125 minutes.
+        by: A. Researcher
+        date: 2026-10-15
+
+``id``, ``action``, ``tubes``, ``reason``, ``by`` and ``date`` are required;
+``evidence`` (a string or list of strings, often a path relative to the
+corrections file) is optional. Evidence that names an existing file is
+hashed into the provenance record.
+"""
+
+import datetime
+import hashlib
+import json
+import os
+
+import numpy as np
+import pandas as pd
+
+from tfscreen.util.io import read_dataframe
+from tfscreen.util.io.read_yaml import read_yaml
+from tfscreen.util import provenance
+
+# Columns that say what a tube was meant to be. A correction moves only
+# these; every other column stays with the physical sample.
+DESIGN_COLUMNS = ("replicate", "condition_pre", "t_pre", "condition_sel",
+                  "t_sel", "titrant_name", "titrant_conc")
+
+# Never changed by a correction: the sample's own name and its library.
+TUBE_ID_COLUMN = "sample"
+LIBRARY_COLUMN = "library"
+
+ACTIONS = {"swap", "relabel", "exclude"}
+
+_REQUIRED_KEYS = ("id", "action", "tubes", "reason", "by", "date")
+_OPTIONAL_KEYS = ("evidence", "set")
+_TOP_KEYS = ("corrections", "description")
+
+# Columns apply_corrections adds; a table that has them was already corrected.
+RECORD_COLUMNS = ("correction_id",)
+
+
+def _sha256(path):
+    h = hashlib.sha256()
+    with open(path, "rb") as fh:
+        for chunk in iter(lambda: fh.read(1 << 20), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def _nonempty_str(value):
+    if isinstance(value, (datetime.date, datetime.datetime)):
+        return value.isoformat()
+    if value is None:
+        return ""
+    return str(value).strip()
+
+
+def read_corrections(corrections_file):
+    """
+    Read and check a corrections file.
+
+    Parameters
+    ----------
+    corrections_file : str
+        YAML file with a ``corrections`` list (and optional ``description``).
+
+    Returns
+    -------
+    dict
+        ``description`` (str or None) and ``corrections``: a list of dicts
+        with ``id``, ``action``, ``tubes`` (list of str), ``reason``, ``by``,
+        ``date`` (str), ``evidence`` (list of str) and ``set`` (dict, relabel
+        only).
+
+    Raises
+    ------
+    ValueError
+        On an unknown key or action, a missing or empty required field, a
+        duplicate id, the wrong number of tubes for the action, a tube named
+        twice, or ``set`` on an action other than relabel.
+    """
+    cf = read_yaml(corrections_file)
+    if not isinstance(cf, dict) or "corrections" not in cf:
+        raise ValueError(f"{corrections_file}: expected a mapping with a "
+                         "'corrections' list.")
+    unknown = sorted(set(cf) - set(_TOP_KEYS))
+    if unknown:
+        raise ValueError(f"{corrections_file}: unknown top-level key(s) "
+                         f"{unknown}; allowed: {list(_TOP_KEYS)}.")
+    entries = cf["corrections"]
+    if not isinstance(entries, list) or not entries:
+        raise ValueError(f"{corrections_file}: 'corrections' must be a "
+                         "non-empty list.")
+
+    out = []
+    seen_ids = set()
+    tube_owner = {}
+    for i, entry in enumerate(entries):
+        where = f"{corrections_file}, correction {i + 1}"
+        if not isinstance(entry, dict):
+            raise ValueError(f"{where}: each correction must be a mapping.")
+        unknown = sorted(set(entry) - set(_REQUIRED_KEYS) - set(_OPTIONAL_KEYS))
+        if unknown:
+            raise ValueError(f"{where}: unknown key(s) {unknown}.")
+        for key in _REQUIRED_KEYS:
+            if key == "tubes":
+                continue
+            if not _nonempty_str(entry.get(key)):
+                raise ValueError(f"{where}: '{key}' is required and must not "
+                                 "be empty.")
+        cid = _nonempty_str(entry["id"])
+        where = f"{corrections_file}, correction '{cid}'"
+        if cid in seen_ids:
+            raise ValueError(f"{where}: duplicate id.")
+        seen_ids.add(cid)
+
+        action = _nonempty_str(entry["action"])
+        if action not in ACTIONS:
+            raise ValueError(f"{where}: unknown action '{action}'; "
+                             f"allowed: {sorted(ACTIONS)}.")
+
+        tubes = entry.get("tubes")
+        if isinstance(tubes, (str, int, float)):
+            tubes = [tubes]
+        if not isinstance(tubes, list) or not tubes:
+            raise ValueError(f"{where}: 'tubes' must name at least one tube.")
+        tubes = [_nonempty_str(t) for t in tubes]
+        if any(not t for t in tubes):
+            raise ValueError(f"{where}: empty tube name in 'tubes'.")
+        if len(set(tubes)) != len(tubes):
+            raise ValueError(f"{where}: a tube is listed twice.")
+        need = {"swap": 2, "relabel": 1}.get(action)
+        if need is not None and len(tubes) != need:
+            raise ValueError(f"{where}: {action} takes exactly {need} "
+                             f"tube(s), got {len(tubes)}.")
+        for t in tubes:
+            if t in tube_owner:
+                raise ValueError(f"{where}: tube '{t}' is also named by "
+                                 f"correction '{tube_owner[t]}'. A tube may "
+                                 "appear in only one correction; combine "
+                                 "them.")
+            tube_owner[t] = cid
+
+        new_values = entry.get("set")
+        if action == "relabel":
+            if not isinstance(new_values, dict) or not new_values:
+                raise ValueError(f"{where}: relabel needs a non-empty 'set' "
+                                 "mapping of design columns to new values.")
+            bad = sorted(set(new_values) - set(DESIGN_COLUMNS))
+            if bad:
+                raise ValueError(f"{where}: relabel can set only design "
+                                 f"columns {list(DESIGN_COLUMNS)}; got {bad}.")
+            new_values = dict(new_values)
+        elif new_values is not None:
+            raise ValueError(f"{where}: 'set' is only for relabel.")
+
+        evidence = entry.get("evidence")
+        if evidence is None:
+            evidence = []
+        elif not isinstance(evidence, list):
+            evidence = [evidence]
+        evidence = [_nonempty_str(e) for e in evidence if _nonempty_str(e)]
+
+        out.append({"id": cid,
+                    "action": action,
+                    "tubes": tubes,
+                    "reason": _nonempty_str(entry["reason"]),
+                    "by": _nonempty_str(entry["by"]),
+                    "date": _nonempty_str(entry["date"]),
+                    "evidence": evidence,
+                    "set": new_values})
+
+    return {"description": cf.get("description"), "corrections": out}
+
+
+def _same_value(a, b):
+    if pd.isna(a) and pd.isna(b):
+        return True
+    try:
+        return bool(np.isclose(float(a), float(b), rtol=1e-12, atol=0.0))
+    except (TypeError, ValueError):
+        return str(a) == str(b)
+
+
+def _coerce(value, series, where, column):
+    """A relabel value in the dtype of its column (numbers stay numbers)."""
+    if pd.api.types.is_numeric_dtype(series.dtype):
+        try:
+            value = float(value)
+        except (TypeError, ValueError):
+            raise ValueError(f"{where}: '{column}' is numeric in the tube "
+                             f"table; got {value!r}.")
+        if pd.api.types.is_integer_dtype(series.dtype):
+            if not float(value).is_integer():
+                raise ValueError(f"{where}: '{column}' holds integers in the "
+                                 f"tube table; got {value!r}.")
+            value = int(value)
+        return value
+    return str(value)
+
+
+def correct_tube_table(tube_df, corrections):
+    """
+    Apply checked corrections to a tube table.
+
+    Parameters
+    ----------
+    tube_df : pandas.DataFrame
+        The original tube table, with a unique ``sample`` column.
+    corrections : list of dict
+        ``read_corrections(...)["corrections"]``.
+
+    Returns
+    -------
+    corrected : pandas.DataFrame
+        The tube table without excluded tubes, design columns corrected, in
+        the original row order, plus ``correction_id`` (empty for tubes no
+        correction touched).
+    changes : pandas.DataFrame
+        One row per changed cell or excluded tube: ``correction_id``,
+        ``action``, ``sample``, ``column``, ``original``, ``corrected``
+        (``column`` and ``corrected`` empty for an exclusion).
+
+    Raises
+    ------
+    ValueError
+        If the table has no or duplicate ``sample`` names, was already
+        corrected, a correction names an unknown tube, a swap crosses
+        libraries or would change nothing, a relabel sets a column the table
+        lacks or changes nothing, or a correction leaves a tube with the same
+        library and design as another tube.
+    """
+    if TUBE_ID_COLUMN not in tube_df.columns:
+        raise ValueError("The tube table has no 'sample' column.")
+    already = [c for c in RECORD_COLUMNS if c in tube_df.columns]
+    if already:
+        raise ValueError(f"The tube table already has {already}: it was "
+                         "corrected before. Apply every correction to the "
+                         "original table in one file.")
+
+    df = tube_df.copy()
+    df[TUBE_ID_COLUMN] = df[TUBE_ID_COLUMN].astype(str)
+    dupes = sorted(df.loc[df[TUBE_ID_COLUMN].duplicated(), TUBE_ID_COLUMN])
+    if dupes:
+        raise ValueError(f"Duplicate tube name(s) in the tube table: {dupes}")
+    df = df.set_index(TUBE_ID_COLUMN, drop=False)
+
+    design = [c for c in DESIGN_COLUMNS if c in df.columns]
+    original = df.copy()
+    df["correction_id"] = ""
+    excluded = []
+    changes = []
+
+    for corr in corrections:
+        cid, action = corr["id"], corr["action"]
+        where = f"correction '{cid}'"
+        missing = [t for t in corr["tubes"] if t not in df.index]
+        if missing:
+            raise ValueError(f"{where}: no tube named {missing} in the tube "
+                             "table.")
+
+        if action == "swap":
+            a, b = corr["tubes"]
+            if LIBRARY_COLUMN in df.columns and not _same_value(
+                    original.at[a, LIBRARY_COLUMN], original.at[b, LIBRARY_COLUMN]):
+                raise ValueError(
+                    f"{where}: tubes '{a}' and '{b}' are in different "
+                    f"libraries ({original.at[a, LIBRARY_COLUMN]}, "
+                    f"{original.at[b, LIBRARY_COLUMN]}). Libraries are "
+                    "transformed and grown separately; a swap across them "
+                    "is not a label swap.")
+            differ = [c for c in design
+                      if not _same_value(original.at[a, c], original.at[b, c])]
+            if not differ:
+                raise ValueError(f"{where}: tubes '{a}' and '{b}' have the "
+                                 "same design; swapping them changes nothing.")
+            for c in design:
+                df.at[a, c] = original.at[b, c]
+                df.at[b, c] = original.at[a, c]
+            for t, other in ((a, b), (b, a)):
+                for c in differ:
+                    changes.append((cid, action, t, c, original.at[t, c],
+                                    original.at[other, c]))
+
+        elif action == "relabel":
+            (t,) = corr["tubes"]
+            absent = sorted(set(corr["set"]) - set(df.columns))
+            if absent:
+                raise ValueError(f"{where}: the tube table has no column(s) "
+                                 f"{absent}.")
+            changed = False
+            for c, value in corr["set"].items():
+                value = _coerce(value, original[c], where, c)
+                if _same_value(original.at[t, c], value):
+                    continue
+                df.at[t, c] = value
+                changes.append((cid, action, t, c, original.at[t, c], value))
+                changed = True
+            if not changed:
+                raise ValueError(f"{where}: tube '{t}' already has those "
+                                 "values; the relabel changes nothing.")
+
+        else:  # exclude
+            for t in corr["tubes"]:
+                excluded.append(t)
+                changes.append((cid, action, t, "", "", ""))
+
+        for t in corr["tubes"]:
+            df.at[t, "correction_id"] = cid
+
+    df = df.drop(index=excluded)
+
+    # A corrected tube must not end up as a copy of another tube's design.
+    key = ([LIBRARY_COLUMN] if LIBRARY_COLUMN in df.columns else []) + design
+    if key:
+        touched = df["correction_id"] != ""
+        dup = df.duplicated(subset=key, keep=False)
+        clash = df.loc[dup & touched, TUBE_ID_COLUMN].tolist()
+        if clash:
+            groups = (df[dup].groupby(key, dropna=False)[TUBE_ID_COLUMN]
+                      .apply(list).tolist())
+            groups = [g for g in groups if set(g) & set(clash)]
+            raise ValueError("After the corrections these tubes share a "
+                             f"library and design: {groups}. Correct every "
+                             "tube involved (three relabels for a rotation).")
+
+    df = df.reset_index(drop=True)
+    changes = pd.DataFrame(changes, columns=["correction_id", "action",
+                                             "sample", "column", "original",
+                                             "corrected"])
+    return df, changes
+
+
+def _evidence_record(evidence, base_dir):
+    out = []
+    for e in evidence:
+        path = e if os.path.isabs(e) else os.path.join(base_dir, e)
+        rec = {"evidence": e}
+        if os.path.isfile(path):
+            rec["path"] = os.path.abspath(path)
+            rec["sha256"] = _sha256(path)
+        out.append(rec)
+    return out
+
+
+def apply_corrections(tube_table_file, corrections_file,
+                      out_prefix="tfs_tubes_corrected"):
+    """
+    Read a tube table and a corrections file and write the corrected table.
+
+    Writes ``{out_prefix}.csv`` (the corrected tube table, for
+    tfs-process-counts), ``{out_prefix}_changes.csv`` (one row per changed
+    cell or excluded tube) and ``{out_prefix}_provenance.json`` (the run's
+    provenance, the SHA-256 of both inputs and every evidence file, the
+    checked corrections and the SHA-256 of the corrected table). The inputs
+    are only read.
+
+    Returns
+    -------
+    str
+        Path of the corrected tube table.
+
+    Raises
+    ------
+    ValueError
+        If ``{out_prefix}.csv`` would overwrite an input, or on any problem
+        ``read_corrections`` or ``correct_tube_table`` raises for.
+    """
+    out_file = f"{out_prefix}.csv"
+    changes_file = f"{out_prefix}_changes.csv"
+    for target in (out_file, changes_file):
+        for src in (tube_table_file, corrections_file):
+            if os.path.abspath(target) == os.path.abspath(src) or (
+                    os.path.exists(target) and os.path.samefile(target, src)):
+                raise ValueError(f"Output {target} would overwrite the input "
+                                 f"{src}. Choose another out_prefix.")
+
+    tube_hash = _sha256(tube_table_file)
+    corr_hash = _sha256(corrections_file)
+
+    cf = read_corrections(corrections_file)
+    tube_df = read_dataframe(tube_table_file)
+    if tube_df.index.name == TUBE_ID_COLUMN:
+        tube_df = tube_df.reset_index()
+
+    corrected, changes = correct_tube_table(tube_df, cf["corrections"])
+
+    parent = os.path.dirname(os.path.abspath(out_file))
+    os.makedirs(parent, exist_ok=True)
+    corrected.to_csv(out_file, index=False)
+    changes.to_csv(changes_file, index=False)
+
+    base_dir = os.path.dirname(os.path.abspath(corrections_file))
+    corrections = []
+    for c in cf["corrections"]:
+        rec = {k: v for k, v in c.items() if not (k == "set" and v is None)}
+        rec["evidence"] = _evidence_record(c["evidence"], base_dir)
+        corrections.append(rec)
+
+    prov = provenance.get_provenance()
+    prov["inputs"] = {
+        "tube_table": {"path": os.path.abspath(tube_table_file),
+                       "sha256": tube_hash},
+        "corrections_file": {"path": os.path.abspath(corrections_file),
+                             "sha256": corr_hash},
+    }
+    prov["description"] = cf["description"]
+    prov["design_columns"] = list(DESIGN_COLUMNS)
+    prov["corrections"] = corrections
+    prov["outputs"] = {
+        "tube_table": {"path": os.path.abspath(out_file),
+                       "sha256": _sha256(out_file),
+                       "num_tubes": int(len(corrected))},
+        "changes": {"path": os.path.abspath(changes_file)},
+    }
+    prov_file = f"{out_prefix}_provenance.json"
+    with open(prov_file, "w") as fh:
+        json.dump(prov, fh, indent=2, default=str)
+        fh.write("\n")
+
+    n_ex = int((changes["action"] == "exclude").sum())
+    print(f"Applied {len(cf['corrections'])} correction(s): "
+          f"{changes['sample'].nunique() - n_ex} tube(s) relabeled, "
+          f"{n_ex} excluded. Wrote {out_file}.")
+    return out_file
diff --git a/src/tfscreen/process_raw/tube_qc.py b/src/tfscreen/process_raw/tube_qc.py
new file mode 100644
index 00000000..db68e96c
--- /dev/null
+++ b/src/tfscreen/process_raw/tube_qc.py
@@ -0,0 +1,430 @@
+"""
+Tube QC: does each selection tube's genotype composition match its titrant
+label?
+
+A tube grown at more titrant shifts the library toward the genotypes that
+titrant favors, so a tube's composition says roughly which concentration it
+saw. ``score_tubes`` turns that into an apparent-titrant score per tube and
+flags tubes whose score is out of order within their row of tubes, the mark
+of a tube or sample-sheet swap.
+
+Method, within each panel (one library x condition_pre x condition_sel x
+titrant_name; rows are replicate x t_pre x t_sel, one tube per
+concentration):
+
+1. Keep the genotypes with at least ``min_counts`` reads in every tube of the
+   panel and take each tube's centered log-ratio composition (log counts
+   less their mean over genotypes). Tube totals and read depth cancel.
+2. Fit every genotype's row x concentration table robustly as
+   ``L[g, r, c] = a[g, r] + b[r] * d[g, c] + residual`` (``_fit_profiles``:
+   a median polish, then alternating medians). The row effect ``a`` holds
+   what a row shares (its time, its replicate's starting pool), the
+   concentration profile ``d`` is the titrant's effect, and the row scale
+   ``b`` lets that effect grow with time; without it a late or early row
+   is shifted. Medians make a swapped pair an outlier rather than part of
+   the profile, and a row missing some concentrations gets its row effect
+   from the profiles of the ones it has, so it is not shifted either (the
+   bias of centering each row on its own mean).
+3. A tube's score is its composition less its row's at the lowest
+   concentration, ``L - a - b * d[:, lowest]``, projected on the axis from
+   the lowest concentration's profile to the highest's and scaled so the
+   profiles score 0 and 1; a row of scale ``b`` scores about
+   ``b * expected``, where ``expected`` is the score of the label's own
+   profile.
+4. Two tests per tube, both leave-one-out within its row. *Order*: fit
+   the rest of the row by isotonic (increasing) regression on
+   concentration; ``order_violation`` is how far the tube's score lies
+   outside the fitted values of its neighbors in concentration (0 between
+   them). *Level*: ``deviation`` is ``score - b * expected``, with ``b``
+   the rest of the row's scale (the median of score/expected over its
+   tubes with expected >= 0.5; 1 if there is none). ``sigma``, one tube's
+   score noise, is 1.4826 x the MAD of every tube's deviation in the panel,
+   each divided by its ``deviation_sd`` (the leave-one-out ``b`` carries
+   noise of its own, more for a high-concentration tube); ``order_z`` is
+   ``order_violation / sigma`` and ``deviation_z`` is
+   ``deviation / (sigma * deviation_sd)``. Of the tubes with either
+   ``|z|`` above ``flag_z``, the one with the largest
+   ``hypot(order_z, deviation_z)`` is flagged and left out (the two tubes
+   of an adjacent swap have equal order violations; the level test says
+   which is off), and the row is tested again until no tube exceeds it.
+   The order test needs no scale;
+   the level test also catches a high-concentration tube that sits low in
+   a row whose other tubes are all low. Rows of fewer than three tubes are
+   not flagged (a pair cannot say which tube is wrong).
+5. Refit steps 2-4 with the flagged tubes left out of the fit (but still
+   scored and tested) until the flags stop changing, so a bad tube does
+   not set its own row's scale.
+
+The scores are relative to the panel's own profiles, so a panel needs rows
+that span its concentrations. A row without any tube at a concentration of
+expected >= 0.5 has no scale of its own and is judged at ``b = 1``: a late
+row of only low concentrations can then look high.
+"""
+
+import numpy as np
+import pandas as pd
+
+from tfscreen.util.dataframe import check_columns
+
+# Panel and row keys, used when present in the growth table.
+PANEL_COLUMNS = ["library", "condition_pre", "condition_sel", "titrant_name"]
+ROW_COLUMNS = ["replicate", "t_pre", "t_sel"]
+
+_UNKNOWN = "__unknown__"
+_MAD_TO_SD = 1.4826
+
+# Tubes whose label's expected score is below this do not set a row's scale.
+SCALE_MIN_EXPECTED = 0.5
+
+
+def _isotonic(y):
+    """Increasing least-squares fit of ``y`` (pool adjacent violators)."""
+    blocks = []  # [mean, weight, length]
+    for v in y:
+        blocks.append([float(v), 1.0, 1])
+        while len(blocks) > 1 and blocks[-2][0] > blocks[-1][0]:
+            m2, w2, n2 = blocks.pop()
+            m1, w1, n1 = blocks.pop()
+            blocks.append([(m1 * w1 + m2 * w2) / (w1 + w2), w1 + w2, n1 + n2])
+    return np.concatenate([np.full(n, m) for m, _, n in blocks])
+
+
+def order_violations(scores):
+    """
+    Leave-one-out order violation of each value in a sequence.
+
+    ``scores`` are ordered by increasing concentration. Each value is
+    compared with an isotonic fit of the others: positive if it lies above
+    the fitted value of the next higher concentration, negative if below
+    that of the next lower, 0 between them.
+    """
+    scores = np.asarray(scores, dtype=float)
+    n = len(scores)
+    out = np.zeros(n)
+    if n < 2:
+        return out
+    for i in range(n):
+        fit = _isotonic(np.delete(scores, i))
+        lower = fit[i - 1] if i > 0 else -np.inf
+        upper = fit[i] if i < n - 1 else np.inf
+        if scores[i] > upper:
+            out[i] = scores[i] - upper
+        elif scores[i] < lower:
+            out[i] = scores[i] - lower
+    return out
+
+
+def _row_scale(score, expected, default=1.0):
+    """
+    How strongly a row shows the titrant's effect, relative to the profile.
+
+    The median of score/expected over the tubes whose expected score is at
+    least ``SCALE_MIN_EXPECTED``; the ratio is unstable for the low
+    concentrations. Unweighted, so one bad high tube cannot set the scale.
+    ``default`` if the row has no such tube. Never below 0.
+    """
+    score = np.asarray(score, dtype=float)
+    expected = np.asarray(expected, dtype=float)
+    keep = (expected >= SCALE_MIN_EXPECTED) & np.isfinite(score)
+    if not keep.any():
+        return default
+    return max(float(np.median(score[keep] / expected[keep])), 0.0)
+
+
+def deviations(score, expected):
+    """
+    Each tube's score less its expectation, ``score - b * expected``.
+
+    ``b`` is the row's scale (``_row_scale``) computed without the tube, so
+    a tube cannot set its own expectation. Also returns each deviation's SD
+    in units of one tube's score noise: ``b`` is itself estimated from k
+    tubes, so ``sqrt(1 + expected^2 var(b))``, with ``var(b)`` the variance
+    of a median of k ratios score/expected (``pi/2`` x the mean's for k >= 3)
+    and 0 where ``b`` is the default. A row of one tube has deviation 0.
+    """
+    score = np.asarray(score, dtype=float)
+    expected = np.asarray(expected, dtype=float)
+    dev = np.zeros(len(score))
+    sd = np.ones(len(score))
+    if len(score) < 2:
+        return dev, sd
+    for i in range(len(score)):
+        s_i, e_i = np.delete(score, i), np.delete(expected, i)
+        dev[i] = score[i] - _row_scale(s_i, e_i) * expected[i]
+        used = e_i[e_i >= SCALE_MIN_EXPECTED]
+        if len(used):
+            var_b = np.mean(1.0 / used ** 2) / len(used)
+            if len(used) >= 3:
+                var_b *= np.pi / 2
+            sd[i] = np.sqrt(1.0 + expected[i] ** 2 * var_b)
+    return dev, sd
+
+
+def _median_polish(L, max_iter=50, tol=1e-10):
+    """
+    Median polish of a (genotype, row, conc) array with NaN for missing tubes.
+
+    Returns the row effects ``a`` (G, R) and concentration effects ``d``
+    (G, C) such that ``L ~ a[:, :, None] + d[:, None, :]``.
+    """
+    resid = L.copy()
+    a = np.zeros(L.shape[:2])
+    d = np.zeros((L.shape[0], L.shape[2]))
+    for _ in range(max_iter):
+        rm = np.nanmedian(resid, axis=2)
+        a += rm
+        resid -= rm[:, :, None]
+        cm = np.nanmedian(resid, axis=1)
+        d += cm
+        resid -= cm[:, None, :]
+        if max(np.nanmax(np.abs(rm)), np.nanmax(np.abs(cm))) < tol:
+            break
+    return a, d
+
+
+def _project(L, a, d, b=None):
+    """
+    Scores (R, C) of every cell and expected scores (C,) of the profiles.
+
+    A cell's score is ``L - a - b * d[:, 0]`` (its composition less its
+    row's at the lowest concentration) on the axis ``d[:, -1] - d[:, 0]``,
+    so a row of scale ``b`` scores ``b * expected``.
+    """
+    if b is None:
+        b = np.ones(L.shape[1])
+    axis = d[:, -1] - d[:, 0]
+    norm = float(axis @ axis)
+    if norm == 0:
+        raise ValueError("The lowest and highest concentrations have the "
+                         "same composition; there is no axis to score.")
+    S = L - a[:, :, None] - b[None, :, None] * d[:, None, [0]]
+    score = np.einsum("grc,g->rc", S, axis) / norm
+    expected = ((d - d[:, [0]]).T @ axis) / norm
+    return score, expected
+
+
+def _fit_profiles(L, n_iter=20, tol=1e-8):
+    """
+    Robust fit of ``L[g, r, c] ~ a[g, r] + b[r] * d[g, c]``; returns
+    ``a``, ``d`` and ``b``.
+
+    Starts from a median polish (b = 1). The titrant's effect on
+    composition grows with time, so rows differ in scale ``b``; an additive
+    row effect alone leaves a row with ``b != 1`` shifted by
+    ``(1 - b)`` times its median concentration's profile. Each pass takes
+    every row's scale from its own scores (``_row_scale``; rows without a
+    high-concentration tube keep 1), then updates ``a`` and ``d`` by
+    medians.
+    """
+    a, d = _median_polish(L)
+    b = np.ones(L.shape[1])
+    for _ in range(n_iter):
+        score, expected = _project(L, a, d, b)
+        new = np.array([_row_scale(score[r], expected, default=np.nan)
+                        for r in range(L.shape[1])])
+        known = np.isfinite(new)
+        if known.any():
+            new[known] /= np.median(new[known])
+        new[~known] = 1.0
+        new = np.maximum(new, 1e-3)
+        done = np.max(np.abs(new - b)) < tol
+        b = new
+        a = np.nanmedian(L - b[None, :, None] * d[:, None, :], axis=2)
+        d = np.nanmedian((L - a[:, :, None]) / b[None, :, None], axis=1)
+        if done:
+            break
+    return a, d, b
+
+
+def _test_rows(score, expected, conc, r_idx, flag_z):
+    """
+    Order and level tests of every tube within its row (module docstring,
+    step 4). Returns per-tube ``deviation``, ``order_violation``,
+    ``flagged`` and the panel's ``sigma``.
+    """
+    n = len(score)
+    deviation = np.zeros(n)
+    dev_sd = np.ones(n)
+    for r in np.unique(r_idx):
+        idx = np.flatnonzero(r_idx == r)
+        deviation[idx], dev_sd[idx] = deviations(score[idx], expected[idx])
+    std = deviation / dev_sd
+    sigma = _MAD_TO_SD * float(np.median(np.abs(std - np.median(std))))
+    sigma = max(sigma, np.finfo(float).tiny)
+
+    violation = np.zeros(n)
+    flagged = np.zeros(n, dtype=bool)
+    for r in np.unique(r_idx):
+        idx = np.flatnonzero(r_idx == r)
+        live = list(idx[np.argsort(conc[idx], kind="stable")])
+        while len(live) >= 3:
+            v = order_violations(score[live])
+            dv, sd = deviations(score[live], expected[live])
+            violation[live] = v
+            deviation[live] = dv
+            dev_sd[live] = sd
+            oz, dz = v / sigma, dv / (sd * sigma)
+            over = np.maximum(np.abs(oz), np.abs(dz)) > flag_z
+            if not over.any():
+                break
+            # An adjacent pair out of order has equal order violations; the
+            # level test says which one is off.
+            worst = int(np.argmax(np.where(over, np.hypot(oz, dz), -1.0)))
+            flagged[live[worst]] = True
+            live.pop(worst)
+
+    return {"deviation": deviation, "deviation_sd": dev_sd,
+            "sigma": np.full(n, sigma),
+            "order_violation": violation, "flagged": flagged}
+
+
+def _score_panel(panel, row_cols, min_counts, min_genotypes, flag_z,
+                 max_rounds=5):
+    """Score and flag the tubes of one panel; see the module docstring."""
+
+    P = panel.pivot_table(index="genotype", columns="_tube", values="counts",
+                          aggfunc="sum")
+    tubes = (panel.drop_duplicates("_tube")
+             .set_index("_tube")
+             .loc[P.columns, row_cols + ["titrant_conc", "sample"]])
+
+    P = P[(P.fillna(0) >= min_counts).all(axis=1)]
+    if len(P) < min_genotypes:
+        raise ValueError(
+            f"Only {len(P)} genotypes have at least {min_counts} reads in "
+            f"every tube of panel {panel['_panel'].iloc[0]}; need "
+            f"{min_genotypes}. Lower min_counts or drop near-empty tubes.")
+
+    logp = np.log(P.to_numpy(dtype=float))
+    logp -= logp.mean(axis=0, keepdims=True)
+
+    row_id = tubes[row_cols].astype(str).agg("|".join, axis=1).to_numpy()
+    rows, r_idx = np.unique(row_id, return_inverse=True)
+    concs, c_idx = np.unique(tubes["titrant_conc"].to_numpy(dtype=float),
+                             return_inverse=True)
+    if len(concs) < 2:
+        raise ValueError(f"Panel {panel['_panel'].iloc[0]} has only one "
+                         "titrant concentration; there is no axis to score.")
+
+    G = logp.shape[0]
+    L = np.full((G, len(rows), len(concs)), np.nan)
+    L[:, r_idx, c_idx] = logp
+    out = tubes.reset_index(drop=True).copy()
+    out["n_genotypes"] = G
+    conc = out["titrant_conc"].to_numpy(dtype=float)
+
+    # Fit the profiles, test the rows, refit without the flagged tubes, and
+    # test every tube again, until the flags stop changing. A bad tube
+    # otherwise sets its own row's scale and shifts the row.
+    flagged = np.zeros(len(out), dtype=bool)
+    for _ in range(max_rounds):
+        Lfit = L.copy()
+        Lfit[:, r_idx[flagged], c_idx[flagged]] = np.nan
+        try:
+            a, d, b = _fit_profiles(Lfit)
+            grid_score, expected_c = _project(L, a, d, b)
+        except ValueError as err:
+            raise ValueError(f"Panel {panel['_panel'].iloc[0]}: {err}") from None
+        score = grid_score[r_idx, c_idx]
+        expected = expected_c[c_idx]
+        result = _test_rows(score, expected, conc, r_idx, flag_z)
+        same = np.array_equal(result["flagged"], flagged)
+        flagged = result["flagged"]
+        if same:
+            break
+
+    out["score"] = score
+    out["expected"] = expected
+    for k, v in result.items():
+        out[k] = v
+    sigma = out["sigma"].iloc[0]
+    out["order_z"] = out["order_violation"] / sigma
+    out["deviation_z"] = out["deviation"] / (sigma * out["deviation_sd"])
+    out["row_scale"] = np.nan
+    for r in np.unique(r_idx):
+        keep = (r_idx == r) & ~flagged
+        out.loc[r_idx == r, "row_scale"] = _row_scale(score[keep], expected[keep])
+
+    return out
+
+
+def score_tubes(growth_df, min_counts=1, min_genotypes=10, flag_z=4.0):
+    """
+    Apparent-titrant score of every tube from its genotype composition.
+
+    Parameters
+    ----------
+    growth_df : pandas.DataFrame
+        Long-form counts, one row per genotype and tube: ``genotype``,
+        ``counts``, ``titrant_conc`` and ``replicate``, plus whichever of
+        ``library``, ``condition_pre``, ``condition_sel``, ``titrant_name``
+        (the panel), ``t_pre``, ``t_sel`` (the row) and ``sample`` (the tube's
+        name) are present. The growth table ``tfs-process-counts`` writes has
+        all of them.
+    min_counts : int
+        A genotype is used only if it has at least this many reads in every
+        tube of its panel.
+    min_genotypes : int
+        Fewest such genotypes a panel may have.
+    flag_z : float
+        Flag a tube when its order violation or deviation exceeds this many
+        noise SDs. On the 2026-10 dev data no tube outside the one swapped
+        row exceeded 2.7.
+
+    Returns
+    -------
+    pandas.DataFrame
+        One row per tube: the panel and row keys, ``titrant_conc``,
+        ``sample``, ``n_genotypes``, ``score`` (0 = the lowest
+        concentration's composition, 1 = the highest's), ``expected`` (the
+        score of the label's own profile), ``deviation``, ``sigma``,
+        ``order_violation``, ``flagged``, ``order_z``, ``deviation_z`` and
+        ``row_scale`` (the row's scale from its unflagged tubes).
+
+    Raises
+    ------
+    ValueError
+        If a required column is missing, a panel has two tubes at one row
+        and concentration, has a single concentration, or keeps fewer than
+        ``min_genotypes`` genotypes.
+    """
+    check_columns(growth_df, required_columns=["genotype", "counts",
+                                               "titrant_conc", "replicate"])
+    df = growth_df[growth_df["genotype"].astype(str) != _UNKNOWN].copy()
+
+    panel_cols = [c for c in PANEL_COLUMNS if c in df.columns]
+    row_cols = [c for c in ROW_COLUMNS if c in df.columns]
+    tube_cols = panel_cols + row_cols + ["titrant_conc"]
+
+    df["_panel"] = (df[panel_cols].astype(str).agg("|".join, axis=1)
+                    if panel_cols else "all")
+    df["_tube"] = df[tube_cols].astype(str).agg("|".join, axis=1)
+    if "sample" in df.columns:
+        n_samples = df.groupby("_tube")["sample"].nunique()
+        dup = n_samples[n_samples > 1]
+        if len(dup):
+            raise ValueError(
+                "More than one tube (sample) shares a row and concentration: "
+                f"{list(dup.index[:5])}. Each replicate x time row needs one "
+                "tube per concentration.")
+    else:
+        df["sample"] = df["_tube"]
+
+    parts = []
+    for _, panel in df.groupby("_panel", sort=True):
+        scored = _score_panel(panel, row_cols, min_counts, min_genotypes, flag_z)
+        for c in panel_cols:
+            scored[c] = panel[c].iloc[0]
+        parts.append(scored)
+
+    out = pd.concat(parts, ignore_index=True)
+    front = panel_cols + row_cols + ["titrant_conc", "sample"]
+    out = out[front + [c for c in out.columns if c not in front]]
+    return out.sort_values(front[:-1], ignore_index=True)
+
+
+def score_table(scores):
+    """Scores pivoted to rows (panel x row keys) by titrant concentration."""
+    keys = [c for c in PANEL_COLUMNS + ROW_COLUMNS if c in scores.columns]
+    return scores.pivot_table(index=keys, columns="titrant_conc",
+                              values="score")
diff --git a/src/tfscreen/simulate/__init__.py b/src/tfscreen/simulate/__init__.py
index b88f1675..7b7ff4ca 100644
--- a/src/tfscreen/simulate/__init__.py
+++ b/src/tfscreen/simulate/__init__.py
@@ -1,6 +1,8 @@
 
 from .build_sample_dataframes import (  # noqa: F401
     build_sample_dataframes,
+    design_conditions,
+    read_design,
 )
 
 from .sim_data_class import (  # noqa: F401
diff --git a/src/tfscreen/simulate/binding_data.py b/src/tfscreen/simulate/binding_data.py
index eea1b89f..39c2f263 100644
--- a/src/tfscreen/simulate/binding_data.py
+++ b/src/tfscreen/simulate/binding_data.py
@@ -33,6 +33,10 @@ def generate_binding_df(binding_cfg, rng, binding_theta_df):
           titrant_name: str, name of the titrant (e.g. 'iptg')
           titrant_conc: list of concentrations (mM)
           noise       : float, sigma for Gaussian noise on theta_obs (default 0)
+          clip_theta_obs : bool, clip noisy theta_obs to [0, 1] (default
+                        False). The fit's binding likelihood is an unclipped
+                        Normal, and clipping biases the anchors near 0 and 1;
+                        True reproduces simulations made before 2026-09-29.
         An optional ``genotypes`` list restricts the output to those genotypes;
         when absent (the normal case under the spiked_binding schema) every
         genotype in ``binding_theta_df`` is emitted.
@@ -49,6 +53,7 @@ def generate_binding_df(binding_cfg, rng, binding_theta_df):
     titrant_name = binding_cfg["titrant_name"]
     titrant_conc = list(binding_cfg["titrant_conc"])
     noise = float(binding_cfg.get("noise", 0.0))
+    clip = bool(binding_cfg.get("clip_theta_obs", False))
 
     # Build lookup: (genotype, conc) → theta_true
     theta_lookup = {
@@ -74,7 +79,10 @@ def generate_binding_df(binding_cfg, rng, binding_theta_df):
                 )
             theta_true = float(theta_lookup[key])
             if noise > 0:
-                theta_obs = float(np.clip(theta_true + rng.normal(0, noise), 0, 1))
+                theta_obs = theta_true + rng.normal(0, noise)
+                if clip:
+                    theta_obs = np.clip(theta_obs, 0, 1)
+                theta_obs = float(theta_obs)
             else:
                 theta_obs = theta_true
             rows.append({
@@ -86,3 +94,41 @@ def generate_binding_df(binding_cfg, rng, binding_theta_df):
             })
 
     return pd.DataFrame(rows)
+
+
+def apply_genotype_offsets(binding_df, offset_sd, rng):
+    """
+    Add one offset per genotype to every one of its theta_obs.
+
+    The simulated counterpart of the fit's ``theta_binding_noise:
+    genotype_offset``: a genotype's binding differs from its in vivo
+    occupancy (or carries its day's normalization error) by a shift shared
+    across its whole curve, offset_g ~ Normal(0, offset_sd).
+
+    Parameters
+    ----------
+    binding_df : pandas.DataFrame
+        Must have ``genotype`` and ``theta_obs``.
+    offset_sd : float
+        SD of the offsets (theta units); 0 returns the table unchanged and
+        draws nothing.
+    rng : numpy.random.Generator
+
+    Returns
+    -------
+    binding_df : pandas.DataFrame
+    truth : pandas.DataFrame or None
+        ``genotype``, ``offset`` (None when offset_sd is 0).
+    """
+    offset_sd = float(offset_sd)
+    if not np.isfinite(offset_sd) or offset_sd < 0:
+        raise ValueError(f"genotype_offset_sd must be >= 0, got {offset_sd}")
+    if offset_sd == 0 or len(binding_df) == 0:
+        return binding_df, None
+    genotypes = list(pd.unique(binding_df["genotype"].astype(str)))
+    offsets = rng.normal(0.0, offset_sd, size=len(genotypes))
+    lookup = dict(zip(genotypes, offsets))
+    out = binding_df.copy()
+    out["theta_obs"] = (out["theta_obs"].astype(float)
+                        + out["genotype"].astype(str).map(lookup))
+    return out, pd.DataFrame({"genotype": genotypes, "offset": offsets})
diff --git a/src/tfscreen/simulate/binding_params.py b/src/tfscreen/simulate/binding_params.py
index 1b5e9ece..9414e0c9 100644
--- a/src/tfscreen/simulate/binding_params.py
+++ b/src/tfscreen/simulate/binding_params.py
@@ -382,6 +382,13 @@ def _horseshoe(size):
             log_K       += _scatter_pair(_horseshoe(P))
             log_n       += _scatter_pair(_horseshoe(P))
 
+    # Optional upper limit on the Hill coefficient (mirrors hill_mut.simulate)
+    max_hill_n = getattr(sim_priors, "max_hill_n", None)
+    if max_hill_n is not None:
+        if max_hill_n <= 0:
+            raise ValueError(f"max_hill_n must be > 0; got {max_hill_n}")
+        log_n = np.minimum(log_n, np.log(max_hill_n))
+
     # Convert to theta: (G, C)
     theta_low_arr  = 1.0 / (1.0 + np.exp(-logit_low))
     theta_high_arr = 1.0 / (1.0 + np.exp(-(logit_low + logit_delta)))
diff --git a/src/tfscreen/simulate/build_sample_dataframes.py b/src/tfscreen/simulate/build_sample_dataframes.py
index 38329b57..e5d765c8 100644
--- a/src/tfscreen/simulate/build_sample_dataframes.py
+++ b/src/tfscreen/simulate/build_sample_dataframes.py
@@ -92,4 +92,77 @@ def build_sample_dataframes(condition_blocks, replicate=1):
     ]
     sample_df = sample_df.sort_values(sort_columns).reset_index(drop=True)
 
-    return sample_df
\ No newline at end of file
+    return sample_df
+
+# Columns of a design file: the tube table tfs-process-counts reads, without
+# the tube totals (the simulator makes those).
+DESIGN_COLUMNS = ["sample", "library", "replicate", "condition_pre", "t_pre",
+                  "condition_sel", "t_sel", "titrant_name", "titrant_conc"]
+
+# The columns that make two tubes the same growth condition.
+CONDITION_COLUMNS = ["library", "titrant_name", "condition_pre", "t_pre",
+                     "condition_sel", "titrant_conc", "t_sel"]
+
+
+def read_design(design):
+    """
+    Read and check a simulation design: one row per sequenced tube.
+
+    The design replaces ``condition_blocks`` when a simulation should follow
+    a real experiment's layout exactly (irregular time points, per-replicate
+    differences). Its format is the tube table ``tfs-process-counts`` reads;
+    extra columns (tube totals, OD600, read counts) are ignored.
+
+    Parameters
+    ----------
+    design : str or pandas.DataFrame
+        Path to the tube table (CSV/TSV/Excel) or the table itself.
+
+    Returns
+    -------
+    pandas.DataFrame
+        The ``DESIGN_COLUMNS``, ``sample`` as str and ``replicate`` as int.
+
+    Raises
+    ------
+    ValueError
+        On a missing column, a repeated sample name, or two tubes of one
+        replicate with the same growth condition.
+    """
+    from tfscreen.util.io import read_dataframe
+
+    df = read_dataframe(design)
+    if df.index.name == "sample":
+        df = df.reset_index()
+    missing = [c for c in DESIGN_COLUMNS if c not in df.columns]
+    if missing:
+        raise ValueError(f"design is missing column(s) {missing}; it needs "
+                         f"{DESIGN_COLUMNS} (a tube table).")
+    df = df[DESIGN_COLUMNS].copy()
+    df["sample"] = df["sample"].astype(str)
+    df["replicate"] = df["replicate"].astype(int)
+    dups = df["sample"][df["sample"].duplicated()].unique().tolist()
+    if dups:
+        raise ValueError(f"design repeats sample name(s): {dups[:10]}")
+    key = ["replicate"] + CONDITION_COLUMNS
+    clash = df[df.duplicated(subset=key, keep=False)]
+    if not clash.empty:
+        raise ValueError("design has more than one tube with the same "
+                         "replicate and growth condition: "
+                         f"{clash['sample'].tolist()[:10]}")
+    return df.reset_index(drop=True)
+
+
+def design_conditions(design_df):
+    """
+    The distinct growth conditions of a design, in ``build_sample_dataframes``
+    form (``replicate`` 1): the union over replicates, whose phenotypes the
+    simulator computes once before each replicate takes its own tubes.
+    """
+    conds = (design_df[CONDITION_COLUMNS].drop_duplicates()
+             .assign(replicate=1))
+    sort_columns = ["replicate", "library", "condition_pre", "condition_sel",
+                    "titrant_name", "titrant_conc", "t_sel"]
+    cols = ["replicate", "library", "titrant_name", "condition_pre", "t_pre",
+            "condition_sel", "titrant_conc", "t_sel"]
+    return conds[cols].sort_values(sort_columns).reset_index(drop=True)
diff --git a/src/tfscreen/simulate/cell_rules.py b/src/tfscreen/simulate/cell_rules.py
new file mode 100644
index 00000000..a4e35ce4
--- /dev/null
+++ b/src/tfscreen/simulate/cell_rules.py
@@ -0,0 +1,215 @@
+"""
+Rules for combining a co-transformed cell's plasmids into cell-level physics.
+
+A cell that carries more than one plasmid has one growth rate. The simulator
+builds that rate from cell-level physical quantities, never by combining the
+plasmids' whole growth rates:
+
+    k_cell = growth_model(theta_cell, activity_cell) + dk_cell
+
+Each rule takes per-slot values for a block of cells and each slot's share of
+its cell (``1/n`` for a cell with ``n`` plasmids, 0 for a masked slot; see
+``selection_experiment._plasmid_shares``).
+
+Theta rules
+-----------
+``fcn(theta, activity, shares) -> (theta_cell, activity_cell)``
+
+- ``theta``, ``activity``: ``(num_cells, max_plasmids, num_conditions)``
+- ``shares``: ``(num_cells, max_plasmids)``
+- returns two ``(num_cells, num_conditions)`` arrays.
+
+TF activity multiplies theta in the growth model, so it is combined by the
+theta rule. Under ``max`` it comes from the plasmid that sets the cell's
+theta. The partition-function rules (``homodimer``, ``heterodimer``) mix the
+variants' occupancies and require activity 1 (the lac repressor is taken as
+fully active; see the congression plan, step 4).
+
+dk rules
+--------
+``fcn(dk_geno, shares, alpha=None) -> dk_cell``
+
+- ``dk_geno``: ``(num_cells, max_plasmids)``
+- returns ``(num_cells,)``.
+
+The soft-min family ``dk_cell = -(1/alpha) log sum_g x_g exp(-alpha dk_g)``
+(congression plan, step 5): ``dilution`` (``alpha -> 0``, the default: the
+share-weighted mean), ``softmin`` (finite ``alpha > 0``, in units of 1/dk)
+and ``min`` (``alpha -> inf``: the worst variant sets the cell's cost). Only
+``softmin`` takes ``alpha``.
+
+See ``planning/congression-physics-plan.md`` for the physics.
+"""
+
+import numpy as np
+from scipy.special import logsumexp
+
+# Theta is clipped to [THETA_EPS, 1 - THETA_EPS] before taking its logit in
+# the partition-function rules (the fit uses the same value).
+THETA_EPS = 1e-6
+
+
+def theta_max(theta, activity, shares):
+    """
+    The plasmid with the highest theta sets the cell's theta and activity.
+
+    Evaluated separately at each condition (the highest-theta plasmid can
+    change with titrant concentration). Masked slots are ignored. If any
+    valid slot's theta is NaN, the cell's theta is NaN.
+
+    Parameters
+    ----------
+    theta : numpy.ndarray
+        ``(num_cells, max_plasmids, num_conditions)``.
+    activity : numpy.ndarray
+        Same shape as ``theta``.
+    shares : numpy.ndarray
+        ``(num_cells, max_plasmids)``; 0 marks a masked slot.
+
+    Returns
+    -------
+    theta_cell, activity_cell : numpy.ndarray
+        ``(num_cells, num_conditions)`` each.
+    """
+    valid = (shares > 0)[:, :, np.newaxis]
+    masked_theta = np.where(valid, theta, -np.inf)
+
+    # np.argmax picks the first NaN if one is present, so a NaN theta in any
+    # valid slot propagates to the cell.
+    best = np.argmax(masked_theta, axis=1)[:, np.newaxis, :]
+    theta_cell = np.take_along_axis(masked_theta, best, axis=1)[:, 0, :]
+    activity_cell = np.take_along_axis(activity, best, axis=1)[:, 0, :]
+
+    return theta_cell, activity_cell
+
+
+def _theta_partition(theta, activity, shares, power):
+    """
+    Partition-function cell theta, in logit space.
+
+    ``logit(theta_cell) = (1 / power) * log sum_g x_g exp(power * l_g)``,
+    with ``l_g = logit(theta_g)`` and ``x_g`` the slot's share. ``power`` 1
+    is homodimers, 0.5 random heterodimers with additive half-site energies.
+    A cell with one plasmid keeps that plasmid's theta. Requires activity 1
+    in every valid slot. Masked slots are ignored; a NaN theta in a valid
+    slot makes the cell's theta NaN.
+    """
+    valid = shares > 0
+    if np.any(np.abs(activity[valid] - 1.0) > 1e-9):
+        raise ValueError(
+            "The partition-function congression theta rules ('homodimer', "
+            "'heterodimer') require TF activity 1 for every genotype (set "
+            "activity_mut_scale to 0 and activity_wt to 1), or use "
+            "congression_theta_rule 'max'. See the congression physics plan, "
+            "step 4.")
+
+    clipped = np.clip(theta, THETA_EPS, 1.0 - THETA_EPS)
+    logit = np.log(clipped) - np.log1p(-clipped)
+    valid3 = valid[:, :, np.newaxis]
+    logit = np.where(valid3, logit, 0.0)
+    weights = np.broadcast_to(shares[:, :, np.newaxis], logit.shape)
+
+    cell_logit = logsumexp(power * logit, axis=1, b=weights) / power
+    theta_cell = 1.0 / (1.0 + np.exp(-cell_logit))
+
+    activity_cell = np.ones(theta_cell.shape)
+    return theta_cell, activity_cell
+
+
+def theta_homodimer(theta, activity, shares):
+    """
+    Homodimers only: the cell's odds are the share-weighted mean of its
+    variants' odds, ``logit(theta_cell) = log sum_g x_g exp(l_g)``.
+
+    A soft maximum between the share-weighted mean logit and the max; a
+    strong binder in a 1:1 cell loses ``log 2`` logit units against the
+    ``max`` rule. Requires activity 1. Arguments and returns as in
+    :func:`theta_max`.
+    """
+    return _theta_partition(theta, activity, shares, 1.0)
+
+
+def theta_heterodimer(theta, activity, shares):
+    """
+    Random heterodimers with additive half-site energies:
+    ``logit(theta_cell) = 2 log sum_g x_g exp(l_g / 2)``.
+
+    Closer to the share-weighted mean logit than :func:`theta_homodimer`;
+    a strong binder in a 1:1 cell loses ``log 4``. Exact per titrant
+    concentration only if subunits bind ligand independently. Requires
+    activity 1. Arguments and returns as in :func:`theta_max`.
+    """
+    return _theta_partition(theta, activity, shares, 0.5)
+
+
+def _check_no_alpha(name, alpha):
+    if alpha is not None:
+        raise ValueError(f"congression_dk_rule '{name}' takes no alpha "
+                         f"(got {alpha}); only 'softmin' does.")
+
+
+def dk_dilution(dk_geno, shares, alpha=None):
+    """
+    Share-weighted mean dk_geno (each variant's burden diluted by its share).
+
+    The ``alpha -> 0`` limit of the soft-min family in the congression plan.
+
+    Parameters
+    ----------
+    dk_geno : numpy.ndarray
+        ``(num_cells, max_plasmids)``.
+    shares : numpy.ndarray
+        ``(num_cells, max_plasmids)``; rows sum to 1.
+    alpha : None
+        Not used; must be None.
+
+    Returns
+    -------
+    numpy.ndarray
+        ``(num_cells,)``.
+    """
+    _check_no_alpha("dilution", alpha)
+    return np.sum(np.where(shares > 0, dk_geno, 0.0) * shares, axis=1)
+
+
+def dk_softmin(dk_geno, shares, alpha=None):
+    """
+    Soft minimum: ``dk_cell = -(1/alpha) log sum_g x_g exp(-alpha dk_g)``.
+
+    Between the share-weighted mean (``alpha -> 0``) and the minimum
+    (``alpha -> inf``); ``alpha`` is in units of 1/dk (minutes when dk is
+    per minute), so it matters once ``alpha`` times the spread of a cell's
+    dk values reaches ~1. Masked slots are ignored; a NaN dk_geno in a
+    valid slot makes the cell's dk NaN. Arguments and returns as in
+    :func:`dk_dilution`, with ``alpha`` a positive float.
+    """
+    if alpha is None or not alpha > 0 or not np.isfinite(alpha):
+        raise ValueError(f"congression_dk_rule 'softmin' needs a finite "
+                         f"alpha > 0, not {alpha}.")
+    valid = shares > 0
+    dk = np.where(valid, dk_geno, 0.0)
+    return -logsumexp(-alpha * dk, axis=1, b=shares) / alpha
+
+
+def dk_min(dk_geno, shares, alpha=None):
+    """
+    The worst variant (lowest dk_geno) sets the cell's cost: the
+    ``alpha -> inf`` limit of the soft-min family. Masked slots are ignored;
+    a NaN dk_geno in a valid slot makes the cell's dk NaN. Arguments and
+    returns as in :func:`dk_dilution`.
+    """
+    _check_no_alpha("min", alpha)
+    return np.min(np.where(shares > 0, dk_geno, np.inf), axis=1)
+
+
+THETA_RULES = {
+    "max": theta_max,
+    "homodimer": theta_homodimer,
+    "heterodimer": theta_heterodimer,
+}
+
+DK_RULES = {
+    "dilution": dk_dilution,
+    "softmin": dk_softmin,
+    "min": dk_min,
+}
diff --git a/src/tfscreen/simulate/empirical/congression.py b/src/tfscreen/simulate/empirical/congression.py
deleted file mode 100644
index c9b4b557..00000000
--- a/src/tfscreen/simulate/empirical/congression.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""
-Backward-compatible shim: Stage 1.5 of the empirical-phenotype pipeline.
-
-The congression de-attenuation moved to
-:mod:`tfscreen.tfmodel.genotype_fit.congression` (it is reused by the standalone
-``tfs-fit-genotypes`` command).  This module re-exports it so existing
-``simulate.empirical.congression`` imports keep working.
-"""
-
-from tfscreen.tfmodel.genotype_fit.congression import *  # noqa: F401,F403
-from tfscreen.tfmodel.genotype_fit.congression import (  # noqa: F401
-    correct_theta_matrix,
-    deattenuate_congression,
-    _theta_from_fit,
-    _refit_hill_theta,
-    _THETA_PARAM_IDX,
-    _THETA_EPS,
-)
diff --git a/src/tfscreen/simulate/growth/growth_linkage.py b/src/tfscreen/simulate/growth/growth_linkage.py
index 05729f26..c54d3736 100644
--- a/src/tfscreen/simulate/growth/growth_linkage.py
+++ b/src/tfscreen/simulate/growth/growth_linkage.py
@@ -19,11 +19,29 @@
 import numpy as np
 
 
+def warp_theta(theta, a, x0):
+    """
+    The monotone warp of linear growth's ``m_warp`` (numpy):
+    f(theta) = (s(theta) - s(0)) / (s(1) - s(0)), s(t) = 1 / (1 + exp(-a (t - x0))),
+    so f(0) = 0 and f(1) = 1; steepness a > 0, threshold x0. Matches
+    ``generative/components/growth/linear.warp_theta``.
+    """
+    theta = np.asarray(theta, dtype=float)
+
+    def s(t):
+        return 1.0 / (1.0 + np.exp(-a * (t - x0)))
+
+    return (s(theta) - s(0.0)) / (s(1.0) - s(0.0))
+
+
 class LinearGrowth:
     """
-    k = b + activity * m * theta
+    k = b + activity * m * f(theta)
 
-    Matches the 'linear' inference component.
+    Matches the 'linear' inference component. f is the identity, or with
+    ``warp_a`` the monotone warp of the component's ``m_warp`` (steepness
+    ``warp_a``, the fit's exp(warp_log_a), and threshold ``warp_x0``), for
+    a marker whose growth is a switch-like or graded function of occupancy.
 
     Parameters
     ----------
@@ -33,9 +51,18 @@ class LinearGrowth:
         Slope: change in growth rate per unit theta.
     activity : float or array-like, default 1.0
         Per-genotype TF activity scaling factor.
+    warp_a : float, optional
+        Warp steepness (> 0). None: no warp.
+    warp_x0 : float, default 0.5
+        Warp threshold.
     """
-    def predict(self, theta, b, m, activity=1.0):
-        return b + np.asarray(activity) * m * np.asarray(theta)
+    def predict(self, theta, b, m, activity=1.0, warp_a=None, warp_x0=0.5):
+        theta = np.asarray(theta)
+        if warp_a is not None:
+            if not float(warp_a) > 0:
+                raise ValueError(f"warp_a must be > 0, got {warp_a}")
+            theta = warp_theta(theta, float(warp_a), float(warp_x0))
+        return b + np.asarray(activity) * m * theta
 
 
 class PowerGrowth:
diff --git a/src/tfscreen/simulate/growth_parameters_output.py b/src/tfscreen/simulate/growth_parameters_output.py
index 273e4433..98fa0a2d 100644
--- a/src/tfscreen/simulate/growth_parameters_output.py
+++ b/src/tfscreen/simulate/growth_parameters_output.py
@@ -22,6 +22,7 @@
 is the single place that mapping is recorded.
 """
 
+import numpy as np
 import pandas as pd
 
 
@@ -42,7 +43,7 @@ def generate_growth_parameters_df(growth_cfg):
         The 'growth' top-level config block: a mapping from condition
         string to a parameter dict. Each parameter dict may contain a
         'model' key ('linear', 'power', or 'saturation'; defaults to
-        'linear' to match simulate.thermo_to_growth._apply_growth_params)
+        'linear' to match simulate.thermo_to_growth.growth_rate_one_condition)
         plus the model's own parameter keys.
 
     Returns
@@ -79,6 +80,10 @@ def generate_growth_parameters_df(growth_cfg):
                     f"is missing required parameter '{src_key}'."
                 )
             row[dst_key] = float(params[src_key])
+        # linear's optional occupancy warp, in the fit's extract names
+        if model_name == "linear" and params.get("warp_a") is not None:
+            row["warp_log_a"] = float(np.log(float(params["warp_a"])))
+            row["warp_x0"] = float(params.get("warp_x0", 0.5))
         rows.append(row)
 
     return pd.DataFrame(rows)
diff --git a/src/tfscreen/simulate/library_binding_data.py b/src/tfscreen/simulate/library_binding_data.py
index 84308dc2..c183a9d9 100644
--- a/src/tfscreen/simulate/library_binding_data.py
+++ b/src/tfscreen/simulate/library_binding_data.py
@@ -7,7 +7,10 @@
 binding *measurement* is added here -- their phenotype (Hill theta) is whatever
 the library simulation produced (a prior-predictive draw for stratified/random
 selection, or the injected file params for the file path), so no theta override
-is needed and consistency with growth is automatic.
+is needed and consistency with growth is automatic.  That theta comes from the
+simulated theta at the binding concentrations (``theta_df``, which
+``library_prediction`` evaluates from the drawn parameters of any theta
+component); the Hill columns of ``parameters_df`` are only a fallback.
 
 Selection for the stratified/random paths is deferred until AFTER the growth
 simulation so it can be restricted to genotypes that actually survived with
@@ -50,6 +53,24 @@ def _theta_matrix(params_df, log_concs):
     return tl + (th - tl) * occupancy
 
 
+def _theta_lookup(theta_df, titrant_conc):
+    """Genotype-indexed theta matrix (columns = ``titrant_conc``) from long form.
+
+    Genotypes with any missing concentration (NaN) are dropped.
+    """
+    df = theta_df.copy()
+    df["genotype"] = df["genotype"].astype(str)
+    df["titrant_conc"] = df["titrant_conc"].astype(float)
+    wide = (df.drop_duplicates(["genotype", "titrant_conc"])
+              .pivot(index="genotype", columns="titrant_conc", values="theta"))
+    missing = [c for c in titrant_conc if c not in wide.columns]
+    if missing:
+        raise ValueError(
+            f"library_binding: theta_df has no theta at binding "
+            f"concentration(s) {missing}.")
+    return wide.loc[:, list(titrant_conc)].dropna()
+
+
 def generate_library_binding_df(library_binding_cfg,
                                 titrant_name,
                                 titrant_conc,
@@ -57,7 +78,9 @@ def generate_library_binding_df(library_binding_cfg,
                                 parameters_df,
                                 growth_df,
                                 spiked_genotypes,
-                                rng):
+                                rng,
+                                clip_theta_obs=False,
+                                theta_df=None):
     """
     Generate binding-curve data for in-library (bulk) genotypes.
 
@@ -74,15 +97,25 @@ def generate_library_binding_df(library_binding_cfg,
     noise : float
         Gaussian sigma on ``theta_obs`` (shared).
     parameters_df : pandas.DataFrame
-        Per-genotype ground-truth phenotype; must carry ``genotype`` plus the
-        Hill columns ``theta_low, theta_high, log_hill_K, hill_n``.  For
-        file-specified genotypes this already reflects the injected file params.
+        Per-genotype ground-truth phenotype with a ``genotype`` column.  Read
+        for theta only when ``theta_df`` is None, and then it must carry the
+        Hill columns ``theta_low, theta_high, log_hill_K, hill_n`` (for
+        file-specified genotypes these reflect the injected file params).
     growth_df : pandas.DataFrame
         The simulated growth data (post selection); its ``genotype`` column is
         the set of survivors used to guarantee coverage.
     spiked_genotypes : iterable of str or None
         Genotypes that are spiked (excluded from the in-library pool).
     rng : numpy.random.Generator
+    clip_theta_obs : bool, optional
+        Clip noisy ``theta_obs`` to [0, 1] (default False; see
+        ``binding_data.generate_binding_df``).
+    theta_df : pandas.DataFrame or None, optional
+        Ground-truth theta at the binding concentrations, long form with
+        columns ``genotype``, ``titrant_conc``, ``theta`` (the last value
+        ``library_prediction(..., return_binding_theta=True)`` returns).  When
+        given it supplies every theta here, so any theta component works.
+        Genotypes with NaN theta are not eligible.
 
     Returns
     -------
@@ -104,17 +137,23 @@ def generate_library_binding_df(library_binding_cfg,
     # Genotypes that actually have growth data (survived selection).
     survivors = set(growth_df["genotype"].astype(str).unique())
 
-    # Index the ground-truth phenotypes by genotype for theta lookups.
-    pdf = parameters_df.copy()
-    pdf["genotype"] = pdf["genotype"].astype(str)
-    pdf = pdf.drop_duplicates("genotype").set_index("genotype")
+    # Ground-truth theta at the binding concentrations, one row per genotype:
+    # the simulated theta when given, else the Hill curve of parameters_df.
+    if theta_df is not None:
+        theta_by_geno = _theta_lookup(theta_df, titrant_conc)
+    else:
+        pdf = parameters_df.copy()
+        pdf["genotype"] = pdf["genotype"].astype(str)
+        pdf = pdf.drop_duplicates("genotype").set_index("genotype")
+        theta_by_geno = pd.DataFrame(_theta_matrix(pdf, log_concs),
+                                     index=pdf.index, columns=titrant_conc)
 
     if _is_file_choice(choose_by):
         # The file names the genotypes; their Hill params were injected into the
         # library phenotype pre-sim, so theta comes from parameters_df.
         named = list(standardize_genotypes(
             list(read_binding_genotype_params(choose_by).keys())))
-        eligible = survivors & set(pdf.index)
+        eligible = survivors & set(theta_by_geno.index)
         missing = [g for g in named if g not in eligible]
         if missing:
             warnings.warn(
@@ -125,7 +164,7 @@ def generate_library_binding_df(library_binding_cfg,
     else:
         # Stratified / random: choose `num` from the surviving bulk
         # (non-spiked, non-wt) so every anchor has matching growth data.
-        pool = sorted((survivors & set(pdf.index)) - spiked_set - {"wt"})
+        pool = sorted((survivors & set(theta_by_geno.index)) - spiked_set - {"wt"})
         if num is None:
             raise ValueError(
                 f"library_binding requires 'num' for choose_by '{choose_by}'."
@@ -135,7 +174,7 @@ def generate_library_binding_df(library_binding_cfg,
                 f"library_binding: only {len(pool)} eligible surviving genotype(s) "
                 f"available, but num={num} requested."
             )
-        pool_theta = _theta_matrix(pdf.loc[pool], log_concs)
+        pool_theta = theta_by_geno.loc[pool].to_numpy(dtype=float)
         if choose_by == "stratified":
             sel_idx = _greedy_maximin(pool_theta, int(num))
         elif choose_by == "random":
@@ -144,15 +183,20 @@ def generate_library_binding_df(library_binding_cfg,
             raise ValueError(f"Unknown library_binding choose_by: '{choose_by}'.")
         genotypes = [pool[int(i)] for i in np.asarray(sel_idx)]
 
-    # Build binding rows: theta from Hill params + Gaussian noise.
-    theta_true = (_theta_matrix(pdf.loc[genotypes], log_concs)
+    # Build binding rows: ground-truth theta + Gaussian noise.
+    theta_true = (theta_by_geno.loc[genotypes].to_numpy(dtype=float)
                   if genotypes else np.zeros((0, len(titrant_conc))))
     rows = []
     for gi, g in enumerate(genotypes):
         for cj, conc in enumerate(titrant_conc):
             tt = float(theta_true[gi, cj])
-            tobs = (float(np.clip(tt + rng.normal(0, noise), 0.0, 1.0))
-                    if noise > 0 else tt)
+            if noise > 0:
+                tobs = tt + rng.normal(0, noise)
+                if clip_theta_obs:
+                    tobs = np.clip(tobs, 0.0, 1.0)
+                tobs = float(tobs)
+            else:
+                tobs = tt
             rows.append({"genotype": g, "titrant_name": titrant_name,
                          "titrant_conc": conc, "theta_obs": tobs,
                          "theta_std": noise})
diff --git a/src/tfscreen/simulate/library_prediction.py b/src/tfscreen/simulate/library_prediction.py
index 5d0c2063..5ce2cff3 100644
--- a/src/tfscreen/simulate/library_prediction.py
+++ b/src/tfscreen/simulate/library_prediction.py
@@ -3,10 +3,16 @@
 
 from tfscreen.simulate import (
     build_sample_dataframes,
+    design_conditions,
+    read_design,
     thermo_to_growth,
 )
 from tfscreen.simulate.sim_data_class import build_sim_data
-from tfscreen.simulate.sample_theta import sample_theta_stratified, sample_theta_prior
+from tfscreen.simulate.sample_theta import (
+    ddG_truth_conditions,
+    sample_theta_prior,
+    sample_theta_stratified,
+)
 from tfscreen.simulate.binding_params import (
     SUPPORTED_COMPONENTS as _BINDING_PARAMS_SUPPORTED,
     read_binding_genotype_params,
@@ -22,6 +28,7 @@
 from tfscreen.simulate.empirical.population import PopulationModel
 
 from tfscreen.genetics import library_manager
+from tfscreen.genetics.library_subsample import subsample_library_df
 from tfscreen.genetics import standardize_genotypes
 
 import jax
@@ -157,6 +164,67 @@ def _measured_params_override(params_file, cf, sim_data, library_df, rng,
     return gc_over, p_over, measured_binding_df.to_dict("records")
 
 
+def growth_titrant_scale_from(cf):
+    """
+    The config's ``growth_titrant_scale`` (s, default 1): growth sees each
+    genotype's curve at s times the nominal titrant, binding at the nominal.
+    """
+    s = cf.get("growth_titrant_scale")
+    if s is None:
+        return 1.0
+    s = float(s)
+    if not np.isfinite(s) or s <= 0:
+        raise ValueError(f"growth_titrant_scale must be a positive number, got {s}")
+    return s
+
+
+def _spiked_keeps_own_theta(theta_component):
+    """Whether spiked stratified/random binding genotypes keep their own theta.
+
+    The thermodynamic components derive each genotype's theta from per-mutation
+    equilibrium constants shared across the library, and their truth is those
+    K's.  Pasting a pool curve over a spiked genotype would leave its K truth
+    describing a curve that was never simulated, so for these components the
+    spiked genotypes keep their own model-generated theta, and stratified or
+    random only chooses which of them are measured.
+    """
+    return str(theta_component).startswith("thermo.")
+
+
+def _select_own_theta_spiked(spiked_names, num, select_mode, binding_theta,
+                             binding_concs, titrant_name, rng):
+    """Choose spiked binding genotypes by their own simulated binding theta.
+
+    ``binding_theta`` is the long-form (genotype, titrant_conc, theta) table
+    from ``thermo_to_growth(..., binding_titrant_conc=...)``.  ``stratified``
+    picks the ``num`` spiked genotypes whose binding curves span the most
+    space (greedy maximin), ``random`` picks ``num`` at random; ``num`` None
+    measures every spiked genotype.  Returns the binding rows
+    (``theta_true`` straight from the table).
+    """
+    from tfscreen.simulate.sample_theta import _greedy_maximin
+
+    concs = [float(c) for c in binding_concs]
+    wide = (binding_theta.assign(genotype=binding_theta["genotype"].astype(str))
+            .pivot(index="genotype", columns="titrant_conc", values="theta"))
+    names = [g for g in standardize_genotypes(spiked_names) if g in wide.index]
+    if num is None or int(num) >= len(names):
+        chosen = names
+    elif select_mode == "stratified":
+        idx = _greedy_maximin(wide.loc[names, concs].to_numpy(dtype=float),
+                              int(num))
+        chosen = [names[int(i)] for i in idx]
+    elif select_mode == "random":
+        idx = rng.choice(len(names), size=int(num), replace=False)
+        chosen = [names[int(i)] for i in idx]
+    else:
+        raise ValueError(f"Unknown spiked_binding choose_by: '{select_mode}'.")
+
+    return [{"genotype": g, "titrant_name": titrant_name, "titrant_conc": c,
+             "theta_true": float(wide.loc[g, c])}
+            for g in chosen for c in concs]
+
+
 def _spiked_stratified_binding(binding_genotypes, cf, sample_df, binding_sample_df,
                                theta_rng_key, conc_to_col, binding_concs,
                                titrant_name, select_mode):
@@ -175,6 +243,8 @@ def _spiked_stratified_binding(binding_genotypes, cf, sample_df, binding_sample_
         single_wt_df = pd.DataFrame({"genotype": ["wt"]})
         binding_wt_sim = build_sim_data(single_wt_df, binding_sample_df,
                                         thermo_data=cf.get('thermo_data'),
+                                        thermo_options=cf.get('thermo_options'),
+                                        theta_component=cf['theta_component'],
                                         skip_pairs=True)
         wt_binding_gc, _ = sample_theta_prior(
             cf['theta_component'], binding_wt_sim, theta_rng_key,
@@ -189,6 +259,7 @@ def _spiked_stratified_binding(binding_genotypes, cf, sample_df, binding_sample_
             rng_key=theta_rng_key,
             n_select=len(non_wt),
             thermo_data=cf.get('thermo_data'),
+            thermo_options=cf.get('thermo_options'),
             pool_size=cf.get('binding_stratify_pool_size', 500),
             priors_overrides=cf.get('theta_priors'),
             sim_priors_overrides=cf.get('theta_sim_priors'),
@@ -209,7 +280,11 @@ def _spiked_stratified_binding(binding_genotypes, cf, sample_df, binding_sample_
 
 
 def library_prediction(cf: Union[Dict[str, Any], str, Path],
-                       override_keys: dict=None):
+                       override_keys: dict=None,
+                       return_mutation_truth: bool=False,
+                       return_binding_theta: bool=False,
+                       library_df: pd.DataFrame=None,
+                       base_dir: str=None):
     """
     Predict the "ground-truth" phenotypes for a transcription factor screen
     experiment given a library, thermodynamic model, and map between that model
@@ -224,6 +299,23 @@ def library_prediction(cf: Union[Dict[str, Any], str, Path],
         after reading the configuration file, replace keys in the configuration
         with the key/value pairs in override keys. No error checking is done
         on these keys; the user is responsible for checking their sanity.
+    return_mutation_truth : bool, default False
+        Also return the per-mutation, per-pair and per-structure ground
+        truth from ``thermo_to_growth`` as a sixth value (``{"mutation":
+        DataFrame or None, "pair": DataFrame or None, "ddG_sigma": DataFrame
+        or None}``).
+    return_binding_theta : bool, default False
+        Also return, last, the ground-truth theta of every library genotype
+        at the ``binding_data`` concentrations (None without a
+        ``binding_data`` block).
+    library_df : pandas.DataFrame, optional
+        The library to simulate, as ``genetics.library_subsample.
+        build_library`` returns it. Default: built from ``cf``, with any
+        ``library_genotypes``/``library_subsample`` applied.
+    base_dir : str, optional
+        Directory a relative ``library_genotypes`` path is read against when
+        the library is built here. Default: the YAML's directory when ``cf``
+        is a path, else the working directory.
 
     Returns
     -------
@@ -248,33 +340,71 @@ def library_prediction(cf: Union[Dict[str, Any], str, Path],
         ``titrant_name``, ``titrant_conc``, ``theta_true``.  Theta values
         here are stratified (greedy maximin) across the binding concentrations
         and are consistent with the growth phenotypes in ``phenotype_df``.
+    mutation_truth : dict
+        Only when ``return_mutation_truth`` is True; see
+        ``thermo_to_growth``.
+    library_binding_theta_df : pandas.DataFrame or None
+        Only when ``return_binding_theta`` is True: columns ``genotype``,
+        ``titrant_name``, ``titrant_conc``, ``theta``, evaluated from each
+        genotype's drawn parameters at the binding concentrations (see
+        ``thermo_to_growth._theta_at_concs``).  ``library_binding`` reads
+        its theta from here.
     """
 
     # -------------------------------------------------------------------------
     # Read inputs and set up simulation
 
+    if isinstance(cf, (str, Path)) and base_dir is None:
+        base_dir = os.path.dirname(os.path.abspath(cf))
     cf = tfscreen.util.read_yaml(cf, override_keys=override_keys)
 
     # -------------------------------------------------------------------------
     # Do main calculation
 
-    # Build library_df
-    lm = library_manager.LibraryManager(cf)
-    library_df = lm.build_library_df()
-
-    # Build sample_df (holds all conditions for the experiment)
-    sample_df = build_sample_dataframes(
-        cf['condition_blocks'],
-        replicate=1
-    )
+    # Build library_df (restricted to the config's subsample, if any)
+    if library_df is None:
+        lm = library_manager.LibraryManager(cf)
+        library_df, _ = subsample_library_df(lm.build_library_df(), cf,
+                                             base_dir=base_dir)
+
+    # Build sample_df (holds all conditions for the experiment): from the
+    # condition blocks, or from a design tube table (the union of its
+    # replicates' conditions; each replicate later takes its own tubes).
+    if cf.get('design') is not None:
+        if cf.get('condition_blocks') is not None:
+            raise ValueError("Give either 'design' or 'condition_blocks', not "
+                             "both.")
+        sample_df = design_conditions(read_design(cf['design']))
+    else:
+        sample_df = build_sample_dataframes(
+            cf['condition_blocks'],
+            replicate=1
+        )
 
     # Build SimData: lightweight container for the theta model
     sim_data = build_sim_data(
         library_df=library_df,
         sample_df=sample_df,
         thermo_data=cf.get('thermo_data'),
+        thermo_options=cf.get('thermo_options'),
+        theta_component=cf.get('theta_component'),
     )
 
+    # In vivo titrant: growth sees each curve at s times the nominal
+    # concentration, binding at the nominal one (the fit's
+    # theta_growth_log_conc_shift = log s, hill_geno). Every growth-side
+    # theta evaluation reads sim_data's concentrations, so scaling them here
+    # covers the prior draw, the wt reference and the overrides; binding
+    # theta is evaluated at the binding concentrations themselves.
+    growth_titrant_scale = growth_titrant_scale_from(cf)
+    growth_sample_df = sample_df
+    if growth_titrant_scale != 1.0:
+        from tfscreen.simulate.sim_data_class import with_titrant_conc
+        nominal = np.sort(sample_df["titrant_conc"].unique()).astype(float)
+        sim_data = with_titrant_conc(sim_data, nominal * growth_titrant_scale)
+        growth_sample_df = sample_df.assign(
+            titrant_conc=sample_df["titrant_conc"] * growth_titrant_scale)
+
     # Derive both RNG objects from the single seed.  They are independent
     # objects with independent state; using the same integer for both introduces
     # no correlation between the two streams.
@@ -301,10 +431,27 @@ def library_prediction(cf: Union[Dict[str, Any], str, Path],
 
     binding_cfg = cf.get('binding_data')
     phenotype_source = cf.get('phenotype_source', 'prior')
+
+    # ddG_truth_sd: SD of the simulated per-structure ΔΔG around the table
+    # (PddG/PnnC), conditioned as the draw's sigma_s (1 kT when absent);
+    # checked before any sampling.
+    ddG_truth_sd = cf.get('ddG_truth_sd')
+    theta_conditions = None
+    if phenotype_source == 'empirical':
+        if ddG_truth_sd is not None:
+            raise ValueError("ddG_truth_sd does not apply with "
+                             "phenotype_source: empirical.")
+    else:
+        theta_conditions = ddG_truth_conditions(
+            ddG_truth_sd, cf.get('theta_component'), cf.get('thermo_options'))
+
     theta_gc_override = {}
     theta_params_override = {}
     dk_geno_override = None
     binding_theta_df = None
+    # spiked stratified/random binding whose genotypes keep their own theta
+    # (thermodynamic components): chosen after the draw, below.
+    deferred_spiked = None
 
     if phenotype_source == 'empirical':
         # Empirical phenotypes: resample every library genotype from the
@@ -348,11 +495,13 @@ def library_prediction(cf: Union[Dict[str, Any], str, Path],
                 theta_gc_override.update(gc_over)
                 theta_params_override.update(p_over)
                 rows.extend(spiked_rows)
+            elif _spiked_keeps_own_theta(theta_component):
+                deferred_spiked = (sb.get("num"), choose_by)
             else:
                 num = sb.get("num")
                 sel = spiked_names if num is None else spiked_names[:int(num)]
                 s_over, s_rows = _spiked_stratified_binding(
-                    sel, cf, sample_df, binding_sample_df, theta_rng_key,
+                    sel, cf, growth_sample_df, binding_sample_df, theta_rng_key,
                     conc_to_col, binding_concs, titrant_name,
                     select_mode=choose_by)
                 theta_gc_override.update(s_over)
@@ -399,7 +548,10 @@ def library_prediction(cf: Union[Dict[str, Any], str, Path],
         activity_wt = cf.get('activity_wt', 1.0)
         activity_mut_scale = cf.get('activity_mut_scale', 0.0)
 
-    phenotype_df, genotype_theta_df, parameters_df = thermo_to_growth(
+    need_binding_theta = binding_cfg is not None and (
+        return_binding_theta or deferred_spiked is not None)
+
+    result = thermo_to_growth(
         genotypes=library_df["genotype"],
         sim_data=sim_data,
         sample_df=sample_df,
@@ -421,6 +573,35 @@ def library_prediction(cf: Union[Dict[str, Any], str, Path],
         theta_gc_override=theta_gc_override,
         theta_params_override=theta_params_override or None,
         dk_geno_override=dk_geno_override,
+        return_mutation_truth=return_mutation_truth,
+        theta_conditions=theta_conditions,
+        **({"binding_titrant_conc": binding_cfg['titrant_conc']}
+           if need_binding_theta else {}),
     )
-
-    return library_df, phenotype_df, genotype_theta_df, parameters_df, binding_theta_df
+    phenotype_df, genotype_theta_df, parameters_df = result[:3]
+
+    library_binding_theta_df = None
+    if need_binding_theta:
+        library_binding_theta_df = result[-1].copy()
+        library_binding_theta_df.insert(1, "titrant_name",
+                                        binding_cfg['titrant_name'])
+
+    # Spiked genotypes that keep their own theta: choose which are measured
+    # from their simulated binding curves, so their binding theta, growth
+    # theta and K truth all come from the same draw.
+    if deferred_spiked is not None:
+        num, choose_by = deferred_spiked
+        spiked_rows = _select_own_theta_spiked(
+            spiked_names, num, choose_by, library_binding_theta_df,
+            binding_cfg['titrant_conc'], binding_cfg['titrant_name'], rng)
+        prior = [] if binding_theta_df is None else \
+            binding_theta_df.to_dict("records")
+        binding_theta_df = pd.DataFrame(spiked_rows + prior)
+
+    out = (library_df, phenotype_df, genotype_theta_df, parameters_df,
+           binding_theta_df)
+    if return_mutation_truth:
+        out += (result[3],)
+    if return_binding_theta:
+        out += (library_binding_theta_df,)
+    return out
diff --git a/src/tfscreen/simulate/od600.py b/src/tfscreen/simulate/od600.py
new file mode 100644
index 00000000..56240381
--- /dev/null
+++ b/src/tfscreen/simulate/od600.py
@@ -0,0 +1,63 @@
+"""
+Simulated OD600 readings of each tube's total population.
+
+The experiment estimates each tube's total cells from one OD600 reading
+through a lab-specific calibration (see ``docs/source/process-raw.rst``,
+"One tube per time-point", and ``tfs-calibrate-od600``). The simulator knows
+each tube's true total, so it runs the calibration backwards to get the true
+OD600, adds the reader's noise and applies its detection threshold, then
+(optionally) runs the calibration forwards again, as the lab does, to get
+the estimated total the pipeline would ingest.
+
+The calibration file is the one ``tfs-calibrate-od600`` writes; reading,
+inverting and applying it live in ``tfscreen.process_raw.od600``.
+"""
+
+import numpy as np
+
+from tfscreen.process_raw.od600 import (
+    as_calibration,
+    cfu_per_mL_to_od600,
+    od600_to_cfu_per_mL,
+    read_calibration,
+)
+
+# The simulator's names for the shared functions.
+read_od600_calibration = read_calibration
+
+__all__ = ["read_od600_calibration", "od600_to_cfu_per_mL",
+           "cfu_per_mL_to_od600", "simulate_od600"]
+
+
+def simulate_od600(total_cfu, volume_mL, cal, rng):
+    """
+    One OD600 reading per tube from its true total population.
+
+    Parameters
+    ----------
+    total_cfu : array_like
+        True total cells in each tube.
+    volume_mL : float
+        Tube volume (the calibration is per mL).
+    cal : dict
+        Calibration from :func:`read_od600_calibration`.
+    rng : numpy.random.Generator
+
+    Returns
+    -------
+    dict of numpy.ndarray
+        ``od600`` (the reading, with multiplicative noise of SD
+        ``reading_rel_sd``), ``od600_detectable`` (reading at or above the
+        detection threshold) and ``od600_in_range`` (reading at or below
+        the top of the calibrated range).
+    """
+    cal = as_calibration(cal)
+    true_od = cfu_per_mL_to_od600(np.asarray(total_cfu, dtype=float) / volume_mL,
+                                  cal)
+    od = true_od * (1.0 + cal["reading_rel_sd"]
+                    * rng.standard_normal(true_od.shape))
+    od = np.maximum(od, 0.0)
+    detectable = od >= cal["detection_threshold"]
+    in_range = od <= cal["calibrated_od600_range"][1]
+    return {"od600": od, "od600_detectable": detectable,
+            "od600_in_range": in_range}
diff --git a/src/tfscreen/simulate/raw_output.py b/src/tfscreen/simulate/raw_output.py
new file mode 100644
index 00000000..9472b1b4
--- /dev/null
+++ b/src/tfscreen/simulate/raw_output.py
@@ -0,0 +1,174 @@
+"""
+A simulated experiment written in the raw formats real data come in.
+
+``tfs-simulate`` builds its growth table in memory (``counts_to_lncfu`` on
+the simulated counts). This module writes the same experiment the way a lab
+hands it to the pipeline, so a simulation enters at ``tfs-process-counts``
+exactly where real data do (pipeline plan step 6):
+
+- ``{out_prefix}_counts/counts_<sample>.csv``: one file per sequenced tube in
+  ``tfs-process-fastq``'s format, ``genotype`` and ``counts`` for every
+  library genotype (zeros included) plus the ``__unknown__`` row (0: the
+  simulator has no reads it cannot assign).
+- ``{out_prefix}_tubes.csv``: the tube table, one row per sequenced tube with
+  ``sample``, ``library`` and the design columns. Ground truth is left out.
+- With an ``od600`` block, ``{out_prefix}_tube_od600.csv`` (``sample``,
+  ``od600``) and a copy of the calibration
+  (``{out_prefix}_od600_calibration.yaml``); ``tfs-process-counts`` turns the
+  readings into tube totals. A tube read below the detection threshold is
+  left out of the tube table, as a lab would drop it. Without an ``od600``
+  block the tube table carries ``sample_cfu`` and ``sample_cfu_std`` (the
+  simulator's totals).
+
+Tubes keep the design's names when the simulation follows a design file
+(``design_sample``) and no name is a substring of another; otherwise they
+are fixed-width (``tube0001``). Either way the count-file glob
+``counts*{sample}*.csv`` matches exactly one file per tube.
+"""
+
+import os
+import shlex
+import shutil
+
+import numpy as np
+import pandas as pd
+
+from tfscreen.genetics import UNKNOWN_GENOTYPE
+
+TUBE_COLUMNS = ["sample", "library", "replicate", "condition_pre", "t_pre",
+                "condition_sel", "t_sel", "titrant_name", "titrant_conc"]
+
+
+def sample_names(n):
+    """Fixed-width tube names, so no name is a substring of another."""
+    width = max(4, len(str(max(n, 1))))
+    return [f"tube{i + 1:0{width}d}" for i in range(n)]
+
+
+def _tube_names(tubes):
+    """
+    The design's tube names (``design_sample``) when every tube has one and no
+    name is a substring of another (the count-file glob would match two
+    files); else fixed-width ``tubeNNNN`` names.
+    """
+    if "design_sample" in tubes.columns:
+        given = tubes["design_sample"]
+        if given.notna().all():
+            given = given.astype(str).tolist()
+            clash = [a for a in given for b in given if a != b and a in b]
+            if not clash and len(set(given)) == len(given):
+                return given
+            print(f"Design tube names cannot name count files uniquely "
+                  f"(e.g. {clash[:3]}); using tube0001, ... instead.",
+                  flush=True)
+    return sample_names(len(tubes))
+
+
+def undetectable_tubes(sample_df, od600_config):
+    """
+    Boolean mask of the tubes whose OD600 read below the detection threshold.
+
+    These are the tubes a lab drops (``tfs-process-counts`` refuses a reading
+    below the threshold, since its total is only an upper bound), so the raw
+    writer leaves them out and ``tfs-simulate`` drops them from its direct
+    growth table too. All False without an ``od600`` block.
+
+    Parameters
+    ----------
+    sample_df : pandas.DataFrame
+        Simulated tubes, with ``od600_detectable`` when there is OD600.
+    od600_config : dict or None
+        The simulate config's ``od600`` block.
+
+    Returns
+    -------
+    numpy.ndarray of bool
+    """
+    if od600_config is None or "od600_detectable" not in sample_df.columns:
+        return np.zeros(len(sample_df), dtype=bool)
+    return ~sample_df["od600_detectable"].astype(bool).to_numpy()
+
+
+def write_raw_experiment(sample_df, counts_df, library_genotypes, out_prefix,
+                         od600_config=None):
+    """
+    Write a simulated experiment in the raw formats (see the module docstring).
+
+    Parameters
+    ----------
+    sample_df : pandas.DataFrame
+        The simulated sequenced tubes, indexed by the simulator's sample id,
+        with the design columns, ``sample_cfu``/``sample_cfu_std`` and, with
+        an ``od600`` block, ``od600`` and ``od600_detectable``.
+    counts_df : pandas.DataFrame
+        ``sample`` (the simulator's id), ``genotype``, ``counts``.
+    library_genotypes : sequence of str
+        Every genotype of the library, the rows ``tfs-process-fastq`` writes.
+    out_prefix : str
+        Output prefix.
+    od600_config : dict or None
+        The simulate config's ``od600`` block (``calibration``,
+        ``tube_volume_mL``), or None.
+
+    Returns
+    -------
+    dict
+        Paths written (``counts_dir``, ``tubes``, and with OD600 ``od600`` and
+        ``calibration``), ``dropped`` (tubes left out for an undetectable
+        reading) and ``command`` (the ``tfs-process-counts`` call that
+        processes them).
+    """
+    tubes = sample_df.copy()
+    tubes = tubes.sort_index()
+    names = dict(zip(tubes.index, _tube_names(tubes)))
+    tubes["sample"] = tubes.index.map(names)
+
+    dropped = []
+    bad = undetectable_tubes(tubes, od600_config)
+    if bad.any():
+        dropped = tubes.loc[bad, "sample"].tolist()
+        tubes = tubes.loc[~bad]
+
+    out_dir = os.path.dirname(out_prefix)
+    if out_dir:
+        os.makedirs(out_dir, exist_ok=True)
+    counts_dir = f"{out_prefix}_counts"
+    os.makedirs(counts_dir, exist_ok=True)
+
+    genotypes = list(dict.fromkeys(str(g) for g in library_genotypes))
+    genotypes = [g for g in genotypes if g != UNKNOWN_GENOTYPE]
+    counts = counts_df.copy()
+    counts["genotype"] = counts["genotype"].astype(str)
+    extra = sorted(set(counts["genotype"]) - set(genotypes) - {UNKNOWN_GENOTYPE})
+    if extra:
+        raise ValueError(f"Simulated counts name genotypes outside the library: "
+                         f"{extra[:10]}")
+    for sim_id, name in names.items():
+        if name in dropped:
+            continue
+        c = (counts.loc[counts["sample"] == sim_id]
+             .groupby("genotype", sort=False)["counts"].sum())
+        out = pd.DataFrame({"genotype": genotypes + [UNKNOWN_GENOTYPE]})
+        out["counts"] = (out["genotype"].map(c).fillna(0)
+                         .astype(np.int64).to_numpy())
+        out.to_csv(os.path.join(counts_dir, f"counts_{name}.csv"), index=False)
+
+    paths = {"counts_dir": counts_dir, "tubes": f"{out_prefix}_tubes.csv",
+             "dropped": dropped}
+    columns = [c for c in TUBE_COLUMNS if c in tubes.columns]
+    args = ["tfs-process-counts", paths["tubes"], counts_dir]
+    if od600_config is not None:
+        tubes[columns].to_csv(paths["tubes"], index=False)
+        paths["od600"] = f"{out_prefix}_tube_od600.csv"
+        tubes[["sample", "od600"]].to_csv(paths["od600"], index=False)
+        paths["calibration"] = f"{out_prefix}_od600_calibration.yaml"
+        shutil.copyfile(od600_config["calibration"], paths["calibration"])
+        args += ["--od600_file", paths["od600"],
+                 "--od600_calibration_file", paths["calibration"],
+                 "--tube_volume_mL", f"{float(od600_config['tube_volume_mL']):g}"]
+    else:
+        tubes[columns + ["sample_cfu", "sample_cfu_std"]].to_csv(paths["tubes"],
+                                                                 index=False)
+    args += ["--out_prefix", f"{out_prefix}_processed_growth"]
+    paths["command"] = " ".join(shlex.quote(a) for a in args)
+    return paths
diff --git a/src/tfscreen/simulate/run_simulation.py b/src/tfscreen/simulate/run_simulation.py
index 374210f1..2b100256 100644
--- a/src/tfscreen/simulate/run_simulation.py
+++ b/src/tfscreen/simulate/run_simulation.py
@@ -4,6 +4,11 @@
     library_prediction,
     selection_experiment
 )
+from tfscreen.genetics.library_subsample import (
+    build_library,
+    has_subsample,
+    subsample_design,
+)
 
 from typing import Any, Dict, Union
 from pathlib import Path
@@ -75,6 +80,8 @@ def run_simulation(cf: Union[Dict[str, Any], str, Path],
     # -------------------------------------------------------------------------
     # Read inputs and set up simulation
 
+    base_dir = (os.path.dirname(os.path.abspath(cf))
+                if isinstance(cf, (str, Path)) else None)
     cf = tfscreen.util.read_yaml(cf, override_keys=override_keys)
     
     # Decide if we are going to write out files, make output directory, and make
@@ -82,10 +89,21 @@ def run_simulation(cf: Union[Dict[str, Any], str, Path],
     file_dict = _setup_file_output(output_dir,output_prefix)
     
     # Build library and predict its phenotypes
-    library_df, phenotype_df, genotype_theta_df, parameters_df, _ = library_prediction(cf)
+    # (a subsampled library is simulated with the settings subsample_design
+    # gives it; see genetics/library_subsample.py)
+    sim_cf = cf
+    library_kwargs = {}
+    if has_subsample(cf):
+        library_df, summary = build_library(cf, base_dir=base_dir)
+        sim_cf = dict(cf)
+        sim_cf.update({k: v for k, v in subsample_design(cf, summary).items()
+                       if k != "kept_pool_share"})
+        library_kwargs["library_df"] = library_df
+    library_df, phenotype_df, genotype_theta_df, parameters_df, _ = \
+        library_prediction(sim_cf, **library_kwargs)
 
     # Perform selection experiment
-    sample_df, counts_df = selection_experiment(cf,library_df,phenotype_df)
+    sample_df, counts_df = selection_experiment(sim_cf,library_df,phenotype_df)
 
     # Prepare outputs
     out_dict = {"library":library_df,
diff --git a/src/tfscreen/simulate/sample_theta.py b/src/tfscreen/simulate/sample_theta.py
index f0cdc250..5d742d01 100644
--- a/src/tfscreen/simulate/sample_theta.py
+++ b/src/tfscreen/simulate/sample_theta.py
@@ -28,13 +28,27 @@
 # Components excluded from simulation (calibration-only, no generative prior).
 _EXCLUDED = frozenset({"_simple"})
 
+# Sample sites returned with the deterministic sites (return_deterministic):
+# the PddG/PnnC per-state ΔΔG SD sigma_s, a latent sample site
+# (thermo/prior.py) but part of the simulation's truth.  The per-state ΔΔG
+# truth itself (theta_ddG) is the per-state draw of _per_state_truth_conditions.
+_TRUTH_SAMPLE_SITES = frozenset({"theta_ddG_sigma"})
+
+# fold_in tag for the key of the per-state ΔΔG truth draw.
+_PER_STATE_TRUTH_FOLD = 7919
+
+# SD (kT) of the simulated ΔΔG about the table when ddG_truth_sd is absent.
+DEFAULT_DDG_TRUTH_SD = 1.0
+
 
 def sample_theta_prior(component_name,
                        sim_data,
                        rng_key,
                        priors_overrides=None,
                        sim_priors_overrides=None,
-                       force_prior_predictive=False):
+                       force_prior_predictive=False,
+                       return_deterministic=False,
+                       conditions=None):
     """
     Draw one sample of theta from a registered theta component.
 
@@ -46,6 +60,10 @@ def sample_theta_prior(component_name,
 
     Otherwise the prior-predictive path is used: ``define_model`` is called
     inside a seeded NumPyro trace, and ``run_model`` produces ``theta_gc``.
+    For the structural models (PddG/PnnC, which put their prior on Δln K)
+    the per-state ΔΔG truth is drawn per state and Δln K is conditioned on
+    its projection, which takes a second traced draw with the same key
+    (``_per_state_truth_conditions``).
     Set ``force_prior_predictive=True`` to always use this path — this is
     required for mutation-decomposed components (e.g. ``hill_mut``) when the
     pool library has only the wt genotype and M=0 mutations, because the
@@ -70,6 +88,22 @@ def sample_theta_prior(component_name,
     force_prior_predictive : bool, default False
         When True, always use the prior-predictive path (``define_model`` +
         ``handlers.seed``), even if the component provides ``simulate``.
+    return_deterministic : bool, default False
+        When True, also return the values of every ``pyro.deterministic``
+        site recorded while drawing the sample (prior-predictive path), plus
+        the structural models' σ_s sample site (``theta_ddG_sigma``; see
+        ``_TRUTH_SAMPLE_SITES``), so callers can write the per-mutation
+        (``theta_d_*``), per-pair (``theta_epi_*``) and per-state
+        (``theta_ddG``, ``theta_ddG_sigma``) truth that ``ThetaParam`` does
+        not carry.  ``theta_ddG`` is the per-state truth draw, not the
+        model's post-hoc ΔΔG site.  Tracing does not change the draw.
+    conditions : dict or None
+        ``{sample_site: value}`` fixing sample sites of the draw
+        (prior-predictive path only; ``numpyro.handlers.condition``), e.g.
+        ``{"theta_ddG_sigma": ...}`` from :func:`ddG_truth_conditions`.  A
+        conditioned site draws no random number; every other site draws as
+        it would with the same value sampled.  A name that is not a sample
+        site of the model is an error.
 
     Returns
     -------
@@ -79,6 +113,9 @@ def sample_theta_prior(component_name,
     theta_param : ThetaParam
         Sampled parameter pytree.  When the perturbation path is used,
         ``mu`` and ``sigma`` fields are ``None``.
+    deterministic : dict[str, np.ndarray]
+        Only when ``return_deterministic`` is True: deterministic site name
+        to value.  Empty for the perturbation path, which has no trace.
 
     Raises
     ------
@@ -106,7 +143,11 @@ def sample_theta_prior(component_name,
         if sim_priors_overrides:
             sim_params.update(sim_priors_overrides)
         sim_priors = module.SimPriors(**sim_params)
-        return module.simulate("theta", sim_data, sim_priors, rng_key)
+        theta_gc, theta_param = module.simulate("theta", sim_data, sim_priors,
+                                                rng_key)
+        if return_deterministic:
+            return theta_gc, theta_param, {}
+        return theta_gc, theta_param
 
     # Prior-predictive fallback: seed the NumPyro trace and call define_model.
     # handlers.seed gives every pyro.sample site a reproducible draw
@@ -118,16 +159,196 @@ def sample_theta_prior(component_name,
         params.update(priors_overrides)
     priors = module.ModelPriors(**params)
 
-    with handlers.seed(rng_seed=rng_key):
-        theta_param = module.define_model("theta", sim_data, priors)
+    # Conditioned sample sites (e.g. the ddG truth-noise SD sigma_s) take
+    # the given value; condition sits inside seed, so they draw no key.
+    conditions = dict(conditions or {})
+
+    # handlers.trace only records the sites; the seeded draw is unchanged.
+    def _draw(conds):
+        with handlers.trace() as tr:
+            with handlers.seed(rng_seed=rng_key):
+                with handlers.condition(data=conds):
+                    param = module.define_model("theta", sim_data, priors)
+        return tr, param
+
+    trace, theta_param = _draw(conditions)
+
+    # Structural models (PddG/PnnC) put their prior on Δln K; the truth is
+    # drawn per state instead, so the per-state ΔΔG truth exists.
+    per_state_ddG = None
+    redraw = _per_state_truth_conditions(module, trace, conditions, rng_key)
+    if redraw is not None:
+        per_state_ddG, redraw_conditions = redraw
+        trace, theta_param = _draw(redraw_conditions)
+
+    if conditions:
+        samples_seen = {n for n, site in trace.items()
+                        if site["type"] == "sample"}
+        unknown = sorted(set(conditions) - samples_seen)
+        if unknown:
+            raise ValueError(
+                f"conditions {unknown} are not sample sites of theta "
+                f"component '{component_name}'.")
 
     # run_model returns (T=1, C, G) for simulation (scatter_theta=0).
     theta_tcg = module.run_model(theta_param, sim_data)
     theta_gc = np.array(theta_tcg[0]).T   # (G, C)
 
+    if return_deterministic:
+        deterministic = {name: np.asarray(site["value"])
+                         for name, site in trace.items()
+                         if site["type"] == "deterministic"
+                         or (site["type"] == "sample"
+                             and name in _TRUTH_SAMPLE_SITES)}
+        if per_state_ddG is not None:
+            deterministic["theta_ddG"] = per_state_ddG
+        return theta_gc, theta_param, deterministic
     return theta_gc, theta_param
 
 
+def _per_state_truth_conditions(module, trace, conditions, rng_key,
+                                name="theta"):
+    """
+    Conditions that make a structural model's Δln K a per-state draw.
+
+    The PddG/PnnC models sample Δln K ~ MVN(P · μ, P · diag(σ²) · Pᵀ)
+    (``thermo/prior.py``).  A simulation needs the per-state ΔΔG truth too,
+    which in the U0 models Δln K does not determine.  So the truth is drawn
+    as the per-state model states it, ΔΔG = μ + σ_s · z (``prior.
+    per_state_truth``), and Δln K is conditioned on P · ΔΔG, which has
+    exactly the model's distribution.  σ_s is the first draw's value (the
+    conditioned ``ddG_truth_sd``, or a draw from its prior); μ is the
+    model's ``theta_ddG_prior_mean`` (the table, or the untrained MLP's 0).
+
+    Parameters
+    ----------
+    module : module
+        The theta component; it has ``DLNK_PROJ`` when it is structural.
+    trace : dict
+        Trace of the first draw.
+    conditions : dict
+        The caller's conditions.
+    rng_key : jax.random.PRNGKey
+        The draw's key; the truth uses ``fold_in(rng_key, 7919)``.
+
+    Returns
+    -------
+    None, or (ddG, conditions)
+        None when the model has no Δln K site to redraw (not structural, no
+        mutations, or Δln K already conditioned).  Otherwise the per-state
+        ΔΔG truth (M, S) and the conditions for the second draw.
+    """
+    from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
+        ddG_sigma_site,
+        dlnK_site,
+        per_state_truth,
+    )
+
+    proj = getattr(module, "DLNK_PROJ", None)
+    site = dlnK_site(name)
+    if (proj is None or site not in trace or site in conditions
+            or trace[site]["type"] != "sample"):
+        return None
+    if isinstance(rng_key, (int, np.integer)):
+        rng_key = jax.random.PRNGKey(int(rng_key))
+    sigma = np.asarray(trace[ddG_sigma_site(name)]["value"])
+    prior_means = np.asarray(trace[f"{name}_ddG_prior_mean"]["value"])
+    ddG, dlnK = per_state_truth(
+        prior_means, sigma, proj,
+        jax.random.fold_in(rng_key, _PER_STATE_TRUTH_FOLD))
+    return ddG, {**conditions, ddG_sigma_site(name): sigma, site: dlnK}
+
+
+def ddG_truth_conditions(ddG_truth_sd, theta_component, thermo_options=None):
+    """
+    Site conditions that set the SD of the simulated per-structure ΔΔG.
+
+    The PddG/PnnC models sample ``theta_ddG_sigma`` (σ_s, one per
+    state) and put the prior on Δln K (``thermo/prior.py``).  The
+    simulator draws the per-state truth ΔΔG = prior_mean + σ_s · z and
+    conditions Δln K on its projection (``sample_theta_prior``), so σ_s is
+    the SD of truth minus the structural prior mean.  The simulation
+    conditions σ_s rather than drawing it from its LogNormal prior, so the
+    truth noise is a known dial.
+
+    Parameters
+    ----------
+    ddG_truth_sd : float, dict or None
+        A scalar SD for every structure, or ``{structure: sd}`` naming every
+        structure in the component's ``STRUCTURE_NAMES``.  In the energy unit
+        of ``thermo_options['ddG_units']`` (kT by default) and converted to
+        kT once, like the ΔΔG table.  0 makes the truth the table exactly.
+        None gives 1 kT for every structure (the default truth noise).
+    theta_component : str
+        Registered theta component.
+    thermo_options : dict or None
+        Supplies ``ddG_units`` and ``temperature_K``.
+
+    Returns
+    -------
+    dict or None
+        ``{"theta_ddG_sigma": np.ndarray (S,)}`` in kT, in the component's
+        ``STRUCTURE_NAMES`` order.  None for a component without the site
+        when ``ddG_truth_sd`` is None.
+
+    Raises
+    ------
+    ValueError
+        ``ddG_truth_sd`` given for a component that is not PddG/PnnC, or
+        not a finite number >= 0, or a mapping that does not name exactly
+        the component's structures.
+    """
+    from tfscreen.tfmodel.generative.components.theta.thermo.io import (
+        ddG_to_kT_factor,
+        normalize_thermo_options,
+        required_struct_names,
+    )
+    from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
+        ddG_sigma_site,
+    )
+
+    struct_names = (required_struct_names(theta_component)
+                    if theta_component is not None else None)
+    if struct_names is None:
+        if ddG_truth_sd is None:
+            return None
+        raise ValueError(
+            f"ddG_truth_sd applies only to the structural thermo components "
+            f"(thermo.*.PddG, thermo.*.PnnC); theta_component is "
+            f"'{theta_component}'.")
+    if ddG_truth_sd is None:
+        return {ddG_sigma_site(): np.full(len(struct_names), DEFAULT_DDG_TRUTH_SD)}
+
+    def _sd(value, label):
+        if isinstance(value, bool) or not isinstance(value, (int, float)):
+            raise ValueError(f"ddG_truth_sd{label} must be a number; got "
+                             f"{value!r}.")
+        value = float(value)
+        if not np.isfinite(value) or value < 0:
+            raise ValueError(f"ddG_truth_sd{label} must be finite and >= 0; "
+                             f"got {value}.")
+        return value
+
+    if isinstance(ddG_truth_sd, dict):
+        given = {str(k): v for k, v in ddG_truth_sd.items()}
+        unknown = sorted(set(given) - set(struct_names))
+        missing = [s for s in struct_names if s not in given]
+        if unknown or missing:
+            raise ValueError(
+                f"ddG_truth_sd mapping must name every structure of "
+                f"'{theta_component}' ({list(struct_names)}); "
+                + (f"unknown {unknown}" if unknown else "")
+                + ("; " if unknown and missing else "")
+                + (f"missing {missing}" if missing else "") + ".")
+        sd = np.array([_sd(given[s], f"[{s}]") for s in struct_names])
+    else:
+        sd = np.full(len(struct_names), _sd(ddG_truth_sd, ""))
+
+    opts = normalize_thermo_options(thermo_options)
+    factor = ddG_to_kT_factor(opts["ddG_units"], opts["temperature_K"])
+    return {ddG_sigma_site(): sd * factor}
+
+
 def _greedy_maximin(theta_gc, n_select):
     """
     Select n_select rows from theta_gc that maximally span the theta-curve space.
@@ -173,6 +394,7 @@ def sample_theta_stratified(component_name,
                              n_select,
                              thermo_data=None,
                              pool_size=500,
+                             thermo_options=None,
                              priors_overrides=None,
                              sim_priors_overrides=None,
                              select_mode="stratified"):
@@ -205,6 +427,8 @@ def sample_theta_stratified(component_name,
         Number of genotypes to select (= number of calibration binding genotypes).
     thermo_data : str or None
         Forwarded to build_sim_data.
+    thermo_options : dict or None
+        Forwarded to build_sim_data.
     pool_size : int, default 500
         Number of candidate parameter sets to draw before selection.
     priors_overrides : dict or None
@@ -241,9 +465,13 @@ def sample_theta_stratified(component_name,
     # regime regardless of pool_size.
     single_library_df = pd.DataFrame({"genotype": ["wt"]})
     binding_sim_data = build_sim_data(single_library_df, binding_sample_df,
-                                      thermo_data=thermo_data, skip_pairs=True)
+                                      thermo_data=thermo_data, skip_pairs=True,
+                                     theta_component=component_name,
+                                     thermo_options=thermo_options)
     growth_sim_data = build_sim_data(single_library_df, growth_sample_df,
-                                     thermo_data=thermo_data, skip_pairs=True)
+                                     thermo_data=thermo_data, skip_pairs=True,
+                                     theta_component=component_name,
+                                     thermo_options=thermo_options)
 
     # Always use the prior-predictive path for pool building.  The perturbation
     # path (simulate()) requires mutations to generate diversity; with a wt-only
diff --git a/src/tfscreen/simulate/scripts/build_empirical_cli.py b/src/tfscreen/simulate/scripts/build_empirical_cli.py
index 570f543b..85143a37 100644
--- a/src/tfscreen/simulate/scripts/build_empirical_cli.py
+++ b/src/tfscreen/simulate/scripts/build_empirical_cli.py
@@ -19,7 +19,7 @@
 
 The configure/prefit intermediates are written under ``<out_prefix>_configure_*``
 and ``<out_prefix>_prefit_*`` so they can be inspected and reused.  The MAP
-prefit is the slow step; pass ``--calibration_file`` (a prefit priors CSV or a
+prefit is the slow step; pass ``--growth_calibration_file`` (a prefit priors CSV or a
 wide ``condition_rep,growth_k,growth_m`` CSV) to skip configure+prefit and
 iterate on the fast Stage-1/2 knobs.
 """
@@ -28,11 +28,10 @@
 import warnings
 
 from tfscreen.tfmodel.genotype_fit.fit import (
-    fit_phenotypes, fits_to_results_df, _natural_from_transformed,
+    fit_phenotypes, _natural_from_transformed,
 )
 from tfscreen.simulate.empirical.population import fit_population
 from tfscreen.util.io import read_dataframe
-from tfscreen.genetics import library_composition_table
 from tfscreen.util.cli.generalized_main import generalized_main
 
 _WT_REF_COLS = ["theta_low", "theta_high", "log_hill_K", "hill_n"]
@@ -43,16 +42,16 @@ def _run_configure_and_prefit(growth_file, binding_file, library_config,
     """Configure a linear/hill_geno model and MAP-calibrate k/m.
 
     Returns the path to the calibrated priors CSV.  Imports the (heavy)
-    inference stack lazily so the ``--calibration_file`` path stays light.
+    inference stack lazily so the ``--growth_calibration_file`` path stays light.
     """
     if binding_file is None:
         raise ValueError(
             "binding data is required to calibrate the growth linkage; pass "
-            "--binding_file, or pass --calibration_file to skip configure+prefit.")
+            "--binding_df, or pass --growth_calibration_file to skip configure+prefit.")
     if library_config is None:
         raise ValueError(
             "--library_config is required to configure the model; pass it, or "
-            "pass --calibration_file to skip configure+prefit.")
+            "pass --growth_calibration_file to skip configure+prefit.")
 
     from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
     from tfscreen.tfmodel.scripts.prefit_calibration_cli import (
@@ -82,15 +81,14 @@ def _run_configure_and_prefit(growth_file, binding_file, library_config,
     return f"{configure_prefix}_priors.csv"
 
 
-def build_empirical(growth_file,
-                    seed,
-                    binding_file=None,
+def build_empirical(growth_df,
+                    binding_df=None,
                     out_prefix="tfs_empirical",
-                    calibration_file=None,
+                    seed=None,
+                    growth_calibration_file=None,
                     library_config=None,
-                    base_growth_file=None,
+                    base_growth_df=None,
                     thermo_data=None,
-                    congression_lambda=None,
                     intercept_cols="replicate",
                     dk_geno_prior_sd=1.0,
                     min_obs=None,
@@ -101,40 +99,34 @@ def build_empirical(growth_file,
 
     Parameters
     ----------
-    growth_file : str
-        Processed ``ln_cfu`` CSV (``tfs-process-counts`` output) for real data.
-    seed : int
-        Random seed for the pre-fit MAP calibration (positional / required for
-        reproducibility; ignored when ``--calibration_file`` is supplied).
-    binding_file : str, optional
-        Real binding CSV.  Required unless ``calibration_file`` is given; it
-        identifies the growth slope ``m`` from data (the in-library anchors).
+    growth_df : str
+        Processed growth CSV (``tfs-process-counts`` output) for real data.
+    binding_df : str, optional
+        Real binding CSV.  Required unless ``growth_calibration_file`` is
+        given; it identifies the growth slope ``m`` from data (the in-library
+        anchors).
     out_prefix : str
-        Output prefix.  Writes ``<prefix>_model.npz`` (+ ``.names.json``),
-        ``<prefix>_stage1_fits.csv`` (the RAW per-genotype fits), and — when
-        ``congression_lambda`` is given — ``<prefix>_stage1p5_fits.csv`` (the
-        de-attenuated fits that feed Stage 2).  Unless ``calibration_file`` is
-        given, also the ``<prefix>_configure_*`` / ``<prefix>_prefit_*`` intermediates.
-    calibration_file : str, optional
+        Output prefix.  Writes ``<prefix>_phenotype_model.json`` (the
+        generating distribution) and ``<prefix>_stage1_fits.csv`` (the
+        per-genotype fits that feed Stage 2). Unless
+        ``growth_calibration_file`` is given, also the
+        ``<prefix>_configure_*`` / ``<prefix>_prefit_*`` intermediates.
+    seed : int, optional
+        Random seed for the pre-fit MAP calibration. Required unless
+        ``growth_calibration_file`` is given.
+    growth_calibration_file : str, optional
         Skip the configure+prefit step and use this calibration directly — a
         prefit priors CSV or a wide ``condition_rep,growth_k,growth_m`` CSV.
         Use this to iterate on the Stage-1/2 knobs without re-running the MAP.
     library_config : str, optional
         Library YAML describing the screened library (the same file handed to
         ``tfs-process-fastq``), forwarded to ``configure_model``.  Required
-        unless ``calibration_file`` is given.  Genotypes encoded by a spiked
-        sequence are congression-free and are left alone by Stage 1.5.
-    base_growth_file : str, optional
+        unless ``growth_calibration_file`` is given.
+    base_growth_df : str, optional
         Direct growth-rate calibration CSV forwarded to ``configure_model``.
     thermo_data : str, optional
         Thermodynamic data path forwarded to ``configure_model`` (unused by
         the default hill_geno theta).
-    congression_lambda : float, optional
-        Zero-truncated Poisson congression rate (the same lambda as the
-        simulator's ``transformation_poisson_lambda``).  When given, run Stage
-        1.5: de-attenuate the bulk theta curves for co-transformation before
-        building the distribution (spiked genotypes are congression-free and
-        left alone).  Omit for no correction.
     intercept_cols : str
         Comma-separated columns whose unique combinations each get a nuisance
         ``ln_cfu0`` (default ``"replicate"``; empty string -> single intercept).
@@ -149,13 +141,8 @@ def build_empirical(growth_file,
         (default) serial; ``-1`` uses ``os.cpu_count() - 1``; ``N`` uses ``N``.
         Recommended for large libraries (the fits are embarrassingly parallel).
     """
+    growth_file = growth_df
     growth_df = read_dataframe(growth_file)
-    spiked = None
-    if library_config is not None:
-        composition = library_composition_table(library_config)
-        spiked = list(composition.loc[composition["in_spiked_origin"],
-                                      "genotype"])
-
     icols = [c.strip() for c in str(intercept_cols).split(",") if c.strip()]
 
     dk_prior = None
@@ -163,9 +150,13 @@ def build_empirical(growth_file,
         dk_prior = (0.0, float(dk_geno_prior_sd))
 
     # Calibration: reuse a supplied one, or configure+prefit to produce it.
+    calibration_file = growth_calibration_file
     if calibration_file is None:
+        if seed is None:
+            raise ValueError("seed is required for the pre-fit calibration "
+                             "(or pass growth_calibration_file to skip it).")
         calibration_file = _run_configure_and_prefit(
-            growth_file, binding_file, library_config, base_growth_file,
+            growth_file, binding_df, library_config, base_growth_df,
             thermo_data, out_prefix, seed)
     else:
         print(f"Using supplied calibration: {calibration_file}", flush=True)
@@ -177,27 +168,13 @@ def build_empirical(growth_file,
         growth_df, calibration_file, intercept_cols=icols,
         dk_geno_prior=dk_prior, min_obs=min_obs, num_workers=num_workers)
 
-    # Stage 1.5 (optional): de-attenuate the bulk theta curves for congression.
-    # The raw Stage-1 table (results_df) is left as-is; the corrected fits are
-    # written to their own table so both are available and unambiguous.
-    stage1p5_df = None
-    if congression_lambda is not None and float(congression_lambda) > 0:
-        from tfscreen.tfmodel.genotype_fit.congression import (
-            deattenuate_congression,
-        )
-        print(f"Stage 1.5: de-attenuating congression "
-              f"(lambda={float(congression_lambda):g})...", flush=True)
-        fits = deattenuate_congression(
-            fits, growth_df, float(congression_lambda), spiked=spiked)
-        stage1p5_df = fits_to_results_df(fits)
-
     # Stage 2: turn the per-genotype fits into ONE generating distribution
     # (deconvolving estimation noise).  This distribution is the deliverable.
     print(f"Stage 2: building the generating distribution from "
           f"{len(fits)} per-genotype fits...", flush=True)
     model = fit_population(fits, drop_railed=drop_railed)
 
-    # Embed wt's actual (congression-corrected, if applied) phenotype so the
+    # Embed wt's actual phenotype so the
     # simulation pins wt to its real value rather than a resampled/mean draw.
     wt_keys = [k for k in fits if k[0] == "wt"]
     if wt_keys:
@@ -214,26 +191,14 @@ def build_empirical(growth_file,
     fits_path = os.path.abspath(f"{out_prefix}_stage1_fits.csv")
     results_df.to_csv(fits_path, index=False)
 
-    # The de-attenuated (Stage-1.5) fits, when congression was applied.  The
-    # Stage-1 table above is always the *raw* (pre-de-attenuation) fit.
-    stage1p5_path = None
-    if stage1p5_df is not None:
-        stage1p5_path = os.path.abspath(f"{out_prefix}_stage1p5_fits.csv")
-        stage1p5_df.to_csv(stage1p5_path, index=False)
-
     bar = "=" * 72
     print(f"\n{bar}")
     print(f"Fit the generating distribution from {model.n_used} genotypes "
           f"(loglik={model.loglik:.4g}, {model.n_iter} EM iters).")
     print("\n  Phenotype model  (the distribution tfs-simulate samples from):")
     print(f"    {model_path}")
-    print("  Per-genotype Stage-1 fits, RAW / pre-de-attenuation "
-          "(diagnostic only, not used downstream):")
+    print("  Per-genotype Stage-1 fits (what feeds Stage 2):")
     print(f"    {fits_path}")
-    if stage1p5_path is not None:
-        print("  Per-genotype Stage-1.5 fits, congression-de-attenuated "
-              "(what feeds Stage 2):")
-        print(f"    {stage1p5_path}")
     print("\nTo simulate from it, add to your tfs-simulate config:")
     print("    phenotype_source: empirical")
     print("    empirical:")
@@ -245,7 +210,8 @@ def build_empirical(growth_file,
 def main():
     return generalized_main(
         build_empirical,
-        manual_arg_types={"binding_file": str, "calibration_file": str,
-                          "library_config": str, "base_growth_file": str,
-                          "thermo_data": str, "seed": int, "min_obs": int,
-                          "congression_lambda": float, "num_workers": int})
+        manual_arg_types={"seed": int, "min_obs": int})
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tfscreen/simulate/scripts/report_cfu0_cli.py b/src/tfscreen/simulate/scripts/report_cfu0_cli.py
deleted file mode 100644
index a74b9afe..00000000
--- a/src/tfscreen/simulate/scripts/report_cfu0_cli.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import numpy as np
-import pandas as pd
-
-import tfscreen
-from tfscreen.simulate import library_prediction, selection_experiment
-from tfscreen.util.cli.generalized_main import generalized_main
-
-_CLASS_ORDER = ["wt", "spiked", "single", "double", "other"]
-
-
-def _classify_genotypes(library_df):
-    """
-    Map each unique genotype in library_df to a class.
-
-    Classes (in priority order): 'wt' (genotype == "wt"), 'spiked' (any row
-    for this genotype has library_origin == "spiked"), 'single'/'double'
-    (one/two "/"-separated mutations), 'other' (anything else, e.g. triple
-    mutants).
-
-    Parameters
-    ----------
-    library_df : pandas.DataFrame
-        Must have "genotype" and "library_origin" columns. Should be the
-        un-expanded library_df returned by library_prediction (one row per
-        genotype per library_origin it was actually drawn from).
-
-    Returns
-    -------
-    dict
-        Maps genotype (str) -> class (str).
-    """
-
-    origins_by_geno = library_df.groupby("genotype", observed=True)["library_origin"].apply(set)
-
-    def classify(g):
-        if g == "wt":
-            return "wt"
-        if "spiked" in origins_by_geno.get(g, set()):
-            return "spiked"
-        num_muts = g.count("/") + 1
-        if num_muts == 1:
-            return "single"
-        if num_muts == 2:
-            return "double"
-        return "other"
-
-    return {g: classify(g) for g in origins_by_geno.index}
-
-
-def report_cfu0(config_file, num_replicates=5, seed=None):
-    """
-    Report average ln_cfu0 by genotype class from a simulate config.
-
-    Runs library_prediction once to get ground-truth phenotypes, then runs
-    selection_experiment num_replicates times (mirroring tfs-simulate's
-    replicate loop, each with an independent seed) to sample independent
-    transformation draws. Genotypes are grouped into four classes -- wt,
-    spiked (non-wt), single (non-spiked), double (non-spiked) -- and, for
-    each class, this prints the number of genotypes present in the library,
-    the mean (across replicates) number of genotypes that survive
-    transformation (nonzero cfu0), and the mean/std of ln_cfu0 pooled across
-    all replicates. Results are printed to stdout.
-
-    Parameters
-    ----------
-    config_file : str
-        Path to the YAML simulate configuration file.
-    num_replicates : int
-        Number of independent simulated transformation replicates to average
-        over. Default 5.
-    seed : int, optional
-        Random seed. Overrides the seed in the config file when provided.
-    """
-
-    cf = tfscreen.util.read_yaml(config_file)
-    if seed is not None:
-        cf["seed"] = seed
-
-    base_seed = cf.get("seed", None)
-
-    library_df, phenotype_df, genotype_theta_df, parameters_df, binding_theta_df = \
-        library_prediction(cf)
-
-    geno_class = _classify_genotypes(library_df)
-    n_in_library = pd.Series(geno_class).value_counts()
-
-    all_rows = []
-    for rep in range(1, num_replicates + 1):
-        print(f"--- Replicate {rep} of {num_replicates} ---", flush=True)
-
-        # Give each replicate a distinct (but reproducible) random seed, same
-        # convention as tfs-simulate.
-        rep_cf = dict(cf)
-        rep_cf["seed"] = (
-            base_seed * num_replicates + rep if base_seed is not None else None
-        )
-
-        rep_phenotype_df = phenotype_df.copy()
-        rep_phenotype_df["replicate"] = rep
-
-        sample_df_rep, counts_df_rep = selection_experiment(
-            rep_cf, library_df, rep_phenotype_df
-        )
-
-        # ln_cfu_0 is constant across all samples for a given (genotype,
-        # replicate, library) -- it is set by transformation, not growth --
-        # so recover replicate/library from sample_df and dedupe down to one
-        # row per independent transformation draw.
-        merged = pd.merge(
-            counts_df_rep,
-            sample_df_rep[["sample", "replicate", "library"]],
-            on="sample",
-            how="left",
-        )
-        deduped = merged.drop_duplicates(subset=["genotype", "replicate", "library"])
-        all_rows.append(deduped[["genotype", "replicate", "library", "ln_cfu_0"]])
-
-    combined = pd.concat(all_rows, ignore_index=True)
-    combined["genotype"] = combined["genotype"].astype(str)
-    combined["class"] = combined["genotype"].map(geno_class)
-
-    print()
-    header = (f"{'class':<8} {'n_library':>10} {'mean_n_observed':>16} "
-              f"{'mean_ln_cfu0':>14} {'std_ln_cfu0':>12}")
-    print(header)
-    print("-" * len(header))
-
-    for cls in _CLASS_ORDER:
-        cls_rows = combined[combined["class"] == cls]
-        if cls_rows.empty and cls not in n_in_library.index:
-            continue
-
-        n_lib = int(n_in_library.get(cls, 0))
-
-        finite = cls_rows[np.isfinite(cls_rows["ln_cfu_0"])]
-
-        n_observed_per_rep = finite.groupby("replicate")["genotype"].nunique()
-        mean_n_observed = (
-            n_observed_per_rep
-            .reindex(range(1, num_replicates + 1), fill_value=0)
-            .mean()
-        )
-
-        mean_ln_cfu0 = finite["ln_cfu_0"].mean() if not finite.empty else float("nan")
-        std_ln_cfu0 = finite["ln_cfu_0"].std() if not finite.empty else float("nan")
-
-        print(f"{cls:<8} {n_lib:>10} {mean_n_observed:>16.1f} "
-              f"{mean_ln_cfu0:>14.2f} {std_ln_cfu0:>12.2f}")
-
-
-def main():
-    return generalized_main(report_cfu0, manual_arg_types={"seed": int})
diff --git a/src/tfscreen/simulate/scripts/setup_sim_grid_cli.py b/src/tfscreen/simulate/scripts/setup_sim_grid_cli.py
index a6a4c1b8..b6d17134 100644
--- a/src/tfscreen/simulate/scripts/setup_sim_grid_cli.py
+++ b/src/tfscreen/simulate/scripts/setup_sim_grid_cli.py
@@ -10,7 +10,7 @@
 
     base_config: ../simulate_config.yaml   # base config to override
 
-    run_name: "{{ theta_component }}__noise{{ growth_rate_noise }}"
+    run_name: "{{ theta_component }}__noise{{ tube_noise_sigma }}__seed{{ seed }}"
     output_file: run.sh   # Jinja2 template; looked up next to this YAML
 
     simulate:              # key-value overrides applied to the base config
@@ -20,8 +20,12 @@
           - theta_component: hill_geno
       - name: noise
         variants:
-          - growth_rate_noise: 0.01
-          - growth_rate_noise: 0.05
+          - tube_noise_sigma: 0.001
+          - tube_noise_sigma: 0.005
+      - name: seed
+        variants:
+          - seed: 0
+          - seed: 42
 
     template:              # variables injected into the Jinja2 template only
       - name: num_replicates
@@ -35,16 +39,38 @@
   keys are not supported — override the entire top-level key if needed.
 - ``simulate`` variables are NOT injected into the template; ``template``
   variables are NOT written to the config.  To share a variable, list it in both.
-- ``run_name`` may reference variables from either section.
+- ``run_name`` may reference variables from either section. Each run
+  directory is named ``run_{index:04d}_<rendered run_name>``.
+- ``simulate`` keys must be valid simulate-config keys
+  (``selection_experiment.SIMULATE_KNOWN_KEYS``); the random seed is ``seed``.
+  Setup does not check them, but tfs-simulate refuses an unknown key.
 - Use the ``basename`` Jinja2 filter to strip path info from filenames:
-      run_name: "{{ thermo_data | basename }}__noise{{ growth_rate_noise }}"
-- Relative paths in ``simulate`` blocks (e.g. ``thermo_data``) are resolved
-  relative to the grid YAML's directory, then re-expressed relative to each
-  subdirectory in the written config.
-- Relative paths already in the base config are resolved relative to the base
-  config's location, then re-expressed relative to each subdirectory.
+      run_name: "{{ thermo_data | basename }}__noise{{ tube_noise_sigma }}"
+
+INPUT FILES
+-----------
+The grid directory is self-contained and can be moved as a unit (on and off a
+cluster, onto another partition). Every input file a run needs is copied into
+``<out_dir>/inputs/`` once, and each run's config and rendered template
+refer to it as ``../inputs/<name>``. Nothing in a run points outside the grid.
+
+- Config file paths are the keys listed in ``_SIM_PATH_KEYS``, nested ones
+  included (``thermo_data``, ``library_genotypes``,
+  ``empirical.phenotype_model``, ``binding_data.*.choose_by``, ...). A relative path resolves against the
+  base config's directory for a base-config value and against the grid YAML's
+  directory for a ``simulate`` override. A ``choose_by`` keyword
+  (``stratified``/``random``) is not a path and is left alone.
+- A ``template`` variable that names an existing file (relative to the grid
+  YAML, or absolute) is copied the same way.
+- Two different files with the same name are kept apart by a numeric suffix
+  (``hill_params_2.csv``); the same file used by many runs is copied once.
+- Setup fails rather than write a run that depends on a location outside the
+  grid: a missing input file, an input that is a directory, or a config value
+  outside ``_SIM_PATH_KEYS`` that names an existing file (add its key to
+  ``_SIM_PATH_KEYS`` if the simulator reads it).
 """
 
+import copy
 import itertools
 import json
 import os
@@ -54,18 +80,37 @@
 
 from tfscreen.util.cli import generalized_main
 from tfscreen.util.grid_utils import (
+    InputStager as _InputStager,
+    check_no_outside_paths as _check_no_outside_paths,
     make_jinja_env as _make_jinja_env,
     make_run_name as _make_run_name,
-    relativize_config_paths as _relativize_config_paths,
-    relativize_template_vars as _relativize_template_vars,
+    render_run_template as _render,
+    stage_template_vars as _stage_template_vars,
+)
+
+# Keys in the simulate config that hold file paths, as key paths into the
+# (possibly nested) config dict. Add any new file-valued key here; setup fails on
+# a config value outside this list that names an existing file.
+_SIM_PATH_KEYS = (
+    ("thermo_data",),
+    ("design",),
+    ("library_genotypes",),
+    ("calibration_file",),
+    ("empirical", "phenotype_model"),
+    ("od600", "calibration"),
+    ("binding_data", "spiked_binding", "choose_by"),
+    ("binding_data", "library_binding", "choose_by"),
 )
 
-# Top-level keys in the simulate config that hold file paths.
-_SIM_PATH_KEYS = frozenset({"thermo_data", "calibration_file"})
+# Values of a path key that are keywords, not paths (see
+# simulate/library_prediction.py::_is_file_choice).
+_PATH_KEYWORDS = frozenset({"stratified", "random"})
 
 # Fixed filename for the per-run config written into each subdirectory.
 _SIM_CONFIG_FILENAME = "tfs_sim_config.yaml"
 
+_TOOL = "tfs-setup-sim-grid"
+
 
 # ---------------------------------------------------------------------------
 # Block expansion
@@ -86,39 +131,91 @@ def _expand_block(block):
 # ---------------------------------------------------------------------------
 
 def _resolve_paths(vars_dict, base_dir):
-    """Return a copy of vars_dict with _SIM_PATH_KEYS resolved to absolute paths."""
-    out = dict(vars_dict)
-    for key in _SIM_PATH_KEYS:
-        if key in out and out[key] and not os.path.isabs(out[key]):
-            out[key] = os.path.normpath(os.path.join(base_dir, out[key]))
+    """Return a copy of vars_dict with _SIM_PATH_KEYS resolved to absolute paths.
+
+    Nested key paths are followed only through dicts; missing or empty values,
+    absolute paths, non-strings and ``_PATH_KEYWORDS`` are left unchanged. The
+    input is not modified.
+    """
+    out = copy.deepcopy(vars_dict)
+    for key_path in _SIM_PATH_KEYS:
+        node = out
+        for key in key_path[:-1]:
+            node = node.get(key) if isinstance(node, dict) else None
+        if not isinstance(node, dict):
+            continue
+        val = node.get(key_path[-1])
+        if (not isinstance(val, str) or not val or os.path.isabs(val)
+                or val in _PATH_KEYWORDS):
+            continue
+        node[key_path[-1]] = os.path.normpath(os.path.join(base_dir, val))
     return out
 
 
+def _get_node(cfg, key_path):
+    """Return the dict holding ``key_path[-1]``, or None if the path is absent."""
+    node = cfg
+    for key in key_path[:-1]:
+        node = node.get(key) if isinstance(node, dict) else None
+    return node if isinstance(node, dict) else None
+
+
+def _phenotype_model_file(path):
+    """Return the file a ``phenotype_model`` value loads (mirrors the simulator)."""
+    for cand in (path, f"{path}.json", f"{path}_phenotype_model.json"):
+        if os.path.isfile(cand):
+            return cand
+    return path
+
+
+def _stage_config_files(run_cfg, stager):
+    """Replace each ``_SIM_PATH_KEYS`` value in run_cfg (in place) with its copy.
+
+    Values must already be absolute (see ``_resolve_paths``).
+    """
+    for key_path in _SIM_PATH_KEYS:
+        node = _get_node(run_cfg, key_path)
+        if node is None:
+            continue
+        val = node.get(key_path[-1])
+        if not isinstance(val, str) or not val or val in _PATH_KEYWORDS:
+            continue
+        if key_path == ("empirical", "phenotype_model"):
+            val = _phenotype_model_file(val)
+        node[key_path[-1]] = stager.stage(
+            val, f"Config key '{'.'.join(key_path)}'"
+        )
+
+
 # ---------------------------------------------------------------------------
 # Public entry point
 # ---------------------------------------------------------------------------
 
-def setup_sim_grid(grid_yaml, out_prefix="sim_grid"):
+def setup_sim_grid(grid_yaml, out_dir="sim_grid"):
     """
     Set up a directory grid for simulation runs.
 
     Reads a grid YAML file, loads a base simulate config, expands all simulate
     and template blocks into their Cartesian product, and for each combination:
 
-    1. Creates a subdirectory under *out_prefix*.
+    1. Creates a subdirectory under *out_dir*.
     2. Merges the simulate-block overrides into the base config and writes
        ``tfs_sim_config.yaml`` inside that subdirectory.
     3. Renders the Jinja2 template (``output_file``) with the template
        variables and writes the result to the subdirectory.
     4. Writes ``combo.json`` recording the variable assignments for that run.
 
-    A ``grid_summary.json`` is written to *out_prefix* listing all created runs.
+    A ``grid_summary.json`` is written to *out_dir* listing all created runs.
+    Input files named by the configs or template variables are copied into
+    ``<out_dir>/inputs/`` and referenced as ``../inputs/<name>``, so the
+    *out_dir* directory can be moved as a unit. Every run is validated
+    before anything is written.
 
     Parameters
     ----------
     grid_yaml : str
         Path to the grid YAML file.
-    out_prefix : str, optional
+    out_dir : str, optional
         Root directory for the grid.  Created if it does not exist.
         Default ``"sim_grid"``.
 
@@ -191,38 +288,53 @@ def setup_sim_grid(grid_yaml, out_prefix="sim_grid"):
         except jinja2.TemplateSyntaxError as e:
             raise ValueError(f"Template syntax error in {output_file}: {e}") from e
 
-    os.makedirs(out_prefix, exist_ok=True)
-
-    all_runs = []
-
+    # Build every run's config and template variables, and validate them
+    # (including rendering the template) before writing anything, so a bad input
+    # cannot leave a half-built grid.
+    runs_to_write = []
+    checker = _InputStager(out_dir, _TOOL, dry_run=True)
     for i, (sim_vars, tmpl_vars) in enumerate(all_combos, start=1):
         all_vars = {**sim_vars, **tmpl_vars}
         run_name = _make_run_name(run_name_template, all_vars, i)
-        subdir = os.path.abspath(os.path.join(out_prefix, run_name))
-        os.makedirs(subdir, exist_ok=True)
 
-        # Build per-run config: base config with known path keys resolved to
-        # absolute (relative to base_cfg_dir), then apply simulate overrides
-        # (path keys resolved relative to grid_yaml_dir).
+        # Base config with known path keys resolved to absolute (relative to
+        # base_cfg_dir), then simulate overrides (relative to grid_yaml_dir).
         run_cfg = _resolve_paths(base_cfg, base_cfg_dir)
         run_cfg.update(_resolve_paths(sim_vars, grid_yaml_dir))
+        source_dirs = {k: grid_yaml_dir if k in sim_vars else base_cfg_dir
+                       for k in run_cfg}
+        _check_no_outside_paths(
+            run_cfg, _SIM_PATH_KEYS, source_dirs,
+            hint=(f"If the simulator reads this file, add the key to "
+                  f"_SIM_PATH_KEYS in {__name__}; otherwise change the value."),
+        )
+        _stage_config_files(copy.deepcopy(run_cfg), checker)
+        if jinja_template is not None:
+            _render(jinja_template, _stage_template_vars(
+                tmpl_vars, grid_yaml_dir, checker), run_name)
+
+        runs_to_write.append((run_name, run_cfg, sim_vars, tmpl_vars))
+
+    os.makedirs(out_dir, exist_ok=True)
+    stager = _InputStager(out_dir, _TOOL)
+
+    all_runs = []
+
+    for run_name, run_cfg, sim_vars, tmpl_vars in runs_to_write:
+        subdir = os.path.abspath(os.path.join(out_dir, run_name))
+        os.makedirs(subdir, exist_ok=True)
 
-        # Write config; _relativize_config_paths then rewrites any absolute
-        # paths that exist on disk to be relative to subdir.
+        # Copy input files into <out_dir>/inputs/; the config refers to them
+        # as ../inputs/<name>.
+        _stage_config_files(run_cfg, stager)
         cfg_path = os.path.join(subdir, _SIM_CONFIG_FILENAME)
         with open(cfg_path, "w") as fh:
             yaml.dump(run_cfg, fh, default_flow_style=False, sort_keys=False)
-        _relativize_config_paths(cfg_path, subdir)
 
         # Render Jinja2 template with template-section variables only.
         if jinja_template is not None:
-            rendered_tmpl_vars = _relativize_template_vars(tmpl_vars, grid_yaml_dir, subdir)
-            try:
-                rendered = jinja_template.render(**rendered_tmpl_vars)
-            except jinja2.UndefinedError as exc:
-                raise ValueError(
-                    f"Undefined template variable for run '{run_name}': {exc}"
-                ) from exc
+            rendered = _render(jinja_template, _stage_template_vars(
+                tmpl_vars, grid_yaml_dir, stager), run_name)
             out_filename = os.path.basename(output_file)
             with open(os.path.join(subdir, out_filename), "w") as fh:
                 fh.write(rendered)
@@ -237,17 +349,17 @@ def setup_sim_grid(grid_yaml, out_prefix="sim_grid"):
 
     summary = {
         "grid_yaml": grid_yaml,
-        "out_prefix": os.path.abspath(out_prefix),
+        "out_dir": os.path.abspath(out_dir),
         "runs": all_runs,
     }
-    summary_path = os.path.join(out_prefix, "grid_summary.json")
+    summary_path = os.path.join(out_dir, "grid_summary.json")
     with open(summary_path, "w") as fh:
         json.dump(summary, fh, indent=2)
         fh.write("\n")
 
     n_ok = len(all_runs)
     print(
-        f"\n{n_ok} run{'s' if n_ok != 1 else ''} created under {out_prefix}/",
+        f"\n{n_ok} run{'s' if n_ok != 1 else ''} created under {out_dir}/",
         flush=True,
     )
 
@@ -257,7 +369,7 @@ def setup_sim_grid(grid_yaml, out_prefix="sim_grid"):
 def main():
     generalized_main(
         setup_sim_grid,
-        manual_arg_types={"grid_yaml": str, "out_prefix": str},
+        manual_arg_types={"grid_yaml": str, "out_dir": str},
     )
 
 
diff --git a/src/tfscreen/simulate/scripts/simulate_cli.py b/src/tfscreen/simulate/scripts/simulate_cli.py
index 2777fa84..e6c3c3c1 100644
--- a/src/tfscreen/simulate/scripts/simulate_cli.py
+++ b/src/tfscreen/simulate/scripts/simulate_cli.py
@@ -5,22 +5,64 @@
 
 import tfscreen
 from tfscreen.simulate import library_prediction, selection_experiment
-from tfscreen.simulate.binding_data import generate_binding_df
+from tfscreen.simulate.binding_data import generate_binding_df, apply_genotype_offsets
+from tfscreen.simulate.library_prediction import growth_titrant_scale_from
 from tfscreen.simulate.library_binding_data import generate_library_binding_df
 from tfscreen.simulate.presplit_data import generate_presplit_df
 from tfscreen.simulate.base_growth_data import generate_base_growth_df, generate_k_ref_df
 from tfscreen.simulate.growth_parameters_output import generate_growth_parameters_df
 from tfscreen.simulate.transformation_lam_output import generate_transformation_lam_df
 from tfscreen.process_raw import counts_to_lncfu
+from tfscreen.genetics.library_subsample import (
+    build_library,
+    format_subsample_summary,
+    has_subsample,
+    subsample_design,
+)
+from tfscreen.simulate.raw_output import (
+    undetectable_tubes,
+    write_raw_experiment,
+)
+from tfscreen.simulate.build_sample_dataframes import (
+    CONDITION_COLUMNS,
+    read_design,
+)
 from tfscreen.util.cli.generalized_main import generalized_main
 
 
+def replicate_read_totals(total_num_reads, rep_ids, num_replicates,
+                          design=None):
+    """
+    Each sequenced replicate's share of ``total_num_reads``, by tube count.
+
+    ``total_num_reads`` is the total over every sequenced tube. With a design
+    the replicates can have different numbers of tubes, so each gets
+    ``total * (its tubes / all tubes)``; otherwise every sequenced replicate
+    has the same tubes and gets ``total / num_replicates``. OD600-only
+    replicates (beyond ``num_replicates``) get none.
+
+    Returns
+    -------
+    dict
+        replicate id -> reads (int, at least 1) for the sequenced replicates.
+    """
+    sequenced = rep_ids[:num_replicates]
+    if design is not None:
+        tubes = design.groupby("replicate").size()
+        share = {r: tubes[r] / tubes[sequenced].sum() for r in sequenced}
+    else:
+        share = {r: 1.0 / num_replicates for r in sequenced}
+    return {r: max(1, int(round(total_num_reads * share[r])))
+            for r in sequenced}
+
+
 def run_simulation_from_config(
     config_file,
-    output_dir,
-    output_prefix="tfs_sim_",
+    out_prefix="tfs_sim",
     num_replicates=2,
     seed=None,
+    write_raw=True,
+    write_growth=True,
 ):
     """
     Simulate a TF selection experiment from a YAML configuration file.
@@ -33,7 +75,16 @@ def run_simulation_from_config(
     tfscreen.simulate.growth_parameters_output.generate_growth_parameters_df),
     and transformation_lam (single-row congression Poisson-rate ground truth;
     see tfscreen.simulate.transformation_lam_output.generate_transformation_lam_df)
-    CSV files. If the config contains a 'binding_data' block, also writes a
+    CSV files. A mutation-decomposed theta model drawn from its prior (the
+    thermodynamic models) also writes mutation_parameters (one row per
+    mutation: the true d_* deltas and, for PddG/PnnC, ddG_{structure}) and,
+    when the library has multi-mutants, pair_parameters (one row per pair:
+    the true epi_* terms); see
+    tfscreen.simulate.thermo_to_growth._mutation_truth_tables. PddG/PnnC
+    also write ddG_sigma (one row per structure: the true SD of ΔΔG about
+    the structural prior mean, set by the config's ddG_truth_sd, 1 kT by
+    default; see _ddG_sigma_truth_table). If the config
+    contains a 'binding_data' block, also writes a
     simulated binding curve CSV (see
     tfscreen.simulate.binding_data.generate_binding_df). If it contains a
     'base_growth_data' block, also writes a simulated direct growth-rate
@@ -41,44 +92,83 @@ def run_simulation_from_config(
     tfscreen.simulate.base_growth_data.generate_base_growth_df and
     .generate_k_ref_df). If it contains a 'presplit_data' block, also writes
     a simulated presplit CSV (see
-    tfscreen.simulate.presplit_data.generate_presplit_df).
+    tfscreen.simulate.presplit_data.generate_presplit_df). If it contains an
+    'od600' block, also writes one OD600 reading per tube for every
+    replicate, sequenced or not ('od600'; see tfscreen.simulate.od600), and
+    simulates od600.num_od_only_replicates extra replicates that get OD600
+    but no reads. With shared_transformation, every replicate (OD-only ones
+    included) draws its cells from one library assembly and transformation.
+
+    With ``library_genotypes`` and/or ``library_subsample`` the library is
+    restricted to a subset of the described genotypes before anything is
+    simulated (``{out_prefix}_library.csv`` lists the kept ones); with the
+    default ``library_subsample_depth: full_library`` each kept sequence
+    keeps the pool share, transformants, reads and PCR templates it has in
+    the full library. See ``tfscreen.genetics.library_subsample``.
 
     Parameters
     ----------
     config_file : str
         Path to the YAML run configuration file.
-    output_dir : str
-        Directory to write output CSV files into (created if absent).
-    output_prefix : str
-        Prefix for all output filenames. Default 'tfs_sim_'.
+    out_prefix : str
+        Output prefix: files are written as ``{out_prefix}_{name}.csv``. A
+        directory part (``sim/tfs_sim``) is created if absent.
     num_replicates : int
         Number of independent experimental replicates to simulate. Default 2.
     seed : int, optional
         Random seed. Overrides seed in the config file when provided.
+    write_raw : bool
+        Also write the experiment in the raw formats real data come in (one
+        count file per tube in ``{out_prefix}_counts/``, the tube table
+        ``{out_prefix}_tubes.csv`` and, with an ``od600`` block, the OD600
+        table and the calibration), so ``tfs-process-counts`` processes it
+        exactly as it would a lab's data. The command is printed. See
+        ``tfscreen.simulate.raw_output``.
+    write_growth : bool
+        Also write ``{out_prefix}_growth.csv``, the growth table built
+        directly from the simulated counts (with each row's true values).
+        ``tfs-process-counts`` builds the same table from the raw files, so a
+        full-size simulation can skip it: on a real library's size it is
+        several GB. With an ``od600`` block, tubes read below the
+        calibration's detection threshold are left out of it, as they are
+        out of the raw tube table (``tfs-process-counts`` refuses them).
     """
     cf = tfscreen.util.read_yaml(config_file)
     if seed is not None:
         cf["seed"] = seed
 
-    os.makedirs(output_dir, exist_ok=True)
+    out_dir = os.path.dirname(out_prefix)
+    if out_dir:
+        os.makedirs(out_dir, exist_ok=True)
 
     def out_path(name):
-        return os.path.join(output_dir, f"{output_prefix}{name}.csv")
+        return f"{out_prefix}_{name}.csv"
 
-    config_out = os.path.join(output_dir, f"{output_prefix}input-config.yaml")
+    config_out = f"{out_prefix}_input-config.yaml"
 
-    output_names = ["library", "parameters", "genotype_theta", "growth",
-                    "growth_parameters", "transformation_lam"]
+    output_names = ["library", "parameters", "genotype_theta",
+                    "growth_parameters", "transformation_lam",
+                    "mutation_parameters", "pair_parameters", "ddG_sigma"]
+    if write_growth:
+        output_names.append("growth")
     if "binding_data" in cf:
         output_names.append("binding")
         if cf["binding_data"].get("library_binding") is not None:
             output_names.append("library_binding")
+        if cf["binding_data"].get("genotype_offset_sd"):
+            output_names.append("binding_offsets")
+    if cf.get("growth_titrant_scale") is not None:
+        output_names.append("growth_titrant_scale")
     if "presplit_data" in cf:
         output_names.append("presplit")
     if "base_growth_data" in cf:
         output_names.append("base_growth")
         output_names.append("k_ref")
+    if cf.get("od600") is not None:
+        output_names.append("od600")
 
+    if write_raw:
+        output_names.append("tubes")
     existing = [out_path(n) for n in output_names if os.path.exists(out_path(n))]
     if os.path.exists(config_out):
         existing.append(config_out)
@@ -86,17 +176,37 @@ def out_path(name):
         paths = ", ".join(existing)
         raise FileExistsError(
             f"Output files already exist: {paths}\n"
-            f"Delete them or choose a different output_dir / output_prefix "
+            f"Delete them or choose a different out_prefix "
             f"before re-running."
         )
 
     base_seed = cf.get("seed", None)
     rng = np.random.default_rng(base_seed)
 
+    # -------------------------------------------------------------------------
+    # The library, restricted to the config's subsample (library_genotypes,
+    # library_subsample; paths relative to the config file). sim_cf carries
+    # the experiment settings the subsample is simulated with.
+
+    sim_cf = dict(cf)
+    library_kwargs = {}
+    if has_subsample(cf):
+        library_df, subsample_summary = build_library(
+            cf, base_dir=os.path.dirname(os.path.abspath(config_file)))
+        design_update = subsample_design(cf, subsample_summary)
+        sim_cf.update({k: v for k, v in design_update.items()
+                       if k != "kept_pool_share"})
+        print(format_subsample_summary(cf, subsample_summary, design_update),
+              flush=True)
+        library_kwargs["library_df"] = library_df
+
     # -------------------------------------------------------------------------
     # Ground-truth library and phenotypes (deterministic given the config)
 
-    library_df, phenotype_df, genotype_theta_df, parameters_df, binding_theta_df = library_prediction(cf)
+    (library_df, phenotype_df, genotype_theta_df, parameters_df,
+     binding_theta_df, mutation_truth, library_binding_theta_df) = \
+        library_prediction(sim_cf, return_mutation_truth=True,
+                           return_binding_theta=True, **library_kwargs)
     growth_parameters_df = generate_growth_parameters_df(cf["growth"])
     transformation_lam_df = generate_transformation_lam_df(cf)
 
@@ -105,24 +215,73 @@ def out_path(name):
 
     all_sample_parts = []
     all_counts_parts = []
+    od_only_parts = []
     sample_id_offset = 0
 
-    for rep in range(1, num_replicates + 1):
-        print(f"\n--- Replicate {rep} of {num_replicates} ---", flush=True)
+    # One library assembly and transformation for every replicate when
+    # shared_transformation is set (selection_experiment ignores it otherwise).
+    shared_state = {}
+
+    od_cf = cf.get("od600") or {}
+    num_od_only = int(od_cf.get("num_od_only_replicates") or 0)
+
+    # A design tube table fixes the replicates and each one's tubes.
+    design = None
+    if cf.get("design") is not None:
+        design = read_design(cf["design"])
+        if num_od_only:
+            raise ValueError("od600.num_od_only_replicates cannot be combined "
+                             "with a design; add the OD-only tubes to the "
+                             "design instead.")
+        rep_ids = sorted(design["replicate"].unique().tolist())
+        if num_replicates != len(rep_ids):
+            print(f"Design has {len(rep_ids)} replicate(s) {rep_ids}; "
+                  f"num_replicates ({num_replicates}) is ignored.", flush=True)
+        num_replicates = len(rep_ids)
+    else:
+        rep_ids = list(range(1, num_replicates + num_od_only + 1))
+
+    # total_num_reads is the total over every sequenced tube, so each
+    # replicate gets its share by tube count. selection_experiment divides
+    # the total it is given among one replicate's tubes, so passing every
+    # replicate the full total multiplied the reads by the number of
+    # replicates (and, with a design, gave a replicate with few tubes far
+    # more reads per tube: 2.5e9 for the dev-data design's two-tube
+    # replicate 3; 2026-10-08).
+    # (selection_experiment validates total_num_reads; none here, none split)
+    rep_reads = {}
+    if sim_cf.get("total_num_reads") is not None:
+        rep_reads = replicate_read_totals(int(sim_cf["total_num_reads"]),
+                                          rep_ids, num_replicates, design)
+
+    for rep_num, rep in enumerate(rep_ids, start=1):
+        sequenced = rep_num <= num_replicates
+        label = ("" if sequenced else " (OD600 only)")
+        print(f"\n--- Replicate {rep} of {len(rep_ids)}{label} ---",
+              flush=True)
 
         # Give each replicate a distinct (but reproducible) random seed so
         # that replicates differ even when a base seed is set.
-        rep_cf = dict(cf)
+        rep_cf = dict(sim_cf)
         rep_cf["seed"] = (
-            base_seed * num_replicates + rep if base_seed is not None else None
+            base_seed * num_replicates + rep_num if base_seed is not None else None
         )
+        if rep in rep_reads:
+            rep_cf["total_num_reads"] = rep_reads[rep]
 
         rep_phenotype_df = phenotype_df.copy()
+        if design is not None:
+            tubes = design.loc[design["replicate"] == rep, CONDITION_COLUMNS]
+            rep_phenotype_df = rep_phenotype_df.merge(tubes, on=CONDITION_COLUMNS)
         rep_phenotype_df["replicate"] = rep
 
         sample_df_rep, counts_df_rep = selection_experiment(
-            rep_cf, library_df, rep_phenotype_df
+            rep_cf, library_df, rep_phenotype_df,
+            shared_state=shared_state, sequence=sequenced
         )
+        if not sequenced:
+            od_only_parts.append(sample_df_rep.assign(sequenced=False))
+            continue
 
         # Shift sample IDs so they are globally unique across replicates
         max_id = int(sample_df_rep.index.max()) + 1
@@ -139,7 +298,70 @@ def out_path(name):
     combined_sample_df = pd.concat(all_sample_parts)
     combined_counts_df = pd.concat(all_counts_parts, ignore_index=True)
 
-    growth_df = counts_to_lncfu(combined_sample_df, combined_counts_df)
+    if cf.get("od600") is not None:
+        od_df = pd.concat([combined_sample_df.assign(sequenced=True)]
+                          + od_only_parts, ignore_index=True)
+        keep = [c for c in ["replicate", "library", "condition_pre", "t_pre",
+                            "condition_sel", "t_sel", "titrant_name",
+                            "titrant_conc", "sequenced", "od600",
+                            "od600_detectable", "od600_in_range",
+                            "sample_cfu_true", "sample_cfu", "sample_cfu_std"]
+                if c in od_df.columns]
+        od_df[keep].to_csv(out_path("od600"), index=False)
+        print(f"Wrote: {out_path('od600')}")
+
+    if design is not None:
+        # The design's tube names, for the raw output.
+        key = ["replicate"] + CONDITION_COLUMNS
+        named = combined_sample_df[key].merge(
+            design[key + ["sample"]].rename(columns={"sample": "design_sample"}),
+            on=key, how="left")
+        combined_sample_df = combined_sample_df.copy()
+        combined_sample_df["design_sample"] = named["design_sample"].to_numpy()
+
+    # Tubes whose OD600 read below the detection threshold: the raw route
+    # leaves them out of the tube table (tfs-process-counts refuses such a
+    # reading, as the lab drops the tube), so the direct growth table drops
+    # them too and both routes hold the same tubes. The OD600 table above
+    # keeps every reading; presplit draws on the library, not on tubes.
+    bad = undetectable_tubes(combined_sample_df, cf.get("od600"))
+    growth_sample_df = combined_sample_df.loc[~bad]
+    growth_counts_df = combined_counts_df
+    if bad.any():
+        bad_ids = set(combined_sample_df.index[bad])
+        growth_counts_df = combined_counts_df.loc[
+            ~combined_counts_df["sample"].isin(bad_ids)]
+        cols = [c for c in ["replicate", "library", "condition_sel",
+                            "titrant_conc", "t_sel", "od600"]
+                if c in combined_sample_df.columns]
+        print(f"\nDropped {int(bad.sum())} of {len(bad)} tube(s) with OD600 "
+              f"below the detection threshold (from the growth table and the "
+              f"raw tube table, as tfs-process-counts would refuse them):\n"
+              f"{combined_sample_df.loc[bad, cols].to_string()}", flush=True)
+
+    # The in-memory growth table: written unless skipped, and needed by the
+    # in-library binding selection (survivors).
+    need_growth = write_growth or (
+        "binding_data" in cf
+        and cf["binding_data"].get("library_binding") is not None)
+    growth_df = None
+    if need_growth:
+        growth_df = counts_to_lncfu(
+            growth_sample_df.drop(columns="design_sample", errors="ignore"),
+            growth_counts_df)
+
+    if write_raw:
+        raw = write_raw_experiment(combined_sample_df, combined_counts_df,
+                                   library_df["genotype"], out_prefix,
+                                   od600_config=cf.get("od600"))
+        print(f"\nWrote the raw experiment: {raw['tubes']}, "
+              f"{raw['counts_dir']}/"
+              + (f", {raw['od600']}, {raw['calibration']}"
+                 if "od600" in raw else ""), flush=True)
+        if raw["dropped"]:
+            print(f"Left out of the tube table (OD600 below the detection "
+                  f"threshold): {raw['dropped']}", flush=True)
+        print(f"Process it as real data with:\n  {raw['command']}", flush=True)
 
     # -------------------------------------------------------------------------
     # Write outputs
@@ -147,10 +369,22 @@ def out_path(name):
     library_df.to_csv(out_path("library"), index=False)
     parameters_df.to_csv(out_path("parameters"), index=False)
     genotype_theta_df.to_csv(out_path("genotype_theta"), index=False)
-    growth_df.to_csv(out_path("growth"), index=False)
+    if write_growth:
+        growth_df.to_csv(out_path("growth"), index=False)
     growth_parameters_df.to_csv(out_path("growth_parameters"), index=False)
     transformation_lam_df.to_csv(out_path("transformation_lam"), index=False)
-    print(f"\nWrote: {', '.join(out_path(n) for n in ['library', 'parameters', 'genotype_theta', 'growth', 'growth_parameters', 'transformation_lam'])}")
+    written = ["library", "parameters", "genotype_theta", "growth_parameters",
+               "transformation_lam"] + (["growth"] if write_growth else [])
+    # Per-mutation / per-pair truth of mutation-decomposed theta models drawn
+    # from their prior (the thermodynamic models); absent otherwise.
+    for key, name in (("mutation", "mutation_parameters"),
+                      ("pair", "pair_parameters"),
+                      ("ddG_sigma", "ddG_sigma")):
+        truth_df = (mutation_truth or {}).get(key)
+        if truth_df is not None:
+            truth_df.to_csv(out_path(name), index=False)
+            written.append(name)
+    print(f"\nWrote: {', '.join(out_path(n) for n in written)}")
 
     if "binding_data" in cf:
         binding_cfg = cf["binding_data"]
@@ -174,13 +408,27 @@ def out_path(name):
                 growth_df,
                 spiked_genotypes=spiked_names,
                 rng=rng,
+                clip_theta_obs=bool(binding_cfg.get("clip_theta_obs", False)),
+                theta_df=library_binding_theta_df,
             )
             binding_df = pd.concat([binding_df, lib_binding_df], ignore_index=True)
             lib_manifest.to_csv(out_path("library_binding"), index=False)
             print(f"Wrote: {out_path('library_binding')}")
+        binding_df, offset_truth = apply_genotype_offsets(
+            binding_df, binding_cfg.get("genotype_offset_sd", 0.0), rng)
+        if offset_truth is not None:
+            offset_truth.to_csv(out_path("binding_offsets"), index=False)
+            print(f"Wrote: {out_path('binding_offsets')}")
         binding_df.to_csv(out_path("binding"), index=False)
         print(f"Wrote: {out_path('binding')}")
 
+    if cf.get("growth_titrant_scale") is not None:
+        s = growth_titrant_scale_from(cf)
+        pd.DataFrame({"parameter": ["growth_log_conc_shift"],
+                      "ref": [float(np.log(s))]}).to_csv(
+            out_path("growth_titrant_scale"), index=False)
+        print(f"Wrote: {out_path('growth_titrant_scale')}")
+
     if "base_growth_data" in cf:
         base_growth_df = generate_base_growth_df(cf["base_growth_data"], parameters_df, rng)
         base_growth_df.to_csv(out_path("base_growth"), index=False)
@@ -194,7 +442,7 @@ def out_path(name):
         print("\nGenerating presplit data...", flush=True)
         presplit_df = generate_presplit_df(combined_sample_df,
                                            combined_counts_df,
-                                           cf, rng)
+                                           sim_cf, rng)
         presplit_df.to_csv(out_path("presplit"), index=False)
         print(f"Wrote: {out_path('presplit')}")
 
diff --git a/src/tfscreen/simulate/selection_experiment.py b/src/tfscreen/simulate/selection_experiment.py
index 317c1ceb..8f04f85f 100644
--- a/src/tfscreen/simulate/selection_experiment.py
+++ b/src/tfscreen/simulate/selection_experiment.py
@@ -7,11 +7,25 @@
 )
 from tfscreen.util.validation import check_unknown_keys
 from tfscreen.simulate.growth.transition_linkage import get_transition_model
+from tfscreen.simulate.thermo_to_growth import (
+    growth_rates,
+    growth_rate_one_condition,
+)
+from tfscreen.simulate.od600 import (
+    read_od600_calibration,
+    simulate_od600,
+    od600_to_cfu_per_mL,
+)
+from tfscreen.simulate.cell_rules import (
+    THETA_RULES,
+    DK_RULES,
+)
 from tfscreen.util.numerical import (
     vstack_padded,
     zero_truncated_poisson,
 )
 from tfscreen.genetics import (
+    UNKNOWN_GENOTYPE,
     set_categorical_genotype
 )
 
@@ -21,10 +35,7 @@
 
 import pandas as pd
 
-from scipy.stats import (
-    gmean,
-    lognorm,
-)
+from scipy.stats import lognorm
 from tqdm.auto import tqdm
 
 # Mostly for type hinting
@@ -39,32 +50,38 @@
 # Type Aliases introduced during review
 _Numeric = TypeVar("_Numeric", int, float)
 
-MULTI_PLASMID_COMBINE_FCNS = {"gmean":gmean,
-                              "mean":ma.mean,
-                              "min":ma.min,
-                              "max":ma.max,
-                              "sum":ma.sum}
-
 # All recognized top-level keys for a simulate config file.
 SIMULATE_KNOWN_KEYS = frozenset({
     # Library genetics (passed to LibraryManager)
     "reading_frame", "first_amplicon_residue", "wt_seq", "degen_sites",
     "tiles", "tile_combos", "spiked_seqs", "expected_5p", "expected_3p",
+    # Library subsample (genetics/library_subsample.py)
+    "library_genotypes", "library_subsample", "library_subsample_seed",
+    "library_subsample_depth",
     # Phenotype / theta calculation
-    "theta_component", "thermo_data", "theta_priors", "theta_rescale",
+    "theta_component", "thermo_data", "thermo_options", "ddG_truth_sd",
+    "theta_priors",
+    "theta_rescale",
     "theta_sim_priors",
+    "growth_titrant_scale",
     # Empirical phenotype source (resample from a fitted PopulationModel)
     "phenotype_source", "empirical",
     # Conditions and growth
-    "condition_blocks", "growth",
+    "condition_blocks", "design", "growth",
     "dk_geno_hyper_loc", "dk_geno_hyper_scale", "dk_geno_hyper_shift", "dk_geno_zero",
     "activity_wt", "activity_mut_scale", "activity_component", "activity_priors",
     # Experimental simulation parameters
     "transform_sizes", "library_mixture", "lib_assembly_skew_sigma",
-    "transformation_poisson_lambda", "multi_plasmid_combine_fcn", "cfu0",
+    "max_congressed_cells",
+    "transformation_poisson_lambda", "cfu0",
+    "congression_theta_rule", "congression_dk_rule", "congression_dk_alpha",
     "tube_noise_sigma", "growth_transition",
+    # Sampling noise (roadmap step 4; all off by default)
+    "founder_sampling", "demographic_growth", "shared_transformation",
+    "pcr_template_molecules", "pcr_amplification_cv",
     # Data collection
-    "total_num_reads", "prob_index_hop", "seed",
+    "total_num_reads", "prob_index_hop", "unassigned_read_fraction",
+    "seed", "od600",
     # Column selectors (rarely overridden)
     "condition_selector", "library_selector",
     # Optional output blocks
@@ -157,6 +174,86 @@ def _check_dict_number(
     return input_dict
 
 
+def _check_bool(key: str, cf: dict[str, Any], default: bool = False) -> None:
+    """Set ``cf[key]`` to a bool (``default`` when missing or None)."""
+    v = cf.get(key)
+    if v is None:
+        cf[key] = default
+    elif isinstance(v, (bool, np.bool_)):
+        cf[key] = bool(v)
+    else:
+        raise ValueError(f"'{key}' must be true or false, not {v!r}.")
+
+
+def _check_sampling_noise(cf: dict[str, Any]) -> None:
+    """
+    Validate the optional sampling-noise settings (roadmap step 4,
+    planning/analysis-roadmap.md) and the ``od600`` block, in place. Every
+    one is off by default, so an existing config simulates as before.
+
+    - ``founder_sampling``: each tube is seeded with a Poisson number of
+      cells from each transformant clone (mean ``cfu0 * frequency``), drawn
+      independently per tube, instead of exactly ``cfu0 * frequency``.
+    - ``demographic_growth``: given its founders, a clone's cells at harvest
+      are Gamma(founders, exp(kt)) when it grows (a birth process started
+      from few cells) and Binomial(founders, exp(kt)) when it shrinks.
+      Requires ``founder_sampling``.
+    - ``shared_transformation``: every replicate draws its cells from one
+      library assembly and transformation (one glycerol stock), rather than
+      each replicate redrawing them.
+    - ``pcr_template_molecules``: reads are drawn from this many template
+      molecules per tube (a bottleneck before sequencing).
+    - ``pcr_amplification_cv``: coefficient of variation of each template's
+      amplification (PCR jackpotting). Requires ``pcr_template_molecules``.
+    - ``od600``: block with ``calibration`` (path to the OD600-to-CFU
+      constants), ``tube_volume_mL`` (default 5.0), ``num_od_only_replicates``
+      (extra replicates that get OD600 but no reads; default 0) and
+      ``sample_cfu_from_od600`` (estimate each tube's total from its
+      simulated OD600, as the lab does, instead of using the true total;
+      default false).
+    """
+    for key in ("founder_sampling", "demographic_growth",
+                "shared_transformation"):
+        _check_bool(key, cf)
+    if cf["demographic_growth"] and not cf["founder_sampling"]:
+        raise ValueError("'demographic_growth' starts each clone from its "
+                         "sampled founders, so it needs "
+                         "'founder_sampling: true'.")
+
+    _check_dict_number("pcr_template_molecules", cf, cast_type=int,
+                       min_allowed=0, inclusive_min=False, allow_none=True)
+    _check_dict_number("pcr_amplification_cv", cf, min_allowed=0,
+                       allow_none=True)
+    if cf["pcr_amplification_cv"] and cf["pcr_template_molecules"] is None:
+        raise ValueError("'pcr_amplification_cv' varies the amplification of "
+                         "each template molecule, so it needs "
+                         "'pcr_template_molecules'.")
+
+    od = cf.get("od600")
+    if od is None:
+        cf["od600"] = None
+        return
+    if not isinstance(od, dict):
+        raise ValueError("'od600' must be a block with at least "
+                         "'calibration'.")
+    unknown = set(od) - {"calibration", "tube_volume_mL",
+                         "num_od_only_replicates", "sample_cfu_from_od600"}
+    if unknown:
+        raise ValueError(f"Unknown keys in 'od600': {sorted(unknown)}.")
+    if od.get("calibration") is None:
+        raise ValueError("'od600' needs 'calibration', the path to the "
+                         "OD600-to-CFU constants.")
+    if od.get("tube_volume_mL") is None:
+        od["tube_volume_mL"] = 5.0
+    _check_dict_number("tube_volume_mL", od, min_allowed=0,
+                       inclusive_min=False)
+    if od.get("num_od_only_replicates") is None:
+        od["num_od_only_replicates"] = 0
+    _check_dict_number("num_od_only_replicates", od, cast_type=int,
+                       min_allowed=0)
+    _check_bool("sample_cfu_from_od600", od)
+
+
 def _check_cf(
     cf: dict[str, Any] | str | Path
 ) -> dict[str, Any]:
@@ -190,16 +287,56 @@ def _check_cf(
     # Load from YAML if a path is provided, otherwise assume it's a dict
     cf = read_yaml(cf)
 
+    # Removed key: say what replaced it rather than failing as "unknown".
+    if "multi_plasmid_combine_fcn" in cf:
+        raise ValueError(
+            "'multi_plasmid_combine_fcn' has been removed. It combined the "
+            "whole growth rates of a cell's plasmids, which let dk_geno decide "
+            "which plasmid set the cell's growth. A co-transformed cell's "
+            "growth is now built from cell-level theta and dk_geno: set "
+            "'congression_theta_rule' (default 'homodimer') and "
+            "'congression_dk_rule' (default 'dilution') instead, or omit both "
+            "for the defaults."
+        )
+
     check_unknown_keys(cf, SIMULATE_KNOWN_KEYS, label="simulate config")
 
     # --- Validate single numerical values ---
     cf = _check_dict_number("prob_index_hop", cf, min_allowed=0, max_allowed=1, allow_none=True)
-    cf = _check_dict_number("lib_assembly_skew_sigma", cf, min_allowed=0, allow_none=True)
+    # lib_assembly_skew_sigma: one number, or one per library origin
+    if isinstance(cf.get("lib_assembly_skew_sigma"), dict):
+        cf["lib_assembly_skew_sigma"] = dict(cf["lib_assembly_skew_sigma"])
+        for origin in list(cf["lib_assembly_skew_sigma"]):
+            _check_dict_number(origin, cf["lib_assembly_skew_sigma"],
+                               min_allowed=0, allow_none=True)
+    else:
+        cf = _check_dict_number("lib_assembly_skew_sigma", cf, min_allowed=0, allow_none=True)
+    cf = _check_dict_number("max_congressed_cells", cf, cast_type=int,
+                            min_allowed=0, inclusive_min=False, allow_none=True)
     cf = _check_dict_number("transformation_poisson_lambda", cf, min_allowed=0, allow_none=True)
     cf = _check_dict_number("tube_noise_sigma", cf, min_allowed=0, allow_none=True)
     cf = _check_dict_number("seed", cf, cast_type=int, min_allowed=0, allow_none=True)
-    cf = _check_dict_number("cfu0", cf, allow_none=False,min_allowed=0)
+    # cfu0: cells per tube at -t_pre, one number or one per library
+    if isinstance(cf.get("cfu0"), dict):
+        for lib_name in list(cf["cfu0"]):
+            cf["cfu0"] = dict(cf["cfu0"])
+            _check_dict_number(lib_name, cf["cfu0"], allow_none=False,
+                               min_allowed=0)
+    else:
+        cf = _check_dict_number("cfu0", cf, allow_none=False,min_allowed=0)
     cf = _check_dict_number("total_num_reads", cf, cast_type=int, min_allowed=0, inclusive_min=False)
+    # unassigned_read_fraction: share of each tube's reads that go to
+    # __unknown__, one number or one per library
+    if isinstance(cf.get("unassigned_read_fraction"), dict):
+        cf["unassigned_read_fraction"] = dict(cf["unassigned_read_fraction"])
+        for lib_name in list(cf["unassigned_read_fraction"]):
+            _check_dict_number(lib_name, cf["unassigned_read_fraction"],
+                               min_allowed=0, max_allowed=1,
+                               inclusive_max=False)
+    else:
+        cf = _check_dict_number("unassigned_read_fraction", cf, min_allowed=0,
+                                max_allowed=1, inclusive_max=False,
+                                allow_none=True)
 
     # --- Validate nested dictionaries ---
     for key in ["transform_sizes", "library_mixture"]:
@@ -230,6 +367,33 @@ def _check_cf(
     if "library_selector" not in cf:
         cf["library_selector"] = ["replicate", "library"]
 
+    # --- Rules for combining a co-transformed cell's plasmids ---
+    for key, rules, default in (("congression_theta_rule", THETA_RULES, "homodimer"),
+                                ("congression_dk_rule", DK_RULES, "dilution")):
+        if cf.get(key) is None:
+            cf[key] = default
+        if cf[key] not in rules:
+            raise ValueError(f"'{key}' must be one of {sorted(rules)}, "
+                             f"not '{cf[key]}'.")
+
+    # congression_dk_alpha: required by 'softmin', refused by the other rules.
+    if cf.get("congression_dk_alpha") is None:
+        cf["congression_dk_alpha"] = None
+    if cf["congression_dk_rule"] == "softmin":
+        cf = _check_dict_number("congression_dk_alpha", cf, cast_type=float,
+                                min_allowed=0, inclusive_min=False)
+        if not np.isfinite(cf["congression_dk_alpha"]):
+            raise ValueError("'congression_dk_alpha' must be finite; use "
+                             "congression_dk_rule 'min' for the alpha -> inf "
+                             "limit.")
+    elif cf["congression_dk_alpha"] is not None:
+        raise ValueError(
+            f"'congression_dk_alpha' is only used by congression_dk_rule "
+            f"'softmin' (got rule '{cf['congression_dk_rule']}'); remove it "
+            f"or set the rule to 'softmin'.")
+
+    _check_sampling_noise(cf)
+
     if not isinstance(cf["condition_selector"], list):
         raise ValueError("condition_selector must be a list of column names.")
     if not isinstance(cf["library_selector"], list):
@@ -302,7 +466,6 @@ def _check_lib_spec(
     2. `library_df` has the required columns.
     3. Keys in `cf["library_mixture"]` and `cf["transform_sizes"]` match
        and are present in `library_df["library_origin"]`.
-    4. Sets defaults for and validates `cf["multi_plasmid_combine_fcn"]`.
 
     Parameters
     ----------
@@ -316,17 +479,12 @@ def _check_lib_spec(
     Returns
     -------
     dict
-        The configuration dictionary, potentially updated with default values
-        for `multi_plasmid_combine_fcn`.
+        The configuration dictionary.
 
     Raises
     ------
     ValueError
         If any of the consistency checks fail.
-
-    Notes
-    -----
-    This function modifies the `cf` dictionary in-place.
     """
 
     # Make sure all genotypes in library_df are in genotype_df
@@ -361,29 +519,71 @@ def _check_lib_spec(
     if sum(cf["library_mixture"].values()) == 0:
         raise ValueError("The sum of ratios in 'library_mixture' cannot be zero.")
 
-    # Deal with multi_plasmid_combine_fcn.
+    return cf
 
-    libraries = np.unique(phenotype_df["library"])
-    if "multi_plasmid_combine_fcn" not in cf:
-        cf["multi_plasmid_combine_fcn"] = {}
-        for lib in libraries:
-            cf["multi_plasmid_combine_fcn"][lib] = "mean"
-    
-    for lib in libraries:
 
-        if lib not in cf["multi_plasmid_combine_fcn"]:
-            err = f"lib '{lib}' not found in 'multi_plasmid_combine_fcn'\n"
-            raise ValueError(err)
-        fcn = cf["multi_plasmid_combine_fcn"][lib]
+# Columns a co-transformed cell's growth is rebuilt from.
+_CELL_COMPONENT_COLUMNS = ("theta", "activity", "dk_geno",
+                           "condition_pre", "condition_sel",
+                           "k_pre", "k_sel")
 
-        if fcn not in MULTI_PLASMID_COMBINE_FCNS:
-            err = f"The multi_plasmid_combine_fcn '{fcn}' for library '{lib}'\n"
-            err += "was not recognized. The function should be one of:\n"
-            for k in MULTI_PLASMID_COMBINE_FCNS:
-                err += f"    {k}\n"
-            raise ValueError(err)
 
-    return cf
+def _check_cell_components(
+    cf: dict[str, Any],
+    phenotype_df: pd.DataFrame,
+) -> None:
+    """
+    Check that co-transformed cells' growth can be rebuilt from components.
+
+    A cell carrying several plasmids gets a growth rate built from its
+    cell-level theta, activity and dk_geno with the same formula
+    ``thermo_to_growth`` uses per genotype (``growth_rates``). That needs the
+    config's ``growth`` block and the phenotype's ``theta``/``activity``/
+    ``dk_geno`` columns, and those must reproduce the phenotype's own
+    ``k_pre``/``k_sel``. Otherwise co-transformed and single-plasmid cells
+    would grow by different formulas.
+
+    Raises
+    ------
+    ValueError
+        If the ``growth`` block or a component column is missing, or if the
+        rebuilt growth rates do not match ``k_pre``/``k_sel``.
+    """
+
+    if "growth" not in cf or not isinstance(cf["growth"], dict):
+        raise ValueError(
+            "With transformation_poisson_lambda > 0, co-transformed cells' "
+            "growth is built from theta, activity and dk_geno, which needs the "
+            "config's 'growth' block."
+        )
+
+    missing = [c for c in _CELL_COMPONENT_COLUMNS if c not in phenotype_df.columns]
+    if missing:
+        raise ValueError(
+            f"With transformation_poisson_lambda > 0, phenotype_df needs the "
+            f"columns {list(_CELL_COMPONENT_COLUMNS)} (as written by "
+            f"thermo_to_growth). Missing: {missing}."
+        )
+
+    theta = phenotype_df["theta"].to_numpy(dtype=float)
+    activity = phenotype_df["activity"].to_numpy(dtype=float)
+    dk_geno = phenotype_df["dk_geno"].to_numpy(dtype=float)
+    theta_rescale = cf.get("theta_rescale", "passthrough")
+
+    for k_col, cond_col in (("k_pre", "condition_pre"), ("k_sel", "condition_sel")):
+        rebuilt = growth_rates(phenotype_df[cond_col].to_numpy(), theta,
+                               activity, dk_geno, cf["growth"], theta_rescale)
+        observed = phenotype_df[k_col].to_numpy(dtype=float)
+        if not np.allclose(rebuilt, observed, rtol=1e-9, atol=1e-12,
+                           equal_nan=True):
+            bad = int(np.sum(~np.isclose(rebuilt, observed, rtol=1e-9,
+                                         atol=1e-12, equal_nan=True)))
+            raise ValueError(
+                f"phenotype_df '{k_col}' does not match growth rebuilt from "
+                f"theta, activity, dk_geno and the 'growth' block ({bad} of "
+                f"{len(observed)} rows differ). Co-transformed cells would "
+                f"grow by a different formula than single-plasmid cells."
+            )
 
 
 def _sim_plasmid_probabilities(
@@ -607,12 +807,109 @@ def _sim_transform(
 
     return transformants, plasmid_mask
 
+def _sim_transform_collapsed(
+    genotype_probs: np.ndarray,
+    num_transformants: int,
+    transformation_poisson_lambda: float | None = None,
+    rng: Generator | None = None,
+    max_congressed_cells: int | None = None,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Transformation with identical cells merged into weighted rows.
+
+    The same transformation as ``_sim_transform``, without one row per cell.
+    Cells with one plasmid are drawn as a clone count per genotype
+    (multinomial) and each genotype with any clones becomes one row carrying
+    that count. Nothing downstream can tell the difference: a row enters
+    growth, sequencing and cfu0 only through its starting abundance, the
+    founder draw is Poisson per cell (a sum of Poissons is Poisson with the
+    summed mean), and demographic growth is Gamma or Binomial per founder
+    (sums stay in the family). So a library can carry the realistic number
+    of transformants (hundreds per genotype) at the memory cost of one row
+    per genotype.
+
+    Cells with several plasmids (congression) are mostly distinct and stay
+    one row each. With ``max_congressed_cells``, more of them than that are
+    represented by a uniform sample of that many cells, each carrying
+    ``n_congressed / max_congressed_cells`` cells: an approximation that
+    keeps the congressed share of every genotype right on average but
+    lumps its noise.
+
+    Parameters
+    ----------
+    genotype_probs, num_transformants, transformation_poisson_lambda, rng
+        As for ``_sim_transform``.
+    max_congressed_cells : int, optional
+        Cap on the rows used for multi-plasmid cells. None (default) keeps
+        every one.
+
+    Returns
+    -------
+    transformants, plasmid_mask : numpy.ndarray
+        As for ``_sim_transform``, one row per merged clone set or
+        congressed cell.
+    cells_per_row : numpy.ndarray
+        ``(num_rows,)`` float, the number of cells each row stands for;
+        sums to ``num_transformants``.
+    """
+    if num_transformants == 0:
+        return (np.empty((0, 0), dtype=int), np.empty((0, 0), dtype=bool),
+                np.empty(0, dtype=float))
+    if rng is None:
+        rng = np.random.default_rng()
+
+    lam = transformation_poisson_lambda
+    if lam is None or lam == 0:
+        n_multi = 0
+    else:
+        # P(M >= 2 | M >= 1) for M ~ Poisson(lam)
+        p_multi = 1.0 - lam*np.exp(-lam)/(1.0 - np.exp(-lam))
+        n_multi = int(rng.binomial(num_transformants, p_multi))
+    n_single = num_transformants - n_multi
+
+    clones = rng.multinomial(n_single, genotype_probs)
+    single_geno = np.flatnonzero(clones)
+    single_cells = clones[single_geno].astype(float)
+
+    num_plas = np.empty(0, dtype=int)
+    n_rows_multi = n_multi
+    if max_congressed_cells is not None:
+        n_rows_multi = min(n_multi, int(max_congressed_cells))
+    if n_rows_multi > 0:
+        # plasmids per congressed cell: zero-truncated Poisson given >= 2
+        draws = []
+        need = n_rows_multi
+        while need > 0:
+            d = zero_truncated_poisson(max(2*need, 1000), lam, rng)
+            d = d[d >= 2][:need]
+            draws.append(d)
+            need -= len(d)
+        num_plas = np.concatenate(draws)
+    max_plas = max(1, int(num_plas.max()) if len(num_plas) else 1)
+
+    multi = rng.choice(len(genotype_probs), size=(n_rows_multi, max_plas),
+                       p=genotype_probs)
+    multi_mask = np.arange(max_plas) >= num_plas[:, np.newaxis]
+
+    single = np.zeros((len(single_geno), max_plas), dtype=int)
+    single[:, 0] = single_geno
+    single_mask = np.ones((len(single_geno), max_plas), dtype=bool)
+    single_mask[:, 0] = False
+
+    transformants = np.vstack([single, multi]).astype(int)
+    plasmid_mask = np.vstack([single_mask, multi_mask])
+    multi_cells = (np.full(n_rows_multi, n_multi/n_rows_multi)
+                   if n_rows_multi > 0 else np.empty(0))
+    cells_per_row = np.concatenate([single_cells, multi_cells])
+    return transformants, plasmid_mask, cells_per_row
+
+
 def _sim_transform_and_mix(
     lib_origin_dict: dict,
     transform_sizes: dict[str, int],
     library_mixture: dict[str, float],
     transformation_poisson_lambda: float | None,
     rng: Generator,
+    max_congressed_cells: int | None = None,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     """Simulate transformation for multiple source libraries and mix them.
 
@@ -634,19 +931,23 @@ def _sim_transform_and_mix(
         weight (float) in the final mixture.
     transformation_poisson_lambda : float, optional
         The lambda parameter for the multi-plasmid transformation simulation,
-        passed to `_sim_transform`.
+        passed to `_sim_transform_collapsed`.
     rng : numpy.random.Generator
         An initialized NumPy random number generator.
+    max_congressed_cells : int, optional
+        Passed to `_sim_transform_collapsed` for each origin.
 
     Returns
     -------
     transformants : numpy.ndarray
-        A 2D integer array representing the mixed population of transformants.
+        A 2D integer array representing the mixed population of
+        transformants, one row per merged clone set or congressed cell
+        (see `_sim_transform_collapsed`).
     trans_mask : numpy.ndarray
         The corresponding boolean mask for the `transformants` array.
     probs : numpy.ndarray
-        A 1D float array representing the frequency of each individual cell
-        (row) in the final mixed population. Sums to 1.0.
+        A 1D float array, the frequency of each row (all the cells it stands
+        for) in the final mixed population. Sums to 1.0.
     """
 
     # Go through all library_origin and transform. 
@@ -669,10 +970,12 @@ def _sim_transform_and_mix(
 
         # Together trans and trans_mask define a ragged array of cells with
         # (possibly) multiple plasmids
-        trans, tr_mask = _sim_transform(origin_sub_df["probs"].to_numpy(),
-                                        transform_sizes[lib_key],
-                                        this_lambda,
-                                        rng)
+        trans, tr_mask, cells = _sim_transform_collapsed(
+            origin_sub_df["probs"].to_numpy(),
+            transform_sizes[lib_key],
+            this_lambda,
+            rng,
+            max_congressed_cells=max_congressed_cells)
         all_trans.append(trans)
         all_trans_mask.append(tr_mask)
 
@@ -682,9 +985,9 @@ def _sim_transform_and_mix(
         # Without this, an origin's pool share becomes
         # library_mixture[lib_key] * transform_sizes[lib_key], entangling the
         # two config values.
-        n_cells = trans.shape[0]
+        n_cells = cells.sum()
         w = library_mixture[lib_key] / n_cells if n_cells > 0 else 0.0
-        all_weights.append(np.full(n_cells,w,dtype=float))
+        all_weights.append(cells*w)
 
     # This is vstack that accounts for the fact that these transformations 
     # might have different numbers of plasmids
@@ -698,42 +1001,155 @@ def _sim_transform_and_mix(
     return transformants, trans_mask, probs
 
 
-def _sim_growth(
+def _cell_kt(
     transformants: np.ndarray,
     trans_mask: np.ndarray,
-    trans_freq: np.ndarray,
     genotype_vs_kt: np.ndarray,
-    total_cfu0: float,
-    multi_plasmid_combine_fcn: str,
+    genotype_vs_theta: np.ndarray,
+    genotype_activity: np.ndarray,
+    genotype_dk_geno: np.ndarray,
+    condition_info: pd.DataFrame,
+    tube_kt: np.ndarray,
+    cf: dict[str, Any],
 ) -> np.ndarray:
-    """Simulate cell growth for a population under multiple conditions.
+    """Total log-growth (k*t) of every cell in every condition.
 
-    This function calculates the final cell abundance (CFU) for each initial
-    transformant across all experimental conditions. It accounts for
-    multi-plasmid transformants by combining their `kt` values using a
-    specified function.
+    A cell with one plasmid grows exactly as its genotype does
+    (``genotype_vs_kt``). A cell with several plasmids has one growth rate,
+    built from cell-level physics rather than by combining its plasmids'
+    growth rates: the ``congression_theta_rule`` gives the cell's theta and
+    activity, the ``congression_dk_rule`` its dk_geno (see
+    ``simulate.cell_rules``), and ``growth_rates`` turns them into ``k_pre``
+    and ``k_sel`` with the same formula used per genotype. The growth
+    transition model and per-tube noise then apply as for any genotype.
 
     Parameters
     ----------
     transformants : numpy.ndarray
-        A 2D integer array of shape `(num_cells, max_plasmids)` holding
-        genotype indices for each plasmid in each cell.
+        ``(num_cells, max_plasmids)`` genotype indices.
     trans_mask : numpy.ndarray
-        A 2D boolean array of shape `(num_cells, max_plasmids)` masking
-        invalid plasmid entries.
+        ``(num_cells, max_plasmids)``; True marks an invalid slot.
+    genotype_vs_kt : numpy.ndarray
+        ``(num_genotypes, num_conditions)`` k*t per genotype, including
+        ``tube_kt``.
+    genotype_vs_theta : numpy.ndarray
+        ``(num_genotypes, num_conditions)`` theta per genotype.
+    genotype_activity, genotype_dk_geno : numpy.ndarray
+        ``(num_genotypes,)`` per-genotype activity and dk_geno.
+    condition_info : pandas.DataFrame
+        One row per condition (column of the arrays above) with
+        ``condition_pre``, ``condition_sel``, ``t_pre`` and ``t_sel``.
+    tube_kt : numpy.ndarray
+        ``(num_conditions,)`` per-tube noise in k*t units.
+    cf : dict
+        Validated config (``growth``, ``theta_rescale``,
+        ``growth_transition``, ``congression_theta_rule``,
+        ``congression_dk_rule``, ``congression_dk_alpha``).
+
+    Returns
+    -------
+    numpy.ndarray
+        ``(num_cells, num_conditions)`` k*t per cell. NaN (a failed phenotype)
+        is set to 0, as for genotypes.
+    """
+
+    num_cells = transformants.shape[0]
+    num_conditions = genotype_vs_kt.shape[1]
+    if num_cells == 0:
+        return np.zeros((0, num_conditions), dtype=float)
+
+    # Single-plasmid cells (and every cell's first slot, overwritten below
+    # for co-transformed cells) take their genotype's k*t.
+    cell_kt = genotype_vs_kt[transformants[:, 0]].astype(float)
+
+    num_in_cell = np.sum(~trans_mask, axis=1)
+    multi = np.where(num_in_cell > 1)[0]
+    if len(multi) == 0:
+        return cell_kt
+
+    multi_trans = transformants[multi]
+    shares = _plasmid_shares(trans_mask[multi])
+
+    theta_rule = THETA_RULES[cf["congression_theta_rule"]]
+    dk_rule = DK_RULES[cf["congression_dk_rule"]]
+
+    # (num_multi, max_plasmids, num_conditions)
+    slot_theta = genotype_vs_theta[multi_trans]
+    slot_activity = np.broadcast_to(genotype_activity[multi_trans][:, :, np.newaxis],
+                                    slot_theta.shape)
+    theta_cell, activity_cell = theta_rule(slot_theta, slot_activity, shares)
+    dk_cell = dk_rule(genotype_dk_geno[multi_trans], shares,
+                      alpha=cf.get("congression_dk_alpha"))
+
+    growth_params = cf["growth"]
+    theta_rescale = cf.get("theta_rescale", "passthrough")
+    growth_transition = cf["growth_transition"]
+    num_multi = len(multi)
+    for c in range(num_conditions):
+        info = condition_info.iloc[c]
+        theta_c = theta_cell[:, c]
+        activity_c = activity_cell[:, c]
+        k_pre = growth_rate_one_condition(info["condition_pre"], theta_c,
+                                          activity_c, dk_cell,
+                                          growth_params, theta_rescale)
+        k_sel = growth_rate_one_condition(info["condition_sel"], theta_c,
+                                          activity_c, dk_cell,
+                                          growth_params, theta_rescale)
+        if growth_transition is None:
+            kt = k_pre*info["t_pre"] + k_sel*info["t_sel"]
+        else:
+            kt = _compute_kt_arrays(k_pre, k_sel,
+                                    np.full(num_multi, info["t_pre"], dtype=float),
+                                    np.full(num_multi, info["t_sel"], dtype=float),
+                                    theta_c,
+                                    np.full(num_multi, info["condition_pre"], dtype=object),
+                                    growth_transition)
+        cell_kt[multi, c] = np.nan_to_num(kt, nan=0.0) + tube_kt[c]
+
+    return cell_kt
+
+
+def _sim_growth(
+    cell_kt: np.ndarray,
+    trans_freq: np.ndarray,
+    total_cfu0: float,
+    rng: Generator | None = None,
+    founder_sampling: bool = False,
+    demographic_growth: bool = False,
+) -> np.ndarray:
+    """Simulate cell growth for a population under multiple conditions.
+
+    Each column of ``cell_kt`` is one tube. By default every tube starts
+    with exactly ``total_cfu0 * trans_freq`` cells of each transformant
+    clone and grows deterministically. The two options add the sampling
+    noise of a real split (study 0b, planning/studies/noise-anatomy/):
+
+    - ``founder_sampling``: a tube is seeded with a Poisson number of cells
+      of each clone (mean ``total_cfu0 * trans_freq``), drawn independently
+      for every tube. A rare clone's starting abundance then differs from
+      tube to tube.
+    - ``demographic_growth``: given ``n0`` founders, a clone's cells at
+      harvest are ``Gamma(n0, exp(kt))`` when ``kt >= 0`` (the sum of
+      ``n0`` birth-process lineages, each with mean ``exp(kt)`` and variance
+      about ``exp(2 kt)``) and ``Binomial(n0, exp(kt))`` when ``kt < 0``
+      (each cell survives with probability ``exp(kt)``). Requires
+      ``founder_sampling``.
+
+    Parameters
+    ----------
+    cell_kt : numpy.ndarray
+        A 2D float array of shape `(num_cells, num_conditions)` holding each
+        cell's total log-growth (`k*t`), from `_cell_kt`.
     trans_freq : numpy.ndarray
         A 1D float array of shape `(num_cells,)` with the initial frequency
         of each cell in the population.
-    genotype_vs_kt : numpy.ndarray
-        A 2D float array of shape `(num_genotypes, num_conditions)` holding
-        the growth rate multiplied by time (`k*t`) for each genotype.
     total_cfu0 : float
         The total initial colony-forming units (CFU) for the entire
         population.
-    multi_plasmid_combine_fcn : str
-        The name of the function used to combine the `kt` values from
-        multiple plasmids within a single cell. Must be a key in
-        `MULTI_PLASMID_COMBINE_FCNS`.
+    rng : numpy.random.Generator, optional
+        Required when either option is on.
+    founder_sampling, demographic_growth : bool, optional
+        See above. Both default to False.
 
     Returns
     -------
@@ -741,32 +1157,56 @@ def _sim_growth(
         A 2D float array of shape `(num_cells, num_conditions)` holding the
         final CFU for each cell after growth in each condition.
     """
-    
-    if multi_plasmid_combine_fcn not in MULTI_PLASMID_COMBINE_FCNS:
-        err = f"multi_plasmid_combine_fcn '{multi_plasmid_combine_fcn}' not recognized. Should be one\n"
-        err += f"of: '{list(MULTI_PLASMID_COMBINE_FCNS.keys())}\n"
-        raise ValueError(err)
+    # -> trans_cfu0 has shape (num_cells,)
+    trans_cfu0 = total_cfu0*trans_freq
 
-    # Look up kt for every plasmid slot in every cell for every condition.
-    # -> all_kt has shape (num_cells, max_plasmids, num_conditions)
-    all_kt = genotype_vs_kt[transformants]
+    if not founder_sampling:
+        if demographic_growth:
+            raise ValueError("demographic_growth requires founder_sampling.")
+        # -> trans_cfu has shape (num_cells, num_conditions)
+        return trans_cfu0[:,np.newaxis]*np.exp(cell_kt)
+
+    # Founders: an independent Poisson draw for every clone in every tube.
+    founders = rng.poisson(np.broadcast_to(trans_cfu0[:, np.newaxis],
+                                           cell_kt.shape))
+    fold = np.exp(cell_kt)
+    if not demographic_growth:
+        return founders*fold
+
+    trans_cfu = np.zeros(cell_kt.shape, dtype=float)
+    grow = (cell_kt >= 0) & (founders > 0)
+    trans_cfu[grow] = rng.gamma(shape=founders[grow], scale=fold[grow])
+    shrink = (cell_kt < 0) & (founders > 0)
+    trans_cfu[shrink] = rng.binomial(founders[shrink], fold[shrink])
+    return trans_cfu
 
-    # Expand the 2D mask to 3D to match the shape of all_kt.
-    # .copy() required: numpy 2.x rejects read-only broadcast arrays as ma masks
-    expanded_mask = np.broadcast_to(trans_mask[:, :, np.newaxis], all_kt.shape).copy()
-    masked_kt = ma.array(all_kt, mask=expanded_mask)
 
-    # Calculate the kt for each cell by combining effects of plasmids
-    # -> trans_kt has shape (num_cells, num_conditions)
-    trans_kt = MULTI_PLASMID_COMBINE_FCNS[multi_plasmid_combine_fcn](masked_kt,axis=1)
+def _plasmid_shares(trans_mask: np.ndarray) -> np.ndarray:
+    """Fraction of its cell that each plasmid slot represents.
 
-    # -> trans_cfu0 has shape (num_cells,)
-    trans_cfu0 = total_cfu0*trans_freq
+    A cell carrying n plasmids splits its plasmid copies among them, so each
+    valid slot is 1/n of the cell. Masked slots are 0. A cell therefore
+    contributes one cell's worth of abundance in total, however many plasmids
+    it carries, and the spiked/bulk make-up of a genotype does not depend on
+    the congression parameter lambda (see
+    ``genetics.library_design.expected_library_composition``).
 
-    # -> trans_cfu has shape (num_cells, num_conditions)
-    trans_cfu = trans_cfu0[:,np.newaxis]*np.exp(trans_kt)
+    Parameters
+    ----------
+    trans_mask : numpy.ndarray
+        A 2D boolean array `(num_cells, max_plasmids)`; True marks an invalid
+        plasmid slot.
 
-    return trans_cfu
+    Returns
+    -------
+    numpy.ndarray
+        A 2D float array with the same shape as `trans_mask`.
+    """
+    valid = ~trans_mask
+    num_in_cell = np.sum(valid, axis=1, keepdims=True)
+    return np.divide(valid, num_in_cell,
+                     out=np.zeros(trans_mask.shape, dtype=float),
+                     where=num_in_cell > 0)
 
 
 def _sim_sequencing(
@@ -776,12 +1216,16 @@ def _sim_sequencing(
     num_genotypes: int,
     reads_per_sample: int,
     rng: Generator,
+    pcr_template_molecules: int | None = None,
+    pcr_amplification_cv: float | None = None,
 ) -> np.ndarray:
     """Simulate the sequencing of plasmids from grown cell populations.
 
     This function models deep sequencing. For each condition, it assumes that
     individual plasmids are sampled for sequencing with a probability
-    proportional to the final abundance (CFU) of their host cell.
+    proportional to the final abundance (CFU) of their host cell, divided
+    among the plasmids that cell carries (a cell with n plasmids gives each
+    1/n of its abundance).
 
     Parameters
     ----------
@@ -798,6 +1242,17 @@ def _sim_sequencing(
         The number of sequencing reads to simulate for each condition/sample.
     rng : numpy.random.Generator
         An initialized NumPy random number generator.
+    pcr_template_molecules : int, optional
+        If given, the reads are drawn from this many template molecules per
+        sample rather than from the cells directly: the templates are a
+        multinomial draw over genotypes by abundance, and each read then
+        picks a template. Models a limited amount of DNA going into PCR
+        (study 0b). None (default) means no bottleneck.
+    pcr_amplification_cv : float, optional
+        Coefficient of variation of each template's amplification (PCR
+        jackpotting). A genotype with ``m`` templates then carries weight
+        ``Gamma(m / cv^2, cv^2)`` (the sum of ``m`` independent
+        amplifications with mean 1). Requires ``pcr_template_molecules``.
 
     Returns
     -------
@@ -816,18 +1271,21 @@ def _sim_sequencing(
     # conditions. 
     all_transformed_plas = ma.array(transformants, mask=trans_mask).compressed()
 
+    # Share of its cell that each plasmid slot represents (1/n for a cell
+    # with n plasmids). Shared across all conditions.
+    shares = _plasmid_shares(trans_mask)
+
     # Loop over all conditions
     for i in range(num_conditions):
 
-        # Get the frequencies of cells at this condition and then broadcast to 
-        # match the shape/alignment of all_transformed_plas
+        # Split each cell's abundance at this condition among its plasmids,
+        # aligned with all_transformed_plas
         trans_freq = trans_cfu[:,i]
-        trans_freq_2d = np.broadcast_to(trans_freq[:, np.newaxis],
-                                        transformants.shape)
-        all_transformed_freq = ma.array(trans_freq_2d, mask=trans_mask).compressed()
+        plasmid_freq_2d = trans_freq[:, np.newaxis]*shares
+        all_transformed_freq = ma.array(plasmid_freq_2d, mask=trans_mask).compressed()
 
-        # Count the number of times each plasmid index appears, weighted by the 
-        # frequency of the cells in the population
+        # Count the number of times each plasmid index appears, weighted by
+        # its share of its cell's abundance
         geno_counts = np.bincount(all_transformed_plas,
                                   weights=all_transformed_freq,
                                   minlength=num_genotypes)
@@ -837,6 +1295,18 @@ def _sim_sequencing(
 
             # Normalize to get relative probability of each drawing each genotype
             geno_probs = geno_counts/np.sum(geno_counts)
+
+            # A limited number of template molecules goes into PCR; reads
+            # are drawn from those, each amplified by a random factor.
+            if pcr_template_molecules is not None:
+                templates = rng.multinomial(pcr_template_molecules, geno_probs)
+                weights = templates.astype(float)
+                if pcr_amplification_cv:
+                    cv2 = pcr_amplification_cv**2
+                    has = templates > 0
+                    weights[has] = rng.gamma(shape=templates[has]/cv2,
+                                             scale=cv2)
+                geno_probs = weights/np.sum(weights)
         
             # Sample genotypes based on their probabilities.
             sampled_plasmids = rng.choice(
@@ -863,8 +1333,10 @@ def _calc_genotype_cfu0(
 
     This function determines the starting abundance (cfu0) of each distinct
     genotype based on the stochastic outcome of the transformation simulation.
-    It aggregates the frequencies of all cells containing a given plasmid
-    and partitions the total library cfu0 accordingly.
+    Each cell's frequency is split among the plasmids it carries (1/n each
+    for n plasmids); these shares are summed per genotype and the total
+    library cfu0 is partitioned accordingly. A genotype's cfu0 is thus in
+    cell-equivalents, matching how `_sim_sequencing` counts reads.
 
     Parameters
     ----------
@@ -891,24 +1363,24 @@ def _calc_genotype_cfu0(
     # successfully transformed plasmid indices.
     all_transformed_plas = ma.array(transformants, mask=trans_mask).compressed()
 
-    # Create an array that has the frequency of the cell from which each 
-    # plasmid came from in the same shape/alignment as all_transformed_plas
-    trans_freq_2d = np.broadcast_to(trans_freq[:, np.newaxis], transformants.shape)
-    all_transformed_freq = ma.array(trans_freq_2d, mask=trans_mask).compressed()
+    # Split each cell's frequency among its plasmids, in the same
+    # shape/alignment as all_transformed_plas
+    plasmid_freq_2d = trans_freq[:, np.newaxis]*_plasmid_shares(trans_mask)
+    all_transformed_freq = ma.array(plasmid_freq_2d, mask=trans_mask).compressed()
 
-    # Count the number of times each plasmid index appears, weighted by the 
-    # frequency of the cells in the population
+    # Count the number of times each plasmid index appears, weighted by its
+    # share of its cell's frequency
     trans_geno_counts = np.bincount(all_transformed_plas,
                                     weights=all_transformed_freq,
                                     minlength=num_genotypes)
 
     # Calculate the frequency of each genotype in the post-transformation pool.
-    total_plasmids = np.sum(trans_geno_counts)
-    if total_plasmids == 0:
+    total_weight = np.sum(trans_geno_counts)
+    if total_weight == 0:
         # Handle edge case where no successful transformations occurred
         trans_freqs = np.zeros(num_genotypes, dtype=float)
     else:
-        trans_freqs = trans_geno_counts / total_plasmids
+        trans_freqs = trans_geno_counts / total_weight
 
     # Partition the total cfu0 according to these new frequencies.
     genotype_cfu0 = total_cfu0 * trans_freqs
@@ -916,16 +1388,23 @@ def _calc_genotype_cfu0(
     return genotype_cfu0 
 
 
-def _compute_kt(
-    phenotype_df: pd.DataFrame,
+def _compute_kt_arrays(
+    k_pre: np.ndarray,
+    k_sel: np.ndarray,
+    t_pre: np.ndarray,
+    t_sel: np.ndarray,
+    theta: np.ndarray,
+    condition_pre: np.ndarray,
     growth_transition: list | None,
 ) -> np.ndarray:
-    """Compute total log-growth (k*t) for every row in phenotype_df.
+    """Compute total log-growth (k*t) from aligned 1D arrays.
 
     Parameters
     ----------
-    phenotype_df : pandas.DataFrame
-        Must have columns: k_pre, k_sel, t_pre, t_sel, theta, condition_pre.
+    k_pre, k_sel, t_pre, t_sel, theta : numpy.ndarray
+        Growth rates, times and (raw) theta, one entry per row.
+    condition_pre : numpy.ndarray
+        Pre-growth condition of each row (selects the transition model).
     growth_transition : list of dict or None
         Validated growth_transition config (from _check_cf). None means instant
         transition for all conditions. Each entry must have 'condition_pre' and
@@ -934,20 +1413,12 @@ def _compute_kt(
     Returns
     -------
     numpy.ndarray
-        1-D array of kt values aligned with phenotype_df rows.
+        1-D array of kt values.
     """
-    k_pre = phenotype_df["k_pre"].to_numpy()
-    k_sel = phenotype_df["k_sel"].to_numpy()
-    t_pre = phenotype_df["t_pre"].to_numpy()
-    t_sel = phenotype_df["t_sel"].to_numpy()
-
     if growth_transition is None:
         return k_pre * t_pre + k_sel * t_sel
 
-    theta = phenotype_df["theta"].to_numpy()
-    condition_pre = phenotype_df["condition_pre"].to_numpy()
-
-    kt = np.zeros(len(phenotype_df))
+    kt = np.zeros(len(k_pre))
     for entry in growth_transition:
         mask = condition_pre == entry["condition_pre"]
         params = {k: v for k, v in entry.items()
@@ -962,6 +1433,41 @@ def _compute_kt(
     return kt
 
 
+def _compute_kt(
+    phenotype_df: pd.DataFrame,
+    growth_transition: list | None,
+) -> np.ndarray:
+    """Compute total log-growth (k*t) for every row in phenotype_df.
+
+    Parameters
+    ----------
+    phenotype_df : pandas.DataFrame
+        Must have columns: k_pre, k_sel, t_pre, t_sel, theta, condition_pre.
+    growth_transition : list of dict or None
+        Validated growth_transition config (from _check_cf). None means instant
+        transition for all conditions.
+
+    Returns
+    -------
+    numpy.ndarray
+        1-D array of kt values aligned with phenotype_df rows.
+    """
+    if growth_transition is None:
+        theta = np.zeros(len(phenotype_df))
+        condition_pre = np.zeros(len(phenotype_df))
+    else:
+        theta = phenotype_df["theta"].to_numpy()
+        condition_pre = phenotype_df["condition_pre"].to_numpy()
+
+    return _compute_kt_arrays(phenotype_df["k_pre"].to_numpy(),
+                              phenotype_df["k_sel"].to_numpy(),
+                              phenotype_df["t_pre"].to_numpy(),
+                              phenotype_df["t_sel"].to_numpy(),
+                              theta,
+                              condition_pre,
+                              growth_transition)
+
+
 def _simulate_library_group(
     sub_df: pd.DataFrame,
     index_offset: int,
@@ -970,6 +1476,8 @@ def _simulate_library_group(
     reads_per_sample: int,
     cf: dict[str, Any],
     rng: Generator,
+    shared_state: dict | None = None,
+    sequence: bool = True,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """
     Run a complete simulation for a single library group.
@@ -996,6 +1504,15 @@ def _simulate_library_group(
         The main, validated configuration dictionary for the simulation.
     rng : numpy.random.Generator
         An initialized NumPy random number generator.
+    shared_state : dict, optional
+        With ``shared_transformation``, the transformation of each library
+        (keyed by its ``library`` value) is taken from here when present and
+        stored here otherwise, so every replicate draws its cells from the
+        same transformants.
+    sequence : bool, optional
+        If False, skip sequencing: the returned ``counts_df`` is empty and
+        only the tube totals (and OD600, with an ``od600`` block) are
+        simulated. Used for OD-only replicates. Default True.
 
     Returns
     -------
@@ -1010,9 +1527,14 @@ def _simulate_library_group(
     tube_noise_sigma = cf["tube_noise_sigma"]
     library_mixture = cf["library_mixture"]
     transform_sizes = cf["transform_sizes"]
-    multi_plasmid_combine_fcn = cf["multi_plasmid_combine_fcn"]
     prob_index_hop = cf["prob_index_hop"]
     total_cfu0 = cf["cfu0"]
+    if isinstance(total_cfu0, dict):
+        lib_names = sub_df["library"].unique().tolist()
+        if len(lib_names) != 1 or lib_names[0] not in total_cfu0:
+            raise ValueError(f"cfu0 is given per library {sorted(total_cfu0)}, "
+                             f"but this group is library {lib_names}.")
+        total_cfu0 = float(total_cfu0[lib_names[0]])
 
     num_genotypes = len(ordered_genotypes)
 
@@ -1038,13 +1560,26 @@ def _simulate_library_group(
 
     # -- simulate transformation and mixing of libraries --
 
-    print("--> simulating transformation",flush=True)
-    transformants, trans_mask, trans_freq = _sim_transform_and_mix(
-        lib_origin_dict,
-        transform_sizes,
-        library_mixture,
-        transformation_poisson_lambda,
-        rng)
+    # With shared_transformation, every replicate reuses one transformation
+    # per library (one glycerol stock); otherwise each group draws its own.
+    share_key = None
+    if cf.get("shared_transformation") and shared_state is not None:
+        share_key = ("transformation",
+                     sub_df["library"].iloc[0] if "library" in sub_df.columns
+                     else None)
+    if share_key is not None and share_key in shared_state:
+        transformants, trans_mask, trans_freq = shared_state[share_key]
+    else:
+        print("--> simulating transformation",flush=True)
+        transformants, trans_mask, trans_freq = _sim_transform_and_mix(
+            lib_origin_dict,
+            transform_sizes,
+            library_mixture,
+            transformation_poisson_lambda,
+            rng,
+            max_congressed_cells=cf.get("max_congressed_cells"))
+        if share_key is not None:
+            shared_state[share_key] = (transformants, trans_mask, trans_freq)
 
     # -- calculate cfu0 of every genotype --
     
@@ -1089,42 +1624,101 @@ def _simulate_library_group(
     # Create a 2D array of (genotype,conditions) holding kt.
     # .copy() required: pandas 3.x CoW returns a read-only view from to_numpy()
     genotype_vs_kt = genotype_vs_kt_pivot.to_numpy().copy()
-    
+
+    # One row per condition (column of genotype_vs_kt), aligned to the pivot.
+    condition_info = (sub_df
+                      .groupby(condition_selector, observed=True)
+                      [["condition_pre", "condition_sel", "t_pre", "t_sel"]]
+                      .first()
+                      .reindex(genotype_vs_kt_pivot.columns))
+
     # Add per-tube environmental noise: one delta_k per condition, shared across
-    # all genotypes in that tube.  tube_noise_sigma is in growth-rate units
-    # (hr⁻¹); the kt contribution is delta_k * t_total, which we read from the
-    # first row of the pivot (t_pre + t_sel is the same for every genotype).
+    # all genotypes (and cells) in that tube.  tube_noise_sigma is in
+    # growth-rate units (per minute, like every time in the simulator); the kt contribution is delta_k * t_total.
+    tube_kt = np.zeros(genotype_vs_kt.shape[1], dtype=float)
     if tube_noise_sigma is not None and tube_noise_sigma > 0:
-        t_pre_per_condition = sub_df.groupby(condition_selector, observed=True)["t_pre"].first().reindex(genotype_vs_kt_pivot.columns).to_numpy()
-        t_sel_per_condition = sub_df.groupby(condition_selector, observed=True)["t_sel"].first().reindex(genotype_vs_kt_pivot.columns).to_numpy()
-        t_total = t_pre_per_condition + t_sel_per_condition
+        t_total = (condition_info["t_pre"].to_numpy(dtype=float)
+                   + condition_info["t_sel"].to_numpy(dtype=float))
         delta_k = rng.normal(scale=tube_noise_sigma, size=len(t_total))
-        genotype_vs_kt += delta_k[np.newaxis, :] * t_total[np.newaxis, :]
-    
-    # Name of the library (e.g. kanR, pheS, ... ) for looking up how to 
-    # combine multiple plasmid effects
-    lib_name = np.unique(sub_df["library"])[0]
+        tube_kt = delta_k * t_total
+    genotype_vs_kt += tube_kt[np.newaxis, :]
+
+    # Per-genotype components for co-transformed cells, aligned to
+    # ordered_genotypes. Only needed when some cell carries >1 plasmid.
+    has_multi = transformants.shape[0] > 0 and np.any(np.sum(~trans_mask, axis=1) > 1)
+    if has_multi:
+        genotype_vs_theta = (sub_df
+                             .pivot_table(index="genotype",
+                                          columns=condition_selector,
+                                          values="theta",
+                                          observed=True,
+                                          dropna=False)
+                             .reindex(index=ordered_genotypes,
+                                      columns=genotype_vs_kt_pivot.columns)
+                             .to_numpy(dtype=float))
+        per_geno = (sub_df
+                    .groupby("genotype", observed=True)[["activity", "dk_geno"]]
+                    .first()
+                    .reindex(ordered_genotypes))
+        genotype_activity = per_geno["activity"].to_numpy(dtype=float)
+        genotype_dk_geno = per_geno["dk_geno"].to_numpy(dtype=float)
+    else:
+        num_cond = genotype_vs_kt.shape[1]
+        genotype_vs_theta = np.zeros((num_genotypes, num_cond), dtype=float)
+        genotype_activity = np.ones(num_genotypes, dtype=float)
+        genotype_dk_geno = np.zeros(num_genotypes, dtype=float)
 
     print("--> simulating growth",flush=True)
-    trans_cfu = _sim_growth(transformants,
-                            trans_mask,
-                            trans_freq,
-                            genotype_vs_kt,
-                            total_cfu0,
-                            multi_plasmid_combine_fcn[lib_name])
-    
-    # Record cfu/mL over all conditions
+    cell_kt = _cell_kt(transformants,
+                       trans_mask,
+                       genotype_vs_kt,
+                       genotype_vs_theta,
+                       genotype_activity,
+                       genotype_dk_geno,
+                       condition_info,
+                       tube_kt,
+                       cf)
+    trans_cfu = _sim_growth(cell_kt, trans_freq, total_cfu0, rng=rng,
+                            founder_sampling=cf.get("founder_sampling", False),
+                            demographic_growth=cf.get("demographic_growth", False))
+
+    # Record the total cells in each tube
     sample_df.loc[:,"sample_cfu"] = np.sum(trans_cfu,axis=0)
     sample_df.loc[:,"sample_cfu_std"] = 0.0
 
+    # One OD600 reading per tube. With sample_cfu_from_od600 the pipeline
+    # sees the lab's estimate of the total, not the truth.
+    od_cf = cf.get("od600")
+    if od_cf is not None:
+        cal = read_od600_calibration(od_cf["calibration"])
+        true_total = sample_df["sample_cfu"].to_numpy(dtype=float)
+        reading = simulate_od600(true_total, od_cf["tube_volume_mL"], cal, rng)
+        sample_df.loc[:,"sample_cfu_true"] = true_total
+        for k, v in reading.items():
+            sample_df.loc[:,k] = v
+        if od_cf["sample_cfu_from_od600"]:
+            cfu, cfu_std, _ = od600_to_cfu_per_mL(reading["od600"], cal)
+            sample_df.loc[:,"sample_cfu"] = cfu*od_cf["tube_volume_mL"]
+            sample_df.loc[:,"sample_cfu_std"] = cfu_std*od_cf["tube_volume_mL"]
+
+    if not sequence:
+        return sample_df, counts_df.iloc[0:0][["sample","genotype"]]
+
     # -- simulate sequencing -- 
+    # Reads that call no library genotype (unassigned_read_fraction; the
+    # dev data lost 66% of kanR and 20% of pheS reads to __unknown__) are
+    # taken off each tube's budget and reported as the __unknown__ row.
+    unassigned = _unassigned_fraction(cf, sub_df)
+    assigned_reads = int(round(reads_per_sample * (1.0 - unassigned)))
     print("--> simulating sequencing",flush=True)
     read_counts_dense = _sim_sequencing(transformants,
                                         trans_mask,
                                         trans_cfu,
                                         num_genotypes,
-                                        reads_per_sample,
-                                        rng)
+                                        assigned_reads,
+                                        rng,
+                                        pcr_template_molecules=cf.get("pcr_template_molecules"),
+                                        pcr_amplification_cv=cf.get("pcr_amplification_cv"))
 
     # Create a DataFrame from the dense counts with the same structure as the pivot
     counts_df_wide = pd.DataFrame(data=read_counts_dense,
@@ -1156,12 +1750,36 @@ def _simulate_library_group(
                            "dk_geno","k_pre","k_sel","theta","ln_cfu_0",
                            "counts"]]
 
+    if reads_per_sample - assigned_reads > 0:
+        unknown = pd.DataFrame({"sample": sample_df["sample"].to_numpy(),
+                                "genotype": UNKNOWN_GENOTYPE,
+                                "counts": reads_per_sample - assigned_reads})
+        counts_df["genotype"] = counts_df["genotype"].astype(str)
+        counts_df = pd.concat([counts_df, unknown], ignore_index=True)
+
     return sample_df, counts_df
 
+
+def _unassigned_fraction(cf, sub_df):
+    """This library group's unassigned_read_fraction (0 when not set)."""
+    frac = cf.get("unassigned_read_fraction")
+    if frac is None:
+        return 0.0
+    if isinstance(frac, dict):
+        libs = sub_df["library"].unique().tolist() if "library" in sub_df else []
+        if len(libs) != 1 or libs[0] not in frac:
+            raise ValueError(f"unassigned_read_fraction is given per library "
+                             f"{sorted(frac)}, but this group is library "
+                             f"{libs}.")
+        return float(frac[libs[0]])
+    return float(frac)
+
 def selection_experiment(
     cf: dict[str, Any] | str | Path,
     library_df: pd.DataFrame | str | Path,
     phenotype_df: pd.DataFrame | str | Path,
+    shared_state: dict | None = None,
+    sequence: bool = True,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """Simulate a high-throughput selection experiment.
 
@@ -1183,12 +1801,21 @@ def selection_experiment(
         A dataframe containing the fitness landscape (growth rates `k_pre`
         and `k_sel`) for each genotype under all possible experimental
         conditions.
+    shared_state : dict, optional
+        Pass the same dict to every call (every replicate) so that, with
+        ``shared_transformation``, they share one library assembly and one
+        transformation. Ignored without ``shared_transformation``.
+    sequence : bool, optional
+        If False, simulate tube totals (and OD600) without sequencing, for
+        OD-only replicates; ``counts_df_final`` is then empty. Default True.
 
     Returns
     -------
     sample_df_final : pandas.DataFrame
         A dataframe describing each unique sample (experimental condition),
-        including the final total CFU/mL.
+        including the final total CFU/mL. With an ``od600`` block it also
+        has ``sample_cfu_true``, ``od600``, ``od600_detectable`` and
+        ``od600_in_range``.
     counts_df_final : pandas.DataFrame
         A dataframe containing the simulated sequencing counts for every
         genotype in every sample, along with the calculated initial
@@ -1220,6 +1847,11 @@ def selection_experiment(
     # Integrated check and update for library specification
     cf = _check_lib_spec(cf,library_df,phenotype_df)
 
+    # Co-transformed cells rebuild their growth from theta/activity/dk_geno.
+    lam = cf["transformation_poisson_lambda"]
+    if lam is not None and lam > 0:
+        _check_cell_components(cf, phenotype_df)
+
     # ------------------------------------------------------------------------
     # Set up calculation
     # ------------------------------------------------------------------------
@@ -1259,11 +1891,27 @@ def selection_experiment(
     # Build base probabilities for library transformation. By putting skew here
     # we are modeling skew that occurs during library construction, not skew 
     # during out growth or transformation. 
-    library_df["probs"] = 0.0
-    for _, origin_sub_df in lib_origin_grouper:
-        p = _sim_plasmid_probabilities(origin_sub_df["weight"],
-                                        cf["lib_assembly_skew_sigma"], rng)
-        library_df.loc[origin_sub_df.index, "probs"] = p
+    # With shared_transformation, one library assembly serves every call
+    # that passes the same shared_state (every replicate), like the
+    # transformation itself (see _simulate_library_group).
+    share = cf.get("shared_transformation") and shared_state is not None
+    if share and "assembly_probs" in shared_state:
+        library_df["probs"] = shared_state["assembly_probs"]
+    else:
+        library_df["probs"] = 0.0
+        for origin, origin_sub_df in lib_origin_grouper:
+            origin = origin[0] if isinstance(origin, tuple) else origin
+            skew = cf["lib_assembly_skew_sigma"]
+            if isinstance(skew, dict):
+                if origin not in skew:
+                    raise ValueError(f"lib_assembly_skew_sigma is given per "
+                                     f"origin {sorted(skew)}, without "
+                                     f"'{origin}'.")
+                skew = skew[origin]
+            p = _sim_plasmid_probabilities(origin_sub_df["weight"], skew, rng)
+            library_df.loc[origin_sub_df.index, "probs"] = p
+        if share:
+            shared_state["assembly_probs"] = library_df["probs"].to_numpy().copy()
 
     # Create a dictionary of grouped DataFrames. 
     lib_origin_dict = dict(list(library_df.groupby(["library_origin"])))
@@ -1317,7 +1965,9 @@ def selection_experiment(
                                                        ordered_genotypes,
                                                        reads_per_sample,
                                                        cf,
-                                                       rng)
+                                                       rng,
+                                                       shared_state=shared_state,
+                                                       sequence=sequence)
 
         sample_df_batches.append(sample_df)
         counts_df_batches.append(counts_df)
diff --git a/src/tfscreen/simulate/sim_data_class.py b/src/tfscreen/simulate/sim_data_class.py
index 885458ea..9c5ebc2d 100644
--- a/src/tfscreen/simulate/sim_data_class.py
+++ b/src/tfscreen/simulate/sim_data_class.py
@@ -97,7 +97,9 @@ class SimData:
 def build_sim_data(library_df,
                    sample_df,
                    thermo_data=None,
-                   skip_pairs=False):
+                   skip_pairs=False,
+                   theta_component=None,
+                   thermo_options=None):
     """
     Build a SimData from the simulation pipeline's core inputs.
 
@@ -121,6 +123,13 @@ def build_sim_data(library_df,
         Skip building pairwise epistasis indices.  Set True for theta
         components that do not model pairwise epistasis to avoid the
         quadratic enumeration over double mutants.
+    theta_component : str or None
+        Theta component name.  When given, a PddG component reads the CSV
+        keeping only (and reordering to) its structures, and the loader is
+        chosen from the component rather than the file extension.
+    thermo_options : dict or None
+        Loader options (ΔΔG units, column map, residue offset, coverage
+        threshold); see ``thermo.io.normalize_thermo_options``.
 
     Returns
     -------
@@ -153,21 +162,15 @@ def build_sim_data(library_df,
         struct_contact_distances=None,
     )
     if thermo_data is not None:
-        path_lower = str(thermo_data).lower()
-        if path_lower.endswith(".csv"):
-            from tfscreen.tfmodel.generative.components.theta.thermo.io import (
-                load_ddG_prior_csv,
-            )
-            raw = load_ddG_prior_csv(thermo_data, mut_labels)
-        else:
-            from tfscreen.tfmodel.generative.components.theta.thermo.io import (
-                load_struct_ensemble,
-            )
-            raw = load_struct_ensemble(
-                h5_path=thermo_data,
-                mut_labels=mut_labels,
-                pair_labels=pair_labels if not skip_pairs else None,
-            )
+        from tfscreen.tfmodel.generative.components.theta.thermo.io import (
+            load_thermo_data,
+        )
+        raw = load_thermo_data(
+            thermo_data, mut_labels,
+            pair_labels=pair_labels if not skip_pairs else None,
+            theta_name=theta_component,
+            thermo_options=thermo_options,
+        )
         struct_kw = dict(
             num_struct=len(raw["struct_names"]),
             struct_names=raw["struct_names"],
@@ -195,3 +198,31 @@ def build_sim_data(library_df,
         pair_nnz_geno_idx=pair_nnz_geno_idx,
         **struct_kw,
     )
+
+
+def with_titrant_conc(sim_data, titrant_conc):
+    """
+    Copy of a SimData evaluated at other titrant concentrations.
+
+    Genotypes, mutations and structural fields are unchanged; only the
+    concentration grid is replaced (sorted unique values, zeros given the
+    same log sentinel as ``build_sim_data``).  A theta component's
+    ``run_model`` on the copy evaluates the same drawn parameters at the new
+    concentrations, e.g. at binding-assay concentrations that differ from
+    the growth concentrations.
+
+    Parameters
+    ----------
+    sim_data : SimData
+    titrant_conc : array-like
+        Concentrations (mM, data units).
+
+    Returns
+    -------
+    SimData
+    """
+    concs = np.sort(np.unique(np.asarray(titrant_conc, dtype=float)))
+    safe_concs = np.where(concs == 0.0, _ZERO_CONC_SENTINEL, concs)
+    return sim_data.replace(num_titrant_conc=len(concs),
+                            titrant_conc=jnp.array(concs),
+                            log_titrant_conc=jnp.array(np.log(safe_concs)))
diff --git a/src/tfscreen/simulate/thermo_to_growth.py b/src/tfscreen/simulate/thermo_to_growth.py
index 3f0642fd..a70010f0 100644
--- a/src/tfscreen/simulate/thermo_to_growth.py
+++ b/src/tfscreen/simulate/thermo_to_growth.py
@@ -153,7 +153,8 @@ def _sample_hierarchical_activity(unique_genotypes,
 _ACTIVITY_COMPONENTS = {"fixed", "horseshoe_geno", "hierarchical_geno"}
 
 
-def _theta_param_to_df(theta_param, unique_genotypes, sim_indices):
+def _theta_param_to_df(theta_param, unique_genotypes, sim_indices,
+                       num_genotype_sim=None):
     """
     Extract per-genotype scalar fields from a ThetaParam pytree.
 
@@ -161,9 +162,14 @@ def _theta_param_to_df(theta_param, unique_genotypes, sim_indices):
     array has exactly two dimensions (T × G_sim) are treated as
     per-genotype parameters: columns are selected via ``sim_indices`` and
     a leading titrant dimension of size 1 is squeezed away; when T > 1 each
-    titrant gets its own column suffixed ``_T{t}``.  Fields with any other
-    dimensionality (e.g. population moments ``mu``/``sigma`` with shape
-    T × C × 1) are silently skipped.
+    titrant gets its own column suffixed ``_T{t}``.  When
+    ``num_genotype_sim`` is given, one-dimensional fields of exactly that
+    length (G_sim,) are per-genotype parameters too and get one column
+    each: the thermodynamic models' titrant-independent constants
+    (``ln_K_op``, ``ln_K_HL``, ``ln_K_U``, ``ln_K_h_l``, ``ln_K_h_o``,
+    ``ln_K_l_o``, ``ln_K_u``).  Fields with any other dimensionality (e.g.
+    population moments ``mu``/``sigma`` with shape T × C × 1, scalars such
+    as ``tf_total``) are silently skipped.
 
     If ``theta_param`` is not a standard or Flax dataclass (e.g. a mock
     during testing) the function returns a DataFrame containing only the
@@ -180,6 +186,10 @@ def _theta_param_to_df(theta_param, unique_genotypes, sim_indices):
     sim_indices : array-like of int
         For each entry in ``unique_genotypes``, its column index in the
         sim_data / ``library_df`` order stored in ``theta_param``.
+    num_genotype_sim : int or None, default None
+        Number of genotypes in sim_data (G_sim).  Required to read the
+        one-dimensional per-genotype fields; None skips them (and any 2-D
+        field whose last axis is not G_sim is skipped when it is given).
 
     Returns
     -------
@@ -201,6 +211,11 @@ def _theta_param_to_df(theta_param, unique_genotypes, sim_indices):
             val = np.array(getattr(theta_param, fname))
         except Exception:
             continue
+        if num_genotype_sim is not None and val.ndim in (1, 2):
+            if val.shape[-1] != num_genotype_sim:
+                continue
+            if val.ndim == 1:
+                val = val[None, :]   # (G,) per-genotype field → (1, G)
         if val.ndim != 2:
             continue  # skip mu, sigma (3-D) and any scalars
         selected = val[:, np.asarray(sim_indices)]   # (T, n_unique)
@@ -215,6 +230,200 @@ def _theta_param_to_df(theta_param, unique_genotypes, sim_indices):
     return df
 
 
+
+_HILL_KEYS = ("theta_low", "theta_high", "log_hill_K", "hill_n")
+
+
+def _theta_at_concs(theta_component, theta_param, sim_data, titrant_conc,
+                    unique_genotypes, unique_sim_indices,
+                    theta_gc_override=None, theta_params_override=None):
+    """
+    Ground-truth theta of every genotype at arbitrary concentrations.
+
+    Evaluates the component's own ``run_model`` on the drawn ``theta_param``
+    with ``sim_data`` moved to ``titrant_conc``, so it works for any theta
+    component the simulator draws (Hill or thermodynamic) and at binding
+    concentrations that need not be growth concentrations.  Genotypes whose
+    growth theta was replaced through ``theta_gc_override`` follow their
+    effective Hill parameters from ``theta_params_override`` when those are
+    complete, and are NaN otherwise (their curve exists only at the growth
+    concentrations; the spiked stratified Hill path supplies their binding
+    theta separately).
+
+    Returns
+    -------
+    pandas.DataFrame
+        Long form, columns ``genotype``, ``titrant_conc``, ``theta``; rows
+        follow ``unique_genotypes`` then ascending concentration.
+    """
+    from tfscreen.tfmodel.generative.registry import model_registry
+    from tfscreen.simulate.sim_data_class import with_titrant_conc
+
+    concs = np.sort(np.unique(np.asarray(titrant_conc, dtype=float)))
+    module = model_registry["theta"][theta_component]
+    conc_data = with_titrant_conc(sim_data, concs)
+    theta_cg = np.asarray(module.run_model(theta_param, conc_data)[0],
+                          dtype=float)                        # (C, G_sim)
+    theta = theta_cg.T[np.asarray(unique_sim_indices)].copy()  # (G, C)
+
+    log_concs = np.log(np.where(concs == 0.0, 1e-20, concs))
+    gc_over = theta_gc_override or {}
+    p_over = theta_params_override or {}
+    for i, g in enumerate(unique_genotypes):
+        params = p_over.get(g)
+        if params is not None and all(k in params for k in _HILL_KEYS):
+            low, high = float(params["theta_low"]), float(params["theta_high"])
+            occ = 1.0 / (1.0 + np.exp(-float(params["hill_n"])
+                                      * (log_concs - float(params["log_hill_K"]))))
+            theta[i] = low + (high - low) * occ
+        elif g in gc_over:
+            theta[i] = np.nan
+
+    return pd.DataFrame({
+        "genotype": np.repeat(np.asarray(unique_genotypes, dtype=str), len(concs)),
+        "titrant_conc": np.tile(concs, len(unique_genotypes)),
+        "theta": theta.ravel(),
+    })
+
+
+def _site_columns(prefix, val, num_items):
+    """
+    Columns for one deterministic site whose last axis indexes items.
+
+    ``val`` of shape (N,) gives column ``prefix``; (T, N) gives ``prefix``
+    when T == 1, else ``prefix_T{t}`` per titrant (the convention of
+    ``_theta_param_to_df``).  Any other shape gives no columns.
+    """
+    val = np.asarray(val)
+    if val.ndim == 0 or val.ndim > 2 or val.shape[-1] != num_items:
+        return {}
+    if val.ndim == 1:
+        return {prefix: val}
+    if val.shape[0] == 1:
+        return {prefix: val[0]}
+    return {f"{prefix}_T{t}": val[t] for t in range(val.shape[0])}
+
+
+def _mutation_truth_tables(deterministic, genotypes, sim_data, name="theta",
+                           struct_names=None):
+    """
+    Per-mutation and per-pair ground truth from a theta draw's trace.
+
+    Mutation-decomposed components record their per-mutation deltas
+    (``{name}_d_*``), pairwise epistasis (``{name}_epi_*``) and, for the
+    structure-based thermodynamic models, per-structure ΔΔG
+    (``{name}_ddG``, shape M × S; ``sample_theta_prior`` replaces the
+    model's post-hoc ΔΔG with the per-state truth draw) as deterministic
+    sites.  ``ThetaParam`` only carries the assembled per-genotype values,
+    so this reads them from the trace returned by
+    ``sample_theta_prior(..., return_deterministic=True)``.
+
+    Column names drop the ``{name}_`` prefix, so they equal the names
+    ``tfs-extract-params`` gives the fitted values (``d_ln_K_op`` →
+    ``*_params_d_ln_K_op.csv``).  ΔΔG gets one column per structure,
+    ``ddG_{structure}``.
+
+    Parameters
+    ----------
+    deterministic : dict[str, np.ndarray]
+        Deterministic site name → value.
+    genotypes : list of str
+        Genotypes in sim_data order (the order ``sim_data`` was built from).
+    sim_data : SimData
+        Supplies ``num_mutation``, ``num_pair`` and ``struct_names``.
+    name : str, default "theta"
+        Site-name prefix used when the component was sampled.
+    struct_names : sequence of str, optional
+        Order of the ΔΔG site's structure axis (the component's
+        ``STRUCTURE_NAMES``; the PddG/PnnC models reorder the data's columns
+        into it).  Falls back to ``sim_data.struct_names``.
+
+    Returns
+    -------
+    mutation_df : pandas.DataFrame or None
+        One row per mutation (``mutation`` column first), or None when the
+        trace has no per-mutation sites.
+    pair_df : pandas.DataFrame or None
+        One row per observed mutation pair (``pair`` column first), or None
+        when the trace has no per-pair sites.
+    """
+    from tfscreen.genetics.build_mut_geno_matrix import build_mut_geno_matrix
+
+    num_mut = int(getattr(sim_data, "num_mutation", 0) or 0)
+    num_pair = int(getattr(sim_data, "num_pair", 0) or 0)
+    if not deterministic or num_mut == 0:
+        return None, None
+
+    mut_labels, pair_labels, *_ = build_mut_geno_matrix(
+        list(genotypes), skip_pairs=(num_pair == 0))
+    if len(mut_labels) != num_mut or len(pair_labels) != num_pair:
+        raise ValueError(
+            f"Mutation/pair labels ({len(mut_labels)}/{len(pair_labels)}) do "
+            f"not match sim_data ({num_mut}/{num_pair}); cannot label the "
+            f"per-mutation truth.")
+
+    d_prefix, epi_prefix = f"{name}_d_", f"{name}_epi_"
+    mut_cols, pair_cols = {}, {}
+    for site, val in deterministic.items():
+        if site.startswith(d_prefix):
+            mut_cols.update(_site_columns(site[len(name) + 1:], val, num_mut))
+        elif site.startswith(epi_prefix) and num_pair > 0:
+            pair_cols.update(_site_columns(site[len(name) + 1:], val, num_pair))
+        elif site == f"{name}_ddG":
+            val = np.asarray(val)
+            if val.ndim == 2 and val.shape[0] == num_mut:
+                structs = (struct_names if struct_names is not None
+                           else getattr(sim_data, "struct_names", None))
+                if structs is None or len(structs) != val.shape[1]:
+                    structs = [str(i) for i in range(val.shape[1])]
+                for i, struct in enumerate(structs):
+                    mut_cols[f"ddG_{struct}"] = val[:, i]
+
+    mutation_df = None
+    if mut_cols:
+        mutation_df = pd.DataFrame(mut_cols)
+        mutation_df.insert(0, "mutation", mut_labels)
+    pair_df = None
+    if pair_cols:
+        pair_df = pd.DataFrame(pair_cols)
+        pair_df.insert(0, "pair", pair_labels)
+    return mutation_df, pair_df
+
+
+def _ddG_sigma_truth_table(sites, struct_names, name="theta"):
+    """
+    The per-structure σ_s truth of a PddG/PnnC draw.
+
+    σ_s (``{name}_ddG_sigma``, shape S) is the SD of the true ΔΔG about the
+    structural prior mean; the simulator conditions it (``ddG_truth_sd``,
+    1 kT by default).  The table matches the fit's extracted ``ddG_sigma``
+    table (``thermo/prior.py``), which ``tfs-summarize-fit`` joins on
+    ``structure``.
+
+    Parameters
+    ----------
+    sites : dict[str, np.ndarray]
+        Site values from ``sample_theta_prior(..., return_deterministic=True)``.
+    struct_names : sequence of str or None
+        The component's ``STRUCTURE_NAMES`` (the order of the site).
+    name : str, default "theta"
+        Site-name prefix.
+
+    Returns
+    -------
+    pandas.DataFrame or None
+        Columns ``structure`` and ``ddG_sigma`` (kT), or None when the draw
+        has no σ_s site.
+    """
+    val = sites.get(f"{name}_ddG_sigma") if sites else None
+    if val is None:
+        return None
+    val = np.asarray(val, dtype=float).reshape(-1)
+    if struct_names is None or len(struct_names) != len(val):
+        struct_names = [str(i) for i in range(len(val))]
+    return pd.DataFrame({"structure": list(struct_names), "ddG_sigma": val})
+
+
 def _assign_dk_geno(unique_genotypes,
                     hyper_loc=-3.5,
                     hyper_scale=1.0,
@@ -250,42 +459,126 @@ def _assign_dk_geno(unique_genotypes,
     return pd.Series(dk_geno)
 
 
-def _apply_growth_params(condition_array, theta_array, growth_params,
-                         activity_array=None):
+def growth_rate_one_condition(condition, theta_array, activity_array,
+                              dk_geno_array, growth_params,
+                              theta_rescale="passthrough"):
     """
-    Compute per-row growth rate k given theta and per-condition model parameters.
+    Growth rate k from its physical components, all rows in one condition.
+
+    ``k = growth_model[condition](theta_rescale(theta), activity) + dk_geno``.
+    This is the one place the simulator turns theta, activity and dk_geno
+    into a growth rate: ``thermo_to_growth`` uses it (through
+    ``growth_rates``) per genotype and ``selection_experiment`` per
+    co-transformed cell, so the two always agree.
 
     Parameters
     ----------
-    condition_array : numpy.ndarray
-        1D array of condition strings.
+    condition : str
+        Condition (key of ``growth_params``).
     theta_array : numpy.ndarray
-        1D array of fractional occupancy values (same length).
+        1D array of fractional occupancy (raw, before rescaling).
+    activity_array : numpy.ndarray
+        1D array of TF activity.
+    dk_geno_array : numpy.ndarray
+        1D array of pleiotropic growth effects.
+    growth_params : dict
+        Mapping from condition string to a parameter dict with a ``'model'``
+        key (``'linear'``, ``'power'`` or ``'saturation'``; default
+        ``'linear'``); remaining keys go to the model's ``predict()``.
+    theta_rescale : str, default "passthrough"
+        Key of ``_THETA_RESCALE``.
+
+    Returns
+    -------
+    numpy.ndarray
+        Growth rate k for each row.
+    """
+    if theta_rescale not in _THETA_RESCALE:
+        raise ValueError(
+            f"theta_rescale '{theta_rescale}' not recognized. "
+            f"It should be one of: {list(_THETA_RESCALE.keys())}"
+        )
+    growth_theta = _THETA_RESCALE[theta_rescale](np.asarray(theta_array, dtype=float))
+    params = growth_params[condition].copy()
+    model = get_growth_model(params.pop("model", "linear"))
+    k = model.predict(growth_theta,
+                      activity=np.asarray(activity_array, dtype=float),
+                      **params)
+    return k + np.asarray(dk_geno_array, dtype=float)
+
+
+def growth_rates(condition_array, theta_array, activity_array, dk_geno_array,
+                 growth_params, theta_rescale="passthrough"):
+    """
+    Growth rate k from its physical components, rows in any conditions.
+
+    Applies ``growth_rate_one_condition`` to the rows of each condition.
+
+    Parameters
+    ----------
+    condition_array : numpy.ndarray
+        1D array of condition strings (keys of ``growth_params``).
+    theta_array, activity_array, dk_geno_array : numpy.ndarray
+        1D arrays aligned with ``condition_array``.
     growth_params : dict
-        Mapping from condition string to a parameter dict.  Must contain a
-        ``'model'`` key (``'linear'``, ``'power'``, or ``'saturation'``);
-        remaining keys are forwarded to the model's ``predict()`` method.
-    activity_array : numpy.ndarray or None
-        Per-row TF activity scaling factors.  Defaults to 1.0.
+        Per-condition growth model parameters.
+    theta_rescale : str, default "passthrough"
+        Key of ``_THETA_RESCALE``.
 
     Returns
     -------
     numpy.ndarray
         Growth rate k for each row.
     """
-    if activity_array is None:
-        activity_array = np.ones(len(theta_array), dtype=float)
+    condition_array = np.asarray(condition_array)
+    theta_array = np.asarray(theta_array, dtype=float)
+    activity_array = np.asarray(activity_array, dtype=float)
+    dk_geno_array = np.asarray(dk_geno_array, dtype=float)
+
     result = np.zeros(len(theta_array), dtype=float)
     for cond in np.unique(condition_array):
         mask = condition_array == cond
-        params = growth_params[cond].copy()
-        model_name = params.pop("model", "linear")
-        model = get_growth_model(model_name)
-        result[mask] = model.predict(theta_array[mask],
-                                     activity=activity_array[mask], **params)
+        result[mask] = growth_rate_one_condition(cond, theta_array[mask],
+                                                 activity_array[mask],
+                                                 dk_geno_array[mask],
+                                                 growth_params, theta_rescale)
     return result
 
 
+def _pin_wt_reference(theta_gc, theta_param, sim_data, wt_idx,
+                      sim_priors_overrides=None):
+    """
+    Give wt the hill_geno reference curve (``wt_theta_low``, ``wt_theta_high``,
+    ``wt_log_K``, ``wt_hill_n`` of its SimPriors, with any overrides).
+
+    The sampler treats wt like every other genotype, so wt could draw a
+    perturbed curve or a special category: seed 1 of the full-size simulation
+    gave it "never binds" (theta 0.006 at every IPTG), which made the
+    relative fit's X gauge, set by wt's own change, 3,600 times too wide.
+    Only wt's row of ``theta_gc`` and its ``theta_param`` entries change.
+    """
+    from tfscreen.tfmodel.generative.components.theta import hill_geno
+
+    sp = hill_geno.get_sim_hyperparameters()
+    if sim_priors_overrides:
+        sp.update(sim_priors_overrides)
+    low, high = float(sp["wt_theta_low"]), float(sp["wt_theta_high"])
+    log_K, n = float(sp["wt_log_K"]), float(sp["wt_hill_n"])
+
+    log_conc = np.asarray(sim_data.log_titrant_conc, dtype=float)
+    occupancy = 1.0 / (1.0 + np.exp(-n * (log_conc - log_K)))
+    theta_gc = np.array(theta_gc)
+    theta_gc[wt_idx, :] = low + (high - low) * occupancy
+
+    theta_param = theta_param.replace(
+        theta_low=theta_param.theta_low.at[:, wt_idx].set(low),
+        theta_high=theta_param.theta_high.at[:, wt_idx].set(high),
+        log_hill_K=theta_param.log_hill_K.at[:, wt_idx].set(log_K),
+        hill_n=theta_param.hill_n.at[:, wt_idx].set(n),
+    )
+    return theta_gc, theta_param
+
+
 def thermo_to_growth(
     genotypes: Iterable[str],
     sim_data,
@@ -309,7 +602,10 @@ def thermo_to_growth(
     theta_gc_override: Optional[dict] = None,
     theta_params_override: Optional[dict] = None,
     dk_geno_override: Optional[dict] = None,
-) -> tuple[pd.DataFrame, pd.DataFrame]:
+    return_mutation_truth: bool = False,
+    binding_titrant_conc: Optional[Iterable[float]] = None,
+    theta_conditions: Optional[dict] = None,
+) -> tuple:
     """
     Generate phenotypes from genotypes via prior-predictive theta sampling.
 
@@ -410,6 +706,19 @@ def thermo_to_growth(
         resampled ``dk_geno`` values alongside ``theta_gc_override`` /
         ``theta_params_override``; genotypes absent from the dict keep their
         drawn (or wt-pinned) value.
+    return_mutation_truth : bool, default False
+        Also return the per-mutation and per-pair ground truth (see
+        ``_mutation_truth_tables``) as a fourth value.
+    binding_titrant_conc : array-like or None, default None
+        Binding-assay concentrations (mM).  When given, also return the
+        ground-truth theta of every genotype at these concentrations (see
+        ``_theta_at_concs``) as the last value.  Binding concentrations need
+        not match the growth concentrations: theta is evaluated from the
+        drawn parameters, not looked up.
+    theta_conditions : dict or None
+        Sample-site values fixed in the prior-predictive draw
+        (``sample_theta_prior``'s ``conditions``), e.g. ``theta_ddG_sigma``
+        from ``sample_theta.ddG_truth_conditions``.
 
     Returns
     -------
@@ -430,7 +739,18 @@ def thermo_to_growth(
         ``dk_geno``, and ``activity``.  Also contains any scalar
         per-genotype fields extracted from the ``theta_param`` pytree
         (e.g. ``theta_low``, ``theta_high``, ``log_hill_K``, ``hill_n``
-        for ``hill_geno``).
+        for ``hill_geno``; ``ln_K_op``, ``ln_K_HL``, ``ln_K_E`` for the
+        C4 thermodynamic models).
+    mutation_truth : dict
+        Only when ``return_mutation_truth`` is True: ``{"mutation":
+        DataFrame or None, "pair": DataFrame or None, "ddG_sigma":
+        DataFrame or None}``, the per-mutation, per-pair and (PddG/PnnC)
+        per-structure σ_s truth (``_ddG_sigma_truth_table``).  All are None
+        for components sampled through ``simulate`` (hill_geno, hill_mut),
+        which have no trace.
+    binding_theta_df : pandas.DataFrame
+        Only when ``binding_titrant_conc`` is given (always last): theta of
+        every genotype at those concentrations, from ``_theta_at_concs``.
     """
 
     print("Sampling theta from prior... ", end="", flush=True)
@@ -461,13 +781,26 @@ def thermo_to_growth(
     # Genotype order matches sim_data / library_df order, not unique_genotypes.
     # We need to build a mapping from unique_genotypes to the sim_data index.
 
-    theta_gc, theta_param = sample_theta_prior(
-        component_name=theta_component,
-        sim_data=sim_data,
-        rng_key=theta_rng_key,
-        priors_overrides=theta_priors_overrides,
-        sim_priors_overrides=theta_sim_priors_overrides,
-    )
+    deterministic = {}
+    if return_mutation_truth:
+        theta_gc, theta_param, deterministic = sample_theta_prior(
+            component_name=theta_component,
+            sim_data=sim_data,
+            rng_key=theta_rng_key,
+            priors_overrides=theta_priors_overrides,
+            sim_priors_overrides=theta_sim_priors_overrides,
+            return_deterministic=True,
+            conditions=theta_conditions,
+        )
+    else:
+        theta_gc, theta_param = sample_theta_prior(
+            component_name=theta_component,
+            sim_data=sim_data,
+            rng_key=theta_rng_key,
+            priors_overrides=theta_priors_overrides,
+            sim_priors_overrides=theta_sim_priors_overrides,
+            conditions=theta_conditions,
+        )
 
     print("Done.", flush=True)
 
@@ -480,6 +813,16 @@ def thermo_to_growth(
     geno_to_sim_idx = {g: i for i, g in enumerate(all_genotypes)}
     unique_sim_indices = np.array([geno_to_sim_idx[g] for g in unique_genotypes])
 
+    # ── wt keeps the reference curve (hill_geno) ──────────────────────────────
+    # hill_geno's sampler draws every genotype's category and perturbation,
+    # wt included (hill_mut gives wt the reference by construction). An
+    # explicit override of wt below still wins.
+    if (theta_component == "hill_geno" and "wt" in geno_to_sim_idx
+            and "wt" not in (theta_gc_override or {})):
+        theta_gc, theta_param = _pin_wt_reference(
+            theta_gc, theta_param, sim_data, geno_to_sim_idx["wt"],
+            theta_sim_priors_overrides)
+
     # ── Inject stratified theta for calibration genotypes ─────────────────────
     if theta_gc_override:
         theta_gc = np.array(theta_gc)  # ensure a mutable copy
@@ -617,15 +960,14 @@ def thermo_to_growth(
     theta = phenotype_df["theta"].to_numpy()
     activity = phenotype_df["activity"].to_numpy()
 
-    growth_theta = _THETA_RESCALE[theta_rescale](theta)
+    dk_geno = phenotype_df["dk_geno"].to_numpy()
 
-    k_pre = _apply_growth_params(phenotype_df["condition_pre"].to_numpy(),
-                                 growth_theta, growth_params, activity_array=activity)
-    phenotype_df["k_pre"] = k_pre + phenotype_df["dk_geno"].to_numpy()
-
-    k_sel = _apply_growth_params(phenotype_df["condition_sel"].to_numpy(),
-                                 growth_theta, growth_params, activity_array=activity)
-    phenotype_df["k_sel"] = k_sel + phenotype_df["dk_geno"].to_numpy()
+    phenotype_df["k_pre"] = growth_rates(phenotype_df["condition_pre"].to_numpy(),
+                                         theta, activity, dk_geno,
+                                         growth_params, theta_rescale)
+    phenotype_df["k_sel"] = growth_rates(phenotype_df["condition_sel"].to_numpy(),
+                                         theta, activity, dk_geno,
+                                         growth_params, theta_rescale)
 
     # ── Final column ordering ─────────────────────────────────────────────────
 
@@ -641,7 +983,8 @@ def thermo_to_growth(
     # phenotype_df keeps dk_geno/activity so selection_experiment still works.
 
     parameters_df = _theta_param_to_df(theta_param, unique_genotypes,
-                                        unique_sim_indices)
+                                        unique_sim_indices,
+                                        num_genotype_sim=len(all_genotypes))
     parameters_df["dk_geno"] = genotype_dk_geno_series.reindex(
         list(unique_genotypes)
     ).values
@@ -667,4 +1010,20 @@ def thermo_to_growth(
 
     print("Done.", flush=True)
 
-    return phenotype_df, genotype_theta_df, parameters_df
+    out = (phenotype_df, genotype_theta_df, parameters_df)
+    if return_mutation_truth:
+        from tfscreen.tfmodel.generative.components.theta.thermo.io import (
+            required_struct_names,
+        )
+        struct_names = required_struct_names(theta_component)
+        mutation_df, pair_df = _mutation_truth_tables(
+            deterministic, all_genotypes, sim_data, struct_names=struct_names)
+        out += ({"mutation": mutation_df, "pair": pair_df,
+                 "ddG_sigma": _ddG_sigma_truth_table(deterministic,
+                                                     struct_names)},)
+    if binding_titrant_conc is not None:
+        out += (_theta_at_concs(theta_component, theta_param, sim_data,
+                                binding_titrant_conc, unique_genotypes,
+                                unique_sim_indices, theta_gc_override,
+                                theta_params_override),)
+    return out
diff --git a/src/tfscreen/simulate/transformation_lam_output.py b/src/tfscreen/simulate/transformation_lam_output.py
index 01e05b0a..a8e4e12a 100644
--- a/src/tfscreen/simulate/transformation_lam_output.py
+++ b/src/tfscreen/simulate/transformation_lam_output.py
@@ -4,11 +4,15 @@
 ``transformation_poisson_lambda`` is a top-level simulate-config scalar
 (number of plasmids/cell during transformation, drawn from a zero-truncated
 Poisson; see ``simulate.selection_experiment``). It has a fit-side analog:
-the ``transformation`` component category's ``empirical``/``logit_norm``
-variants both sample a global congression rate (see
-``generative/components/transformation/_congression.py``) and extract it as
+the ``transformation`` component category's ``mixture`` variant samples a
+global congression rate (see
+``generative/components/transformation/mixture.py``) and extracts it as
 ``lam`` (see those modules' ``get_extract_specs``, which writes
-``*_params_lam.csv``). This mirrors ``base_growth_data.generate_k_ref_df``:
+``*_params_lam.csv``). The two are the same quantity: the fit's class
+weights are the abundance shares of zero-truncated Poisson(lambda) cells
+whose plasmids split the cell's abundance, as in the simulator
+(``selection_experiment._plasmid_shares``), so the fit's ``lam`` is compared
+to this value directly. This mirrors ``base_growth_data.generate_k_ref_df``:
 a single global scalar, not genotype- or condition-indexed, echoed to its
 own single-row ground-truth file.
 """
diff --git a/src/tfscreen/tfmodel/analysis/error_calibration.py b/src/tfscreen/tfmodel/analysis/error_calibration.py
index ea6f3cf3..37947485 100644
--- a/src/tfscreen/tfmodel/analysis/error_calibration.py
+++ b/src/tfscreen/tfmodel/analysis/error_calibration.py
@@ -395,7 +395,8 @@ def _load_h5_params(path: str) -> Dict[str, np.ndarray]:
     out = {}
     with h5py.File(path, "r") as hf:
         for key in hf.keys():
-            out[key] = hf[key][:]
+            if isinstance(hf[key], h5py.Dataset):   # skip model_params group
+                out[key] = hf[key][:]
     return out
 
 
diff --git a/src/tfscreen/tfmodel/analysis/extraction.py b/src/tfscreen/tfmodel/analysis/extraction.py
index 9bb94281..741a11de 100644
--- a/src/tfscreen/tfmodel/analysis/extraction.py
+++ b/src/tfscreen/tfmodel/analysis/extraction.py
@@ -7,6 +7,7 @@
 from tqdm import tqdm
 from tfscreen.tfmodel.inference.posteriors import load_posteriors, get_posterior_samples
 from tfscreen.tfmodel.generative.registry import model_registry
+from tfscreen.tfmodel.analysis.shared_shift import apply_shared_shift
 
 
 @dataclass
@@ -16,6 +17,22 @@ class ExtractionContext:
     mut_labels: List[str]
     pair_labels: List[str]
     growth_shares_replicates: bool
+    theta_priors: Any = None
+    # Binding genotypes in binding-tensor order (None without binding data).
+    binding_genotypes: Any = None
+    # The condition_growth component's priors (static flags such as m_warp).
+    condition_growth_priors: Any = None
+
+
+def _binding_genotypes(orchestrator):
+    """Binding genotype labels in binding-tensor order, or None."""
+    tm = getattr(orchestrator, "binding_tm", None)
+    if tm is None:
+        return None
+    names = list(tm.tensor_dim_names)
+    if "genotype" not in names:
+        return None
+    return [str(g) for g in tm.tensor_dim_labels[names.index("genotype")]]
 
 
 def _extract_param_est(input_df,
@@ -24,7 +41,8 @@ def _extract_param_est(input_df,
                        get_columns,
                        in_run_prefix,
                        param_posteriors,
-                       q_to_get):
+                       q_to_get,
+                       out_names=None):
     """
     Extract parameter estimates and quantiles from posterior samples.
 
@@ -50,12 +68,19 @@ def _extract_param_est(input_df,
     q_to_get : dict
         Dictionary mapping output column names to quantile values (between 0 and 1)
         to extract from the posterior samples.
+    out_names : list of str, optional
+        Output table name for each entry of `params_to_get` (same length).
+        Lets several specs read slices of one site into separately named
+        tables, e.g. the thermo models' (M, S) ``theta_ddG`` into one
+        ``ddG_{structure}`` table per structure.  Defaults to the parameter
+        names.
 
     Returns
     -------
     out_dfs : dict
-        Dictionary mapping each parameter name to a DataFrame containing the
-        requested quantiles for each parameter, with metadata columns.
+        Dictionary mapping each output name (the parameter name unless
+        `out_names` is given) to a DataFrame containing the requested
+        quantiles for each parameter, with metadata columns.
 
     Raises
     ------
@@ -71,9 +96,16 @@ def _extract_param_est(input_df,
           .reset_index(drop=True)
           .copy())
 
+    if out_names is None:
+        out_names = list(params_to_get)
+    if len(out_names) != len(params_to_get):
+        raise ValueError(
+            f"out_names ({len(out_names)}) must match params_to_get "
+            f"({len(params_to_get)}).")
+
     # Go through all parameters requested
     out_dfs = {}
-    for param in params_to_get:
+    for param, out_name in zip(params_to_get, out_names):
 
         # Grab the posterior distribution of this parameters and flatten.
         model_param = f"{in_run_prefix}{param}"
@@ -96,7 +128,7 @@ def _extract_param_est(input_df,
             to_write[q_name] = q[to_write[map_column].values]
 
         # Record the final dataframe
-        out_dfs[param] = to_write.drop(columns=[map_column])
+        out_dfs[out_name] = to_write.drop(columns=[map_column])
 
     return out_dfs
 
@@ -120,8 +152,9 @@ def extract_parameters(orchestrator, posteriors, q_to_get=None):
     q_to_get : dict, optional
         Dictionary mapping output column names to quantile values (between 0 and 1)
         to extract from the posterior samples. If None, a default set of quantiles
-        is used (min, lower_95, lower_std, lower_quartile, median, upper_std,
-        upper_quartile, upper_95, max).
+        is used: the 17 levels of
+        ``tfscreen.tfmodel.inference.posteriors._DEFAULT_QUANTILE_LEVELS``
+        (0.001 to 0.999), written as bare ``q<level>`` columns.
 
     Returns
     -------
@@ -142,6 +175,11 @@ def extract_parameters(orchestrator, posteriors, q_to_get=None):
         mut_labels=orchestrator.mut_labels,
         pair_labels=orchestrator.pair_labels,
         growth_shares_replicates=orchestrator._growth_shares_replicates,
+        theta_priors=getattr(getattr(orchestrator, "priors", None), "theta", None),
+        binding_genotypes=_binding_genotypes(orchestrator),
+        condition_growth_priors=getattr(getattr(getattr(orchestrator, "priors", None),
+                                                "growth", None),
+                                        "condition_growth", None),
     )
 
     component_selections = [
@@ -152,7 +190,11 @@ def extract_parameters(orchestrator, posteriors, q_to_get=None):
         ("activity", orchestrator._activity),
         ("theta", orchestrator._theta),
         ("transformation", orchestrator._transformation),
+        ("sample_offset", getattr(orchestrator, "_sample_offset", "zero")),
     ]
+    if getattr(orchestrator, "_has_binding", False):
+        component_selections.append(
+            ("theta_binding_noise", orchestrator._theta_binding_noise))
 
     extract = []
     for category, selection in component_selections:
@@ -181,7 +223,8 @@ def extract_parameters(orchestrator, posteriors, q_to_get=None):
                                              q_to_get=q_to_get,
                                              **kwargs))
         except KeyError as exc:
-            param_names = kwargs.get("params_to_get", [])
+            param_names = (kwargs.get("out_names")
+                           or kwargs.get("params_to_get", []))
             warnings.warn(
                 f"Skipping extraction of {param_names}: {exc}. "
                 "This can happen with MAP checkpoints because computed "
@@ -194,7 +237,7 @@ def extract_parameters(orchestrator, posteriors, q_to_get=None):
     return params
 
 def extract_theta_curves(orchestrator, posteriors, q_to_get=None, manual_titrant_df=None,
-                         num_samples=100):
+                         num_samples=100, shared_shift=None, shift_seed=0):
     """
     Extract theta curves by sampling from the joint posterior distribution.
 
@@ -213,8 +256,9 @@ def extract_theta_curves(orchestrator, posteriors, q_to_get=None, manual_titrant
     q_to_get : dict, optional
         Dictionary mapping output column names to quantile values (between 0 and 1)
         to extract from the posterior samples. If None, a default set of quantiles
-        is used (min, lower_95, lower_std, lower_quartile, median, upper_std,
-        upper_quartile, upper_95, max).
+        is used: the 17 levels of
+        ``tfscreen.tfmodel.inference.posteriors._DEFAULT_QUANTILE_LEVELS``
+        (0.001 to 0.999), written as bare ``q<level>`` columns.
     manual_titrant_df : pd.DataFrame, optional
         A DataFrame specifying 'titrant_name' and 'titrant_conc' values
         at which to calculate theta. If provided, it overrides the default
@@ -227,6 +271,15 @@ def extract_theta_curves(orchestrator, posteriors, q_to_get=None, manual_titrant
         columns. Sampling is with replacement when ``num_samples`` exceeds the
         total number of posterior samples. Set to ``None`` to suppress sample
         columns and return only quantiles. Default 100.
+    shared_shift : str, pandas.DataFrame or None, optional
+        A shift shared by every non-wt genotype (``genotype``,
+        ``titrant_name``, ``titrant_conc``, ``shift``; the two-stage fit's
+        k uncertainty). Each draw gets one standard-normal multiple of it
+        before quantiles and samples are taken
+        (``analysis.shared_shift.apply_shared_shift``). Needs more than one
+        draw. Default None.
+    shift_seed : int, optional
+        Seed for the shared multiples. Default 0.
 
     Returns
     -------
@@ -255,6 +308,9 @@ def extract_theta_curves(orchestrator, posteriors, q_to_get=None, manual_titrant
 
     calc_df, internal_cols, extra_kwargs = module.build_calc_df(orchestrator, manual_titrant_df)
     theta_samples = module.compute_theta_samples(calc_df, param_posteriors, **extra_kwargs)
+    if shared_shift is not None:
+        theta_samples = apply_shared_shift(theta_samples, calc_df,
+                                           shared_shift, seed=shift_seed)
 
     for q_name, q_val in q_to_get.items():
         calc_df[q_name] = np.quantile(theta_samples, q_val, axis=0)
@@ -275,7 +331,8 @@ def extract_theta_epistasis(orchestrator, posteriors, q_to_get=None,
                             manual_titrant_df=None, scale="logit",
                             scale_constant=1.0,
                             group_by=("titrant_name", "titrant_conc"),
-                            regime_eps=0.01, regime_ci=0.95):
+                            regime_eps=0.01, regime_ci=0.95,
+                            shared_shift=None, shift_seed=0):
     """
     Extract second-order epistasis quantiles from the joint theta posterior.
 
@@ -331,6 +388,13 @@ def extract_theta_epistasis(orchestrator, posteriors, q_to_get=None,
         Central posterior-mass fraction that must lie inside the band for a
         corner to count as in-regime (e.g. 0.95 requires the theta 2.5-97.5%
         interval within ``[regime_eps, 1 - regime_eps]``).  Must be in (0, 1).
+    shared_shift : str, pandas.DataFrame or None, optional
+        A shift shared by every non-wt genotype, added to each joint draw
+        as one standard-normal multiple before epistasis is computed (see
+        ``extract_theta_curves``); the cycle's wt corner does not move, so
+        epistasis keeps one copy of it. Default None.
+    shift_seed : int, optional
+        Seed for the shared multiples. Default 0.
 
     Returns
     -------
@@ -386,6 +450,16 @@ def extract_theta_epistasis(orchestrator, posteriors, q_to_get=None,
             f"'{orchestrator._theta}' does not support this interface."
         )
 
+    # A relative theta component gives X, defined only up to an affine map
+    # (roadmap C8): additive epistasis is the only scale that means anything
+    # until the map to theta is known, and the in_regime band is a theta band.
+    relative = getattr(module, "THETA_SCALE", "theta") == "X"
+    if relative and scale != "add":
+        raise ValueError(
+            f"theta component '{orchestrator._theta}' predicts the "
+            f"wt-relative growth variable X, not an occupancy in (0, 1); only "
+            f"scale='add' applies to it (got scale={scale!r}).")
+
     q_to_get, param_posteriors = load_posteriors(posteriors, q_to_get)
 
     # Joint sample matrix: (num_sample, num_row), rows aligned to calc_df.
@@ -393,6 +467,9 @@ def extract_theta_epistasis(orchestrator, posteriors, q_to_get=None,
         orchestrator, manual_titrant_df)
     theta_samples = module.compute_theta_samples(
         calc_df, param_posteriors, **extra_kwargs)
+    if shared_shift is not None:
+        theta_samples = apply_shared_shift(theta_samples, calc_df,
+                                           shared_shift, seed=shift_seed)
 
     group_by = list(group_by)
 
@@ -414,8 +491,8 @@ def extract_theta_epistasis(orchestrator, posteriors, q_to_get=None,
         cycles = cycles.dropna(subset=idx_cols)
 
     if cycles.empty:
-        return pd.DataFrame(columns=["genotype"] + group_by
-                            + list(q_to_get) + ["in_regime"])
+        return pd.DataFrame(columns=["genotype"] + group_by + list(q_to_get)
+                            + ([] if relative else ["in_regime"]))
 
     idx_00 = cycles["00__row_idx"].values.astype(int)
     idx_10 = cycles["10__row_idx"].values.astype(int)
@@ -436,6 +513,9 @@ def extract_theta_epistasis(orchestrator, posteriors, q_to_get=None,
     for q_name, q_val in q_to_get.items():
         out[q_name] = np.quantile(ep_samples, q_val, axis=0)
 
+    if relative:
+        return out.sort_values(["genotype"] + group_by).reset_index(drop=True)
+
     # in_regime: are all four cycle corners' theta posteriors inside the
     # resolvable band [regime_eps, 1 - regime_eps]?  Outside it logit(theta)
     # saturates and the epistasis leans on the theta-model extrapolation, so the
@@ -522,8 +602,6 @@ def extract_theta_unmeasured(orchestrator, posteriors, target_genotypes,
     theta_priors = orchestrator.priors.theta
     if hasattr(theta_priors, "theta_tf_total_M"):
         extra_kwargs["tf_total"] = float(theta_priors.theta_tf_total_M)
-    if hasattr(theta_priors, "theta_op_total_M"):
-        extra_kwargs["op_total"] = float(theta_priors.theta_op_total_M)
     if hasattr(theta_priors, "theta_conc_unit_scale"):
         extra_kwargs["conc_unit_scale"] = float(theta_priors.theta_conc_unit_scale)
 
@@ -588,8 +666,9 @@ def extract_growth_predictions(orchestrator,
     q_to_get : dict, optional
         Dictionary mapping output column names to quantile values (between 0 and 1)
         to extract from the posterior samples. If None, a default set of quantiles
-        is used (min, lower_95, lower_std, lower_quartile, median, upper_std,
-        upper_quartile, upper_95, max).
+        is used: the 17 levels of
+        ``tfscreen.tfmodel.inference.posteriors._DEFAULT_QUANTILE_LEVELS``
+        (0.001 to 0.999), written as bare ``q<level>`` columns.
     num_samples : int or None, optional
         Randomly select this many joint posterior samples and return them as
         columns ``sample_0``, ``sample_1``, ... alongside the quantile
diff --git a/src/tfscreen/tfmodel/analysis/prediction.py b/src/tfscreen/tfmodel/analysis/prediction.py
index 3e5d0659..39d9b66d 100644
--- a/src/tfscreen/tfmodel/analysis/prediction.py
+++ b/src/tfscreen/tfmodel/analysis/prediction.py
@@ -1,13 +1,19 @@
 import pandas as pd
 import numpy as np
 import itertools
-import warnings
-from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator, _RELATIVE_THETA
 from tfscreen.tfmodel.inference.posteriors import load_posteriors, get_posterior_samples
+from tfscreen.tfmodel.inference.model_params import (
+    hold_model_params,
+    read_model_params,
+    sample_keys,
+)
 from tfscreen.tfmodel.tensors.batch import get_batch as _get_batch
+from tfscreen.tfmodel.generative.model import growth_titrant
+from tfscreen.tfmodel.inference.initialization import structure_trace
 import jax
 from jax import numpy as jnp
-from numpyro.handlers import seed, trace
+from numpyro.handlers import seed, substitute, trace
 from numpyro.infer import Predictive
 
 def _align_site_to_tm_dims(spatial_shape, tm_sizes):
@@ -162,6 +168,11 @@ def _condition_rep_output_df(growth_tm, site_samples, q_to_get, num_samples):
     return df
 
 
+def _is_relative(orchestrator):
+    """True when the model's theta component is on the relative X scale."""
+    return orchestrator.settings.get("theta") in _RELATIVE_THETA
+
+
 def copy_orchestrator(orchestrator,
                       t_pre=None,
                       t_sel=None,
@@ -190,7 +201,8 @@ def copy_orchestrator(orchestrator,
         values from `orchestrator.growth_df`.
     genotypes : list, optional
         Subset of genotypes to include. Must be a subset of those in
-        ``orchestrator.growth_df``. If None, uses all genotypes.
+        ``orchestrator.growth_df``. If None, uses all genotypes. A relative
+        theta component is gauged on wt, so wt is always kept.
 
     Returns
     -------
@@ -247,6 +259,8 @@ def _get_input(value, col_name):
         missing = [g for g in geno_strs if g not in all_genos]
         if missing:
             raise ValueError(f"Genotype(s) not found in orchestrator: {missing}")
+        if _is_relative(orchestrator) and "wt" not in geno_strs:
+            geno_strs = geno_strs + ["wt"]
         unique_cats = unique_cats[
             unique_cats["genotype"].astype(str).isin(set(geno_strs))
         ]
@@ -261,10 +275,18 @@ def _get_input(value, col_name):
     # Add required data columns with dummy values
     new_growth_df["ln_cfu"] = 0.0
     new_growth_df["ln_cfu_std"] = 1.0
+    if orchestrator.settings.get("growth_likelihood") == "counts":
+        # The count observer's inputs; placeholders too, since prediction
+        # reads growth_pred, not the observation.
+        new_growth_df["counts"] = 0
+        new_growth_df["sample_reads"] = 1.0
+        new_growth_df["sample_ln_cfu"] = 0.0
 
     # We keep the binding_df as is, as it's keyed by genotype/titrant_name
-    # and we aren't subsetting those in this step.
-    new_binding_df = orchestrator.binding_df.copy()
+    # and we aren't subsetting those in this step. A growth-only model has
+    # none.
+    new_binding_df = (None if orchestrator.binding_df is None
+                      else orchestrator.binding_df.copy())
 
     # Create new ModelOrchestrator using settings from the old one
     settings = orchestrator.settings.copy()
@@ -276,17 +298,38 @@ def _get_input(value, col_name):
     # the subset must remain marked as spiked; those outside the subset are
     # simply omitted.  Dropping all spiked_genotypes would silently mis-classify
     # in-subset spiked genotypes as library genotypes and corrupt predictions.
-    if genotypes is not None:
+    # A model built from library_file reports the spiked list it derived, but
+    # the library table is the definition (and covers any subset), so the
+    # derived list is dropped, as write_configuration does.
+    if settings.get("library_file") is not None:
+        settings.pop("spiked_genotypes", None)
+    elif genotypes is not None:
         geno_set = set(geno_strs)
         orig_spiked = settings.get("spiked_genotypes") or []
         settings["spiked_genotypes"] = [
             g for g in orig_spiked if str(g) in geno_set
         ]
 
+    # Per-mutation and per-pair parameters (the *_mutation_plate and
+    # *_pair_plate sites of hill_mut, the *_mut activity components and the
+    # thermodynamic theta models, and the per-structure data built from
+    # those labels) are indexed by the fitted model's mut_labels and
+    # pair_labels. Built from a genotype subset alone, the labels would be
+    # the subset's mutations in a different first-seen order, and posterior
+    # arrays would land on the wrong mutations and pairs. Keep the fitted
+    # model's index space and rebuild only the genotype columns.
+    mut_label_kwargs = {}
+    if len(getattr(orchestrator, "mut_labels", None) or []) > 0:
+        mut_label_kwargs = {
+            "mut_labels": list(orchestrator.mut_labels),
+            "pair_labels": list(orchestrator.pair_labels),
+        }
+
     return ModelOrchestrator(
         growth_df=new_growth_df,
         binding_df=new_binding_df,
-        **settings
+        **settings,
+        **mut_label_kwargs,
     )
 
 def _convert_map_params(map_params, model_trace):
@@ -335,65 +378,134 @@ def _convert_map_params(map_params, model_trace):
     return constrained
 
 
-def _build_population_theta_reference(param_posteriors, max_samples=20):
+# Deterministic sites holding every genotype's library-ordered value, and the
+# GrowthData field each one fills for the congression mixture.
+_POPULATION_SITES = (("dk_geno", "external_dk_population"),
+                     ("activity", "external_activity_population"))
+
+
+def _missing_site_error(site):
+    return ValueError(
+        f"The mixture transformation needs every genotype's '{site}' from the "
+        f"posterior to build congressed cells, but '{site}' is not in "
+        f"param_posteriors. Raw MAP checkpoints store only guide parameters; "
+        f"run tfs-sample-posterior to write a full posterior file first."
+    )
+
+
+def _posterior_median(param_posteriors, site, max_samples):
+    """Median over the first ``max_samples`` posterior draws of ``site``."""
+    try:
+        values = get_posterior_samples(param_posteriors, site)
+    except KeyError:
+        raise _missing_site_error(site) from None
+    n_use = min(max_samples, values.shape[0])
+    return np.median(np.asarray(values[:n_use]), axis=0)
+
+
+def _binding_labels(orchestrator):
+    """Binding genotypes in binding-tensor order ([] without binding)."""
+    tm = getattr(orchestrator, "binding_tm", None)
+    if tm is None:
+        return []
+    names = list(tm.tensor_dim_names)
+    return [str(g) for g in tm.tensor_dim_labels[names.index("genotype")]]
+
+
+def _population_theta(orchestrator, param_posteriors, pred_growth,
+                      max_samples, model_params=None):
     """
-    Build a single population-wide theta_growth reference for transformation
-    components whose congression correction needs one (see
-    transformation/_congression.py and generative/model.py).
-
-    Reads the ``theta_growth_pred`` deterministic site, which get_posteriors()
-    always stores at full genotype-population size regardless of any
-    genotype batching used during training or posterior sampling (unlike a
-    fresh forward pass through a genotype-subsetted orchestrator, which only
-    ever sees the requested subset -- see copy_orchestrator()).
-
-    Reduces across a small number of posterior draws (median) rather than
-    threading the full posterior sample axis through this call: the
-    population reference only needs to characterize the theta *distribution*
-    across genotypes for the congression correction, not track each specific
-    joint posterior draw, and this keeps the one-time cost independent of
-    both ``num_marginal_samples`` and the number of stored posterior draws.
+    Every fitted genotype's theta at the prediction's titrant concentrations.
+
+    The theta component's posterior parameters (median over draws) are
+    evaluated over the whole fitted library on the prediction's
+    concentration grid, which can differ from the fit's (so the stored
+    ``theta_growth_pred`` cannot be used directly).
+    """
+    sample_theta, calc_theta, _ = orchestrator.main_control_kwargs["theta"]
+    num_genotype = orchestrator.data.growth.num_genotype
+    full = jnp.arange(num_genotype)
+    growth = orchestrator.get_batch(orchestrator.data, full).growth
+
+    theta_trace = trace(seed(sample_theta, rng_seed=0)).get_trace(
+        "theta", growth, orchestrator.priors.theta)
+    latents = {
+        name: jnp.asarray(_posterior_median(param_posteriors, name,
+                                            max_samples))
+        for name, site in theta_trace.items()
+        if site["type"] == "sample" and not site.get("is_observed", False)
+    }
+    # the theta component's own learned params (thermo PnnC NN) too
+    theta_param = substitute(seed(sample_theta, rng_seed=0),
+                             data={**(model_params or {}), **latents})(
+        "theta", growth, orchestrator.priors.theta)
+
+    view = growth.replace(batch_idx=full,
+                          geno_theta_idx=full,
+                          titrant_conc=pred_growth.titrant_conc,
+                          log_titrant_conc=pred_growth.log_titrant_conc,
+                          num_titrant_conc=pred_growth.num_titrant_conc)
+    # the titrant the growth sees (s times nominal), as in the model
+    return calc_theta(theta_param, growth_titrant(theta_param, view))
+
+
+def _build_population_references(orchestrator, param_posteriors, pred_growth,
+                                 max_samples=20, model_params=None):
+    """
+    Library-wide theta, dk_geno and activity references for the congression
+    mixture (transformation components with ``NEEDS_POPULATION``).
+
+    A prediction runs a model rebuilt for the requested genotypes only (see
+    copy_orchestrator), whose latents cover just that subset. The mixture's
+    congressed cells, though, look their co-resident plasmids up across the
+    whole fitted library, so the library-wide values come from the posterior:
+    theta from the theta component's parameters, evaluated at the
+    prediction's concentrations (``_population_theta``); dk_geno and activity
+    from their deterministic sites, which get_posteriors() stores for every
+    genotype in library order.
+
+    Each reference comes from the median over up to ``max_samples``
+    posterior draws, not per-draw values: the background only needs to
+    characterize the co-residents, and this keeps the cost independent of the
+    number of draws.
 
     Parameters
     ----------
+    orchestrator : ModelOrchestrator
+        The fitted model.
     param_posteriors : dict-like
-        Full (not genotype-subsetted) posterior samples, as returned by
-        load_posteriors().
+        Full (not genotype-subsetted) posterior samples.
+    pred_growth : GrowthData
+        The prediction's growth data (supplies the concentration grid).
     max_samples : int, optional
-        Maximum number of posterior draws to read when computing the
-        median. Default 20.
+        Maximum number of posterior draws to read. Default 20.
 
     Returns
     -------
-    jnp.ndarray or None
-        Shape matches theta_growth_pred's stored shape with the leading
-        sample axis reduced away (typically (num_titrant_name,
-        num_titrant_conc, num_genotype), possibly with extra broadcast dims
-        from scatter_theta=1). None if theta_growth_pred is not present in
-        param_posteriors (e.g. a raw MAP checkpoint, which stores guide
-        parameters rather than deterministic site values).
+    dict
+        Maps each ``external_*_population`` GrowthData field to its array.
+
+    Raises
+    ------
+    ValueError
+        If a site is missing (a raw MAP checkpoint stores guide parameters,
+        not deterministic sites) or is not library-sized.
     """
-    try:
-        theta_growth_pred = get_posterior_samples(param_posteriors, "theta_growth_pred")
-    except KeyError:
-        warnings.warn(
-            "The configured transformation component needs a population-wide "
-            "theta reference for its congression correction, but "
-            "'theta_growth_pred' was not found in param_posteriors (expected "
-            "for raw MAP checkpoints, which only store guide parameters, not "
-            "deterministic site values). Falling back to no population "
-            "reference; predictions may reproduce the population-CDF bug this "
-            "mechanism exists to avoid. Run tfs-sample-posterior to generate "
-            "a full posterior file instead.",
-            UserWarning,
-            stacklevel=2,
-        )
-        return None
+    num_genotype = orchestrator.data.growth.num_genotype
+    references = {}
+    for site, field_name in _POPULATION_SITES:
+        values = _posterior_median(param_posteriors, site, max_samples)
+        if values.shape[-1] != num_genotype:
+            raise ValueError(
+                f"Posterior '{site}' has {values.shape[-1]} genotypes on its "
+                f"last axis; expected the whole library ({num_genotype})."
+            )
+        references[field_name] = jnp.asarray(values)
 
-    n_available = theta_growth_pred.shape[0]
-    n_use = min(max_samples, n_available)
-    theta_growth_pred = np.asarray(theta_growth_pred[:n_use])
-    return jnp.asarray(np.median(theta_growth_pred, axis=0))
+    references["external_theta_population"] = _population_theta(
+        orchestrator, param_posteriors, pred_growth, max_samples,
+        model_params=model_params)
+    return references
 
 
 def predict(orchestrator,
@@ -461,21 +573,21 @@ def predict(orchestrator,
     if isinstance(predict_sites, str):
         predict_sites = [predict_sites]
 
-    # Some transformation components (currently just "empirical") correct
-    # theta using a background CDF that must be estimated from the *full*
-    # genotype population, not whatever subset of genotypes this call
-    # happens to request (see generative/model.py and
-    # transformation/_congression.py). Build that reference once, up front,
-    # from the full-population posterior -- never from new_orchestrator,
-    # which copy_orchestrator() below rebuilds scoped to just the requested
-    # genotypes.
+    # The mixture transformation builds congressed cells from co-resident
+    # plasmids looked up across the whole library (see
+    # transformation/mixture.py and generative/model.py). Build the
+    # library-wide references once, up front, from the full-population
+    # posterior -- never from new_orchestrator, which copy_orchestrator()
+    # below rebuilds scoped to just the requested genotypes.
     transformation_control = orchestrator.main_control_kwargs.get("transformation")
     transformation_needs_population = (
         bool(transformation_control[2]) if transformation_control is not None else False
     )
-    external_theta_population = None
+    # Checked here, before any model work, so a raw MAP checkpoint fails fast.
     if transformation_needs_population:
-        external_theta_population = _build_population_theta_reference(param_posteriors)
+        for site, _ in _POPULATION_SITES:
+            if site not in param_posteriors:
+                raise _missing_site_error(site)
 
     # Create the expanded prediction model, subsetting genotypes if requested.
     # Passing genotypes to copy_orchestrator ensures the new orchestrator's TM
@@ -488,6 +600,11 @@ def predict(orchestrator,
                                          titrant_conc=titrant_conc,
                                          genotypes=genotypes)
 
+    # A relative theta component's gauge needs wt, so copy_orchestrator keeps
+    # it in any subset; it is dropped from the output below if not asked for.
+    drop_wt = (genotypes is not None and _is_relative(orchestrator)
+               and "wt" not in [str(g) for g in genotypes])
+
     # After copy_orchestrator the new TM contains exactly the requested
     # genotypes (or all genotypes when genotypes=None).
     genotypes = new_orchestrator.growth_tm.tensor_dim_labels[-1].tolist()
@@ -497,10 +614,11 @@ def predict(orchestrator,
     #
     # We use the original orchestrator because posteriors match its structure.
 
-    seeded_model = seed(orchestrator.jax_model, rng_seed=0)
-    traced_model = trace(seeded_model)
-    model_trace = traced_model.get_trace(data=orchestrator.data,
-                                         priors=orchestrator.priors)
+    # A structure trace: a prior draw that predicts a non-finite ln_cfu must
+    # not stop it at the count observation (see structure_trace).
+    model_trace = structure_trace(orchestrator.jax_model,
+                                  data=orchestrator.data,
+                                  priors=orchestrator.priors)
 
     # -------------------------------------------------------------------------
     # MAP param conversion (if needed)
@@ -516,6 +634,14 @@ def predict(orchestrator,
     # already store constrained values keyed by bare site name and are left
     # unchanged.
 
+    # The model's own learned params (thermo PnnC NN weights): a
+    # posterior file stores them in its model_params group, a raw MAP params
+    # file under their own names. They are held fixed in every model run
+    # below; without them the model would use their init values.
+    model_param_names = [name for name, site in model_trace.items()
+                         if site["type"] == "param"]
+    model_params = read_model_params(param_posteriors, model_param_names)
+
     if any(str(k).endswith("_auto_loc") for k in param_posteriors.keys()):
         param_posteriors = _convert_map_params(param_posteriors, model_trace)
 
@@ -523,7 +649,7 @@ def predict(orchestrator,
     # Sample selection from posterior
 
     # Identify how many samples we have
-    first_key = next(iter(param_posteriors.keys()))
+    first_key = sample_keys(param_posteriors)[0]
     total_available = param_posteriors[first_key].shape[0]
 
     # Sample indices for running through the model (quantile computation)
@@ -550,6 +676,18 @@ def predict(orchestrator,
             
         val = val[sample_indices]
 
+        # Per-tube offsets (sample_offset: one value per tube, on a
+        # "{name}_tubes" plate) describe particular tubes. A prediction is
+        # for a typical tube, and its tube grid (every t_pre x t_sel x
+        # concentration asked for) is not the training grid, so these are
+        # set to zero: the expected growth, without any one tube's shift.
+        if any(frame.name.endswith("_tubes")
+               for frame in site.get("cond_indep_stack", [])):
+            num_new_tubes = int(np.prod(new_orchestrator.growth_tm.tensor_shape[:-1]))
+            sliced_samples[site_name] = jnp.zeros((val.shape[0], num_new_tubes),
+                                                  dtype=val.dtype)
+            continue
+
         # Slice any plated dimension to match the new data labels.
         # This handles genotype subsetting and any other model plates (like
         # titrant_conc in congression/categorical models).
@@ -586,6 +724,25 @@ def predict(orchestrator,
 
                 continue
 
+            # Latents that exist only for binding genotypes (noise/
+            # genotype_offset's "*_binding_plate") follow the binding
+            # genotypes by label: a subset's binding table holds only the
+            # binding genotypes that are in its growth table.
+            if plate_name.endswith("_binding_plate"):
+                old_b = _binding_labels(orchestrator)
+                new_b = _binding_labels(new_orchestrator)
+                if old_b != new_b:
+                    try:
+                        indices = [old_b.index(g) for g in new_b]
+                    except ValueError:
+                        raise ValueError(
+                            f"Site '{site_name}' is plated on the binding "
+                            f"genotypes and has no value for "
+                            f"{sorted(set(new_b) - set(old_b))}.")
+                    val = jnp.take(val, jnp.array(indices, dtype=int),
+                                   axis=frame.dim)
+                continue
+
             # Find the corresponding dimension in the TensorManager
             dim_idx = None
             for i, name in enumerate(orchestrator.growth_tm.tensor_dim_names):
@@ -614,7 +771,8 @@ def predict(orchestrator,
     # -------------------------------------------------------------------------
     # Run Prediction
     
-    predictive = Predictive(new_orchestrator.jax_model,
+    predictive = Predictive(hold_model_params(new_orchestrator.jax_model,
+                                              model_params),
                             posterior_samples=sliced_samples,
                             return_sites=predict_sites)
 
@@ -637,13 +795,51 @@ def predict(orchestrator,
     all_indices = jnp.arange(num_geno, dtype=jnp.int32)
     pred_data = _get_batch(new_orchestrator.data, all_indices)
 
-    if external_theta_population is not None:
+    if transformation_needs_population:
+        population_references = _build_population_references(
+            orchestrator, param_posteriors, pred_data.growth,
+            model_params=model_params)
+        # The subset model draws its own co-resident sets over the subset;
+        # use the fitted model's instead. Their entries index the original
+        # library, which is what the external references cover.
+        orig_labels = list(orchestrator.growth_tm.tensor_dim_labels[-1])
+        new_labels = list(new_orchestrator.growth_tm.tensor_dim_labels[-1])
+        rows = jnp.array([orig_labels.index(g) for g in new_labels],
+                         dtype=jnp.int32)
+        orig_growth = orchestrator.data.growth
         pred_data = pred_data.replace(
             growth=pred_data.growth.replace(
-                external_theta_population=external_theta_population
+                coresident_idx=orig_growth.coresident_idx[rows],
+                bulk_fraction=orig_growth.bulk_fraction[rows],
+                **population_references,
             )
         )
 
+    # Every posterior array must have the shape the prediction model expects.
+    # A plate the slicing loop above does not know how to remap (one sized by
+    # something other than a growth-tensor dimension or condition_rep) would
+    # otherwise be indexed silently by the wrong entries, as the mutation and
+    # pair plates were before copy_orchestrator kept the fitted labels.
+    # Until 2026-10-11 this trace ran with argument validation on: with the
+    # PddG fits' learned sigma_s held, the prior draw of ln K reached +/-60,
+    # ln_cfu went non-finite and the count observation refused it, which
+    # stopped tfs-summarize-fit's trajectory plots.
+    new_trace = structure_trace(hold_model_params(new_orchestrator.jax_model,
+                                                  model_params),
+                                data=pred_data, priors=orchestrator.priors)
+    for site_name, val in sliced_samples.items():
+        new_site = new_trace.get(site_name)
+        if new_site is None:
+            continue
+        expected = tuple(jnp.shape(new_site["value"]))
+        if tuple(val.shape[1:]) != expected:
+            raise ValueError(
+                f"Posterior site '{site_name}' has shape "
+                f"{tuple(val.shape[1:])} but the prediction model expects "
+                f"{expected}; its plate cannot be mapped onto the requested "
+                f"genotypes/conditions."
+            )
+
     # Use the ORIGINAL orchestrator's priors so that any pinned hyperpriors
     # (e.g. activity_hyper_loc/activity_hyper_scale in the calibration model)
     # remain pinned at their calibrated values rather than being sampled from
@@ -756,6 +952,11 @@ def predict(orchestrator,
 
         all_dfs[site] = df
 
+    if drop_wt:
+        for site, df in all_dfs.items():
+            if "genotype" in df.columns:
+                all_dfs[site] = df[df["genotype"].astype(str) != "wt"]
+
     if len(predict_sites) == 1:
         return all_dfs[predict_sites[0]]
 
diff --git a/src/tfscreen/tfmodel/analysis/prior_predictive.py b/src/tfscreen/tfmodel/analysis/prior_predictive.py
index 2e46c66f..b2ea0f14 100644
--- a/src/tfscreen/tfmodel/analysis/prior_predictive.py
+++ b/src/tfscreen/tfmodel/analysis/prior_predictive.py
@@ -53,7 +53,12 @@ def draw_prior(orchestrator, rng_key=0, num_draws=1):
         if site["type"] == "deterministic"
     }
 
-    return_sites = sorted(set(tr.keys()) - observed)
+    # Plate sites also appear in the trace (their value is the index array);
+    # they are neither latents nor predictions.
+    return_sites = sorted(
+        name for name, site in tr.items()
+        if site["type"] in ("sample", "deterministic") and name not in observed
+    )
 
     predictive = Predictive(
         orchestrator.jax_model,
diff --git a/src/tfscreen/tfmodel/analysis/shared_shift.py b/src/tfscreen/tfmodel/analysis/shared_shift.py
new file mode 100644
index 00000000..c62ef157
--- /dev/null
+++ b/src/tfscreen/tfmodel/analysis/shared_shift.py
@@ -0,0 +1,144 @@
+"""
+A shift shared by every genotype, added to posterior draws of X.
+
+The two-stage fit (``planning/two-stage-fit.md``) pins every condition's
+growth baseline k at a stage-1 estimate. Its uncertainty, measured by a
+stage-1 jackknife, moves every non-wt genotype's X by nearly the same
+amount (on the full-size simulation one standard deviation moved X by
+-0.032 to -0.038, with a genotype-to-genotype SD of 0.005), while wt is
+fixed by the X gauge. Treated as independent width per genotype it would
+widen comparisons between genotypes that it barely touches. Added to the
+joint posterior draws as one standard-normal multiple per draw, shared by
+every row, it widens each genotype's own interval by the right amount
+while differences between genotypes keep it only where they should (an
+epistasis on X, X_ab - X_a - X_b + X_wt, keeps one copy, since wt does not
+move).
+
+The shift table has columns ``genotype``, ``titrant_name``,
+``titrant_conc`` and ``shift`` (one standard deviation's signed change in
+X), written by ``tfscreen.tfmodel.two_stage.shift_table``.
+"""
+
+import numpy as np
+import pandas as pd
+
+SHIFT_KEY = ["genotype", "titrant_name", "titrant_conc"]
+
+
+def read_shift_table(shift):
+    """
+    The shift table, from a path or a DataFrame.
+
+    Parameters
+    ----------
+    shift : str or pandas.DataFrame
+        Path to a CSV, or a DataFrame, with ``genotype``, ``titrant_name``,
+        ``titrant_conc`` and ``shift``.
+
+    Returns
+    -------
+    pandas.DataFrame
+
+    Raises
+    ------
+    ValueError
+        On a missing column or a key given twice.
+    """
+    df = pd.read_csv(shift) if isinstance(shift, str) else shift.copy()
+    missing = [c for c in SHIFT_KEY + ["shift"] if c not in df.columns]
+    if missing:
+        raise ValueError(f"Shared-shift table is missing column(s) {missing}.")
+    df = df[SHIFT_KEY + ["shift"]].copy()
+    df["genotype"] = df["genotype"].astype(str)
+    df["titrant_name"] = df["titrant_name"].astype(str)
+    df["titrant_conc"] = df["titrant_conc"].astype(float)
+    dup = df.duplicated(SHIFT_KEY)
+    if dup.any():
+        raise ValueError("Shared-shift table gives a row more than once: "
+                         f"{df.loc[dup, SHIFT_KEY].values.tolist()[:5]}")
+    return df
+
+
+def row_shifts(calc_df, shift):
+    """
+    The shift for each row of a prediction table, aligned to its rows.
+
+    wt's shift is 0 whatever the table says (the X gauge fixes wt).
+
+    Parameters
+    ----------
+    calc_df : pandas.DataFrame
+        Prediction rows with ``genotype``, ``titrant_name`` and
+        ``titrant_conc``.
+    shift : str or pandas.DataFrame
+        The shift table (see ``read_shift_table``).
+
+    Returns
+    -------
+    numpy.ndarray
+        One shift per row of ``calc_df``.
+
+    Raises
+    ------
+    ValueError
+        If a non-wt row has no shift.
+    """
+    table = read_shift_table(shift)
+    rows = calc_df[SHIFT_KEY].copy()
+    rows["genotype"] = rows["genotype"].astype(str)
+    rows["titrant_name"] = rows["titrant_name"].astype(str)
+    rows["titrant_conc"] = rows["titrant_conc"].astype(float)
+    merged = rows.merge(table, on=SHIFT_KEY, how="left")
+    is_wt = merged["genotype"] == "wt"
+    lacking = merged["shift"].isna() & ~is_wt
+    if lacking.any():
+        bad = merged.loc[lacking, SHIFT_KEY].drop_duplicates()
+        raise ValueError(
+            f"Shared-shift table has no shift for {len(bad)} predicted "
+            f"row(s), e.g. {bad.values.tolist()[:5]}. It must cover every "
+            "non-wt genotype and concentration predicted.")
+    # a copy: under pandas 3's copy-on-write to_numpy returns a read-only
+    # view, which the wt assignment below cannot write into
+    out = merged["shift"].to_numpy(dtype=float, copy=True)
+    out[is_wt.to_numpy()] = 0.0
+    return out
+
+
+def apply_shared_shift(samples, calc_df, shift, seed=0):
+    """
+    Add one shared standard-normal multiple of the shift to each draw.
+
+    Parameters
+    ----------
+    samples : numpy.ndarray
+        Posterior draws, shape (num_draws, num_rows), rows aligned to
+        ``calc_df``.
+    calc_df : pandas.DataFrame
+        The rows (``genotype``, ``titrant_name``, ``titrant_conc``).
+    shift : str or pandas.DataFrame
+        The shift table.
+    seed : int, optional
+        Seed for the per-draw multiples. Default 0.
+
+    Returns
+    -------
+    numpy.ndarray
+        ``samples + z[:, None] * shift[None, :]``, with one ``z`` per draw.
+
+    Raises
+    ------
+    ValueError
+        With a single draw (a MAP point has no draws to share a shift
+        across) or a row count that does not match ``calc_df``.
+    """
+    samples = np.asarray(samples, dtype=float)
+    if samples.ndim != 2 or samples.shape[1] != len(calc_df):
+        raise ValueError(f"samples have shape {samples.shape}; expected "
+                         f"(num_draws, {len(calc_df)}).")
+    if samples.shape[0] < 2:
+        raise ValueError("A shared shift needs posterior draws; this "
+                         "posterior has one (a MAP point). Run "
+                         "tfs-sample-posterior first.")
+    per_row = row_shifts(calc_df, shift)
+    z = np.random.default_rng(seed).standard_normal(samples.shape[0])
+    return samples + z[:, None] * per_row[None, :]
diff --git a/src/tfscreen/tfmodel/analysis/tube_offsets.py b/src/tfscreen/tfmodel/analysis/tube_offsets.py
new file mode 100644
index 00000000..71cbfdde
--- /dev/null
+++ b/src/tfscreen/tfmodel/analysis/tube_offsets.py
@@ -0,0 +1,177 @@
+"""
+Diagnostic for the per-tube sample offsets (``sample_offset: level``).
+
+A level offset should be tube noise: an error in a tube's supplied total or
+its composition, independent from tube to tube. On the dev data the MAP's
+better optimum put the offsets in a pattern instead (the "offset mode",
+``planning/offset-mode-growth-transition.md``): within each selection
+condition they followed IPTG, from +2 to -4 ln units across 0 to 1 mM, and
+held constant over time, carrying growth the model could not express. This
+module reports the offsets in units of their prior SD and tests, within each
+condition, whether they trend with titrant or with time.
+
+``tfs-summarize-fit`` calls ``tube_offset_diagnostic`` on the
+``*_sample_offset_offset.csv`` that ``tfs-extract-params`` writes.
+"""
+
+import warnings
+
+import numpy as np
+import pandas as pd
+from scipy import stats
+
+from tfscreen.analysis.cat_response.cat_assess import benjamini_hochberg
+
+GROUP_COLUMNS = ["library", "condition_pre", "condition_sel", "titrant_name"]
+
+
+def _spearman(x, y):
+    """Spearman rho and p, NaN when either side is constant or n < 3."""
+    x = np.asarray(x, dtype=float)
+    y = np.asarray(y, dtype=float)
+    if len(x) < 3 or np.ptp(x) == 0 or np.ptp(y) == 0:
+        return np.nan, np.nan
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        res = stats.spearmanr(x, y)
+    return float(res.statistic), float(res.pvalue)
+
+
+def _r2_by(values, labels):
+    """Fraction of the variance of ``values`` explained by the means by label."""
+    values = np.asarray(values, dtype=float)
+    total = np.sum((values - values.mean()) ** 2)
+    if total == 0 or len(np.unique(labels)) < 2:
+        return np.nan
+    means = pd.Series(values).groupby(np.asarray(labels)).transform("mean")
+    return float(np.sum((means.to_numpy() - values.mean()) ** 2) / total)
+
+
+def tube_offset_diagnostic(offsets_df, sigma=None, alpha=0.05,
+                           value_column="q0.5"):
+    """
+    Size and structure of the per-tube offsets.
+
+    Parameters
+    ----------
+    offsets_df : pandas.DataFrame
+        One row per tube: the design columns (``condition_sel``,
+        ``titrant_conc``, ``t_sel`` and whichever of ``library``,
+        ``condition_pre``, ``titrant_name``, ``replicate`` are present) and
+        ``value_column``, the offset's point estimate.
+    sigma : float or None
+        The offsets' prior SD (held ``sigma_fixed`` or the fit's sigma). With
+        None, sizes are reported in ln units only.
+    alpha : float
+        A condition is called structured when the Benjamini-Hochberg q of its
+        titrant or its time trend is below ``alpha`` (q over all conditions
+        and both trends at once).
+    value_column : str
+        Column holding the offset.
+
+    Returns
+    -------
+    tubes : pandas.DataFrame
+        ``offsets_df`` with ``offset`` and, given ``sigma``, ``z`` (offset in
+        prior SDs).
+    trends : pandas.DataFrame
+        One row per condition (``library``, ``condition_pre``,
+        ``condition_sel``, ``titrant_name``): ``n_tubes``,
+        ``n_titrant_conc``, ``mean``, ``sd``, ``mean_z``, ``sd_z``,
+        Spearman ``rho_titrant``/``p_titrant``/``q_titrant`` (offset against
+        titrant concentration), ``rho_time``/``p_time``/``q_time`` (against
+        ``t_sel``), ``r2_titrant`` (variance explained by the mean at each
+        concentration, any shape) and ``structured``.
+    summary : dict
+        ``n_tubes``, ``sigma``, ``sd``, ``sd_over_sigma``, ``abs_z_median``,
+        ``abs_z_q95``, ``abs_z_max``, ``range``, ``alpha``,
+        ``n_conditions``, ``n_structured`` and ``structured`` (any
+        structured condition), with the structured conditions listed.
+    """
+    if value_column not in offsets_df.columns:
+        raise ValueError(f"offsets table has no '{value_column}' column")
+    for col in ("condition_sel", "titrant_conc", "t_sel"):
+        if col not in offsets_df.columns:
+            raise ValueError(f"offsets table has no '{col}' column")
+
+    tubes = offsets_df.copy()
+    tubes["offset"] = tubes[value_column].astype(float)
+    have_sigma = sigma is not None and np.isfinite(sigma) and sigma > 0
+    if have_sigma:
+        tubes["z"] = tubes["offset"] / float(sigma)
+
+    group_cols = [c for c in GROUP_COLUMNS if c in tubes.columns]
+    rows = []
+    for key, g in tubes.groupby(group_cols, observed=True, sort=True):
+        key = key if isinstance(key, tuple) else (key,)
+        row = dict(zip(group_cols, key))
+        off = g["offset"].to_numpy()
+        row["n_tubes"] = len(g)
+        row["n_titrant_conc"] = int(g["titrant_conc"].nunique())
+        row["mean"] = float(off.mean())
+        row["sd"] = float(off.std(ddof=1)) if len(off) > 1 else np.nan
+        row["mean_z"] = row["mean"] / sigma if have_sigma else np.nan
+        row["sd_z"] = row["sd"] / sigma if have_sigma else np.nan
+        row["rho_titrant"], row["p_titrant"] = _spearman(g["titrant_conc"], off)
+        row["rho_time"], row["p_time"] = _spearman(g["t_sel"], off)
+        row["r2_titrant"] = _r2_by(off, g["titrant_conc"].to_numpy())
+        rows.append(row)
+    trends = pd.DataFrame(rows)
+
+    n = len(trends)
+    q = benjamini_hochberg(np.concatenate([trends["p_titrant"].to_numpy(),
+                                           trends["p_time"].to_numpy()]))
+    trends["q_titrant"] = q[:n]
+    trends["q_time"] = q[n:]
+    trends["structured"] = ((trends["q_titrant"] < alpha)
+                            | (trends["q_time"] < alpha))
+    order = group_cols + ["n_tubes", "n_titrant_conc", "mean", "sd",
+                          "mean_z", "sd_z", "rho_titrant", "p_titrant",
+                          "q_titrant", "r2_titrant", "rho_time", "p_time",
+                          "q_time", "structured"]
+    trends = trends[order]
+
+    off = tubes["offset"].to_numpy()
+    sd = float(off.std(ddof=1)) if len(off) > 1 else np.nan
+    abs_z = np.abs(tubes["z"].to_numpy()) if have_sigma else None
+    structured = trends.loc[trends["structured"], group_cols]
+    summary = {
+        "n_tubes": int(len(tubes)),
+        "sigma": float(sigma) if have_sigma else None,
+        "sd": sd,
+        "sd_over_sigma": sd / sigma if have_sigma else None,
+        "abs_z_median": float(np.median(abs_z)) if have_sigma else None,
+        "abs_z_q95": float(np.quantile(abs_z, 0.95)) if have_sigma else None,
+        "abs_z_max": float(abs_z.max()) if have_sigma else None,
+        "range": [float(off.min()), float(off.max())],
+        "alpha": float(alpha),
+        "n_conditions": int(n),
+        "n_structured": int(trends["structured"].sum()),
+        "structured": bool(trends["structured"].any()),
+        "structured_conditions": structured.astype(str).to_dict("records"),
+    }
+    return tubes, trends, summary
+
+
+def format_tube_offset_summary(summary, trends):
+    """A few printable lines: size, then each structured condition."""
+    lines = [f"Tube offsets: {summary['n_tubes']} tubes, SD {summary['sd']:.3g}, "
+             f"range {summary['range'][0]:.3g} to {summary['range'][1]:.3g}"]
+    if summary["sigma"] is not None:
+        lines[0] += (f"; prior SD {summary['sigma']:.3g}, SD/prior "
+                     f"{summary['sd_over_sigma']:.3g}, max |z| "
+                     f"{summary['abs_z_max']:.3g}")
+    if summary["structured"]:
+        lines.append(f"  {summary['n_structured']} of {summary['n_conditions']} "
+                     f"conditions trend with titrant or time (BH q < "
+                     f"{summary['alpha']:g}); the offsets carry structure "
+                     f"(the offset mode):")
+        for _, r in trends[trends["structured"]].iterrows():
+            lines.append(f"    {r['condition_sel']}: rho titrant "
+                         f"{r['rho_titrant']:.2f} (q {r['q_titrant']:.2g}), "
+                         f"rho time {r['rho_time']:.2f} (q {r['q_time']:.2g}), "
+                         f"mean {r['mean']:.3g}")
+    else:
+        lines.append("  no condition trends with titrant or time "
+                     f"(BH q < {summary['alpha']:g})")
+    return "\n".join(lines)
diff --git a/src/tfscreen/tfmodel/configuration_io.py b/src/tfscreen/tfmodel/configuration_io.py
index e1a4c839..3a177077 100644
--- a/src/tfscreen/tfmodel/configuration_io.py
+++ b/src/tfscreen/tfmodel/configuration_io.py
@@ -1,9 +1,11 @@
+import ast
 import os
 import yaml
 import pandas as pd
 import numpy as np
 import jax.numpy as jnp
 import warnings
+from tfscreen.util.provenance import get_provenance
 from tfscreen.__version__ import __version__
 from tfscreen.util.validation import check_unknown_keys
 
@@ -16,6 +18,7 @@
     "guesses_file",
     "library_file",
     "library",
+    "provenance",
 })
 
 def _extract_scalars(obj, prefix=""):
@@ -25,6 +28,11 @@ def _extract_scalars(obj, prefix=""):
         if k.startswith("_") or k in ['replace', 'asdict', '__class__', 'tree_flatten', 'tree_unflatten']:
             continue
         v = getattr(obj, k)
+        if v is None:
+            # An absent component (e.g. binding priors in a growth-only model,
+            # growth priors in a binding-only one). Writing it would come back
+            # as NaN; leaving it out keeps it None on read.
+            continue
         if hasattr(v, '__dataclass_fields__'):
             out.update(_extract_scalars(v, prefix + k + "."))
         elif isinstance(v, dict):
@@ -67,7 +75,92 @@ def _gather_dict_field(full_key, flat_dict):
     return sub_dict
 
 
-def _update_dataclass(dc, prefix, flat_dict):
+def _dataclass_fields(dc):
+    """Fields of a standard or flax struct dataclass."""
+    import dataclasses
+    if dataclasses.is_dataclass(dc):
+        return dataclasses.fields(dc)
+    return dc.__dataclass_fields__.values()
+
+
+def _known_keys(dc, prefix=""):
+    """
+    The flat keys a dataclass accepts, as ``(exact_keys, dict_prefixes)``.
+
+    Every field name is an exact key, whatever its value (a field left at
+    None, or a static tuple that :func:`_update_dataclass` keeps, is still a
+    field). Nested dataclasses contribute their own keys under
+    ``"{field}."``. A dict-typed field accepts any one-level key
+    ``"{field}.<sub>"``, so its ``"{field}."`` is returned as a prefix.
+    """
+    exact, prefixes = set(), []
+    if not hasattr(dc, "__dataclass_fields__"):
+        return exact, prefixes
+    for f in _dataclass_fields(dc):
+        full_key = f"{prefix}{f.name}"
+        val = getattr(dc, f.name)
+        if hasattr(val, "__dataclass_fields__"):
+            sub_exact, sub_prefixes = _known_keys(val, full_key + ".")
+            exact |= sub_exact
+            prefixes.extend(sub_prefixes)
+        elif isinstance(val, dict):
+            prefixes.append(full_key + ".")
+        else:
+            exact.add(full_key)
+    return exact, prefixes
+
+
+def _check_unknown_keys(dc, prefix, flat_dict):
+    """
+    Raise if ``flat_dict`` names a key that is not a field of ``dc``.
+
+    A priors CSV row that names no field used to be ignored. A file written
+    for another version of a model then loaded without complaint, and rows
+    whose names a model kept were read with whatever meaning they have now.
+    """
+    exact, prefixes = _known_keys(dc, prefix)
+    unknown = []
+    for k in flat_dict:
+        if k in exact:
+            continue
+        if any(k.startswith(p) and "." not in k[len(p):] for p in prefixes):
+            continue
+        unknown.append(k)
+    if unknown:
+        raise ValueError(
+            f"The priors file has {len(unknown)} row(s) that name no prior of "
+            f"the configured model: {sorted(unknown)}. The file was likely "
+            f"written for another model or an older tfscreen. Regenerate it "
+            f"with tfs-configure-model."
+        )
+
+
+def _static_flag(value):
+    """
+    A bool field's value read from the priors CSV.
+
+    Scalar rows give a bool (``1.0``, ``"True"``). Indexed per-condition rows
+    (``condition_growth.m_pinned`` per ``condition_rep``) arrive as an array
+    and become a tuple of bools, hashable as a static pytree field must be;
+    an array whose flags all agree collapses to that bool. A tuple written as
+    text (``"(True, False)"``) is parsed back.
+    """
+    if isinstance(value, str):
+        text = value.strip()
+        if text.startswith("("):
+            value = ast.literal_eval(text)
+        elif text.lower() in ("true", "false"):
+            return text.lower() == "true"
+        else:
+            value = float(text)
+    arr = np.asarray(value)
+    if arr.ndim == 0:
+        return bool(arr)
+    flags = tuple(bool(v) for v in arr.ravel())
+    return flags[0] if len(set(flags)) == 1 else flags
+
+
+def _update_dataclass(dc, prefix, flat_dict, check_unknown=True):
     """
     Recursively update a dataclass from a flat dictionary of values.
 
@@ -78,21 +171,32 @@ def _update_dataclass(dc, prefix, flat_dict):
       via :func:`_gather_dict_field`).  Used by the ``pinned`` field on
       component ``ModelPriors`` to inject empirical-Bayes hyperprior
       values from the calibration pre-fit.
+
+    A key in ``flat_dict`` that names none of these raises ``ValueError``
+    (see :func:`_check_unknown_keys`); ``check_unknown=False`` skips the
+    check, which the recursion does since the top call has made it.
     """
     import dataclasses
+    if check_unknown:
+        _check_unknown_keys(dc, prefix, flat_dict)
     updates = {}
 
     # Check if this is a standard dataclass or a flax struct dataclass
     if hasattr(dc, '__dataclass_fields__'):
-        fields = dataclasses.fields(dc) if dataclasses.is_dataclass(dc) else dc.__dataclass_fields__.values()
+        fields = _dataclass_fields(dc)
 
         for field in fields:
             field_name = field.name
             full_key = f"{prefix}{field_name}" if prefix else field_name
             attr_val = getattr(dc, field_name)
 
+            if attr_val is None:
+                # Absent component; nothing to update (see _extract_scalars).
+                continue
             if hasattr(attr_val, '__dataclass_fields__'):
-                updates[field_name] = _update_dataclass(attr_val, full_key + ".", flat_dict)
+                updates[field_name] = _update_dataclass(attr_val, full_key + ".",
+                                                        flat_dict,
+                                                        check_unknown=False)
             elif isinstance(attr_val, dict):
                 # Dict-typed field — gather any "{full_key}.<suffix>" rows.
                 # Merge with the existing dict so previously-set entries
@@ -109,6 +213,10 @@ def _update_dataclass(dc, prefix, flat_dict):
                 # overwrite the correctly-typed tuple with that string — keep
                 # the freshly-built value.
                 continue
+            elif isinstance(attr_val, bool) and full_key in flat_dict:
+                # Flags such as condition_growth.m_pinned: a bool, or a tuple
+                # of per-condition flags (see _static_flag).
+                updates[field_name] = _static_flag(flat_dict[full_key])
             elif full_key in flat_dict:
                 new_val = flat_dict[full_key]
                 if isinstance(attr_val, int) and not isinstance(attr_val, bool):
@@ -324,6 +432,7 @@ def write_configuration(orchestrator,
 
     config = {
         "tfscreen_version": __version__,
+        "provenance": get_provenance(),
         "data": data_paths,
         "components": settings,
         "priors_file": os.path.basename(priors_path),
diff --git a/src/tfscreen/tfmodel/data_class.py b/src/tfscreen/tfmodel/data_class.py
index 89c3daad..ae979f19 100644
--- a/src/tfscreen/tfmodel/data_class.py
+++ b/src/tfscreen/tfmodel/data_class.py
@@ -22,7 +22,6 @@ class GrowthData:
     t_pre: jnp.ndarray
     t_sel: jnp.ndarray
     good_mask: jnp.ndarray
-    congression_mask: jnp.ndarray
 
         
     # Tensor shape
@@ -65,8 +64,57 @@ class GrowthData:
     ln_cfu0_library_masks: Any = field(default=None)
     num_ln_cfu0_library_classes: int = field(pytree_node=False, default=1)
 
+    # Float array, shape (num_genotype,): the fraction of each genotype's
+    # cells that come from the bulk (co-transformed) sub-libraries, in
+    # [0, 1]. 0 = pure monoclonal spike, 1 = pure bulk. This is a genotype's
+    # congression *purity*, kept separate from its ln_cfu0 prior class
+    # (ln_cfu0_spiked_mask), which it no longer shares a flag with. Built by
+    # ModelOrchestrator from the library composition table's bulk_fraction
+    # (legacy spiked_genotypes: 0 for spiked, 1 otherwise). Library-sized;
+    # consumers index it with batch_idx. Sets the congressed fraction of the
+    # mixture transformation (transformation/mixture.py).
+    bulk_fraction: Any = field(default=None)
+
+    # Fixed co-resident plasmid sets for the congression mixture, drawn once
+    # by ModelOrchestrator._draw_coresident_sets. coresident_idx: int,
+    # (num_genotype, K, N_max), library (growth-tensor) genotype indices,
+    # -1 = empty slot; coresident_n: int, (K,), co-residents per set.
+    # Library-sized; consumers index the first axis with batch_idx. Used by
+    # the mixture transformation (transformation/mixture.py).
+    coresident_idx: Any = field(default=None)
+    coresident_n: Any = field(default=None)
+
+    # How a congressed cell's theta is built from its plasmids' thetas
+    # ("max", "homodimer", "heterodimer"; see transformation/mixture.py).
+    # Static: set by ModelOrchestrator from its congression_theta_rule.
+    congression_theta_rule: str = field(pytree_node=False, default="homodimer")
+
+    # How a congressed cell's dk_geno is built from its plasmids' ("dilution",
+    # "softmin" with congression_dk_alpha, "min"). Static: set by
+    # ModelOrchestrator.
+    congression_dk_rule: str = field(pytree_node=False, default="dilution")
+    congression_dk_alpha: Any = field(pytree_node=False, default=None)
+
     growth_shares_replicates: bool = field(pytree_node=False, default=False)
 
+    # (ln c_lo, ln c_hi): the concentrations at which the relative theta
+    # component (theta/hill_relative.py) pins wt's X to 1 and 0. None for
+    # every other theta component. Set by ModelOrchestrator from its
+    # theta_gauge_conc (zero concentration mapped like log_titrant_conc).
+    theta_gauge_log_conc: Any = field(default=None)
+
+    # Growth observation model: "lncfu" (Student-t on ln_cfu, observe/growth.py)
+    # or "counts" (negative binomial on read counts, observe/growth_counts.py).
+    # Static: set by ModelOrchestrator from its growth_likelihood.
+    growth_likelihood: str = field(pytree_node=False, default="lncfu")
+    # Count-likelihood tensors, same layout as ln_cfu; None unless
+    # growth_likelihood == "counts". counts: reads of the genotype in the
+    # tube; ln_sample_reads: ln of the tube's total reads (__unknown__
+    # included); sample_ln_cfu: ln of the tube's total cells as supplied.
+    counts: Any = field(default=None)
+    ln_sample_reads: Any = field(default=None)
+    sample_ln_cfu: Any = field(default=None)
+
     # Optional mutation-decomposition matrices (set when using *_mut_decomp components).
     # Stored as pytree_node=False so they are treated as static by JAX tracing.
     # Shape: mut_geno_matrix (num_mutation, num_genotype).
@@ -103,8 +151,8 @@ class GrowthData:
 
     # Precomputed theta at growth titrant concentrations for *every* genotype
     # in the library (shape (num_titrant_name, num_titrant_conc, true_num_genotype)),
-    # used only by transformation components whose congression correction needs
-    # a population-wide reference distribution (see transformation/_congression.py).
+    # used only by transformation components that need a population-wide
+    # reference (NEEDS_POPULATION; see transformation/mixture.py).
     # None (the default) tells `jax_model` to compute this locally from
     # `data.growth` itself, which is only correct when `data.growth` already
     # spans the full genotype population (true during SVI training, since
@@ -113,6 +161,16 @@ class GrowthData:
     # supply this explicitly, computed against the true, unsubsetted library.
     external_theta_population: Any = field(default=None)
 
+    # Library-ordered dk_geno and activity for *every* genotype (shape
+    # (num_genotype,), or with leading sample dims), the counterparts of
+    # external_theta_population for the congression mixture's co-resident
+    # lookup (planning/congression-physics-plan.md, step 3). During training
+    # the dk_geno/activity components supply these themselves
+    # (return_population=True). A posterior forward pass that substitutes
+    # batch-sized latents cannot, so prediction code must pass them here.
+    external_dk_population: Any = field(default=None)
+    external_activity_population: Any = field(default=None)
+
 @dataclass(frozen=True)
 class BindingData:
 
@@ -160,6 +218,13 @@ class BindingData:
     struct_contact_pair_idx: Any = field(pytree_node=False, default=None)
     struct_contact_distances: Any = field(pytree_node=False, default=None)
 
+    # Each batch position's ordinal among the binding genotypes (their order
+    # on the binding tensors' genotype axis), for latents that exist only
+    # for binding genotypes (noise/genotype_offset). Joint models:
+    # arange(num_binding), since the binding tensors are never sliced and
+    # batch_idx holds library indices. Binding-only: the batch's indices.
+    binding_ordinal: Any = None
+
 
 @dataclass(frozen=True)
 class PreSplitData:
diff --git a/src/tfscreen/tfmodel/generative/components/_horseshoe.py b/src/tfscreen/tfmodel/generative/components/_horseshoe.py
new file mode 100644
index 00000000..c2ca3c7f
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/components/_horseshoe.py
@@ -0,0 +1,46 @@
+"""
+Overflow-safe pieces of the regularized horseshoe prior.
+
+The horseshoe components (theta ``hill_mut``, the thermo ``PK``/``PnnC``/
+``PddG`` variants and ``thermo/horseshoe.py``, activity ``horseshoe_mut``)
+draw local scales ``lambda ~ HalfCauchy`` and use the slab-regularized scale
+
+    lambda_tilde = sqrt(c2 lambda^2 / (c2 + tau^2 lambda^2)).
+
+Both were written with ``lambda ** 2``. The half-Cauchy tail is heavy and,
+where the data stop constraining an effect (a theta already saturated), a
+mean-field guide can widen a local scale's LogNormal until a draw reaches
+~1e20; its square overflows float32, ``lambda_tilde`` becomes inf / inf =
+NaN, the half-Cauchy log density becomes -inf, and the whole fit goes NaN
+(count-likelihood grid, run_0007, 2026-09-27). The forms here use
+``jnp.hypot``, which does not overflow, and give the same values.
+"""
+
+import jax.numpy as jnp
+import numpyro.distributions as dist
+
+
+def regularized_scale(lam, tau, c2):
+    """
+    ``sqrt(c2 lam^2 / (c2 + tau^2 lam^2))`` without squaring ``lam``.
+
+    Written as ``lam / hypot(1, tau lam / sqrt(c2))``: it tends to
+    ``sqrt(c2) / tau`` as ``lam`` grows, and equals ``lam`` at ``tau = 0``
+    (epistasis switched off) with finite gradients.
+    """
+    return lam / jnp.hypot(1.0, tau * lam / jnp.sqrt(c2))
+
+
+class HalfCauchy(dist.HalfCauchy):
+    """
+    numpyro's HalfCauchy with a log density that does not overflow:
+    ``log1p(z^2)`` is computed as ``2 log hypot(1, z)``. numpyro's returns
+    -inf (and NaN gradients) once ``(x / scale)^2`` exceeds float32.
+    """
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        z = value / self.scale
+        return (jnp.log(2.0 / jnp.pi) - jnp.log(self.scale)
+                - 2.0 * jnp.log(jnp.hypot(1.0, z)))
diff --git a/src/tfscreen/tfmodel/generative/components/_population.py b/src/tfscreen/tfmodel/generative/components/_population.py
new file mode 100644
index 00000000..985ecc05
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/components/_population.py
@@ -0,0 +1,62 @@
+"""
+Per-genotype values for the batch, and optionally for the whole library.
+
+Per-genotype components (``dk_geno``, ``activity``) sample their latents at
+library size and slice them to the batch with ``batch_idx``. The congression
+mixture also needs the *library-ordered* values of every genotype, so it can
+look up a genotype's co-resident plasmids by index (see
+``planning/congression-physics-plan.md``, step 3). :func:`per_genotype`
+computes both from the same draw, without adding sample sites.
+"""
+
+import jax.numpy as jnp
+
+
+def per_genotype(compute, latents, data, return_population=False):
+    """
+    Evaluate a per-genotype quantity for the batch and, optionally, the library.
+
+    Parameters
+    ----------
+    compute : callable
+        ``compute(*latents, genotype_idx) -> values``: elementwise in the
+        latents along their last (genotype) axis; ``genotype_idx`` gives the
+        library index of each entry (used, e.g., to pin wildtype).
+    latents : sequence of jnp.ndarray
+        Per-genotype latents, genotype on the last axis. Each is either
+        library-sized (``num_genotype``, sampled in a library plate) or an
+        already-sliced batch-sized substitution (the posterior forward pass).
+    data : GrowthData
+        Uses ``num_genotype``, ``batch_idx``.
+    return_population : bool, default False
+        Also return the library-ordered values.
+
+    Returns
+    -------
+    batch_values : jnp.ndarray
+        Values for the batch's genotypes, in batch order.
+    population : jnp.ndarray or None
+        Library-ordered values for every genotype, shape ``(...,
+        num_genotype)``. None unless ``return_population`` and every latent
+        is library-sized; a batch-sized substitution carries no information
+        about the rest of the library, so callers must fall back to an
+        external reference.
+
+    Notes
+    -----
+    With ``return_population=False`` this evaluates exactly the old batch-only
+    path (slice, then compute), so it costs nothing extra under
+    mini-batching. With ``return_population=True`` it computes on the library
+    and then slices; because ``compute`` is elementwise the batch values are
+    identical.
+    """
+
+    library_sized = all(jnp.shape(x)[-1] == data.num_genotype for x in latents)
+
+    if return_population and library_sized:
+        population = compute(*latents, jnp.arange(data.num_genotype))
+        return population[..., data.batch_idx], population
+
+    sliced = [x[..., data.batch_idx] if jnp.shape(x)[-1] == data.num_genotype
+              else x for x in latents]
+    return compute(*sliced, data.batch_idx), None
diff --git a/src/tfscreen/tfmodel/generative/components/activity/fixed.py b/src/tfscreen/tfmodel/generative/components/activity/fixed.py
index eb076106..3b074ea8 100644
--- a/src/tfscreen/tfmodel/generative/components/activity/fixed.py
+++ b/src/tfscreen/tfmodel/generative/components/activity/fixed.py
@@ -15,7 +15,8 @@ class ModelPriors:
 
 def define_model(name: str, 
                  data: GrowthData, 
-                 priors: ModelPriors) -> jnp.ndarray:
+                 priors: ModelPriors,
+                 return_population: bool = False) -> jnp.ndarray:
     """
     Defines the fixed model for genotype-specific activity.
 
@@ -36,6 +37,9 @@ def define_model(name: str,
           per-genotype parameters to the full set of observations.
     priors : ModelPriors
         A Pytree of hyperparameters. (Unused in this model).
+    return_population : bool, default False
+        Also return the library-ordered per-genotype values, shape
+        ``(num_genotype,)``, as a ``(tensor, population)`` tuple.
 
     Returns
     -------
@@ -53,12 +57,15 @@ def define_model(name: str,
     # Broadcast to full-sized tensor
     activity = activity_dists[None,None,None,None,None,None,:]
 
+    if return_population:
+        return activity, jnp.ones(data.num_genotype, dtype=float)
     return activity
 
 
 def guide(name: str, 
           data: GrowthData, 
-          priors: ModelPriors) -> jnp.ndarray:
+          priors: ModelPriors,
+          return_population: bool = False) -> jnp.ndarray:
     """
     Guide for the fixed activity model.
 
@@ -73,6 +80,8 @@ def guide(name: str,
     # Broadcast to full-sized tensor
     activity = activity_dists[None,None,None,None,None,None,:]
 
+    if return_population:
+        return activity, jnp.ones(data.num_genotype, dtype=float)
     return activity
 
 def get_hyperparameters() -> Dict[str, Any]:
diff --git a/src/tfscreen/tfmodel/generative/components/activity/hierarchical_geno.py b/src/tfscreen/tfmodel/generative/components/activity/hierarchical_geno.py
index 25c5e402..c62a7bb3 100644
--- a/src/tfscreen/tfmodel/generative/components/activity/hierarchical_geno.py
+++ b/src/tfscreen/tfmodel/generative/components/activity/hierarchical_geno.py
@@ -5,6 +5,7 @@
 from typing import Dict, Any, Mapping
 
 from tfscreen.tfmodel.data_class import GrowthData
+from tfscreen.tfmodel.generative.components._population import per_genotype
 from tfscreen.tfmodel.generative.components._pinning import (
     _hyper,
     _pinned_value,
@@ -43,9 +44,19 @@ class ModelPriors:
         pytree_node=False, default_factory=dict
     )
 
+def _activity_values(hyper_loc, hyper_scale, wt_indexes):
+    """Per-genotype activity from log-space offsets; wt pinned to 1."""
+    def compute(activity_offset, genotype_idx):
+        log_activity = hyper_loc + activity_offset * hyper_scale
+        activity = jnp.clip(jnp.exp(log_activity), max=1e30)
+        return jnp.where(jnp.isin(genotype_idx, wt_indexes), 1.0, activity)
+    return compute
+
+
 def define_model(name: str, 
                  data: GrowthData, 
-                 priors: ModelPriors) -> jnp.ndarray:
+                 priors: ModelPriors,
+                 return_population: bool = False) -> jnp.ndarray:
     """
     Defines the hierarchical model for genotype-specific activity.
 
@@ -73,12 +84,18 @@ def define_model(name: str,
     priors : ModelPriors
         A Pytree (Flax dataclass) containing the hyperparameters for
         the pooled priors.
+    return_population : bool, default False
+        Also return the library-ordered per-genotype values (shape
+        ``(num_genotype,)``, or ``None`` when the latents arrived as a
+        batch-sized substitution), for callers that look genotypes up by
+        library index (the congression mixture).
 
     Returns
     -------
     jnp.ndarray
         The sampled `activity` values, expanded to match the shape of
-        the observations via ``data.map_genotype``.
+        the observations via ``data.map_genotype``. With
+        ``return_population``, a ``(tensor, population)`` tuple.
     """
 
     pinned = priors.pinned
@@ -97,22 +114,16 @@ def define_model(name: str,
     )
 
     # Sample non-centered offsets for mutant genotypes only
-    with pyro.plate("shared_genotype_plate", size=data.batch_size,dim=-1):
-        with pyro.handlers.scale(scale=data.scale_vector):
-            activity_offset = pyro.sample(f"{name}_offset", dist.Normal(0.0, 1.0))
-
-    # Guard against full-sized array substitution during initialization or re-runs 
-    # with full-sized initial values
-    if activity_offset.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        activity_offset = activity_offset[..., data.batch_idx]
-    
-    # Calculate in log-space, then exponentiate
-    log_activity_mutant_dists = log_activity_hyper_loc + activity_offset * log_activity_hyper_scale
-    activity = jnp.clip(jnp.exp(log_activity_mutant_dists), max=1e30)
+    with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+        activity_offset = pyro.sample(f"{name}_offset", dist.Normal(0.0, 1.0))
 
-    # Set wildtype activity to 1.0
-    is_wt_mask = jnp.isin(data.batch_idx, data.wt_indexes)
-    activity = jnp.where(is_wt_mask, 1.0, activity)
+    # Sampled at library size: slice to this batch's genotypes (a value of
+    # any other size is an already-sliced substitution from the posterior
+    # forward pass). Computed in log-space; wt activity is 1.0.
+    activity, population = per_genotype(
+        _activity_values(log_activity_hyper_loc, log_activity_hyper_scale,
+                         data.wt_indexes),
+        [activity_offset], data, return_population)
 
     # Register per-genotype values for inspection
     pyro.deterministic(name, activity)  
@@ -120,11 +131,14 @@ def define_model(name: str,
     # Broadcast to full-sized tensor
     activity = activity[None,None,None,None,None,None,:] 
 
+    if return_population:
+        return activity, population
     return activity
 
 def guide(name: str, 
           data: GrowthData, 
-          priors: ModelPriors) -> jnp.ndarray:
+          priors: ModelPriors,
+          return_population: bool = False) -> jnp.ndarray:
     """
     Guide corresponding to the hierarchical activity model.
 
@@ -167,30 +181,22 @@ def guide(name: str,
                                constraint=dist.constraints.positive)
 
     # Sample non-centered offsets for mutant genotypes only
-    with pyro.plate("shared_genotype_plate", size=data.batch_size,dim=-1):
-        with pyro.handlers.scale(scale=data.scale_vector):
-
-            batch_locs = offset_locs[data.batch_idx]
-            batch_scales = offset_scales[data.batch_idx]
-
-            activity_offset = pyro.sample(f"{name}_offset", dist.Normal(batch_locs, batch_scales))
-
-    # Guard against full-sized array substitution during initialization or re-runs 
-    # with full-sized initial values
-    if activity_offset.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        activity_offset = activity_offset[..., data.batch_idx]
-    
-    # Calculate in log-space, then exponentiate
-    log_activity_mutant_dists = log_activity_hyper_loc + activity_offset * log_activity_hyper_scale
-    activity = jnp.clip(jnp.exp(log_activity_mutant_dists), max=1e30)
+    with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+        activity_offset = pyro.sample(f"{name}_offset", dist.Normal(offset_locs, offset_scales))
 
-    # Set wildtype activity to 1.0
-    is_wt_mask = jnp.isin(data.batch_idx, data.wt_indexes)
-    activity = jnp.where(is_wt_mask, 1.0, activity)
+    # Sampled at library size: slice to this batch's genotypes (a value of
+    # any other size is an already-sliced substitution from the posterior
+    # forward pass). Computed in log-space; wt activity is 1.0.
+    activity, population = per_genotype(
+        _activity_values(log_activity_hyper_loc, log_activity_hyper_scale,
+                         data.wt_indexes),
+        [activity_offset], data, return_population)
 
     # Broadcast to full-sized tensor
     activity = activity[None,None,None,None,None,None,:] 
 
+    if return_population:
+        return activity, population
     return activity
 
 
diff --git a/src/tfscreen/tfmodel/generative/components/activity/hierarchical_mut.py b/src/tfscreen/tfmodel/generative/components/activity/hierarchical_mut.py
index 2a0a78bc..e8ab0784 100644
--- a/src/tfscreen/tfmodel/generative/components/activity/hierarchical_mut.py
+++ b/src/tfscreen/tfmodel/generative/components/activity/hierarchical_mut.py
@@ -34,7 +34,8 @@ class ModelPriors:
 
 def define_model(name: str,
                  data: GrowthData,
-                 priors: ModelPriors) -> jnp.ndarray:
+                 priors: ModelPriors,
+                 return_population: bool = False) -> jnp.ndarray:
     """
     Define the mutation-decomposed activity model.
 
@@ -45,11 +46,14 @@ def define_model(name: str,
         Must have ``mut_geno_matrix`` (num_mutation x G) and ``num_mutation``.
         If ``num_pair > 0``, must also have ``pair_geno_matrix``.
     priors : ModelPriors
+    return_population : bool, default False
+        Also return the library-ordered per-genotype activity, shape
+        ``(num_genotype,)``, as a ``(tensor, population)`` tuple.
 
     Returns
     -------
     jnp.ndarray
-        Activity values broadcast to shape ``(1, 1, 1, 1, 1, 1, num_genotype)``.
+        Activity values for the batch's genotypes, shape ``(1, 1, 1, 1, 1, 1, batch_size)``.
     """
     mut_nnz_mut_idx  = jnp.array(data.mut_nnz_mut_idx)    # COO row (mutation)
     mut_nnz_geno_idx = jnp.array(data.mut_nnz_geno_idx)   # COO col (genotype)
@@ -98,14 +102,19 @@ def define_model(name: str,
 
     pyro.deterministic(name, activity)
 
-    if data.batch_size < data.num_genotype:
-        activity = activity[data.batch_idx]
-    return activity[None, None, None, None, None, None, :]
+    # activity is library-ordered; always slice to the batch's genotypes.
+    # The full-batch index is a reshuffled permutation, not the identity.
+    population = activity
+    activity = activity[data.batch_idx][None, None, None, None, None, None, :]
+    if return_population:
+        return activity, population
+    return activity
 
 
 def guide(name: str,
           data: GrowthData,
-          priors: ModelPriors) -> jnp.ndarray:
+          priors: ModelPriors,
+          return_population: bool = False) -> jnp.ndarray:
     """Variational guide for the mutation-decomposed activity model."""
 
     num_mut = data.num_mutation
@@ -160,9 +169,13 @@ def guide(name: str,
 
     activity = jnp.clip(jnp.exp(log_activity), max=1e30)
 
-    if data.batch_size < data.num_genotype:
-        activity = activity[data.batch_idx]
-    return activity[None, None, None, None, None, None, :]
+    # activity is library-ordered; always slice to the batch's genotypes.
+    # The full-batch index is a reshuffled permutation, not the identity.
+    population = activity
+    activity = activity[data.batch_idx][None, None, None, None, None, None, :]
+    if return_population:
+        return activity, population
+    return activity
 
 
 def get_hyperparameters() -> Dict[str, Any]:
diff --git a/src/tfscreen/tfmodel/generative/components/activity/horseshoe_geno.py b/src/tfscreen/tfmodel/generative/components/activity/horseshoe_geno.py
index b6c8b2d3..8e82b5bf 100644
--- a/src/tfscreen/tfmodel/generative/components/activity/horseshoe_geno.py
+++ b/src/tfscreen/tfmodel/generative/components/activity/horseshoe_geno.py
@@ -5,6 +5,7 @@
 from typing import Dict, Any
 
 from tfscreen.tfmodel.data_class import GrowthData
+from tfscreen.tfmodel.generative.components._population import per_genotype
 
 @dataclass(frozen=True)
 class ModelPriors:
@@ -20,9 +21,19 @@ class ModelPriors:
 
     global_scale_tau_scale: float
 
+def _activity_values(global_scale_tau, wt_indexes):
+    """Per-genotype activity: log(activity) = z * tau * lambda; wt pinned to 1."""
+    def compute(local_scale_lambda, activity_offset, genotype_idx):
+        log_activity = activity_offset * (global_scale_tau * local_scale_lambda)
+        activity = jnp.clip(jnp.exp(log_activity), max=1e30)
+        return jnp.where(jnp.isin(genotype_idx, wt_indexes), 1.0, activity)
+    return compute
+
+
 def define_model(name: str, 
                  data: GrowthData, 
-                 priors: ModelPriors) -> jnp.ndarray:
+                 priors: ModelPriors,
+                 return_population: bool = False) -> jnp.ndarray:
     """
     Defines the Horseshoe-regularized model for genotype-specific activity.
 
@@ -49,12 +60,18 @@ def define_model(name: str,
     priors : ModelPriors
         A Pytree (Flax dataclass) containing the hyperparameters for
         the Horseshoe prior.
+    return_population : bool, default False
+        Also return the library-ordered per-genotype values (shape
+        ``(num_genotype,)``, or ``None`` when the latents arrived as a
+        batch-sized substitution), for callers that look genotypes up by
+        library index (the congression mixture).
 
     Returns
     -------
     jnp.ndarray
         The sampled `activity` values, expanded to match the shape of
-        the observations via ``data.map_genotype``.
+        the observations via ``data.map_genotype``. With
+        ``return_population``, a ``(tensor, population)`` tuple.
     """
 
     # Global scale: How big can the "slab" (real effects) be?
@@ -63,34 +80,22 @@ def define_model(name: str,
                                    dist.HalfNormal(priors.global_scale_tau_scale)) 
     
     # Sample local scales and offsets 
-    with pyro.plate("shared_genotype_plate", size=data.batch_size,dim=-1):
-        with pyro.handlers.scale(scale=data.scale_vector):
-        
-            # Local scale `lambda`. HalfNormal(1) is the standard Horseshoe.
-            local_scale_lambda = pyro.sample(f"{name}_local_scale",
-                                            dist.HalfNormal(1.0))
-
-            # Non-centered offset `z` (always Normal(0,1))
-            activity_offset = pyro.sample(f"{name}_offset", dist.Normal(0.0, 1.0))
-
-    # Guard against full-sized array substitution during initialization or re-runs 
-    # with full-sized initial values
-    if local_scale_lambda.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        local_scale_lambda = local_scale_lambda[..., data.batch_idx]
-    if activity_offset.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        activity_offset = activity_offset[..., data.batch_idx]
-
-    # Combine scales: `beta = z * (tau * lambda)`
-    # The mean `log(activity)` is 0.0 (i.e., activity = 1.0)
-    # The effective scale allows for deviations from 0.0
-    effective_scale = global_scale_tau * local_scale_lambda
-    log_activity_mutant_dists = activity_offset * effective_scale
-    
-    activity = jnp.clip(jnp.exp(log_activity_mutant_dists), max=1e30)
+    with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+
+        # Local scale `lambda`. HalfNormal(1) is the standard Horseshoe.
+        local_scale_lambda = pyro.sample(f"{name}_local_scale",
+                                        dist.HalfNormal(1.0))
 
-    # Set wildtype activity to 1.0
-    is_wt_mask = jnp.isin(data.batch_idx, data.wt_indexes)
-    activity = jnp.where(is_wt_mask, 1.0, activity)
+        # Non-centered offset `z` (always Normal(0,1))
+        activity_offset = pyro.sample(f"{name}_offset", dist.Normal(0.0, 1.0))
+
+    # Sampled at library size: slice to this batch's genotypes (a value of
+    # any other size is an already-sliced substitution from the posterior
+    # forward pass). Combine scales, `beta = z * (tau * lambda)`, around a
+    # mean log(activity) of 0.0; wt activity is 1.0.
+    activity, population = per_genotype(
+        _activity_values(global_scale_tau, data.wt_indexes),
+        [local_scale_lambda, activity_offset], data, return_population)
 
     # Register per-genotype values for inspection
     pyro.deterministic(name, activity)  
@@ -98,12 +103,15 @@ def define_model(name: str,
     # Broadcast to full-sized tensor
     activity = activity[None,None,None,None,None,None,:]
 
+    if return_population:
+        return activity, population
     return activity
 
 
 def guide(name: str, 
           data: GrowthData, 
-          priors: ModelPriors) -> jnp.ndarray:
+          priors: ModelPriors,
+          return_population: bool = False) -> jnp.ndarray:
     """
     Guide corresponding to the Horseshoe-regularized activity model.
 
@@ -132,44 +140,30 @@ def guide(name: str,
                                         constraint=dist.constraints.positive)
 
     # Sample local scales and offsets 
-    with pyro.plate("shared_genotype_plate", size=data.batch_size,dim=-1):
-        with pyro.handlers.scale(scale=data.scale_vector):
-        
-            lambda_batch_locs = lambda_locs[data.batch_idx]
-            lambda_batch_scales = lambda_scales[data.batch_idx]
-
-            activity_batch_locs = activity_offset_locs[data.batch_idx]
-            activity_batch_scales = activity_offset_scales[data.batch_idx]
-
-            # Local scale `lambda`. HalfNormal(1) is the standard Horseshoe.
-            local_scale_lambda = pyro.sample(f"{name}_local_scale",
-                                            dist.LogNormal(lambda_batch_locs, lambda_batch_scales))
-
-            # Non-centered offset `z` (always Normal(0,1))
-            activity_offset = pyro.sample(f"{name}_offset", dist.Normal(activity_batch_locs, activity_batch_scales))
-
-    # Guard against full-sized array substitution during initialization or re-runs 
-    # with full-sized initial values
-    if local_scale_lambda.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        local_scale_lambda = local_scale_lambda[..., data.batch_idx]
-    if activity_offset.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        activity_offset = activity_offset[..., data.batch_idx]
-
-    # Combine scales: `beta = z * (tau * lambda)`
-    # The mean `log(activity)` is 0.0 (i.e., activity = 1.0)
-    # The effective scale allows for deviations from 0.0
-    effective_scale = global_scale_tau * local_scale_lambda
-    log_activity_mutant_dists = activity_offset * effective_scale
-    
-    activity = jnp.clip(jnp.exp(log_activity_mutant_dists), max=1e30)
+    with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+
+        # Local scale `lambda`. HalfNormal(1) is the standard Horseshoe.
+        local_scale_lambda = pyro.sample(f"{name}_local_scale",
+                                         dist.LogNormal(lambda_locs, lambda_scales))
+
+        # Non-centered offset `z` (always Normal(0,1))
+        activity_offset = pyro.sample(f"{name}_offset",
+                                      dist.Normal(activity_offset_locs,
+                                                  activity_offset_scales))
 
-    # Set wildtype activity to 1.0
-    is_wt_mask = jnp.isin(data.batch_idx, data.wt_indexes)
-    activity = jnp.where(is_wt_mask, 1.0, activity)
+    # Sampled at library size: slice to this batch's genotypes (a value of
+    # any other size is an already-sliced substitution from the posterior
+    # forward pass). Combine scales, `beta = z * (tau * lambda)`, around a
+    # mean log(activity) of 0.0; wt activity is 1.0.
+    activity, population = per_genotype(
+        _activity_values(global_scale_tau, data.wt_indexes),
+        [local_scale_lambda, activity_offset], data, return_population)
 
     # Broadcast to full-sized tensor
     activity = activity[None,None,None,None,None,None,:]
 
+    if return_population:
+        return activity, population
     return activity
 
 def get_hyperparameters() -> Dict[str, Any]:
diff --git a/src/tfscreen/tfmodel/generative/components/activity/horseshoe_mut.py b/src/tfscreen/tfmodel/generative/components/activity/horseshoe_mut.py
index c6304d4a..4e7d1545 100644
--- a/src/tfscreen/tfmodel/generative/components/activity/horseshoe_mut.py
+++ b/src/tfscreen/tfmodel/generative/components/activity/horseshoe_mut.py
@@ -27,6 +27,7 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale, HalfCauchy
 import pandas as pd
 from flax.struct import dataclass
 from typing import Dict, Any
@@ -52,7 +53,8 @@ class ModelPriors:
 
 def define_model(name: str,
                  data: GrowthData,
-                 priors: ModelPriors) -> jnp.ndarray:
+                 priors: ModelPriors,
+                 return_population: bool = False) -> jnp.ndarray:
     """
     Define the regularised-horseshoe mutation-decomposed activity model.
 
@@ -63,11 +65,14 @@ def define_model(name: str,
         Must have ``mut_geno_matrix`` (num_mutation x G) and ``num_mutation``.
         If ``num_pair > 0``, must also have ``pair_geno_matrix``.
     priors : ModelPriors
+    return_population : bool, default False
+        Also return the library-ordered per-genotype activity, shape
+        ``(num_genotype,)``, as a ``(tensor, population)`` tuple.
 
     Returns
     -------
     jnp.ndarray
-        Activity values broadcast to shape ``(1, 1, 1, 1, 1, 1, num_genotype)``.
+        Activity values for the batch's genotypes, shape ``(1, 1, 1, 1, 1, 1, batch_size)``.
     """
     mut_nnz_mut_idx  = jnp.array(data.mut_nnz_mut_idx)    # COO row (mutation)
     mut_nnz_geno_idx = jnp.array(data.mut_nnz_geno_idx)   # COO col (genotype)
@@ -79,18 +84,18 @@ def define_model(name: str,
     # ------------------------------------------------------------------
     tau_d = pyro.sample(
         f"{name}_d_tau",
-        dist.HalfCauchy(priors.activity_d_tau_scale))
+        HalfCauchy(priors.activity_d_tau_scale))
     c2_d = pyro.sample(
         f"{name}_d_c2",
         dist.InverseGamma(priors.activity_d_slab_df / 2.0,
                           priors.activity_d_slab_df * priors.activity_d_slab_scale ** 2 / 2.0))
 
     with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
-        lambda_d = pyro.sample(f"{name}_d_lambda", dist.HalfCauchy(1.0))
+        lambda_d = pyro.sample(f"{name}_d_lambda", HalfCauchy(1.0))
         d_offset = pyro.sample(f"{name}_d_offset", dist.Normal(0.0, 1.0))
 
     # Regularised local scale, then non-centred delta (symmetric about 0)
-    lambda_d_tilde = jnp.sqrt(c2_d * lambda_d ** 2 / (c2_d + tau_d ** 2 * lambda_d ** 2))
+    lambda_d_tilde = regularized_scale(lambda_d, tau_d, c2_d)
     d_log_activity = d_offset * tau_d * lambda_d_tilde   # [num_mutation]
     pyro.deterministic(f"{name}_d_log_activity", d_log_activity)
 
@@ -107,18 +112,17 @@ def define_model(name: str,
 
         tau_epi = pyro.sample(
             f"{name}_epi_tau",
-            dist.HalfCauchy(priors.activity_epi_tau_scale))
+            HalfCauchy(priors.activity_epi_tau_scale))
         c2_epi = pyro.sample(
             f"{name}_epi_c2",
             dist.InverseGamma(priors.activity_epi_slab_df / 2.0,
                               priors.activity_epi_slab_df * priors.activity_epi_slab_scale ** 2 / 2.0))
 
         with pyro.plate(f"{name}_pair_plate", num_pair, dim=-1):
-            lambda_epi = pyro.sample(f"{name}_epi_lambda", dist.HalfCauchy(1.0))
+            lambda_epi = pyro.sample(f"{name}_epi_lambda", HalfCauchy(1.0))
             epi_offset = pyro.sample(f"{name}_epi_offset", dist.Normal(0.0, 1.0))
 
-        lambda_epi_tilde = jnp.sqrt(
-            c2_epi * lambda_epi ** 2 / (c2_epi + tau_epi ** 2 * lambda_epi ** 2))
+        lambda_epi_tilde = regularized_scale(lambda_epi, tau_epi, c2_epi)
         epi_log_activity = epi_offset * tau_epi * lambda_epi_tilde   # [num_pair]
         pyro.deterministic(f"{name}_epi_log_activity", epi_log_activity)
         log_activity = log_activity + apply_pair_matrix(
@@ -127,14 +131,19 @@ def define_model(name: str,
     activity = jnp.clip(jnp.exp(log_activity), max=1e30)
     pyro.deterministic(name, activity)
 
-    if data.batch_size < data.num_genotype:
-        activity = activity[data.batch_idx]
-    return activity[None, None, None, None, None, None, :]
+    # activity is library-ordered; always slice to the batch's genotypes.
+    # The full-batch index is a reshuffled permutation, not the identity.
+    population = activity
+    activity = activity[data.batch_idx][None, None, None, None, None, None, :]
+    if return_population:
+        return activity, population
+    return activity
 
 
 def guide(name: str,
           data: GrowthData,
-          priors: ModelPriors) -> jnp.ndarray:
+          priors: ModelPriors,
+          return_population: bool = False) -> jnp.ndarray:
     """Variational guide for the regularised-horseshoe mutation-decomposed activity model."""
 
     num_mut = data.num_mutation
@@ -171,7 +180,7 @@ def guide(name: str,
         d_offset = pyro.sample(f"{name}_d_offset",
                                dist.Normal(d_offset_locs, d_offset_scales))
 
-    lambda_d_tilde = jnp.sqrt(c2_d * lambda_d ** 2 / (c2_d + tau_d ** 2 * lambda_d ** 2))
+    lambda_d_tilde = regularized_scale(lambda_d, tau_d, c2_d)
     d_log_activity = d_offset * tau_d * lambda_d_tilde
     log_activity = apply_mut_matrix(
         d_log_activity, mut_nnz_mut_idx, mut_nnz_geno_idx, data.num_genotype)
@@ -207,17 +216,20 @@ def guide(name: str,
             epi_offset = pyro.sample(f"{name}_epi_offset",
                                      dist.Normal(epi_offset_locs, epi_offset_scales))
 
-        lambda_epi_tilde = jnp.sqrt(
-            c2_epi * lambda_epi ** 2 / (c2_epi + tau_epi ** 2 * lambda_epi ** 2))
+        lambda_epi_tilde = regularized_scale(lambda_epi, tau_epi, c2_epi)
         epi_log_activity = epi_offset * tau_epi * lambda_epi_tilde
         log_activity = log_activity + apply_pair_matrix(
             epi_log_activity, pair_nnz_pair_idx, pair_nnz_geno_idx, data.num_genotype)
 
     activity = jnp.clip(jnp.exp(log_activity), max=1e30)
 
-    if data.batch_size < data.num_genotype:
-        activity = activity[data.batch_idx]
-    return activity[None, None, None, None, None, None, :]
+    # activity is library-ordered; always slice to the batch's genotypes.
+    # The full-batch index is a reshuffled permutation, not the identity.
+    population = activity
+    activity = activity[data.batch_idx][None, None, None, None, None, None, :]
+    if return_population:
+        return activity, population
+    return activity
 
 
 def get_hyperparameters() -> Dict[str, Any]:
diff --git a/src/tfscreen/tfmodel/generative/components/dk_geno/fixed.py b/src/tfscreen/tfmodel/generative/components/dk_geno/fixed.py
index 0d6b0f50..846ea975 100644
--- a/src/tfscreen/tfmodel/generative/components/dk_geno/fixed.py
+++ b/src/tfscreen/tfmodel/generative/components/dk_geno/fixed.py
@@ -13,7 +13,8 @@ class ModelPriors:
 
 def define_model(name: str, 
                  data: GrowthData, 
-                 priors: ModelPriors) -> jnp.ndarray:
+                 priors: ModelPriors,
+                 return_population: bool = False) -> jnp.ndarray:
     """
     The pleiotropic effect of a genotype on growth rate independent of
     transcription factor occupancy. Fixed to zero. Returns a full tensor. 
@@ -30,6 +31,9 @@ def define_model(name: str,
           per-genotype parameters to the full set of observations.
     priors : ModelPriors
         A Pytree of hyperparameters. (Unused in this model).
+    return_population : bool, default False
+        Also return the library-ordered per-genotype values, shape
+        ``(num_genotype,)``, as a ``(tensor, population)`` tuple.
         
     Returns
     -------
@@ -47,11 +51,14 @@ def define_model(name: str,
     # Expand to full-sized tensor
     dk_geno = dk_geno_per_genotype[None,None,None,None,None,None,:]
 
+    if return_population:
+        return dk_geno, jnp.zeros(data.num_genotype, dtype=float)
     return dk_geno
 
 def guide(name: str, 
           data: GrowthData, 
-          priors: ModelPriors) -> jnp.ndarray:
+          priors: ModelPriors,
+          return_population: bool = False) -> jnp.ndarray:
     """
     Guide for the fixed dk_geno model.
 
@@ -65,6 +72,8 @@ def guide(name: str,
     # Expand to full-sized tensor
     dk_geno = dk_geno_per_genotype[None,None,None,None,None,None,:]
 
+    if return_population:
+        return dk_geno, jnp.zeros(data.num_genotype, dtype=float)
     return dk_geno
 
 def get_hyperparameters():
diff --git a/src/tfscreen/tfmodel/generative/components/dk_geno/hierarchical_geno.py b/src/tfscreen/tfmodel/generative/components/dk_geno/hierarchical_geno.py
index 1c1801c7..d39a7035 100644
--- a/src/tfscreen/tfmodel/generative/components/dk_geno/hierarchical_geno.py
+++ b/src/tfscreen/tfmodel/generative/components/dk_geno/hierarchical_geno.py
@@ -6,6 +6,7 @@
 from typing import Mapping
 
 from tfscreen.tfmodel.data_class import GrowthData
+from tfscreen.tfmodel.generative.components._population import per_genotype
 from tfscreen.tfmodel.generative.components._pinning import (
     _hyper,
     _pinned_value,
@@ -35,9 +36,20 @@ class ModelPriors:
     )
     
 
+def _dk_geno_values(hyper_loc, hyper_scale, hyper_shift, wt_indexes):
+    """Per-genotype dk_geno from offsets (shift - lognormal); wt pinned to 0."""
+    def compute(dk_offset, genotype_idx):
+        lognormal = jnp.clip(jnp.exp(hyper_loc + dk_offset * hyper_scale),
+                             max=1e30)
+        values = hyper_shift - lognormal
+        return jnp.where(jnp.isin(genotype_idx, wt_indexes), 0.0, values)
+    return compute
+
+
 def define_model(name: str, 
                  data: GrowthData, 
-                 priors: ModelPriors) -> jnp.ndarray:
+                 priors: ModelPriors,
+                 return_population: bool = False) -> jnp.ndarray:
     """
     The pleiotropic effect of a genotype on growth rate independent of 
     transcription factor occupancy. 
@@ -64,12 +76,18 @@ def define_model(name: str,
         - ``priors.hyper_loc_loc``
         - ``priors.hyper_loc_scale``
         - ``priors.hyper_scale_loc``
+    return_population : bool, default False
+        Also return the library-ordered per-genotype values (shape
+        ``(num_genotype,)``, or ``None`` when the latents arrived as a
+        batch-sized substitution), for callers that look genotypes up by
+        library index (the congression mixture).
 
     Returns
     -------
     jnp.ndarray
         full tensor with shape (num_replicate,num_time,num_treatment,num_genotype)
-        with dk_geno values for each position in the tensor.
+        with dk_geno values for each position in the tensor. With
+        ``return_population``, a ``(tensor, population)`` tuple.
     """
 
     pinned = priors.pinned
@@ -93,33 +111,31 @@ def define_model(name: str,
         pinned,
     )
 
-    with pyro.plate("shared_genotype_plate", size=data.batch_size,dim=-1):
-        with pyro.handlers.scale(scale=data.scale_vector):
-            dk_geno_offset = pyro.sample(f"{name}_offset", dist.Normal(0.0, 1.0))
+    with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+        dk_geno_offset = pyro.sample(f"{name}_offset", dist.Normal(0.0, 1.0))
 
-    # Guard against full-sized array substitution during initialization or re-runs 
-    # with full-sized initial values
-    if dk_geno_offset.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        dk_geno_offset = dk_geno_offset[..., data.batch_idx]
-    
-    dk_geno_lognormal_values = jnp.clip(jnp.exp(dk_geno_hyper_loc + dk_geno_offset * dk_geno_hyper_scale),max=1e30)
-    dk_geno_per_genotype = dk_geno_hyper_shift - dk_geno_lognormal_values
+    # Sampled at library size: slice to this batch's genotypes (a value of
+    # any other size is an already-sliced substitution from the posterior
+    # forward pass). wt is forced to zero.
+    dk_geno_per_genotype, population = per_genotype(
+        _dk_geno_values(dk_geno_hyper_loc, dk_geno_hyper_scale,
+                        dk_geno_hyper_shift, data.wt_indexes),
+        [dk_geno_offset], data, return_population)
 
-    # Force wildtype to be zero. 
-    is_wt_mask = jnp.isin(data.batch_idx, data.wt_indexes)
-    dk_geno_per_genotype = jnp.where(is_wt_mask, 0.0, dk_geno_per_genotype)
-    
     # Register dists
     pyro.deterministic(name, dk_geno_per_genotype)    
 
     # Expand to full-sized tensor
     dk_geno = dk_geno_per_genotype[None,None,None,None,None,None,:]
 
+    if return_population:
+        return dk_geno, population
     return dk_geno
 
 def guide(name: str, 
           data: GrowthData, 
-          priors: ModelPriors) -> jnp.ndarray:
+          priors: ModelPriors,
+          return_population: bool = False) -> jnp.ndarray:
     """
     Guide corresponding to the hierarchical dk_geno model.
 
@@ -171,33 +187,23 @@ def guide(name: str,
     offset_scales = pyro.param(f"{name}_offset_scales", jnp.ones(data.num_genotype,dtype=float), 
                                constraint=dist.constraints.positive)
 
-    # --- Batching ---
-    with pyro.plate("shared_genotype_plate", size=data.batch_size,dim=-1):
-        with pyro.handlers.scale(scale=data.scale_vector):
-        
-            batch_locs = offset_locs[...,data.batch_idx]
-            batch_scales = offset_scales[...,data.batch_idx]
-
-            dk_geno_offset = pyro.sample(f"{name}_offset", dist.Normal(batch_locs, batch_scales))
-
-    # Guard against full-sized array substitution during initialization or re-runs 
-    # with full-sized initial values
-    if dk_geno_offset.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        dk_geno_offset = dk_geno_offset[..., data.batch_idx]
+    with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+        dk_geno_offset = pyro.sample(f"{name}_offset", dist.Normal(offset_locs, offset_scales))
 
     # --- Deterministic Calculation ---
-    
-    # Replicate the Shift - LogNormal logic
-    dk_geno_lognormal_values = jnp.clip(jnp.exp(dk_geno_hyper_loc + dk_geno_offset * dk_geno_hyper_scale), max=1e30)
-    dk_geno_per_genotype = dk_geno_hyper_shift - dk_geno_lognormal_values
 
-    # Force wildtype to be zero
-    is_wt_mask = jnp.isin(data.batch_idx, data.wt_indexes)
-    dk_geno_per_genotype = jnp.where(is_wt_mask, 0.0, dk_geno_per_genotype)
-    
+    # Same shift - lognormal logic as the model, sliced to the batch; wt is
+    # forced to zero.
+    dk_geno_per_genotype, population = per_genotype(
+        _dk_geno_values(dk_geno_hyper_loc, dk_geno_hyper_scale,
+                        dk_geno_hyper_shift, data.wt_indexes),
+        [dk_geno_offset], data, return_population)
+
     # Expand to full-sized tensor
     dk_geno = dk_geno_per_genotype[None,None,None,None,None,None,:]
 
+    if return_population:
+        return dk_geno, population
     return dk_geno
 
 def get_hyperparameters():
diff --git a/src/tfscreen/tfmodel/generative/components/dk_geno/pinned.py b/src/tfscreen/tfmodel/generative/components/dk_geno/pinned.py
index 39568cc5..42044da9 100644
--- a/src/tfscreen/tfmodel/generative/components/dk_geno/pinned.py
+++ b/src/tfscreen/tfmodel/generative/components/dk_geno/pinned.py
@@ -64,16 +64,30 @@ class ModelPriors:
     dk_geno_values: jnp.ndarray
 
 
-def _compute_dk_geno(data: GrowthData, priors: ModelPriors) -> jnp.ndarray:
-    """Shared per-genotype dk_geno lookup, with wildtype forced to 0."""
-    dk_geno_per_genotype = priors.dk_geno_values[data.batch_idx]
-    is_wt_mask = jnp.isin(data.batch_idx, data.wt_indexes)
+def _compute_dk_geno(data: GrowthData, priors: ModelPriors,
+                     genotype_idx=None) -> jnp.ndarray:
+    """
+    Shared per-genotype dk_geno lookup, with wildtype forced to 0.
+
+    ``genotype_idx`` defaults to the batch (``data.batch_idx``); pass
+    ``arange(num_genotype)`` for the library-ordered population.
+    """
+    if genotype_idx is None:
+        genotype_idx = data.batch_idx
+    dk_geno_per_genotype = priors.dk_geno_values[genotype_idx]
+    is_wt_mask = jnp.isin(genotype_idx, data.wt_indexes)
     return jnp.where(is_wt_mask, 0.0, dk_geno_per_genotype)
 
 
+def _population(data: GrowthData, priors: ModelPriors) -> jnp.ndarray:
+    """Library-ordered pinned dk_geno for every genotype."""
+    return _compute_dk_geno(data, priors, jnp.arange(data.num_genotype))
+
+
 def define_model(name: str,
                  data: GrowthData,
-                 priors: ModelPriors) -> jnp.ndarray:
+                 priors: ModelPriors,
+                 return_population: bool = False) -> jnp.ndarray:
     """
     The pleiotropic effect of a genotype on growth rate independent of
     transcription factor occupancy, pinned to caller-supplied per-genotype
@@ -93,6 +107,9 @@ def define_model(name: str,
           wildtype (always forced to dk_geno = 0).
     priors : ModelPriors
         Holds the pinned ``dk_geno_values``.
+    return_population : bool, default False
+        Also return the library-ordered per-genotype values, shape
+        ``(num_genotype,)``, as a ``(tensor, population)`` tuple.
 
     Returns
     -------
@@ -106,12 +123,15 @@ def define_model(name: str,
 
     dk_geno = dk_geno_per_genotype[None, None, None, None, None, None, :]
 
+    if return_population:
+        return dk_geno, _population(data, priors)
     return dk_geno
 
 
 def guide(name: str,
           data: GrowthData,
-          priors: ModelPriors) -> jnp.ndarray:
+          priors: ModelPriors,
+          return_population: bool = False) -> jnp.ndarray:
     """
     Guide for the pinned dk_geno model.
 
@@ -122,6 +142,8 @@ def guide(name: str,
 
     dk_geno = dk_geno_per_genotype[None, None, None, None, None, None, :]
 
+    if return_population:
+        return dk_geno, _population(data, priors)
     return dk_geno
 
 
diff --git a/src/tfscreen/tfmodel/generative/components/growth/linear.py b/src/tfscreen/tfmodel/generative/components/growth/linear.py
index 468b64ad..fb6252ad 100644
--- a/src/tfscreen/tfmodel/generative/components/growth/linear.py
+++ b/src/tfscreen/tfmodel/generative/components/growth/linear.py
@@ -1,4 +1,5 @@
 import numpy as np
+import jax
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
@@ -24,6 +25,12 @@ class LinearParams:
     m_pre: jnp.ndarray
     k_sel: jnp.ndarray
     m_sel: jnp.ndarray
+    # Per-element warp of theta (see ModelPriors.m_warp); a = 0 is no warp.
+    # None when no condition is warped.
+    warp_a_pre: jnp.ndarray = None
+    warp_x0_pre: jnp.ndarray = None
+    warp_a_sel: jnp.ndarray = None
+    warp_x0_sel: jnp.ndarray = None
 
 @dataclass(frozen=True)
 class ModelPriors:
@@ -46,28 +53,72 @@ class ModelPriors:
     m_scale : float
         Standard deviation of the Normal prior on m (used for all conditions
         when ``m_is_selection`` is ``None``).
-    m_scale_minus : float
+    m_scale_minus : float or array
         Prior scale on m for control ('-') conditions, where theta is not
         expected to meaningfully affect growth.  Applied per-condition when
-        ``m_is_selection`` is provided.
-    m_scale_plus : float
+        ``m_is_selection`` is provided.  A per-condition array (indexed rows
+        in the priors CSV) sets each condition's scale; only the entries of
+        control conditions are used.
+    m_scale_plus : float or array
         Prior scale on m for selection ('+') conditions, where theta drives
         differential growth.  Applied per-condition when ``m_is_selection``
-        is provided.
+        is provided.  Scalar or per-condition array, as ``m_scale_minus``
+        (e.g. a tight prior from monoculture data for one selection and a
+        loose one for another).
     m_is_selection : tuple of bool or None
         Length-``num_condition_rep`` tuple; ``True`` for selection ('+')
         conditions, ``False`` for control ('-') conditions.  ``None``
         disables the per-condition logic and uses ``m_scale`` for all
         conditions (backward-compatible default).
-    m_pinned : bool
+    m_pinned : bool or tuple of bool
         When ``True``, the per-condition slope m is *clamped* to ``m_loc``
         (a ``deterministic`` site) instead of sampled, so the growth
         likelihood cannot inflate it away from the calibration-pinned value.
         A soft Normal prior — however tight — is only a KL penalty in SVI and
         the likelihood over many observations can override it; this is a hard
-        clamp.  ``k`` is intentionally never clamped this way: it carries real
+        clamp.  A tuple (one flag per ``condition_rep``) clamps only the
+        conditions flagged; the others are sampled as ``{name}_m_free`` and
+        ``{name}_m`` becomes a deterministic site holding every condition
+        (indexed 0/1 rows in the priors CSV; ``tfs-configure-model
+        --growth_priors`` takes ``k_pinned``/``m_pinned`` columns).  ``k`` is
+        not clamped this way in production fits: it carries real
         per-experiment (tube-noise) variance and sits in the additive
         k/dk_geno slide, so it keeps a floored soft prior.  Default ``False``.
+    k_pinned : bool or tuple of bool
+        When ``True``, ``k`` is clamped to ``k_loc`` the same way, per
+        condition with a tuple.  For conditional fits at a fixed (k, m), such
+        as the two-stage fit of ``planning/studies/svi-overconfidence/`` or a
+        fit holding one marker's k and m at an independent estimate
+        (``planning/studies/m-bias-factorial/``).  Default ``False``.
+    m_warp : bool or tuple of bool
+        True for a condition (a tuple: per condition) makes its growth
+        follow a monotone warp of theta instead of theta itself,
+        ``f(x) = (s(x) - s(0)) / (s(1) - s(0))``, ``s(x) = sigmoid(a (x -
+        x0))``, so f(0) = 0 and f(1) = 1 (wt's gauge and m keep their
+        meaning) and f is linear as a -> 0. Per warped condition
+        ``{name}_warp_log_a`` ~ Normal(warp_log_a_loc, warp_log_a_scale) and
+        ``{name}_warp_x0`` ~ Normal(warp_x0_loc, warp_x0_scale). For markers
+        that read occupancy through differently shaped maps
+        (``planning/studies/relative-m/``: kanR switch-like, pheS graded).
+        Default False. ``--growth_priors`` takes a 0/1 ``m_warp`` column.
+        Experimental: tested on the relative stage 1 only, and not carried
+        through ``tfs-fit-two-stage``, which refuses it
+        (planning/release-0.5.0.md, decision 2).
+    warp_log_a_loc, warp_log_a_scale, warp_x0_loc, warp_x0_scale : float
+        Priors of the warp's log steepness and threshold.
+    k_titrant_loglinear_scale : float
+        > 0: in every selection condition (``m_is_selection``), the selection
+        phase's k gets a shift shared by every genotype that is linear in
+        log titrant concentration, ``s * log(max(c, c_floor) / c_floor)``
+        with ``c_floor`` a tenth of the lowest nonzero concentration (so 0 at
+        no titrant), one slope ``{name}_k_titrant_slope`` ~ Normal(0, scale)
+        per selection condition.  0 (the default) is off.  The within-tube
+        study (``planning/studies/within-tube-m/``) found flat genotypes
+        growing faster at 1 mM IPTG than at none in both selection
+        conditions and not in the controls.  A free shift at every
+        concentration was tried and dropped: with a curve per genotype it
+        trades with m along wt's own curve (on a simulation with no such
+        term it made m 2.5x too steep).
     """
     k_loc: float
     k_scale: float
@@ -76,7 +127,14 @@ class ModelPriors:
     m_scale_minus: float
     m_scale_plus: float
     m_is_selection: tuple = field(pytree_node=False, default=None)
-    m_pinned: bool = field(pytree_node=False, default=False)
+    m_pinned: bool | tuple = field(pytree_node=False, default=False)
+    k_pinned: bool | tuple = field(pytree_node=False, default=False)
+    k_titrant_loglinear_scale: float = field(pytree_node=False, default=0.0)
+    m_warp: bool | tuple = field(pytree_node=False, default=False)
+    warp_log_a_loc: float = field(pytree_node=False, default=1.0)
+    warp_log_a_scale: float = field(pytree_node=False, default=1.5)
+    warp_x0_loc: float = field(pytree_node=False, default=0.5)
+    warp_x0_scale: float = field(pytree_node=False, default=0.5)
 
 
 # ---------------------------------------------------------------------------
@@ -133,6 +191,178 @@ def _parse_condition_label(label: str) -> bool:
     )
 
 
+def _pin_mask(pinned, num_cr: int) -> np.ndarray:
+    """
+    Per-condition pin flags as a static numpy bool array.
+
+    ``pinned`` is a bool (every condition) or a tuple with one flag per
+    ``condition_rep``.
+    """
+    mask = np.asarray(pinned, dtype=bool)
+    if mask.ndim == 0:
+        return np.full(num_cr, bool(mask))
+    if mask.shape != (num_cr,):
+        raise ValueError(f"Per-condition pin flags {tuple(mask)} do not match "
+                         f"the model's {num_cr} condition(s).")
+    return mask
+
+
+def _with_free(loc_arr, free, values, num_cr):
+    """``loc_arr`` with the free conditions' entries replaced by ``values``."""
+    full = jnp.broadcast_to(loc_arr, values.shape[:-1] + (num_cr,))
+    return full.at[..., free].set(values)
+
+
+def _sample_condition_param(name, param, loc_arr, scale_arr, mask, num_cr):
+    """
+    Model side of k or m: sampled per condition, clamped to ``loc_arr``
+    (deterministic), or, with some conditions pinned, the free ones sampled
+    as ``{name}_{param}_free`` and the full array a deterministic site.
+    """
+    site = f"{name}_{param}"
+    if not mask.any():
+        with pyro.plate(f"{name}_condition_parameters", num_cr) as idx:
+            return pyro.sample(site, dist.Normal(loc_arr[idx], scale_arr[idx]))
+    if mask.all():
+        # Hard clamp, registered as a site so extraction / posterior sampling
+        # still find "{name}_{param}".
+        return pyro.deterministic(site, loc_arr)
+    free = np.flatnonzero(~mask)
+    with pyro.plate(f"{name}_{param}_free_conditions", free.size):
+        values = pyro.sample(f"{site}_free",
+                             dist.Normal(loc_arr[free], scale_arr[free]))
+    return pyro.deterministic(site, _with_free(loc_arr, free, values, num_cr))
+
+
+def _guide_condition_param(name, param, loc_arr, scale_arr, mask, num_cr):
+    """Guide side of ``_sample_condition_param``."""
+    site = f"{name}_{param}"
+    if mask.all():
+        # Clamped in the model (deterministic); no variational site here.
+        return loc_arr
+    if not mask.any():
+        locs = pyro.param(f"{site}_locs", loc_arr)
+        scales = pyro.param(f"{site}_scales", scale_arr,
+                            constraint=dist.constraints.positive)
+        with pyro.plate(f"{name}_condition_parameters", num_cr) as idx:
+            return pyro.sample(site, dist.Normal(locs[..., idx],
+                                                 scales[..., idx]))
+    free = np.flatnonzero(~mask)
+    locs = pyro.param(f"{site}_free_locs", loc_arr[free])
+    scales = pyro.param(f"{site}_free_scales", scale_arr[free],
+                        constraint=dist.constraints.positive)
+    with pyro.plate(f"{name}_{param}_free_conditions", free.size):
+        values = pyro.sample(f"{site}_free", dist.Normal(locs, scales))
+    return _with_free(loc_arr, free, values, num_cr)
+
+
+
+def _titrant_scale(priors):
+    """The log-linear titrant k's prior SD, or 0 when it is off."""
+    scale = float(getattr(priors, "k_titrant_loglinear_scale", 0.0))
+    if scale < 0:
+        raise ValueError("k_titrant_loglinear_scale must be >= 0")
+    if scale > 0 and priors.m_is_selection is None:
+        raise ValueError("a titrant-dependent k needs the selection flags "
+                         "(m_is_selection) to know which conditions get it")
+    return scale
+
+
+def _log_titrant_offsets(titrant_conc):
+    """log(max(c, c_floor) / c_floor), c_floor a tenth of the lowest nonzero
+    concentration, so 0 at no titrant. jax only: titrant_conc is traced
+    inside the jitted fit."""
+    conc = jnp.asarray(titrant_conc, dtype=float)
+    floor = jnp.min(jnp.where(conc > 0, conc, jnp.inf)) / 10.0
+    return jnp.log(jnp.maximum(conc, floor) / floor)
+
+
+def _titrant_table(name, data, priors, guide=False):
+    """
+    The selection-phase k shift per (condition_rep, titrant_conc), or None.
+    Rows of control conditions and the no-titrant column are 0.
+    """
+    scale = _titrant_scale(priors)
+    if scale == 0:
+        return None
+    num_cr, C = data.num_condition_rep, data.num_titrant_conc
+    sel = np.flatnonzero(np.asarray(priors.m_is_selection, dtype=bool))
+    site = f"{name}_k_titrant_slope"
+    if guide:
+        locs = pyro.param(f"{site}_locs", jnp.zeros(sel.size))
+        scales = pyro.param(f"{site}_scales", jnp.full(sel.size, 1e-4),
+                            constraint=dist.constraints.positive)
+    else:
+        locs, scales = jnp.zeros(sel.size), jnp.full(sel.size, scale)
+    with pyro.plate(f"{name}_k_titrant_conditions", sel.size):
+        slope = pyro.sample(site, dist.Normal(locs, scales))
+    L = _log_titrant_offsets(data.titrant_conc)
+    table = jnp.zeros(slope.shape[:-1] + (num_cr, C))
+    return table.at[..., sel, :].set(slope[..., :, None] * L)
+
+
+
+def _warp_tables(name, data, priors, guide=False):
+    """
+    Per-condition warp steepness a (0 = no warp) and threshold x0, or
+    (None, None) when no condition is warped.
+    """
+    num_cr = data.num_condition_rep
+    mask = _pin_mask(getattr(priors, "m_warp", False), num_cr)
+    if not mask.any():
+        return None, None
+    idx = np.flatnonzero(mask)
+    la_loc = jnp.full(idx.size, float(priors.warp_log_a_loc))
+    x0_loc = jnp.full(idx.size, float(priors.warp_x0_loc))
+    if guide:
+        la_l = pyro.param(f"{name}_warp_log_a_locs", la_loc)
+        la_s = pyro.param(f"{name}_warp_log_a_scales", jnp.full(idx.size, 0.1),
+                          constraint=dist.constraints.positive)
+        x0_l = pyro.param(f"{name}_warp_x0_locs", x0_loc)
+        x0_s = pyro.param(f"{name}_warp_x0_scales", jnp.full(idx.size, 0.1),
+                          constraint=dist.constraints.positive)
+    else:
+        la_l, la_s = la_loc, jnp.full(idx.size, float(priors.warp_log_a_scale))
+        x0_l, x0_s = x0_loc, jnp.full(idx.size, float(priors.warp_x0_scale))
+    with pyro.plate(f"{name}_warp_conditions", idx.size):
+        log_a = pyro.sample(f"{name}_warp_log_a", dist.Normal(la_l, la_s))
+        x0 = pyro.sample(f"{name}_warp_x0", dist.Normal(x0_l, x0_s))
+    a = jnp.zeros(log_a.shape[:-1] + (num_cr,)).at[..., idx].set(jnp.exp(log_a))
+    x0_full = jnp.full(x0.shape[:-1] + (num_cr,), 0.5).at[..., idx].set(x0)
+    return a, x0_full
+
+
+def warp_theta(theta, a, x0):
+    """
+    The monotone warp f(theta) of ModelPriors.m_warp; theta itself where
+    a == 0 (computed with a safe a so the unused branch cannot give NaN).
+    """
+    if a is None:
+        return theta
+    warped = a > 0
+    a_safe = jnp.where(warped, a, 1.0)
+    s0 = jax.nn.sigmoid(-a_safe * x0)
+    s1 = jax.nn.sigmoid(a_safe * (1.0 - x0))
+    f = (jax.nn.sigmoid(a_safe * (theta - x0)) - s0) / (s1 - s0)
+    return jnp.where(warped, f, theta)
+
+
+def _add_titrant_shift(k_sel, table, data):
+    """k_sel plus the shift for each element's condition and titrant
+    concentration (the growth tensor's second-to-last axis)."""
+    if table is None:
+        return k_sel
+    num_titrant_conc = data.num_titrant_conc
+    m = jnp.asarray(data.map_condition_sel)
+    if m.ndim < 2 or m.shape[-2] not in (num_titrant_conc,):
+        raise ValueError("a titrant-dependent k needs the full growth-tensor "
+                         "condition map (titrant_conc on its second-to-last "
+                         f"axis); got shape {tuple(m.shape)}")
+    ci = jnp.arange(num_titrant_conc).reshape((1,) * (m.ndim - 2)
+                                              + (num_titrant_conc, 1))
+    return k_sel + table[..., m, ci]
+
+
 def define_model(name: str,
                  data: GrowthData,
                  priors: ModelPriors) -> LinearParams:
@@ -159,12 +389,16 @@ def define_model(name: str,
     num_cr = data.num_condition_rep
 
     if priors.m_is_selection is None:
-        m_scale_arr = jnp.full(num_cr, priors.m_scale)
+        m_scale_arr = jnp.broadcast_to(jnp.asarray(priors.m_scale, dtype=float),
+                                       (num_cr,))
     else:
-        m_scale_arr = jnp.array([
-            priors.m_scale_plus if sel else priors.m_scale_minus
-            for sel in priors.m_is_selection
-        ])
+        # m_scale_plus / m_scale_minus may be scalars or per-condition arrays
+        # (indexed rows in the priors CSV); each condition takes the one its
+        # selection flag names.
+        m_scale_arr = jnp.where(
+            jnp.asarray(priors.m_is_selection, dtype=bool),
+            jnp.broadcast_to(jnp.asarray(priors.m_scale_plus, dtype=float), (num_cr,)),
+            jnp.broadcast_to(jnp.asarray(priors.m_scale_minus, dtype=float), (num_cr,)))
 
     # Broadcast scalar-or-array priors to a per-condition array so k, m can be
     # pinned condition-by-condition (see prefit calibration).  A scalar prior
@@ -173,27 +407,25 @@ def define_model(name: str,
     k_scale_arr = jnp.broadcast_to(jnp.asarray(priors.k_scale, dtype=float), (num_cr,))
     m_loc_arr = jnp.broadcast_to(jnp.asarray(priors.m_loc, dtype=float), (num_cr,))
 
-    m_pinned = bool(priors.m_pinned)
-
-    with pyro.plate(f"{name}_condition_parameters", num_cr) as idx:
-        growth_k = pyro.sample(f"{name}_k",
-                               dist.Normal(k_loc_arr[idx], k_scale_arr[idx]))
-        if not m_pinned:
-            growth_m = pyro.sample(f"{name}_m",
-                                   dist.Normal(m_loc_arr[idx], m_scale_arr[idx]))
-
-    if m_pinned:
-        # Hard clamp: hold m at its per-condition loc (deterministic) rather
-        # than sampling it.  Registered as a site so extraction / posterior
-        # sampling still find "{name}_m".
-        growth_m = pyro.deterministic(f"{name}_m", m_loc_arr)
+    growth_k = _sample_condition_param(name, "k", k_loc_arr, k_scale_arr,
+                                       _pin_mask(priors.k_pinned, num_cr), num_cr)
+    growth_m = _sample_condition_param(name, "m", m_loc_arr, m_scale_arr,
+                                       _pin_mask(priors.m_pinned, num_cr), num_cr)
 
     k_pre = growth_k[data.map_condition_pre]
     m_pre = growth_m[data.map_condition_pre]
     k_sel = growth_k[data.map_condition_sel]
     m_sel = growth_m[data.map_condition_sel]
+    k_sel = _add_titrant_shift(k_sel, _titrant_table(name, data, priors), data)
 
-    return LinearParams(k_pre=k_pre, m_pre=m_pre, k_sel=k_sel, m_sel=m_sel)
+    a, x0 = _warp_tables(name, data, priors)
+    if a is None:
+        return LinearParams(k_pre=k_pre, m_pre=m_pre, k_sel=k_sel, m_sel=m_sel)
+    return LinearParams(k_pre=k_pre, m_pre=m_pre, k_sel=k_sel, m_sel=m_sel,
+                        warp_a_pre=a[..., data.map_condition_pre],
+                        warp_x0_pre=x0[..., data.map_condition_pre],
+                        warp_a_sel=a[..., data.map_condition_sel],
+                        warp_x0_sel=x0[..., data.map_condition_sel])
 
 
 def guide(name: str,
@@ -205,36 +437,31 @@ def guide(name: str,
     Maintains per-condition variational parameters for k and m.
     """
     num_cr = data.num_condition_rep
-    m_pinned = bool(priors.m_pinned)
-    k_locs = pyro.param(f"{name}_k_locs",
-                        jnp.broadcast_to(jnp.asarray(priors.k_loc, dtype=float), (num_cr,)))
-    k_scales = pyro.param(f"{name}_k_scales",
-                          jnp.broadcast_to(jnp.asarray(priors.k_scale, dtype=float), (num_cr,)),
-                          constraint=dist.constraints.positive)
-    if not m_pinned:
-        m_locs = pyro.param(f"{name}_m_locs",
-                            jnp.broadcast_to(jnp.asarray(priors.m_loc, dtype=float), (num_cr,)))
-        m_scales = pyro.param(f"{name}_m_scales",
-                              jnp.broadcast_to(jnp.asarray(priors.m_scale, dtype=float), (num_cr,)),
-                              constraint=dist.constraints.positive)
-
-    with pyro.plate(f"{name}_condition_parameters", data.num_condition_rep) as idx:
-        growth_k = pyro.sample(f"{name}_k",
-                               dist.Normal(k_locs[..., idx], k_scales[..., idx]))
-        if not m_pinned:
-            growth_m = pyro.sample(f"{name}_m",
-                                   dist.Normal(m_locs[..., idx], m_scales[..., idx]))
-
-    if m_pinned:
-        # Clamped in the model (deterministic); no variational site here.
-        growth_m = jnp.broadcast_to(jnp.asarray(priors.m_loc, dtype=float), (num_cr,))
+
+    def _arr(v):
+        return jnp.broadcast_to(jnp.asarray(v, dtype=float), (num_cr,))
+
+    growth_k = _guide_condition_param(name, "k", _arr(priors.k_loc),
+                                      _arr(priors.k_scale),
+                                      _pin_mask(priors.k_pinned, num_cr), num_cr)
+    growth_m = _guide_condition_param(name, "m", _arr(priors.m_loc),
+                                      _arr(priors.m_scale),
+                                      _pin_mask(priors.m_pinned, num_cr), num_cr)
 
     k_pre = growth_k[data.map_condition_pre]
     m_pre = growth_m[data.map_condition_pre]
     k_sel = growth_k[data.map_condition_sel]
     m_sel = growth_m[data.map_condition_sel]
+    k_sel = _add_titrant_shift(k_sel, _titrant_table(name, data, priors, guide=True), data)
 
-    return LinearParams(k_pre=k_pre, m_pre=m_pre, k_sel=k_sel, m_sel=m_sel)
+    a, x0 = _warp_tables(name, data, priors, guide=True)
+    if a is None:
+        return LinearParams(k_pre=k_pre, m_pre=m_pre, k_sel=k_sel, m_sel=m_sel)
+    return LinearParams(k_pre=k_pre, m_pre=m_pre, k_sel=k_sel, m_sel=m_sel,
+                        warp_a_pre=a[..., data.map_condition_pre],
+                        warp_x0_pre=x0[..., data.map_condition_pre],
+                        warp_a_sel=a[..., data.map_condition_sel],
+                        warp_x0_sel=x0[..., data.map_condition_sel])
 
 
 def calculate_growth(params: LinearParams,
@@ -262,8 +489,10 @@ def calculate_growth(params: LinearParams,
     g_sel : jnp.ndarray
         Selection growth rate tensor.
     """
-    g_pre = params.k_pre + dk_geno + activity * params.m_pre * theta
-    g_sel = params.k_sel + dk_geno + activity * params.m_sel * theta
+    theta_pre = warp_theta(theta, params.warp_a_pre, params.warp_x0_pre)
+    theta_sel = warp_theta(theta, params.warp_a_sel, params.warp_x0_sel)
+    g_pre = params.k_pre + dk_geno + activity * params.m_pre * theta_pre
+    g_sel = params.k_sel + dk_geno + activity * params.m_sel * theta_sel
 
     return g_pre, g_sel
 
@@ -279,7 +508,14 @@ def get_hyperparameters():
     parameters["m_scale"] = 0.01        # used when m_is_selection is None
     parameters["m_scale_minus"] = 0.001 # tight prior for '-' control conditions
     parameters["m_scale_plus"] = 0.01   # normal prior for '+' selection conditions
-    parameters["m_pinned"] = False      # hard-clamp m to m_loc when True
+    parameters["m_pinned"] = False      # hard-clamp m to m_loc (bool or per-condition tuple)
+    parameters["k_pinned"] = False      # hard-clamp k to k_loc (bool or per-condition tuple)
+    parameters["k_titrant_loglinear_scale"] = 0.0  # > 0: k shift linear in log conc (selection)
+    parameters["m_warp"] = False        # monotone warp of theta (bool or per-condition tuple)
+    parameters["warp_log_a_loc"] = 1.0
+    parameters["warp_log_a_scale"] = 1.5
+    parameters["warp_x0_loc"] = 0.5
+    parameters["warp_x0_scale"] = 0.5
 
     return parameters
 
@@ -441,10 +677,29 @@ def get_extract_specs(ctx):
         return []
     cond_rep_cols = (["condition_rep"] if ctx.growth_shares_replicates
                      else ["replicate", "condition_rep"])
-    return [dict(
-        input_df=ctx.growth_tm.map_groups["condition_rep"],
+    cond_df = ctx.growth_tm.map_groups["condition_rep"]
+    specs = [dict(
+        input_df=cond_df,
         params_to_get=["growth_m", "growth_k"],
         map_column="map_condition_rep",
         get_columns=cond_rep_cols,
         in_run_prefix="condition_",
     )]
+
+    # The warp's steepness and threshold, one per warped condition, in the
+    # order of the {name}_warp_conditions plate (map_condition_rep order).
+    flags = getattr(getattr(ctx, "condition_growth_priors", None), "m_warp", False)
+    if isinstance(flags, (bool, np.bool_, tuple)) and np.any(flags):
+        cond_df = cond_df.sort_values("map_condition_rep")
+        mask = _pin_mask(flags, len(cond_df))
+        if mask.any():
+            warped = cond_df[mask].copy()
+            warped["map_warp"] = np.arange(len(warped))
+            specs.append(dict(
+                input_df=warped,
+                params_to_get=["warp_log_a", "warp_x0"],
+                map_column="map_warp",
+                get_columns=cond_rep_cols,
+                in_run_prefix="condition_growth_",
+            ))
+    return specs
diff --git a/src/tfscreen/tfmodel/generative/components/ln_cfu0/hierarchical.py b/src/tfscreen/tfmodel/generative/components/ln_cfu0/hierarchical.py
index eeb6e663..267c79d4 100644
--- a/src/tfscreen/tfmodel/generative/components/ln_cfu0/hierarchical.py
+++ b/src/tfscreen/tfmodel/generative/components/ln_cfu0/hierarchical.py
@@ -247,13 +247,12 @@ def define_model(name: str,
     # Sample non-centered offsets for each ln_cfu0 group
     with pyro.plate(f"{name}_replicate", data.num_replicate, dim=-3):
         with pyro.plate(f"{name}_condition_pre", data.num_condition_pre, dim=-2):
-            with pyro.plate("shared_genotype_plate", size=data.batch_size, dim=-1):
-                with pyro.handlers.scale(scale=data.scale_vector):
-                    ln_cfu0_offsets = pyro.sample(f"{name}_offset", dist.Normal(0.0, 1.0))
+            with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+                ln_cfu0_offsets = pyro.sample(f"{name}_offset", dist.Normal(0.0, 1.0))
 
-    # Guard against full-sized array substitution during initialization or re-runs
-    # with full-sized initial values
-    if ln_cfu0_offsets.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
+    # Sampled at library size: slice to this batch's genotypes. A value of
+    # any other size is an already-sliced substitution (posterior forward pass).
+    if ln_cfu0_offsets.shape[-1] == data.num_genotype:
         ln_cfu0_offsets = ln_cfu0_offsets[..., data.batch_idx]
 
     per_geno_loc, per_geno_scale = _per_geno_loc_scale(
@@ -364,17 +363,13 @@ def guide(name: str,
     # Sample non-centered offsets for each ln_cfu0 group
     with pyro.plate(f"{name}_replicate", data.num_replicate, dim=-3):
         with pyro.plate(f"{name}_condition_pre", data.num_condition_pre, dim=-2):
-            with pyro.plate("shared_genotype_plate", size=data.batch_size, dim=-1):
-                with pyro.handlers.scale(scale=data.scale_vector):
+            with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+                ln_cfu0_offsets = pyro.sample(f"{name}_offset",
+                                              dist.Normal(offset_locs, offset_scales))
 
-                    batch_locs   = offset_locs[..., data.batch_idx]
-                    batch_scales = offset_scales[..., data.batch_idx]
-                    ln_cfu0_offsets = pyro.sample(f"{name}_offset",
-                                                  dist.Normal(batch_locs, batch_scales))
-
-    # Guard against full-sized array substitution during initialization or re-runs
-    # with full-sized initial values
-    if ln_cfu0_offsets.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
+    # Sampled at library size: slice to this batch's genotypes. A value of
+    # any other size is an already-sliced substitution (posterior forward pass).
+    if ln_cfu0_offsets.shape[-1] == data.num_genotype:
         ln_cfu0_offsets = ln_cfu0_offsets[..., data.batch_idx]
 
     per_geno_loc, per_geno_scale = _per_geno_loc_scale(
@@ -721,13 +716,49 @@ def get_priors(data: Optional[GrowthData] = None,
     return ModelPriors(**params)
 
 
-def get_extract_specs(ctx):
-    if "map_ln_cfu0" not in ctx.growth_tm.df.columns:
+def ln_cfu0_extract_spec(ctx):
+    """
+    Extraction spec for ``ln_cfu0``, shape (replicate, condition_pre,
+    genotype) in the model.
+
+    Each row is keyed by its position in that array, computed from the
+    growth tensor's axis labels. ``map_ln_cfu0`` numbers only the
+    (replicate, library, condition_pre, genotype) combinations present in
+    the data, so where a genotype is missing from a replicate or library
+    its numbers run behind the array's positions, and every later row was
+    labeled with another genotype's value (all ln_cfu0 outputs before
+    2026-09-29 on data with such gaps).
+    """
+    tm = ctx.growth_tm
+    df = tm.df
+    if "map_ln_cfu0" not in df.columns:
         return []
+    labels = dict(zip(tm.tensor_dim_names, tm.tensor_dim_labels))
+    index = {dim: {str(v): i for i, v in enumerate(labels[dim])}
+             for dim in ("replicate", "condition_pre", "genotype")}
+    n_cp = len(labels["condition_pre"])
+    n_g = len(labels["genotype"])
+    # the growth tensor's condition_pre axis is keyed library/condition_pre
+    cp = df["condition_pre"].astype(str)
+    if "library" in df.columns:
+        cp = df["library"].astype(str) + "/" + cp
+    rep = df["replicate"].astype(str).map(index["replicate"])
+    cp = cp.map(index["condition_pre"]).fillna(
+        df["condition_pre"].astype(str).map(index["condition_pre"]))
+    geno = df["genotype"].astype(str).map(index["genotype"])
+    if rep.isna().any() or cp.isna().any() or geno.isna().any():
+        raise ValueError("ln_cfu0 extraction: growth rows whose replicate, "
+                         "library/condition_pre or genotype is not a label "
+                         "of the growth tensor.")
+    flat = (rep.astype(int) * n_cp + cp.astype(int)) * n_g + geno.astype(int)
     return [dict(
-        input_df=ctx.growth_tm.df,
+        input_df=df.assign(_ln_cfu0_flat=flat.to_numpy()),
         params_to_get=["ln_cfu0"],
-        map_column="map_ln_cfu0",
+        map_column="_ln_cfu0_flat",
         get_columns=["replicate", "condition_pre", "genotype"],
         in_run_prefix="",
     )]
+
+
+def get_extract_specs(ctx):
+    return ln_cfu0_extract_spec(ctx)
diff --git a/src/tfscreen/tfmodel/generative/components/ln_cfu0/hierarchical_factored.py b/src/tfscreen/tfmodel/generative/components/ln_cfu0/hierarchical_factored.py
index 932a378b..9b818086 100644
--- a/src/tfscreen/tfmodel/generative/components/ln_cfu0/hierarchical_factored.py
+++ b/src/tfscreen/tfmodel/generative/components/ln_cfu0/hierarchical_factored.py
@@ -94,9 +94,16 @@ def define_model(name: str,
     ``hierarchical`` component (pooled Normal per library class, separate
     locations for spiked/wt genotypes) but without the condition_pre
     dimension — reflecting the biology that all tubes were split from the
-    same stock at the same OD600.  ``tube_offset`` is a small centred Normal
-    per (replicate, condition_pre) pair that absorbs any remaining systematic
-    tube-to-tube dilution differences.
+    same stock at the same OD600.  ``tube_offset`` is a small Normal per
+    (replicate, condition_pre) pair that absorbs any remaining systematic
+    tube-to-tube dilution differences.  It is non-centred,
+    ``tube_offset = tube_scale * tube_offset_z`` with ``tube_offset_z ~
+    N(0, 1)``: the offsets trade against the genotype baselines, so the
+    data let them sit at 0, and the centred form ``Normal(0, tube_scale)``
+    then has a joint density that grows without bound as ``tube_scale``
+    goes to 0 (about ``tube_scale^-4`` for 4 offsets). No MAP existed, and a
+    full-covariance guide followed the scale to a loss near -1e23
+    (``planning/studies/svi-overconfidence/``, 2026-09-28).
 
     Parameters
     ----------
@@ -151,21 +158,21 @@ def define_model(name: str,
 
     # Per-(replicate, genotype) non-centred offsets — shape (R, batch)
     with pyro.plate(f"{name}_geno_replicate", data.num_replicate, dim=-2):
-        with pyro.plate("shared_genotype_plate", size=data.batch_size, dim=-1):
-            with pyro.handlers.scale(scale=data.scale_vector):
-                offset_geno = pyro.sample(f"{name}_offset_geno", dist.Normal(0.0, 1.0))
+        with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+            offset_geno = pyro.sample(f"{name}_offset_geno", dist.Normal(0.0, 1.0))
 
-    # Guard against full-sized array substitution during initialisation
-    if offset_geno.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
+    # Sampled at library size: slice to this batch's genotypes. A value of
+    # any other size is an already-sliced substitution (posterior forward pass).
+    if offset_geno.shape[-1] == data.num_genotype:
         offset_geno = offset_geno[..., data.batch_idx]
 
-    # Per-(replicate, condition_pre) tube offsets — shape (R, C)
+    # Per-(replicate, condition_pre) tube offsets, non-centred — shape (R, C)
     with pyro.plate(f"{name}_tube_replicate", data.num_replicate, dim=-2):
         with pyro.plate(f"{name}_condition_pre", data.num_condition_pre, dim=-1):
-            tube_offset = pyro.sample(
-                f"{name}_tube_offset",
-                dist.Normal(0.0, tube_scale)
-            )
+            tube_offset_z = pyro.sample(f"{name}_tube_offset_z",
+                                        dist.Normal(0.0, 1.0))
+    tube_offset = pyro.deterministic(f"{name}_tube_offset",
+                                     tube_scale * tube_offset_z)
 
     per_geno_loc, per_geno_scale = _per_geno_loc_scale(
         data, priors,
@@ -192,7 +199,8 @@ def guide(name: str,
 
     Variational parameters:
     - ``{name}_offset_geno_locs/scales``: shape ``(num_replicate, num_genotype)``
-    - ``{name}_tube_offset_locs/scales``: shape ``(num_replicate, num_condition_pre)``
+    - ``{name}_tube_offset_z_locs/scales``: shape ``(num_replicate, num_condition_pre)``,
+      the non-centred tube offsets (``tube_offset = tube_scale * tube_offset_z``)
     - Per-class hyper_loc/hyper_scale variational params (skipped when pinned)
     - ``{name}_tube_scale``: LogNormal variational posterior for positive scalar
     """
@@ -259,7 +267,8 @@ def guide(name: str,
     ts_loc = pyro.param(f"{name}_tube_scale_loc", jnp.array(-1.0))
     ts_scale = pyro.param(f"{name}_tube_scale_scale", jnp.array(0.1),
                           constraint=dist.constraints.greater_than(1e-4))
-    pyro.sample(f"{name}_tube_scale", dist.LogNormal(ts_loc, ts_scale))
+    tube_scale = pyro.sample(f"{name}_tube_scale",
+                             dist.LogNormal(ts_loc, ts_scale))
 
     # Per-(replicate, genotype) offset variational params — shape (R, G)
     geno_param_shape = (data.num_replicate, data.num_genotype)
@@ -270,29 +279,29 @@ def guide(name: str,
                                     constraint=dist.constraints.positive)
 
     with pyro.plate(f"{name}_geno_replicate", data.num_replicate, dim=-2):
-        with pyro.plate("shared_genotype_plate", size=data.batch_size, dim=-1):
-            with pyro.handlers.scale(scale=data.scale_vector):
-                batch_geno_locs   = offset_geno_locs[..., data.batch_idx]
-                batch_geno_scales = offset_geno_scales[..., data.batch_idx]
-                offset_geno = pyro.sample(f"{name}_offset_geno",
-                                          dist.Normal(batch_geno_locs, batch_geno_scales))
-
-    # Guard against full-sized substitution
-    if offset_geno.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
+        with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+            offset_geno = pyro.sample(f"{name}_offset_geno",
+                                      dist.Normal(offset_geno_locs, offset_geno_scales))
+
+    # Sampled at library size: slice to this batch's genotypes. A value of
+    # any other size is an already-sliced substitution (posterior forward pass).
+    if offset_geno.shape[-1] == data.num_genotype:
         offset_geno = offset_geno[..., data.batch_idx]
 
-    # Per-(replicate, condition_pre) tube offset variational params — shape (R, C)
+    # Per-(replicate, condition_pre) non-centred tube offsets — shape (R, C)
     tube_param_shape = (data.num_replicate, data.num_condition_pre)
-    tube_offset_locs = pyro.param(f"{name}_tube_offset_locs",
-                                  jnp.zeros(tube_param_shape, dtype=float))
-    tube_offset_scales = pyro.param(f"{name}_tube_offset_scales",
-                                    jnp.full(tube_param_shape, 0.1, dtype=float),
-                                    constraint=dist.constraints.positive)
+    tube_offset_z_locs = pyro.param(f"{name}_tube_offset_z_locs",
+                                    jnp.zeros(tube_param_shape, dtype=float))
+    tube_offset_z_scales = pyro.param(f"{name}_tube_offset_z_scales",
+                                      jnp.ones(tube_param_shape, dtype=float),
+                                      constraint=dist.constraints.positive)
 
     with pyro.plate(f"{name}_tube_replicate", data.num_replicate, dim=-2):
         with pyro.plate(f"{name}_condition_pre", data.num_condition_pre, dim=-1):
-            tube_offset = pyro.sample(f"{name}_tube_offset",
-                                      dist.Normal(tube_offset_locs, tube_offset_scales))
+            tube_offset_z = pyro.sample(
+                f"{name}_tube_offset_z",
+                dist.Normal(tube_offset_z_locs, tube_offset_z_scales))
+    tube_offset = tube_scale * tube_offset_z
 
     per_geno_loc, per_geno_scale = _per_geno_loc_scale(
         data, priors,
@@ -361,8 +370,9 @@ def get_guesses(name: str, data: GrowthData,
     median across condition_pre, unless a direct pre-split measurement is
     available (see ``presplit``), in which case it is preferred wherever
     valid.  The tube offset is the per-(replicate, condition_pre) median
-    residual after subtracting that baseline.  The tube scale is estimated
-    from the MAD of the tube offsets.
+    residual after subtracting that baseline, given as ``tube_offset_z`` =
+    offset / tube scale.  The tube scale is estimated from the MAD of the
+    tube offsets.
 
     Falls back to hard-coded defaults for any group or dimension with no
     valid observations.
@@ -386,7 +396,7 @@ def get_guesses(name: str, data: GrowthData,
             f"{name}_tube_scale":  _FALLBACK_TUBE_SCALE,
             f"{name}_offset_geno": jnp.zeros(
                 (data.num_replicate, data.num_genotype), dtype=float),
-            f"{name}_tube_offset": jnp.zeros(
+            f"{name}_tube_offset_z": jnp.zeros(
                 (data.num_replicate, data.num_condition_pre), dtype=float),
         }
         for i in range(num_classes):
@@ -444,7 +454,8 @@ def get_guesses(name: str, data: GrowthData,
         f"{name}_wt_loc":      float(estimates["wt_loc"]),
         f"{name}_tube_scale":  float(tube_scale_est),
         f"{name}_offset_geno": jnp.array(offset_geno, dtype=float),
-        f"{name}_tube_offset": jnp.array(tube_offset_est, dtype=float),
+        f"{name}_tube_offset_z": jnp.array(tube_offset_est / tube_scale_est,
+                                           dtype=float),
     }
     for i in range(len(class_masks)):
         guesses[f"{name}_hyper_loc_{i}"]   = float(estimates["hyper_locs"][i])
@@ -454,12 +465,8 @@ def get_guesses(name: str, data: GrowthData,
 
 
 def get_extract_specs(ctx):
-    if "map_ln_cfu0" not in ctx.growth_tm.df.columns:
-        return []
-    return [dict(
-        input_df=ctx.growth_tm.df,
-        params_to_get=["ln_cfu0"],
-        map_column="map_ln_cfu0",
-        get_columns=["replicate", "condition_pre", "genotype"],
-        in_run_prefix="",
-    )]
+    # same array layout as hierarchical; see its ln_cfu0_extract_spec
+    from tfscreen.tfmodel.generative.components.ln_cfu0.hierarchical import (
+        ln_cfu0_extract_spec,
+    )
+    return ln_cfu0_extract_spec(ctx)
diff --git a/src/tfscreen/tfmodel/generative/components/noise/beta.py b/src/tfscreen/tfmodel/generative/components/noise/beta.py
index 1f8a8977..b5ea1bef 100644
--- a/src/tfscreen/tfmodel/generative/components/noise/beta.py
+++ b/src/tfscreen/tfmodel/generative/components/noise/beta.py
@@ -24,9 +24,10 @@ class ModelPriors:
     beta_kappa_scale: float
 
 
-def define_model(name: str, 
-                 fx_calc: jnp.ndarray, 
-                 priors: ModelPriors) -> jnp.ndarray:
+def define_model(name: str,
+                 fx_calc: jnp.ndarray,
+                 priors: ModelPriors,
+                 data: GrowthData = None) -> jnp.ndarray:
     """
     Applies Beta-distributed noise to a deterministic value.
 
@@ -51,6 +52,14 @@ def define_model(name: str,
     priors : ModelPriors
         A Pytree (Flax dataclass) containing the hyperparameters for
         the `kappa` prior.
+    data : GrowthData, optional
+        Batch data, accepted for a uniform noise-component signature and
+        unused.  The ``{name}_dist`` latent is the noisy value itself, drawn
+        per observation around the batch's ``fx_calc``, so unlike the other
+        per-genotype latents it cannot be sampled at library size: it is
+        batch-shaped and therefore not safe to fit with an autoguide
+        (including AutoDelta/MAP) under genotype mini-batching.  See
+        inference/batch_safety.py.
 
     Returns
     -------
@@ -85,9 +94,10 @@ def define_model(name: str,
     
     return fx_noisy
 
-def guide(name: str, 
-          fx_calc: jnp.ndarray, 
-          priors: ModelPriors) -> jnp.ndarray:
+def guide(name: str,
+          fx_calc: jnp.ndarray,
+          priors: ModelPriors,
+          data: GrowthData = None) -> jnp.ndarray:
     """
     Guide for the Beta noise model.
     
diff --git a/src/tfscreen/tfmodel/generative/components/noise/genotype_offset.py b/src/tfscreen/tfmodel/generative/components/noise/genotype_offset.py
new file mode 100644
index 00000000..8a1e0549
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/components/noise/genotype_offset.py
@@ -0,0 +1,184 @@
+"""
+Per-genotype binding offset: binding sees each binding genotype's theta
+shifted by one offset of its own.
+
+Experimental: part of the joint growth/binding fit
+(planning/joint-fit-return.md), paused 2026-10-18 while the link between
+binding and in vivo occupancy is open (planning/findings-ledger.md). Not
+part of the two-stage route, which refuses it.
+
+    theta_binding = theta_calc + delta_g,    delta_g = tau * z_g,
+    z_g ~ Normal(0, 1),    tau ~ HalfNormal(offset_scale_scale)
+
+One offset per binding genotype, the same at every titrant concentration,
+added in theta units (the binding likelihood is an unclipped Normal, so the
+sum need not stay in [0, 1]). For ``theta_binding_noise`` only: the growth
+side never sees it, so the curve shared with growth is the in vivo one and
+binding measures it up to the offset.
+
+Why (planning/joint-fit-return.md step 4b): a genotype's in vitro binding
+can differ from its in vivo occupancy (M42I grows 0.3-0.4 below its 2 uM
+binding at low IPTG), and each binding curve carries its day's
+normalization error, which is shared by the whole curve (wt's day-to-day
+scatter at 2 uM is about three times the point-to-point scatter within a
+curve). Without an offset, one such genotype pulls the shared growth
+parameters; with it, the genotype disagrees alone, and tau says how far
+binding and growth disagree across genotypes. tau is learned unless
+``offset_scale_fixed`` > 0 holds it.
+
+Non-centered (z, not delta, is the latent) so a MAP has no funnel at
+tau -> 0. The offsets live in a plate over the binding genotypes only
+(``{name}_binding_plate``, sized ``data.num_genotype`` of the binding data)
+and are indexed by ``data.binding_ordinal``, so a library of 200,000
+genotypes does not carry 200,000 prior-only offsets. The plate name holds no
+growth tensor dimension name, so prediction remaps it by binding genotype
+label (``analysis/prediction.py``).
+"""
+
+from typing import Any, Dict
+
+import jax.numpy as jnp
+import numpyro as pyro
+import numpyro.distributions as dist
+from flax.struct import dataclass, field
+
+from tfscreen.tfmodel.data_class import BindingData
+
+
+@dataclass(frozen=True)
+class ModelPriors:
+    """
+    Attributes
+    ----------
+    offset_scale_scale : float
+        Scale of the HalfNormal prior on tau, in theta units.
+    offset_scale_fixed : float
+        If > 0, tau is held at this value (a deterministic site, no guide
+        parameters) instead of learned. Default 0 keeps it learned.
+    """
+    offset_scale_scale: float
+    offset_scale_fixed: float = field(pytree_node=False, default=0.0)
+
+
+def _tau_fixed(priors):
+    return float(getattr(priors, "offset_scale_fixed", 0.0))
+
+
+def _ordinal(fx_calc, data):
+    if data is None or getattr(data, "binding_ordinal", None) is None:
+        return jnp.arange(fx_calc.shape[-1])
+    return data.binding_ordinal
+
+
+def _num_binding(fx_calc, data):
+    return fx_calc.shape[-1] if data is None else data.num_genotype
+
+
+def _apply(name, fx_calc, tau, z, data):
+    offset = tau * z
+    pyro.deterministic(f"{name}_offset", offset)
+    if offset.shape[-1] == _num_binding(fx_calc, data):
+        offset = offset[..., _ordinal(fx_calc, data)]
+    return fx_calc + offset
+
+
+def define_model(name: str,
+                 fx_calc: jnp.ndarray,
+                 priors: ModelPriors,
+                 data: BindingData = None) -> jnp.ndarray:
+    """
+    Add each binding genotype's offset to its theta.
+
+    Parameters
+    ----------
+    name : str
+        Prefix for the Numpyro sites.
+    fx_calc : jnp.ndarray
+        Theta at the binding concentrations; the last axis is the batch's
+        binding genotypes.
+    priors : ModelPriors
+    data : BindingData, optional
+        Supplies ``num_genotype`` (the plate size) and ``binding_ordinal``.
+        None: one offset per position of ``fx_calc``'s last axis.
+
+    Returns
+    -------
+    jnp.ndarray
+        ``fx_calc`` plus the offsets, same shape.
+    """
+    fixed = _tau_fixed(priors)
+    if fixed > 0:
+        tau = pyro.deterministic(f"{name}_offset_scale", jnp.asarray(fixed))
+    else:
+        tau = pyro.sample(f"{name}_offset_scale",
+                          dist.HalfNormal(priors.offset_scale_scale))
+    with pyro.plate(f"{name}_binding_plate", _num_binding(fx_calc, data), dim=-1):
+        z = pyro.sample(f"{name}_offset_z", dist.Normal(0.0, 1.0))
+    return _apply(name, fx_calc, tau, z, data)
+
+
+def guide(name: str,
+          fx_calc: jnp.ndarray,
+          priors: ModelPriors,
+          data: BindingData = None) -> jnp.ndarray:
+    """Mean-field guide: LogNormal tau, Normal z per binding genotype."""
+    fixed = _tau_fixed(priors)
+    if fixed > 0:
+        tau = jnp.asarray(fixed)
+    else:
+        loc = pyro.param(f"{name}_offset_scale_loc",
+                         jnp.log(jnp.asarray(priors.offset_scale_scale) / 2.0))
+        scale = pyro.param(f"{name}_offset_scale_scale", jnp.asarray(0.1),
+                           constraint=dist.constraints.greater_than(1e-4))
+        tau = pyro.sample(f"{name}_offset_scale", dist.LogNormal(loc, scale))
+    n = _num_binding(fx_calc, data)
+    z_locs = pyro.param(f"{name}_offset_z_locs", jnp.zeros(n))
+    z_scales = pyro.param(f"{name}_offset_z_scales", jnp.ones(n),
+                          constraint=dist.constraints.greater_than(1e-4))
+    with pyro.plate(f"{name}_binding_plate", n, dim=-1):
+        z = pyro.sample(f"{name}_offset_z", dist.Normal(z_locs, z_scales))
+    offset = tau * z
+    if offset.shape[-1] == n:
+        offset = offset[..., _ordinal(fx_calc, data)]
+    return fx_calc + offset
+
+
+def get_hyperparameters() -> Dict[str, Any]:
+    """
+    ``offset_scale_scale`` 0.2: tau mostly below 0.4 in theta, about the
+    largest disagreement seen (M42I) and the day-to-day scatter (0.13).
+    """
+    return {"offset_scale_scale": 0.2, "offset_scale_fixed": 0.0}
+
+
+def get_priors() -> ModelPriors:
+    return ModelPriors(**get_hyperparameters())
+
+
+def get_guesses(name: str, data: BindingData) -> Dict[str, Any]:
+    """Start at no offsets and a small tau."""
+    return {f"{name}_offset_scale": jnp.asarray(0.05),
+            f"{name}_offset_z": jnp.zeros(data.num_genotype)}
+
+
+def get_extract_specs(ctx) -> list:
+    """tau, and one offset per binding genotype."""
+    import pandas as pd
+    specs = [dict(
+        input_df=pd.DataFrame({"parameter": ["offset_scale"], "map_all": [0]}),
+        params_to_get=["offset_scale"],
+        map_column="map_all",
+        get_columns=["parameter"],
+        in_run_prefix="theta_binding_noise_",
+    )]
+    labels = getattr(ctx, "binding_genotypes", None)
+    if labels is not None:
+        specs.append(dict(
+            input_df=pd.DataFrame({"genotype": list(labels),
+                                   "map_binding": range(len(labels))}),
+            params_to_get=["offset"],
+            map_column="map_binding",
+            get_columns=["genotype"],
+            in_run_prefix="theta_binding_noise_",
+        ))
+    return specs
diff --git a/src/tfscreen/tfmodel/generative/components/noise/logit_normal.py b/src/tfscreen/tfmodel/generative/components/noise/logit_normal.py
index e1227c8d..952ee126 100644
--- a/src/tfscreen/tfmodel/generative/components/noise/logit_normal.py
+++ b/src/tfscreen/tfmodel/generative/components/noise/logit_normal.py
@@ -45,6 +45,38 @@
 _LOGIT_EPS = 1e-6
 
 
+def _sample_epsilon(name, fx_calc, sigma_logit, data):
+    """
+    Sample the per-(titrant_conc, genotype) epsilon, returned at fx_calc's shape.
+
+    With ``data``, epsilon is sampled at library size (``data.num_genotype``)
+    and sliced to the batch with ``data.batch_idx``, so it stays attached to
+    the same genotype under mini-batching (see inference/batch_safety.py).  A
+    value of any other size is an already-sliced substitution (posterior
+    forward pass).  Without ``data`` the genotype plate is sized from
+    ``fx_calc`` itself, for standalone callers.
+    """
+    num_genotype = fx_calc.shape[-1] if data is None else data.num_genotype
+    eps_shape = fx_calc.shape[:-1] + (num_genotype,)
+
+    # Plate epsilon on titrant_conc (dim=-2) and genotype (dim=-1) so that
+    # prediction.py's slicing logic can match these named plates to TensorManager
+    # dim names and correctly reduce epsilon to the prediction-time shape.
+    # Without plates, prediction.py passes training-time epsilon unchanged into
+    # a model expecting prediction-time shapes, causing a broadcast error.
+    with pyro.plate(f"{name}_titrant_conc", fx_calc.shape[-2], dim=-2):
+        with pyro.plate(f"{name}_genotype", num_genotype, dim=-1):
+            epsilon = pyro.sample(
+                f"{name}_epsilon",
+                dist.Normal(jnp.zeros(eps_shape), sigma_logit)
+            )
+
+    if data is not None and epsilon.shape[-1] == data.num_genotype:
+        epsilon = epsilon[..., data.batch_idx]
+
+    return epsilon
+
+
 @dataclass(frozen=True)
 class ModelPriors:
     """
@@ -61,7 +93,8 @@ class ModelPriors:
 
 def define_model(name: str,
                  fx_calc: jnp.ndarray,
-                 priors: ModelPriors) -> jnp.ndarray:
+                 priors: ModelPriors,
+                 data: GrowthData = None) -> jnp.ndarray:
     """
     Apply logit-normal noise to a deterministic occupancy array.
 
@@ -70,9 +103,14 @@ def define_model(name: str,
     name : str
         Prefix for all Numpyro sample / deterministic sites.
     fx_calc : jnp.ndarray
-        Deterministic theta values in (0, 1).
+        Deterministic theta values in (0, 1).  The last axis is the batch's
+        genotypes.
     priors : ModelPriors
         Pytree containing ``sigma_logit_scale``.
+    data : GrowthData, optional
+        When given, epsilon is sampled at library size and sliced with
+        ``data.batch_idx`` (mini-batch safe).  ``None`` sizes it from
+        ``fx_calc``.
 
     Returns
     -------
@@ -88,17 +126,7 @@ def define_model(name: str,
     fx_safe = jnp.clip(fx_calc, _LOGIT_EPS, 1.0 - _LOGIT_EPS)
     logit_calc = jnp.log(fx_safe / (1.0 - fx_safe))
 
-    # Plate epsilon on titrant_conc (dim=-2) and genotype (dim=-1) so that
-    # prediction.py's slicing logic can match these named plates to TensorManager
-    # dim names and correctly reduce epsilon to the prediction-time shape.
-    # Without plates, prediction.py passes training-time epsilon unchanged into
-    # a model expecting prediction-time shapes, causing a broadcast error.
-    with pyro.plate(f"{name}_titrant_conc", fx_calc.shape[-2], dim=-2):
-        with pyro.plate(f"{name}_genotype", fx_calc.shape[-1], dim=-1):
-            epsilon = pyro.sample(
-                f"{name}_epsilon",
-                dist.Normal(jnp.zeros_like(fx_calc), sigma_logit)
-            )
+    epsilon = _sample_epsilon(name, fx_calc, sigma_logit, data)
 
     fx_noisy = jax_nn.sigmoid(logit_calc + epsilon)
     pyro.deterministic(name, fx_noisy)
@@ -107,7 +135,8 @@ def define_model(name: str,
 
 def guide(name: str,
           fx_calc: jnp.ndarray,
-          priors: ModelPriors) -> jnp.ndarray:
+          priors: ModelPriors,
+          data: GrowthData = None) -> jnp.ndarray:
     """
     Variational guide for the logit-normal theta noise model.
 
@@ -141,12 +170,7 @@ def guide(name: str,
     )
     sigma_logit = pyro.sample(f"{name}_sigma_logit", dist.LogNormal(loc, scale))
 
-    with pyro.plate(f"{name}_titrant_conc", fx_calc.shape[-2], dim=-2):
-        with pyro.plate(f"{name}_genotype", fx_calc.shape[-1], dim=-1):
-            epsilon = pyro.sample(
-                f"{name}_epsilon",
-                dist.Normal(jnp.zeros_like(fx_calc), sigma_logit)
-            )
+    epsilon = _sample_epsilon(name, fx_calc, sigma_logit, data)
 
     fx_safe = jnp.clip(fx_calc, _LOGIT_EPS, 1.0 - _LOGIT_EPS)
     logit_calc = jnp.log(fx_safe / (1.0 - fx_safe))
diff --git a/src/tfscreen/tfmodel/generative/components/noise/zero.py b/src/tfscreen/tfmodel/generative/components/noise/zero.py
index ee5b1857..3081d42d 100644
--- a/src/tfscreen/tfmodel/generative/components/noise/zero.py
+++ b/src/tfscreen/tfmodel/generative/components/noise/zero.py
@@ -15,9 +15,10 @@ class ModelPriors:
     pass
 
 
-def define_model(name: str, 
-                 fx_calc: jnp.ndarray, 
-                 priors: ModelPriors) -> jnp.ndarray:
+def define_model(name: str,
+                 fx_calc: jnp.ndarray,
+                 priors: ModelPriors,
+                 data: DataClass = None) -> jnp.ndarray:
     """
     A null model that applies no noise to the input.
 
@@ -34,6 +35,9 @@ def define_model(name: str,
         occupancy).
     priors : ModelPriors
         A Pytree of hyperparameters. (Unused in this model).
+    data : DataClass, optional
+        Batch data, accepted for a uniform noise-component signature.
+        (Unused in this model).
 
     Returns
     -------
@@ -43,9 +47,10 @@ def define_model(name: str,
 
     return fx_calc
 
-def guide(name: str, 
-          fx_calc: jnp.ndarray, 
-          priors: ModelPriors) -> jnp.ndarray:
+def guide(name: str,
+          fx_calc: jnp.ndarray,
+          priors: ModelPriors,
+          data: DataClass = None) -> jnp.ndarray:
     """
     Guide for the no-noise model.
 
diff --git a/src/tfscreen/tfmodel/generative/components/sample_offset/_tubes.py b/src/tfscreen/tfmodel/generative/components/sample_offset/_tubes.py
new file mode 100644
index 00000000..2cd89be6
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/components/sample_offset/_tubes.py
@@ -0,0 +1,80 @@
+"""
+Tube labels for the per-tube sample offsets.
+
+Both ``level`` and ``normal`` sample one value per position of the growth
+tensor's tube axes (replicate, time, condition_pre, condition_sel,
+titrant_name, titrant_conc), flattened in C order. ``tube_extract_spec``
+labels each flat position with the tube design read off the growth rows
+that occupy it, so ``tfs-extract-params`` writes one row per measured tube.
+"""
+
+import numpy as np
+import pandas as pd
+
+TUBE_DIMS = ("replicate", "time", "condition_pre", "condition_sel",
+             "titrant_name", "titrant_conc")
+
+TUBE_COLUMNS = ["replicate", "library", "condition_pre", "condition_sel",
+                "titrant_name", "titrant_conc", "t_pre", "t_sel"]
+
+
+def tube_index(tm):
+    """
+    Flat tube position of every growth row, from the tensor's ``{dim}_idx``
+    columns, in the order the offset components reshape it.
+    """
+    df = tm.df
+    names = list(tm.tensor_dim_names)
+    sizes = {d: len(lab) for d, lab in zip(names, tm.tensor_dim_labels)}
+    missing = [d for d in TUBE_DIMS if f"{d}_idx" not in df.columns]
+    if missing:
+        raise ValueError(f"growth rows lack the tube index columns "
+                         f"{[f'{d}_idx' for d in missing]}")
+    idx = tuple(df[f"{d}_idx"].to_numpy(dtype=int) for d in TUBE_DIMS)
+    shape = tuple(sizes[d] for d in TUBE_DIMS)
+    return np.ravel_multi_index(idx, shape)
+
+
+def tube_extract_spec(ctx, name, offset_site, sigma_site):
+    """
+    Extraction specs for a per-tube offset site and its population SD.
+
+    Parameters
+    ----------
+    ctx : ExtractionContext
+    name : str
+        The component's registry name (``sample_offset``).
+    offset_site : str
+        Site suffix of the per-tube values (``offset``, ``delta_k``).
+    sigma_site : str
+        Site suffix of their SD (``sigma``, ``sigma_env``).
+
+    Returns
+    -------
+    list of dict
+        One spec for the per-tube values (``{name}_{offset_site}``, one row per
+        measured tube labeled by ``TUBE_COLUMNS``) and one for the SD. A held
+        SD is a deterministic site, absent from a MAP checkpoint;
+        ``extract_parameters`` then skips it with a warning.
+    """
+    tm = ctx.growth_tm
+    df = tm.df
+    flat = tube_index(tm)
+    columns = [c for c in TUBE_COLUMNS if c in df.columns]
+    # every genotype in a tube shares its labels; keep one row per tube
+    tubes = (df[columns].assign(_tube_flat=flat)
+             .drop_duplicates("_tube_flat"))
+    sigma = pd.DataFrame({"parameter": [f"{name}_{sigma_site}"],
+                          "_map": [0]})
+    return [
+        dict(input_df=tubes,
+             params_to_get=[f"{name}_{offset_site}"],
+             map_column="_tube_flat",
+             get_columns=columns,
+             in_run_prefix=""),
+        dict(input_df=sigma,
+             params_to_get=[f"{name}_{sigma_site}"],
+             map_column="_map",
+             get_columns=["parameter"],
+             in_run_prefix=""),
+    ]
diff --git a/src/tfscreen/tfmodel/generative/components/sample_offset/level.py b/src/tfscreen/tfmodel/generative/components/sample_offset/level.py
new file mode 100644
index 00000000..7c335820
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/components/sample_offset/level.py
@@ -0,0 +1,131 @@
+"""
+Per-tube level offset: one ln_cfu shift per tube, shared by every genotype
+in it, with a constant prior.
+
+A tube is one sequenced sample (replicate x time x condition x titrant; see
+``docs/source/process-raw.rst``, "One tube per time-point"). Anything that
+moves every genotype's reads in a tube together lands here: the tube's
+composition offset (PCR and genotype-calling efficiency, the ``__unknown__``
+share; study 0b measured about 0.02 ln units) and any error in the tube's
+supplied total cells (per-tube OD600 scattered about 0.17 ln units around a
+smooth curve in study 0c). Roadmap D2 (planning/analysis-roadmap.md): unlike
+``normal``, whose offset is a growth-rate shift scaled by elapsed time, this
+one is a level with no time dependence.
+
+Prior
+-----
+    sigma          ~ HalfNormal(sigma_prior_scale)
+    offset[tube]   ~ Normal(0, sigma)
+
+Guide
+-----
+    sigma          ~ LogNormal(sigma_loc, sigma_scale)
+    offset[tube]   ~ Normal(offset_loc, offset_scale)
+
+The offset is a per-tube global site (not per genotype), so it is
+mini-batch safe. Its contribution has shape ``(*tube_shape, 1)`` and
+broadcasts over the genotype axis of ``ln_cfu_pred``.
+"""
+
+import math
+from typing import Any, Dict
+
+import jax.numpy as jnp
+import numpyro as pyro
+import numpyro.distributions as dist
+from flax.struct import dataclass, field
+
+from tfscreen.tfmodel.data_class import GrowthData
+
+from ._tubes import tube_extract_spec
+
+
+@dataclass(frozen=True)
+class ModelPriors:
+    """
+    sigma_prior_scale : float
+        Scale of the HalfNormal prior on sigma, the SD of the per-tube
+        offsets (ln units).
+    sigma_fixed : float
+        If > 0, sigma is held at this value (a deterministic site, no guide
+        parameters) instead of learned. Default 0 (learned). A learned sigma
+        lets the offsets grow into a free level per tube: on the first
+        real-data fit (2026-09-29) it went from its 0.2 prior scale to 0.53,
+        the offsets ran from -2.7 to +3.0 in a smooth pattern over IPTG and
+        time, and they carried the population's growth in place of k and m,
+        which went unphysical. Holding sigma near the per-tube OD600 scatter
+        (about 0.17, study 0c) keeps the tube totals binding. Static.
+    """
+    sigma_prior_scale: float
+    sigma_fixed: float = field(pytree_node=False, default=0.0)
+
+
+def _tube_shape(data: GrowthData):
+    return (data.num_replicate, data.num_time, data.num_condition_pre,
+            data.num_condition_sel, data.num_titrant_name, data.num_titrant_conc)
+
+
+def define_model(name: str,
+                 data: GrowthData,
+                 priors: ModelPriors) -> jnp.ndarray:
+    """
+    Sample the per-tube offsets and return them shaped
+    ``(*tube_shape, 1)`` for adding to ``ln_cfu_pred``.
+    """
+    if float(priors.sigma_fixed) > 0:
+        sigma = pyro.deterministic(f"{name}_sigma",
+                                   jnp.array(float(priors.sigma_fixed)))
+    else:
+        sigma = pyro.sample(f"{name}_sigma",
+                            dist.HalfNormal(priors.sigma_prior_scale))
+    shape = _tube_shape(data)
+    num_tubes = math.prod(shape)
+    with pyro.plate(f"{name}_tubes", num_tubes, dim=-1):
+        offset = pyro.sample(f"{name}_offset", dist.Normal(0.0, sigma))
+    return offset.reshape(*shape, 1)
+
+
+def guide(name: str,
+          data: GrowthData,
+          priors: ModelPriors) -> jnp.ndarray:
+    """LogNormal guide for sigma, per-tube Normal guides for the offsets."""
+    shape = _tube_shape(data)
+    num_tubes = math.prod(shape)
+
+    if not float(priors.sigma_fixed) > 0:
+        sigma_loc = pyro.param(f"{name}_sigma_loc",
+                               jnp.log(jnp.array(priors.sigma_prior_scale) / 4.0))
+        sigma_scale = pyro.param(f"{name}_sigma_scale", jnp.array(0.5),
+                                 constraint=dist.constraints.greater_than(1e-4))
+        pyro.sample(f"{name}_sigma", dist.LogNormal(sigma_loc, sigma_scale))
+
+    offset_loc = pyro.param(f"{name}_offset_loc", jnp.zeros(num_tubes))
+    offset_scale = pyro.param(f"{name}_offset_scale", jnp.full(num_tubes, 1e-2),
+                              constraint=dist.constraints.greater_than(1e-4))
+    with pyro.plate(f"{name}_tubes", num_tubes, dim=-1):
+        offset = pyro.sample(f"{name}_offset",
+                             dist.Normal(offset_loc, offset_scale))
+    return offset.reshape(*shape, 1)
+
+
+def get_hyperparameters() -> Dict[str, Any]:
+    """
+    sigma_prior_scale = 0.2: wide enough for the per-tube OD600 scatter of
+    study 0c (about 0.17 ln units) when each tube's total comes from its own
+    OD600, and for the small composition offset (about 0.02, study 0b).
+    """
+    return {"sigma_prior_scale": 0.2, "sigma_fixed": 0.0}
+
+
+def get_priors() -> ModelPriors:
+    return ModelPriors(**get_hyperparameters())
+
+
+def get_guesses(name: str, data: GrowthData) -> Dict[str, Any]:
+    # a guess for a held sigma is unused (the site is deterministic)
+    return {f"{name}_sigma": jnp.array(0.05)}
+
+
+def get_extract_specs(ctx) -> list:
+    """Per-tube offsets labeled by tube design, and their SD."""
+    return tube_extract_spec(ctx, "sample_offset", "offset", "sigma")
diff --git a/src/tfscreen/tfmodel/generative/components/sample_offset/normal.py b/src/tfscreen/tfmodel/generative/components/sample_offset/normal.py
index 24c7ae2c..11a76ad4 100644
--- a/src/tfscreen/tfmodel/generative/components/sample_offset/normal.py
+++ b/src/tfscreen/tfmodel/generative/components/sample_offset/normal.py
@@ -43,6 +43,8 @@
 from tfscreen.tfmodel.data_class import GrowthData
 from typing import Dict, Any
 
+from ._tubes import tube_extract_spec
+
 
 @dataclass(frozen=True)
 class ModelPriors:
@@ -178,4 +180,5 @@ def get_guesses(name: str, data: GrowthData) -> Dict[str, Any]:
 
 
 def get_extract_specs(ctx) -> list:
-    return []
+    """Per-tube growth-rate offsets labeled by tube design, and their SD."""
+    return tube_extract_spec(ctx, "sample_offset", "delta_k", "sigma_env")
diff --git a/src/tfscreen/tfmodel/generative/components/sample_offset/zero.py b/src/tfscreen/tfmodel/generative/components/sample_offset/zero.py
index 690a142c..c6bb8520 100644
--- a/src/tfscreen/tfmodel/generative/components/sample_offset/zero.py
+++ b/src/tfscreen/tfmodel/generative/components/sample_offset/zero.py
@@ -1,8 +1,10 @@
 """
 Pass-through sample offset: no per-tube noise (delta_sample = 0).
 
-This is the default. All tube-to-tube growth-rate variation is absent;
-noise is captured entirely by data.ln_cfu_std and the Student-T nu.
+The ModelOrchestrator default (so configs written before the count
+likelihood read back unchanged); tfs-configure-model defaults to 'level'.
+All tube-to-tube variation is absent; noise is captured by the likelihood
+alone.
 """
 
 import jax.numpy as jnp
diff --git a/src/tfscreen/tfmodel/generative/components/theta/_simple.py b/src/tfscreen/tfmodel/generative/components/theta/_simple.py
index 37940331..0bb1740e 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/_simple.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/_simple.py
@@ -31,8 +31,8 @@
 * 3-D input: ``mu = mean(logit(theta_values), axis=-1, keepdims=True)``;
   ``sigma = max(std(logit(theta_values), axis=-1, keepdims=True), floor)``.
 
-Both variants return moments of shape ``(T, C, 1)`` to satisfy the
-contract used by downstream transformations (e.g. ``transformation.logit_norm``).
+Both variants return moments of shape ``(T, C, 1)`` to satisfy the theta
+component interface (``get_population_moments``).
 """
 
 import jax.numpy as jnp
@@ -209,7 +209,7 @@ def run_model(theta_param: ThetaParam, data: DataClass) -> jnp.ndarray:
     theta_param : ThetaParam
         Output of ``define_model`` / ``guide``.
     data : DataClass
-        Must expose ``geno_theta_idx``, ``titrant_conc``, and
+        Must expose ``batch_idx``, ``geno_theta_idx``, ``titrant_conc``, and
         ``scatter_theta``.
 
     Returns
@@ -222,7 +222,11 @@ def run_model(theta_param: ThetaParam, data: DataClass) -> jnp.ndarray:
     """
 
     # Subset on the genotype axis using the run-time genotype map.
-    theta_base = theta_param.theta[..., data.geno_theta_idx]
+    # theta is library-ordered: translate batch-relative geno_theta_idx to
+    # library indices through batch_idx (the hill_geno pattern). Indexing
+    # by geno_theta_idx alone reads library positions as batch positions,
+    # which is wrong whenever batch_idx is not the identity.
+    theta_base = theta_param.theta[..., data.batch_idx[data.geno_theta_idx]]
 
     # Map this experiment's concentrations onto the columns of theta_base.
     conc_idx = jnp.searchsorted(theta_param.concentrations, data.titrant_conc)
diff --git a/src/tfscreen/tfmodel/generative/components/theta/categorical_geno.py b/src/tfscreen/tfmodel/generative/components/theta/categorical_geno.py
index 9c0a762b..2c61f77b 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/categorical_geno.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/categorical_geno.py
@@ -104,18 +104,16 @@ def define_model(name: str,
 
     with pyro.plate(f"{name}_titrant_name_plate", data.num_titrant_name, dim=-3):
         with pyro.plate(f"{name}_titrant_conc_plate", data.num_titrant_conc, dim=-2):
-            with pyro.plate("theta_genotype_plate", size=data.batch_size, dim=-1):
-                with pyro.handlers.scale(scale=data.scale_vector):
+            with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
 
-                    logit_theta_offset = pyro.sample(
-                        f"{name}_logit_theta_offset",
-                        dist.Normal(0.0, 1.0)
-                    )
+                logit_theta_offset = pyro.sample(
+                    f"{name}_logit_theta_offset",
+                    dist.Normal(0.0, 1.0)
+                )
 
-    # Guard against full-sized array substitution during initialization or re-runs
-    # with full-sized initial values
-    if logit_theta_offset.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        logit_theta_offset = logit_theta_offset[..., data.batch_idx]
+    # Kept at library size (library order); run_model selects the batch's
+    # genotypes through batch_idx, so the full population is available to
+    # callers that evaluate every genotype (the congression background).
 
     # Calculate parameters in logit-space
     # logit_theta_hyper_loc result from plating is (Name, Conc) if rank-minimized,
@@ -194,28 +192,20 @@ def guide(name: str,
                                jnp.ones(param_shape, dtype=float),
                                constraint=dist.constraints.positive)
 
-    # --- 3. Sampling (Sliced by Genotype) ---
+    # --- 3. Sampling (full library; sliced to the batch below) ---
 
     with pyro.plate(f"{name}_titrant_name_plate", data.num_titrant_name, dim=-3):
         with pyro.plate(f"{name}_titrant_conc_plate", data.num_titrant_conc, dim=-2):
-            # Batching on Genotype (dim=-1)
-            with pyro.plate("theta_genotype_plate", size=data.batch_size, dim=-1):
-                with pyro.handlers.scale(scale=data.scale_vector):
+            with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
 
-                    # Slice the last dimension (Genotype) using the batch indices
-                    # The ellipsis (...) preserves the TitrantName and TitrantConc dimensions
-                    batch_locs = offset_locs[..., data.batch_idx]
-                    batch_scales = offset_scales[..., data.batch_idx]
+                logit_theta_offset = pyro.sample(
+                    f"{name}_logit_theta_offset",
+                    dist.Normal(offset_locs, offset_scales)
+                )
 
-                    logit_theta_offset = pyro.sample(
-                        f"{name}_logit_theta_offset",
-                        dist.Normal(batch_locs, batch_scales)
-                    )
-
-    # Guard against full-sized array substitution during initialization or re-runs
-    # with full-sized initial values
-    if logit_theta_offset.shape[-1] == data.num_genotype and data.batch_size < data.num_genotype:
-        logit_theta_offset = logit_theta_offset[..., data.batch_idx]
+    # Kept at library size (library order); run_model selects the batch's
+    # genotypes through batch_idx, so the full population is available to
+    # callers that evaluate every genotype (the congression background).
 
     # --- 4. Reconstruction (Deterministic) ---
 
@@ -271,8 +261,10 @@ def run_model(theta_param: ThetaParam, data: DataClass) -> jnp.ndarray:
     """
 
     # 1. Select the correct genotypes for this dataset
-    # theta_param.theta: (Name, Conc, Genotypes)
-    theta_base = theta_param.theta[..., data.geno_theta_idx]
+    # theta_param.theta: (Name, Conc, Genotypes), library-ordered.
+    # geno_theta_idx is batch-relative; translating through batch_idx gives
+    # library indices (the hill_geno pattern).
+    theta_base = theta_param.theta[..., data.batch_idx[data.geno_theta_idx]]
 
     # 2. Map concentrations
     # We find the indices of data.titrant_conc in theta_param.concentrations.
@@ -464,9 +456,8 @@ def compute_theta_samples(calc_df, param_posteriors):
     """
     Reconstruct posterior theta samples for the categorical model.
 
-    Does NOT use the ``theta_theta`` deterministic site, which is computed
-    inside a forward batch and only covers ``batch_size`` genotypes in the
-    HDF5 file (not the full genotype set).  Instead, reconstructs theta from
+    Does NOT use the ``theta_theta`` deterministic site (older posterior
+    files store it for only ``batch_size`` genotypes).  Instead, reconstructs theta from
     its three constituent posterior arrays, all of which are correctly stored
     for every genotype:
 
diff --git a/src/tfscreen/tfmodel/generative/components/theta/hill_geno.py b/src/tfscreen/tfmodel/generative/components/theta/hill_geno.py
index 3fef9861..8747e43c 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/hill_geno.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/hill_geno.py
@@ -1,9 +1,10 @@
 import numpy as np
+import pandas as pd
 import jax
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
-from flax.struct import dataclass
+from flax.struct import dataclass, field
 from typing import Dict, Any, Optional
 
 from tfscreen.tfmodel.data_class import DataClass
@@ -28,6 +29,31 @@ class ModelPriors:
     theta_log_hill_n_hyper_loc_loc : float
     theta_log_hill_n_hyper_loc_scale : float
     theta_log_hill_n_hyper_scale : float
+    theta_log_hill_n_hyper_scale_fixed : float
+        If > 0, the population SD of log(hill_n) is held at this value (a
+        deterministic site, no guide parameters) instead of learned. Learned,
+        a MAP on the dev-data screen drove it to about 19: n spread from
+        0.01 to 13, and a tiny n hid each genotype's shrunken response in the
+        0-IPTG tubes, below the lowest measured concentration
+        (planning/dev-data/real_fit). Default 0 keeps it learned.
+    theta_logit_low_hyper_scale_fixed, theta_logit_delta_hyper_scale_fixed, theta_log_hill_K_hyper_scale_fixed : float
+        The same for the other population SDs (> 0 holds it). A MAP has no
+        reason to get them right, and their HalfNormal priors are tight
+        (log K's at 0.1): on the joint-fit simulation's stage 1 they and the
+        population locations compressed the singles' theta range and
+        steepened m by 14% (planning/studies/joint-fit-sim, run 5).
+        Default 0 keeps them learned.
+    theta_growth_log_conc_shift_loc, theta_growth_log_conc_shift_scale : float
+        The titrant scale between binding and growth. With scale > 0, one
+        global site ``{name}_growth_log_conc_shift`` (log s) ~ Normal(loc,
+        scale) and growth sees each genotype's curve at s times the nominal
+        titrant concentration, while binding sees it at the nominal one: the
+        curve is in binding (in vitro) units, and s is the ratio of the
+        effective in vivo titrant to the nominal. Equivalent to log K - log s
+        on the growth side only. Identified only with binding data. Default
+        scale 0: no site, s = 1 (planning/joint-fit-return.md step 4a).
+        Experimental: the joint fit is paused (2026-10-18) while the link
+        between binding and in vivo occupancy is open.
     """
 
     theta_logit_low_hyper_loc_loc: float
@@ -44,6 +70,18 @@ class ModelPriors:
     theta_log_hill_n_hyper_loc_loc: float
     theta_log_hill_n_hyper_loc_scale: float
     theta_log_hill_n_hyper_scale: float
+    theta_log_hill_n_hyper_scale_fixed: float = field(pytree_node=False,
+                                                      default=0.0)
+    theta_logit_low_hyper_scale_fixed: float = field(pytree_node=False,
+                                                     default=0.0)
+    theta_logit_delta_hyper_scale_fixed: float = field(pytree_node=False,
+                                                       default=0.0)
+    theta_log_hill_K_hyper_scale_fixed: float = field(pytree_node=False,
+                                                      default=0.0)
+    theta_growth_log_conc_shift_loc: float = field(pytree_node=False,
+                                                   default=0.0)
+    theta_growth_log_conc_shift_scale: float = field(pytree_node=False,
+                                                     default=0.0)
 
 
 @dataclass(frozen=True)
@@ -68,6 +106,9 @@ class ThetaParam:
         Population mean of logit(theta) at each concentration, shape (T, C, 1).
     sigma : jnp.ndarray
         Population std of logit(theta) at each concentration, shape (T, C, 1).
+    growth_log_conc_shift : jnp.ndarray or None
+        log s, added to the growth data's log titrant concentrations before
+        theta is evaluated for growth (``ModelPriors``); None for s = 1.
     """
 
     theta_low: jnp.ndarray
@@ -76,6 +117,7 @@ class ThetaParam:
     hill_n: jnp.ndarray
     mu: Optional[jnp.ndarray]
     sigma: Optional[jnp.ndarray]
+    growth_log_conc_shift: Optional[jnp.ndarray] = None
 
 
 @dataclass(frozen=True)
@@ -188,6 +230,23 @@ def _population_moments(
     return mu, sigma
 
 
+_HYPER = ("logit_low", "logit_delta", "log_hill_K", "log_hill_n")
+
+
+def _held(priors, h):
+    """The held population SD of hyperparameter h, or 0 when learned."""
+    return float(getattr(priors, f"theta_{h}_hyper_scale_fixed", 0.0))
+
+
+def _hyper_scale(name, h, priors, T):
+    """A population SD: a deterministic site at its held value, or sampled."""
+    held = _held(priors, h)
+    if held > 0:
+        return pyro.deterministic(f"{name}_{h}_hyper_scale", jnp.full(T, held))
+    return pyro.sample(f"{name}_{h}_hyper_scale",
+                       dist.HalfNormal(getattr(priors, f"theta_{h}_hyper_scale")))
+
+
 def define_model(name: str,
                  data: DataClass,
                  priors: ModelPriors) -> ThetaParam:
@@ -226,30 +285,22 @@ def define_model(name: str,
             f"{name}_logit_low_hyper_loc",
             dist.Normal(priors.theta_logit_low_hyper_loc_loc,
                         priors.theta_logit_low_hyper_loc_scale))
-        logit_low_hyper_scale = pyro.sample(
-            f"{name}_logit_low_hyper_scale",
-            dist.HalfNormal(priors.theta_logit_low_hyper_scale))
+        logit_low_hyper_scale = _hyper_scale(name, "logit_low", priors, T)
         logit_delta_hyper_loc = pyro.sample(
             f"{name}_logit_delta_hyper_loc",
             dist.Normal(priors.theta_logit_delta_hyper_loc_loc,
                         priors.theta_logit_delta_hyper_loc_scale))
-        logit_delta_hyper_scale = pyro.sample(
-            f"{name}_logit_delta_hyper_scale",
-            dist.HalfNormal(priors.theta_logit_delta_hyper_scale))
+        logit_delta_hyper_scale = _hyper_scale(name, "logit_delta", priors, T)
         log_K_hyper_loc = pyro.sample(
             f"{name}_log_hill_K_hyper_loc",
             dist.Normal(priors.theta_log_hill_K_hyper_loc_loc,
                         priors.theta_log_hill_K_hyper_loc_scale))
-        log_K_hyper_scale = pyro.sample(
-            f"{name}_log_hill_K_hyper_scale",
-            dist.HalfNormal(priors.theta_log_hill_K_hyper_scale))
+        log_K_hyper_scale = _hyper_scale(name, "log_hill_K", priors, T)
         log_n_hyper_loc = pyro.sample(
             f"{name}_log_hill_n_hyper_loc",
             dist.Normal(priors.theta_log_hill_n_hyper_loc_loc,
                         priors.theta_log_hill_n_hyper_loc_scale))
-        log_n_hyper_scale = pyro.sample(
-            f"{name}_log_hill_n_hyper_scale",
-            dist.HalfNormal(priors.theta_log_hill_n_hyper_scale))
+        log_n_hyper_scale = _hyper_scale(name, "log_hill_n", priors, T)
 
     # ------------------------------------------------------------------
     # Full-population per-genotype offsets: shape (T, G)
@@ -288,9 +339,21 @@ def define_model(name: str,
         data.log_titrant_conc,
     )
 
+    shift = None
+    if _shift_on(priors):
+        shift = pyro.sample(
+            f"{name}_growth_log_conc_shift",
+            dist.Normal(float(priors.theta_growth_log_conc_shift_loc),
+                        float(priors.theta_growth_log_conc_shift_scale)))
+
     return ThetaParam(theta_low=theta_low, theta_high=theta_high,
                       log_hill_K=log_hill_K, hill_n=hill_n,
-                      mu=mu, sigma=sigma)
+                      mu=mu, sigma=sigma, growth_log_conc_shift=shift)
+
+
+def _shift_on(priors):
+    """Whether the growth titrant shift log s is sampled (scale > 0)."""
+    return float(getattr(priors, "theta_growth_log_conc_shift_scale", 0.0)) > 0
 
 
 def guide(name: str,
@@ -314,54 +377,50 @@ def guide(name: str,
     h_low_loc_scale = pyro.param(f"{name}_logit_low_hyper_loc_scale",
                                  jnp.full(T, priors.theta_logit_low_hyper_loc_scale),
                                  constraint=dist.constraints.greater_than(1e-4))
-    h_low_scale_loc   = pyro.param(f"{name}_logit_low_hyper_scale_loc",   jnp.full(T, -1.0))
-    h_low_scale_scale = pyro.param(f"{name}_logit_low_hyper_scale_scale", jnp.full(T, 0.1),
-                                   constraint=dist.constraints.greater_than(1e-4))
 
     h_delta_loc_loc   = pyro.param(f"{name}_logit_delta_hyper_loc_loc",
                                    jnp.full(T, priors.theta_logit_delta_hyper_loc_loc))
     h_delta_loc_scale = pyro.param(f"{name}_logit_delta_hyper_loc_scale",
                                    jnp.full(T, priors.theta_logit_delta_hyper_loc_scale),
                                    constraint=dist.constraints.greater_than(1e-4))
-    h_delta_scale_loc   = pyro.param(f"{name}_logit_delta_hyper_scale_loc",   jnp.full(T, -1.0))
-    h_delta_scale_scale = pyro.param(f"{name}_logit_delta_hyper_scale_scale", jnp.full(T, 0.1),
-                                     constraint=dist.constraints.greater_than(1e-4))
 
     h_K_loc_loc   = pyro.param(f"{name}_log_hill_K_hyper_loc_loc",
                                jnp.full(T, priors.theta_log_hill_K_hyper_loc_loc))
     h_K_loc_scale = pyro.param(f"{name}_log_hill_K_hyper_loc_scale",
                                jnp.full(T, priors.theta_log_hill_K_hyper_loc_scale),
                                constraint=dist.constraints.greater_than(1e-4))
-    h_K_scale_loc   = pyro.param(f"{name}_log_hill_K_hyper_scale_loc",   jnp.full(T, -1.0))
-    h_K_scale_scale = pyro.param(f"{name}_log_hill_K_hyper_scale_scale", jnp.full(T, 0.1),
-                                 constraint=dist.constraints.greater_than(1e-4))
 
     h_n_loc_loc   = pyro.param(f"{name}_log_hill_n_hyper_loc_loc",
                                jnp.full(T, priors.theta_log_hill_n_hyper_loc_loc))
     h_n_loc_scale = pyro.param(f"{name}_log_hill_n_hyper_loc_scale",
                                jnp.full(T, priors.theta_log_hill_n_hyper_loc_scale),
                                constraint=dist.constraints.greater_than(1e-4))
-    h_n_scale_loc   = pyro.param(f"{name}_log_hill_n_hyper_scale_loc",   jnp.full(T, -1.0))
-    h_n_scale_scale = pyro.param(f"{name}_log_hill_n_hyper_scale_scale", jnp.full(T, 0.1),
-                                 constraint=dist.constraints.greater_than(1e-4))
+    scale_q = {}
+    for h in _HYPER:
+        if not _held(priors, h) > 0:
+            scale_q[h] = (
+                pyro.param(f"{name}_{h}_hyper_scale_loc", jnp.full(T, -1.0)),
+                pyro.param(f"{name}_{h}_hyper_scale_scale", jnp.full(T, 0.1),
+                           constraint=dist.constraints.greater_than(1e-4)))
+
+    def guide_scale(h):
+        if h not in scale_q:
+            return jnp.full(T, _held(priors, h))
+        return pyro.sample(f"{name}_{h}_hyper_scale", dist.LogNormal(*scale_q[h]))
 
     with pyro.plate(f"{name}_hyper_plate", T, dim=-1):
         logit_low_hyper_loc   = pyro.sample(f"{name}_logit_low_hyper_loc",
                                             dist.Normal(h_low_loc_loc, h_low_loc_scale))
-        logit_low_hyper_scale = pyro.sample(f"{name}_logit_low_hyper_scale",
-                                            dist.LogNormal(h_low_scale_loc, h_low_scale_scale))
+        logit_low_hyper_scale = guide_scale("logit_low")
         logit_delta_hyper_loc   = pyro.sample(f"{name}_logit_delta_hyper_loc",
                                               dist.Normal(h_delta_loc_loc, h_delta_loc_scale))
-        logit_delta_hyper_scale = pyro.sample(f"{name}_logit_delta_hyper_scale",
-                                              dist.LogNormal(h_delta_scale_loc, h_delta_scale_scale))
+        logit_delta_hyper_scale = guide_scale("logit_delta")
         log_K_hyper_loc   = pyro.sample(f"{name}_log_hill_K_hyper_loc",
                                         dist.Normal(h_K_loc_loc, h_K_loc_scale))
-        log_K_hyper_scale = pyro.sample(f"{name}_log_hill_K_hyper_scale",
-                                        dist.LogNormal(h_K_scale_loc, h_K_scale_scale))
+        log_K_hyper_scale = guide_scale("log_hill_K")
         log_n_hyper_loc   = pyro.sample(f"{name}_log_hill_n_hyper_loc",
                                         dist.Normal(h_n_loc_loc, h_n_loc_scale))
-        log_n_hyper_scale = pyro.sample(f"{name}_log_hill_n_hyper_scale",
-                                        dist.LogNormal(h_n_scale_loc, h_n_scale_scale))
+        log_n_hyper_scale = guide_scale("log_hill_n")
 
     # ------------------------------------------------------------------
     # Full-population per-genotype offset variational parameters: shape (T, G)
@@ -412,9 +471,19 @@ def guide(name: str,
         data.log_titrant_conc,
     )
 
+    shift = None
+    if _shift_on(priors):
+        s_loc = pyro.param(f"{name}_growth_log_conc_shift_loc",
+                           jnp.asarray(float(priors.theta_growth_log_conc_shift_loc)))
+        s_scale = pyro.param(f"{name}_growth_log_conc_shift_scale",
+                             jnp.asarray(0.1),
+                             constraint=dist.constraints.greater_than(1e-4))
+        shift = pyro.sample(f"{name}_growth_log_conc_shift",
+                            dist.Normal(s_loc, s_scale))
+
     return ThetaParam(theta_low=theta_low, theta_high=theta_high,
                       log_hill_K=log_hill_K, hill_n=hill_n,
-                      mu=mu, sigma=sigma)
+                      mu=mu, sigma=sigma, growth_log_conc_shift=shift)
 
 
 def simulate(name: str,
@@ -610,6 +679,12 @@ def get_hyperparameters() -> Dict[str, Any]:
     parameters["theta_log_hill_n_hyper_loc_loc"]   = 0.7
     parameters["theta_log_hill_n_hyper_loc_scale"]  = 0.5
     parameters["theta_log_hill_n_hyper_scale"]      = 1.0
+    parameters["theta_log_hill_n_hyper_scale_fixed"] = 0.0
+    parameters["theta_logit_low_hyper_scale_fixed"] = 0.0
+    parameters["theta_logit_delta_hyper_scale_fixed"] = 0.0
+    parameters["theta_log_hill_K_hyper_scale_fixed"] = 0.0
+    parameters["theta_growth_log_conc_shift_loc"] = 0.0
+    parameters["theta_growth_log_conc_shift_scale"] = 0.0
 
     return parameters
 
@@ -684,6 +759,8 @@ def get_guesses(name: str, data: DataClass) -> Dict[str, Any]:
     guesses[f"{name}_logit_delta_offset"] = jnp.zeros((T, G), dtype=float)
     guesses[f"{name}_log_hill_K_offset"]  = jnp.zeros((T, G), dtype=float)
     guesses[f"{name}_log_hill_n_offset"]  = jnp.zeros((T, G), dtype=float)
+    # Read only when the shift is on (no sample site matches it otherwise).
+    guesses[f"{name}_growth_log_conc_shift"] = 0.0
 
     return guesses
 
@@ -700,13 +777,23 @@ def get_priors() -> ModelPriors:
 
 
 def get_extract_specs(ctx):
-    return [dict(
+    specs = [dict(
         input_df=ctx.growth_tm.df,
         params_to_get=["hill_n", "log_hill_K", "theta_high", "theta_low"],
         map_column="map_theta_group",
         get_columns=["genotype", "titrant_name"],
         in_run_prefix="theta_",
     )]
+    if _shift_on(getattr(ctx, "theta_priors", None)):
+        specs.append(dict(
+            input_df=pd.DataFrame({"parameter": ["growth_log_conc_shift"],
+                                   "map_all": [0]}),
+            params_to_get=["growth_log_conc_shift"],
+            map_column="map_all",
+            get_columns=["parameter"],
+            in_run_prefix="theta_",
+        ))
+    return specs
 
 
 _ZERO_CONC_VALUE = 1e-20
diff --git a/src/tfscreen/tfmodel/generative/components/theta/hill_mut.py b/src/tfscreen/tfmodel/generative/components/theta/hill_mut.py
index d3b66b51..877e7f6f 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/hill_mut.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/hill_mut.py
@@ -30,8 +30,9 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale, HalfCauchy
 import pandas as pd
-from flax.struct import dataclass
+from flax.struct import dataclass, field
 from typing import Dict, Any, Optional, Union
 from functools import partial
 from tfscreen.tfmodel.data_class import GrowthData, BindingData
@@ -120,6 +121,12 @@ class SimPriors:
         scale of the InvGamma slab (c²).
     epi_slab_df : float
         Degrees of freedom for the InvGamma slab prior on c².  Usually 4.
+    max_hill_n : float or None
+        Upper limit on the simulated Hill coefficient (``None``, the
+        default, applies none).  Per-mutation deltas and horseshoe epistasis
+        on log(hill_n) add up, so double mutants can reach Hill coefficients
+        far beyond anything physical (hundreds); with a limit, larger draws
+        are set to it.
     """
 
     wt_theta_low: float
@@ -139,6 +146,9 @@ class SimPriors:
     epi_slab_scale: float   # typical magnitude of a large epistasis effect
     epi_slab_df: float      # InvGamma degrees of freedom (slab; usually 4)
 
+    # Optional upper limit on the simulated Hill coefficient.
+    max_hill_n: Optional[float] = field(pytree_node=False, default=None)
+
 
 # ---------------------------------------------------------------------------
 # Internal helpers shared by model and guide
@@ -305,7 +315,7 @@ def define_model(name: str,
         # Shared global scale and slab variance across all Hill parameters
         tau_epi = pyro.sample(
             f"{name}_epi_tau",
-            dist.HalfCauchy(priors.theta_epi_tau_scale))
+            HalfCauchy(priors.theta_epi_tau_scale))
         c2_epi = pyro.sample(
             f"{name}_epi_c2",
             dist.InverseGamma(priors.theta_epi_slab_df / 2.0,
@@ -315,19 +325,19 @@ def define_model(name: str,
         with pyro.plate(f"{name}_titrant_pair_outer_plate", T, dim=-2):
             with pyro.plate(f"{name}_pair_plate", num_pair, dim=-1):
                 epi_low_lam = pyro.sample(
-                    f"{name}_epi_logit_low_lambda", dist.HalfCauchy(1.0))
+                    f"{name}_epi_logit_low_lambda", HalfCauchy(1.0))
                 epi_low_off = pyro.sample(
                     f"{name}_epi_logit_low_offset", dist.Normal(0.0, 1.0))
                 epi_delta_lam = pyro.sample(
-                    f"{name}_epi_logit_delta_lambda", dist.HalfCauchy(1.0))
+                    f"{name}_epi_logit_delta_lambda", HalfCauchy(1.0))
                 epi_delta_off = pyro.sample(
                     f"{name}_epi_logit_delta_offset", dist.Normal(0.0, 1.0))
                 epi_K_lam = pyro.sample(
-                    f"{name}_epi_log_hill_K_lambda", dist.HalfCauchy(1.0))
+                    f"{name}_epi_log_hill_K_lambda", HalfCauchy(1.0))
                 epi_K_off = pyro.sample(
                     f"{name}_epi_log_hill_K_offset", dist.Normal(0.0, 1.0))
                 epi_n_lam = pyro.sample(
-                    f"{name}_epi_log_hill_n_lambda", dist.HalfCauchy(1.0))
+                    f"{name}_epi_log_hill_n_lambda", HalfCauchy(1.0))
                 epi_n_off = pyro.sample(
                     f"{name}_epi_log_hill_n_offset", dist.Normal(0.0, 1.0))
     else:
@@ -349,7 +359,7 @@ def define_model(name: str,
 
     if has_epi:
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         epi_logit_low   = epi_low_off   * tau_epi * _lam_tilde(epi_low_lam)    # [T, P]
         epi_logit_delta = epi_delta_off * tau_epi * _lam_tilde(epi_delta_lam)
@@ -612,7 +622,7 @@ def guide(name: str,
 
     if has_epi:
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         logit_theta_low   = logit_theta_low   + pair_scatter(epi_low_off   * tau_epi * _lam_tilde(epi_low_lam))
         logit_theta_delta = logit_theta_delta + pair_scatter(epi_delta_off * tau_epi * _lam_tilde(epi_delta_lam))
@@ -751,6 +761,13 @@ def _horseshoe(size):
             log_n       += _scatter_pair(_horseshoe(P))
         # else: tau == 0.0 → all epistasis effects are exactly zero; nothing to add
 
+    # Optional upper limit on the Hill coefficient
+    if sim_priors.max_hill_n is not None:
+        if sim_priors.max_hill_n <= 0:
+            raise ValueError(
+                f"max_hill_n must be > 0; got {sim_priors.max_hill_n}")
+        log_n = np.minimum(log_n, np.log(sim_priors.max_hill_n))
+
     # Convert to probability space
     theta_low_arr  = 1.0 / (1.0 + np.exp(-logit_low))               # (G,)
     theta_high_arr = 1.0 / (1.0 + np.exp(-(logit_low + logit_delta))) # (G,)
@@ -852,6 +869,7 @@ def get_sim_hyperparameters() -> Dict[str, Any]:
         "epi_tau_scale":       0.1,   # matches theta_epi_tau_scale in get_hyperparameters()
         "epi_slab_scale":      2.0,   # matches theta_epi_slab_scale
         "epi_slab_df":         4.0,   # matches theta_epi_slab_df
+        "max_hill_n":          None,  # no limit on the simulated Hill coefficient
     }
 
 
diff --git a/src/tfscreen/tfmodel/generative/components/theta/hill_relative.py b/src/tfscreen/tfmodel/generative/components/theta/hill_relative.py
new file mode 100644
index 00000000..1c0a903b
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/components/theta/hill_relative.py
@@ -0,0 +1,454 @@
+"""
+Relative Hill curves: a wt-relative growth variable X instead of theta.
+
+Roadmap step 5 (``planning/analysis-roadmap.md``). Growth data alone fix an
+occupancy-like variable only up to an affine map (``X -> a X + b`` is absorbed
+by each condition's ``k`` and ``m``), so this component does not claim
+absolute theta. Each genotype's X follows hill_geno's curve,
+
+    X_g(c) = X_low_g + (X_high_g - X_low_g) * sigmoid(n_g (ln c - ln K_g)),
+
+with real-valued baselines (a mutant may repress more than wt, X > 1, or
+less than wt does at saturating IPTG, X < 0). The gauge is fixed on wt (D4):
+
+    X_wt(c_lo) = 1,   X_wt(c_hi) = 0,
+
+at the two gauge concentrations ``data.theta_gauge_log_conc`` (an
+orchestrator setting, ``theta_gauge_conc``; default the lowest and highest
+measured concentration). wt keeps its K and n free; its baselines follow from
+them. Then ``k_c`` is wt's growth at ``c_hi`` and ``m_c`` wt's growth change
+between the two, in every condition, and X is shared by all conditions (D5).
+
+Only valid with activity 'fixed', theta_rescale 'passthrough', linear
+condition growth, no theta noise, no binding data and, under the congression
+mixture, the 'max' theta rule (C1); ModelOrchestrator enforces these.
+
+Outputs are on the X scale (``THETA_SCALE = "X"``): the deterministic sites
+are ``theta_X_low``/``theta_X_high`` and tfs-predict-theta labels its rows.
+"""
+from typing import Any, Dict
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import numpyro as pyro
+import numpyro.distributions as dist
+from flax.struct import dataclass, field
+
+from tfscreen.tfmodel.data_class import DataClass
+
+# What run_model returns; read by tfs-predict-theta and the X-scale checks.
+THETA_SCALE = "X"
+
+# Floor on wt's occupancy change between the gauge concentrations. The gauge
+# divides by it; a wt curve that barely moves across the range would
+# otherwise send wt's baselines to infinity.
+_MIN_GAUGE_SPAN = 1e-3
+
+
+@dataclass(frozen=True)
+class ModelPriors:
+    """
+    Hyperpriors for the relative Hill model.
+
+    ``X_low`` is X at no titrant, ``X_delta`` = ``X_high - X_low``. The
+    defaults are centered on wt under the gauge (1 and -1).
+    """
+
+    theta_X_low_hyper_loc_loc: float
+    theta_X_low_hyper_loc_scale: float
+    theta_X_low_hyper_scale: float
+    theta_X_delta_hyper_loc_loc: float
+    theta_X_delta_hyper_loc_scale: float
+    theta_X_delta_hyper_scale: float
+
+    theta_log_hill_K_hyper_loc_loc: float
+    theta_log_hill_K_hyper_loc_scale: float
+    theta_log_hill_K_hyper_scale: float
+
+    theta_log_hill_n_hyper_loc_loc: float
+    theta_log_hill_n_hyper_loc_scale: float
+    theta_log_hill_n_hyper_scale: float
+    # > 0: hold that population SD here instead of learning it (hill_geno's
+    # log(n) field of the same name explains why). On the dev-data MAP the
+    # learned SDs ran to 3.6 (X_low), 4.3 (X_delta) and 11 (log K), against
+    # about 0.5, 0.5 and 0.7 among well-measured genotypes, so nothing
+    # shrank the low-depth genotypes.
+    theta_X_low_hyper_scale_fixed: float = field(pytree_node=False,
+                                                 default=0.0)
+    theta_X_delta_hyper_scale_fixed: float = field(pytree_node=False,
+                                                   default=0.0)
+    theta_log_hill_K_hyper_scale_fixed: float = field(pytree_node=False,
+                                                      default=0.0)
+    theta_log_hill_n_hyper_scale_fixed: float = field(pytree_node=False,
+                                                      default=0.0)
+    # > 0: hold wt's Hill K (a titrant concentration, the units of
+    # titrant_conc) or Hill n at this value; 0 leaves it free. wt's curve
+    # sets the gauge, and a one-library fit can collapse it (the pheS-only
+    # dev-data fit put wt's whole drop below 1 uM IPTG, where wt's own
+    # trajectories put it at 10-30 uM; planning/studies/x-vs-binding/).
+    # wt's own K or n offset then leaves the likelihood and sits at its
+    # prior.
+    theta_wt_hill_K_fixed: float = field(pytree_node=False, default=0.0)
+    theta_wt_hill_n_fixed: float = field(pytree_node=False, default=0.0)
+
+
+@dataclass(frozen=True)
+class ThetaParam:
+    """
+    Per-genotype relative Hill parameters, each shape ``(num_titrant_name,
+    num_genotype)``, library-ordered. wt's ``X_low``/``X_high`` are set by
+    the gauge.
+    """
+
+    X_low: jnp.ndarray
+    X_high: jnp.ndarray
+    log_hill_K: jnp.ndarray
+    hill_n: jnp.ndarray
+
+
+def gauge_baselines(log_hill_K, hill_n, gauge_log_conc):
+    """
+    wt's X baselines that put ``X = 1`` at ``c_lo`` and ``X = 0`` at ``c_hi``.
+
+    Parameters
+    ----------
+    log_hill_K, hill_n : jnp.ndarray
+        wt's Hill constant (log) and coefficient, any matching shape.
+    gauge_log_conc : jnp.ndarray
+        ``(ln c_lo, ln c_hi)``.
+
+    Returns
+    -------
+    X_low, X_high : jnp.ndarray
+    """
+    occ_lo = jax.nn.sigmoid(hill_n * (gauge_log_conc[0] - log_hill_K))
+    occ_hi = jax.nn.sigmoid(hill_n * (gauge_log_conc[1] - log_hill_K))
+    span = jnp.maximum(occ_hi - occ_lo, _MIN_GAUGE_SPAN)
+    X_low = 1.0 + occ_lo / span
+    X_high = X_low - 1.0 / span
+    return X_low, X_high
+
+
+def _assemble(hyper, offsets, data, priors):
+    """Per-genotype parameters from hyperparameters and offsets, wt gauged
+    (and wt's K and n held when the priors say so)."""
+    (low_loc, low_scale, delta_loc, delta_scale,
+     K_loc, K_scale, n_loc, n_scale) = hyper
+    low_off, delta_off, K_off, n_off = offsets
+
+    X_low = low_loc[:, None] + low_off * low_scale[:, None]
+    X_delta = delta_loc[:, None] + delta_off * delta_scale[:, None]
+    log_hill_K = K_loc[:, None] + K_off * K_scale[:, None]
+    hill_n = jnp.exp(n_loc[:, None] + n_off * n_scale[:, None])
+
+    is_wt = jnp.zeros(data.num_genotype, dtype=bool).at[data.wt_indexes].set(True)
+    wt_K = float(getattr(priors, "theta_wt_hill_K_fixed", 0.0))
+    wt_n = float(getattr(priors, "theta_wt_hill_n_fixed", 0.0))
+    if wt_K < 0 or wt_n < 0:
+        raise ValueError("theta_wt_hill_K_fixed and theta_wt_hill_n_fixed "
+                         "must be > 0 to hold, or 0 to leave free")
+    if wt_K > 0:
+        log_hill_K = jnp.where(is_wt[None, :], np.log(wt_K), log_hill_K)
+    if wt_n > 0:
+        hill_n = jnp.where(is_wt[None, :], wt_n, hill_n)
+
+    wt = data.wt_indexes[0]
+    wt_low, wt_high = gauge_baselines(log_hill_K[:, wt], hill_n[:, wt],
+                                      data.theta_gauge_log_conc)
+    X_low = jnp.where(is_wt[None, :], wt_low[:, None], X_low)
+    X_high = jnp.where(is_wt[None, :], wt_high[:, None], X_low + X_delta)
+
+    return ThetaParam(X_low=X_low, X_high=X_high,
+                      log_hill_K=log_hill_K, hill_n=hill_n)
+
+
+_HYPER = ("X_low", "X_delta", "log_hill_K", "log_hill_n")
+_OFFSETS = ("X_low_offset", "X_delta_offset", "log_hill_K_offset",
+            "log_hill_n_offset")
+
+
+def _fixed(priors, h):
+    """The held population SD of hyperparameter ``h``, or 0 if learned."""
+    return float(getattr(priors, f"theta_{h}_hyper_scale_fixed", 0.0))
+
+
+def define_model(name: str,
+                 data: DataClass,
+                 priors: ModelPriors) -> ThetaParam:
+    """
+    Hierarchical relative Hill model with per-titrant hyperpriors and
+    library-sized per-genotype offsets (``run_model`` slices by
+    ``data.batch_idx[data.geno_theta_idx]``).
+    """
+    T = data.num_titrant_name
+
+    hyper = []
+    with pyro.plate(f"{name}_hyper_plate", T, dim=-1):
+        for h in _HYPER:
+            hyper.append(pyro.sample(
+                f"{name}_{h}_hyper_loc",
+                dist.Normal(getattr(priors, f"theta_{h}_hyper_loc_loc"),
+                            getattr(priors, f"theta_{h}_hyper_loc_scale"))))
+            if _fixed(priors, h) > 0:
+                hyper.append(pyro.deterministic(f"{name}_{h}_hyper_scale",
+                                                jnp.full(T, _fixed(priors, h))))
+            else:
+                hyper.append(pyro.sample(
+                    f"{name}_{h}_hyper_scale",
+                    dist.HalfNormal(getattr(priors, f"theta_{h}_hyper_scale"))))
+
+    with pyro.plate(f"{name}_titrant_name_plate", T, dim=-2):
+        with pyro.plate(f"{name}_genotype_plate", data.num_genotype, dim=-1):
+            offsets = [pyro.sample(f"{name}_{o}", dist.Normal(0.0, 1.0))
+                       for o in _OFFSETS]
+
+    theta_param = _assemble(hyper, offsets, data, priors)
+
+    pyro.deterministic(f"{name}_X_low", theta_param.X_low)
+    pyro.deterministic(f"{name}_X_high", theta_param.X_high)
+    pyro.deterministic(f"{name}_log_hill_K", theta_param.log_hill_K)
+    pyro.deterministic(f"{name}_hill_n", theta_param.hill_n)
+
+    return theta_param
+
+
+def guide(name: str,
+          data: DataClass,
+          priors: ModelPriors) -> ThetaParam:
+    """Mean-field guide mirroring ``define_model``."""
+    T = data.num_titrant_name
+    G = data.num_genotype
+
+    hyper = []
+    with pyro.plate(f"{name}_hyper_plate", T, dim=-1):
+        for h in _HYPER:
+            loc_loc = pyro.param(
+                f"{name}_{h}_hyper_loc_loc",
+                jnp.full(T, getattr(priors, f"theta_{h}_hyper_loc_loc")))
+            loc_scale = pyro.param(
+                f"{name}_{h}_hyper_loc_scale",
+                jnp.full(T, getattr(priors, f"theta_{h}_hyper_loc_scale")),
+                constraint=dist.constraints.greater_than(1e-4))
+            hyper.append(pyro.sample(f"{name}_{h}_hyper_loc",
+                                     dist.Normal(loc_loc, loc_scale)))
+            if _fixed(priors, h) > 0:
+                hyper.append(jnp.full(T, _fixed(priors, h)))
+                continue
+            scale_loc = pyro.param(f"{name}_{h}_hyper_scale_loc",
+                                   jnp.full(T, -1.0))
+            scale_scale = pyro.param(
+                f"{name}_{h}_hyper_scale_scale", jnp.full(T, 0.1),
+                constraint=dist.constraints.greater_than(1e-4))
+            hyper.append(pyro.sample(f"{name}_{h}_hyper_scale",
+                                     dist.LogNormal(scale_loc, scale_scale)))
+
+    locs = [pyro.param(f"{name}_{o}_locs", jnp.zeros((T, G), dtype=float))
+            for o in _OFFSETS]
+    scales = [pyro.param(f"{name}_{o}_scales", jnp.ones((T, G), dtype=float),
+                         constraint=dist.constraints.positive)
+              for o in _OFFSETS]
+
+    with pyro.plate(f"{name}_titrant_name_plate", T, dim=-2):
+        with pyro.plate(f"{name}_genotype_plate", G, dim=-1):
+            offsets = [pyro.sample(f"{name}_{o}", dist.Normal(l, s))
+                       for o, l, s in zip(_OFFSETS, locs, scales)]
+
+    return _assemble(hyper, offsets, data, priors)
+
+
+def run_model(theta_param: ThetaParam, data: DataClass) -> jnp.ndarray:
+    """
+    X at every titrant concentration for the batch's genotypes.
+
+    Returns shape ``(T, C, G_subset)``, or ``(1, 1, 1, 1, T, C, G_subset)``
+    when ``data.scatter_theta == 1`` (the hill_geno contract).
+    """
+    geno_idx = data.batch_idx[data.geno_theta_idx]
+    X_low = theta_param.X_low[:, None, geno_idx]
+    X_high = theta_param.X_high[:, None, geno_idx]
+    log_hill_K = theta_param.log_hill_K[:, None, geno_idx]
+    hill_n = theta_param.hill_n[:, None, geno_idx]
+
+    log_titrant = data.log_titrant_conc[None, :, None]
+    occupancy = jax.nn.sigmoid(hill_n * (log_titrant - log_hill_K))
+    X = X_low + (X_high - X_low) * occupancy
+
+    if data.scatter_theta == 1:
+        X = X[None, None, None, None, :, :, :]
+
+    return X
+
+
+def get_population_moments(theta_param: ThetaParam, data: DataClass) -> tuple:
+    """No logit-space moments: X is not an occupancy (theta noise is refused)."""
+    return None, None
+
+
+def get_hyperparameters() -> Dict[str, Any]:
+    """Default hyperparameters (X centered on wt's gauge; K/n as hill_geno)."""
+    return {
+        "theta_X_low_hyper_loc_loc": 1.0,
+        "theta_X_low_hyper_loc_scale": 0.5,
+        "theta_X_low_hyper_scale": 1.0,
+        "theta_X_delta_hyper_loc_loc": -1.0,
+        "theta_X_delta_hyper_loc_scale": 0.5,
+        "theta_X_delta_hyper_scale": 1.0,
+        "theta_log_hill_K_hyper_loc_loc": -4.1,
+        "theta_log_hill_K_hyper_loc_scale": 1.0,
+        "theta_log_hill_K_hyper_scale": 0.1,
+        "theta_log_hill_n_hyper_loc_loc": 0.7,
+        "theta_log_hill_n_hyper_loc_scale": 0.5,
+        "theta_log_hill_n_hyper_scale": 1.0,
+        "theta_X_low_hyper_scale_fixed": 0.0,
+        "theta_X_delta_hyper_scale_fixed": 0.0,
+        "theta_log_hill_K_hyper_scale_fixed": 0.0,
+        "theta_log_hill_n_hyper_scale_fixed": 0.0,
+        "theta_wt_hill_K_fixed": 0.0,
+        "theta_wt_hill_n_fixed": 0.0,
+    }
+
+
+def get_priors() -> ModelPriors:
+    return ModelPriors(**get_hyperparameters())
+
+
+def get_guesses(name: str, data: DataClass) -> Dict[str, Any]:
+    """Starting values: wt-like baselines, K at the median concentration."""
+    log_conc = np.array(data.log_titrant_conc)
+    finite = log_conc[np.isfinite(log_conc)]
+    log_K_guess = float(np.median(finite)) if len(finite) > 0 else -4.1
+
+    T = data.num_titrant_name
+    G = data.num_genotype
+
+    guesses = {
+        f"{name}_X_low_hyper_loc": jnp.full(T, 1.0),
+        f"{name}_X_low_hyper_scale": jnp.full(T, 0.5),
+        f"{name}_X_delta_hyper_loc": jnp.full(T, -1.0),
+        f"{name}_X_delta_hyper_scale": jnp.full(T, 0.5),
+        f"{name}_log_hill_K_hyper_loc": jnp.full(T, log_K_guess),
+        f"{name}_log_hill_K_hyper_scale": jnp.full(T, 1.0),
+        f"{name}_log_hill_n_hyper_loc": jnp.full(T, 0.7),
+        f"{name}_log_hill_n_hyper_scale": jnp.full(T, 0.3),
+    }
+    for o in _OFFSETS:
+        guesses[f"{name}_{o}"] = jnp.zeros((T, G), dtype=float)
+    return guesses
+
+
+def get_extract_specs(ctx):
+    return [dict(
+        input_df=ctx.growth_tm.df,
+        params_to_get=["hill_n", "log_hill_K", "X_high", "X_low"],
+        map_column="map_theta_group",
+        get_columns=["genotype", "titrant_name"],
+        in_run_prefix="theta_",
+    )]
+
+
+_ZERO_CONC_VALUE = 1e-20
+
+
+def _log_conc(conc):
+    conc = np.asarray(conc, dtype=float).copy()
+    conc[conc == 0] = _ZERO_CONC_VALUE
+    return np.log(conc)
+
+
+def build_calc_df(model, manual_titrant_df):
+    """Concentration grid for X extraction (same grid as hill_geno)."""
+    from tfscreen.tfmodel.generative.components.theta.hill_geno import (
+        build_calc_df as _hill_build_calc_df,
+    )
+    return _hill_build_calc_df(model, manual_titrant_df)
+
+
+def compute_theta_samples(calc_df, param_posteriors):
+    """Posterior X samples, shape ``(S, len(calc_df))``."""
+    from tfscreen.tfmodel.inference.posteriors import get_posterior_samples
+
+    indices = calc_df["map_theta_group"].values.astype(int)
+    log_titrant = _log_conc(calc_df["titrant_conc"].values)[None, :]
+
+    def _load_flat(key):
+        v = get_posterior_samples(param_posteriors, key)
+        if hasattr(v, "shape") and not hasattr(v, "reshape"):
+            v = v[:]
+        return v.reshape(v.shape[0], -1)
+
+    h_n = _load_flat("theta_hill_n")[:, indices]
+    l_K = _load_flat("theta_log_hill_K")[:, indices]
+    x_h = _load_flat("theta_X_high")[:, indices]
+    x_l = _load_flat("theta_X_low")[:, indices]
+
+    occupancy = 1.0 / (1.0 + np.exp(-h_n * (log_titrant - l_K)))
+    return x_l + (x_h - x_l) * occupancy
+
+
+def predict_unmeasured(target_genotypes,
+                       titrant_names,
+                       manual_titrant_df,
+                       mut_labels,
+                       pair_labels,
+                       param_posteriors,
+                       q_to_get):
+    """
+    Population-mean X for genotypes not seen in training (no per-mutation
+    structure, as for hill_geno).
+    """
+    from tfscreen.tfmodel.inference.posteriors import get_posterior_samples
+    from tfscreen.tfmodel.analysis.predict_unmeasured import (
+        _build_prediction_grid,
+    )
+
+    calc_df, _, titrant_idx = _build_prediction_grid(
+        list(target_genotypes), titrant_names, manual_titrant_df)
+
+    def _load(key):
+        v = get_posterior_samples(param_posteriors, key)
+        if hasattr(v, "shape") and not hasattr(v, "reshape"):
+            v = v[:]
+        return np.array(v)
+
+    X_low = _load("theta_X_low_hyper_loc")[:, titrant_idx]
+    X_high = X_low + _load("theta_X_delta_hyper_loc")[:, titrant_idx]
+    l_K = _load("theta_log_hill_K_hyper_loc")[:, titrant_idx]
+    h_n = np.exp(_load("theta_log_hill_n_hyper_loc"))[:, titrant_idx]
+
+    log_conc = _log_conc(calc_df["titrant_conc"].values)[None, :]
+    occupancy = 1.0 / (1.0 + np.exp(-h_n * (log_conc - l_K)))
+    samples = X_low + (X_high - X_low) * occupancy
+
+    result_df = calc_df[["genotype", "titrant_name", "titrant_conc"]].copy()
+    for q_name, q_val in q_to_get.items():
+        result_df[q_name] = np.quantile(samples, q_val, axis=0)
+    return result_df
+
+
+def x_scale_truth(theta, theta_wt_lo, theta_wt_hi,
+                  activity=1.0, activity_wt=1.0):
+    """
+    Map simulated occupancy onto the X gauge (roadmap C8).
+
+    With growth linear in ``activity * theta`` and the gauge ``X_wt(c_lo) =
+    1``, ``X_wt(c_hi) = 0``:
+
+        X = (A theta - A_wt theta_wt(c_hi)) / (A_wt (theta_wt(c_lo) - theta_wt(c_hi)))
+
+    Parameters are arrays or scalars that broadcast together.
+    """
+    theta = np.asarray(theta, dtype=float)
+    span = activity_wt * (np.asarray(theta_wt_lo) - np.asarray(theta_wt_hi))
+    return (activity * theta - activity_wt * np.asarray(theta_wt_hi)) / span
+
+
+def x_scale_growth_truth(k, m, theta_wt_lo, theta_wt_hi, activity_wt=1.0):
+    """
+    A condition's (k, m) on the X gauge, from its (k, m) on the theta scale.
+
+    ``g = k + m A theta`` with ``A theta = A_wt theta_wt(c_hi) + X * span``
+    gives ``k_X = k + m A_wt theta_wt(c_hi)`` and ``m_X = m * span``.
+    """
+    span = activity_wt * (theta_wt_lo - theta_wt_hi)
+    return k + m * activity_wt * theta_wt_hi, m * span
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PK.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PK.py
index 49a1062f..e93d026a 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PK.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PK.py
@@ -27,7 +27,7 @@
 different effector species to have different WT affinities; the per-mutation
 delta is shared across effector types.
 
-Epistasis uses a regularised horseshoe prior (shared τ, slab c², per-pair λ)
+Epistasis uses a regularized horseshoe prior (shared τ, slab c², per-pair λ)
 and is only sampled when data.num_pair > 0.
 
 Thermodynamic functions (partition function, Newton solve for free effector,
@@ -37,6 +37,7 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale, HalfCauchy
 import pandas as pd
 from flax.struct import dataclass
 from functools import partial
@@ -45,6 +46,13 @@
 from tfscreen.tfmodel.data_class import GrowthData, BindingData
 from tfscreen.genetics.build_mut_geno_matrix import apply_pair_matrix, apply_mut_matrix
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_H_E,
+    DEFAULT_LN_K_H_L,
+    DEFAULT_LN_K_H_O,
+    DEFAULT_LN_K_L_E,
+    DEFAULT_LN_K_L_O,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -73,12 +81,9 @@ class ModelPriors:
     theta_ln_K_l_e_wt_loc:   float
     theta_ln_K_l_e_wt_scale: float
 
-    # Physical concentrations (M).
-    # theta_tf_total_M is the total TF in MONOMER units (same convention as
-    # MWCDimerModel.r_total in src/tfscreen/models/lac_model/mwc_dimer.py).
-    # The dimer concentration used in the mass-balance is tf_total_M / 2.
-    theta_tf_total_M: float   # ≈ 6.5e-7 M monomer (= 325 nM dimer)
-    theta_op_total_M: float   # ≈ 2.5e-8 M (25 nM)
+    # Total TF (M) in MONOMER units; the dimer concentration in the mass
+    # balance is tf_total_M / 2 (thermo/mwc.py).
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
 
     # Unit-conversion factor applied to data.titrant_conc before it enters
     # the thermodynamic equations.  Set to 1e-3 when concentrations in the
@@ -92,7 +97,7 @@ class ModelPriors:
     theta_sigma_d_ln_K_h_e_scale: float
     theta_sigma_d_ln_K_l_e_scale: float
 
-    # Shared regularised horseshoe hyperparameters for pairwise epistasis
+    # Shared regularized horseshoe hyperparameters for pairwise epistasis
     theta_epi_tau_scale:  float   # HalfCauchy scale for global τ
     theta_epi_slab_scale: float   # typical size of a large epistasis effect
     theta_epi_slab_df:    float   # InvGamma shape ν (usually 4)
@@ -161,7 +166,7 @@ def define_model(name: str,
 
     Samples WT equilibrium constants and per-mutation delta parameters, then
     assembles per-genotype constants via matrix multiplication.  Epistasis uses
-    a regularised horseshoe prior (shared τ and slab c², per-pair local λ).
+    a regularized horseshoe prior (shared τ and slab c², per-pair local λ).
 
     Parameters
     ----------
@@ -186,7 +191,6 @@ def define_model(name: str,
     has_epi = data.num_pair > 0
 
     tf_total         = priors.theta_tf_total_M
-    op_total         = priors.theta_op_total_M
     conc_unit_scale  = priors.theta_conc_unit_scale
 
     # ------------------------------------------------------------------
@@ -232,19 +236,26 @@ def define_model(name: str,
     # Per-mutation delta offsets
     # Scalar K_h_l, K_h_o, K_l_o: shape (num_mutation,)
     # ------------------------------------------------------------------
-    with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
-        d_K_h_l_off = pyro.sample(f"{name}_d_ln_K_h_l_offset", dist.Normal(0.0, 1.0))
-        d_K_h_o_off = pyro.sample(f"{name}_d_ln_K_h_o_offset", dist.Normal(0.0, 1.0))
-        d_K_l_o_off = pyro.sample(f"{name}_d_ln_K_l_o_offset", dist.Normal(0.0, 1.0))
-
-    # T-dimensional K_h_e, K_l_e: shape (T, num_mutation)
-    with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
-        with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
-            d_K_h_e_off = pyro.sample(f"{name}_d_ln_K_h_e_offset", dist.Normal(0.0, 1.0))
-            d_K_l_e_off = pyro.sample(f"{name}_d_ln_K_l_e_offset", dist.Normal(0.0, 1.0))
+    if num_mut > 0:
+        with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
+            d_K_h_l_off = pyro.sample(f"{name}_d_ln_K_h_l_offset", dist.Normal(0.0, 1.0))
+            d_K_h_o_off = pyro.sample(f"{name}_d_ln_K_h_o_offset", dist.Normal(0.0, 1.0))
+            d_K_l_o_off = pyro.sample(f"{name}_d_ln_K_l_o_offset", dist.Normal(0.0, 1.0))
+
+        # T-dimensional K_h_e, K_l_e: shape (T, num_mutation)
+        with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
+            with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
+                d_K_h_e_off = pyro.sample(f"{name}_d_ln_K_h_e_offset", dist.Normal(0.0, 1.0))
+                d_K_l_e_off = pyro.sample(f"{name}_d_ln_K_l_e_offset", dist.Normal(0.0, 1.0))
+    else:
+        # num_mut == 0 (wt-only library, e.g. a stratified binding
+        # pool): numpyro.plate needs size > 0, so skip the sites.  The
+        # empty offsets scatter to zeros, so every genotype is wt.
+        d_K_h_l_off = d_K_h_o_off = d_K_l_o_off = jnp.zeros((0,))
+        d_K_h_e_off = d_K_l_e_off = jnp.zeros((T, 0))
 
     # ------------------------------------------------------------------
-    # Optional epistasis — regularised horseshoe prior
+    # Optional epistasis — regularized horseshoe prior
     # ------------------------------------------------------------------
     if has_epi:
         pair_scatter = partial(apply_pair_matrix,
@@ -255,7 +266,7 @@ def define_model(name: str,
 
         tau_epi = pyro.sample(
             f"{name}_epi_tau",
-            dist.HalfCauchy(priors.theta_epi_tau_scale))
+            HalfCauchy(priors.theta_epi_tau_scale))
         c2_epi = pyro.sample(
             f"{name}_epi_c2",
             dist.InverseGamma(priors.theta_epi_slab_df / 2.0,
@@ -263,19 +274,19 @@ def define_model(name: str,
 
         # Scalar K_h_l, K_h_o, K_l_o: (num_pair,) each
         with pyro.plate(f"{name}_pair_scalar_plate", num_pair, dim=-1):
-            epi_K_h_l_lam = pyro.sample(f"{name}_epi_ln_K_h_l_lambda", dist.HalfCauchy(1.0))
+            epi_K_h_l_lam = pyro.sample(f"{name}_epi_ln_K_h_l_lambda", HalfCauchy(1.0))
             epi_K_h_l_off = pyro.sample(f"{name}_epi_ln_K_h_l_offset", dist.Normal(0.0, 1.0))
-            epi_K_h_o_lam = pyro.sample(f"{name}_epi_ln_K_h_o_lambda", dist.HalfCauchy(1.0))
+            epi_K_h_o_lam = pyro.sample(f"{name}_epi_ln_K_h_o_lambda", HalfCauchy(1.0))
             epi_K_h_o_off = pyro.sample(f"{name}_epi_ln_K_h_o_offset", dist.Normal(0.0, 1.0))
-            epi_K_l_o_lam = pyro.sample(f"{name}_epi_ln_K_l_o_lambda", dist.HalfCauchy(1.0))
+            epi_K_l_o_lam = pyro.sample(f"{name}_epi_ln_K_l_o_lambda", HalfCauchy(1.0))
             epi_K_l_o_off = pyro.sample(f"{name}_epi_ln_K_l_o_offset", dist.Normal(0.0, 1.0))
 
         # T-dimensional K_h_e, K_l_e: (T, num_pair) each
         with pyro.plate(f"{name}_titrant_epi_outer_plate", T, dim=-2):
             with pyro.plate(f"{name}_pair_plate", num_pair, dim=-1):
-                epi_K_h_e_lam = pyro.sample(f"{name}_epi_ln_K_h_e_lambda", dist.HalfCauchy(1.0))
+                epi_K_h_e_lam = pyro.sample(f"{name}_epi_ln_K_h_e_lambda", HalfCauchy(1.0))
                 epi_K_h_e_off = pyro.sample(f"{name}_epi_ln_K_h_e_offset", dist.Normal(0.0, 1.0))
-                epi_K_l_e_lam = pyro.sample(f"{name}_epi_ln_K_l_e_lambda", dist.HalfCauchy(1.0))
+                epi_K_l_e_lam = pyro.sample(f"{name}_epi_ln_K_l_e_lambda", HalfCauchy(1.0))
                 epi_K_l_e_off = pyro.sample(f"{name}_epi_ln_K_l_e_offset", dist.Normal(0.0, 1.0))
     else:
         pair_scatter = None
@@ -297,7 +308,7 @@ def define_model(name: str,
 
     if has_epi:
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         epi_ln_K_h_l = epi_K_h_l_off * tau_epi * _lam_tilde(epi_K_h_l_lam)   # (P,)
         epi_ln_K_h_o = epi_K_h_o_off * tau_epi * _lam_tilde(epi_K_h_o_lam)   # (P,)
@@ -356,12 +367,12 @@ def _lam_tilde(lam):
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -383,7 +394,6 @@ def guide(name: str,
     has_epi = data.num_pair > 0
 
     tf_total         = priors.theta_tf_total_M
-    op_total         = priors.theta_op_total_M
     conc_unit_scale  = priors.theta_conc_unit_scale
 
     # ------------------------------------------------------------------
@@ -475,22 +485,29 @@ def guide(name: str,
         sigma_d_K_h_e = pyro.sample(f"{name}_sigma_d_ln_K_h_e", dist.LogNormal(sigma_d_K_h_e_locs, sigma_d_K_h_e_scales))
         sigma_d_K_l_e = pyro.sample(f"{name}_sigma_d_ln_K_l_e", dist.LogNormal(sigma_d_K_l_e_locs, sigma_d_K_l_e_scales))
 
-    with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
-        d_K_h_l_off = pyro.sample(f"{name}_d_ln_K_h_l_offset", dist.Normal(d_K_h_l_locs, d_K_h_l_scales))
-        d_K_h_o_off = pyro.sample(f"{name}_d_ln_K_h_o_offset", dist.Normal(d_K_h_o_locs, d_K_h_o_scales))
-        d_K_l_o_off = pyro.sample(f"{name}_d_ln_K_l_o_offset", dist.Normal(d_K_l_o_locs, d_K_l_o_scales))
+    if num_mut > 0:
+        with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
+            d_K_h_l_off = pyro.sample(f"{name}_d_ln_K_h_l_offset", dist.Normal(d_K_h_l_locs, d_K_h_l_scales))
+            d_K_h_o_off = pyro.sample(f"{name}_d_ln_K_h_o_offset", dist.Normal(d_K_h_o_locs, d_K_h_o_scales))
+            d_K_l_o_off = pyro.sample(f"{name}_d_ln_K_l_o_offset", dist.Normal(d_K_l_o_locs, d_K_l_o_scales))
 
-    with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
-        with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
-            d_K_h_e_off = pyro.sample(f"{name}_d_ln_K_h_e_offset", dist.Normal(d_K_h_e_locs, d_K_h_e_scales))
-            d_K_l_e_off = pyro.sample(f"{name}_d_ln_K_l_e_offset", dist.Normal(d_K_l_e_locs, d_K_l_e_scales))
+        with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
+            with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
+                d_K_h_e_off = pyro.sample(f"{name}_d_ln_K_h_e_offset", dist.Normal(d_K_h_e_locs, d_K_h_e_scales))
+                d_K_l_e_off = pyro.sample(f"{name}_d_ln_K_l_e_offset", dist.Normal(d_K_l_e_locs, d_K_l_e_scales))
+    else:
+        # num_mut == 0 (wt-only library, e.g. a stratified binding
+        # pool): numpyro.plate needs size > 0, so skip the sites.  The
+        # empty offsets scatter to zeros, so every genotype is wt.
+        d_K_h_l_off = d_K_h_o_off = d_K_l_o_off = jnp.zeros((0,))
+        d_K_h_e_off = d_K_l_e_off = jnp.zeros((T, 0))
 
     if has_epi:
         tau_epi = pyro.sample(f"{name}_epi_tau", dist.LogNormal(tau_epi_loc, tau_epi_scale))
         c2_epi  = pyro.sample(f"{name}_epi_c2",  dist.LogNormal(c2_epi_loc,  c2_epi_scale))
 
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         # Scalar K_h_l epistasis: (num_pair,)
         epi_K_h_l_lam_locs   = pyro.param(f"{name}_epi_ln_K_h_l_lambda_locs",   jnp.zeros(num_pair))
@@ -575,12 +592,12 @@ def _lam_tilde(lam):
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -590,46 +607,27 @@ def _lam_tilde(lam):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    # All values calibrated to Sochor et al. 2014 (PeerJ 2:e498), Table 1.
-    # Concentrations must be in Molar; equilibrium constants in M⁻¹.
-    #
-    # K_h_l = K_RR* = [L]/[H] ≈ 6.3 → ln ≈ 1.84
-    # The L (low-affinity) state is DOMINANT without IPTG (~86% of TF).
-    # K_h_l > 1 is required for the effector to produce derepression:
-    # when K_h_l << 1, adding effector has no effect on theta (flat curves).
-    p["theta_ln_K_h_l_wt_loc"]   = 1.84
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_h_l_wt_loc"]   = DEFAULT_LN_K_H_L   # Sochor (2014) Table 1: K_h_l = 6.3
     p["theta_ln_K_h_l_wt_scale"] = 2.0
-    # K_h_o = K_RO ≈ 0.42 nM⁻¹ = 4.2×10⁸ M⁻¹ → ln ≈ 19.9
-    p["theta_ln_K_h_o_wt_loc"]   = 19.9
+    p["theta_ln_K_h_o_wt_loc"]   = DEFAULT_LN_K_H_O   # Sochor: K_h_o = 4.2e8 M⁻¹
     p["theta_ln_K_h_o_wt_scale"] = 2.0
-    # K_l_o = K_R*O ≈ 0 (L-state does not bind operator DNA).
-    # Sochor fixes this at 10⁻¹⁰ nM⁻¹ = 0.1 M⁻¹ → ln ≈ -2.3.
-    p["theta_ln_K_l_o_wt_loc"]   = -2.3
+    p["theta_ln_K_l_o_wt_loc"]   = DEFAULT_LN_K_L_O   # Sochor: K_l_o = 0.1 M⁻¹
     p["theta_ln_K_l_o_wt_scale"] = 3.0
-    # K_h_e = K_RE ≈ 5.6×10⁻⁵ nM⁻¹ = 5.6×10⁴ M⁻¹ → ln ≈ 10.9
-    p["theta_ln_K_h_e_wt_loc"]   = 10.9
+    p["theta_ln_K_h_e_wt_loc"]   = DEFAULT_LN_K_H_E   # per site; ≈ 3.7e3 M⁻¹ = K_l_e/79.6, theta_sat 0.05
     p["theta_ln_K_h_e_wt_scale"] = 2.0
-    # K_l_e = K_R*E ≈ 7.6×10⁻⁴ nM⁻¹ = 7.6×10⁵ M⁻¹ → ln ≈ 13.5
-    # L-state binds IPTG ~14× more tightly than H-state (K_l_e/K_h_e ≈ 13.6).
-    p["theta_ln_K_l_e_wt_loc"]   = 13.5
+    p["theta_ln_K_l_e_wt_loc"]   = DEFAULT_LN_K_L_E   # per site; ≈ 3.0e5 M⁻¹ = 0.39 × Sochor, midpoint 80 µM
     p["theta_ln_K_l_e_wt_scale"] = 2.0
-    # Physical concentrations — theta_tf_total_M is in MONOMER units.
-    # Sochor in vitro: [TF]≈664 nM dimer = ~1328 nM monomer.
-    # 6.5e-7 M monomer = 650 nM monomer = 325 nM dimer is a round-number
-    # approximation suitable as the prior centre.
-    p["theta_tf_total_M"] = 6.5e-7   # monomer
-    p["theta_op_total_M"] = 2.5e-8   # operator (M)
-    # Unit-conversion factor: multiply data.titrant_conc by this before
-    # entering thermodynamic equations.  Default 1e-3 assumes concentrations
-    # are in mM and K values are in M⁻¹.
-    p["theta_conc_unit_scale"] = 1e-3
+    p["theta_tf_total_M"] = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     # Mutation effect scales
     p["theta_sigma_d_ln_K_h_l_scale"] = 1.0
     p["theta_sigma_d_ln_K_h_o_scale"] = 1.0
     p["theta_sigma_d_ln_K_l_o_scale"] = 1.0
     p["theta_sigma_d_ln_K_h_e_scale"] = 1.0
     p["theta_sigma_d_ln_K_l_e_scale"] = 1.0
-    # Shared regularised horseshoe hyperparameters
+    # Shared regularized horseshoe hyperparameters
     p["theta_epi_tau_scale"]   = 0.1
     p["theta_epi_slab_scale"]  = 2.0
     p["theta_epi_slab_df"]     = 4.0
@@ -640,11 +638,11 @@ def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, An
     T = data.num_titrant_name
     M = data.num_mutation
     g = {}
-    g[f"{name}_ln_K_h_l_wt"]          = jnp.array(1.84)
-    g[f"{name}_ln_K_h_o_wt"]          = jnp.array(19.9)
-    g[f"{name}_ln_K_l_o_wt"]          = jnp.array(-2.3)
-    g[f"{name}_ln_K_h_e_wt"]          = jnp.full(T, 10.9)
-    g[f"{name}_ln_K_l_e_wt"]          = jnp.full(T, 13.5)
+    g[f"{name}_ln_K_h_l_wt"]          = jnp.array(DEFAULT_LN_K_H_L)
+    g[f"{name}_ln_K_h_o_wt"]          = jnp.array(DEFAULT_LN_K_H_O)
+    g[f"{name}_ln_K_l_o_wt"]          = jnp.array(DEFAULT_LN_K_L_O)
+    g[f"{name}_ln_K_h_e_wt"]          = jnp.full(T, DEFAULT_LN_K_H_E)
+    g[f"{name}_ln_K_l_e_wt"]          = jnp.full(T, DEFAULT_LN_K_L_E)
     g[f"{name}_sigma_d_ln_K_h_l"]     = jnp.array(0.3)
     g[f"{name}_sigma_d_ln_K_h_o"]     = jnp.array(0.3)
     g[f"{name}_sigma_d_ln_K_l_o"]     = jnp.array(0.3)
@@ -686,7 +684,6 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
     conc_unit_scale=1.0,
 ):
     """
@@ -702,7 +699,6 @@ def predict_unmeasured(
     param_posteriors : dict-like
     q_to_get : dict
     tf_total : float — monomer units (M)
-    op_total : float
     conc_unit_scale : float
         Multiply titrant_conc by this factor before thermodynamic equations.
         Use 1e-3 when concentrations are in mM and K values are in M⁻¹.
@@ -782,7 +778,7 @@ def _load(key):
     theta_samples = _solve_theta_np(
         ln_K_h_l_rows, ln_K_h_o_rows, ln_K_h_e_rows,
         ln_K_l_o_rows, ln_K_l_e_rows,
-        conc, tf_total, op_total,
+        conc, tf_total,
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PddG.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PddG.py
index 232d24f8..480c84b2 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PddG.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PddG.py
@@ -4,19 +4,29 @@
 Each mutation's effect on the five equilibrium constants (K_h_l, K_h_o, K_l_o,
 K_h_e, K_l_e) is derived from per-structure ΔΔG latent variables whose prior
 means come from a user-supplied CSV file (columns: mut, H, HO, L, LO, HE2, LE2).
-A learned per-structure trust scale σ_s controls how far the posterior can deviate
+A per-structure scale σ_s, sampled under a LogNormal prior, sets how far ΔΔG may deviate
 from the CSV values.  Registry name: ``theta_mwc_dimer_lnK_ddG_prior``.
 
 Contrast with PK (Δln_K inferred from data alone, no structure) and PnnC
 (ΔΔG prior means predicted by a per-structure MLP rather than supplied by the user).
 
-Implements the full MWC two-state lac-dimer model (Sochor 2014) with five
+Implements the standard MWC two-state lac-dimer model (``thermo/mwc.py``;
+constants and values from Sochor 2014) with five
 equilibrium constants, using six structural states as the free-energy basis:
 
     Structures: H, HO, L, LO, HE2, LE2
 
-    ΔΔG[m, s]  ~ Normal( ddG_prior[m, s],  σ_s )
-    σ_s        — per-structure trust scale (``pyro.param``, init=1.0)
+    σ_s        ~ LogNormal(theta_ddG_sigma_loc, theta_ddG_sigma_scale), one per
+                 structure (SD of the true ΔΔG about its prior mean)
+    Δln K[m]   ~ MVN( P · ddG_prior[m],  P · diag(σ_s²) · Pᵀ )      (site theta_dlnK)
+
+with P = ``DLNK_PROJ``, the projection below. This is the distribution the
+per-structure model ΔΔG[m, s] ~ Normal(ddG_prior[m, s], σ_s) induces on the
+constants, which are all the data see (thermo/prior.py). The per-structure
+ΔΔG is the deterministic site ``theta_ddG``: the conditional mean under that
+per-structure prior, whose common shift across structures comes from the
+prior means, not the data (no unfolded state anchors it; score per-structure
+ΔΔG only for U1 models).
 
 where ``ddG_prior`` is a (M, S) float32 array loaded from a CSV spreadsheet
 (columns: ``mut``, ``H``, ``HO``, ``L``, ``LO``, ``HE2``, ``LE2``).
@@ -33,38 +43,62 @@
 Data requirements
 -----------------
 ``data`` must have (stored as ``pytree_node=False`` statics):
-    struct_names            — tuple containing exactly {'H', 'HO', 'L', 'LO', 'HE2', 'LE2'}
+    struct_names            — tuple containing at least {'H', 'HO', 'L', 'LO', 'HE2', 'LE2'} (extras ignored)
     struct_features         — (M, S) float32  per-mutation per-structure ΔΔG prior means
     mut_geno_matrix         — (M, G) for mutation → genotype scatter
     num_mutation            — M
-    num_struct              — S (must equal 6)
+    num_struct              — S (at least 6)
 
 Input CSV format (passed via ``thermo_data``)
 ------------------------------------------------------
-A CSV with columns ``mut`` (mutation label) and one column per structure
-(``H``, ``HO``, ``L``, ``LO``, ``HE2``, ``LE2`` in any order).  Mutations
-absent from the CSV receive a prior mean of 0.0 (no structural prediction).
+A CSV with a mutation column (``mut``, ``Mutation`` or ``mutation``) and one
+column per structure (``H``, ``HO``, ``L``, ``LO``, ``HE2``, ``LE2``, any order; other columns are
+ignored, and ``thermo_options`` ``column_map`` can name the column for a
+structure).  ΔΔG_X is the shift a mutation makes to state X's free energy,
+so positive destabilizes X, and Δln K_{A→B} = ΔΔG_A − ΔΔG_B.  The model
+needs kT; ``thermo_options`` ``ddG_units: kcal/mol`` (or ``kJ/mol``, with
+``temperature_K``) converts at load, and the default ``kT`` uses the values
+as-is.  The loader (``thermo.io.load_ddG_prior_csv``) reports coverage, errors
+on zero coverage or a wt-residue mismatch (numbering offset; see
+``residue_offset``), and gives absent mutations a prior mean of 0.0 with a
+warning when more than ``max_missing_fraction`` are absent.
 """
 
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale
 import numpyro.distributions.constraints as constraints
 import pandas as pd
-from flax.struct import dataclass
+from flax.struct import dataclass, field
 from functools import partial
 from typing import Dict, Any, Union
 
 from tfscreen.tfmodel.data_class import GrowthData, BindingData
 from tfscreen.genetics.build_mut_geno_matrix import apply_pair_matrix, apply_mut_matrix
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
 from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
-    sample_ddG,
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    ddG_sigma_fixed,
+    ddG_extract_specs,
+    dlnK_guesses,
+    guide_dlnK,
+    projection,
+    sample_dlnK,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.horseshoe import (
     sample_pair_ddG,
     _DEFAULT_D0,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_H_E,
+    DEFAULT_LN_K_H_L,
+    DEFAULT_LN_K_H_O,
+    DEFAULT_LN_K_L_E,
+    DEFAULT_LN_K_L_O,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -72,18 +106,16 @@
     get_population_moments,
 )
 
-# Expected structure names — column order matches _PROJ below.
-STRUCTURE_NAMES = ('H', 'HO', 'L', 'LO', 'HE2', 'LE2')
+# Structure basis and projection rows (thermo/mwc.py). Convention: ΔΔG_X
+# adds to state X's free energy (kT), Δln K_{A→B} = ΔΔG_A − ΔΔG_B, and the
+# effector rows are per site, half of ΔΔG_X − ΔΔG_XE2.
+STRUCTURE_NAMES = mwc.STRUCTURE_NAMES_C12
+
+# Δln_K = ddG @ _PROJ.T; rows = constants, cols = STRUCTURE_NAMES.
+_PROJ = jnp.asarray(mwc.PROJ_C12)
 
-# Fixed thermodynamic projection: rows = (K_h_l, K_h_o, K_h_e, K_l_o, K_l_e),
-# cols = (H, HO, L, LO, HE2, LE2).
-_PROJ = jnp.array([
-    [ 1.,   0., -1.,   0.,   0.,   0. ],  # K_h_l = ΔΔG_H − ΔΔG_L
-    [ 1.,  -1.,  0.,   0.,   0.,   0. ],  # K_h_o = ΔΔG_H − ΔΔG_HO
-    [ 0.5,  0.,  0.,   0.,  -0.5,  0. ],  # K_h_e = (ΔΔG_H − ΔΔG_HE2)/2
-    [ 0.,   0.,  1.,  -1.,   0.,   0. ],  # K_l_o = ΔΔG_L − ΔΔG_LO
-    [ 0.,   0.,  0.5,  0.,   0.,  -0.5],  # K_l_e = (ΔΔG_L − ΔΔG_LE2)/2
-])
+# Δln K (sampled, M × 5) = ΔΔG @ DLNK_PROJ.T (thermo/prior.py).
+DLNK_PROJ = projection(mwc.PROJ_C12)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -106,20 +138,27 @@ class ModelPriors:
     theta_ln_K_l_e_wt_loc:   float
     theta_ln_K_l_e_wt_scale: float
 
-    # Physical concentrations (M); Sochor, PeerJ 2014
-    theta_tf_total_M: float    # ≈ 6.5e-7 M (650 nM)
-    theta_op_total_M: float    # ≈ 2.5e-8 M (25 nM)
+    # Total TF (M, monomer units)
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
 
     # Unit-conversion factor applied to data.titrant_conc before thermodynamics.
     # Default 1e-3 assumes concentrations are in mM and K values are in M⁻¹.
     theta_conc_unit_scale: float
 
-    # Regularised horseshoe for pairwise epistasis
+    # Regularized horseshoe for pairwise epistasis
     theta_epi_tau_scale:  float
     theta_epi_slab_scale: float
     theta_epi_slab_df:    float
     theta_epi_d0:         float
 
+    # LogNormal prior on the per-structure ΔΔG scale σ_s (log kT); see
+    # thermo/prior.py.
+    theta_ddG_sigma_loc:   float
+    theta_ddG_sigma_scale: float
+    # > 0 holds every σ_s at this value (kT) instead of sampling it;
+    # 0 (default) samples σ_s (thermo/prior.py, "Holding σ_s").
+    theta_ddG_sigma_fixed: float = field(pytree_node=False, default=0.0)
+
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Internal helpers
@@ -127,14 +166,16 @@ class ModelPriors:
 
 def _get_struct_perm(data):
     """
-    Verify struct_names contains exactly the required structures and return a
-    permutation index mapping data's S ordering to STRUCTURE_NAMES order.
+    Verify struct_names contains every required structure (extras are
+    ignored) and return the index of each STRUCTURE_NAMES entry in it.
     """
-    data_names = data.struct_names
-    if set(data_names) != set(STRUCTURE_NAMES):
+    data_names = tuple(data.struct_names or ())
+    missing = [s for s in STRUCTURE_NAMES if s not in data_names]
+    if missing:
         raise ValueError(
-            f"lnK_ddG_prior (mwc_dimer) requires struct_names to contain exactly "
-            f"{set(STRUCTURE_NAMES)}; got {data_names}."
+            f"lnK_ddG_prior (mwc_dimer) needs structures {STRUCTURE_NAMES}; "
+            f"struct_names {data_names} lacks {missing}. Extra "
+            f"structures are ignored."
         )
     name_to_idx = {n: i for i, n in enumerate(data_names)}
     return [name_to_idx[s] for s in STRUCTURE_NAMES]
@@ -231,7 +272,6 @@ def define_model(name: str,
     ddG_prior_means = jnp.array(data.struct_features)[:, perm_idx]   # (M, S)
 
     tf_total        = priors.theta_tf_total_M
-    op_total        = priors.theta_op_total_M
     conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── WT equilibrium constants ─────────────────────────────────────────────
@@ -252,12 +292,14 @@ def define_model(name: str,
             f"{name}_ln_K_l_e_wt",
             dist.Normal(priors.theta_ln_K_l_e_wt_loc, priors.theta_ln_K_l_e_wt_scale))
 
-    # ── Per-mutation per-structure ΔΔG latent variables ──────────────────────
-    # ddG_prior_means (M, S) serve as the Normal prior means; σ_s is learned.
-    ddG = sample_ddG(name, list(STRUCTURE_NAMES), num_mut, ddG_prior_means)  # (M, S)
-
-    # Project (M, 6) → (M, 5) Δln_K
-    delta_lnK  = _project_ddG(ddG)
+    # ── Per-mutation Δln K under the structural prior ─────────────────────────
+    # Δln K[m] ~ MVN(P · prior_mean[m], P · diag(σ_s²) · Pᵀ), σ_s per state
+    # (thermo/prior.py); the post-hoc per-state ΔΔG is the site {name}_ddG.
+    dlnK = sample_dlnK(name, list(STRUCTURE_NAMES), num_mut, ddG_prior_means, DLNK_PROJ,
+                       sigma_loc=priors.theta_ddG_sigma_loc,
+                       sigma_scale=priors.theta_ddG_sigma_scale,
+                       sigma_fixed=ddG_sigma_fixed(priors))  # (M, C)
+    delta_lnK  = dlnK[:, :5]
 
     pyro.deterministic(f"{name}_d_ln_K_h_l", delta_lnK[:, 0])
     pyro.deterministic(f"{name}_d_ln_K_h_o", delta_lnK[:, 1])
@@ -308,12 +350,12 @@ def define_model(name: str,
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -335,12 +377,11 @@ def guide(name: str,
                           mut_nnz_geno_idx=jnp.array(data.mut_nnz_geno_idx),
                           num_genotype=data.num_genotype)
     num_mut  = data.num_mutation
-    S        = data.num_struct
+    S        = len(STRUCTURE_NAMES)
     has_epi  = data.num_pair > 0
     ddG_prior_means = jnp.array(data.struct_features)[:, perm_idx]   # (M, S)
 
     tf_total        = priors.theta_tf_total_M
-    op_total        = priors.theta_op_total_M
     conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── Variational params for WT K values ────────────────────────────────────
@@ -355,18 +396,6 @@ def guide(name: str,
     ln_K_l_e_wt_locs   = pyro.param(f"{name}_ln_K_l_e_wt_locs",   jnp.full(T, priors.theta_ln_K_l_e_wt_loc))
     ln_K_l_e_wt_scales = pyro.param(f"{name}_ln_K_l_e_wt_scales", jnp.ones(T), constraint=constraints.positive)
 
-    # ── Register σ_s so param store includes it before sampling ──────────────
-    pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-
-    # ── Variational params for ddG offsets, shape (S, M) in plate layout ─────
-    ddG_offset_locs   = pyro.param(f"{name}_ddG_offset_locs",
-                                   jnp.zeros((S, num_mut)))
-    ddG_offset_scales = pyro.param(f"{name}_ddG_offset_scales",
-                                   jnp.ones((S, num_mut)),
-                                   constraint=constraints.positive)
-
     # ── Optional epistasis variational params ─────────────────────────────────
     if has_epi:
         num_pair = data.num_pair
@@ -387,19 +416,9 @@ def guide(name: str,
         ln_K_h_e_wt = pyro.sample(f"{name}_ln_K_h_e_wt", dist.Normal(ln_K_h_e_wt_locs, ln_K_h_e_wt_scales))
         ln_K_l_e_wt = pyro.sample(f"{name}_ln_K_l_e_wt", dist.Normal(ln_K_l_e_wt_locs, ln_K_l_e_wt_scales))
 
-    # ── Sample ddG offsets (matches model's prior.sample_ddG plate layout) ────
-    with pyro.plate(f"{name}_struct_plate", S, dim=-2):
-        with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
-            offsets = pyro.sample(
-                f"{name}_ddG_offset",
-                dist.Normal(ddG_offset_locs, ddG_offset_scales),
-            )  # (S, M)
-
-    sigma_s = pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-    ddG_SM = ddG_prior_means.T + sigma_s[:, None] * offsets   # (S, M)
-    ddG    = ddG_SM.T                                          # (M, S)
+    # ── Sample σ_s and Δln K (matches prior.sample_dlnK) ────────────────────
+    dlnK = guide_dlnK(name, num_mut, S, DLNK_PROJ, ddG_prior_means,
+                      sigma_fixed=ddG_sigma_fixed(priors))   # (M, C)
 
     # ── Sample epistasis ──────────────────────────────────────────────────────
     epi_delta_lnK = None
@@ -429,7 +448,7 @@ def guide(name: str,
                 )
 
         def _lam_tilde(l):
-            return jnp.sqrt(c2_epi * l ** 2 / (c2_epi + tau_epi ** 2 * l ** 2))
+            return regularized_scale(l, tau_epi, c2_epi)
 
         epi_SP        = epi_off * tau_epi * _lam_tilde(lam)   # (S, P)
         epi_ddG       = epi_SP.T                               # (P, S)
@@ -441,7 +460,7 @@ def _lam_tilde(l):
                                num_genotype=data.num_genotype)
 
     # ── Assemble per-genotype K values ────────────────────────────────────────
-    delta_lnK = _project_ddG(ddG)
+    delta_lnK = dlnK[:, :5]
     ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e = _assemble_K(
         ln_K_h_l_wt, ln_K_h_o_wt, ln_K_h_e_wt,
         ln_K_l_o_wt, ln_K_l_e_wt,
@@ -452,12 +471,12 @@ def _lam_tilde(l):
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -467,38 +486,42 @@ def _lam_tilde(l):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    # All values calibrated to Sochor et al. 2014 (PeerJ 2:e498), Table 1.
-    p["theta_ln_K_h_l_wt_loc"]   = 1.84
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_h_l_wt_loc"]   = DEFAULT_LN_K_H_L   # Sochor (2014) Table 1: K_h_l = 6.3
     p["theta_ln_K_h_l_wt_scale"] = 2.0
-    p["theta_ln_K_h_o_wt_loc"]   = 19.9
+    p["theta_ln_K_h_o_wt_loc"]   = DEFAULT_LN_K_H_O   # Sochor: K_h_o = 4.2e8 M⁻¹
     p["theta_ln_K_h_o_wt_scale"] = 2.0
-    p["theta_ln_K_h_e_wt_loc"]   = 10.9
+    p["theta_ln_K_h_e_wt_loc"]   = DEFAULT_LN_K_H_E   # per site; ≈ 3.7e3 M⁻¹ = K_l_e/79.6, theta_sat 0.05
     p["theta_ln_K_h_e_wt_scale"] = 2.0
-    p["theta_ln_K_l_o_wt_loc"]   = -2.3
+    p["theta_ln_K_l_o_wt_loc"]   = DEFAULT_LN_K_L_O   # Sochor: K_l_o = 0.1 M⁻¹
     p["theta_ln_K_l_o_wt_scale"] = 3.0
-    p["theta_ln_K_l_e_wt_loc"]   = 13.5
+    p["theta_ln_K_l_e_wt_loc"]   = DEFAULT_LN_K_L_E   # per site; ≈ 3.0e5 M⁻¹ = 0.39 × Sochor, midpoint 80 µM
     p["theta_ln_K_l_e_wt_scale"] = 2.0
-    p["theta_tf_total_M"]        = 6.5e-7
-    p["theta_op_total_M"]        = 2.5e-8
-    p["theta_conc_unit_scale"]   = 1e-3
+    p["theta_tf_total_M"]        = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"]   = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     p["theta_epi_tau_scale"]     = 0.1
     p["theta_epi_slab_scale"]    = 2.0
     p["theta_epi_slab_df"]       = 4.0
     p["theta_epi_d0"]            = float(_DEFAULT_D0)
+    p["theta_ddG_sigma_loc"]   = DEFAULT_DDG_SIGMA_LOC     # median σ_s 1 kT
+    p["theta_ddG_sigma_scale"] = DEFAULT_DDG_SIGMA_SCALE   # 95%: 0.1-10 kT
+    p["theta_ddG_sigma_fixed"] = 0.0   # > 0 holds σ_s (kT)
     return p
 
 
 def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, Any]:
     T = data.num_titrant_name
     M = data.num_mutation
-    S = data.num_struct
+    S = len(STRUCTURE_NAMES)
     g = {}
-    g[f"{name}_ln_K_h_l_wt"]  = jnp.array(1.84)
-    g[f"{name}_ln_K_h_o_wt"]  = jnp.array(19.9)
-    g[f"{name}_ln_K_h_e_wt"]  = jnp.full(T, 10.9)
-    g[f"{name}_ln_K_l_o_wt"]  = jnp.array(-2.3)
-    g[f"{name}_ln_K_l_e_wt"]  = jnp.full(T, 13.5)
-    g[f"{name}_ddG_offset"]    = jnp.zeros((S, M))
+    g[f"{name}_ln_K_h_l_wt"]  = jnp.array(DEFAULT_LN_K_H_L)
+    g[f"{name}_ln_K_h_o_wt"]  = jnp.array(DEFAULT_LN_K_H_O)
+    g[f"{name}_ln_K_h_e_wt"]  = jnp.full(T, DEFAULT_LN_K_H_E)
+    g[f"{name}_ln_K_l_o_wt"]  = jnp.array(DEFAULT_LN_K_L_O)
+    g[f"{name}_ln_K_l_e_wt"]  = jnp.full(T, DEFAULT_LN_K_L_E)
+    # Δln K starts at P · (ΔΔG prior means), σ_s at 1 kT.
+    g.update(dlnK_guesses(name, jnp.asarray(data.struct_features)[:, jnp.array(_get_struct_perm(data))], DLNK_PROJ))
     if data.num_pair > 0:
         P = data.num_pair
         g[f"{name}_epi_tau"]    = jnp.array(0.05)
@@ -573,6 +596,10 @@ def get_extract_specs(ctx):
             in_run_prefix="theta_",
         ))
 
+    # Per-structure ddG and its prior mean, in STRUCTURE_NAMES order (the
+    # order the model samples in and the simulator labels its truth with).
+    specs.extend(ddG_extract_specs(ctx, STRUCTURE_NAMES))
+
     return specs
 
 
@@ -586,7 +613,6 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
     conc_unit_scale=1.0,
 ):
     """
@@ -606,7 +632,6 @@ def predict_unmeasured(
     param_posteriors : dict-like
     q_to_get : dict
     tf_total : float — monomer units (M)
-    op_total : float
     conc_unit_scale : float
         Multiply titrant_conc by this factor before thermodynamic equations.
         Use 1e-3 when concentrations are in mM and K values are in M⁻¹.
@@ -685,7 +710,7 @@ def _load(key):
     theta_samples = _solve_theta_np(
         ln_K_h_l_rows, ln_K_h_o_rows, ln_K_h_e_rows,
         ln_K_l_o_rows, ln_K_l_e_rows,
-        conc, tf_total, op_total,
+        conc, tf_total,
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PnnC.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PnnC.py
index dd862d5d..27b5d551 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PnnC.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/PnnC.py
@@ -3,20 +3,31 @@
 
 Identical to PddG, but prior means for the per-structure ΔΔG latent variables
 are predicted by per-structure two-layer MLPs trained on LigandMPNN structural
-features rather than read from a user-supplied CSV.  The MLPs are optimised
+features rather than read from a user-supplied CSV.  The MLPs are optimized
 jointly with the model (empirical Bayes).  Requires a structural ensemble HDF5
 file as input.  Registry name: ``theta_mwc_dimer_lnK_nn_prior``.
 
 Contrast with PK (Δln_K inferred from data alone, no structure) and PddG
 (ΔΔG prior means from a user-supplied CSV rather than a trained NN).
 
-Implements the full MWC two-state lac-dimer model (Sochor 2014) with five
+Implements the standard MWC two-state lac-dimer model (``thermo/mwc.py``;
+constants and values from Sochor 2014) with five
 equilibrium constants, using six structural states as the free-energy basis:
 
     Structures: H, HO, L, LO, HE2, LE2
 
-    ΔΔG[m, s]  ~ Normal( MLP_s(features[m, s, :]) · n_chains[s],  σ_s )
-    σ_s        — per-structure trust scale (``pyro.param``, init=1.0)
+    σ_s        ~ LogNormal(theta_ddG_sigma_loc, theta_ddG_sigma_scale), one per
+                 structure (SD of the true ΔΔG about its prior mean)
+    Δln K[m]   ~ MVN( P · μ[m],  P · diag(σ_s²) · Pᵀ )      (site theta_dlnK)
+    μ[m, s]    = MLP_s(features[m, s, :]) · n_chains[s]
+
+with P = ``DLNK_PROJ``, the projection below. This is the distribution the
+per-structure model ΔΔG[m, s] ~ Normal(MLP_s(features[m, s, :]) ·
+n_chains[s], σ_s) induces on the constants, which are all the data see
+(thermo/prior.py). The per-structure ΔΔG is the deterministic site
+``theta_ddG``: the conditional mean under that per-structure prior, whose
+common shift across structures comes from the prior means, not the data (no
+unfolded state anchors it; score per-structure ΔΔG only for U1 models).
 
 Projection from per-structure ΔΔG to Δln_K (rows = K, cols = structures):
 
@@ -58,6 +69,7 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale
 import numpyro.distributions.constraints as constraints
 import pandas as pd
 from flax.struct import dataclass, field
@@ -70,14 +82,29 @@
     compute_nn_predictions,
     _DEFAULT_HIDDEN_SIZE,
 )
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
 from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
-    sample_ddG,
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    ddG_sigma_fixed,
+    ddG_extract_specs,
+    dlnK_guesses,
+    guide_dlnK,
+    projection,
+    sample_dlnK,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.horseshoe import (
     sample_pair_ddG,
     _DEFAULT_D0,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_H_E,
+    DEFAULT_LN_K_H_L,
+    DEFAULT_LN_K_H_O,
+    DEFAULT_LN_K_L_E,
+    DEFAULT_LN_K_L_O,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -85,25 +112,19 @@
     get_population_moments,
 )
 
-# Expected structure names — column order matches _PROJ below.
-STRUCTURE_NAMES = ('H', 'HO', 'L', 'LO', 'HE2', 'LE2')
-
-# Fixed thermodynamic projection: rows = (K_h_l, K_h_o, K_h_e, K_l_o, K_l_e),
-# cols = (H, HO, L, LO, HE2, LE2).
-# Δln_K = ddG @ _PROJ.T   where ddG is (..., 6) → result (..., 5)
-#
-# K_h_e and K_l_e are per-site constants (M⁻¹).  The HE2/LE2 structures have
-# both sites occupied, so ΔΔG_HE2 = 2·ΔΔG_HE and Δln_K_h_e = (ΔΔG_H − ΔΔG_HE2)/2.
-# Using HE2/LE2 (fully symmetric, both chains loaded) rather than HE/LE
-# (half-saturated, asymmetric chains) gives LigandMPNN a coherent single
-# structural environment to score, matching what _aggregate_logP expects.
-_PROJ = jnp.array([
-    [ 1.,   0., -1.,   0.,   0.,   0. ],  # K_h_l = ΔΔG_H − ΔΔG_L
-    [ 1.,  -1.,  0.,   0.,   0.,   0. ],  # K_h_o = ΔΔG_H − ΔΔG_HO
-    [ 0.5,  0.,  0.,   0.,  -0.5,  0. ],  # K_h_e = (ΔΔG_H − ΔΔG_HE2)/2
-    [ 0.,   0.,  1.,  -1.,   0.,   0. ],  # K_l_o = ΔΔG_L − ΔΔG_LO
-    [ 0.,   0.,  0.5,  0.,   0.,  -0.5],  # K_l_e = (ΔΔG_L − ΔΔG_LE2)/2
-])
+# Structure basis and projection rows (thermo/mwc.py). Convention: ΔΔG_X
+# adds to state X's free energy (kT), Δln K_{A→B} = ΔΔG_A − ΔΔG_B, and the
+# effector rows are per site, half of ΔΔG_X − ΔΔG_XE2.
+STRUCTURE_NAMES = mwc.STRUCTURE_NAMES_C12
+
+# Δln_K = ddG @ _PROJ.T; rows = constants, cols = STRUCTURE_NAMES.
+_PROJ = jnp.asarray(mwc.PROJ_C12)
+
+# Δln K (sampled, M × 5) = ΔΔG @ DLNK_PROJ.T (thermo/prior.py).
+DLNK_PROJ = projection(mwc.PROJ_C12)
+# The basis uses HE2/LE2 (both sites filled, symmetric chains) rather than
+# HE/LE (one site filled, asymmetric chains): it gives LigandMPNN one coherent
+# structural environment to score, as _aggregate_logP expects.
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -126,12 +147,9 @@ class ModelPriors:
     theta_ln_K_l_e_wt_loc:   float
     theta_ln_K_l_e_wt_scale: float
 
-    # Physical concentrations (M).
-    # theta_tf_total_M is the total TF in MONOMER units (same convention as
-    # MWCDimerModel.r_total in src/tfscreen/models/lac_model/mwc_dimer.py).
-    # The dimer concentration used in the mass-balance is tf_total_M / 2.
-    theta_tf_total_M: float    # ≈ 6.5e-7 M monomer (= 325 nM dimer)
-    theta_op_total_M: float    # ≈ 2.5e-8 M (25 nM)
+    # Total TF (M) in MONOMER units; the dimer concentration in the mass
+    # balance is tf_total_M / 2 (thermo/mwc.py).
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
 
     # Unit-conversion factor applied to data.titrant_conc before thermodynamics.
     # Default 1e-3 assumes concentrations are in mM and K values are in M⁻¹.
@@ -140,7 +158,7 @@ class ModelPriors:
     # Per-structure MLP hidden width (static shape — not a float parameter)
     theta_nn_hidden_size: int = field(pytree_node=False)
 
-    # Regularised horseshoe for pairwise epistasis
+    # Regularized horseshoe for pairwise epistasis
     theta_epi_tau_scale:  float
     theta_epi_slab_scale: float
     theta_epi_slab_df:    float
@@ -148,6 +166,14 @@ class ModelPriors:
     # Distance scale for distance-dependent λ prior (Å)
     theta_epi_d0: float
 
+    # LogNormal prior on the per-structure ΔΔG scale σ_s (log kT); see
+    # thermo/prior.py.
+    theta_ddG_sigma_loc:   float
+    theta_ddG_sigma_scale: float
+    # > 0 holds every σ_s at this value (kT) instead of sampling it;
+    # 0 (default) samples σ_s (thermo/prior.py, "Holding σ_s").
+    theta_ddG_sigma_fixed: float = field(pytree_node=False, default=0.0)
+
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Internal helpers
@@ -268,7 +294,6 @@ def define_model(name: str,
                 and data.struct_contact_distances is not None)
 
     tf_total        = priors.theta_tf_total_M
-    op_total        = priors.theta_op_total_M
     conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── WT equilibrium constants ─────────────────────────────────────────────
@@ -298,11 +323,14 @@ def define_model(name: str,
         hidden_size=priors.theta_nn_hidden_size,
     )  # (M, S)
 
-    # ── Per-mutation per-structure ΔΔG latent variables ──────────────────────
-    ddG = sample_ddG(name, list(data.struct_names), num_mut, nn_means)  # (M, S)
-
-    # Project (M, 6) → (M, 5) Δln_K
-    delta_lnK  = _project_ddG(ddG)         # (M, 5)
+    # ── Per-mutation Δln K under the structural prior ─────────────────────────
+    # Δln K[m] ~ MVN(P · prior_mean[m], P · diag(σ_s²) · Pᵀ), σ_s per state
+    # (thermo/prior.py); the post-hoc per-state ΔΔG is the site {name}_ddG.
+    dlnK = sample_dlnK(name, list(data.struct_names), num_mut, nn_means, DLNK_PROJ,
+                       sigma_loc=priors.theta_ddG_sigma_loc,
+                       sigma_scale=priors.theta_ddG_sigma_scale,
+                       sigma_fixed=ddG_sigma_fixed(priors))  # (M, C)
+    delta_lnK  = dlnK[:, :5]
     d_ln_K_h_l = delta_lnK[:, 0]           # (M,)
     d_ln_K_h_o = delta_lnK[:, 1]           # (M,)
     d_ln_K_h_e = delta_lnK[:, 2]           # (M,)
@@ -359,12 +387,12 @@ def define_model(name: str,
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -393,7 +421,6 @@ def guide(name: str,
                 and data.struct_contact_distances is not None)
 
     tf_total        = priors.theta_tf_total_M
-    op_total        = priors.theta_op_total_M
     conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── Variational params for WT K values ────────────────────────────────────
@@ -414,18 +441,6 @@ def guide(name: str,
         hidden_size=priors.theta_nn_hidden_size,
     )  # (M, S)
 
-    # ── Register sigma_s so the param store includes it before sampling ───────
-    pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-
-    # ── Variational params for ddG offsets, shape (S, M) in plate layout ─────
-    ddG_offset_locs   = pyro.param(f"{name}_ddG_offset_locs",
-                                   jnp.zeros((S, num_mut)))
-    ddG_offset_scales = pyro.param(f"{name}_ddG_offset_scales",
-                                   jnp.ones((S, num_mut)),
-                                   constraint=constraints.positive)
-
     # ── Optional epistasis variational params ─────────────────────────────────
     if has_epi:
         num_pair = data.num_pair
@@ -446,19 +461,9 @@ def guide(name: str,
         ln_K_h_e_wt = pyro.sample(f"{name}_ln_K_h_e_wt", dist.Normal(ln_K_h_e_wt_locs, ln_K_h_e_wt_scales))
         ln_K_l_e_wt = pyro.sample(f"{name}_ln_K_l_e_wt", dist.Normal(ln_K_l_e_wt_locs, ln_K_l_e_wt_scales))
 
-    # ── Sample ddG offsets (matches model's prior.sample_ddG plate layout) ────
-    with pyro.plate(f"{name}_struct_plate", S, dim=-2):
-        with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
-            offsets = pyro.sample(
-                f"{name}_ddG_offset",
-                dist.Normal(ddG_offset_locs, ddG_offset_scales),
-            )  # (S, M)
-
-    sigma_s = pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-    ddG_SM = nn_means.T + sigma_s[:, None] * offsets   # (S, M)
-    ddG    = ddG_SM.T                                   # (M, S)
+    # ── Sample σ_s and Δln K (matches prior.sample_dlnK) ────────────────────
+    dlnK = guide_dlnK(name, num_mut, S, DLNK_PROJ, jnp.zeros((num_mut, S)),
+                      sigma_fixed=ddG_sigma_fixed(priors))   # (M, C)
 
     # ── Sample epistasis ──────────────────────────────────────────────────────
     epi_delta_lnK = None
@@ -488,7 +493,7 @@ def guide(name: str,
                 )
 
         def _lam_tilde(l):
-            return jnp.sqrt(c2_epi * l ** 2 / (c2_epi + tau_epi ** 2 * l ** 2))
+            return regularized_scale(l, tau_epi, c2_epi)
 
         epi_SP    = epi_off * tau_epi * _lam_tilde(lam)   # (S, P)
         epi_ddG   = epi_SP.T                              # (P, S)
@@ -500,7 +505,7 @@ def _lam_tilde(l):
                                num_genotype=data.num_genotype)
 
     # ── Assemble per-genotype K values ────────────────────────────────────────
-    delta_lnK = _project_ddG(ddG)   # (M, 5)
+    delta_lnK = dlnK[:, :5]
     ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e = _assemble_K(
         ln_K_h_l_wt, ln_K_h_o_wt, ln_K_h_e_wt,
         ln_K_l_o_wt, ln_K_l_e_wt,
@@ -511,12 +516,12 @@ def _lam_tilde(l):
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -526,39 +531,20 @@ def _lam_tilde(l):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    # All values calibrated to Sochor et al. 2014 (PeerJ 2:e498), Table 1.
-    # Concentrations must be in Molar; equilibrium constants in M⁻¹.
-    #
-    # K_h_l = K_RR* = [L]/[H] ≈ 6.3 → ln ≈ 1.84
-    # The L (low-affinity) state is DOMINANT without IPTG (~86% of TF).
-    # K_h_l > 1 is required for the effector to produce derepression:
-    # when K_h_l << 1, adding effector has no effect on theta (flat curves).
-    p["theta_ln_K_h_l_wt_loc"]   = 1.84
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_h_l_wt_loc"]   = DEFAULT_LN_K_H_L   # Sochor (2014) Table 1: K_h_l = 6.3
     p["theta_ln_K_h_l_wt_scale"] = 2.0
-    # K_h_o = K_RO ≈ 0.42 nM⁻¹ = 4.2×10⁸ M⁻¹ → ln ≈ 19.9
-    p["theta_ln_K_h_o_wt_loc"]   = 19.9
+    p["theta_ln_K_h_o_wt_loc"]   = DEFAULT_LN_K_H_O   # Sochor: K_h_o = 4.2e8 M⁻¹
     p["theta_ln_K_h_o_wt_scale"] = 2.0
-    # K_h_e = K_RE ≈ 5.6×10⁻⁵ nM⁻¹ = 5.6×10⁴ M⁻¹ → ln ≈ 10.9
-    p["theta_ln_K_h_e_wt_loc"]   = 10.9
+    p["theta_ln_K_h_e_wt_loc"]   = DEFAULT_LN_K_H_E   # per site; ≈ 3.7e3 M⁻¹ = K_l_e/79.6, theta_sat 0.05
     p["theta_ln_K_h_e_wt_scale"] = 2.0
-    # K_l_o = K_R*O ≈ 0 (L-state does not bind operator DNA).
-    # Sochor fixes this at 10⁻¹⁰ nM⁻¹ = 0.1 M⁻¹ → ln ≈ -2.3.
-    p["theta_ln_K_l_o_wt_loc"]   = -2.3
+    p["theta_ln_K_l_o_wt_loc"]   = DEFAULT_LN_K_L_O   # Sochor: K_l_o = 0.1 M⁻¹
     p["theta_ln_K_l_o_wt_scale"] = 3.0
-    # K_l_e = K_R*E ≈ 7.6×10⁻⁴ nM⁻¹ = 7.6×10⁵ M⁻¹ → ln ≈ 13.5
-    # L-state binds IPTG ~14× more tightly than H-state (K_l_e/K_h_e ≈ 13.6).
-    p["theta_ln_K_l_e_wt_loc"]   = 13.5
+    p["theta_ln_K_l_e_wt_loc"]   = DEFAULT_LN_K_L_E   # per site; ≈ 3.0e5 M⁻¹ = 0.39 × Sochor, midpoint 80 µM
     p["theta_ln_K_l_e_wt_scale"] = 2.0
-    # Physical concentrations — theta_tf_total_M is in MONOMER units.
-    # Sochor in vitro: [TF]≈664 nM dimer = ~1328 nM monomer.
-    # 6.5e-7 M monomer = 650 nM monomer = 325 nM dimer is a round-number
-    # approximation suitable as the prior centre.
-    p["theta_tf_total_M"] = 6.5e-7   # monomer
-    p["theta_op_total_M"] = 2.5e-8   # operator (M)
-    # Unit-conversion factor: multiply data.titrant_conc by this before
-    # entering thermodynamic equations.  Default 1e-3 assumes concentrations
-    # are in mM and K values are in M⁻¹.
-    p["theta_conc_unit_scale"] = 1e-3
+    p["theta_tf_total_M"] = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     # NN architecture
     p["theta_nn_hidden_size"]   = _DEFAULT_HIDDEN_SIZE
     # Horseshoe hyperparameters for pairwise epistasis
@@ -566,6 +552,9 @@ def get_hyperparameters() -> Dict[str, Any]:
     p["theta_epi_slab_scale"]  = 2.0
     p["theta_epi_slab_df"]     = 4.0
     p["theta_epi_d0"]          = float(_DEFAULT_D0)
+    p["theta_ddG_sigma_loc"]   = DEFAULT_DDG_SIGMA_LOC     # median σ_s 1 kT
+    p["theta_ddG_sigma_scale"] = DEFAULT_DDG_SIGMA_SCALE   # 95%: 0.1-10 kT
+    p["theta_ddG_sigma_fixed"] = 0.0   # > 0 holds σ_s (kT)
     return p
 
 
@@ -574,12 +563,13 @@ def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, An
     M = data.num_mutation
     S = data.num_struct
     g = {}
-    g[f"{name}_ln_K_h_l_wt"]  = jnp.array(1.84)
-    g[f"{name}_ln_K_h_o_wt"]  = jnp.array(19.9)
-    g[f"{name}_ln_K_h_e_wt"]  = jnp.full(T, 10.9)
-    g[f"{name}_ln_K_l_o_wt"]  = jnp.array(-2.3)
-    g[f"{name}_ln_K_l_e_wt"]  = jnp.full(T, 13.5)
-    g[f"{name}_ddG_offset"]    = jnp.zeros((S, M))
+    g[f"{name}_ln_K_h_l_wt"]  = jnp.array(DEFAULT_LN_K_H_L)
+    g[f"{name}_ln_K_h_o_wt"]  = jnp.array(DEFAULT_LN_K_H_O)
+    g[f"{name}_ln_K_h_e_wt"]  = jnp.full(T, DEFAULT_LN_K_H_E)
+    g[f"{name}_ln_K_l_o_wt"]  = jnp.array(DEFAULT_LN_K_L_O)
+    g[f"{name}_ln_K_l_e_wt"]  = jnp.full(T, DEFAULT_LN_K_L_E)
+    # Δln K starts at P · (ΔΔG prior means), σ_s at 1 kT.
+    g.update(dlnK_guesses(name, jnp.zeros((M, S)), DLNK_PROJ))
     if data.num_pair > 0 and data.struct_contact_distances is not None:
         P = data.num_pair
         g[f"{name}_epi_tau"]    = jnp.array(0.05)
@@ -603,7 +593,6 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
     conc_unit_scale=1.0,
 ):
     """
@@ -625,7 +614,6 @@ def predict_unmeasured(
     param_posteriors : dict-like
     q_to_get : dict
     tf_total : float
-    op_total : float
     conc_unit_scale : float
 
     Returns
@@ -702,7 +690,7 @@ def _load(key):
     theta_samples = _solve_theta_np(
         ln_K_h_l_rows, ln_K_h_o_rows, ln_K_h_e_rows,
         ln_K_l_o_rows, ln_K_l_e_rows,
-        conc, tf_total, op_total,
+        conc, tf_total,
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
@@ -775,6 +763,10 @@ def get_extract_specs(ctx):
             in_run_prefix="theta_",
         ))
 
+    # Per-structure ddG and its prior mean, in STRUCTURE_NAMES order (the
+    # order the model samples in and the simulator labels its truth with).
+    specs.extend(ddG_extract_specs(ctx, STRUCTURE_NAMES))
+
     return specs
 
 
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/thermo.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/thermo.py
index 4ca4043d..f7972739 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/thermo.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/thermo.py
@@ -1,68 +1,57 @@
 """
-Shared thermodynamic functions for the MWC lac-dimer theta model.
+Shared thermodynamic functions for the M3 MWC lac-dimer θ model (O2_C12 U0).
 
-Full Monod-Wyman-Changeux (MWC) two-state model with five independent
-equilibrium constants (Sochor 2014).  Unlike the simplified lac-dimer model,
-this separately parameterises H- and L-state affinities for both operator
-(K_h_o, K_l_o) and effector (K_h_e, K_l_e), making it more expressive at the
-cost of identifiability with sparse data.  Used by
-``theta_mwc_dimer_lnK_mut`` (PK), ``theta_mwc_dimer_lnK_ddG_prior`` (PddG),
-and ``theta_mwc_dimer_lnK_nn_prior`` (PnnC).
+The standard Monod-Wyman-Changeux (MWC) two-conformation dimer with five
+equilibrium constants (Sochor 2014, PeerJ 2:e498, for the constants and
+values). Unlike M2 (``O2_C4``), both conformations may bind operator and
+effector. Used by ``thermo.O2_C12_K5_U0_a.PK``, ``.PddG`` and ``.PnnC``.
+The equations are in ``thermo/mwc.py`` and shared with M2, which is this
+model with K_h_e = K_l_o = 0. **All concentrations and K values are in M.**
 
-Full MWC two-state model (Sochor 2014, PeerJ 2:e498) with five equilibrium
-constants. **All concentrations and K values must be in Molar units.**
-
-    K_h_l  — H → L conformational equilibrium (dimensionless); K_h_l = [L]/[H]
+    K_h_l  — [L]/[H] (dimensionless)
     K_h_o  — H + O → HO  (M⁻¹)
-    K_h_e  — H + E → HE per step; HE + E → HE2 uses the same K_h_e (M⁻¹)
-    K_l_o  — L + O → LO  (M⁻¹); ≈ 0 for wild-type lac repressor (Sochor 2014)
-    K_l_e  — L + E → LE per step; LE + E → LE2 uses the same K_l_e (M⁻¹)
-
-Physical intuition (Sochor 2014, Table 1)
------------------------------------------
-Wild-type lac repressor in vivo:
-  K_h_l ≈ 6.3  →  L-state (low operator affinity) is DOMINANT (~86% of TF).
-  K_h_o ≈ 4.2×10⁸ M⁻¹  →  H-state binds operator tightly.
-  K_l_o ≈ 0.1  M⁻¹    →  L-state essentially does not bind operator.
-  K_h_e ≈ 5.6×10⁴ M⁻¹  →  H-state binds effector (IPTG) weakly.
-  K_l_e ≈ 7.6×10⁵ M⁻¹  →  L-state binds IPTG ~14× more tightly.
-
-IPTG mechanism: effector preferentially stabilises the L-state → free H-state
-decreases → operator occupancy (θ) decreases.
-
-⚠️  K_h_l > 1 is required for effector-induced derepression.  If K_h_l << 1,
-    F(e) ≈ P_H(e) and h_free × P_H is approximately constant — theta is flat
-    regardless of effector concentration.
-
-Operator-depletion approximation
----------------------------------
-[TF_monomer_total] ≈ 6.5×10⁻⁷ M (650 nM monomer = 325 nM dimer) >>
-[op_total] ≈ 2.5×10⁻⁸ M (25 nM).  Because operator-bound TF is < 4 % of
-total TF, operator-bound forms are omitted from the TF mass balance.
-Operator occupancy is then computed via the Langmuir formula:
-
-NOTE: tf_total is the **monomer** concentration, consistent with the
-scipy-based MWCDimerModel in src/tfscreen/models/lac_model/mwc_dimer.py
-(which also takes r_total in monomer units).  The dimer concentration
-entering the mass-balance is r = tf_total / 2.
-
-    h_free  = r / F(e_free)
-    F(e)    = P_H(e) + K_h_l · P_L(e)   (= B0 + B1·e + B2·e²)
-    P_H(e)  = 1 + K_h_e · e + K_h_e² · e²
-    P_L(e)  = 1 + K_l_e · e + K_l_e² · e²
-    W       = h_free · (K_h_o · P_H + K_h_l · K_l_o · P_L)
-    θ       = W / (1 + W)
-
-Free-effector cubic
--------------------
-Defining B0 = 1 + K_h_l,  B1 = K_h_e + K_h_l·K_l_e,  B2 = K_h_e² + K_h_l·K_l_e²
-and r = tf_total / 2 (dimer units), the effector mass balance gives the cubic:
+    K_h_e  — per-site effector association constant of H (M⁻¹)
+    K_l_o  — L + O → LO  (M⁻¹); ≈ 0 for wild-type lac repressor
+    K_l_e  — per-site effector association constant of L (M⁻¹)
+
+Each conformation has two independent, equivalent effector sites:
+
+    P_H(e) = (1 + K_h_e·e)²,    P_L(e) = (1 + K_l_e·e)²
+
+Until 2026-10-11 the model used Sochor's polynomial 1 + K·e + K²·e², which
+fixes the ratio of macroscopic constants within a conformation at
+K2/K1 = 1 instead of the independent-site 1/4 (see ``mwc.py``).
+
+Wild-type defaults
+------------------
+K_h_l = 6.3, K_h_o = 4.2×10⁸ M⁻¹ and K_l_o = 0.1 M⁻¹ are Sochor's (Table 1).
+The effector selectivity K_l_e/K_h_e = ``mwc.MWC_EFFECTOR_SELECTIVITY``
+≈ 79.6, wider than Sochor's fitted 13.6, puts θ at saturating effector at
+0.05 for tf_total = 1e-5 M. K_h_e and K_l_e (≈ 3.7×10³ and 3.0×10⁵ M⁻¹)
+then share the scale ``mwc.MWC_EFFECTOR_SCALE`` ≈ 0.39 on Sochor's K_l_e,
+which puts the midpoint of the default wt θ(IPTG) curve at 80 µM with
+effector depletion. The derivation is in ``mwc.py``.
+
+K_h_l > 1 is required for effector-induced derepression. If K_h_l << 1,
+F(e) ≈ P_H(e) and h·P_H is about constant, so θ is flat in effector.
+
+Bookkeeping
+-----------
+tf_total is in **monomer** units (default 1e-5 M); r = tf_total/2 is the
+dimer concentration. TF is about 400-fold in excess over operator, so
+operator-bound TF is left out of the TF mass balance and operator total does
+not enter the model:
+
+    h = r / F(e)
+    F(e) = P_H(e) + K_h_l·P_L(e) = B0 + B1·e + B2·e²
+    W = h·(K_h_o·P_H + K_h_l·K_l_o·P_L)
+    θ = W / (1 + W)
+
+with B0 = 1 + K_h_l, B1 = 2·(K_h_e + K_h_l·K_l_e), B2 = K_h_e² + K_h_l·K_l_e².
+Free effector solves the cubic in ``mwc.py``:
 
     B2·x³ + (B1 + B2·(2r − e_total))·x²
           + (B0 + B1·(r − e_total))·x − B0·e_total = 0
-
-Eight Newton steps from x₀ = e_total converge for all biophysically
-plausible parameters.
 """
 
 import jax
@@ -71,7 +60,17 @@
 import pandas as pd
 from flax.struct import dataclass, field
 
-NEWTON_ITERATIONS = 8
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
+from tfscreen.tfmodel.generative.components.theta.thermo.mwc import (  # noqa: F401
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_TF_TOTAL_M,
+    DEFAULT_LN_K_H_L,
+    DEFAULT_LN_K_H_O,
+    DEFAULT_LN_K_H_E,
+    DEFAULT_LN_K_L_O,
+    DEFAULT_LN_K_L_E,
+    NEWTON_ITERATIONS,
+)
 
 
 # ---------------------------------------------------------------------------
@@ -84,11 +83,10 @@ class ThetaParam:
 
     ln_K_h_l: jnp.ndarray   # (G,)    — log conformational equilibrium
     ln_K_h_o: jnp.ndarray   # (G,)    — log H-state DNA-binding constant
-    ln_K_h_e: jnp.ndarray   # (T, G)  — log H-state effector-binding constant
+    ln_K_h_e: jnp.ndarray   # (T, G)  — log per-site H-state effector constant
     ln_K_l_o: jnp.ndarray   # (G,)    — log L-state DNA-binding constant
-    ln_K_l_e: jnp.ndarray   # (T, G)  — log L-state effector-binding constant
+    ln_K_l_e: jnp.ndarray   # (T, G)  — log per-site L-state effector constant
     tf_total: float           # M — monomer units; r = tf_total/2 is the dimer concentration
-    op_total: float           # M
     mu:       jnp.ndarray    # (T, C, 1) — population mean logit-theta
     sigma:    jnp.ndarray    # (T, C, 1) — population std  logit-theta
     # Scale factor applied to data.titrant_conc before entering thermodynamic
@@ -102,7 +100,7 @@ class ThetaParam:
 # ---------------------------------------------------------------------------
 
 def _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e,
-                   titrant_conc, tf_total, op_total):
+                   titrant_conc, tf_total):
     """
     Evaluate MWC operator-occupancy for all (T, C, G) combinations.
 
@@ -113,57 +111,21 @@ def _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e,
     ln_K_h_e : (T, G)
     ln_K_l_o : (G,)
     ln_K_l_e : (T, G)
-    titrant_conc : (C,)
+    titrant_conc : (C,)  — M (already scaled)
     tf_total : scalar — total TF in **monomer** units (M); dimer units = tf_total/2
-    op_total : scalar
 
     Returns
     -------
     theta : (T, C, G)
     """
-    K_h_l = jnp.exp(ln_K_h_l)[None, None, :]    # (1, 1, G)
-    K_h_o = jnp.exp(ln_K_h_o)[None, None, :]    # (1, 1, G)
-    K_h_e = jnp.exp(ln_K_h_e)[:, None, :]       # (T, 1, G)
-    K_l_o = jnp.exp(ln_K_l_o)[None, None, :]    # (1, 1, G)
-    K_l_e = jnp.exp(ln_K_l_e)[:, None, :]       # (T, 1, G)
-    e_total = titrant_conc[None, :, None]        # (1, C, 1)
-
-    r = tf_total / 2.0   # dimer units
-
-    # TF partition-function coefficients (excluding operator-bound forms)
-    # B0: (1, 1, G)   B1, B2: (T, 1, G)
-    B0 = 1.0 + K_h_l
-    B1 = K_h_e + K_h_l * K_l_e
-    B2 = K_h_e ** 2 + K_h_l * K_l_e ** 2
-
-    # Cubic for free effector — all broadcast to (T, C, G)
-    c3 = B2
-    c2 = B1 + B2 * (2.0 * r - e_total)
-    c1 = B0 + B1 * (r - e_total)
-    c0 = -B0 * e_total
-
-    def f(x):
-        return c3 * x ** 3 + c2 * x ** 2 + c1 * x + c0
-
-    def df(x):
-        return 3.0 * c3 * x ** 2 + 2.0 * c2 * x + c1
-
-    def step(_, x):
-        dfx = df(x)
-        return x - f(x) / jnp.where(jnp.abs(dfx) < 1e-30, 1e-30, dfx)
-
-    x0 = jnp.broadcast_to(e_total, c2.shape)
-    e_free = jax.lax.fori_loop(0, NEWTON_ITERATIONS, step, x0)
-    e_free = jnp.clip(e_free, 0.0, e_total)
-
-    P_H = 1.0 + K_h_e * e_free + K_h_e ** 2 * e_free ** 2    # (T, C, G)
-    P_L = 1.0 + K_l_e * e_free + K_l_e ** 2 * e_free ** 2    # (T, C, G)
-
-    F = B0 + B1 * e_free + B2 * e_free ** 2                   # (T, C, G)
-    h_free = r / jnp.where(F < 1e-30, 1e-30, F)               # (T, C, G)
-
-    W = h_free * (K_h_o * P_H + K_h_l * K_l_o * P_L)
-    return W / (1.0 + W)                                       # (T, C, G)
+    return mwc.theta_c12(ln_K_h_l[None, None, :],
+                         ln_K_h_o[None, None, :],
+                         ln_K_h_e[:, None, :],
+                         ln_K_l_o[None, None, :],
+                         ln_K_l_e[:, None, :],
+                         None,
+                         titrant_conc[None, :, None],
+                         tf_total)
 
 
 def _population_moments(theta, data):
@@ -203,7 +165,6 @@ def run_model(theta_param: ThetaParam, data) -> jnp.ndarray:
         theta_param.ln_K_l_e,
         data.titrant_conc * theta_param.conc_unit_scale,
         theta_param.tf_total,
-        theta_param.op_total,
     )                                               # (T, C, G)
 
     geno_idx = data.batch_idx[data.geno_theta_idx]
@@ -227,9 +188,9 @@ def get_population_moments(theta_param: ThetaParam, data) -> tuple:
 
 
 def _solve_theta_np(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e,
-                    conc, tf_total, op_total):
+                    conc, tf_total):
     """
-    Pure-numpy Newton solve for MWC-dimer theta at per-row K values.
+    Numpy MWC-dimer θ at per-row K values (the same ``mwc.theta_c12`` as JAX).
 
     Parameters
     ----------
@@ -240,45 +201,15 @@ def _solve_theta_np(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e,
     ln_K_l_e : (S, N)
     conc     : (N,)  — effector concentrations in M (zero already replaced)
     tf_total : float  — monomer units; r = tf_total / 2 is the dimer concentration
-    op_total : float  — unused (operator-depletion approx.)
 
     Returns
     -------
     theta : np.ndarray, shape (S, N)
     """
-    K_h_l = np.exp(ln_K_h_l)
-    K_h_o = np.exp(ln_K_h_o)
-    K_h_e = np.exp(ln_K_h_e)
-    K_l_o = np.exp(ln_K_l_o)
-    K_l_e = np.exp(ln_K_l_e)
-
-    e_total = conc[np.newaxis, :]
-    r = tf_total / 2.0
-
-    B0 = 1.0 + K_h_l
-    B1 = K_h_e + K_h_l * K_l_e
-    B2 = K_h_e ** 2 + K_h_l * K_l_e ** 2
-
-    c3 = B2
-    c2 = B1 + B2 * (2.0 * r - e_total)
-    c1 = B0 + B1 * (r - e_total)
-    c0 = -B0 * e_total
-
-    e_free = np.broadcast_to(e_total, c2.shape).copy()
-    for _ in range(NEWTON_ITERATIONS):
-        f  = c3 * e_free**3 + c2 * e_free**2 + c1 * e_free + c0
-        df = 3.0 * c3 * e_free**2 + 2.0 * c2 * e_free + c1
-        e_free = e_free - f / np.where(np.abs(df) < 1e-30, 1e-30, df)
-    e_free = np.clip(e_free, 0.0, e_total)
-
-    P_H = 1.0 + K_h_e * e_free + K_h_e ** 2 * e_free ** 2
-    P_L = 1.0 + K_l_e * e_free + K_l_e ** 2 * e_free ** 2
-
-    F = B0 + B1 * e_free + B2 * e_free ** 2
-    h_free = r / np.where(F < 1e-30, 1e-30, F)
-
-    W = h_free * (K_h_o * P_H + K_h_l * K_l_o * P_L)
-    return W / (1.0 + W)
+    f = lambda v: np.asarray(v, float)  # noqa: E731
+    return mwc.theta_c12(f(ln_K_h_l), f(ln_K_h_o), f(ln_K_h_e), f(ln_K_l_o),
+                         f(ln_K_l_e), None, f(conc)[np.newaxis, :], tf_total,
+                         xp=np)
 
 
 def build_calc_df(model, manual_titrant_df):
@@ -290,7 +221,8 @@ def build_calc_df(model, manual_titrant_df):
     calc_df : pd.DataFrame
     internal_cols : list of str
     extra_kwargs : dict
-        ``tf_total`` and ``op_total`` required by ``compute_theta_samples``.
+        ``tf_total`` (M, monomer units) and ``conc_unit_scale`` required
+        by ``compute_theta_samples``.
     """
     import tfscreen.util.dataframe
 
@@ -331,18 +263,17 @@ def build_calc_df(model, manual_titrant_df):
 
     extra = {
         "tf_total": float(model.priors.theta.theta_tf_total_M),
-        "op_total": float(model.priors.theta.theta_op_total_M),
         "conc_unit_scale": float(model.priors.theta.theta_conc_unit_scale),
     }
     return calc_df, ["genotype_idx", "titrant_name_idx"], extra
 
 
-def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total,
+def compute_theta_samples(calc_df, param_posteriors, *, tf_total,
                           conc_unit_scale=1.0):
     """
     Compute posterior theta samples for the MWC dimer model.
 
-    Uses a numpy Newton solve identical in structure to the JAX version.
+    Uses the same equations as the JAX path (``mwc.theta_c12``) in numpy.
 
     Parameters
     ----------
@@ -352,7 +283,7 @@ def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total,
     param_posteriors : dict-like
         Posterior samples keyed by parameter name (with ``theta_`` prefix).
     tf_total : float
-    op_total : float
+        Total TF concentration in M, monomer units.
     conc_unit_scale : float, optional
         Multiply ``titrant_conc`` by this factor before passing to the
         thermodynamic equations.  Use ``1e-3`` when concentrations are in
@@ -390,4 +321,4 @@ def _load(key):
 
     return _solve_theta_np(ln_K_h_l_pts, ln_K_h_o_pts, ln_K_h_e_pts,
                            ln_K_l_o_pts, ln_K_l_e_pts,
-                           conc, tf_total, op_total)
+                           conc, tf_total)
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PK.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PK.py
index 49c79d2e..95f10743 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PK.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PK.py
@@ -7,15 +7,15 @@
 is used.  Registry name: ``theta_mwc_dimer_unfolded_lnK_mut``.
 
 Contrast with PddG (Δln_K derived from user-supplied structural ΔΔG) and PnnC
-(Δln_K derived from NN-predicted structural ΔΔG).  Note that PddG and PnnC in
-this family hold d_ln_K_u = 0 for all mutations by design.
+(Δln_K derived from NN-predicted structural ΔΔG).  In PddG and PnnC,
+d_ln_K_u is not free: it equals ΔΔG_H, since mutations leave U unchanged.
 
 Identical to mwc_dimer/lnK_mut.py with one additional scalar parameter:
 
     ln_K_u[g] = ln_K_u_wt + (d_ln_K_u · M)[g]
 
 where K_u = [U]/[H] is the H→U unfolding equilibrium.  In WT K_u << 1.
-Mutations that destabilise the protein increase K_u (positive d_ln_K_u).
+Mutations that destabilize the protein increase K_u (positive d_ln_K_u).
 When K_u dominates the partition function, θ collapses toward zero for all
 effector concentrations regardless of the folded-state equilibria.
 
@@ -27,6 +27,7 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale, HalfCauchy
 import pandas as pd
 from flax.struct import dataclass
 from functools import partial
@@ -35,6 +36,13 @@
 from tfscreen.tfmodel.data_class import GrowthData, BindingData
 from tfscreen.genetics.build_mut_geno_matrix import apply_pair_matrix, apply_mut_matrix
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_H_E,
+    DEFAULT_LN_K_H_L,
+    DEFAULT_LN_K_H_O,
+    DEFAULT_LN_K_L_E,
+    DEFAULT_LN_K_L_O,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -65,12 +73,9 @@ class ModelPriors:
     theta_ln_K_u_wt_loc:     float
     theta_ln_K_u_wt_scale:   float
 
-    # Physical concentrations (M).
-    # theta_tf_total_M is the total TF in MONOMER units (same convention as
-    # MWCDimerModel.r_total in src/tfscreen/models/lac_model/mwc_dimer.py).
-    # The dimer concentration used in the mass-balance is tf_total_M / 2.
-    theta_tf_total_M: float   # ≈ 6.5e-7 M monomer (= 325 nM dimer)
-    theta_op_total_M: float   # ≈ 2.5e-8 M (25 nM)
+    # Total TF (M) in MONOMER units; the dimer concentration in the mass
+    # balance is tf_total_M / 2 (thermo/mwc.py).
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
 
     # Unit-conversion factor applied to data.titrant_conc before it enters
     # the thermodynamic equations.  Set to 1e-3 when concentrations in the
@@ -85,7 +90,7 @@ class ModelPriors:
     theta_sigma_d_ln_K_l_e_scale: float
     theta_sigma_d_ln_K_u_scale:   float
 
-    # Shared regularised horseshoe hyperparameters for pairwise epistasis
+    # Shared regularized horseshoe hyperparameters for pairwise epistasis
     theta_epi_tau_scale:  float   # HalfCauchy scale for global τ
     theta_epi_slab_scale: float   # typical size of a large epistasis effect
     theta_epi_slab_df:    float   # InvGamma shape ν (usually 4)
@@ -154,7 +159,7 @@ def define_model(name: str,
 
     Samples WT equilibrium constants and per-mutation delta parameters, then
     assembles per-genotype constants via matrix multiplication.  Epistasis uses
-    a regularised horseshoe prior (shared τ and slab c², per-pair local λ)
+    a regularized horseshoe prior (shared τ and slab c², per-pair local λ)
     and is applied only to folded-state K values (K_h_l, K_h_o, K_l_o, K_h_e,
     K_l_e).  K_u carries no epistasis term.
 
@@ -181,7 +186,6 @@ def define_model(name: str,
     has_epi = data.num_pair > 0
 
     tf_total         = priors.theta_tf_total_M
-    op_total         = priors.theta_op_total_M
     conc_unit_scale  = priors.theta_conc_unit_scale
 
     # ------------------------------------------------------------------
@@ -233,20 +237,27 @@ def define_model(name: str,
     # Per-mutation delta offsets
     # Scalar K_h_l, K_h_o, K_l_o, K_u: shape (num_mutation,)
     # ------------------------------------------------------------------
-    with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
-        d_K_h_l_off = pyro.sample(f"{name}_d_ln_K_h_l_offset", dist.Normal(0.0, 1.0))
-        d_K_h_o_off = pyro.sample(f"{name}_d_ln_K_h_o_offset", dist.Normal(0.0, 1.0))
-        d_K_l_o_off = pyro.sample(f"{name}_d_ln_K_l_o_offset", dist.Normal(0.0, 1.0))
-        d_K_u_off   = pyro.sample(f"{name}_d_ln_K_u_offset",   dist.Normal(0.0, 1.0))
-
-    # T-dimensional K_h_e, K_l_e: shape (T, num_mutation)
-    with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
-        with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
-            d_K_h_e_off = pyro.sample(f"{name}_d_ln_K_h_e_offset", dist.Normal(0.0, 1.0))
-            d_K_l_e_off = pyro.sample(f"{name}_d_ln_K_l_e_offset", dist.Normal(0.0, 1.0))
+    if num_mut > 0:
+        with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
+            d_K_h_l_off = pyro.sample(f"{name}_d_ln_K_h_l_offset", dist.Normal(0.0, 1.0))
+            d_K_h_o_off = pyro.sample(f"{name}_d_ln_K_h_o_offset", dist.Normal(0.0, 1.0))
+            d_K_l_o_off = pyro.sample(f"{name}_d_ln_K_l_o_offset", dist.Normal(0.0, 1.0))
+            d_K_u_off   = pyro.sample(f"{name}_d_ln_K_u_offset",   dist.Normal(0.0, 1.0))
+
+        # T-dimensional K_h_e, K_l_e: shape (T, num_mutation)
+        with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
+            with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
+                d_K_h_e_off = pyro.sample(f"{name}_d_ln_K_h_e_offset", dist.Normal(0.0, 1.0))
+                d_K_l_e_off = pyro.sample(f"{name}_d_ln_K_l_e_offset", dist.Normal(0.0, 1.0))
+    else:
+        # num_mut == 0 (wt-only library, e.g. a stratified binding
+        # pool): numpyro.plate needs size > 0, so skip the sites.  The
+        # empty offsets scatter to zeros, so every genotype is wt.
+        d_K_h_l_off = d_K_h_o_off = d_K_l_o_off = d_K_u_off = jnp.zeros((0,))
+        d_K_h_e_off = d_K_l_e_off = jnp.zeros((T, 0))
 
     # ------------------------------------------------------------------
-    # Optional epistasis — regularised horseshoe prior (folded-state only)
+    # Optional epistasis — regularized horseshoe prior (folded-state only)
     # ------------------------------------------------------------------
     if has_epi:
         pair_scatter = partial(apply_pair_matrix,
@@ -257,7 +268,7 @@ def define_model(name: str,
 
         tau_epi = pyro.sample(
             f"{name}_epi_tau",
-            dist.HalfCauchy(priors.theta_epi_tau_scale))
+            HalfCauchy(priors.theta_epi_tau_scale))
         c2_epi = pyro.sample(
             f"{name}_epi_c2",
             dist.InverseGamma(priors.theta_epi_slab_df / 2.0,
@@ -265,19 +276,19 @@ def define_model(name: str,
 
         # Scalar K_h_l, K_h_o, K_l_o: (num_pair,) each
         with pyro.plate(f"{name}_pair_scalar_plate", num_pair, dim=-1):
-            epi_K_h_l_lam = pyro.sample(f"{name}_epi_ln_K_h_l_lambda", dist.HalfCauchy(1.0))
+            epi_K_h_l_lam = pyro.sample(f"{name}_epi_ln_K_h_l_lambda", HalfCauchy(1.0))
             epi_K_h_l_off = pyro.sample(f"{name}_epi_ln_K_h_l_offset", dist.Normal(0.0, 1.0))
-            epi_K_h_o_lam = pyro.sample(f"{name}_epi_ln_K_h_o_lambda", dist.HalfCauchy(1.0))
+            epi_K_h_o_lam = pyro.sample(f"{name}_epi_ln_K_h_o_lambda", HalfCauchy(1.0))
             epi_K_h_o_off = pyro.sample(f"{name}_epi_ln_K_h_o_offset", dist.Normal(0.0, 1.0))
-            epi_K_l_o_lam = pyro.sample(f"{name}_epi_ln_K_l_o_lambda", dist.HalfCauchy(1.0))
+            epi_K_l_o_lam = pyro.sample(f"{name}_epi_ln_K_l_o_lambda", HalfCauchy(1.0))
             epi_K_l_o_off = pyro.sample(f"{name}_epi_ln_K_l_o_offset", dist.Normal(0.0, 1.0))
 
         # T-dimensional K_h_e, K_l_e: (T, num_pair) each
         with pyro.plate(f"{name}_titrant_epi_outer_plate", T, dim=-2):
             with pyro.plate(f"{name}_pair_plate", num_pair, dim=-1):
-                epi_K_h_e_lam = pyro.sample(f"{name}_epi_ln_K_h_e_lambda", dist.HalfCauchy(1.0))
+                epi_K_h_e_lam = pyro.sample(f"{name}_epi_ln_K_h_e_lambda", HalfCauchy(1.0))
                 epi_K_h_e_off = pyro.sample(f"{name}_epi_ln_K_h_e_offset", dist.Normal(0.0, 1.0))
-                epi_K_l_e_lam = pyro.sample(f"{name}_epi_ln_K_l_e_lambda", dist.HalfCauchy(1.0))
+                epi_K_l_e_lam = pyro.sample(f"{name}_epi_ln_K_l_e_lambda", HalfCauchy(1.0))
                 epi_K_l_e_off = pyro.sample(f"{name}_epi_ln_K_l_e_offset", dist.Normal(0.0, 1.0))
     else:
         pair_scatter = None
@@ -301,7 +312,7 @@ def define_model(name: str,
 
     if has_epi:
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         epi_ln_K_h_l = epi_K_h_l_off * tau_epi * _lam_tilde(epi_K_h_l_lam)   # (P,)
         epi_ln_K_h_o = epi_K_h_o_off * tau_epi * _lam_tilde(epi_K_h_o_lam)   # (P,)
@@ -363,12 +374,12 @@ def _lam_tilde(lam):
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e, ln_K_u,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e, ln_K_u=ln_K_u,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -390,7 +401,6 @@ def guide(name: str,
     has_epi = data.num_pair > 0
 
     tf_total         = priors.theta_tf_total_M
-    op_total         = priors.theta_op_total_M
     conc_unit_scale  = priors.theta_conc_unit_scale
 
     # ------------------------------------------------------------------
@@ -490,23 +500,30 @@ def guide(name: str,
         sigma_d_K_h_e = pyro.sample(f"{name}_sigma_d_ln_K_h_e", dist.LogNormal(sigma_d_K_h_e_locs, sigma_d_K_h_e_scales))
         sigma_d_K_l_e = pyro.sample(f"{name}_sigma_d_ln_K_l_e", dist.LogNormal(sigma_d_K_l_e_locs, sigma_d_K_l_e_scales))
 
-    with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
-        d_K_h_l_off = pyro.sample(f"{name}_d_ln_K_h_l_offset", dist.Normal(d_K_h_l_locs, d_K_h_l_scales))
-        d_K_h_o_off = pyro.sample(f"{name}_d_ln_K_h_o_offset", dist.Normal(d_K_h_o_locs, d_K_h_o_scales))
-        d_K_l_o_off = pyro.sample(f"{name}_d_ln_K_l_o_offset", dist.Normal(d_K_l_o_locs, d_K_l_o_scales))
-        d_K_u_off   = pyro.sample(f"{name}_d_ln_K_u_offset",   dist.Normal(d_K_u_locs,   d_K_u_scales))
-
-    with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
-        with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
-            d_K_h_e_off = pyro.sample(f"{name}_d_ln_K_h_e_offset", dist.Normal(d_K_h_e_locs, d_K_h_e_scales))
-            d_K_l_e_off = pyro.sample(f"{name}_d_ln_K_l_e_offset", dist.Normal(d_K_l_e_locs, d_K_l_e_scales))
+    if num_mut > 0:
+        with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
+            d_K_h_l_off = pyro.sample(f"{name}_d_ln_K_h_l_offset", dist.Normal(d_K_h_l_locs, d_K_h_l_scales))
+            d_K_h_o_off = pyro.sample(f"{name}_d_ln_K_h_o_offset", dist.Normal(d_K_h_o_locs, d_K_h_o_scales))
+            d_K_l_o_off = pyro.sample(f"{name}_d_ln_K_l_o_offset", dist.Normal(d_K_l_o_locs, d_K_l_o_scales))
+            d_K_u_off   = pyro.sample(f"{name}_d_ln_K_u_offset",   dist.Normal(d_K_u_locs,   d_K_u_scales))
+
+        with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
+            with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
+                d_K_h_e_off = pyro.sample(f"{name}_d_ln_K_h_e_offset", dist.Normal(d_K_h_e_locs, d_K_h_e_scales))
+                d_K_l_e_off = pyro.sample(f"{name}_d_ln_K_l_e_offset", dist.Normal(d_K_l_e_locs, d_K_l_e_scales))
+    else:
+        # num_mut == 0 (wt-only library, e.g. a stratified binding
+        # pool): numpyro.plate needs size > 0, so skip the sites.  The
+        # empty offsets scatter to zeros, so every genotype is wt.
+        d_K_h_l_off = d_K_h_o_off = d_K_l_o_off = d_K_u_off = jnp.zeros((0,))
+        d_K_h_e_off = d_K_l_e_off = jnp.zeros((T, 0))
 
     if has_epi:
         tau_epi = pyro.sample(f"{name}_epi_tau", dist.LogNormal(tau_epi_loc, tau_epi_scale))
         c2_epi  = pyro.sample(f"{name}_epi_c2",  dist.LogNormal(c2_epi_loc,  c2_epi_scale))
 
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         # Scalar K_h_l epistasis: (num_pair,)
         epi_K_h_l_lam_locs   = pyro.param(f"{name}_epi_ln_K_h_l_lambda_locs",   jnp.zeros(num_pair))
@@ -593,12 +610,12 @@ def _lam_tilde(lam):
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e, ln_K_u,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e, ln_K_u=ln_K_u,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -608,42 +625,23 @@ def _lam_tilde(lam):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    # All values calibrated to Sochor et al. 2014 (PeerJ 2:e498), Table 1.
-    # Concentrations must be in Molar; equilibrium constants in M⁻¹.
-    #
-    # K_h_l = K_RR* = [L]/[H] ≈ 6.3 → ln ≈ 1.84
-    # The L (low-affinity) state is DOMINANT without IPTG (~86% of TF).
-    # K_h_l > 1 is required for the effector to produce derepression:
-    # when K_h_l << 1, adding effector has no effect on theta (flat curves).
-    p["theta_ln_K_h_l_wt_loc"]   = 1.84
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_h_l_wt_loc"]   = DEFAULT_LN_K_H_L   # Sochor (2014) Table 1: K_h_l = 6.3
     p["theta_ln_K_h_l_wt_scale"] = 2.0
-    # K_h_o = K_RO ≈ 0.42 nM⁻¹ = 4.2×10⁸ M⁻¹ → ln ≈ 19.9
-    p["theta_ln_K_h_o_wt_loc"]   = 19.9
+    p["theta_ln_K_h_o_wt_loc"]   = DEFAULT_LN_K_H_O   # Sochor: K_h_o = 4.2e8 M⁻¹
     p["theta_ln_K_h_o_wt_scale"] = 2.0
-    # K_l_o = K_R*O ≈ 0 (L-state does not bind operator DNA).
-    # Sochor fixes this at 10⁻¹⁰ nM⁻¹ = 0.1 M⁻¹ → ln ≈ -2.3.
-    p["theta_ln_K_l_o_wt_loc"]   = -2.3
+    p["theta_ln_K_l_o_wt_loc"]   = DEFAULT_LN_K_L_O   # Sochor: K_l_o = 0.1 M⁻¹
     p["theta_ln_K_l_o_wt_scale"] = 3.0
-    # K_h_e = K_RE ≈ 5.6×10⁻⁵ nM⁻¹ = 5.6×10⁴ M⁻¹ → ln ≈ 10.9
-    p["theta_ln_K_h_e_wt_loc"]   = 10.9
+    p["theta_ln_K_h_e_wt_loc"]   = DEFAULT_LN_K_H_E   # per site; ≈ 3.7e3 M⁻¹ = K_l_e/79.6, theta_sat 0.05
     p["theta_ln_K_h_e_wt_scale"] = 2.0
-    # K_l_e = K_R*E ≈ 7.6×10⁻⁴ nM⁻¹ = 7.6×10⁵ M⁻¹ → ln ≈ 13.5
-    # L-state binds IPTG ~14× more tightly than H-state (K_l_e/K_h_e ≈ 13.6).
-    p["theta_ln_K_l_e_wt_loc"]   = 13.5
+    p["theta_ln_K_l_e_wt_loc"]   = DEFAULT_LN_K_L_E   # per site; ≈ 3.0e5 M⁻¹ = 0.39 × Sochor, midpoint 80 µM
     p["theta_ln_K_l_e_wt_scale"] = 2.0
     # WT unfolded state: K_u << 1 in WT (ln_K_u ≈ -12 → K_u ≈ 6e-6)
     p["theta_ln_K_u_wt_loc"]     = -12.0
     p["theta_ln_K_u_wt_scale"]   = 3.0
-    # Physical concentrations — theta_tf_total_M is in MONOMER units.
-    # Sochor in vitro: [TF]≈664 nM dimer = ~1328 nM monomer.
-    # 6.5e-7 M monomer = 650 nM monomer = 325 nM dimer is a round-number
-    # approximation suitable as the prior centre.
-    p["theta_tf_total_M"] = 6.5e-7   # monomer
-    p["theta_op_total_M"] = 2.5e-8   # operator (M)
-    # Unit-conversion factor: multiply data.titrant_conc by this before
-    # entering thermodynamic equations.  Default 1e-3 assumes concentrations
-    # are in mM and K values are in M⁻¹.
-    p["theta_conc_unit_scale"] = 1e-3
+    p["theta_tf_total_M"] = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     # Mutation effect scales
     p["theta_sigma_d_ln_K_h_l_scale"] = 1.0
     p["theta_sigma_d_ln_K_h_o_scale"] = 1.0
@@ -651,7 +649,7 @@ def get_hyperparameters() -> Dict[str, Any]:
     p["theta_sigma_d_ln_K_h_e_scale"] = 1.0
     p["theta_sigma_d_ln_K_l_e_scale"] = 1.0
     p["theta_sigma_d_ln_K_u_scale"]   = 1.0
-    # Shared regularised horseshoe hyperparameters
+    # Shared regularized horseshoe hyperparameters
     p["theta_epi_tau_scale"]   = 0.1
     p["theta_epi_slab_scale"]  = 2.0
     p["theta_epi_slab_df"]     = 4.0
@@ -662,11 +660,11 @@ def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, An
     T = data.num_titrant_name
     M = data.num_mutation
     g = {}
-    g[f"{name}_ln_K_h_l_wt"]          = jnp.array(1.84)
-    g[f"{name}_ln_K_h_o_wt"]          = jnp.array(19.9)
-    g[f"{name}_ln_K_l_o_wt"]          = jnp.array(-2.3)
-    g[f"{name}_ln_K_h_e_wt"]          = jnp.full(T, 10.9)
-    g[f"{name}_ln_K_l_e_wt"]          = jnp.full(T, 13.5)
+    g[f"{name}_ln_K_h_l_wt"]          = jnp.array(DEFAULT_LN_K_H_L)
+    g[f"{name}_ln_K_h_o_wt"]          = jnp.array(DEFAULT_LN_K_H_O)
+    g[f"{name}_ln_K_l_o_wt"]          = jnp.array(DEFAULT_LN_K_L_O)
+    g[f"{name}_ln_K_h_e_wt"]          = jnp.full(T, DEFAULT_LN_K_H_E)
+    g[f"{name}_ln_K_l_e_wt"]          = jnp.full(T, DEFAULT_LN_K_L_E)
     g[f"{name}_ln_K_u_wt"]            = jnp.array(-12.0)
     g[f"{name}_sigma_d_ln_K_h_l"]     = jnp.array(0.3)
     g[f"{name}_sigma_d_ln_K_h_o"]     = jnp.array(0.3)
@@ -711,7 +709,6 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
     conc_unit_scale=1.0,
 ):
     """
@@ -727,7 +724,6 @@ def predict_unmeasured(
     param_posteriors : dict-like
     q_to_get : dict
     tf_total : float — monomer units (M)
-    op_total : float
     conc_unit_scale : float
         Multiply titrant_conc by this factor before thermodynamic equations.
         Use 1e-3 when concentrations are in mM and K values are in M⁻¹.
@@ -811,7 +807,7 @@ def _load(key):
     theta_samples = _solve_theta_np(
         ln_K_h_l_rows, ln_K_h_o_rows, ln_K_h_e_rows,
         ln_K_l_o_rows, ln_K_l_e_rows, ln_K_u_rows,
-        conc, tf_total, op_total,
+        conc, tf_total,
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PddG.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PddG.py
index 0f14b88b..cb105ade 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PddG.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PddG.py
@@ -3,23 +3,29 @@
 
 Extends the MWC dimer PddG model with a WT-level unfolding constant (ln_K_u_wt).
 Per-mutation effects on the five folded-state K values (K_h_l, K_h_o, K_l_o,
-K_h_e, K_l_e) are derived from user-supplied structural ΔΔG (CSV: columns mut,
-H, HO, L, LO, HE2, LE2); K_u is held at its WT value for all genotypes
-(d_ln_K_u = 0) because structure-based predictors describe folded-state
-environments, not global stability.  Registry name:
+K_h_e, K_l_e) and on K_u are derived from user-supplied structural ΔΔG (CSV:
+columns mut, H, HO, L, LO, HE2, LE2).  Registry name:
 ``theta_mwc_dimer_unfolded_lnK_ddG_prior``.
 
 Contrast with PK (all K shifts inferred from data, including K_u per mutation)
 and PnnC (ΔΔG prior means from a per-structure MLP rather than a user CSV).
 
-Implements the full MWC two-state lac-dimer model (Sochor 2014) with five
+Implements the standard MWC two-state lac-dimer model (``thermo/mwc.py``;
+constants and values from Sochor 2014) with five
 equilibrium constants plus a global unfolding equilibrium, using six structural
 states as the free-energy basis:
 
     Structures: H, HO, L, LO, HE2, LE2
 
-    ΔΔG[m, s]  ~ Normal( ddG_prior[m, s],  σ_s )
-    σ_s        — per-structure trust scale (``pyro.param``, init=1.0)
+    σ_s        ~ LogNormal(theta_ddG_sigma_loc, theta_ddG_sigma_scale), one per
+                 structure (SD of the true ΔΔG about its prior mean)
+    Δln K[m]   ~ MVN( P · ddG_prior[m],  P · diag(σ_s²) · Pᵀ )      (site theta_dlnK)
+
+with P = ``DLNK_PROJ``, the projection below plus the unfolding row. This is
+the distribution the per-structure model ΔΔG[m, s] ~ Normal(ddG_prior[m, s],
+σ_s) induces on the constants, which are all the data see (thermo/prior.py).
+The per-structure ΔΔG is the deterministic site ``theta_ddG``: P⁻¹ · Δln K
+exactly (ΔΔG_U = 0; the unfolding row makes P square and invertible).
 
 where ``ddG_prior`` is a (M, S) float32 array loaded from a CSV spreadsheet
 (columns: ``mut``, ``H``, ``HO``, ``L``, ``LO``, ``HE2``, ``LE2``).
@@ -35,53 +41,81 @@
 
 Unfolding extension
 -------------------
-A single WT-level unfolding constant ``ln_K_u_wt`` is sampled and broadcast
-uniformly across all genotypes:
+A WT-level unfolding constant ``ln_K_u_wt`` is sampled, and each mutation
+shifts it through the H column of its ΔΔG.  A mutation shifts the free energy
+of each folded structure X by ΔΔG_X and leaves the unfolded state alone
+(ΔΔG_U = 0, the standard assumption).  Because K_u = [U]/[H], the projection
+convention above (Δln K_{A→B} = ΔΔG_A − ΔΔG_B) gives
 
+    Δln_K_u   = ΔΔG_H − ΔΔG_U = ΔΔG_H
     ln_K_u_wt ~ Normal(theta_ln_K_u_wt_loc, theta_ln_K_u_wt_scale)
-    ln_K_u[g]  = ln_K_u_wt   for all g
+    ln_K_u[g] = ln_K_u_wt + Σ_m ΔΔG_H[m]   (plus the pairwise ΔΔG_H epistasis)
 
-K_u is homogeneous across genotypes; per-mutation unfolding effects are absent
-by design.  This mirrors lac_dimer_unfolded, where the unfolded state adds a
-global thermodynamic sink but mutation-level ΔΔG contributions to unfolding
-are not modelled — that extension would require a separate structural ensemble
-and is deferred to a future component.
+A mutation that raises every folded state by the same ΔΔG leaves the five
+folded-state constants unchanged and raises K_u by ΔΔG: a pure stability
+effect.  No extra structure is needed.  The per-mutation shift is exposed as
+``d_ln_K_u`` (and ``epi_ln_K_u`` per pair), the same name the PK model uses
+for its free per-mutation unfolding shift.
 
 Data requirements
 -----------------
 ``data`` must have (stored as ``pytree_node=False`` statics):
-    struct_names            — tuple containing exactly {'H', 'HO', 'L', 'LO', 'HE2', 'LE2'}
+    struct_names            — tuple containing at least {'H', 'HO', 'L', 'LO', 'HE2', 'LE2'} (extras ignored)
     struct_features         — (M, S) float32  per-mutation per-structure ΔΔG prior means
     mut_geno_matrix         — (M, G) for mutation → genotype scatter
     num_mutation            — M
-    num_struct              — S (must equal 6)
+    num_struct              — S (at least 6)
 
 Input CSV format (passed via ``thermo_data``)
 ------------------------------------------------------
-A CSV with columns ``mut`` (mutation label) and one column per structure
-(``H``, ``HO``, ``L``, ``LO``, ``HE2``, ``LE2`` in any order).  Mutations
-absent from the CSV receive a prior mean of 0.0 (no structural prediction).
+A CSV with a mutation column (``mut``, ``Mutation`` or ``mutation``) and one
+column per structure (``H``, ``HO``, ``L``, ``LO``, ``HE2``, ``LE2``, any order; other columns are
+ignored, and ``thermo_options`` ``column_map`` can name the column for a
+structure).  ΔΔG_X is the shift a mutation makes to state X's free energy,
+so positive destabilizes X, and Δln K_{A→B} = ΔΔG_A − ΔΔG_B.  The model
+needs kT; ``thermo_options`` ``ddG_units: kcal/mol`` (or ``kJ/mol``, with
+``temperature_K``) converts at load, and the default ``kT`` uses the values
+as-is.  The loader (``thermo.io.load_ddG_prior_csv``) reports coverage, errors
+on zero coverage or a wt-residue mismatch (numbering offset; see
+``residue_offset``), and gives absent mutations a prior mean of 0.0 with a
+warning when more than ``max_missing_fraction`` are absent.
 """
 
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale
 import numpyro.distributions.constraints as constraints
 import pandas as pd
-from flax.struct import dataclass
+from flax.struct import dataclass, field
 from functools import partial
 from typing import Dict, Any, Union
 
 from tfscreen.tfmodel.data_class import GrowthData, BindingData
 from tfscreen.genetics.build_mut_geno_matrix import apply_pair_matrix, apply_mut_matrix
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
 from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
-    sample_ddG,
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    ddG_sigma_fixed,
+    ddG_extract_specs,
+    dlnK_guesses,
+    guide_dlnK,
+    projection,
+    sample_dlnK,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.horseshoe import (
     sample_pair_ddG,
     _DEFAULT_D0,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_H_E,
+    DEFAULT_LN_K_H_L,
+    DEFAULT_LN_K_H_O,
+    DEFAULT_LN_K_L_E,
+    DEFAULT_LN_K_L_O,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -89,18 +123,20 @@
     get_population_moments,
 )
 
-# Expected structure names — column order matches _PROJ below.
-STRUCTURE_NAMES = ('H', 'HO', 'L', 'LO', 'HE2', 'LE2')
+# Structure basis and projection rows (thermo/mwc.py). Convention: ΔΔG_X
+# adds to state X's free energy (kT), Δln K_{A→B} = ΔΔG_A − ΔΔG_B, and the
+# effector rows are per site, half of ΔΔG_X − ΔΔG_XE2.
+STRUCTURE_NAMES = mwc.STRUCTURE_NAMES_C12
+
+# Δln_K = ddG @ _PROJ.T; rows = constants, cols = STRUCTURE_NAMES.
+_PROJ = jnp.asarray(mwc.PROJ_C12)
 
-# Fixed thermodynamic projection: rows = (K_h_l, K_h_o, K_h_e, K_l_o, K_l_e),
-# cols = (H, HO, L, LO, HE2, LE2).
-_PROJ = jnp.array([
-    [ 1.,   0., -1.,   0.,   0.,   0. ],  # K_h_l = ΔΔG_H − ΔΔG_L
-    [ 1.,  -1.,  0.,   0.,   0.,   0. ],  # K_h_o = ΔΔG_H − ΔΔG_HO
-    [ 0.5,  0.,  0.,   0.,  -0.5,  0. ],  # K_h_e = (ΔΔG_H − ΔΔG_HE2)/2
-    [ 0.,   0.,  1.,  -1.,   0.,   0. ],  # K_l_o = ΔΔG_L − ΔΔG_LO
-    [ 0.,   0.,  0.5,  0.,   0.,  -0.5],  # K_l_e = (ΔΔG_L − ΔΔG_LE2)/2
-])
+# Unfolding row. K_u = [U]/[H] and ΔΔG_U = 0, so Δln K_u = ΔΔG_H.
+_PROJ_U = jnp.asarray(mwc.PROJ_U_C12)
+
+# Δln K (sampled, M × 6) = ΔΔG @ DLNK_PROJ.T: the folded rows, then the
+# unfolding row; square and invertible (thermo/prior.py).
+DLNK_PROJ = projection(mwc.PROJ_C12, mwc.PROJ_U_C12)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -127,20 +163,27 @@ class ModelPriors:
     theta_ln_K_u_wt_loc:   float
     theta_ln_K_u_wt_scale: float
 
-    # Physical concentrations (M); Sochor, PeerJ 2014
-    theta_tf_total_M: float    # ≈ 6.5e-7 M (650 nM)
-    theta_op_total_M: float    # ≈ 2.5e-8 M (25 nM)
+    # Total TF (M, monomer units)
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
 
     # Unit-conversion factor applied to data.titrant_conc before thermodynamics.
     # Default 1e-3 assumes concentrations are in mM and K values are in M⁻¹.
     theta_conc_unit_scale: float
 
-    # Regularised horseshoe for pairwise epistasis
+    # Regularized horseshoe for pairwise epistasis
     theta_epi_tau_scale:  float
     theta_epi_slab_scale: float
     theta_epi_slab_df:    float
     theta_epi_d0:         float
 
+    # LogNormal prior on the per-structure ΔΔG scale σ_s (log kT); see
+    # thermo/prior.py.
+    theta_ddG_sigma_loc:   float
+    theta_ddG_sigma_scale: float
+    # > 0 holds every σ_s at this value (kT) instead of sampling it;
+    # 0 (default) samples σ_s (thermo/prior.py, "Holding σ_s").
+    theta_ddG_sigma_fixed: float = field(pytree_node=False, default=0.0)
+
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Internal helpers
@@ -148,14 +191,16 @@ class ModelPriors:
 
 def _get_struct_perm(data):
     """
-    Verify struct_names contains exactly the required structures and return a
-    permutation index mapping data's S ordering to STRUCTURE_NAMES order.
+    Verify struct_names contains every required structure (extras are
+    ignored) and return the index of each STRUCTURE_NAMES entry in it.
     """
-    data_names = data.struct_names
-    if set(data_names) != set(STRUCTURE_NAMES):
+    data_names = tuple(data.struct_names or ())
+    missing = [s for s in STRUCTURE_NAMES if s not in data_names]
+    if missing:
         raise ValueError(
-            f"lnK_ddG_prior (mwc_dimer_unfolded) requires struct_names to contain exactly "
-            f"{set(STRUCTURE_NAMES)}; got {data_names}."
+            f"lnK_ddG_prior (mwc_dimer_unfolded) needs structures {STRUCTURE_NAMES}; "
+            f"struct_names {data_names} lacks {missing}. Extra "
+            f"structures are ignored."
         )
     name_to_idx = {n: i for i, n in enumerate(data_names)}
     return [name_to_idx[s] for s in STRUCTURE_NAMES]
@@ -176,6 +221,20 @@ def _project_ddG(ddG):
     return ddG @ _PROJ.T
 
 
+def _project_unfolding(ddG):
+    """Per-mutation Δln K_u = ΔΔG_H (ΔΔG_U = 0); (..., 6) → (...,)."""
+    return ddG @ _PROJ_U
+
+
+def _assemble_K_u(ln_K_u_wt, d_ln_K_u, mut_scatter,
+                  epi_ln_K_u=None, pair_scatter=None):
+    """ln_K_u (G,) from the wt value plus scattered per-mutation/pair shifts."""
+    ln_K_u = ln_K_u_wt + mut_scatter(d_ln_K_u)
+    if epi_ln_K_u is not None and pair_scatter is not None:
+        ln_K_u = ln_K_u + pair_scatter(epi_ln_K_u)
+    return ln_K_u
+
+
 def _assemble_K(ln_K_h_l_wt, ln_K_h_o_wt, ln_K_h_e_wt,
                 ln_K_l_o_wt, ln_K_l_e_wt,
                 delta_lnK, mut_scatter,
@@ -251,7 +310,6 @@ def define_model(name: str,
     ddG_prior_means = jnp.array(data.struct_features)[:, perm_idx]   # (M, S)
 
     tf_total        = priors.theta_tf_total_M
-    op_total        = priors.theta_op_total_M
     conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── WT equilibrium constants ─────────────────────────────────────────────
@@ -276,24 +334,27 @@ def define_model(name: str,
     ln_K_u_wt = pyro.sample(
         f"{name}_ln_K_u_wt",
         dist.Normal(priors.theta_ln_K_u_wt_loc, priors.theta_ln_K_u_wt_scale))
-    ln_K_u = jnp.full(data.num_genotype, ln_K_u_wt)
-    pyro.deterministic(f"{name}_ln_K_u", ln_K_u)
 
-    # ── Per-mutation per-structure ΔΔG latent variables ──────────────────────
-    # ddG_prior_means (M, S) serve as the Normal prior means; σ_s is learned.
-    ddG = sample_ddG(name, list(STRUCTURE_NAMES), num_mut, ddG_prior_means)  # (M, S)
-
-    # Project (M, 6) → (M, 5) Δln_K
-    delta_lnK  = _project_ddG(ddG)
+    # ── Per-mutation Δln K under the structural prior ─────────────────────────
+    # Δln K[m] ~ MVN(P · prior_mean[m], P · diag(σ_s²) · Pᵀ), σ_s per state
+    # (thermo/prior.py); the post-hoc per-state ΔΔG is the site {name}_ddG.
+    dlnK = sample_dlnK(name, list(STRUCTURE_NAMES), num_mut, ddG_prior_means, DLNK_PROJ,
+                       sigma_loc=priors.theta_ddG_sigma_loc,
+                       sigma_scale=priors.theta_ddG_sigma_scale,
+                       sigma_fixed=ddG_sigma_fixed(priors))  # (M, C)
+    delta_lnK  = dlnK[:, :5]
 
     pyro.deterministic(f"{name}_d_ln_K_h_l", delta_lnK[:, 0])
     pyro.deterministic(f"{name}_d_ln_K_h_o", delta_lnK[:, 1])
     pyro.deterministic(f"{name}_d_ln_K_h_e", delta_lnK[:, 2])
     pyro.deterministic(f"{name}_d_ln_K_l_o", delta_lnK[:, 3])
     pyro.deterministic(f"{name}_d_ln_K_l_e", delta_lnK[:, 4])
+    d_ln_K_u = dlnK[:, 5]
+    pyro.deterministic(f"{name}_d_ln_K_u",   d_ln_K_u)
 
     # ── Optional pairwise epistasis ───────────────────────────────────────────
     epi_delta_lnK = None
+    epi_ln_K_u    = None
     pair_scatter  = None
     if has_epi:
         P = data.num_pair
@@ -315,6 +376,8 @@ def define_model(name: str,
         pyro.deterministic(f"{name}_epi_ln_K_h_e", epi_delta_lnK[:, 2])
         pyro.deterministic(f"{name}_epi_ln_K_l_o", epi_delta_lnK[:, 3])
         pyro.deterministic(f"{name}_epi_ln_K_l_e", epi_delta_lnK[:, 4])
+        epi_ln_K_u = _project_unfolding(epi_ddG)   # (P,)
+        pyro.deterministic(f"{name}_epi_ln_K_u",   epi_ln_K_u)
 
     # ── Assemble per-genotype K values ────────────────────────────────────────
     ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e = _assemble_K(
@@ -329,18 +392,21 @@ def define_model(name: str,
     pyro.deterministic(f"{name}_ln_K_h_e", ln_K_h_e)
     pyro.deterministic(f"{name}_ln_K_l_o", ln_K_l_o)
     pyro.deterministic(f"{name}_ln_K_l_e", ln_K_l_e)
+    ln_K_u = _assemble_K_u(ln_K_u_wt, d_ln_K_u, mut_scatter,
+                           epi_ln_K_u=epi_ln_K_u, pair_scatter=pair_scatter)
+    pyro.deterministic(f"{name}_ln_K_u",   ln_K_u)
 
     # ── Population moments for transformation model ───────────────────────────
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 ln_K_u,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e, ln_K_u=ln_K_u,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -362,11 +428,10 @@ def guide(name: str,
                           mut_nnz_geno_idx=jnp.array(data.mut_nnz_geno_idx),
                           num_genotype=data.num_genotype)
     num_mut = data.num_mutation
-    S       = data.num_struct
+    S       = len(STRUCTURE_NAMES)
     ddG_prior_means = jnp.array(data.struct_features)[:, perm_idx]   # (M, S)
 
     tf_total        = priors.theta_tf_total_M
-    op_total        = priors.theta_op_total_M
     conc_unit_scale = priors.theta_conc_unit_scale
     has_epi         = data.num_pair > 0
 
@@ -386,18 +451,6 @@ def guide(name: str,
     ln_K_u_wt_loc   = pyro.param(f"{name}_ln_K_u_wt_loc",   jnp.array(priors.theta_ln_K_u_wt_loc))
     ln_K_u_wt_scale = pyro.param(f"{name}_ln_K_u_wt_scale", jnp.array(1.0), constraint=constraints.positive)
 
-    # ── Register σ_s so param store includes it before sampling ──────────────
-    pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-
-    # ── Variational params for ddG offsets, shape (S, M) in plate layout ─────
-    ddG_offset_locs   = pyro.param(f"{name}_ddG_offset_locs",
-                                   jnp.zeros((S, num_mut)))
-    ddG_offset_scales = pyro.param(f"{name}_ddG_offset_scales",
-                                   jnp.ones((S, num_mut)),
-                                   constraint=constraints.positive)
-
     # ── Optional epistasis variational params ─────────────────────────────────
     if has_epi:
         num_pair = data.num_pair
@@ -420,24 +473,14 @@ def guide(name: str,
 
     # ── Sample WT unfolding constant ──────────────────────────────────────────
     ln_K_u_wt = pyro.sample(f"{name}_ln_K_u_wt", dist.Normal(ln_K_u_wt_loc, ln_K_u_wt_scale))
-    ln_K_u = jnp.full(data.num_genotype, ln_K_u_wt)
-
-    # ── Sample ddG offsets (matches model's prior.sample_ddG plate layout) ────
-    with pyro.plate(f"{name}_struct_plate", S, dim=-2):
-        with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
-            offsets = pyro.sample(
-                f"{name}_ddG_offset",
-                dist.Normal(ddG_offset_locs, ddG_offset_scales),
-            )  # (S, M)
-
-    sigma_s = pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-    ddG_SM = ddG_prior_means.T + sigma_s[:, None] * offsets   # (S, M)
-    ddG    = ddG_SM.T                                          # (M, S)
+
+    # ── Sample σ_s and Δln K (matches prior.sample_dlnK) ────────────────────
+    dlnK = guide_dlnK(name, num_mut, S, DLNK_PROJ, ddG_prior_means,
+                      sigma_fixed=ddG_sigma_fixed(priors))   # (M, C)
 
     # ── Sample epistasis ──────────────────────────────────────────────────────
     epi_delta_lnK = None
+    epi_ln_K_u    = None
     pair_scatter  = None
     if has_epi:
         tau_loc    = pyro.param(f"{name}_epi_tau_loc",    jnp.array(-2.0))
@@ -464,11 +507,12 @@ def guide(name: str,
                 )
 
         def _lam_tilde(l):
-            return jnp.sqrt(c2_epi * l ** 2 / (c2_epi + tau_epi ** 2 * l ** 2))
+            return regularized_scale(l, tau_epi, c2_epi)
 
         epi_SP        = epi_off * tau_epi * _lam_tilde(lam)   # (S, P)
         epi_ddG       = epi_SP.T                               # (P, S)
         epi_delta_lnK = _project_ddG(epi_ddG)                 # (P, 5)
+        epi_ln_K_u    = _project_unfolding(epi_ddG)           # (P,)
 
         pair_scatter = partial(apply_pair_matrix,
                                pair_nnz_pair_idx=jnp.array(data.pair_nnz_pair_idx),
@@ -476,24 +520,26 @@ def _lam_tilde(l):
                                num_genotype=data.num_genotype)
 
     # ── Assemble per-genotype K values ────────────────────────────────────────
-    delta_lnK = _project_ddG(ddG)
+    delta_lnK = dlnK[:, :5]
     ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e = _assemble_K(
         ln_K_h_l_wt, ln_K_h_o_wt, ln_K_h_e_wt,
         ln_K_l_o_wt, ln_K_l_e_wt,
         delta_lnK, mut_scatter,
         epi_delta_lnK=epi_delta_lnK, pair_scatter=pair_scatter,
     )
+    ln_K_u = _assemble_K_u(ln_K_u_wt, dlnK[:, 5], mut_scatter,
+                           epi_ln_K_u=epi_ln_K_u, pair_scatter=pair_scatter)
 
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 ln_K_u,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e, ln_K_u=ln_K_u,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -503,43 +549,46 @@ def _lam_tilde(l):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    # All values calibrated to Sochor et al. 2014 (PeerJ 2:e498), Table 1.
-    p["theta_ln_K_h_l_wt_loc"]   = 1.84
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_h_l_wt_loc"]   = DEFAULT_LN_K_H_L   # Sochor (2014) Table 1: K_h_l = 6.3
     p["theta_ln_K_h_l_wt_scale"] = 2.0
-    p["theta_ln_K_h_o_wt_loc"]   = 19.9
+    p["theta_ln_K_h_o_wt_loc"]   = DEFAULT_LN_K_H_O   # Sochor: K_h_o = 4.2e8 M⁻¹
     p["theta_ln_K_h_o_wt_scale"] = 2.0
-    p["theta_ln_K_h_e_wt_loc"]   = 10.9
+    p["theta_ln_K_h_e_wt_loc"]   = DEFAULT_LN_K_H_E   # per site; ≈ 3.7e3 M⁻¹ = K_l_e/79.6, theta_sat 0.05
     p["theta_ln_K_h_e_wt_scale"] = 2.0
-    p["theta_ln_K_l_o_wt_loc"]   = -2.3
+    p["theta_ln_K_l_o_wt_loc"]   = DEFAULT_LN_K_L_O   # Sochor: K_l_o = 0.1 M⁻¹
     p["theta_ln_K_l_o_wt_scale"] = 3.0
-    p["theta_ln_K_l_e_wt_loc"]   = 13.5
+    p["theta_ln_K_l_e_wt_loc"]   = DEFAULT_LN_K_L_E   # per site; ≈ 3.0e5 M⁻¹ = 0.39 × Sochor, midpoint 80 µM
     p["theta_ln_K_l_e_wt_scale"] = 2.0
-    # Unfolding constant prior: -12.0 corresponds to K_u ~ e^{-12} ≈ 6e-6 M⁻¹,
-    # a strongly folded protein (unfolded state strongly disfavoured).
+    # WT unfolded state: K_u << 1 in WT (ln_K_u ≈ -12 → K_u ≈ 6e-6)
     p["theta_ln_K_u_wt_loc"]     = -12.0
     p["theta_ln_K_u_wt_scale"]   = 3.0
-    p["theta_tf_total_M"]        = 6.5e-7
-    p["theta_op_total_M"]        = 2.5e-8
-    p["theta_conc_unit_scale"]   = 1e-3
+    p["theta_tf_total_M"]        = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"]   = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     p["theta_epi_tau_scale"]     = 0.1
     p["theta_epi_slab_scale"]    = 2.0
     p["theta_epi_slab_df"]       = 4.0
     p["theta_epi_d0"]            = float(_DEFAULT_D0)
+    p["theta_ddG_sigma_loc"]   = DEFAULT_DDG_SIGMA_LOC     # median σ_s 1 kT
+    p["theta_ddG_sigma_scale"] = DEFAULT_DDG_SIGMA_SCALE   # 95%: 0.1-10 kT
+    p["theta_ddG_sigma_fixed"] = 0.0   # > 0 holds σ_s (kT)
     return p
 
 
 def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, Any]:
     T = data.num_titrant_name
     M = data.num_mutation
-    S = data.num_struct
+    S = len(STRUCTURE_NAMES)
     g = {}
-    g[f"{name}_ln_K_h_l_wt"]  = jnp.array(1.84)
-    g[f"{name}_ln_K_h_o_wt"]  = jnp.array(19.9)
-    g[f"{name}_ln_K_h_e_wt"]  = jnp.full(T, 10.9)
-    g[f"{name}_ln_K_l_o_wt"]  = jnp.array(-2.3)
-    g[f"{name}_ln_K_l_e_wt"]  = jnp.full(T, 13.5)
+    g[f"{name}_ln_K_h_l_wt"]  = jnp.array(DEFAULT_LN_K_H_L)
+    g[f"{name}_ln_K_h_o_wt"]  = jnp.array(DEFAULT_LN_K_H_O)
+    g[f"{name}_ln_K_h_e_wt"]  = jnp.full(T, DEFAULT_LN_K_H_E)
+    g[f"{name}_ln_K_l_o_wt"]  = jnp.array(DEFAULT_LN_K_L_O)
+    g[f"{name}_ln_K_l_e_wt"]  = jnp.full(T, DEFAULT_LN_K_L_E)
     g[f"{name}_ln_K_u_wt"]    = jnp.array(-12.0)
-    g[f"{name}_ddG_offset"]    = jnp.zeros((S, M))
+    # Δln K starts at P · (ΔΔG prior means), σ_s at 1 kT.
+    g.update(dlnK_guesses(name, jnp.asarray(data.struct_features)[:, jnp.array(_get_struct_perm(data))], DLNK_PROJ))
     if data.num_pair > 0:
         P = data.num_pair
         g[f"{name}_epi_tau"]    = jnp.array(0.05)
@@ -560,7 +609,7 @@ def get_extract_specs(ctx):
     titrant_names = list(ctx.growth_tm.tensor_dim_labels[titrant_dim])
     num_mut = len(ctx.mut_labels)
 
-    # Scalar per-genotype K values (includes ln_K_u broadcast from WT)
+    # Scalar per-genotype K values (includes ln_K_u)
     geno_df = (ctx.growth_tm.df[["genotype", "genotype_idx"]]
                .drop_duplicates().copy())
     geno_df["map_geno"] = geno_df["genotype_idx"]
@@ -594,7 +643,7 @@ def get_extract_specs(ctx):
     specs.append(dict(
         input_df=mut_df,
         params_to_get=["d_ln_K_h_l", "d_ln_K_h_o", "d_ln_K_h_e",
-                        "d_ln_K_l_o", "d_ln_K_l_e"],
+                        "d_ln_K_l_o", "d_ln_K_l_e", "d_ln_K_u"],
         map_column="map_mut",
         get_columns=["mutation"],
         in_run_prefix="theta_",
@@ -608,12 +657,16 @@ def get_extract_specs(ctx):
         specs.append(dict(
             input_df=pair_df,
             params_to_get=["epi_ln_K_h_l", "epi_ln_K_h_o", "epi_ln_K_h_e",
-                           "epi_ln_K_l_o", "epi_ln_K_l_e"],
+                           "epi_ln_K_l_o", "epi_ln_K_l_e", "epi_ln_K_u"],
             map_column="map_pair",
             get_columns=["pair"],
             in_run_prefix="theta_",
         ))
 
+    # Per-structure ddG and its prior mean, in STRUCTURE_NAMES order (the
+    # order the model samples in and the simulator labels its truth with).
+    specs.extend(ddG_extract_specs(ctx, STRUCTURE_NAMES))
+
     return specs
 
 
@@ -627,7 +680,6 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
     conc_unit_scale=1.0,
 ):
     """
@@ -635,8 +687,9 @@ def predict_unmeasured(
 
     The d_ln_K_h_e and d_ln_K_l_e deterministic sites have shape (S, M) with no
     T dimension; the scalar per-mutation effect is broadcast uniformly across
-    effectors.  ln_K_u is homogeneous across genotypes (WT only, no per-mutation
-    delta).
+    effectors.  ln_K_u is assembled like the folded-state constants, from
+    ln_K_u_wt plus the per-mutation ``d_ln_K_u`` (= ΔΔG_H) and per-pair
+    ``epi_ln_K_u``.
 
     Parameters
     ----------
@@ -648,7 +701,6 @@ def predict_unmeasured(
     param_posteriors : dict-like
     q_to_get : dict
     tf_total : float — monomer units (M)
-    op_total : float
     conc_unit_scale : float
         Multiply titrant_conc by this factor before thermodynamic equations.
         Use 1e-3 when concentrations are in mM and K values are in M⁻¹.
@@ -694,6 +746,7 @@ def _load(key):
     d_l_o = _load("theta_d_ln_K_l_o")   # (S, M)
     d_h_e = _load("theta_d_ln_K_h_e")   # (S, M) — scalar delta, no T dim
     d_l_e = _load("theta_d_ln_K_l_e")   # (S, M) — scalar delta, no T dim
+    d_u   = _load("theta_d_ln_K_u")     # (S, M)
 
     ln_K_h_l_geno = ln_K_h_l_wt[:, None] + np.einsum("sm,nm->sn", d_h_l, mut_mat)
     ln_K_h_o_geno = ln_K_h_o_wt[:, None] + np.einsum("sm,nm->sn", d_h_o, mut_mat)
@@ -703,9 +756,7 @@ def _load(key):
                      + np.einsum("sm,nm->sn", d_h_e, mut_mat)[:, None, :])
     ln_K_l_e_geno = (ln_K_l_e_wt[:, :, None]
                      + np.einsum("sm,nm->sn", d_l_e, mut_mat)[:, None, :])
-    # ln_K_u is homogeneous (WT only); broadcast across all target genotypes
-    ln_K_u_geno   = np.broadcast_to(ln_K_u_wt[:, None],
-                                     (ln_K_u_wt.shape[0], len(target_genotypes)))
+    ln_K_u_geno   = ln_K_u_wt[:, None] + np.einsum("sm,nm->sn", d_u, mut_mat)
 
     if len(pair_labels) > 0:
         epi_h_l = _load("theta_epi_ln_K_h_l")   # (S, P)
@@ -713,11 +764,13 @@ def _load(key):
         epi_l_o = _load("theta_epi_ln_K_l_o")   # (S, P)
         epi_h_e = _load("theta_epi_ln_K_h_e")   # (S, P) — scalar, no T dim
         epi_l_e = _load("theta_epi_ln_K_l_e")   # (S, P) — scalar, no T dim
+        epi_u   = _load("theta_epi_ln_K_u")     # (S, P)
         ln_K_h_l_geno += np.einsum("sp,np->sn", epi_h_l, pair_mat)
         ln_K_h_o_geno += np.einsum("sp,np->sn", epi_h_o, pair_mat)
         ln_K_l_o_geno += np.einsum("sp,np->sn", epi_l_o, pair_mat)
         ln_K_h_e_geno += np.einsum("sp,np->sn", epi_h_e, pair_mat)[:, None, :]
         ln_K_l_e_geno += np.einsum("sp,np->sn", epi_l_e, pair_mat)[:, None, :]
+        ln_K_u_geno   += np.einsum("sp,np->sn", epi_u,   pair_mat)
 
     ln_K_h_l_rows = ln_K_h_l_geno[:, geno_idx]
     ln_K_h_o_rows = ln_K_h_o_geno[:, geno_idx]
@@ -732,7 +785,7 @@ def _load(key):
     theta_samples = _solve_theta_np(
         ln_K_h_l_rows, ln_K_h_o_rows, ln_K_h_e_rows,
         ln_K_l_o_rows, ln_K_l_e_rows, ln_K_u_rows,
-        conc, tf_total, op_total,
+        conc, tf_total,
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PnnC.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PnnC.py
index b928f979..470c75b8 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PnnC.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/PnnC.py
@@ -2,23 +2,32 @@
 PnnC — mutation effects on equilibrium constants from NN-predicted ΔΔG prior means (MWC dimer unfolded).
 
 Extends the MWC dimer PnnC model with a WT-level unfolding constant (ln_K_u_wt).
-Per-mutation effects on the five folded-state K values use MLP-predicted ΔΔG
-prior means from LigandMPNN structural features; K_u is held at its WT value for
-all genotypes (d_ln_K_u = 0) because NN features describe local folded-state
-environments, not global stability.  Registry name:
+Per-mutation effects on the five folded-state K values and on K_u use
+MLP-predicted ΔΔG prior means from LigandMPNN structural features.  Registry
+name:
 ``theta_mwc_dimer_unfolded_lnK_nn_prior``.
 
 Contrast with PK (all K shifts inferred from data, including K_u per mutation)
 and PddG (ΔΔG prior means from a user-supplied CSV rather than a trained NN).
 
-Implements the full MWC two-state lac-dimer model (Sochor 2014) with five
+Implements the standard MWC two-state lac-dimer model (``thermo/mwc.py``;
+constants and values from Sochor 2014) with five
 equilibrium constants plus a global unfolding equilibrium, using six structural
 states as the free-energy basis:
 
     Structures: H, HO, L, LO, HE2, LE2
 
-    ΔΔG[m, s]  ~ Normal( MLP_s(features[m, s, :]) · n_chains[s],  σ_s )
-    σ_s        — per-structure trust scale (``pyro.param``, init=1.0)
+    σ_s        ~ LogNormal(theta_ddG_sigma_loc, theta_ddG_sigma_scale), one per
+                 structure (SD of the true ΔΔG about its prior mean)
+    Δln K[m]   ~ MVN( P · μ[m],  P · diag(σ_s²) · Pᵀ )      (site theta_dlnK)
+    μ[m, s]    = MLP_s(features[m, s, :]) · n_chains[s]
+
+with P = ``DLNK_PROJ``, the projection below plus the unfolding row. This is
+the distribution the per-structure model ΔΔG[m, s] ~
+Normal(MLP_s(features[m, s, :]) · n_chains[s], σ_s) induces on the
+constants, which are all the data see (thermo/prior.py). The per-structure
+ΔΔG is the deterministic site ``theta_ddG``: P⁻¹ · Δln K exactly (ΔΔG_U = 0;
+the unfolding row makes P square and invertible).
 
 Projection from per-structure ΔΔG to Δln_K (rows = K, cols = structures):
 
@@ -30,17 +39,21 @@
 
 Unfolding extension
 -------------------
-A single WT-level unfolding constant ``ln_K_u_wt`` is sampled and broadcast
-uniformly across all genotypes:
+A WT-level unfolding constant ``ln_K_u_wt`` is sampled, and each mutation
+shifts it through the H column of its ΔΔG.  A mutation shifts the free energy
+of each folded structure X by ΔΔG_X and leaves the unfolded state alone
+(ΔΔG_U = 0, the standard assumption).  Because K_u = [U]/[H], the projection
+convention above (Δln K_{A→B} = ΔΔG_A − ΔΔG_B) gives
 
+    Δln_K_u   = ΔΔG_H − ΔΔG_U = ΔΔG_H
     ln_K_u_wt ~ Normal(theta_ln_K_u_wt_loc, theta_ln_K_u_wt_scale)
-    ln_K_u[g]  = ln_K_u_wt   for all g
+    ln_K_u[g] = ln_K_u_wt + Σ_m ΔΔG_H[m]   (plus the pairwise ΔΔG_H epistasis)
 
-K_u is homogeneous across genotypes; per-mutation unfolding effects are absent
-by design.  This mirrors lac_dimer_unfolded, where the unfolded state adds a
-global thermodynamic sink but mutation-level ΔΔG contributions to unfolding
-are not modelled — that extension would require a separate structural ensemble
-and is deferred to a future component.
+A mutation that raises every folded state by the same ΔΔG leaves the five
+folded-state constants unchanged and raises K_u by ΔΔG: a pure stability
+effect.  No extra structure is needed.  The per-mutation shift is exposed as
+``d_ln_K_u`` (and ``epi_ln_K_u`` per pair), the same name the PK model uses
+for its free per-mutation unfolding shift.
 
 Data requirements
 -----------------
@@ -63,6 +76,7 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale
 import numpyro.distributions.constraints as constraints
 import pandas as pd
 from flax.struct import dataclass, field
@@ -75,14 +89,29 @@
     compute_nn_predictions,
     _DEFAULT_HIDDEN_SIZE,
 )
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
 from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
-    sample_ddG,
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    ddG_sigma_fixed,
+    ddG_extract_specs,
+    dlnK_guesses,
+    guide_dlnK,
+    projection,
+    sample_dlnK,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.horseshoe import (
     sample_pair_ddG,
     _DEFAULT_D0,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_H_E,
+    DEFAULT_LN_K_H_L,
+    DEFAULT_LN_K_H_O,
+    DEFAULT_LN_K_L_E,
+    DEFAULT_LN_K_L_O,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -90,19 +119,23 @@
     get_population_moments,
 )
 
-# Expected structure names — column order matches _PROJ below.
-STRUCTURE_NAMES = ('H', 'HO', 'L', 'LO', 'HE2', 'LE2')
+# Structure basis and projection rows (thermo/mwc.py). Convention: ΔΔG_X
+# adds to state X's free energy (kT), Δln K_{A→B} = ΔΔG_A − ΔΔG_B, and the
+# effector rows are per site, half of ΔΔG_X − ΔΔG_XE2.
+STRUCTURE_NAMES = mwc.STRUCTURE_NAMES_C12
+
+# Δln_K = ddG @ _PROJ.T; rows = constants, cols = STRUCTURE_NAMES.
+_PROJ = jnp.asarray(mwc.PROJ_C12)
+# The basis uses HE2/LE2 (both sites filled, symmetric chains) rather than
+# HE/LE (one site filled, asymmetric chains): it gives LigandMPNN one coherent
+# structural environment to score, as _aggregate_logP expects.
 
-# Fixed thermodynamic projection: rows = (K_h_l, K_h_o, K_h_e, K_l_o, K_l_e),
-# cols = (H, HO, L, LO, HE2, LE2).
-# Δln_K = ddG @ _PROJ.T   where ddG is (..., 6) → result (..., 5)
-_PROJ = jnp.array([
-    [ 1.,   0., -1.,   0.,   0.,   0. ],  # K_h_l = ΔΔG_H − ΔΔG_L
-    [ 1.,  -1.,  0.,   0.,   0.,   0. ],  # K_h_o = ΔΔG_H − ΔΔG_HO
-    [ 0.5,  0.,  0.,   0.,  -0.5,  0. ],  # K_h_e = (ΔΔG_H − ΔΔG_HE2)/2
-    [ 0.,   0.,  1.,  -1.,   0.,   0. ],  # K_l_o = ΔΔG_L − ΔΔG_LO
-    [ 0.,   0.,  0.5,  0.,   0.,  -0.5],  # K_l_e = (ΔΔG_L − ΔΔG_LE2)/2
-])
+# Unfolding row. K_u = [U]/[H] and ΔΔG_U = 0, so Δln K_u = ΔΔG_H.
+_PROJ_U = jnp.asarray(mwc.PROJ_U_C12)
+
+# Δln K (sampled, M × 6) = ΔΔG @ DLNK_PROJ.T: the folded rows, then the
+# unfolding row; square and invertible (thermo/prior.py).
+DLNK_PROJ = projection(mwc.PROJ_C12, mwc.PROJ_U_C12)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -131,8 +164,7 @@ class ModelPriors:
 
     # Physical concentrations (M).
     # theta_tf_total_M is the total TF in MONOMER units.
-    theta_tf_total_M: float    # ≈ 6.5e-7 M monomer (= 325 nM dimer)
-    theta_op_total_M: float    # ≈ 2.5e-8 M (25 nM)
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
 
     # Unit-conversion factor applied to data.titrant_conc before thermodynamics.
     # Default 1e-3 assumes concentrations are in mM and K values are in M⁻¹.
@@ -141,7 +173,7 @@ class ModelPriors:
     # Per-structure MLP hidden width (static shape — not a float parameter)
     theta_nn_hidden_size: int = field(pytree_node=False)
 
-    # Regularised horseshoe for pairwise epistasis
+    # Regularized horseshoe for pairwise epistasis
     theta_epi_tau_scale:  float
     theta_epi_slab_scale: float
     theta_epi_slab_df:    float
@@ -149,6 +181,14 @@ class ModelPriors:
     # Distance scale for distance-dependent λ prior (Å)
     theta_epi_d0: float
 
+    # LogNormal prior on the per-structure ΔΔG scale σ_s (log kT); see
+    # thermo/prior.py.
+    theta_ddG_sigma_loc:   float
+    theta_ddG_sigma_scale: float
+    # > 0 holds every σ_s at this value (kT) instead of sampling it;
+    # 0 (default) samples σ_s (thermo/prior.py, "Holding σ_s").
+    theta_ddG_sigma_fixed: float = field(pytree_node=False, default=0.0)
+
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Internal helpers
@@ -188,6 +228,20 @@ def _project_ddG(ddG):
     return ddG @ _PROJ.T
 
 
+def _project_unfolding(ddG):
+    """Per-mutation Δln K_u = ΔΔG_H (ΔΔG_U = 0); (..., 6) → (...,)."""
+    return ddG @ _PROJ_U
+
+
+def _assemble_K_u(ln_K_u_wt, d_ln_K_u, mut_scatter,
+                  epi_ln_K_u=None, pair_scatter=None):
+    """ln_K_u (G,) from the wt value plus scattered per-mutation/pair shifts."""
+    ln_K_u = ln_K_u_wt + mut_scatter(d_ln_K_u)
+    if epi_ln_K_u is not None and pair_scatter is not None:
+        ln_K_u = ln_K_u + pair_scatter(epi_ln_K_u)
+    return ln_K_u
+
+
 def _assemble_K(ln_K_h_l_wt, ln_K_h_o_wt, ln_K_h_e_wt,
                 ln_K_l_o_wt, ln_K_l_e_wt,
                 delta_lnK, mut_scatter,
@@ -267,7 +321,6 @@ def define_model(name: str,
                 and data.struct_contact_distances is not None)
 
     tf_total        = priors.theta_tf_total_M
-    op_total        = priors.theta_op_total_M
     conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── WT equilibrium constants ─────────────────────────────────────────────
@@ -293,8 +346,6 @@ def define_model(name: str,
     ln_K_u_wt = pyro.sample(
         f"{name}_ln_K_u_wt",
         dist.Normal(priors.theta_ln_K_u_wt_loc, priors.theta_ln_K_u_wt_scale))
-    ln_K_u = jnp.full(data.num_genotype, ln_K_u_wt)
-    pyro.deterministic(f"{name}_ln_K_u", ln_K_u)
 
     # ── NN prior means (pyro.param — shared with guide) ──────────────────────
     nn_means = compute_nn_predictions(
@@ -302,11 +353,14 @@ def define_model(name: str,
         hidden_size=priors.theta_nn_hidden_size,
     )  # (M, S)
 
-    # ── Per-mutation per-structure ΔΔG latent variables ──────────────────────
-    ddG = sample_ddG(name, list(data.struct_names), num_mut, nn_means)  # (M, S)
-
-    # Project (M, 6) → (M, 5) Δln_K
-    delta_lnK  = _project_ddG(ddG)         # (M, 5)
+    # ── Per-mutation Δln K under the structural prior ─────────────────────────
+    # Δln K[m] ~ MVN(P · prior_mean[m], P · diag(σ_s²) · Pᵀ), σ_s per state
+    # (thermo/prior.py); the post-hoc per-state ΔΔG is the site {name}_ddG.
+    dlnK = sample_dlnK(name, list(data.struct_names), num_mut, nn_means, DLNK_PROJ,
+                       sigma_loc=priors.theta_ddG_sigma_loc,
+                       sigma_scale=priors.theta_ddG_sigma_scale,
+                       sigma_fixed=ddG_sigma_fixed(priors))  # (M, C)
+    delta_lnK  = dlnK[:, :5]
     d_ln_K_h_l = delta_lnK[:, 0]           # (M,)
     d_ln_K_h_o = delta_lnK[:, 1]           # (M,)
     d_ln_K_h_e = delta_lnK[:, 2]           # (M,)
@@ -318,9 +372,12 @@ def define_model(name: str,
     pyro.deterministic(f"{name}_d_ln_K_h_e", d_ln_K_h_e)
     pyro.deterministic(f"{name}_d_ln_K_l_o", d_ln_K_l_o)
     pyro.deterministic(f"{name}_d_ln_K_l_e", d_ln_K_l_e)
+    d_ln_K_u = dlnK[:, 5]     # (M,)
+    pyro.deterministic(f"{name}_d_ln_K_u",   d_ln_K_u)
 
     # ── Optional pairwise epistasis ───────────────────────────────────────────
     epi_delta_lnK = None
+    epi_ln_K_u    = None
     pair_scatter  = None
     if has_epi:
         pair_scatter = partial(apply_pair_matrix,
@@ -344,6 +401,8 @@ def define_model(name: str,
         pyro.deterministic(f"{name}_epi_ln_K_h_e", epi_delta_lnK[:, 2])
         pyro.deterministic(f"{name}_epi_ln_K_l_o", epi_delta_lnK[:, 3])
         pyro.deterministic(f"{name}_epi_ln_K_l_e", epi_delta_lnK[:, 4])
+        epi_ln_K_u = _project_unfolding(epi_ddG)   # (P,)
+        pyro.deterministic(f"{name}_epi_ln_K_u",   epi_ln_K_u)
 
     # ── Assemble per-genotype K values ────────────────────────────────────────
     ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e = _assemble_K(
@@ -358,18 +417,21 @@ def define_model(name: str,
     pyro.deterministic(f"{name}_ln_K_h_e", ln_K_h_e)
     pyro.deterministic(f"{name}_ln_K_l_o", ln_K_l_o)
     pyro.deterministic(f"{name}_ln_K_l_e", ln_K_l_e)
+    ln_K_u = _assemble_K_u(ln_K_u_wt, d_ln_K_u, mut_scatter,
+                           epi_ln_K_u=epi_ln_K_u, pair_scatter=pair_scatter)
+    pyro.deterministic(f"{name}_ln_K_u",   ln_K_u)
 
     # ── Population moments for transformation model ───────────────────────────
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 ln_K_u,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e, ln_K_u=ln_K_u,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -398,7 +460,6 @@ def guide(name: str,
                 and data.struct_contact_distances is not None)
 
     tf_total        = priors.theta_tf_total_M
-    op_total        = priors.theta_op_total_M
     conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── Variational params for WT K values ────────────────────────────────────
@@ -423,18 +484,6 @@ def guide(name: str,
         hidden_size=priors.theta_nn_hidden_size,
     )  # (M, S)
 
-    # ── Register sigma_s so the param store includes it before sampling ───────
-    pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-
-    # ── Variational params for ddG offsets, shape (S, M) in plate layout ─────
-    ddG_offset_locs   = pyro.param(f"{name}_ddG_offset_locs",
-                                   jnp.zeros((S, num_mut)))
-    ddG_offset_scales = pyro.param(f"{name}_ddG_offset_scales",
-                                   jnp.ones((S, num_mut)),
-                                   constraint=constraints.positive)
-
     # ── Optional epistasis variational params ─────────────────────────────────
     if has_epi:
         num_pair = data.num_pair
@@ -457,24 +506,14 @@ def guide(name: str,
 
     # ── Sample WT unfolding constant ──────────────────────────────────────────
     ln_K_u_wt = pyro.sample(f"{name}_ln_K_u_wt", dist.Normal(ln_K_u_wt_loc, ln_K_u_wt_scale))
-    ln_K_u = jnp.full(data.num_genotype, ln_K_u_wt)
-
-    # ── Sample ddG offsets (matches model's prior.sample_ddG plate layout) ────
-    with pyro.plate(f"{name}_struct_plate", S, dim=-2):
-        with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
-            offsets = pyro.sample(
-                f"{name}_ddG_offset",
-                dist.Normal(ddG_offset_locs, ddG_offset_scales),
-            )  # (S, M)
-
-    sigma_s = pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-    ddG_SM = nn_means.T + sigma_s[:, None] * offsets   # (S, M)
-    ddG    = ddG_SM.T                                   # (M, S)
+
+    # ── Sample σ_s and Δln K (matches prior.sample_dlnK) ────────────────────
+    dlnK = guide_dlnK(name, num_mut, S, DLNK_PROJ, jnp.zeros((num_mut, S)),
+                      sigma_fixed=ddG_sigma_fixed(priors))   # (M, C)
 
     # ── Sample epistasis ──────────────────────────────────────────────────────
     epi_delta_lnK = None
+    epi_ln_K_u    = None
     pair_scatter  = None
     if has_epi:
         tau_loc    = pyro.param(f"{name}_epi_tau_loc",    jnp.array(-2.0))
@@ -501,11 +540,12 @@ def guide(name: str,
                 )
 
         def _lam_tilde(l):
-            return jnp.sqrt(c2_epi * l ** 2 / (c2_epi + tau_epi ** 2 * l ** 2))
+            return regularized_scale(l, tau_epi, c2_epi)
 
         epi_SP    = epi_off * tau_epi * _lam_tilde(lam)   # (S, P)
         epi_ddG   = epi_SP.T                              # (P, S)
         epi_delta_lnK = _project_ddG(epi_ddG)             # (P, 5)
+        epi_ln_K_u    = _project_unfolding(epi_ddG)       # (P,)
 
         pair_scatter = partial(apply_pair_matrix,
                                pair_nnz_pair_idx=jnp.array(data.pair_nnz_pair_idx),
@@ -513,24 +553,26 @@ def _lam_tilde(l):
                                num_genotype=data.num_genotype)
 
     # ── Assemble per-genotype K values ────────────────────────────────────────
-    delta_lnK = _project_ddG(ddG)   # (M, 5)
+    delta_lnK = dlnK[:, :5]
     ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e = _assemble_K(
         ln_K_h_l_wt, ln_K_h_o_wt, ln_K_h_e_wt,
         ln_K_l_o_wt, ln_K_l_e_wt,
         delta_lnK, mut_scatter,
         epi_delta_lnK=epi_delta_lnK, pair_scatter=pair_scatter,
     )
+    ln_K_u = _assemble_K_u(ln_K_u_wt, dlnK[:, 5], mut_scatter,
+                           epi_ln_K_u=epi_ln_K_u, pair_scatter=pair_scatter)
 
     theta_vals = _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e,
                                 ln_K_l_o, ln_K_l_e,
                                 ln_K_u,
                                 data.titrant_conc * conc_unit_scale,
-                                tf_total, op_total)
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_h_l=ln_K_h_l, ln_K_h_o=ln_K_h_o, ln_K_h_e=ln_K_h_e,
                       ln_K_l_o=ln_K_l_o, ln_K_l_e=ln_K_l_e, ln_K_u=ln_K_u,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma,
+                      tf_total=tf_total, mu=mu, sigma=sigma,
                       conc_unit_scale=conc_unit_scale)
 
 
@@ -540,26 +582,23 @@ def _lam_tilde(l):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    # All values calibrated to Sochor et al. 2014 (PeerJ 2:e498), Table 1.
-    p["theta_ln_K_h_l_wt_loc"]   = 1.84
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_h_l_wt_loc"]   = DEFAULT_LN_K_H_L   # Sochor (2014) Table 1: K_h_l = 6.3
     p["theta_ln_K_h_l_wt_scale"] = 2.0
-    p["theta_ln_K_h_o_wt_loc"]   = 19.9
+    p["theta_ln_K_h_o_wt_loc"]   = DEFAULT_LN_K_H_O   # Sochor: K_h_o = 4.2e8 M⁻¹
     p["theta_ln_K_h_o_wt_scale"] = 2.0
-    p["theta_ln_K_h_e_wt_loc"]   = 10.9
+    p["theta_ln_K_h_e_wt_loc"]   = DEFAULT_LN_K_H_E   # per site; ≈ 3.7e3 M⁻¹ = K_l_e/79.6, theta_sat 0.05
     p["theta_ln_K_h_e_wt_scale"] = 2.0
-    p["theta_ln_K_l_o_wt_loc"]   = -2.3
+    p["theta_ln_K_l_o_wt_loc"]   = DEFAULT_LN_K_L_O   # Sochor: K_l_o = 0.1 M⁻¹
     p["theta_ln_K_l_o_wt_scale"] = 3.0
-    p["theta_ln_K_l_e_wt_loc"]   = 13.5
+    p["theta_ln_K_l_e_wt_loc"]   = DEFAULT_LN_K_L_E   # per site; ≈ 3.0e5 M⁻¹ = 0.39 × Sochor, midpoint 80 µM
     p["theta_ln_K_l_e_wt_scale"] = 2.0
-    # Unfolding constant prior: -12.0 corresponds to K_u ~ e^{-12} ≈ 6e-6 M⁻¹,
-    # a strongly folded protein (unfolded state strongly disfavoured).
+    # WT unfolded state: K_u << 1 in WT (ln_K_u ≈ -12 → K_u ≈ 6e-6)
     p["theta_ln_K_u_wt_loc"]     = -12.0
     p["theta_ln_K_u_wt_scale"]   = 3.0
-    # Physical concentrations — theta_tf_total_M is in MONOMER units.
-    p["theta_tf_total_M"] = 6.5e-7   # monomer
-    p["theta_op_total_M"] = 2.5e-8   # operator (M)
-    # Unit-conversion factor
-    p["theta_conc_unit_scale"] = 1e-3
+    p["theta_tf_total_M"] = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     # NN architecture
     p["theta_nn_hidden_size"]   = _DEFAULT_HIDDEN_SIZE
     # Horseshoe hyperparameters for pairwise epistasis
@@ -567,6 +606,9 @@ def get_hyperparameters() -> Dict[str, Any]:
     p["theta_epi_slab_scale"]  = 2.0
     p["theta_epi_slab_df"]     = 4.0
     p["theta_epi_d0"]          = float(_DEFAULT_D0)
+    p["theta_ddG_sigma_loc"]   = DEFAULT_DDG_SIGMA_LOC     # median σ_s 1 kT
+    p["theta_ddG_sigma_scale"] = DEFAULT_DDG_SIGMA_SCALE   # 95%: 0.1-10 kT
+    p["theta_ddG_sigma_fixed"] = 0.0   # > 0 holds σ_s (kT)
     return p
 
 
@@ -575,13 +617,14 @@ def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, An
     M = data.num_mutation
     S = data.num_struct
     g = {}
-    g[f"{name}_ln_K_h_l_wt"]  = jnp.array(1.84)
-    g[f"{name}_ln_K_h_o_wt"]  = jnp.array(19.9)
-    g[f"{name}_ln_K_h_e_wt"]  = jnp.full(T, 10.9)
-    g[f"{name}_ln_K_l_o_wt"]  = jnp.array(-2.3)
-    g[f"{name}_ln_K_l_e_wt"]  = jnp.full(T, 13.5)
+    g[f"{name}_ln_K_h_l_wt"]  = jnp.array(DEFAULT_LN_K_H_L)
+    g[f"{name}_ln_K_h_o_wt"]  = jnp.array(DEFAULT_LN_K_H_O)
+    g[f"{name}_ln_K_h_e_wt"]  = jnp.full(T, DEFAULT_LN_K_H_E)
+    g[f"{name}_ln_K_l_o_wt"]  = jnp.array(DEFAULT_LN_K_L_O)
+    g[f"{name}_ln_K_l_e_wt"]  = jnp.full(T, DEFAULT_LN_K_L_E)
     g[f"{name}_ln_K_u_wt"]    = jnp.array(-12.0)
-    g[f"{name}_ddG_offset"]    = jnp.zeros((S, M))
+    # Δln K starts at P · (ΔΔG prior means), σ_s at 1 kT.
+    g.update(dlnK_guesses(name, jnp.zeros((M, S)), DLNK_PROJ))
     if data.num_pair > 0 and data.struct_contact_distances is not None:
         P = data.num_pair
         g[f"{name}_epi_tau"]    = jnp.array(0.05)
@@ -605,16 +648,13 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
     conc_unit_scale=1.0,
 ):
     """
     Predict theta for unmeasured genotypes using NN-prior MWC-dimer-unfolded assembly.
 
-    The unfolding constant K_u is homogeneous across genotypes; per-mutation
-    unfolding effects are absent by design (same rationale as lac_dimer_unfolded).
-    ``ln_K_u_wt`` is loaded from the posterior and broadcast as a constant
-    across all genotypes and rows of the prediction grid.
+    ln_K_u is assembled like the folded-state constants, from ln_K_u_wt plus
+    the per-mutation ``d_ln_K_u`` (= ΔΔG_H) and per-pair ``epi_ln_K_u``.
 
     Parameters
     ----------
@@ -626,7 +666,6 @@ def predict_unmeasured(
     param_posteriors : dict-like
     q_to_get : dict
     tf_total : float
-    op_total : float
     conc_unit_scale : float
 
     Returns
@@ -670,6 +709,7 @@ def _load(key):
     d_l_o = _load("theta_d_ln_K_l_o")   # (S, M)
     d_h_e = _load("theta_d_ln_K_h_e")   # (S, M)
     d_l_e = _load("theta_d_ln_K_l_e")   # (S, M)
+    d_u   = _load("theta_d_ln_K_u")     # (S, M)
 
     ln_K_h_l_geno = ln_K_h_l_wt[:, None] + np.einsum("sm,nm->sn", d_h_l, mut_mat)
     ln_K_h_o_geno = ln_K_h_o_wt[:, None] + np.einsum("sm,nm->sn", d_h_o, mut_mat)
@@ -679,6 +719,7 @@ def _load(key):
                      + np.einsum("sm,nm->sn", d_h_e, mut_mat)[:, None, :])
     ln_K_l_e_geno = (ln_K_l_e_wt[:, :, None]
                      + np.einsum("sm,nm->sn", d_l_e, mut_mat)[:, None, :])
+    ln_K_u_geno   = ln_K_u_wt[:, None] + np.einsum("sm,nm->sn", d_u, mut_mat)
 
     if len(pair_labels) > 0:
         epi_h_l = _load("theta_epi_ln_K_h_l")   # (S, P)
@@ -686,19 +727,20 @@ def _load(key):
         epi_l_o = _load("theta_epi_ln_K_l_o")   # (S, P)
         epi_h_e = _load("theta_epi_ln_K_h_e")   # (S, P)
         epi_l_e = _load("theta_epi_ln_K_l_e")   # (S, P)
+        epi_u   = _load("theta_epi_ln_K_u")     # (S, P)
         ln_K_h_l_geno += np.einsum("sp,np->sn", epi_h_l, pair_mat)
         ln_K_h_o_geno += np.einsum("sp,np->sn", epi_h_o, pair_mat)
         ln_K_l_o_geno += np.einsum("sp,np->sn", epi_l_o, pair_mat)
         ln_K_h_e_geno += np.einsum("sp,np->sn", epi_h_e, pair_mat)[:, None, :]
         ln_K_l_e_geno += np.einsum("sp,np->sn", epi_l_e, pair_mat)[:, None, :]
+        ln_K_u_geno   += np.einsum("sp,np->sn", epi_u,   pair_mat)
 
     ln_K_h_l_rows = ln_K_h_l_geno[:, geno_idx]
     ln_K_h_o_rows = ln_K_h_o_geno[:, geno_idx]
     ln_K_l_o_rows = ln_K_l_o_geno[:, geno_idx]
     ln_K_h_e_rows = ln_K_h_e_geno[:, titrant_idx, geno_idx]
     ln_K_l_e_rows = ln_K_l_e_geno[:, titrant_idx, geno_idx]
-    # K_u is homogeneous — broadcast the scalar WT value as a constant row
-    ln_K_u_rows   = ln_K_u_wt[:, None]   # (S, 1) broadcasts over prediction rows
+    ln_K_u_rows   = ln_K_u_geno[:, geno_idx]
 
     conc = calc_df["titrant_conc"].values.copy().astype(float) * conc_unit_scale
     conc[conc == 0] = _ZERO_CONC_VALUE
@@ -707,7 +749,7 @@ def _load(key):
         ln_K_h_l_rows, ln_K_h_o_rows, ln_K_h_e_rows,
         ln_K_l_o_rows, ln_K_l_e_rows,
         ln_K_u_rows,
-        conc, tf_total, op_total,
+        conc, tf_total,
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
@@ -725,7 +767,7 @@ def get_extract_specs(ctx):
     titrant_names = list(ctx.growth_tm.tensor_dim_labels[titrant_dim])
     num_mut = len(ctx.mut_labels)
 
-    # Scalar per-genotype K values (includes ln_K_u broadcast from WT)
+    # Scalar per-genotype K values (includes ln_K_u)
     geno_df = (ctx.growth_tm.df[["genotype", "genotype_idx"]]
                .drop_duplicates().copy())
     geno_df["map_geno"] = geno_df["genotype_idx"]
@@ -759,7 +801,7 @@ def get_extract_specs(ctx):
     specs.append(dict(
         input_df=mut_df,
         params_to_get=["d_ln_K_h_l", "d_ln_K_h_o", "d_ln_K_h_e",
-                        "d_ln_K_l_o", "d_ln_K_l_e"],
+                        "d_ln_K_l_o", "d_ln_K_l_e", "d_ln_K_u"],
         map_column="map_mut",
         get_columns=["mutation"],
         in_run_prefix="theta_",
@@ -774,12 +816,16 @@ def get_extract_specs(ctx):
         specs.append(dict(
             input_df=pair_df,
             params_to_get=["epi_ln_K_h_l", "epi_ln_K_h_o", "epi_ln_K_h_e",
-                           "epi_ln_K_l_o", "epi_ln_K_l_e"],
+                           "epi_ln_K_l_o", "epi_ln_K_l_e", "epi_ln_K_u"],
             map_column="map_pair",
             get_columns=["pair"],
             in_run_prefix="theta_",
         ))
 
+    # Per-structure ddG and its prior mean, in STRUCTURE_NAMES order (the
+    # order the model samples in and the simulator labels its truth with).
+    specs.extend(ddG_extract_specs(ctx, STRUCTURE_NAMES))
+
     return specs
 
 
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/thermo.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/thermo.py
index 4e951875..eb1bf632 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/thermo.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/thermo.py
@@ -1,44 +1,37 @@
 """
-Shared thermodynamic functions for the MWC lac-dimer-unfolded theta model.
+Shared thermodynamic functions for the M3 MWC lac-dimer θ model with unfolding (O2_C12 U1).
 
-Extends the five-parameter MWC model with a globally unfolded state U.
-Destabilising mutations shift population into U, collapsing θ toward zero at
-all effector concentrations.  Used by ``theta_mwc_dimer_unfolded_lnK_mut``
-(PK), ``theta_mwc_dimer_unfolded_lnK_ddG_prior`` (PddG), and
-``theta_mwc_dimer_unfolded_lnK_nn_prior`` (PnnC).
+Extends the five-constant MWC model (``O2_C12_K5_U0_a/thermo.py``) with a
+globally unfolded state U that binds neither operator nor effector.
+Destabilizing mutations shift the dimer into U and pull θ toward zero at all
+effector concentrations. Used by ``thermo.O2_C12_K5_U1_a.PK``, ``.PddG`` and
+``.PnnC``.
 
-Extends the MWC two-state model (Sochor 2014, PeerJ 2:e498) with an explicit
-unfolded state U:
+Species not bound to operator, with weights relative to free H (h), for two
+independent, equivalent effector sites per conformation:
 
     State       Weight relative to H
     H           1                           (high-affinity conformation)
-    H·op        K_h_o · [op_free]
-    H·E         K_h_e · [e_free]
-    H·E²        K_h_e² · [e_free]²
+    H·E         2·K_h_e·e
+    H·E²        K_h_e²·e²
     L           K_h_l                       (low-affinity conformation)
-    L·op        K_h_l · K_l_o · [op_free]
-    L·E         K_h_l · K_l_e · [e_free]
-    L·E²        K_h_l · K_l_e² · [e_free]²
+    L·E         2·K_h_l·K_l_e·e
+    L·E²        K_h_l·K_l_e²·e²
     U           K_u                         (globally unfolded; no binding)
 
-K_u = [U]/[H] is the H→U equilibrium constant.  In WT K_u << 1.  Mutations
-accumulate destabilising effects as additive shifts in ln(K_u); when K_u
-dominates the partition function, θ collapses toward zero.
-
-U does not bind operator or effector, so the thermodynamic projection matrix
-for all other equilibrium constants is unchanged.  The only modification to
-the MWC partition function is that K_u is added to B0 (the ligand-free
-coefficient), which propagates into the free-effector cubic and into F(e):
+The operator-bound species HO·E_n and LO·E_n carry the extra factor K_h_o·[O]
+or K_l_o·[O]. K_u = [U]/[H]; in wt K_u << 1. K_u adds to B0, the ligand-free
+coefficient of F(e), which enters the free-effector cubic and h:
 
     B0 = 1 + K_h_l + K_u
     F(e) = B0 + B1·e + B2·e²
-    h_free = r / F(e_free)
-    W = h_free · (K_h_o·P_H + K_h_l·K_l_o·P_L)
+    h = r / F(e_free)
+    W = h·(K_h_o·P_H + K_h_l·K_l_o·P_L)
     θ = W / (1 + W)
 
-All other details (operator-depletion approximation, eight Newton steps,
-monomer-unit convention for tf_total, conc_unit_scale) are identical to
-mwc_dimer/thermo.py.  See that module for full derivations.
+Everything else (polynomial, bookkeeping, Newton solve, monomer units for
+tf_total, conc_unit_scale, defaults) is that of the U0 model and
+``thermo/mwc.py``.
 """
 
 import jax
@@ -47,7 +40,17 @@
 import pandas as pd
 from flax.struct import dataclass, field
 
-NEWTON_ITERATIONS = 8
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
+from tfscreen.tfmodel.generative.components.theta.thermo.mwc import (  # noqa: F401
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_TF_TOTAL_M,
+    DEFAULT_LN_K_H_L,
+    DEFAULT_LN_K_H_O,
+    DEFAULT_LN_K_H_E,
+    DEFAULT_LN_K_L_O,
+    DEFAULT_LN_K_L_E,
+    NEWTON_ITERATIONS,
+)
 
 
 # ---------------------------------------------------------------------------
@@ -60,12 +63,11 @@ class ThetaParam:
 
     ln_K_h_l: jnp.ndarray   # (G,)    — log conformational equilibrium
     ln_K_h_o: jnp.ndarray   # (G,)    — log H-state DNA-binding constant
-    ln_K_h_e: jnp.ndarray   # (T, G)  — log H-state effector-binding constant
+    ln_K_h_e: jnp.ndarray   # (T, G)  — log per-site H-state effector constant
     ln_K_l_o: jnp.ndarray   # (G,)    — log L-state DNA-binding constant
-    ln_K_l_e: jnp.ndarray   # (T, G)  — log L-state effector-binding constant
+    ln_K_l_e: jnp.ndarray   # (T, G)  — log per-site L-state effector constant
     ln_K_u:   jnp.ndarray   # (G,)    — log H→U unfolding equilibrium constant
     tf_total: float           # M — monomer units; r = tf_total/2 is the dimer concentration
-    op_total: float           # M
     mu:       jnp.ndarray    # (T, C, 1) — population mean logit-theta
     sigma:    jnp.ndarray    # (T, C, 1) — population std  logit-theta
     conc_unit_scale: float = field(pytree_node=False)
@@ -76,7 +78,7 @@ class ThetaParam:
 # ---------------------------------------------------------------------------
 
 def _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e, ln_K_u,
-                   titrant_conc, tf_total, op_total):
+                   titrant_conc, tf_total):
     """
     Evaluate MWC-unfolded operator-occupancy for all (T, C, G) combinations.
 
@@ -88,56 +90,21 @@ def _compute_theta(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e, ln_K_u,
     ln_K_l_o : (G,)
     ln_K_l_e : (T, G)
     ln_K_u   : (G,)
-    titrant_conc : (C,)
+    titrant_conc : (C,)  — M (already scaled)
     tf_total : scalar — total TF in **monomer** units (M); dimer = tf_total/2
-    op_total : scalar
 
     Returns
     -------
     theta : (T, C, G)
     """
-    K_h_l = jnp.exp(ln_K_h_l)[None, None, :]    # (1, 1, G)
-    K_h_o = jnp.exp(ln_K_h_o)[None, None, :]    # (1, 1, G)
-    K_h_e = jnp.exp(ln_K_h_e)[:, None, :]       # (T, 1, G)
-    K_l_o = jnp.exp(ln_K_l_o)[None, None, :]    # (1, 1, G)
-    K_l_e = jnp.exp(ln_K_l_e)[:, None, :]       # (T, 1, G)
-    K_u   = jnp.exp(ln_K_u)[None, None, :]      # (1, 1, G)
-    e_total = titrant_conc[None, :, None]        # (1, C, 1)
-
-    r = tf_total / 2.0   # dimer units
-
-    # B0 includes K_u; B1, B2 are unchanged (U has no effector binding)
-    B0 = 1.0 + K_h_l + K_u
-    B1 = K_h_e + K_h_l * K_l_e
-    B2 = K_h_e ** 2 + K_h_l * K_l_e ** 2
-
-    c3 = B2
-    c2 = B1 + B2 * (2.0 * r - e_total)
-    c1 = B0 + B1 * (r - e_total)
-    c0 = -B0 * e_total
-
-    def f(x):
-        return c3 * x ** 3 + c2 * x ** 2 + c1 * x + c0
-
-    def df(x):
-        return 3.0 * c3 * x ** 2 + 2.0 * c2 * x + c1
-
-    def step(_, x):
-        dfx = df(x)
-        return x - f(x) / jnp.where(jnp.abs(dfx) < 1e-30, 1e-30, dfx)
-
-    x0 = jnp.broadcast_to(e_total, c2.shape)
-    e_free = jax.lax.fori_loop(0, NEWTON_ITERATIONS, step, x0)
-    e_free = jnp.clip(e_free, 0.0, e_total)
-
-    P_H = 1.0 + K_h_e * e_free + K_h_e ** 2 * e_free ** 2
-    P_L = 1.0 + K_l_e * e_free + K_l_e ** 2 * e_free ** 2
-
-    F = B0 + B1 * e_free + B2 * e_free ** 2
-    h_free = r / jnp.where(F < 1e-30, 1e-30, F)
-
-    W = h_free * (K_h_o * P_H + K_h_l * K_l_o * P_L)
-    return W / (1.0 + W)
+    return mwc.theta_c12(ln_K_h_l[None, None, :],
+                         ln_K_h_o[None, None, :],
+                         ln_K_h_e[:, None, :],
+                         ln_K_l_o[None, None, :],
+                         ln_K_l_e[:, None, :],
+                         ln_K_u[None, None, :],
+                         titrant_conc[None, :, None],
+                         tf_total)
 
 
 def _population_moments(theta, data):
@@ -178,7 +145,6 @@ def run_model(theta_param: ThetaParam, data) -> jnp.ndarray:
         theta_param.ln_K_u,
         data.titrant_conc * theta_param.conc_unit_scale,
         theta_param.tf_total,
-        theta_param.op_total,
     )                                               # (T, C, G)
 
     geno_idx = data.batch_idx[data.geno_theta_idx]
@@ -202,9 +168,9 @@ def get_population_moments(theta_param: ThetaParam, data) -> tuple:
 
 
 def _solve_theta_np(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e, ln_K_u,
-                    conc, tf_total, op_total):
+                    conc, tf_total):
     """
-    Pure-numpy Newton solve for MWC-dimer-unfolded theta at per-row K values.
+    Numpy MWC-dimer-unfolded θ at per-row K values (same ``mwc.theta_c12``).
 
     Parameters
     ----------
@@ -214,48 +180,17 @@ def _solve_theta_np(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e, ln_K_u,
     ln_K_l_o : (S, N)
     ln_K_l_e : (S, N)
     ln_K_u   : (S, N)  or broadcastable to (S, N)
-    conc     : (N,)
+    conc     : (N,)  — effector concentrations in M (zero already replaced)
     tf_total : float — monomer units
-    op_total : float — unused (operator-depletion approx.)
 
     Returns
     -------
     theta : np.ndarray, shape (S, N)
     """
-    K_h_l = np.exp(ln_K_h_l)
-    K_h_o = np.exp(ln_K_h_o)
-    K_h_e = np.exp(ln_K_h_e)
-    K_l_o = np.exp(ln_K_l_o)
-    K_l_e = np.exp(ln_K_l_e)
-    K_u   = np.exp(ln_K_u)
-
-    e_total = conc[np.newaxis, :]
-    r = tf_total / 2.0
-
-    B0 = 1.0 + K_h_l + K_u
-    B1 = K_h_e + K_h_l * K_l_e
-    B2 = K_h_e ** 2 + K_h_l * K_l_e ** 2
-
-    c3 = B2
-    c2 = B1 + B2 * (2.0 * r - e_total)
-    c1 = B0 + B1 * (r - e_total)
-    c0 = -B0 * e_total
-
-    e_free = np.broadcast_to(e_total, c2.shape).copy()
-    for _ in range(NEWTON_ITERATIONS):
-        f  = c3 * e_free**3 + c2 * e_free**2 + c1 * e_free + c0
-        df = 3.0 * c3 * e_free**2 + 2.0 * c2 * e_free + c1
-        e_free = e_free - f / np.where(np.abs(df) < 1e-30, 1e-30, df)
-    e_free = np.clip(e_free, 0.0, e_total)
-
-    P_H = 1.0 + K_h_e * e_free + K_h_e ** 2 * e_free ** 2
-    P_L = 1.0 + K_l_e * e_free + K_l_e ** 2 * e_free ** 2
-
-    F = B0 + B1 * e_free + B2 * e_free ** 2
-    h_free = r / np.where(F < 1e-30, 1e-30, F)
-
-    W = h_free * (K_h_o * P_H + K_h_l * K_l_o * P_L)
-    return W / (1.0 + W)
+    f = lambda v: np.asarray(v, float)  # noqa: E731
+    return mwc.theta_c12(f(ln_K_h_l), f(ln_K_h_o), f(ln_K_h_e), f(ln_K_l_o),
+                         f(ln_K_l_e), f(ln_K_u), f(conc)[np.newaxis, :],
+                         tf_total, xp=np)
 
 
 def build_calc_df(model, manual_titrant_df):
@@ -267,7 +202,7 @@ def build_calc_df(model, manual_titrant_df):
     calc_df : pd.DataFrame
     internal_cols : list of str
     extra_kwargs : dict
-        ``tf_total``, ``op_total``, and ``conc_unit_scale``.
+        ``tf_total`` (M, monomer units) and ``conc_unit_scale``.
     """
     import tfscreen.util.dataframe
 
@@ -308,13 +243,12 @@ def build_calc_df(model, manual_titrant_df):
 
     extra = {
         "tf_total": float(model.priors.theta.theta_tf_total_M),
-        "op_total": float(model.priors.theta.theta_op_total_M),
         "conc_unit_scale": float(model.priors.theta.theta_conc_unit_scale),
     }
     return calc_df, ["genotype_idx", "titrant_name_idx"], extra
 
 
-def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total,
+def compute_theta_samples(calc_df, param_posteriors, *, tf_total,
                           conc_unit_scale=1.0):
     """
     Compute posterior theta samples for the MWC-dimer-unfolded model.
@@ -324,7 +258,7 @@ def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total,
     calc_df : pd.DataFrame
     param_posteriors : dict-like
     tf_total : float
-    op_total : float
+        Total TF concentration in M, monomer units.
     conc_unit_scale : float
 
     Returns
@@ -361,4 +295,4 @@ def _load(key):
 
     return _solve_theta_np(ln_K_h_l_pts, ln_K_h_o_pts, ln_K_h_e_pts,
                            ln_K_l_o_pts, ln_K_l_e_pts, ln_K_u_pts,
-                           conc, tf_total, op_total)
+                           conc, tf_total)
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PK.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PK.py
index 0fe2b65a..000f8660 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PK.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PK.py
@@ -3,6 +3,9 @@
 
 Each mutation shifts the three equilibrium constants (K_op, K_HL, K_E) by an
 independent Δln_K drawn from a hierarchical Normal prior with learned scale.
+K_op = K_h_o (M⁻¹), K_HL = K_h_l and K_E = K_l_e, the per-site effector
+constant of L (M⁻¹), of the MWC dimer in ``thermo/mwc.py``; this model (M2)
+fixes K_h_e = K_l_o = 0.
 No structural information is used; all mutation effects are inferred directly
 from the observations.  This is the simplest and most data-efficient K-assembly
 variant.  Registry name: ``theta_lac_dimer_lnK_mut``.
@@ -23,7 +26,7 @@
 K_op and K_HL are protein properties with no dependence on effector identity
 (no T dimension). K_E is per effector species (T dimension).
 
-Epistasis uses a regularised horseshoe prior (shared τ, slab c², per-pair λ)
+Epistasis uses a regularized horseshoe prior (shared τ, slab c², per-pair λ)
 and is only sampled when data.num_pair > 0.
 
 Thermodynamic functions (partition function, Newton solve for free effector,
@@ -33,6 +36,7 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale, HalfCauchy
 import pandas as pd
 from flax.struct import dataclass
 from functools import partial
@@ -41,6 +45,11 @@
 from tfscreen.tfmodel.data_class import GrowthData
 from tfscreen.genetics.build_mut_geno_matrix import apply_pair_matrix, apply_mut_matrix
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_E_WT_LOC,
+    DEFAULT_LN_K_HL_WT_LOC,
+    DEFAULT_LN_K_OP_WT_LOC,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -65,17 +74,17 @@ class ModelPriors:
     theta_ln_K_E_wt_loc: float
     theta_ln_K_E_wt_scale: float
 
-    # Total TF and operator concentrations (M); treated as fixed physical constants.
-    # Default values from Sochor, PeerJ 2014, https://doi.org/10.7717/peerj.498
-    theta_tf_total_M: float   # ≈ 6.5e-7 M (650 nM)
-    theta_op_total_M: float   # ≈ 2.5e-8 M (25 nM)
+    # Total TF (M, monomer units); a fixed physical constant.
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
+    # Factor converting data titrant_conc (mM) to M (1e-3); see thermo.py.
+    theta_conc_unit_scale: float
 
     # HalfNormal scales for mutation delta distributions
     theta_sigma_d_ln_K_op_scale: float
     theta_sigma_d_ln_K_HL_scale: float
     theta_sigma_d_ln_K_E_scale: float
 
-    # Shared regularised horseshoe hyperparameters for pairwise epistasis
+    # Shared regularized horseshoe hyperparameters for pairwise epistasis
     theta_epi_tau_scale: float     # HalfCauchy scale for global τ (shared across K_op, K_HL, K_E)
     theta_epi_slab_scale: float    # typical size of a large epistasis effect
     theta_epi_slab_df: float       # InvGamma shape ν (usually 4)
@@ -157,7 +166,7 @@ def define_model(name: str,
 
     Samples WT equilibrium constants and per-mutation delta parameters, then
     assembles per-genotype constants via matrix multiplication.  Epistasis uses
-    a regularised horseshoe prior (shared τ and slab c², per-pair local λ).
+    a regularized horseshoe prior (shared τ and slab c², per-pair local λ).
 
     Parameters
     ----------
@@ -181,7 +190,7 @@ def define_model(name: str,
     has_epi = data.num_pair > 0
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     # ------------------------------------------------------------------
     # WT parameters for K_op and K_HL (scalars — no T dimension)
@@ -214,22 +223,29 @@ def define_model(name: str,
     # ------------------------------------------------------------------
     # Mutation delta offsets for K_op and K_HL: shape (num_mutation,)
     # ------------------------------------------------------------------
-    with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
-        d_K_op_off = pyro.sample(
-            f"{name}_d_ln_K_op_offset", dist.Normal(0.0, 1.0))
-        d_K_HL_off = pyro.sample(
-            f"{name}_d_ln_K_HL_offset", dist.Normal(0.0, 1.0))
-
-    # ------------------------------------------------------------------
-    # Mutation delta offsets for K_E: shape (T, num_mutation)
-    # ------------------------------------------------------------------
-    with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
-        with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
-            d_K_E_off = pyro.sample(
-                f"{name}_d_ln_K_E_offset", dist.Normal(0.0, 1.0))
+    if num_mut > 0:
+        with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
+            d_K_op_off = pyro.sample(
+                f"{name}_d_ln_K_op_offset", dist.Normal(0.0, 1.0))
+            d_K_HL_off = pyro.sample(
+                f"{name}_d_ln_K_HL_offset", dist.Normal(0.0, 1.0))
+
+        # ------------------------------------------------------------------
+        # Mutation delta offsets for K_E: shape (T, num_mutation)
+        # ------------------------------------------------------------------
+        with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
+            with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
+                d_K_E_off = pyro.sample(
+                    f"{name}_d_ln_K_E_offset", dist.Normal(0.0, 1.0))
+    else:
+        # num_mut == 0 (wt-only library, e.g. a stratified binding
+        # pool): numpyro.plate needs size > 0, so skip the sites.  The
+        # empty offsets scatter to zeros, so every genotype is wt.
+        d_K_op_off = d_K_HL_off = jnp.zeros((0,))
+        d_K_E_off = jnp.zeros((T, 0))
 
     # ------------------------------------------------------------------
-    # Optional epistasis — regularised horseshoe prior
+    # Optional epistasis — regularized horseshoe prior
     # ------------------------------------------------------------------
     if has_epi:
         pair_scatter = partial(apply_pair_matrix,
@@ -241,7 +257,7 @@ def define_model(name: str,
         # Shared global scale and slab variance across all equilibrium constants
         tau_epi = pyro.sample(
             f"{name}_epi_tau",
-            dist.HalfCauchy(priors.theta_epi_tau_scale))
+            HalfCauchy(priors.theta_epi_tau_scale))
         c2_epi = pyro.sample(
             f"{name}_epi_c2",
             dist.InverseGamma(priors.theta_epi_slab_df / 2.0,
@@ -250,11 +266,11 @@ def define_model(name: str,
         # Scalar K_op and K_HL: local scales and offsets shape (num_pair,)
         with pyro.plate(f"{name}_pair_scalar_plate", num_pair, dim=-1):
             epi_K_op_lam = pyro.sample(
-                f"{name}_epi_ln_K_op_lambda", dist.HalfCauchy(1.0))
+                f"{name}_epi_ln_K_op_lambda", HalfCauchy(1.0))
             epi_K_op_off = pyro.sample(
                 f"{name}_epi_ln_K_op_offset", dist.Normal(0.0, 1.0))
             epi_K_HL_lam = pyro.sample(
-                f"{name}_epi_ln_K_HL_lambda", dist.HalfCauchy(1.0))
+                f"{name}_epi_ln_K_HL_lambda", HalfCauchy(1.0))
             epi_K_HL_off = pyro.sample(
                 f"{name}_epi_ln_K_HL_offset", dist.Normal(0.0, 1.0))
 
@@ -262,7 +278,7 @@ def define_model(name: str,
         with pyro.plate(f"{name}_titrant_epi_outer_plate", T, dim=-2):
             with pyro.plate(f"{name}_pair_plate", num_pair, dim=-1):
                 epi_K_E_lam = pyro.sample(
-                    f"{name}_epi_ln_K_E_lambda", dist.HalfCauchy(1.0))
+                    f"{name}_epi_ln_K_E_lambda", HalfCauchy(1.0))
                 epi_K_E_off = pyro.sample(
                     f"{name}_epi_ln_K_E_offset", dist.Normal(0.0, 1.0))
     else:
@@ -281,7 +297,7 @@ def define_model(name: str,
 
     if has_epi:
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         epi_ln_K_op = epi_K_op_off * tau_epi * _lam_tilde(epi_K_op_lam)          # (P,)
         epi_ln_K_HL = epi_K_HL_off * tau_epi * _lam_tilde(epi_K_HL_lam)          # (P,)
@@ -311,16 +327,17 @@ def _lam_tilde(lam):
     # Population moments over growth concentrations
     # ------------------------------------------------------------------
     theta_for_moments = _compute_theta(ln_K_op, ln_K_HL, ln_K_E,
-                                       data.titrant_conc, tf_total, op_total)
+                                       data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma = _population_moments(theta_for_moments, data)
 
     return ThetaParam(ln_K_op=ln_K_op,
                       ln_K_HL=ln_K_HL,
                       ln_K_E=ln_K_E,
                       tf_total=tf_total,
-                      op_total=op_total,
                       mu=mu,
-                      sigma=sigma)
+                      sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ---------------------------------------------------------------------------
@@ -341,7 +358,7 @@ def guide(name: str,
     has_epi = data.num_pair > 0
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     # ------------------------------------------------------------------
     # Scalar variational parameters for K_op_wt, K_HL_wt, sigma_d_K_op/HL
@@ -407,7 +424,7 @@ def guide(name: str,
         constraint=dist.constraints.positive)
 
     # ------------------------------------------------------------------
-    # Optional epistasis — regularised horseshoe variational parameters
+    # Optional epistasis — regularized horseshoe variational parameters
     # ------------------------------------------------------------------
     if has_epi:
         pair_scatter = partial(apply_pair_matrix,
@@ -484,19 +501,26 @@ def guide(name: str,
             f"{name}_sigma_d_ln_K_E",
             dist.LogNormal(sigma_d_K_E_locs, sigma_d_K_E_scales))
 
-    with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
-        d_K_op_off = pyro.sample(
-            f"{name}_d_ln_K_op_offset",
-            dist.Normal(d_K_op_locs, d_K_op_scales))
-        d_K_HL_off = pyro.sample(
-            f"{name}_d_ln_K_HL_offset",
-            dist.Normal(d_K_HL_locs, d_K_HL_scales))
-
-    with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
-        with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
-            d_K_E_off = pyro.sample(
-                f"{name}_d_ln_K_E_offset",
-                dist.Normal(d_K_E_locs, d_K_E_scales))
+    if num_mut > 0:
+        with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
+            d_K_op_off = pyro.sample(
+                f"{name}_d_ln_K_op_offset",
+                dist.Normal(d_K_op_locs, d_K_op_scales))
+            d_K_HL_off = pyro.sample(
+                f"{name}_d_ln_K_HL_offset",
+                dist.Normal(d_K_HL_locs, d_K_HL_scales))
+
+        with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
+            with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
+                d_K_E_off = pyro.sample(
+                    f"{name}_d_ln_K_E_offset",
+                    dist.Normal(d_K_E_locs, d_K_E_scales))
+    else:
+        # num_mut == 0 (wt-only library, e.g. a stratified binding
+        # pool): numpyro.plate needs size > 0, so skip the sites.  The
+        # empty offsets scatter to zeros, so every genotype is wt.
+        d_K_op_off = d_K_HL_off = jnp.zeros((0,))
+        d_K_E_off = jnp.zeros((T, 0))
 
     if has_epi:
         tau_epi = pyro.sample(f"{name}_epi_tau",
@@ -536,23 +560,24 @@ def guide(name: str,
 
     if has_epi:
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         ln_K_op = ln_K_op + pair_scatter(epi_K_op_off * tau_epi * _lam_tilde(epi_K_op_lam))
         ln_K_HL = ln_K_HL + pair_scatter(epi_K_HL_off * tau_epi * _lam_tilde(epi_K_HL_lam))
         ln_K_E  = ln_K_E  + pair_scatter(epi_K_E_off  * tau_epi * _lam_tilde(epi_K_E_lam))
 
     theta_for_moments = _compute_theta(ln_K_op, ln_K_HL, ln_K_E,
-                                       data.titrant_conc, tf_total, op_total)
+                                       data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma = _population_moments(theta_for_moments, data)
 
     return ThetaParam(ln_K_op=ln_K_op,
                       ln_K_HL=ln_K_HL,
                       ln_K_E=ln_K_E,
                       tf_total=tf_total,
-                      op_total=op_total,
                       mu=mu,
-                      sigma=sigma)
+                      sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ---------------------------------------------------------------------------
@@ -561,23 +586,21 @@ def _lam_tilde(lam):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    # K_op ≈ 10 nM⁻¹ = 1e10 M⁻¹  → ln_K_op ≈ 23.0  (protein-DNA association)
-    p["theta_ln_K_op_wt_loc"]   = 23.0
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_op_wt_loc"]   = DEFAULT_LN_K_OP_WT_LOC   # = K_h_o; Sochor: 4.2e8 M⁻¹
     p["theta_ln_K_op_wt_scale"] = 2.0
-    # K_HL ≈ 1e-4 → ln_K_HL ≈ -9.0  (dimensionless conformational equilibrium)
-    p["theta_ln_K_HL_wt_loc"]   = -9.0
+    p["theta_ln_K_HL_wt_loc"]   = DEFAULT_LN_K_HL_WT_LOC   # = K_h_l; Sochor: 6.3
     p["theta_ln_K_HL_wt_scale"] = 3.0
-    # K_E ≈ 1e-4 nM⁻² = 1e14 M⁻²  → ln_K_E ≈ 33.4  (effector-binding constant)
-    p["theta_ln_K_E_wt_loc"]    = 33.4
+    p["theta_ln_K_E_wt_loc"]    = DEFAULT_LN_K_E_WT_LOC   # = K_l_e, per site (M⁻¹); midpoint 80 µM
     p["theta_ln_K_E_wt_scale"]  = 3.0
-    # Physical concentrations (M); Sochor, PeerJ 2014
-    p["theta_tf_total_M"]  = 6.5e-7   # 650 nM
-    p["theta_op_total_M"]  = 2.5e-8   # 25 nM
+    p["theta_tf_total_M"]  = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     # Mutation effect scales
     p["theta_sigma_d_ln_K_op_scale"]  = 1.0
     p["theta_sigma_d_ln_K_HL_scale"]  = 1.0
     p["theta_sigma_d_ln_K_E_scale"]   = 1.0
-    # Shared regularised horseshoe hyperparameters for pairwise epistasis
+    # Shared regularized horseshoe hyperparameters for pairwise epistasis
     p["theta_epi_tau_scale"]   = 0.1
     p["theta_epi_slab_scale"]  = 2.0
     p["theta_epi_slab_df"]     = 4.0
@@ -588,9 +611,9 @@ def get_guesses(name: str, data: GrowthData) -> Dict[str, Any]:
     T = data.num_titrant_name
     M = data.num_mutation
     g = {}
-    g[f"{name}_ln_K_op_wt"]         = jnp.array(23.0)
-    g[f"{name}_ln_K_HL_wt"]         = jnp.array(-9.0)
-    g[f"{name}_ln_K_E_wt"]          = jnp.full(T, 33.4)
+    g[f"{name}_ln_K_op_wt"]         = jnp.array(DEFAULT_LN_K_OP_WT_LOC)
+    g[f"{name}_ln_K_HL_wt"]         = jnp.array(DEFAULT_LN_K_HL_WT_LOC)
+    g[f"{name}_ln_K_E_wt"]          = jnp.full(T, DEFAULT_LN_K_E_WT_LOC)
     g[f"{name}_sigma_d_ln_K_op"]    = jnp.array(0.5)
     g[f"{name}_sigma_d_ln_K_HL"]    = jnp.array(0.5)
     g[f"{name}_sigma_d_ln_K_E"]     = jnp.full(T, 0.5)
@@ -624,7 +647,7 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
+    conc_unit_scale=1.0,
 ):
     """
     Predict theta for unmeasured genotypes using additive ln-K assembly.
@@ -640,13 +663,13 @@ def predict_unmeasured(
     param_posteriors : dict-like
     q_to_get : dict  mapping column name -> quantile value
     tf_total : float  — total TF concentration in M
-    op_total : float  — total operator concentration in M
+    conc_unit_scale : float  — converts titrant_conc to M (1e-3 for mM data)
 
     Returns
     -------
     pd.DataFrame
         Columns: 'genotype', 'titrant_name', 'titrant_conc', then one per q_to_get key.
-        Rows with unrecognised mutations have NaN quantiles.
+        Rows with unrecognized mutations have NaN quantiles.
     """
     import numpy as np
     from tfscreen.tfmodel.inference.posteriors import get_posterior_samples
@@ -703,11 +726,11 @@ def _load(key):
     ln_K_HL_rows = ln_K_HL_geno[:, geno_idx]
     ln_K_E_rows  = ln_K_E_geno[:, titrant_idx, geno_idx]        # (S, N_rows)
 
-    conc = calc_df["titrant_conc"].values.copy().astype(float)
+    conc = calc_df["titrant_conc"].values.copy().astype(float) * conc_unit_scale
     conc[conc == 0] = _ZERO_CONC_VALUE
 
     theta_samples = _solve_theta_np(
-        ln_K_op_rows, ln_K_HL_rows, ln_K_E_rows, conc, tf_total, op_total
+        ln_K_op_rows, ln_K_HL_rows, ln_K_E_rows, conc, tf_total
     )   # (S, N_rows)
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PddG.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PddG.py
index 39700d06..8a7cee2e 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PddG.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PddG.py
@@ -3,20 +3,31 @@
 
 Each mutation's effect on the three equilibrium constants (K_op, K_HL, K_E) is
 derived from per-structure ΔΔG latent variables whose prior means come from a
-user-supplied CSV file (columns: mut, H, HD, L, LE2).  A learned per-structure
-trust scale σ_s controls how far the posterior can deviate from the CSV values.
+user-supplied CSV file (columns: mut, H, HD, L, LE2).  A per-structure
+scale σ_s, sampled under a LogNormal prior, sets how far ΔΔG may deviate
+from the CSV values.
 Registry name: ``theta_lac_dimer_lnK_ddG_prior``.
 
 Contrast with PK (Δln_K inferred from data alone, no structure) and PnnC
 (ΔΔG prior means predicted by a per-structure MLP rather than supplied by the user).
 
-Implements the lac-dimer four-state partition function with three equilibrium
-constants, using four structural states as the free-energy basis:
+Implements M2, the standard MWC dimer restricted to K_h_e = K_l_o = 0
+(``thermo/mwc.py``), with three equilibrium constants, using four structural
+states as the free-energy basis:
 
     Structures: H, HD, L, LE2
 
-    ΔΔG[m, s]  ~ Normal( ddG_prior[m, s],  σ_s )
-    σ_s        — per-structure trust scale (``pyro.param``, init=1.0)
+    σ_s        ~ LogNormal(theta_ddG_sigma_loc, theta_ddG_sigma_scale), one per
+                 structure (SD of the true ΔΔG about its prior mean)
+    Δln K[m]   ~ MVN( P · ddG_prior[m],  P · diag(σ_s²) · Pᵀ )      (site theta_dlnK)
+
+with P = ``DLNK_PROJ``, the projection below. This is the distribution the
+per-structure model ΔΔG[m, s] ~ Normal(ddG_prior[m, s], σ_s) induces on the
+constants, which are all the data see (thermo/prior.py). The per-structure
+ΔΔG is the deterministic site ``theta_ddG``: the conditional mean under that
+per-structure prior, whose common shift across structures comes from the
+prior means, not the data (no unfolded state anchors it; score per-structure
+ΔΔG only for U1 models).
 
 where ``ddG_prior`` is a (M, S) float32 array loaded from a CSV spreadsheet
 (columns: ``mut``, ``H``, ``HD``, ``L``, ``LE2``).
@@ -25,43 +36,69 @@
 
     Δln_K_op[m] = ΔΔG[m, H]  − ΔΔG[m, HD]
     Δln_K_HL[m] = ΔΔG[m, H]  − ΔΔG[m, L]
-    Δln_K_E[m]  = ΔΔG[m, L]  − ΔΔG[m, LE2]   (broadcast uniformly across effectors)
+    Δln_K_E[m]  = (ΔΔG[m, L] − ΔΔG[m, LE2]) / 2   (broadcast uniformly across effectors)
+
+ΔΔG_X adds to state X's free energy, and Δln K_{A→B} = ΔΔG_A − ΔΔG_B. K_E
+is the per-site constant of L (M⁻¹); LE2 has weight K_E²·e² relative to L, so
+ΔΔG_L − ΔΔG_LE2 = 2·Δln K_E. HD is the operator-bound H structure.
 
 Data requirements
 -----------------
 ``data`` must have (stored as ``pytree_node=False`` statics):
-    struct_names            — tuple containing exactly {'H', 'HD', 'L', 'LE2'}
+    struct_names            — tuple containing at least {'H', 'HD', 'L', 'LE2'} (extras ignored)
     struct_features         — (M, S) float32  per-mutation per-structure ΔΔG prior means
     mut_geno_matrix         — (M, G) for mutation → genotype scatter
     num_mutation            — M
-    num_struct              — S (must equal 4)
+    num_struct              — S (at least 4)
 
 Input CSV format (passed via ``thermo_data``)
 ------------------------------------------------------
-A CSV with columns ``mut`` (mutation label) and one column per structure
-(``H``, ``HD``, ``L``, ``LE2`` in any order).  Mutations absent from the CSV
-receive a prior mean of 0.0 (no structural prediction).
+A CSV with a mutation column (``mut``, ``Mutation`` or ``mutation``) and one
+column per structure (``H``, ``HD``, ``L``, ``LE2``, any order; other columns are
+ignored, and ``thermo_options`` ``column_map`` can name the column for a
+structure).  ΔΔG_X is the shift a mutation makes to state X's free energy,
+so positive destabilizes X, and Δln K_{A→B} = ΔΔG_A − ΔΔG_B.  The model
+needs kT; ``thermo_options`` ``ddG_units: kcal/mol`` (or ``kJ/mol``, with
+``temperature_K``) converts at load, and the default ``kT`` uses the values
+as-is.  The loader (``thermo.io.load_ddG_prior_csv``) reports coverage, errors
+on zero coverage or a wt-residue mismatch (numbering offset; see
+``residue_offset``), and gives absent mutations a prior mean of 0.0 with a
+warning when more than ``max_missing_fraction`` are absent.
 """
 
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale
 import numpyro.distributions.constraints as constraints
 import pandas as pd
-from flax.struct import dataclass
+from flax.struct import dataclass, field
 from functools import partial
 from typing import Dict, Any, Union
 
 from tfscreen.tfmodel.data_class import GrowthData, BindingData
 from tfscreen.genetics.build_mut_geno_matrix import apply_pair_matrix, apply_mut_matrix
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
 from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
-    sample_ddG,
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    ddG_sigma_fixed,
+    ddG_extract_specs,
+    dlnK_guesses,
+    guide_dlnK,
+    projection,
+    sample_dlnK,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.horseshoe import (
     sample_pair_ddG,
     _DEFAULT_D0,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_E_WT_LOC,
+    DEFAULT_LN_K_HL_WT_LOC,
+    DEFAULT_LN_K_OP_WT_LOC,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -69,14 +106,16 @@
     get_population_moments,
 )
 
-# Expected structure names — column order matches _PROJ below.
-STRUCTURE_NAMES = ('H', 'HD', 'L', 'LE2')
+# Structure basis and projection rows (thermo/mwc.py). Convention: ΔΔG_X
+# adds to state X's free energy (kT), Δln K_{A→B} = ΔΔG_A − ΔΔG_B, and the
+# effector rows are per site, half of ΔΔG_X − ΔΔG_XE2.
+STRUCTURE_NAMES = mwc.STRUCTURE_NAMES_C4
+
+# Δln_K = ddG @ _PROJ.T; rows = constants, cols = STRUCTURE_NAMES.
+_PROJ = jnp.asarray(mwc.PROJ_C4)
 
-# Fixed thermodynamic projection: rows = (K_op, K_HL, K_E), cols = (H, HD, L, LE2).
-# Δln_K = ddG @ _PROJ.T   where ddG is (..., 4) → result (..., 3)
-_PROJ = jnp.array([[1., -1.,  0.,  0.],   # K_op = ΔΔG_H − ΔΔG_HD
-                   [1.,  0., -1.,  0.],    # K_HL = ΔΔG_H − ΔΔG_L
-                   [0.,  0.,  1., -1.]])   # K_E  = ΔΔG_L − ΔΔG_LE2
+# Δln K (sampled, M × 3) = ΔΔG @ DLNK_PROJ.T (thermo/prior.py).
+DLNK_PROJ = projection(mwc.PROJ_C4)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -95,16 +134,25 @@ class ModelPriors:
     theta_ln_K_E_wt_loc:    float
     theta_ln_K_E_wt_scale:  float
 
-    # Physical concentrations (M); Sochor, PeerJ 2014
-    theta_tf_total_M: float   # ≈ 6.5e-7 M (650 nM)
-    theta_op_total_M: float   # ≈ 2.5e-8 M (25 nM)
+    # Total TF (M, monomer units); see thermo.py Units
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
+    # Factor converting data titrant_conc (mM) to M (1e-3); see thermo.py.
+    theta_conc_unit_scale: float
 
-    # Regularised horseshoe for pairwise epistasis
+    # Regularized horseshoe for pairwise epistasis
     theta_epi_tau_scale:  float
     theta_epi_slab_scale: float
     theta_epi_slab_df:    float
     theta_epi_d0:         float
 
+    # LogNormal prior on the per-structure ΔΔG scale σ_s (log kT); see
+    # thermo/prior.py.
+    theta_ddG_sigma_loc:   float
+    theta_ddG_sigma_scale: float
+    # > 0 holds every σ_s at this value (kT) instead of sampling it;
+    # 0 (default) samples σ_s (thermo/prior.py, "Holding σ_s").
+    theta_ddG_sigma_fixed: float = field(pytree_node=False, default=0.0)
+
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Internal helpers
@@ -112,14 +160,16 @@ class ModelPriors:
 
 def _get_struct_perm(data):
     """
-    Verify struct_names contains exactly the required structures and return a
-    permutation index mapping data's S ordering to STRUCTURE_NAMES order.
+    Verify struct_names contains every required structure (extras are
+    ignored) and return the index of each STRUCTURE_NAMES entry in it.
     """
-    data_names = data.struct_names
-    if set(data_names) != set(STRUCTURE_NAMES):
+    data_names = tuple(data.struct_names or ())
+    missing = [s for s in STRUCTURE_NAMES if s not in data_names]
+    if missing:
         raise ValueError(
-            f"lnK_ddG_prior (lac_dimer) requires struct_names to contain exactly "
-            f"{set(STRUCTURE_NAMES)}; got {data_names}."
+            f"lnK_ddG_prior (lac_dimer) needs structures {STRUCTURE_NAMES}; "
+            f"struct_names {data_names} lacks {missing}. Extra "
+            f"structures are ignored."
         )
     name_to_idx = {n: i for i, n in enumerate(data_names)}
     return [name_to_idx[s] for s in STRUCTURE_NAMES]
@@ -209,7 +259,7 @@ def define_model(name: str,
     ddG_prior_means = jnp.array(data.struct_features)[:, perm_idx]   # (M, S)
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── WT equilibrium constants ─────────────────────────────────────────────
     ln_K_op_wt = pyro.sample(
@@ -223,12 +273,14 @@ def define_model(name: str,
             f"{name}_ln_K_E_wt",
             dist.Normal(priors.theta_ln_K_E_wt_loc, priors.theta_ln_K_E_wt_scale))
 
-    # ── Per-mutation per-structure ΔΔG latent variables ──────────────────────
-    # ddG_prior_means (M, S) serve as the Normal prior means; σ_s is learned.
-    ddG = sample_ddG(name, list(STRUCTURE_NAMES), num_mut, ddG_prior_means)  # (M, S)
-
-    # Project (M, 4) → (M, 3) Δln_K
-    delta_lnK = _project_ddG(ddG)
+    # ── Per-mutation Δln K under the structural prior ─────────────────────────
+    # Δln K[m] ~ MVN(P · prior_mean[m], P · diag(σ_s²) · Pᵀ), σ_s per state
+    # (thermo/prior.py); the post-hoc per-state ΔΔG is the site {name}_ddG.
+    dlnK = sample_dlnK(name, list(STRUCTURE_NAMES), num_mut, ddG_prior_means, DLNK_PROJ,
+                       sigma_loc=priors.theta_ddG_sigma_loc,
+                       sigma_scale=priors.theta_ddG_sigma_scale,
+                       sigma_fixed=ddG_sigma_fixed(priors))  # (M, C)
+    delta_lnK = dlnK[:, :3]
 
     pyro.deterministic(f"{name}_d_ln_K_op", delta_lnK[:, 0])
     pyro.deterministic(f"{name}_d_ln_K_HL", delta_lnK[:, 1])
@@ -268,11 +320,13 @@ def define_model(name: str,
 
     # ── Population moments for transformation model ───────────────────────────
     theta_vals = _compute_theta(ln_K_op, ln_K_HL, ln_K_E,
-                                data.titrant_conc, tf_total, op_total)
+                                data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -293,11 +347,11 @@ def guide(name: str,
                           mut_nnz_geno_idx=jnp.array(data.mut_nnz_geno_idx),
                           num_genotype=data.num_genotype)
     num_mut = data.num_mutation
-    S       = data.num_struct
+    S       = len(STRUCTURE_NAMES)
     ddG_prior_means = jnp.array(data.struct_features)[:, perm_idx]   # (M, S)
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
     has_epi  = data.num_pair > 0
 
     # ── Variational params for WT K values ────────────────────────────────────
@@ -308,18 +362,6 @@ def guide(name: str,
     ln_K_E_wt_locs   = pyro.param(f"{name}_ln_K_E_wt_locs",   jnp.full(T, priors.theta_ln_K_E_wt_loc))
     ln_K_E_wt_scales = pyro.param(f"{name}_ln_K_E_wt_scales", jnp.ones(T), constraint=constraints.positive)
 
-    # ── Register σ_s so param store includes it before sampling ──────────────
-    pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-
-    # ── Variational params for ddG offsets, shape (S, M) in plate layout ─────
-    ddG_offset_locs   = pyro.param(f"{name}_ddG_offset_locs",
-                                   jnp.zeros((S, num_mut)))
-    ddG_offset_scales = pyro.param(f"{name}_ddG_offset_scales",
-                                   jnp.ones((S, num_mut)),
-                                   constraint=constraints.positive)
-
     # ── Optional epistasis variational params ─────────────────────────────────
     if has_epi:
         num_pair = data.num_pair
@@ -338,19 +380,9 @@ def guide(name: str,
     with pyro.plate(f"{name}_wt_plate", T, dim=-1):
         ln_K_E_wt = pyro.sample(f"{name}_ln_K_E_wt", dist.Normal(ln_K_E_wt_locs, ln_K_E_wt_scales))
 
-    # ── Sample ddG offsets (matches model's prior.sample_ddG plate layout) ────
-    with pyro.plate(f"{name}_struct_plate", S, dim=-2):
-        with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
-            offsets = pyro.sample(
-                f"{name}_ddG_offset",
-                dist.Normal(ddG_offset_locs, ddG_offset_scales),
-            )  # (S, M)
-
-    sigma_s = pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-    ddG_SM = ddG_prior_means.T + sigma_s[:, None] * offsets   # (S, M)
-    ddG    = ddG_SM.T                                          # (M, S)
+    # ── Sample σ_s and Δln K (matches prior.sample_dlnK) ────────────────────
+    dlnK = guide_dlnK(name, num_mut, S, DLNK_PROJ, ddG_prior_means,
+                      sigma_fixed=ddG_sigma_fixed(priors))   # (M, C)
 
     # ── Sample epistasis ──────────────────────────────────────────────────────
     epi_delta_lnK = None
@@ -380,7 +412,7 @@ def guide(name: str,
                 )
 
         def _lam_tilde(l):
-            return jnp.sqrt(c2_epi * l ** 2 / (c2_epi + tau_epi ** 2 * l ** 2))
+            return regularized_scale(l, tau_epi, c2_epi)
 
         epi_SP        = epi_off * tau_epi * _lam_tilde(lam)   # (S, P)
         epi_ddG       = epi_SP.T                               # (P, S)
@@ -392,18 +424,20 @@ def _lam_tilde(l):
                                num_genotype=data.num_genotype)
 
     # ── Assemble per-genotype K values ────────────────────────────────────────
-    delta_lnK = _project_ddG(ddG)
+    delta_lnK = dlnK[:, :3]
     ln_K_op, ln_K_HL, ln_K_E = _assemble_K(
         ln_K_op_wt, ln_K_HL_wt, ln_K_E_wt, delta_lnK, mut_scatter,
         epi_delta_lnK=epi_delta_lnK, pair_scatter=pair_scatter,
     )
 
     theta_vals = _compute_theta(ln_K_op, ln_K_HL, ln_K_E,
-                                data.titrant_conc, tf_total, op_total)
+                                data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -412,31 +446,36 @@ def _lam_tilde(l):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    # WT K priors (same values as lnK_mut for continuity)
-    p["theta_ln_K_op_wt_loc"]   = 23.0
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_op_wt_loc"]   = DEFAULT_LN_K_OP_WT_LOC   # = K_h_o; Sochor: 4.2e8 M⁻¹
     p["theta_ln_K_op_wt_scale"] = 2.0
-    p["theta_ln_K_HL_wt_loc"]   = -9.0
+    p["theta_ln_K_HL_wt_loc"]   = DEFAULT_LN_K_HL_WT_LOC   # = K_h_l; Sochor: 6.3
     p["theta_ln_K_HL_wt_scale"] = 3.0
-    p["theta_ln_K_E_wt_loc"]    = 33.4
+    p["theta_ln_K_E_wt_loc"]    = DEFAULT_LN_K_E_WT_LOC   # = K_l_e, per site (M⁻¹); midpoint 80 µM
     p["theta_ln_K_E_wt_scale"]  = 3.0
-    p["theta_tf_total_M"]     = 6.5e-7
-    p["theta_op_total_M"]     = 2.5e-8
+    p["theta_tf_total_M"]     = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     p["theta_epi_tau_scale"]  = 0.1
     p["theta_epi_slab_scale"] = 2.0
     p["theta_epi_slab_df"]    = 4.0
     p["theta_epi_d0"]         = float(_DEFAULT_D0)
+    p["theta_ddG_sigma_loc"]   = DEFAULT_DDG_SIGMA_LOC     # median σ_s 1 kT
+    p["theta_ddG_sigma_scale"] = DEFAULT_DDG_SIGMA_SCALE   # 95%: 0.1-10 kT
+    p["theta_ddG_sigma_fixed"] = 0.0   # > 0 holds σ_s (kT)
     return p
 
 
 def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, Any]:
     T = data.num_titrant_name
     M = data.num_mutation
-    S = data.num_struct
+    S = len(STRUCTURE_NAMES)
     g = {}
-    g[f"{name}_ln_K_op_wt"]  = jnp.array(23.0)
-    g[f"{name}_ln_K_HL_wt"]  = jnp.array(-9.0)
-    g[f"{name}_ln_K_E_wt"]   = jnp.full(T, 33.4)
-    g[f"{name}_ddG_offset"]   = jnp.zeros((S, M))
+    g[f"{name}_ln_K_op_wt"]  = jnp.array(DEFAULT_LN_K_OP_WT_LOC)
+    g[f"{name}_ln_K_HL_wt"]  = jnp.array(DEFAULT_LN_K_HL_WT_LOC)
+    g[f"{name}_ln_K_E_wt"]   = jnp.full(T, DEFAULT_LN_K_E_WT_LOC)
+    # Δln K starts at P · (ΔΔG prior means), σ_s at 1 kT.
+    g.update(dlnK_guesses(name, jnp.asarray(data.struct_features)[:, jnp.array(_get_struct_perm(data))], DLNK_PROJ))
     if data.num_pair > 0:
         P = data.num_pair
         g[f"{name}_epi_tau"]    = jnp.array(0.05)
@@ -460,7 +499,7 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
+    conc_unit_scale=1.0,
 ):
     """
     Predict theta for unmeasured genotypes using ddG-prior ln-K assembly.
@@ -478,7 +517,8 @@ def predict_unmeasured(
     param_posteriors : dict-like
     q_to_get : dict
     tf_total : float
-    op_total : float
+    conc_unit_scale : float
+        Converts titrant_conc to M (1e-3 for mM data).
 
     Returns
     -------
@@ -536,11 +576,11 @@ def _load(key):
     ln_K_HL_rows = ln_K_HL_geno[:, geno_idx]
     ln_K_E_rows  = ln_K_E_geno[:, titrant_idx, geno_idx]
 
-    conc = calc_df["titrant_conc"].values.copy().astype(float)
+    conc = calc_df["titrant_conc"].values.copy().astype(float) * conc_unit_scale
     conc[conc == 0] = _ZERO_CONC_VALUE
 
     theta_samples = _solve_theta_np(
-        ln_K_op_rows, ln_K_HL_rows, ln_K_E_rows, conc, tf_total, op_total
+        ln_K_op_rows, ln_K_HL_rows, ln_K_E_rows, conc, tf_total
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
@@ -607,6 +647,10 @@ def get_extract_specs(ctx):
             in_run_prefix="theta_",
         ))
 
+    # Per-structure ddG and its prior mean, in STRUCTURE_NAMES order (the
+    # order the model samples in and the simulator labels its truth with).
+    specs.extend(ddG_extract_specs(ctx, STRUCTURE_NAMES))
+
     return specs
 
 
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PnnC.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PnnC.py
index eb9e2bbc..006584f9 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PnnC.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/PnnC.py
@@ -3,7 +3,7 @@
 
 Identical to PddG, but prior means for the per-structure ΔΔG latent variables
 are predicted by per-structure two-layer MLPs trained on LigandMPNN structural
-features rather than read from a user-supplied CSV.  The MLPs are optimised
+features rather than read from a user-supplied CSV.  The MLPs are optimized
 jointly with the model (empirical Bayes).  Requires a structural ensemble HDF5
 file as input.  Registry name: ``theta_lac_dimer_lnK_nn_prior``.
 
@@ -13,21 +13,37 @@
 Per-mutation per-structure ΔΔG latents with MLP-predicted prior means
 (empirical-Bayes via LigandMPNN structural features):
 
-    ΔΔG[m, s]  ~ Normal( MLP_s(features[m, s, :]) · n_chains[s],  σ_s )
-    σ_s        — per-structure trust scale (``pyro.param``, init=1.0)
+    σ_s        ~ LogNormal(theta_ddG_sigma_loc, theta_ddG_sigma_scale), one per
+                 structure (SD of the true ΔΔG about its prior mean)
+    Δln K[m]   ~ MVN( P · μ[m],  P · diag(σ_s²) · Pᵀ )      (site theta_dlnK)
+    μ[m, s]    = MLP_s(features[m, s, :]) · n_chains[s]
+
+with P = ``DLNK_PROJ``, the projection below. This is the distribution the
+per-structure model ΔΔG[m, s] ~ Normal(MLP_s(features[m, s, :]) ·
+n_chains[s], σ_s) induces on the constants, which are all the data see
+(thermo/prior.py). The per-structure ΔΔG is the deterministic site
+``theta_ddG``: the conditional mean under that per-structure prior, whose
+common shift across structures comes from the prior means, not the data (no
+unfolded state anchors it; score per-structure ΔΔG only for U1 models).
 
 When σ_s → 0 the posterior collapses to the deterministic MLP output.
-When σ_s → ∞ the prior is flat and ΔΔG is unconstrained (like ``lnK_mut``).  The data learn the balance per structure.
+When σ_s → ∞ the prior is flat and ΔΔG is unconstrained (like ``lnK_mut``).  The data set
+the balance per structure.
 
 The four per-structure ΔΔG values are projected onto Δln_K via the fixed
 thermodynamic matrix:
 
     Δln_K_op[m] = ΔΔG[m, H]  − ΔΔG[m, HD]
     Δln_K_HL[m] = ΔΔG[m, H]  − ΔΔG[m, L]
-    Δln_K_E[m]  = ΔΔG[m, L]  − ΔΔG[m, LE2]
+    Δln_K_E[m]  = (ΔΔG[m, L] − ΔΔG[m, LE2]) / 2
+
+ΔΔG_X adds to state X's free energy, and Δln K_{A→B} = ΔΔG_A − ΔΔG_B. K_E
+is the per-site constant of L (M⁻¹); LE2 has weight K_E²·e² relative to L, so
+ΔΔG_L − ΔΔG_LE2 = 2·Δln K_E. The model is M2, the MWC dimer of
+``thermo/mwc.py`` with K_h_e = K_l_o = 0.
 
 Epistasis (when ``data.num_pair > 0`` and ``data.struct_contact_distances``
-is not None) uses a distance-dependent regularised horseshoe applied at the
+is not None) uses a distance-dependent regularized horseshoe applied at the
 ΔΔG level per structure, projected to Δln_K in the same way.  Distant pairs
 are strongly shrunk toward zero.
 
@@ -53,6 +69,7 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale
 import numpyro.distributions.constraints as constraints
 import pandas as pd
 from flax.struct import dataclass, field
@@ -65,14 +82,27 @@
     compute_nn_predictions,
     _DEFAULT_HIDDEN_SIZE,
 )
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
 from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
-    sample_ddG,
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    ddG_sigma_fixed,
+    ddG_extract_specs,
+    dlnK_guesses,
+    guide_dlnK,
+    projection,
+    sample_dlnK,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.horseshoe import (
     sample_pair_ddG,
     _DEFAULT_D0,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_E_WT_LOC,
+    DEFAULT_LN_K_HL_WT_LOC,
+    DEFAULT_LN_K_OP_WT_LOC,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -80,14 +110,16 @@
     get_population_moments,
 )
 
-# Expected structure names — column order matches _PROJ below.
-STRUCTURE_NAMES = ('H', 'HD', 'L', 'LE2')
+# Structure basis and projection rows (thermo/mwc.py). Convention: ΔΔG_X
+# adds to state X's free energy (kT), Δln K_{A→B} = ΔΔG_A − ΔΔG_B, and the
+# effector rows are per site, half of ΔΔG_X − ΔΔG_XE2.
+STRUCTURE_NAMES = mwc.STRUCTURE_NAMES_C4
+
+# Δln_K = ddG @ _PROJ.T; rows = constants, cols = STRUCTURE_NAMES.
+_PROJ = jnp.asarray(mwc.PROJ_C4)
 
-# Fixed thermodynamic projection: rows = (K_op, K_HL, K_E), cols = (H, HD, L, LE2).
-# Δln_K = ddG @ _PROJ.T   where ddG is (..., 4) → result (..., 3)
-_PROJ = jnp.array([[1., -1.,  0.,  0.],   # K_op = ΔΔG_H − ΔΔG_HD
-                   [1.,  0., -1.,  0.],    # K_HL = ΔΔG_H − ΔΔG_L
-                   [0.,  0.,  1., -1.]])   # K_E  = ΔΔG_L − ΔΔG_LE2  (S=4, K=3)
+# Δln K (sampled, M × 3) = ΔΔG @ DLNK_PROJ.T (thermo/prior.py).
+DLNK_PROJ = projection(mwc.PROJ_C4)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -106,14 +138,15 @@ class ModelPriors:
     theta_ln_K_E_wt_loc:    float
     theta_ln_K_E_wt_scale:  float
 
-    # Physical concentrations (M); Sochor, PeerJ 2014
-    theta_tf_total_M: float    # ≈ 6.5e-7 M (650 nM)
-    theta_op_total_M: float    # ≈ 2.5e-8 M (25 nM)
+    # Total TF (M, monomer units); see thermo.py Units
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
+    # Factor converting data titrant_conc (mM) to M (1e-3); see thermo.py.
+    theta_conc_unit_scale: float
 
     # Per-structure MLP hidden width (static shape — not a float parameter)
     theta_nn_hidden_size: int = field(pytree_node=False)
 
-    # Regularised horseshoe for pairwise epistasis
+    # Regularized horseshoe for pairwise epistasis
     theta_epi_tau_scale:  float
     theta_epi_slab_scale: float
     theta_epi_slab_df:    float
@@ -121,6 +154,14 @@ class ModelPriors:
     # Distance scale for distance-dependent λ prior (Å)
     theta_epi_d0: float
 
+    # LogNormal prior on the per-structure ΔΔG scale σ_s (log kT); see
+    # thermo/prior.py.
+    theta_ddG_sigma_loc:   float
+    theta_ddG_sigma_scale: float
+    # > 0 holds every σ_s at this value (kT) instead of sampling it;
+    # 0 (default) samples σ_s (thermo/prior.py, "Holding σ_s").
+    theta_ddG_sigma_fixed: float = field(pytree_node=False, default=0.0)
+
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Internal helpers
@@ -191,7 +232,7 @@ def define_model(name: str,
                 and data.struct_contact_distances is not None)
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── WT equilibrium constants ─────────────────────────────────────────────
     ln_K_op_wt = pyro.sample(
@@ -211,12 +252,14 @@ def define_model(name: str,
         hidden_size=priors.theta_nn_hidden_size,
     )  # (M, S)
 
-    # ── Per-mutation per-structure ΔΔG latent variables ──────────────────────
-    # sigma_s (pyro.param) controls trust in the NN; sample_ddG also records it.
-    ddG = sample_ddG(name, list(data.struct_names), num_mut, nn_means)  # (M, S)
-
-    # Project (M, 4) → (M, 3) Δln_K
-    delta_lnK = _project_ddG(ddG)        # (M, 3)
+    # ── Per-mutation Δln K under the structural prior ─────────────────────────
+    # Δln K[m] ~ MVN(P · prior_mean[m], P · diag(σ_s²) · Pᵀ), σ_s per state
+    # (thermo/prior.py); the post-hoc per-state ΔΔG is the site {name}_ddG.
+    dlnK = sample_dlnK(name, list(data.struct_names), num_mut, nn_means, DLNK_PROJ,
+                       sigma_loc=priors.theta_ddG_sigma_loc,
+                       sigma_scale=priors.theta_ddG_sigma_scale,
+                       sigma_fixed=ddG_sigma_fixed(priors))  # (M, C)
+    delta_lnK = dlnK[:, :3]
     d_ln_K_op = delta_lnK[:, 0]          # (M,)
     d_ln_K_HL = delta_lnK[:, 1]          # (M,)
     d_ln_K_E  = delta_lnK[:, 2]          # (M,) — uniform across effectors
@@ -265,11 +308,13 @@ def define_model(name: str,
 
     # ── Population moments for transformation model ───────────────────────────
     theta_vals = _compute_theta(ln_K_op, ln_K_HL, ln_K_E,
-                                data.titrant_conc, tf_total, op_total)
+                                data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -296,7 +341,7 @@ def guide(name: str,
                 and data.struct_contact_distances is not None)
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── Variational params for WT K values ────────────────────────────────────
     ln_K_op_wt_loc   = pyro.param(f"{name}_ln_K_op_wt_loc",   jnp.array(priors.theta_ln_K_op_wt_loc))
@@ -312,18 +357,6 @@ def guide(name: str,
         hidden_size=priors.theta_nn_hidden_size,
     )  # (M, S)
 
-    # ── Register sigma_s so the param store includes it before sampling ───────
-    pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )
-
-    # ── Variational params for ddG offsets, shape (S, M) in plate layout ─────
-    ddG_offset_locs   = pyro.param(f"{name}_ddG_offset_locs",
-                                   jnp.zeros((S, num_mut)))
-    ddG_offset_scales = pyro.param(f"{name}_ddG_offset_scales",
-                                   jnp.ones((S, num_mut)),
-                                   constraint=constraints.positive)
-
     # ── Optional epistasis variational params ─────────────────────────────────
     if has_epi:
         num_pair = data.num_pair
@@ -342,20 +375,9 @@ def guide(name: str,
     with pyro.plate(f"{name}_wt_plate", T, dim=-1):
         ln_K_E_wt = pyro.sample(f"{name}_ln_K_E_wt", dist.Normal(ln_K_E_wt_locs, ln_K_E_wt_scales))
 
-    # ── Sample ddG offsets (matches model's prior.sample_ddG plate layout) ────
-    with pyro.plate(f"{name}_struct_plate", S, dim=-2):
-        with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
-            offsets = pyro.sample(
-                f"{name}_ddG_offset",
-                dist.Normal(ddG_offset_locs, ddG_offset_scales),
-            )  # (S, M)
-
-    # Reconstruct ddG from guide offsets and current sigma_s
-    sigma_s = pyro.param(
-        f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive,
-    )  # (S,) — already registered above; second call returns same tensor
-    ddG_SM = nn_means.T + sigma_s[:, None] * offsets   # (S, M)
-    ddG    = ddG_SM.T                                   # (M, S)
+    # ── Sample σ_s and Δln K (matches prior.sample_dlnK) ────────────────────
+    dlnK = guide_dlnK(name, num_mut, S, DLNK_PROJ, jnp.zeros((num_mut, S)),
+                      sigma_fixed=ddG_sigma_fixed(priors))   # (M, C)
 
     # ── Sample epistasis ──────────────────────────────────────────────────────
     if has_epi:
@@ -374,7 +396,7 @@ def guide(name: str,
                 )
 
     # ── Assemble per-genotype K values ────────────────────────────────────────
-    delta_lnK = _project_ddG(ddG)
+    delta_lnK = dlnK[:, :3]
     d_ln_K_op = delta_lnK[:, 0]
     d_ln_K_HL = delta_lnK[:, 1]
     d_ln_K_E  = delta_lnK[:, 2]
@@ -390,7 +412,7 @@ def guide(name: str,
                                num_genotype=data.num_genotype)
 
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         epi_SP       = epi_off * tau_epi * _lam_tilde(lam)   # (S, P)
         epi_ddG      = epi_SP.T                              # (P, S)
@@ -401,11 +423,13 @@ def _lam_tilde(lam):
         ln_K_E  = ln_K_E  + pair_scatter(epi_delta[:, 2])[None, :]
 
     theta_vals = _compute_theta(ln_K_op, ln_K_HL, ln_K_E,
-                                data.titrant_conc, tf_total, op_total)
+                                data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -414,16 +438,16 @@ def _lam_tilde(lam):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    # WT K priors (same values as lnK_mut for continuity)
-    p["theta_ln_K_op_wt_loc"]   = 23.0
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_op_wt_loc"]   = DEFAULT_LN_K_OP_WT_LOC   # = K_h_o; Sochor: 4.2e8 M⁻¹
     p["theta_ln_K_op_wt_scale"] = 2.0
-    p["theta_ln_K_HL_wt_loc"]   = -9.0
+    p["theta_ln_K_HL_wt_loc"]   = DEFAULT_LN_K_HL_WT_LOC   # = K_h_l; Sochor: 6.3
     p["theta_ln_K_HL_wt_scale"] = 3.0
-    p["theta_ln_K_E_wt_loc"]    = 33.4
+    p["theta_ln_K_E_wt_loc"]    = DEFAULT_LN_K_E_WT_LOC   # = K_l_e, per site (M⁻¹); midpoint 80 µM
     p["theta_ln_K_E_wt_scale"]  = 3.0
-    # Physical concentrations
-    p["theta_tf_total_M"] = 6.5e-7
-    p["theta_op_total_M"] = 2.5e-8
+    p["theta_tf_total_M"] = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     # NN architecture
     p["theta_nn_hidden_size"]   = _DEFAULT_HIDDEN_SIZE
     # Horseshoe hyperparameters for pairwise epistasis
@@ -431,6 +455,9 @@ def get_hyperparameters() -> Dict[str, Any]:
     p["theta_epi_slab_scale"]  = 2.0
     p["theta_epi_slab_df"]     = 4.0
     p["theta_epi_d0"]          = float(_DEFAULT_D0)
+    p["theta_ddG_sigma_loc"]   = DEFAULT_DDG_SIGMA_LOC     # median σ_s 1 kT
+    p["theta_ddG_sigma_scale"] = DEFAULT_DDG_SIGMA_SCALE   # 95%: 0.1-10 kT
+    p["theta_ddG_sigma_fixed"] = 0.0   # > 0 holds σ_s (kT)
     return p
 
 
@@ -439,10 +466,11 @@ def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, An
     M       = data.num_mutation
     S       = data.num_struct
     g = {}
-    g[f"{name}_ln_K_op_wt"]  = jnp.array(23.0)
-    g[f"{name}_ln_K_HL_wt"]  = jnp.array(-9.0)
-    g[f"{name}_ln_K_E_wt"]   = jnp.full(T, 33.4)
-    g[f"{name}_ddG_offset"]  = jnp.zeros((S, M))
+    g[f"{name}_ln_K_op_wt"]  = jnp.array(DEFAULT_LN_K_OP_WT_LOC)
+    g[f"{name}_ln_K_HL_wt"]  = jnp.array(DEFAULT_LN_K_HL_WT_LOC)
+    g[f"{name}_ln_K_E_wt"]   = jnp.full(T, DEFAULT_LN_K_E_WT_LOC)
+    # Δln K starts at P · (ΔΔG prior means), σ_s at 1 kT.
+    g.update(dlnK_guesses(name, jnp.zeros((M, S)), DLNK_PROJ))
     if data.num_pair > 0 and data.struct_contact_distances is not None:
         P = data.num_pair
         g[f"{name}_epi_tau"]      = jnp.array(0.05)
@@ -466,7 +494,7 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
+    conc_unit_scale=1.0,
 ):
     """
     Predict theta for unmeasured genotypes using NN-prior ln-K assembly.
@@ -485,7 +513,8 @@ def predict_unmeasured(
     param_posteriors : dict-like
     q_to_get : dict
     tf_total : float
-    op_total : float
+    conc_unit_scale : float
+        Converts titrant_conc to M (1e-3 for mM data).
 
     Returns
     -------
@@ -543,11 +572,11 @@ def _load(key):
     ln_K_HL_rows = ln_K_HL_geno[:, geno_idx]
     ln_K_E_rows  = ln_K_E_geno[:, titrant_idx, geno_idx]
 
-    conc = calc_df["titrant_conc"].values.copy().astype(float)
+    conc = calc_df["titrant_conc"].values.copy().astype(float) * conc_unit_scale
     conc[conc == 0] = _ZERO_CONC_VALUE
 
     theta_samples = _solve_theta_np(
-        ln_K_op_rows, ln_K_HL_rows, ln_K_E_rows, conc, tf_total, op_total
+        ln_K_op_rows, ln_K_HL_rows, ln_K_E_rows, conc, tf_total
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
@@ -616,6 +645,10 @@ def get_extract_specs(ctx):
             in_run_prefix="theta_",
         ))
 
+    # Per-structure ddG and its prior mean, in STRUCTURE_NAMES order (the
+    # order the model samples in and the simulator labels its truth with).
+    specs.extend(ddG_extract_specs(ctx, STRUCTURE_NAMES))
+
     return specs
 
 
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/thermo.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/thermo.py
index 6d0e2b6b..0fcc3d39 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/thermo.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/thermo.py
@@ -1,54 +1,76 @@
 """
-Shared thermodynamic functions for the lac-dimer partition-function θ model.
+Shared thermodynamic functions for the M2 lac-dimer θ model (O2_C4 U0).
 
-The TF homodimer occupies two conformations: H (high operator affinity) and
-L (low operator affinity, high effector affinity).  Effector binding shifts
-population toward L, reducing operator occupancy (θ).  Used by
-``theta_lac_dimer_lnK_mut`` (PK), ``theta_lac_dimer_lnK_ddG_prior`` (PddG),
-and ``theta_lac_dimer_lnK_nn_prior`` (PnnC).
+M2 is the standard MWC dimer of ``thermo/mwc.py`` restricted to K_h_e = 0
+(H does not bind effector) and K_l_o = 0 (L does not bind operator). The
+dimer is in conformation H, which binds operator, or L, which binds two
+effector molecules at independent, equivalent sites. Effector pulls the
+dimer into L and lowers operator occupancy θ. Used by
+``thermo.O2_C4_K3_U0_a.PK``, ``.PddG`` and ``.PnnC``.
 
-Four-state partition function for the TF homodimer:
+Three constants, named for M3's (O2_C12) constants they equal:
 
-    State      Weight
-    H          1                               (apo, high-affinity conformation)
-    H·op       K_op · [op_free]                (operator-bound, repressing state)
-    L          K_HL                            (low-affinity conformation)
-    L·E²       K_HL · K_E · [E_free]²          (effector-bound, n=2 hardcoded)
+    K_op = K_h_o   H + O → HO (M⁻¹)
+    K_HL = K_h_l   [L]/[H] (dimensionless)
+    K_E  = K_l_e   per-site effector association constant of L (M⁻¹)
 
-    Z     = 1 + K_op·[op_free] + K_HL + K_HL·K_E·[E_free]²
-    θ     = K_op·[op_free] / Z
+Species not bound to operator, with weights relative to free H (h):
 
-Units: all concentrations in M; K_op in M⁻¹; K_E in M⁻²; K_HL dimensionless.
+    H        1
+    L        K_HL
+    LE       2·K_HL·K_E·e         (two ways to fill one site)
+    LE2      K_HL·K_E²·e²
 
-Operator depletion approximation
----------------------------------
-[TF_total] ≈ 6.5e-7 M, [op_total] ≈ 2.5e-8 M
-(Sochor, PeerJ 2014, https://doi.org/10.7717/peerj.498),
-giving [TF]/[op] ≈ 26.  Because the TF is in large excess we treat
-[op_free] ≈ [op_total] = const.
+    F(e) = 1 + K_HL·(1 + K_E·e)²
 
-Free effector (Newton solve)
-----------------------------
-Free-effector concentration satisfies the cubic mass balance:
+TF (1e-5 M monomer by default) is about 400-fold in excess over operator, so
+operator-bound TF is left out of the TF mass balance and operator total does
+not enter the model. With r = tf_total/2 (tf_total in MONOMER units):
 
-    a·x³ + a·(2·[TF_total] − [E_total])·x² + Z₀·x − Z₀·[E_total] = 0
+    h = r / F(e),    W = h·K_op,    θ = W / (1 + W)
 
-where  a  = K_HL·K_E (shape T×G)  and  Z₀ = 1 + K_op·[op_total] + K_HL (shape G).
-Eight Newton iterations from x₀ = [E_total] converge for all
-biophysically plausible parameter combinations.
+Free effector e solves mwc.py's cubic with B0 = 1 + K_HL,
+B1 = 2·K_HL·K_E and B2 = K_HL·K_E². θ here is M3's θ with K_h_e and K_l_o at
+0, computed by the same function (``mwc.theta_c4`` calls ``mwc.mwc_theta``),
+so the nesting is exact.
 
-This module is imported by all K-assembly variants
-(lnK_additive, nn_additive, …). It contains no Numpyro sample sites.
+History. Before 2026-10-11 M2 had a different partition function,
+Z = 1 + K_op·[op_total] + K_HL + K_HL·K_E·e² with K_E in M⁻² for a one-step
+L + 2E → LE2, operator total as a fixed free-operator concentration, and a
+mass balance that counted operator-bound TF. The C4/K3 labels in the
+registry keys are historical: the model now has the species above, three
+constants and no operator-bound species in the TF mass balance.
+
+Units
+-----
+Data concentrations (``titrant_conc``) are mM; ``theta_conc_unit_scale``
+(1e-3) converts them to M, so K_op and K_E are in M⁻¹ and tf_total in M.
+
+This module contains no Numpyro sample sites.
 """
 
 import jax
 import jax.numpy as jnp
 import numpy as np
+
 import pandas as pd
-from flax.struct import dataclass
+from flax.struct import dataclass, field
+
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
+from tfscreen.tfmodel.generative.components.theta.thermo.mwc import (  # noqa: F401
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_TF_TOTAL_M,
+    DEFAULT_LN_K_H_O,
+    DEFAULT_LN_K_H_L,
+    DEFAULT_LN_K_E_C4,
+    NEWTON_ITERATIONS,
+)
 
-# Number of Newton iterations for the free-effector cubic solve.
-NEWTON_ITERATIONS = 8
+# Default wt constants (see mwc.py): Sochor's K_h_o and K_h_l, and the per-site
+# K_E that puts the default wt midpoint at 80 µM IPTG.
+DEFAULT_LN_K_OP_WT_LOC = DEFAULT_LN_K_H_O
+DEFAULT_LN_K_HL_WT_LOC = DEFAULT_LN_K_H_L
+DEFAULT_LN_K_E_WT_LOC = DEFAULT_LN_K_E_C4
 
 
 # ---------------------------------------------------------------------------
@@ -59,90 +81,43 @@
 class ThetaParam:
     """Assembled partition-function parameters passed between K-assembly and thermo."""
 
-    ln_K_op:  jnp.ndarray   # (G,)    — per-genotype log protein-DNA affinity
-    ln_K_HL:  jnp.ndarray   # (G,)    — per-genotype log conformational equilibrium
-    ln_K_E:   jnp.ndarray   # (T, G)  — per-genotype log effector binding constant
-    tf_total: float          # M
-    op_total: float          # M
+    ln_K_op:  jnp.ndarray   # (G,)    — per-genotype log K_op (= K_h_o), M⁻¹
+    ln_K_HL:  jnp.ndarray   # (G,)    — per-genotype log K_HL (= K_h_l)
+    ln_K_E:   jnp.ndarray   # (T, G)  — per-genotype log per-site K_E (= K_l_e), M⁻¹
+    tf_total: float          # M, monomer units
     mu:       jnp.ndarray   # (T, C, 1) — population mean logit-theta
     sigma:    jnp.ndarray   # (T, C, 1) — population std  logit-theta
+    # Factor that converts data.titrant_conc (mM) to M before it enters the
+    # thermodynamic equations (theta_conc_unit_scale, 1e-3 by default).
+    conc_unit_scale: float = field(pytree_node=False)
 
 
 # ---------------------------------------------------------------------------
 # Internal helpers
 # ---------------------------------------------------------------------------
 
-def _solve_free_effector(e_total, tf_total, a, Z0):
-    """
-    Solve the cubic mass-balance equation for free effector concentration.
-
-        a·x³ + a·(2·tf_total − e_total)·x² + Z₀·x − Z₀·e_total = 0
-
-    Uses NEWTON_ITERATIONS Newton iterations starting from x₀ = e_total.
-
-    Parameters
-    ----------
-    e_total : (1, C, 1)  — total effector concentration grid
-    tf_total : scalar    — total TF concentration (M)
-    a : (T, 1, G)        — K_HL · K_E
-    Z0 : (1, 1, G)       — 1 + K_op·[op_total] + K_HL
-
-    Returns
-    -------
-    x_free : (T, C, G)   — free effector concentration, clipped to [0, e_total]
-    """
-    coeff_a = a                                     # (T, 1, G)
-    coeff_b = a * (2.0 * tf_total - e_total)        # (T, C, G)
-    coeff_c = Z0                                    # (1, 1, G)
-    coeff_d = -Z0 * e_total                         # (1, C, G)
-
-    def f(x):
-        return coeff_a * x**3 + coeff_b * x**2 + coeff_c * x + coeff_d
-
-    def df(x):
-        return 3.0 * coeff_a * x**2 + 2.0 * coeff_b * x + coeff_c
-
-    def step(i, x):
-        dfx = df(x)
-        return x - f(x) / jnp.where(jnp.abs(dfx) < 1e-30, 1e-30, dfx)
-
-    x0 = jnp.broadcast_to(e_total, coeff_b.shape)
-    x_free = jax.lax.fori_loop(0, NEWTON_ITERATIONS, step, x0)
-    return jnp.clip(x_free, 0.0, e_total)
-
-
-def _compute_theta(ln_K_op, ln_K_HL, ln_K_E, titrant_conc, tf_total, op_total):
+def _compute_theta(ln_K_op, ln_K_HL, ln_K_E, titrant_conc, tf_total):
     """
-    Evaluate the partition-function theta for all (T, C, G) combinations.
+    Evaluate θ for all (T, C, G) combinations.
 
     Parameters
     ----------
     ln_K_op : (G,)
     ln_K_HL : (G,)
     ln_K_E  : (T, G)
-    titrant_conc : (C,)  — effector concentrations in M
-    tf_total : scalar
-    op_total : scalar
+    titrant_conc : (C,)  — effector concentrations in M (already scaled)
+    tf_total : scalar   — total TF in M, monomer units
 
     Returns
     -------
     theta : (T, C, G)
     """
-    K_op = jnp.exp(ln_K_op)[None, None, :]     # (1, 1, G)
-    K_HL = jnp.exp(ln_K_HL)[None, None, :]     # (1, 1, G)
-    K_E  = jnp.exp(ln_K_E)[:, None, :]         # (T, 1, G)
-    e_total = titrant_conc[None, :, None]       # (1, C, 1)
-
-    Z0 = 1.0 + K_op * op_total + K_HL          # (1, 1, G)
-    a  = K_HL * K_E                             # (T, 1, G)
-
-    e_free = _solve_free_effector(e_total, tf_total, a, Z0)  # (T, C, G)
-
-    w_Hop = K_op * op_total                     # (1, 1, G)
-    w_LE  = K_HL * K_E * e_free**2             # (T, C, G)
-    Z     = 1.0 + w_Hop + K_HL + w_LE          # (T, C, G)
-
-    return w_Hop / Z                            # (T, C, G)
+    return mwc.theta_c4(ln_K_op[None, None, :],
+                        ln_K_HL[None, None, :],
+                        ln_K_E[:, None, :],
+                        None,
+                        titrant_conc[None, :, None],
+                        tf_total)
 
 
 def _population_moments(theta, data):
@@ -187,9 +162,8 @@ def run_model(theta_param: ThetaParam, data) -> jnp.ndarray:
         theta_param.ln_K_op,
         theta_param.ln_K_HL,
         theta_param.ln_K_E,
-        data.titrant_conc,
+        data.titrant_conc * theta_param.conc_unit_scale,
         theta_param.tf_total,
-        theta_param.op_total,
     )                                              # (T, C, G)
 
     geno_idx = data.batch_idx[data.geno_theta_idx]
@@ -212,9 +186,9 @@ def get_population_moments(theta_param: ThetaParam, data) -> tuple:
 _ZERO_CONC_VALUE = 1e-20
 
 
-def _solve_theta_np(ln_K_op, ln_K_HL, ln_K_E, conc, tf_total, op_total):
+def _solve_theta_np(ln_K_op, ln_K_HL, ln_K_E, conc, tf_total):
     """
-    Pure-numpy Newton solve for lac_dimer theta at per-row K values.
+    Numpy θ at per-row K values (the same ``mwc.theta_c4`` as the JAX path).
 
     Parameters
     ----------
@@ -222,33 +196,16 @@ def _solve_theta_np(ln_K_op, ln_K_HL, ln_K_E, conc, tf_total, op_total):
     ln_K_HL : (S, N)
     ln_K_E  : (S, N)
     conc    : (N,)  — effector concentrations in M (zero already replaced)
-    tf_total : float
-    op_total : float
+    tf_total : float — M, monomer units
 
     Returns
     -------
     theta : np.ndarray, shape (S, N)
     """
-    K_op = np.exp(ln_K_op)
-    K_HL = np.exp(ln_K_HL)
-    K_E  = np.exp(ln_K_E)
-
-    e_total = conc[np.newaxis, :]
-    Z0      = 1.0 + K_op * op_total + K_HL
-    a       = K_HL * K_E
-    coeff_b = a * (2.0 * tf_total - e_total)
-
-    e_free = e_total * np.ones_like(a)
-    for _ in range(NEWTON_ITERATIONS):
-        f  = a * e_free**3 + coeff_b * e_free**2 + Z0 * e_free - Z0 * e_total
-        df = 3.0 * a * e_free**2 + 2.0 * coeff_b * e_free + Z0
-        e_free = e_free - f / np.where(np.abs(df) < 1e-30, 1e-30, df)
-    e_free = np.clip(e_free, 0.0, e_total)
-
-    w_Hop = K_op * op_total
-    w_LE  = a * e_free**2
-    Z     = 1.0 + w_Hop + K_HL + w_LE
-    return w_Hop / Z
+    return mwc.theta_c4(np.asarray(ln_K_op, float), np.asarray(ln_K_HL, float),
+                        np.asarray(ln_K_E, float), None,
+                        np.asarray(conc, float)[np.newaxis, :], tf_total,
+                        xp=np)
 
 
 def build_calc_df(model, manual_titrant_df):
@@ -265,8 +222,8 @@ def build_calc_df(model, manual_titrant_df):
     internal_cols : list of str
         Columns to strip before returning results to the caller.
     extra_kwargs : dict
-        ``tf_total`` and ``op_total`` scalars (in M) required by
-        ``compute_theta_samples``.
+        ``tf_total`` (M, monomer units) and ``conc_unit_scale`` required
+        by ``compute_theta_samples``.
     """
     import tfscreen.util.dataframe
 
@@ -307,18 +264,19 @@ def build_calc_df(model, manual_titrant_df):
 
     extra = {
         "tf_total": float(model.priors.theta.theta_tf_total_M),
-        "op_total": float(model.priors.theta.theta_op_total_M),
+        "conc_unit_scale": float(model.priors.theta.theta_conc_unit_scale),
     }
     return calc_df, ["genotype_idx", "titrant_name_idx"], extra
 
 
-def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total):
+def compute_theta_samples(calc_df, param_posteriors, *, tf_total,
+                          conc_unit_scale=1.0):
     """
     Compute posterior theta samples for the lac_dimer partition-function model.
 
-    Uses a pure-numpy Newton solve identical in structure to the JAX version
-    in ``_compute_theta``, operating on flattened per-row posterior slices
-    rather than the full (T, C, G) grid.
+    Uses the same equations as the JAX path (``mwc.theta_c4``) in numpy,
+    on flattened per-row posterior slices rather than the full (T, C, G)
+    grid.
 
     Parameters
     ----------
@@ -328,9 +286,10 @@ def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total):
     param_posteriors : dict-like
         Posterior samples keyed by parameter name (with ``theta_`` prefix).
     tf_total : float
-        Total TF concentration in M.
-    op_total : float
-        Total operator concentration in M.
+        Total TF concentration in M, monomer units.
+    conc_unit_scale : float, optional
+        Multiply ``titrant_conc`` by this factor to get M. ``build_calc_df``
+        passes ``theta_conc_unit_scale`` (1e-3 for mM data).
 
     Returns
     -------
@@ -342,7 +301,7 @@ def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total):
     geno_indices    = calc_df["genotype_idx"].values.astype(int)
     titrant_indices = calc_df["titrant_name_idx"].values.astype(int)
 
-    conc = calc_df["titrant_conc"].values.copy().astype(float)   # (N,)
+    conc = calc_df["titrant_conc"].values.copy().astype(float) * conc_unit_scale   # (N,)
     conc[conc == 0] = _ZERO_CONC_VALUE
 
     def _load(key):
@@ -361,4 +320,4 @@ def _load(key):
     ln_K_E_pts  = ln_K_E_all[:, titrant_indices, geno_indices]
 
     return _solve_theta_np(ln_K_op_pts, ln_K_HL_pts, ln_K_E_pts,
-                           conc, tf_total, op_total)
+                           conc, tf_total)
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PK.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PK.py
index 43c88ba2..900435e6 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PK.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PK.py
@@ -4,19 +4,19 @@
 Extends the lac-dimer PK model with a per-mutation unfolding shift (d_ln_K_U).
 All four equilibrium constants (K_op, K_HL, K_E, K_U) are shifted by Δln_K
 values inferred from the observations; no structural information is used.
-Use this when mutations may significantly destabilise the protein and a flat
+Use this when mutations may significantly destabilize the protein and a flat
 θ curve is observed.  Registry name: ``theta_lac_dimer_unfolded_lnK_mut``.
 
 Contrast with PddG (Δln_K derived from user-supplied structural ΔΔG) and PnnC
-(Δln_K derived from NN-predicted structural ΔΔG).  Note that PddG and PnnC in
-this family hold d_ln_K_U = 0 for all mutations by design.
+(Δln_K derived from NN-predicted structural ΔΔG).  In PddG and PnnC,
+d_ln_K_U is not free: it equals ΔΔG_H, since mutations leave U unchanged.
 
 Identical to lac_dimer/lnK_mut.py with one additional scalar parameter:
 
     ln_K_U[g] = ln_K_U_wt + (d_ln_K_U · M)[g]
 
 where K_U = [U]/[H] is the H→U unfolding equilibrium.  In WT K_U << 1.
-Mutations that destabilise the protein increase K_U (positive d_ln_K_U).
+Mutations that destabilize the protein increase K_U (positive d_ln_K_U).
 When K_U dominates the partition function, θ collapses toward zero for all
 effector concentrations regardless of the folded-state equilibria.
 
@@ -28,6 +28,7 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale, HalfCauchy
 import pandas as pd
 from flax.struct import dataclass
 from functools import partial
@@ -36,6 +37,11 @@
 from tfscreen.tfmodel.data_class import GrowthData
 from tfscreen.genetics.build_mut_geno_matrix import apply_pair_matrix, apply_mut_matrix
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_E_WT_LOC,
+    DEFAULT_LN_K_HL_WT_LOC,
+    DEFAULT_LN_K_OP_WT_LOC,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -62,9 +68,10 @@ class ModelPriors:
     theta_ln_K_U_wt_loc: float
     theta_ln_K_U_wt_scale: float
 
-    # Physical concentrations (M)
-    theta_tf_total_M: float
-    theta_op_total_M: float
+    # Total TF (M, monomer units)
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
+    # Factor converting data titrant_conc (mM) to M (1e-3); see thermo.py.
+    theta_conc_unit_scale: float
 
     # HalfNormal scales for mutation delta distributions
     theta_sigma_d_ln_K_op_scale: float
@@ -72,7 +79,7 @@ class ModelPriors:
     theta_sigma_d_ln_K_E_scale: float
     theta_sigma_d_ln_K_U_scale: float
 
-    # Shared regularised horseshoe hyperparameters for pairwise epistasis
+    # Shared regularized horseshoe hyperparameters for pairwise epistasis
     theta_epi_tau_scale: float
     theta_epi_slab_scale: float
     theta_epi_slab_df: float
@@ -122,7 +129,7 @@ def define_model(name: str,
     has_epi = data.num_pair > 0
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── WT parameters ────────────────────────────────────────────────────────
     ln_K_op_wt = pyro.sample(
@@ -154,14 +161,21 @@ def define_model(name: str,
             dist.HalfNormal(priors.theta_sigma_d_ln_K_E_scale))
 
     # ── Mutation delta offsets ────────────────────────────────────────────────
-    with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
-        d_K_op_off = pyro.sample(f"{name}_d_ln_K_op_offset", dist.Normal(0.0, 1.0))
-        d_K_HL_off = pyro.sample(f"{name}_d_ln_K_HL_offset", dist.Normal(0.0, 1.0))
-        d_K_U_off  = pyro.sample(f"{name}_d_ln_K_U_offset",  dist.Normal(0.0, 1.0))
-
-    with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
-        with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
-            d_K_E_off = pyro.sample(f"{name}_d_ln_K_E_offset", dist.Normal(0.0, 1.0))
+    if num_mut > 0:
+        with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
+            d_K_op_off = pyro.sample(f"{name}_d_ln_K_op_offset", dist.Normal(0.0, 1.0))
+            d_K_HL_off = pyro.sample(f"{name}_d_ln_K_HL_offset", dist.Normal(0.0, 1.0))
+            d_K_U_off  = pyro.sample(f"{name}_d_ln_K_U_offset",  dist.Normal(0.0, 1.0))
+
+        with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
+            with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
+                d_K_E_off = pyro.sample(f"{name}_d_ln_K_E_offset", dist.Normal(0.0, 1.0))
+    else:
+        # num_mut == 0 (wt-only library, e.g. a stratified binding
+        # pool): numpyro.plate needs size > 0, so skip the sites.  The
+        # empty offsets scatter to zeros, so every genotype is wt.
+        d_K_op_off = d_K_HL_off = d_K_U_off = jnp.zeros((0,))
+        d_K_E_off = jnp.zeros((T, 0))
 
     # ── Optional epistasis (folded-state K values only) ───────────────────────
     if has_epi:
@@ -173,21 +187,21 @@ def define_model(name: str,
 
         tau_epi = pyro.sample(
             f"{name}_epi_tau",
-            dist.HalfCauchy(priors.theta_epi_tau_scale))
+            HalfCauchy(priors.theta_epi_tau_scale))
         c2_epi = pyro.sample(
             f"{name}_epi_c2",
             dist.InverseGamma(priors.theta_epi_slab_df / 2.0,
                               priors.theta_epi_slab_df * priors.theta_epi_slab_scale ** 2 / 2.0))
 
         with pyro.plate(f"{name}_pair_scalar_plate", num_pair, dim=-1):
-            epi_K_op_lam = pyro.sample(f"{name}_epi_ln_K_op_lambda", dist.HalfCauchy(1.0))
+            epi_K_op_lam = pyro.sample(f"{name}_epi_ln_K_op_lambda", HalfCauchy(1.0))
             epi_K_op_off = pyro.sample(f"{name}_epi_ln_K_op_offset", dist.Normal(0.0, 1.0))
-            epi_K_HL_lam = pyro.sample(f"{name}_epi_ln_K_HL_lambda", dist.HalfCauchy(1.0))
+            epi_K_HL_lam = pyro.sample(f"{name}_epi_ln_K_HL_lambda", HalfCauchy(1.0))
             epi_K_HL_off = pyro.sample(f"{name}_epi_ln_K_HL_offset", dist.Normal(0.0, 1.0))
 
         with pyro.plate(f"{name}_titrant_epi_outer_plate", T, dim=-2):
             with pyro.plate(f"{name}_pair_plate", num_pair, dim=-1):
-                epi_K_E_lam = pyro.sample(f"{name}_epi_ln_K_E_lambda", dist.HalfCauchy(1.0))
+                epi_K_E_lam = pyro.sample(f"{name}_epi_ln_K_E_lambda", HalfCauchy(1.0))
                 epi_K_E_off = pyro.sample(f"{name}_epi_ln_K_E_offset", dist.Normal(0.0, 1.0))
     else:
         pair_scatter = None
@@ -205,7 +219,7 @@ def define_model(name: str,
 
     if has_epi:
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         epi_ln_K_op = epi_K_op_off * tau_epi * _lam_tilde(epi_K_op_lam)
         epi_ln_K_HL = epi_K_HL_off * tau_epi * _lam_tilde(epi_K_HL_lam)
@@ -233,11 +247,13 @@ def _lam_tilde(lam):
 
     # ── Population moments ────────────────────────────────────────────────────
     theta_for_moments = _compute_theta(ln_K_op, ln_K_HL, ln_K_E, ln_K_U,
-                                       data.titrant_conc, tf_total, op_total)
+                                       data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma = _population_moments(theta_for_moments, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E, ln_K_U=ln_K_U,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ---------------------------------------------------------------------------
@@ -258,7 +274,7 @@ def guide(name: str,
     has_epi = data.num_pair > 0
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── Variational params for WT K values ────────────────────────────────────
     ln_K_op_wt_loc   = pyro.param(f"{name}_ln_K_op_wt_loc",   jnp.array(priors.theta_ln_K_op_wt_loc))
@@ -330,14 +346,21 @@ def guide(name: str,
         ln_K_E_wt   = pyro.sample(f"{name}_ln_K_E_wt",   dist.Normal(ln_K_E_wt_locs,   ln_K_E_wt_scales))
         sigma_d_K_E = pyro.sample(f"{name}_sigma_d_ln_K_E", dist.LogNormal(sigma_d_K_E_locs, sigma_d_K_E_scales))
 
-    with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
-        d_K_op_off = pyro.sample(f"{name}_d_ln_K_op_offset", dist.Normal(d_K_op_locs, d_K_op_scales))
-        d_K_HL_off = pyro.sample(f"{name}_d_ln_K_HL_offset", dist.Normal(d_K_HL_locs, d_K_HL_scales))
-        d_K_U_off  = pyro.sample(f"{name}_d_ln_K_U_offset",  dist.Normal(d_K_U_locs,  d_K_U_scales))
+    if num_mut > 0:
+        with pyro.plate(f"{name}_mutation_scalar_plate", num_mut, dim=-1):
+            d_K_op_off = pyro.sample(f"{name}_d_ln_K_op_offset", dist.Normal(d_K_op_locs, d_K_op_scales))
+            d_K_HL_off = pyro.sample(f"{name}_d_ln_K_HL_offset", dist.Normal(d_K_HL_locs, d_K_HL_scales))
+            d_K_U_off  = pyro.sample(f"{name}_d_ln_K_U_offset",  dist.Normal(d_K_U_locs,  d_K_U_scales))
 
-    with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
-        with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
-            d_K_E_off = pyro.sample(f"{name}_d_ln_K_E_offset", dist.Normal(d_K_E_locs, d_K_E_scales))
+        with pyro.plate(f"{name}_titrant_mut_outer_plate", T, dim=-2):
+            with pyro.plate(f"{name}_mutation_plate", num_mut, dim=-1):
+                d_K_E_off = pyro.sample(f"{name}_d_ln_K_E_offset", dist.Normal(d_K_E_locs, d_K_E_scales))
+    else:
+        # num_mut == 0 (wt-only library, e.g. a stratified binding
+        # pool): numpyro.plate needs size > 0, so skip the sites.  The
+        # empty offsets scatter to zeros, so every genotype is wt.
+        d_K_op_off = d_K_HL_off = d_K_U_off = jnp.zeros((0,))
+        d_K_E_off = jnp.zeros((T, 0))
 
     if has_epi:
         tau_epi = pyro.sample(f"{name}_epi_tau", dist.LogNormal(tau_epi_loc, tau_epi_scale))
@@ -362,18 +385,20 @@ def guide(name: str,
 
     if has_epi:
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         ln_K_op = ln_K_op + pair_scatter(epi_K_op_off * tau_epi * _lam_tilde(epi_K_op_lam))
         ln_K_HL = ln_K_HL + pair_scatter(epi_K_HL_off * tau_epi * _lam_tilde(epi_K_HL_lam))
         ln_K_E  = ln_K_E  + pair_scatter(epi_K_E_off  * tau_epi * _lam_tilde(epi_K_E_lam))
 
     theta_for_moments = _compute_theta(ln_K_op, ln_K_HL, ln_K_E, ln_K_U,
-                                       data.titrant_conc, tf_total, op_total)
+                                       data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma = _population_moments(theta_for_moments, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E, ln_K_U=ln_K_U,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ---------------------------------------------------------------------------
@@ -382,17 +407,19 @@ def _lam_tilde(lam):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    p["theta_ln_K_op_wt_loc"]   = 23.0
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_op_wt_loc"]   = DEFAULT_LN_K_OP_WT_LOC   # = K_h_o; Sochor: 4.2e8 M⁻¹
     p["theta_ln_K_op_wt_scale"] = 2.0
-    p["theta_ln_K_HL_wt_loc"]   = -9.0
+    p["theta_ln_K_HL_wt_loc"]   = DEFAULT_LN_K_HL_WT_LOC   # = K_h_l; Sochor: 6.3
     p["theta_ln_K_HL_wt_scale"] = 3.0
-    p["theta_ln_K_E_wt_loc"]    = 33.4
+    p["theta_ln_K_E_wt_loc"]    = DEFAULT_LN_K_E_WT_LOC   # = K_l_e, per site (M⁻¹); midpoint 80 µM
     p["theta_ln_K_E_wt_scale"]  = 3.0
     # WT unfolded state: K_U << 1 in WT (ln_K_U ≈ -12 → K_U ≈ 6e-6)
     p["theta_ln_K_U_wt_loc"]    = -12.0
     p["theta_ln_K_U_wt_scale"]  = 3.0
-    p["theta_tf_total_M"]  = 6.5e-7
-    p["theta_op_total_M"]  = 2.5e-8
+    p["theta_tf_total_M"]  = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     p["theta_sigma_d_ln_K_op_scale"]  = 1.0
     p["theta_sigma_d_ln_K_HL_scale"]  = 1.0
     p["theta_sigma_d_ln_K_E_scale"]   = 1.0
@@ -407,9 +434,9 @@ def get_guesses(name: str, data: GrowthData) -> Dict[str, Any]:
     T = data.num_titrant_name
     M = data.num_mutation
     g = {}
-    g[f"{name}_ln_K_op_wt"]       = jnp.array(23.0)
-    g[f"{name}_ln_K_HL_wt"]       = jnp.array(-9.0)
-    g[f"{name}_ln_K_E_wt"]        = jnp.full(T, 33.4)
+    g[f"{name}_ln_K_op_wt"]       = jnp.array(DEFAULT_LN_K_OP_WT_LOC)
+    g[f"{name}_ln_K_HL_wt"]       = jnp.array(DEFAULT_LN_K_HL_WT_LOC)
+    g[f"{name}_ln_K_E_wt"]        = jnp.full(T, DEFAULT_LN_K_E_WT_LOC)
     g[f"{name}_ln_K_U_wt"]        = jnp.array(-12.0)
     g[f"{name}_sigma_d_ln_K_op"]  = jnp.array(0.5)
     g[f"{name}_sigma_d_ln_K_HL"]  = jnp.array(0.5)
@@ -446,7 +473,7 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
+    conc_unit_scale=1.0,
 ):
     """Predict theta for unmeasured genotypes (lac_dimer_unfolded/lnK_mut)."""
     import numpy as np
@@ -502,12 +529,12 @@ def _load(key):
     ln_K_E_rows  = ln_K_E_geno[:, titrant_idx, geno_idx]
     ln_K_U_rows  = ln_K_U_geno[:, geno_idx]
 
-    conc = calc_df["titrant_conc"].values.copy().astype(float)
+    conc = calc_df["titrant_conc"].values.copy().astype(float) * conc_unit_scale
     conc[conc == 0] = _ZERO_CONC_VALUE
 
     theta_samples = _solve_theta_np(
         ln_K_op_rows, ln_K_HL_rows, ln_K_E_rows, ln_K_U_rows,
-        conc, tf_total, op_total,
+        conc, tf_total,
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PddG.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PddG.py
index c2986a21..7a4ecbb4 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PddG.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PddG.py
@@ -2,10 +2,8 @@
 PddG — mutation effects on equilibrium constants from user-supplied ΔΔG prior means (lac-dimer-unfolded).
 
 Extends the lac-dimer PddG model with a WT-level unfolding constant (ln_K_U_wt).
-Per-mutation effects on K_op, K_HL, and K_E are derived from user-supplied
-structural ΔΔG (CSV: columns mut, H, HD, L, LE2); K_U is held at its WT value
-for all genotypes (d_ln_K_U = 0) because structure-based predictors describe
-folded-state environments, not global stability.  Registry name:
+Per-mutation effects on K_op, K_HL, K_E, and K_U are derived from user-supplied
+structural ΔΔG (CSV: columns mut, H, HD, L, LE2).  Registry name:
 ``theta_lac_dimer_unfolded_lnK_ddG_prior``.
 
 Contrast with PK (all K shifts inferred from data, including K_U per mutation)
@@ -13,34 +11,77 @@
 
 Identical to lac_dimer/lnK_ddG_prior.py with one additional scalar parameter:
 
-    ln_K_U_wt  — WT H→U unfolding equilibrium constant (same for all genotypes)
-
-Per-mutation effects on the unfolded state are deliberately absent: structure-
-based ΔΔG predictors describe effects on the *folded* conformational equilibria,
-not global protein stability.  Setting d_ln_K_U = 0 for all mutations is an
-explicit design choice — use lac_dimer_unfolded/lnK_mut if per-mutation
-unfolding effects should be inferred from the data.
+    ln_K_U_wt  — WT H→U unfolding equilibrium constant
+
+and one additional projection row.  A mutation shifts the free energy of each
+folded structure X by ΔΔG_X and leaves the unfolded state alone (ΔΔG_U = 0,
+the standard assumption).  Because K_U = [U]/[H], the projection convention
+Δln K_{A→B} = ΔΔG_A − ΔΔG_B gives
+
+    Δln K_U = ΔΔG_H − ΔΔG_U = ΔΔG_H
+
+so ln_K_U[g] = ln_K_U_wt + Σ_m ΔΔG_H[m] (plus the pairwise ΔΔG_H epistasis).
+A mutation that raises every folded state by the same ΔΔG leaves K_op, K_HL
+and K_E unchanged and raises K_U by ΔΔG: a pure stability effect.  The
+per-mutation shift is exposed as ``d_ln_K_U`` (and ``epi_ln_K_U`` per pair),
+the same name the PK model uses for its free per-mutation unfolding shift.
+
+The structural prior sits on the constants (thermo/prior.py): σ_s ~
+LogNormal(theta_ddG_sigma_loc, theta_ddG_sigma_scale) per structure and Δln
+K[m] ~ MVN(P · μ[m], P · diag(σ_s²) · Pᵀ), μ the ΔΔG table, P =
+``DLNK_PROJ`` (the folded rows plus the unfolding row; site ``theta_dlnK``,
+M × 4).  P is square and invertible, so this is exactly the per-structure
+model ΔΔG[m, s] ~ Normal(μ[m, s], σ_s) written in the constants, and the
+per-structure ΔΔG is the deterministic site ``theta_ddG`` = P⁻¹ · Δln K.
+
+Input CSV format (passed via ``thermo_data``)
+------------------------------------------------------
+A CSV with a mutation column (``mut``, ``Mutation`` or ``mutation``) and one
+column per structure (``H``, ``HD``, ``L``, ``LE2``, any order; other columns are
+ignored, and ``thermo_options`` ``column_map`` can name the column for a
+structure).  ΔΔG_X is the shift a mutation makes to state X's free energy,
+so positive destabilizes X, and Δln K_{A→B} = ΔΔG_A − ΔΔG_B.  The model
+needs kT; ``thermo_options`` ``ddG_units: kcal/mol`` (or ``kJ/mol``, with
+``temperature_K``) converts at load, and the default ``kT`` uses the values
+as-is.  The loader (``thermo.io.load_ddG_prior_csv``) reports coverage, errors
+on zero coverage or a wt-residue mismatch (numbering offset; see
+``residue_offset``), and gives absent mutations a prior mean of 0.0 with a
+warning when more than ``max_missing_fraction`` are absent.
 """
 
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale
 import numpyro.distributions.constraints as constraints
 import pandas as pd
-from flax.struct import dataclass
+from flax.struct import dataclass, field
 from functools import partial
 from typing import Dict, Any, Union
 
 from tfscreen.tfmodel.data_class import GrowthData, BindingData
 from tfscreen.genetics.build_mut_geno_matrix import apply_pair_matrix, apply_mut_matrix
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
 from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
-    sample_ddG,
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    ddG_sigma_fixed,
+    ddG_extract_specs,
+    dlnK_guesses,
+    guide_dlnK,
+    projection,
+    sample_dlnK,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.horseshoe import (
     sample_pair_ddG,
     _DEFAULT_D0,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_E_WT_LOC,
+    DEFAULT_LN_K_HL_WT_LOC,
+    DEFAULT_LN_K_OP_WT_LOC,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -48,11 +89,20 @@
     get_population_moments,
 )
 
-STRUCTURE_NAMES = ('H', 'HD', 'L', 'LE2')
+# Structure basis and projection rows (thermo/mwc.py). Convention: ΔΔG_X
+# adds to state X's free energy (kT), Δln K_{A→B} = ΔΔG_A − ΔΔG_B, and the
+# effector rows are per site, half of ΔΔG_X − ΔΔG_XE2.
+STRUCTURE_NAMES = mwc.STRUCTURE_NAMES_C4
+
+# Δln_K = ddG @ _PROJ.T; rows = constants, cols = STRUCTURE_NAMES.
+_PROJ = jnp.asarray(mwc.PROJ_C4)
 
-_PROJ = jnp.array([[1., -1.,  0.,  0.],
-                   [1.,  0., -1.,  0.],
-                   [0.,  0.,  1., -1.]])
+# Unfolding row. K_U = [U]/[H] and ΔΔG_U = 0, so Δln K_U = ΔΔG_H.
+_PROJ_U = jnp.asarray(mwc.PROJ_U_C4)
+
+# Δln K (sampled, M × 4) = ΔΔG @ DLNK_PROJ.T: the folded rows, then the
+# unfolding row; square and invertible (thermo/prior.py).
+DLNK_PROJ = projection(mwc.PROJ_C4, mwc.PROJ_U_C4)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -72,26 +122,37 @@ class ModelPriors:
     theta_ln_K_U_wt_loc:    float
     theta_ln_K_U_wt_scale:  float
 
-    theta_tf_total_M: float
-    theta_op_total_M: float
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
+    # Factor converting data titrant_conc (mM) to M (1e-3); see thermo.py.
+    theta_conc_unit_scale: float
 
-    # Regularised horseshoe for pairwise epistasis
+    # Regularized horseshoe for pairwise epistasis
     theta_epi_tau_scale:  float
     theta_epi_slab_scale: float
     theta_epi_slab_df:    float
     theta_epi_d0:         float
 
+    # LogNormal prior on the per-structure ΔΔG scale σ_s (log kT); see
+    # thermo/prior.py.
+    theta_ddG_sigma_loc:   float
+    theta_ddG_sigma_scale: float
+    # > 0 holds every σ_s at this value (kT) instead of sampling it;
+    # 0 (default) samples σ_s (thermo/prior.py, "Holding σ_s").
+    theta_ddG_sigma_fixed: float = field(pytree_node=False, default=0.0)
+
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Internal helpers
 # ──────────────────────────────────────────────────────────────────────────────
 
 def _get_struct_perm(data):
-    data_names = data.struct_names
-    if set(data_names) != set(STRUCTURE_NAMES):
+    data_names = tuple(data.struct_names or ())
+    missing = [s for s in STRUCTURE_NAMES if s not in data_names]
+    if missing:
         raise ValueError(
-            f"lnK_ddG_prior (lac_dimer_unfolded) requires struct_names to contain exactly "
-            f"{set(STRUCTURE_NAMES)}; got {data_names}."
+            f"lnK_ddG_prior (lac_dimer_unfolded) needs structures {STRUCTURE_NAMES}; "
+            f"struct_names {data_names} lacks {missing}. Extra "
+            f"structures are ignored."
         )
     name_to_idx = {n: i for i, n in enumerate(data_names)}
     return [name_to_idx[s] for s in STRUCTURE_NAMES]
@@ -101,6 +162,11 @@ def _project_ddG(ddG):
     return ddG @ _PROJ.T
 
 
+def _project_unfolding(ddG):
+    """Per-mutation Δln K_U = ΔΔG_H (ΔΔG_U = 0); (..., 4) → (...,)."""
+    return ddG @ _PROJ_U
+
+
 def _assemble_K(ln_K_op_wt, ln_K_HL_wt, ln_K_E_wt, delta_lnK, mut_scatter,
                 epi_delta_lnK=None, pair_scatter=None):
     d_op = delta_lnK[:, 0]
@@ -116,6 +182,15 @@ def _assemble_K(ln_K_op_wt, ln_K_HL_wt, ln_K_E_wt, delta_lnK, mut_scatter,
     return ln_K_op, ln_K_HL, ln_K_E
 
 
+def _assemble_K_U(ln_K_U_wt, d_ln_K_U, mut_scatter,
+                  epi_ln_K_U=None, pair_scatter=None):
+    """ln_K_U (G,) from the wt value plus scattered per-mutation/pair shifts."""
+    ln_K_U = ln_K_U_wt + mut_scatter(d_ln_K_U)
+    if epi_ln_K_U is not None and pair_scatter is not None:
+        ln_K_U = ln_K_U + pair_scatter(epi_ln_K_U)
+    return ln_K_U
+
+
 # ──────────────────────────────────────────────────────────────────────────────
 # Model
 # ──────────────────────────────────────────────────────────────────────────────
@@ -127,7 +202,7 @@ def define_model(name: str,
     Define the lnK_ddG_prior lac-dimer-unfolded theta model.
 
     Adds ``ln_K_U_wt`` to the standard lnK_ddG_prior model.  Per-mutation
-    effects on K_U are fixed at zero (no d_ln_K_U parameter).
+    effects on K_U follow from ΔΔG_H (``d_ln_K_U = ΔΔG_H``; ΔΔG_U = 0).
     """
     perm = _get_struct_perm(data)
     perm_idx = jnp.array(perm)
@@ -143,7 +218,7 @@ def define_model(name: str,
     ddG_prior_means = jnp.array(data.struct_features)[:, perm_idx]
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     ln_K_op_wt = pyro.sample(
         f"{name}_ln_K_op_wt",
@@ -159,15 +234,24 @@ def define_model(name: str,
             f"{name}_ln_K_E_wt",
             dist.Normal(priors.theta_ln_K_E_wt_loc, priors.theta_ln_K_E_wt_scale))
 
-    ddG = sample_ddG(name, list(STRUCTURE_NAMES), num_mut, ddG_prior_means)
-    delta_lnK = _project_ddG(ddG)
+    # ── Per-mutation Δln K under the structural prior ─────────────────────────
+    # Δln K[m] ~ MVN(P · prior_mean[m], P · diag(σ_s²) · Pᵀ), σ_s per state
+    # (thermo/prior.py); the post-hoc per-state ΔΔG is the site {name}_ddG.
+    dlnK = sample_dlnK(name, list(STRUCTURE_NAMES), num_mut, ddG_prior_means, DLNK_PROJ,
+                       sigma_loc=priors.theta_ddG_sigma_loc,
+                       sigma_scale=priors.theta_ddG_sigma_scale,
+                       sigma_fixed=ddG_sigma_fixed(priors))  # (M, C)
+    delta_lnK = dlnK[:, :3]
 
     pyro.deterministic(f"{name}_d_ln_K_op", delta_lnK[:, 0])
     pyro.deterministic(f"{name}_d_ln_K_HL", delta_lnK[:, 1])
     pyro.deterministic(f"{name}_d_ln_K_E",  delta_lnK[:, 2])
+    d_ln_K_U = dlnK[:, 3]
+    pyro.deterministic(f"{name}_d_ln_K_U",  d_ln_K_U)
 
     # ── Optional pairwise epistasis ───────────────────────────────────────────
     epi_delta_lnK = None
+    epi_ln_K_U    = None
     pair_scatter  = None
     if has_epi:
         P = data.num_pair
@@ -187,13 +271,15 @@ def define_model(name: str,
         pyro.deterministic(f"{name}_epi_ln_K_op", epi_delta_lnK[:, 0])
         pyro.deterministic(f"{name}_epi_ln_K_HL", epi_delta_lnK[:, 1])
         pyro.deterministic(f"{name}_epi_ln_K_E",  epi_delta_lnK[:, 2])
+        epi_ln_K_U = _project_unfolding(epi_ddG)  # (P,)
+        pyro.deterministic(f"{name}_epi_ln_K_U",  epi_ln_K_U)
 
     ln_K_op, ln_K_HL, ln_K_E = _assemble_K(
         ln_K_op_wt, ln_K_HL_wt, ln_K_E_wt, delta_lnK, mut_scatter,
         epi_delta_lnK=epi_delta_lnK, pair_scatter=pair_scatter,
     )
-    # K_U is homogeneous across genotypes — no per-mutation effects
-    ln_K_U = jnp.full(data.num_genotype, ln_K_U_wt)
+    ln_K_U = _assemble_K_U(ln_K_U_wt, d_ln_K_U, mut_scatter,
+                           epi_ln_K_U=epi_ln_K_U, pair_scatter=pair_scatter)
 
     pyro.deterministic(f"{name}_ln_K_op", ln_K_op)
     pyro.deterministic(f"{name}_ln_K_HL", ln_K_HL)
@@ -201,11 +287,13 @@ def define_model(name: str,
     pyro.deterministic(f"{name}_ln_K_U",  ln_K_U)
 
     theta_vals = _compute_theta(ln_K_op, ln_K_HL, ln_K_E, ln_K_U,
-                                data.titrant_conc, tf_total, op_total)
+                                data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E, ln_K_U=ln_K_U,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -226,11 +314,11 @@ def guide(name: str,
                           mut_nnz_geno_idx=jnp.array(data.mut_nnz_geno_idx),
                           num_genotype=data.num_genotype)
     num_mut = data.num_mutation
-    S       = data.num_struct
+    S       = len(STRUCTURE_NAMES)
     ddG_prior_means = jnp.array(data.struct_features)[:, perm_idx]
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
     has_epi  = data.num_pair > 0
 
     ln_K_op_wt_loc   = pyro.param(f"{name}_ln_K_op_wt_loc",   jnp.array(priors.theta_ln_K_op_wt_loc))
@@ -242,11 +330,6 @@ def guide(name: str,
     ln_K_E_wt_locs   = pyro.param(f"{name}_ln_K_E_wt_locs",   jnp.full(T, priors.theta_ln_K_E_wt_loc))
     ln_K_E_wt_scales = pyro.param(f"{name}_ln_K_E_wt_scales", jnp.ones(T), constraint=constraints.positive)
 
-    pyro.param(f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive)
-
-    ddG_offset_locs   = pyro.param(f"{name}_ddG_offset_locs",   jnp.zeros((S, num_mut)))
-    ddG_offset_scales = pyro.param(f"{name}_ddG_offset_scales", jnp.ones((S, num_mut)), constraint=constraints.positive)
-
     # ── Optional epistasis variational params ─────────────────────────────────
     if has_epi:
         num_pair = data.num_pair
@@ -265,19 +348,13 @@ def guide(name: str,
     with pyro.plate(f"{name}_wt_plate", T, dim=-1):
         ln_K_E_wt = pyro.sample(f"{name}_ln_K_E_wt", dist.Normal(ln_K_E_wt_locs, ln_K_E_wt_scales))
 
-    with pyro.plate(f"{name}_struct_plate", S, dim=-2):
-        with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
-            offsets = pyro.sample(
-                f"{name}_ddG_offset",
-                dist.Normal(ddG_offset_locs, ddG_offset_scales),
-            )
-
-    sigma_s = pyro.param(f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive)
-    ddG_SM  = ddG_prior_means.T + sigma_s[:, None] * offsets
-    ddG     = ddG_SM.T
+    # ── Sample σ_s and Δln K (matches prior.sample_dlnK) ────────────────────
+    dlnK = guide_dlnK(name, num_mut, S, DLNK_PROJ, ddG_prior_means,
+                      sigma_fixed=ddG_sigma_fixed(priors))   # (M, C)
 
     # ── Sample epistasis ──────────────────────────────────────────────────────
     epi_delta_lnK = None
+    epi_ln_K_U    = None
     pair_scatter  = None
     if has_epi:
         tau_loc    = pyro.param(f"{name}_epi_tau_loc",    jnp.array(-2.0))
@@ -304,30 +381,34 @@ def guide(name: str,
                 )
 
         def _lam_tilde(l):
-            return jnp.sqrt(c2_epi * l ** 2 / (c2_epi + tau_epi ** 2 * l ** 2))
+            return regularized_scale(l, tau_epi, c2_epi)
 
         epi_SP        = epi_off * tau_epi * _lam_tilde(lam)   # (S, P)
         epi_ddG       = epi_SP.T                               # (P, S)
         epi_delta_lnK = _project_ddG(epi_ddG)                 # (P, 3)
+        epi_ln_K_U    = _project_unfolding(epi_ddG)           # (P,)
 
         pair_scatter = partial(apply_pair_matrix,
                                pair_nnz_pair_idx=jnp.array(data.pair_nnz_pair_idx),
                                pair_nnz_geno_idx=jnp.array(data.pair_nnz_geno_idx),
                                num_genotype=data.num_genotype)
 
-    delta_lnK = _project_ddG(ddG)
+    delta_lnK = dlnK[:, :3]
     ln_K_op, ln_K_HL, ln_K_E = _assemble_K(
         ln_K_op_wt, ln_K_HL_wt, ln_K_E_wt, delta_lnK, mut_scatter,
         epi_delta_lnK=epi_delta_lnK, pair_scatter=pair_scatter,
     )
-    ln_K_U = jnp.full(data.num_genotype, ln_K_U_wt)
+    ln_K_U = _assemble_K_U(ln_K_U_wt, dlnK[:, 3], mut_scatter,
+                           epi_ln_K_U=epi_ln_K_U, pair_scatter=pair_scatter)
 
     theta_vals = _compute_theta(ln_K_op, ln_K_HL, ln_K_E, ln_K_U,
-                                data.titrant_conc, tf_total, op_total)
+                                data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E, ln_K_U=ln_K_U,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -336,33 +417,39 @@ def _lam_tilde(l):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    p["theta_ln_K_op_wt_loc"]   = 23.0
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_op_wt_loc"]   = DEFAULT_LN_K_OP_WT_LOC   # = K_h_o; Sochor: 4.2e8 M⁻¹
     p["theta_ln_K_op_wt_scale"] = 2.0
-    p["theta_ln_K_HL_wt_loc"]   = -9.0
+    p["theta_ln_K_HL_wt_loc"]   = DEFAULT_LN_K_HL_WT_LOC   # = K_h_l; Sochor: 6.3
     p["theta_ln_K_HL_wt_scale"] = 3.0
-    p["theta_ln_K_E_wt_loc"]    = 33.4
+    p["theta_ln_K_E_wt_loc"]    = DEFAULT_LN_K_E_WT_LOC   # = K_l_e, per site (M⁻¹); midpoint 80 µM
     p["theta_ln_K_E_wt_scale"]  = 3.0
     p["theta_ln_K_U_wt_loc"]    = -12.0
     p["theta_ln_K_U_wt_scale"]  = 3.0
-    p["theta_tf_total_M"]     = 6.5e-7
-    p["theta_op_total_M"]     = 2.5e-8
+    p["theta_tf_total_M"]     = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     p["theta_epi_tau_scale"]  = 0.1
     p["theta_epi_slab_scale"] = 2.0
     p["theta_epi_slab_df"]    = 4.0
     p["theta_epi_d0"]         = float(_DEFAULT_D0)
+    p["theta_ddG_sigma_loc"]   = DEFAULT_DDG_SIGMA_LOC     # median σ_s 1 kT
+    p["theta_ddG_sigma_scale"] = DEFAULT_DDG_SIGMA_SCALE   # 95%: 0.1-10 kT
+    p["theta_ddG_sigma_fixed"] = 0.0   # > 0 holds σ_s (kT)
     return p
 
 
 def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, Any]:
     T = data.num_titrant_name
     M = data.num_mutation
-    S = data.num_struct
+    S = len(STRUCTURE_NAMES)
     g = {}
-    g[f"{name}_ln_K_op_wt"]  = jnp.array(23.0)
-    g[f"{name}_ln_K_HL_wt"]  = jnp.array(-9.0)
-    g[f"{name}_ln_K_E_wt"]   = jnp.full(T, 33.4)
+    g[f"{name}_ln_K_op_wt"]  = jnp.array(DEFAULT_LN_K_OP_WT_LOC)
+    g[f"{name}_ln_K_HL_wt"]  = jnp.array(DEFAULT_LN_K_HL_WT_LOC)
+    g[f"{name}_ln_K_E_wt"]   = jnp.full(T, DEFAULT_LN_K_E_WT_LOC)
     g[f"{name}_ln_K_U_wt"]   = jnp.array(-12.0)
-    g[f"{name}_ddG_offset"]   = jnp.zeros((S, M))
+    # Δln K starts at P · (ΔΔG prior means), σ_s at 1 kT.
+    g.update(dlnK_guesses(name, jnp.asarray(data.struct_features)[:, jnp.array(_get_struct_perm(data))], DLNK_PROJ))
     if data.num_pair > 0:
         P = data.num_pair
         g[f"{name}_epi_tau"]    = jnp.array(0.05)
@@ -386,12 +473,13 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
+    conc_unit_scale=1.0,
 ):
     """
     Predict theta for unmeasured genotypes (lac_dimer_unfolded/lnK_ddG_prior).
 
-    K_U is homogeneous across genotypes: ln_K_U_wt is broadcast to all rows.
+    ln_K_U is assembled like the folded-state constants, from ln_K_U_wt plus
+    the per-mutation ``d_ln_K_U`` (= ΔΔG_H) and per-pair ``epi_ln_K_U``.
     """
     import numpy as np
     from tfscreen.tfmodel.inference.posteriors import get_posterior_samples
@@ -426,31 +514,35 @@ def _load(key):
     d_op = _load("theta_d_ln_K_op")
     d_HL = _load("theta_d_ln_K_HL")
     d_E  = _load("theta_d_ln_K_E")
+    d_U  = _load("theta_d_ln_K_U")
 
     ln_K_op_geno = ln_K_op_wt[:, None] + np.einsum("sm,nm->sn", d_op, mut_mat)
     ln_K_HL_geno = ln_K_HL_wt[:, None] + np.einsum("sm,nm->sn", d_HL, mut_mat)
     ln_K_E_geno  = (ln_K_E_wt[:, :, None]
                     + np.einsum("sm,nm->sn", d_E, mut_mat)[:, None, :])
-    ln_K_U_rows  = ln_K_U_wt[:, None]
+    ln_K_U_geno  = ln_K_U_wt[:, None] + np.einsum("sm,nm->sn", d_U, mut_mat)
 
     if len(pair_labels) > 0:
         epi_op = _load("theta_epi_ln_K_op")   # (S, P)
         epi_HL = _load("theta_epi_ln_K_HL")   # (S, P)
         epi_E  = _load("theta_epi_ln_K_E")    # (S, P) — scalar, no T dim
+        epi_U  = _load("theta_epi_ln_K_U")    # (S, P)
         ln_K_op_geno += np.einsum("sp,np->sn", epi_op, pair_mat)
         ln_K_HL_geno += np.einsum("sp,np->sn", epi_HL, pair_mat)
         ln_K_E_geno  += np.einsum("sp,np->sn", epi_E,  pair_mat)[:, None, :]
+        ln_K_U_geno  += np.einsum("sp,np->sn", epi_U,  pair_mat)
 
     ln_K_op_rows = ln_K_op_geno[:, geno_idx]
     ln_K_HL_rows = ln_K_HL_geno[:, geno_idx]
     ln_K_E_rows  = ln_K_E_geno[:, titrant_idx, geno_idx]
+    ln_K_U_rows  = ln_K_U_geno[:, geno_idx]
 
-    conc = calc_df["titrant_conc"].values.copy().astype(float)
+    conc = calc_df["titrant_conc"].values.copy().astype(float) * conc_unit_scale
     conc[conc == 0] = _ZERO_CONC_VALUE
 
     theta_samples = _solve_theta_np(
         ln_K_op_rows, ln_K_HL_rows, ln_K_E_rows, ln_K_U_rows,
-        conc, tf_total, op_total,
+        conc, tf_total,
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
@@ -494,7 +586,7 @@ def get_extract_specs(ctx):
     mut_df = pd.DataFrame({"mutation": ctx.mut_labels, "map_mut": range(num_mut)})
     specs.append(dict(
         input_df=mut_df,
-        params_to_get=["d_ln_K_op", "d_ln_K_HL", "d_ln_K_E"],
+        params_to_get=["d_ln_K_op", "d_ln_K_HL", "d_ln_K_E", "d_ln_K_U"],
         map_column="map_mut",
         get_columns=["mutation"],
         in_run_prefix="theta_",
@@ -507,12 +599,17 @@ def get_extract_specs(ctx):
         })
         specs.append(dict(
             input_df=pair_df,
-            params_to_get=["epi_ln_K_op", "epi_ln_K_HL", "epi_ln_K_E"],
+            params_to_get=["epi_ln_K_op", "epi_ln_K_HL", "epi_ln_K_E",
+                           "epi_ln_K_U"],
             map_column="map_pair",
             get_columns=["pair"],
             in_run_prefix="theta_",
         ))
 
+    # Per-structure ddG and its prior mean, in STRUCTURE_NAMES order (the
+    # order the model samples in and the simulator labels its truth with).
+    specs.extend(ddG_extract_specs(ctx, STRUCTURE_NAMES))
+
     return specs
 
 
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PnnC.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PnnC.py
index bfab38f5..0ddda84b 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PnnC.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/PnnC.py
@@ -2,10 +2,8 @@
 PnnC — mutation effects on equilibrium constants from NN-predicted ΔΔG prior means (lac-dimer-unfolded).
 
 Extends the lac-dimer PnnC model with a WT-level unfolding constant (ln_K_U_wt).
-Per-mutation effects on K_op, K_HL, and K_E use MLP-predicted ΔΔG prior means
-from LigandMPNN structural features; K_U is held at its WT value for all
-genotypes (d_ln_K_U = 0) because NN features describe local folded-state
-environments, not global stability.  Registry name:
+Per-mutation effects on K_op, K_HL, K_E, and K_U use MLP-predicted ΔΔG prior
+means from LigandMPNN structural features.  Registry name:
 ``theta_lac_dimer_unfolded_lnK_nn_prior``.
 
 Contrast with PK (all K shifts inferred from data, including K_U per mutation)
@@ -13,21 +11,34 @@
 
 Identical to lac_dimer/lnK_nn_prior.py with one additional scalar parameter:
 
-    ln_K_U_wt  — WT H→U unfolding equilibrium constant (same for all genotypes)
+    ln_K_U_wt  — WT H→U unfolding equilibrium constant
 
-Per-mutation effects on the unfolded state are deliberately absent: the NN
-features describe the local structural environment of the *folded* protein and
-have no information about global unfolding stability.  Setting d_ln_K_U = 0
-for all mutations is an explicit design choice — use lac_dimer_unfolded/lnK_mut
-if per-mutation unfolding effects should be inferred from the data.
+and one additional projection row.  A mutation shifts the free energy of each
+folded structure X by ΔΔG_X and leaves the unfolded state alone (ΔΔG_U = 0,
+the standard assumption).  Because K_U = [U]/[H], the projection convention
+Δln K_{A→B} = ΔΔG_A − ΔΔG_B gives
 
-The assembled ln_K_U is constant across genotypes:
-    ln_K_U[g] = ln_K_U_wt    for all g
+    Δln K_U = ΔΔG_H − ΔΔG_U = ΔΔG_H
+
+so ln_K_U[g] = ln_K_U_wt + Σ_m ΔΔG_H[m] (plus the pairwise ΔΔG_H epistasis).
+A mutation that raises every folded state by the same ΔΔG leaves K_op, K_HL
+and K_E unchanged and raises K_U by ΔΔG: a pure stability effect.  The
+per-mutation shift is exposed as ``d_ln_K_U`` (and ``epi_ln_K_U`` per pair),
+the same name the PK model uses for its free per-mutation unfolding shift.
+
+The structural prior sits on the constants (thermo/prior.py): σ_s ~
+LogNormal(theta_ddG_sigma_loc, theta_ddG_sigma_scale) per structure and Δln
+K[m] ~ MVN(P · μ[m], P · diag(σ_s²) · Pᵀ), μ the MLP output, P =
+``DLNK_PROJ`` (the folded rows plus the unfolding row; site ``theta_dlnK``,
+M × 4).  P is square and invertible, so this is exactly the per-structure
+model ΔΔG[m, s] ~ Normal(μ[m, s], σ_s) written in the constants, and the
+per-structure ΔΔG is the deterministic site ``theta_ddG`` = P⁻¹ · Δln K.
 """
 
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale
 import numpyro.distributions.constraints as constraints
 import pandas as pd
 from flax.struct import dataclass, field
@@ -40,14 +51,27 @@
     compute_nn_predictions,
     _DEFAULT_HIDDEN_SIZE,
 )
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
 from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
-    sample_ddG,
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    ddG_sigma_fixed,
+    ddG_extract_specs,
+    dlnK_guesses,
+    guide_dlnK,
+    projection,
+    sample_dlnK,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.horseshoe import (
     sample_pair_ddG,
     _DEFAULT_D0,
 )
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.thermo import (
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_E_WT_LOC,
+    DEFAULT_LN_K_HL_WT_LOC,
+    DEFAULT_LN_K_OP_WT_LOC,
+    DEFAULT_TF_TOTAL_M,
     ThetaParam,
     _compute_theta,
     _population_moments,
@@ -55,11 +79,20 @@
     get_population_moments,
 )
 
-STRUCTURE_NAMES = ('H', 'HD', 'L', 'LE2')
+# Structure basis and projection rows (thermo/mwc.py). Convention: ΔΔG_X
+# adds to state X's free energy (kT), Δln K_{A→B} = ΔΔG_A − ΔΔG_B, and the
+# effector rows are per site, half of ΔΔG_X − ΔΔG_XE2.
+STRUCTURE_NAMES = mwc.STRUCTURE_NAMES_C4
+
+# Δln_K = ddG @ _PROJ.T; rows = constants, cols = STRUCTURE_NAMES.
+_PROJ = jnp.asarray(mwc.PROJ_C4)
 
-_PROJ = jnp.array([[1., -1.,  0.,  0.],
-                   [1.,  0., -1.,  0.],
-                   [0.,  0.,  1., -1.]])
+# Unfolding row. K_U = [U]/[H] and ΔΔG_U = 0, so Δln K_U = ΔΔG_H.
+_PROJ_U = jnp.asarray(mwc.PROJ_U_C4)
+
+# Δln K (sampled, M × 4) = ΔΔG @ DLNK_PROJ.T: the folded rows, then the
+# unfolding row; square and invertible (thermo/prior.py).
+DLNK_PROJ = projection(mwc.PROJ_C4, mwc.PROJ_U_C4)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -79,8 +112,9 @@ class ModelPriors:
     theta_ln_K_U_wt_loc:    float
     theta_ln_K_U_wt_scale:  float
 
-    theta_tf_total_M: float
-    theta_op_total_M: float
+    theta_tf_total_M: float   # M, monomer units; default 1e-5 (thermo/mwc.py)
+    # Factor converting data titrant_conc (mM) to M (1e-3); see thermo.py.
+    theta_conc_unit_scale: float
 
     theta_nn_hidden_size: int = field(pytree_node=False)
 
@@ -89,6 +123,14 @@ class ModelPriors:
     theta_epi_slab_df:    float
     theta_epi_d0:         float
 
+    # LogNormal prior on the per-structure ΔΔG scale σ_s (log kT); see
+    # thermo/prior.py.
+    theta_ddG_sigma_loc:   float
+    theta_ddG_sigma_scale: float
+    # > 0 holds every σ_s at this value (kT) instead of sampling it;
+    # 0 (default) samples σ_s (thermo/prior.py, "Holding σ_s").
+    theta_ddG_sigma_fixed: float = field(pytree_node=False, default=0.0)
+
 
 # ──────────────────────────────────────────────────────────────────────────────
 # Internal helpers
@@ -106,6 +148,11 @@ def _project_ddG(ddG):
     return ddG @ _PROJ.T
 
 
+def _project_unfolding(ddG):
+    """Per-mutation Δln K_U = ΔΔG_H (ΔΔG_U = 0); (..., 4) → (...,)."""
+    return ddG @ _PROJ_U
+
+
 # ──────────────────────────────────────────────────────────────────────────────
 # Model
 # ──────────────────────────────────────────────────────────────────────────────
@@ -117,7 +164,7 @@ def define_model(name: str,
     Define the lnK_nn_prior lac-dimer-unfolded theta model.
 
     Adds ``ln_K_U_wt`` to the standard lnK_nn_prior model.  Per-mutation
-    effects on K_U are fixed at zero (no d_ln_K_U parameter).
+    effects on K_U follow from ΔΔG_H (``d_ln_K_U = ΔΔG_H``; ΔΔG_U = 0).
     """
     _check_struct_names(data)
 
@@ -133,7 +180,7 @@ def define_model(name: str,
                 and data.struct_contact_distances is not None)
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     # ── WT equilibrium constants ─────────────────────────────────────────────
     ln_K_op_wt = pyro.sample(
@@ -156,10 +203,15 @@ def define_model(name: str,
         hidden_size=priors.theta_nn_hidden_size,
     )
 
-    # ── Per-mutation per-structure ΔΔG latent variables ──────────────────────
-    ddG = sample_ddG(name, list(data.struct_names), num_mut, nn_means)
+    # ── Per-mutation Δln K under the structural prior ─────────────────────────
+    # Δln K[m] ~ MVN(P · prior_mean[m], P · diag(σ_s²) · Pᵀ), σ_s per state
+    # (thermo/prior.py); the post-hoc per-state ΔΔG is the site {name}_ddG.
+    dlnK = sample_dlnK(name, list(data.struct_names), num_mut, nn_means, DLNK_PROJ,
+                       sigma_loc=priors.theta_ddG_sigma_loc,
+                       sigma_scale=priors.theta_ddG_sigma_scale,
+                       sigma_fixed=ddG_sigma_fixed(priors))  # (M, C)
 
-    delta_lnK = _project_ddG(ddG)
+    delta_lnK = dlnK[:, :3]
     d_ln_K_op = delta_lnK[:, 0]
     d_ln_K_HL = delta_lnK[:, 1]
     d_ln_K_E  = delta_lnK[:, 2]
@@ -167,15 +219,16 @@ def define_model(name: str,
     pyro.deterministic(f"{name}_d_ln_K_op", d_ln_K_op)
     pyro.deterministic(f"{name}_d_ln_K_HL", d_ln_K_HL)
     pyro.deterministic(f"{name}_d_ln_K_E",  d_ln_K_E)
+    d_ln_K_U = dlnK[:, 3]
+    pyro.deterministic(f"{name}_d_ln_K_U",  d_ln_K_U)
 
     # ── Assemble per-genotype K values ────────────────────────────────────────
     ln_K_op = ln_K_op_wt + mut_scatter(d_ln_K_op)
     ln_K_HL = ln_K_HL_wt + mut_scatter(d_ln_K_HL)
     ln_K_E  = ln_K_E_wt[:, None] + mut_scatter(d_ln_K_E)[None, :]
-    # K_U is homogeneous across genotypes — no per-mutation effects
-    ln_K_U  = jnp.full(data.num_genotype, ln_K_U_wt)
+    ln_K_U  = ln_K_U_wt + mut_scatter(d_ln_K_U)
 
-    # ── Optional pairwise epistasis (folded-state K values only) ─────────────
+    # ── Optional pairwise epistasis ───────────────────────────────────────────
     if has_epi:
         pair_scatter = partial(apply_pair_matrix,
                                pair_nnz_pair_idx=jnp.array(data.pair_nnz_pair_idx),
@@ -199,10 +252,13 @@ def define_model(name: str,
         pyro.deterministic(f"{name}_epi_ln_K_op", epi_ln_K_op)
         pyro.deterministic(f"{name}_epi_ln_K_HL", epi_ln_K_HL)
         pyro.deterministic(f"{name}_epi_ln_K_E",  epi_ln_K_E)
+        epi_ln_K_U    = _project_unfolding(epi_ddG)
+        pyro.deterministic(f"{name}_epi_ln_K_U",  epi_ln_K_U)
 
         ln_K_op = ln_K_op + pair_scatter(epi_ln_K_op)
         ln_K_HL = ln_K_HL + pair_scatter(epi_ln_K_HL)
         ln_K_E  = ln_K_E  + pair_scatter(epi_ln_K_E)[None, :]
+        ln_K_U  = ln_K_U  + pair_scatter(epi_ln_K_U)
 
     pyro.deterministic(f"{name}_ln_K_op", ln_K_op)
     pyro.deterministic(f"{name}_ln_K_HL", ln_K_HL)
@@ -210,11 +266,13 @@ def define_model(name: str,
     pyro.deterministic(f"{name}_ln_K_U",  ln_K_U)
 
     theta_vals = _compute_theta(ln_K_op, ln_K_HL, ln_K_E, ln_K_U,
-                                data.titrant_conc, tf_total, op_total)
+                                data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E, ln_K_U=ln_K_U,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -241,7 +299,7 @@ def guide(name: str,
                 and data.struct_contact_distances is not None)
 
     tf_total = priors.theta_tf_total_M
-    op_total = priors.theta_op_total_M
+    conc_unit_scale = priors.theta_conc_unit_scale
 
     ln_K_op_wt_loc   = pyro.param(f"{name}_ln_K_op_wt_loc",   jnp.array(priors.theta_ln_K_op_wt_loc))
     ln_K_op_wt_scale = pyro.param(f"{name}_ln_K_op_wt_scale", jnp.array(1.0), constraint=constraints.positive)
@@ -257,11 +315,6 @@ def guide(name: str,
         hidden_size=priors.theta_nn_hidden_size,
     )
 
-    pyro.param(f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive)
-
-    ddG_offset_locs   = pyro.param(f"{name}_ddG_offset_locs",   jnp.zeros((S, num_mut)))
-    ddG_offset_scales = pyro.param(f"{name}_ddG_offset_scales", jnp.ones((S, num_mut)), constraint=constraints.positive)
-
     if has_epi:
         num_pair = data.num_pair
         pyro.param(f"{name}_epi_tau_loc",    jnp.array(-2.0))
@@ -279,16 +332,9 @@ def guide(name: str,
     with pyro.plate(f"{name}_wt_plate", T, dim=-1):
         ln_K_E_wt = pyro.sample(f"{name}_ln_K_E_wt", dist.Normal(ln_K_E_wt_locs, ln_K_E_wt_scales))
 
-    with pyro.plate(f"{name}_struct_plate", S, dim=-2):
-        with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
-            offsets = pyro.sample(
-                f"{name}_ddG_offset",
-                dist.Normal(ddG_offset_locs, ddG_offset_scales),
-            )
-
-    sigma_s = pyro.param(f"{name}_ddG_sigma_s", jnp.ones(S), constraint=constraints.positive)
-    ddG_SM = nn_means.T + sigma_s[:, None] * offsets
-    ddG    = ddG_SM.T
+    # ── Sample σ_s and Δln K (matches prior.sample_dlnK) ────────────────────
+    dlnK = guide_dlnK(name, num_mut, S, DLNK_PROJ, jnp.zeros((num_mut, S)),
+                      sigma_fixed=ddG_sigma_fixed(priors))   # (M, C)
 
     if has_epi:
         tau_loc   = pyro.param(f"{name}_epi_tau_loc",    jnp.array(-2.0))
@@ -308,7 +354,7 @@ def guide(name: str,
                 lam = pyro.sample(f"{name}_epi_lambda", dist.LogNormal(lam_locs, lam_scales))
                 epi_off = pyro.sample(f"{name}_epi_offset", dist.Normal(epi_locs, epi_scales))
 
-    delta_lnK = _project_ddG(ddG)
+    delta_lnK = dlnK[:, :3]
     d_ln_K_op = delta_lnK[:, 0]
     d_ln_K_HL = delta_lnK[:, 1]
     d_ln_K_E  = delta_lnK[:, 2]
@@ -316,7 +362,7 @@ def guide(name: str,
     ln_K_op = ln_K_op_wt + mut_scatter(d_ln_K_op)
     ln_K_HL = ln_K_HL_wt + mut_scatter(d_ln_K_HL)
     ln_K_E  = ln_K_E_wt[:, None] + mut_scatter(d_ln_K_E)[None, :]
-    ln_K_U  = jnp.full(data.num_genotype, ln_K_U_wt)
+    ln_K_U  = ln_K_U_wt + mut_scatter(dlnK[:, 3])
 
     if has_epi:
         pair_scatter = partial(apply_pair_matrix,
@@ -325,7 +371,7 @@ def guide(name: str,
                                num_genotype=data.num_genotype)
 
         def _lam_tilde(lam):
-            return jnp.sqrt(c2_epi * lam ** 2 / (c2_epi + tau_epi ** 2 * lam ** 2))
+            return regularized_scale(lam, tau_epi, c2_epi)
 
         epi_SP    = epi_off * tau_epi * _lam_tilde(lam)
         epi_ddG   = epi_SP.T
@@ -334,13 +380,16 @@ def _lam_tilde(lam):
         ln_K_op = ln_K_op + pair_scatter(epi_delta[:, 0])
         ln_K_HL = ln_K_HL + pair_scatter(epi_delta[:, 1])
         ln_K_E  = ln_K_E  + pair_scatter(epi_delta[:, 2])[None, :]
+        ln_K_U  = ln_K_U  + pair_scatter(_project_unfolding(epi_ddG))
 
     theta_vals = _compute_theta(ln_K_op, ln_K_HL, ln_K_E, ln_K_U,
-                                data.titrant_conc, tf_total, op_total)
+                                data.titrant_conc * conc_unit_scale,
+                                tf_total)
     mu, sigma  = _population_moments(theta_vals, data)
 
     return ThetaParam(ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E, ln_K_U=ln_K_U,
-                      tf_total=tf_total, op_total=op_total, mu=mu, sigma=sigma)
+                      tf_total=tf_total, mu=mu, sigma=sigma,
+                      conc_unit_scale=conc_unit_scale)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -349,21 +398,26 @@ def _lam_tilde(lam):
 
 def get_hyperparameters() -> Dict[str, Any]:
     p = {}
-    p["theta_ln_K_op_wt_loc"]   = 23.0
+    # Wild-type defaults: see thermo/mwc.py (Sochor 2014 constants and the
+    # 80 µM midpoint calibration at tf_total = 1e-5 M monomer).
+    p["theta_ln_K_op_wt_loc"]   = DEFAULT_LN_K_OP_WT_LOC   # = K_h_o; Sochor: 4.2e8 M⁻¹
     p["theta_ln_K_op_wt_scale"] = 2.0
-    p["theta_ln_K_HL_wt_loc"]   = -9.0
+    p["theta_ln_K_HL_wt_loc"]   = DEFAULT_LN_K_HL_WT_LOC   # = K_h_l; Sochor: 6.3
     p["theta_ln_K_HL_wt_scale"] = 3.0
-    p["theta_ln_K_E_wt_loc"]    = 33.4
+    p["theta_ln_K_E_wt_loc"]    = DEFAULT_LN_K_E_WT_LOC   # = K_l_e, per site (M⁻¹); midpoint 80 µM
     p["theta_ln_K_E_wt_scale"]  = 3.0
     p["theta_ln_K_U_wt_loc"]    = -12.0
     p["theta_ln_K_U_wt_scale"]  = 3.0
-    p["theta_tf_total_M"] = 6.5e-7
-    p["theta_op_total_M"] = 2.5e-8
+    p["theta_tf_total_M"] = DEFAULT_TF_TOTAL_M   # M, monomer units (5e-6 M dimer)
+    p["theta_conc_unit_scale"] = DEFAULT_CONC_UNIT_SCALE   # titrant_conc mM → M
     p["theta_nn_hidden_size"]   = _DEFAULT_HIDDEN_SIZE
     p["theta_epi_tau_scale"]   = 0.1
     p["theta_epi_slab_scale"]  = 2.0
     p["theta_epi_slab_df"]     = 4.0
     p["theta_epi_d0"]          = float(_DEFAULT_D0)
+    p["theta_ddG_sigma_loc"]   = DEFAULT_DDG_SIGMA_LOC     # median σ_s 1 kT
+    p["theta_ddG_sigma_scale"] = DEFAULT_DDG_SIGMA_SCALE   # 95%: 0.1-10 kT
+    p["theta_ddG_sigma_fixed"] = 0.0   # > 0 holds σ_s (kT)
     return p
 
 
@@ -372,11 +426,12 @@ def get_guesses(name: str, data: Union[GrowthData, BindingData]) -> Dict[str, An
     M = data.num_mutation
     S = data.num_struct
     g = {}
-    g[f"{name}_ln_K_op_wt"]  = jnp.array(23.0)
-    g[f"{name}_ln_K_HL_wt"]  = jnp.array(-9.0)
-    g[f"{name}_ln_K_E_wt"]   = jnp.full(T, 33.4)
+    g[f"{name}_ln_K_op_wt"]  = jnp.array(DEFAULT_LN_K_OP_WT_LOC)
+    g[f"{name}_ln_K_HL_wt"]  = jnp.array(DEFAULT_LN_K_HL_WT_LOC)
+    g[f"{name}_ln_K_E_wt"]   = jnp.full(T, DEFAULT_LN_K_E_WT_LOC)
     g[f"{name}_ln_K_U_wt"]   = jnp.array(-12.0)
-    g[f"{name}_ddG_offset"]  = jnp.zeros((S, M))
+    # Δln K starts at P · (ΔΔG prior means), σ_s at 1 kT.
+    g.update(dlnK_guesses(name, jnp.zeros((M, S)), DLNK_PROJ))
     if data.num_pair > 0 and data.struct_contact_distances is not None:
         P = data.num_pair
         g[f"{name}_epi_tau"]      = jnp.array(0.05)
@@ -400,12 +455,13 @@ def predict_unmeasured(
     q_to_get,
     *,
     tf_total,
-    op_total,
+    conc_unit_scale=1.0,
 ):
     """
     Predict theta for unmeasured genotypes (lac_dimer_unfolded/lnK_nn_prior).
 
-    K_U is homogeneous across genotypes: ln_K_U_wt is broadcast to all rows.
+    ln_K_U is assembled like the folded-state constants, from ln_K_U_wt plus
+    the per-mutation ``d_ln_K_U`` (= ΔΔG_H) and per-pair ``epi_ln_K_U``.
     """
     import numpy as np
     from tfscreen.tfmodel.inference.posteriors import get_posterior_samples
@@ -440,32 +496,35 @@ def _load(key):
     d_op = _load("theta_d_ln_K_op")   # (S, M)
     d_HL = _load("theta_d_ln_K_HL")   # (S, M)
     d_E  = _load("theta_d_ln_K_E")    # (S, M)
+    d_U  = _load("theta_d_ln_K_U")    # (S, M)
 
     ln_K_op_geno = ln_K_op_wt[:, None] + np.einsum("sm,nm->sn", d_op, mut_mat)
     ln_K_HL_geno = ln_K_HL_wt[:, None] + np.einsum("sm,nm->sn", d_HL, mut_mat)
     ln_K_E_geno  = (ln_K_E_wt[:, :, None]
                     + np.einsum("sm,nm->sn", d_E, mut_mat)[:, None, :])
-    # K_U is constant across genotypes — broadcast over rows
-    ln_K_U_rows  = ln_K_U_wt[:, None]   # (S, 1) → broadcasts to (S, N)
+    ln_K_U_geno  = ln_K_U_wt[:, None] + np.einsum("sm,nm->sn", d_U, mut_mat)
 
     if len(pair_labels) > 0:
         epi_op = _load("theta_epi_ln_K_op")
         epi_HL = _load("theta_epi_ln_K_HL")
         epi_E  = _load("theta_epi_ln_K_E")
+        epi_U  = _load("theta_epi_ln_K_U")
         ln_K_op_geno += np.einsum("sp,np->sn", epi_op, pair_mat)
         ln_K_HL_geno += np.einsum("sp,np->sn", epi_HL, pair_mat)
         ln_K_E_geno  += np.einsum("sp,np->sn", epi_E, pair_mat)[:, None, :]
+        ln_K_U_geno  += np.einsum("sp,np->sn", epi_U, pair_mat)
 
     ln_K_op_rows = ln_K_op_geno[:, geno_idx]
     ln_K_HL_rows = ln_K_HL_geno[:, geno_idx]
     ln_K_E_rows  = ln_K_E_geno[:, titrant_idx, geno_idx]
+    ln_K_U_rows  = ln_K_U_geno[:, geno_idx]
 
-    conc = calc_df["titrant_conc"].values.copy().astype(float)
+    conc = calc_df["titrant_conc"].values.copy().astype(float) * conc_unit_scale
     conc[conc == 0] = _ZERO_CONC_VALUE
 
     theta_samples = _solve_theta_np(
         ln_K_op_rows, ln_K_HL_rows, ln_K_E_rows, ln_K_U_rows,
-        conc, tf_total, op_total,
+        conc, tf_total,
     )
 
     theta_samples[:, ~is_valid[geno_idx]] = np.nan
@@ -510,7 +569,7 @@ def get_extract_specs(ctx):
     mut_df = pd.DataFrame({"mutation": ctx.mut_labels, "map_mut": range(num_mut)})
     specs.append(dict(
         input_df=mut_df,
-        params_to_get=["d_ln_K_op", "d_ln_K_HL", "d_ln_K_E"],
+        params_to_get=["d_ln_K_op", "d_ln_K_HL", "d_ln_K_E", "d_ln_K_U"],
         map_column="map_mut",
         get_columns=["mutation"],
         in_run_prefix="theta_",
@@ -521,12 +580,17 @@ def get_extract_specs(ctx):
         pair_df = pd.DataFrame({"pair": ctx.pair_labels, "map_pair": range(num_pair)})
         specs.append(dict(
             input_df=pair_df,
-            params_to_get=["epi_ln_K_op", "epi_ln_K_HL", "epi_ln_K_E"],
+            params_to_get=["epi_ln_K_op", "epi_ln_K_HL", "epi_ln_K_E",
+                           "epi_ln_K_U"],
             map_column="map_pair",
             get_columns=["pair"],
             in_run_prefix="theta_",
         ))
 
+    # Per-structure ddG and its prior mean, in STRUCTURE_NAMES order (the
+    # order the model samples in and the simulator labels its truth with).
+    specs.extend(ddG_extract_specs(ctx, STRUCTURE_NAMES))
+
     return specs
 
 
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/thermo.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/thermo.py
index 13975823..8256e945 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/thermo.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/thermo.py
@@ -1,47 +1,38 @@
 """
-Shared thermodynamic functions for the lac-dimer-unfolded partition-function θ model.
-
-Extends the four-state lac-dimer model with a globally unfolded state U.
-Destabilising mutations shift population into U, where neither operator nor
-effector can bind, collapsing θ toward zero at all effector concentrations.
-Used by ``theta_lac_dimer_unfolded_lnK_mut`` (PK),
-``theta_lac_dimer_unfolded_lnK_ddG_prior`` (PddG), and
-``theta_lac_dimer_unfolded_lnK_nn_prior`` (PnnC).
-
-Five-state partition function for the TF homodimer:
-
-    State      Weight
-    H          1                               (apo, high-affinity conformation)
-    H·op       K_op · [op_free]                (operator-bound, repressing state)
-    L          K_HL                            (low-affinity conformation)
-    L·E²       K_HL · K_E · [E_free]²          (effector-bound, n=2 hardcoded)
-    U          K_U                             (globally unfolded; no DNA/effector binding)
-
-    Z     = 1 + K_op·[op_free] + K_HL + K_HL·K_E·[E_free]² + K_U
-    θ     = K_op·[op_free] / Z
-
-The U state is the H→U equilibrium: K_U = [U]/[H].  In WT K_U << 1 (U is
-strongly disfavoured).  Mutations accumulate destabilising effects as additive
-shifts in ln(K_U); when K_U dominates Z, θ collapses toward zero for all
-conditions regardless of effector concentration.
-
-U has no DNA-binding or effector-binding interactions, so the thermodynamic
-projection matrix for the folded states is unchanged.  The only modification
-to the Newton solve for free effector is that K_U appears in Z₀ (the
-ligand-free part of the partition function), which enters the cubic mass
-balance for free effector.
-
-Operator depletion approximation, free-effector cubic, and units are identical
-to lac_dimer/thermo.py.  See that module for details.
+Shared thermodynamic functions for the M2 lac-dimer θ model with unfolding (O2_C4 U1).
+
+Extends M2 (``O2_C4_K3_U0_a/thermo.py``) with a globally unfolded state U
+that binds neither operator nor effector. K_U = [U]/[H]. In wt K_U << 1;
+destabilizing mutations raise it, shift the dimer into U and pull θ toward
+zero at every effector concentration. Used by ``thermo.O2_C4_K3_U1_a.PK``,
+``.PddG`` and ``.PnnC``.
+
+U adds K_U to the ligand-free part of the TF polynomial:
+
+    F(e) = 1 + K_HL·(1 + K_E·e)² + K_U
+    h = r / F(e),    W = h·K_op,    θ = W / (1 + W)
+
+It is M3 with unfolding (``O2_C12_K5_U1_a``) restricted to K_h_e = K_l_o = 0,
+computed by the same ``mwc.mwc_theta``. Constants, units, bookkeeping and the
+free-effector cubic are those of the U0 model and ``thermo/mwc.py``.
 """
 
 import jax
 import jax.numpy as jnp
 import numpy as np
 import pandas as pd
-from flax.struct import dataclass
+from flax.struct import dataclass, field
 
-NEWTON_ITERATIONS = 8
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
+# Unit convention and the defaults are shared with the U0 model.
+from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.thermo import (  # noqa: F401
+    DEFAULT_CONC_UNIT_SCALE,
+    DEFAULT_LN_K_E_WT_LOC,
+    DEFAULT_LN_K_HL_WT_LOC,
+    DEFAULT_LN_K_OP_WT_LOC,
+    DEFAULT_TF_TOTAL_M,
+    NEWTON_ITERATIONS,
+)
 
 
 # ---------------------------------------------------------------------------
@@ -52,49 +43,25 @@
 class ThetaParam:
     """Assembled partition-function parameters passed between K-assembly and thermo."""
 
-    ln_K_op:  jnp.ndarray   # (G,)    — per-genotype log protein-DNA affinity
-    ln_K_HL:  jnp.ndarray   # (G,)    — per-genotype log conformational equilibrium
-    ln_K_E:   jnp.ndarray   # (T, G)  — per-genotype log effector binding constant
+    ln_K_op:  jnp.ndarray   # (G,)    — per-genotype log K_op (= K_h_o), M⁻¹
+    ln_K_HL:  jnp.ndarray   # (G,)    — per-genotype log K_HL (= K_h_l)
+    ln_K_E:   jnp.ndarray   # (T, G)  — per-genotype log per-site K_E (= K_l_e), M⁻¹
     ln_K_U:   jnp.ndarray   # (G,)    — per-genotype log H→U unfolding equilibrium
-    tf_total: float          # M
-    op_total: float          # M
+    tf_total: float          # M, monomer units
     mu:       jnp.ndarray   # (T, C, 1) — population mean logit-theta
     sigma:    jnp.ndarray   # (T, C, 1) — population std  logit-theta
+    # Factor that converts data.titrant_conc (mM) to M before it enters the
+    # thermodynamic equations (theta_conc_unit_scale, 1e-3 by default).
+    conc_unit_scale: float = field(pytree_node=False)
 
 
 # ---------------------------------------------------------------------------
 # Internal helpers
 # ---------------------------------------------------------------------------
 
-def _solve_free_effector(e_total, tf_total, a, Z0):
-    """
-    Solve the cubic mass-balance equation for free effector concentration.
-
-    Identical to lac_dimer/thermo.py; Z0 now includes the K_U term.
+def _compute_theta(ln_K_op, ln_K_HL, ln_K_E, ln_K_U, titrant_conc, tf_total):
     """
-    coeff_a = a
-    coeff_b = a * (2.0 * tf_total - e_total)
-    coeff_c = Z0
-    coeff_d = -Z0 * e_total
-
-    def f(x):
-        return coeff_a * x**3 + coeff_b * x**2 + coeff_c * x + coeff_d
-
-    def df(x):
-        return 3.0 * coeff_a * x**2 + 2.0 * coeff_b * x + coeff_c
-
-    def step(i, x):
-        dfx = df(x)
-        return x - f(x) / jnp.where(jnp.abs(dfx) < 1e-30, 1e-30, dfx)
-
-    x0 = jnp.broadcast_to(e_total, coeff_b.shape)
-    x_free = jax.lax.fori_loop(0, NEWTON_ITERATIONS, step, x0)
-    return jnp.clip(x_free, 0.0, e_total)
-
-
-def _compute_theta(ln_K_op, ln_K_HL, ln_K_E, ln_K_U, titrant_conc, tf_total, op_total):
-    """
-    Evaluate the five-state partition-function theta for all (T, C, G) combinations.
+    Evaluate θ with the unfolded state for all (T, C, G) combinations.
 
     Parameters
     ----------
@@ -102,31 +69,19 @@ def _compute_theta(ln_K_op, ln_K_HL, ln_K_E, ln_K_U, titrant_conc, tf_total, op_
     ln_K_HL : (G,)
     ln_K_E  : (T, G)
     ln_K_U  : (G,)
-    titrant_conc : (C,)
-    tf_total : scalar
-    op_total : scalar
+    titrant_conc : (C,)  — effector concentrations in M (already scaled)
+    tf_total : scalar   — total TF in M, monomer units
 
     Returns
     -------
     theta : (T, C, G)
     """
-    K_op = jnp.exp(ln_K_op)[None, None, :]     # (1, 1, G)
-    K_HL = jnp.exp(ln_K_HL)[None, None, :]     # (1, 1, G)
-    K_E  = jnp.exp(ln_K_E)[:, None, :]         # (T, 1, G)
-    K_U  = jnp.exp(ln_K_U)[None, None, :]      # (1, 1, G)
-    e_total = titrant_conc[None, :, None]       # (1, C, 1)
-
-    # Z₀ includes K_U: the ligand-free part of the partition function
-    Z0 = 1.0 + K_op * op_total + K_HL + K_U    # (1, 1, G)
-    a  = K_HL * K_E                             # (T, 1, G)
-
-    e_free = _solve_free_effector(e_total, tf_total, a, Z0)  # (T, C, G)
-
-    w_Hop = K_op * op_total                     # (1, 1, G)
-    w_LE  = K_HL * K_E * e_free**2             # (T, C, G)
-    Z     = 1.0 + w_Hop + K_HL + w_LE + K_U   # (T, C, G)
-
-    return w_Hop / Z                            # (T, C, G)
+    return mwc.theta_c4(ln_K_op[None, None, :],
+                        ln_K_HL[None, None, :],
+                        ln_K_E[:, None, :],
+                        ln_K_U[None, None, :],
+                        titrant_conc[None, :, None],
+                        tf_total)
 
 
 def _population_moments(theta, data):
@@ -163,9 +118,8 @@ def run_model(theta_param: ThetaParam, data) -> jnp.ndarray:
         theta_param.ln_K_HL,
         theta_param.ln_K_E,
         theta_param.ln_K_U,
-        data.titrant_conc,
+        data.titrant_conc * theta_param.conc_unit_scale,
         theta_param.tf_total,
-        theta_param.op_total,
     )                                              # (T, C, G)
 
     geno_idx = data.batch_idx[data.geno_theta_idx]
@@ -188,9 +142,9 @@ def get_population_moments(theta_param: ThetaParam, data) -> tuple:
 _ZERO_CONC_VALUE = 1e-20
 
 
-def _solve_theta_np(ln_K_op, ln_K_HL, ln_K_E, ln_K_U, conc, tf_total, op_total):
+def _solve_theta_np(ln_K_op, ln_K_HL, ln_K_E, ln_K_U, conc, tf_total):
     """
-    Pure-numpy Newton solve for lac_dimer_unfolded theta at per-row K values.
+    Numpy θ at per-row K values (the same ``mwc.theta_c4`` as the JAX path).
 
     Parameters
     ----------
@@ -199,34 +153,16 @@ def _solve_theta_np(ln_K_op, ln_K_HL, ln_K_E, ln_K_U, conc, tf_total, op_total):
     ln_K_E  : (S, N)
     ln_K_U  : (S, N)  or broadcastable to (S, N)
     conc    : (N,)  — effector concentrations in M (zero already replaced)
-    tf_total : float
-    op_total : float
+    tf_total : float — M, monomer units
 
     Returns
     -------
     theta : np.ndarray, shape (S, N)
     """
-    K_op = np.exp(ln_K_op)
-    K_HL = np.exp(ln_K_HL)
-    K_E  = np.exp(ln_K_E)
-    K_U  = np.exp(ln_K_U)
-
-    e_total = conc[np.newaxis, :]
-    Z0      = 1.0 + K_op * op_total + K_HL + K_U
-    a       = K_HL * K_E
-    coeff_b = a * (2.0 * tf_total - e_total)
-
-    e_free = e_total * np.ones_like(a)
-    for _ in range(NEWTON_ITERATIONS):
-        f  = a * e_free**3 + coeff_b * e_free**2 + Z0 * e_free - Z0 * e_total
-        df = 3.0 * a * e_free**2 + 2.0 * coeff_b * e_free + Z0
-        e_free = e_free - f / np.where(np.abs(df) < 1e-30, 1e-30, df)
-    e_free = np.clip(e_free, 0.0, e_total)
-
-    w_Hop = K_op * op_total
-    w_LE  = a * e_free**2
-    Z     = 1.0 + w_Hop + K_HL + w_LE + K_U
-    return w_Hop / Z
+    return mwc.theta_c4(np.asarray(ln_K_op, float), np.asarray(ln_K_HL, float),
+                        np.asarray(ln_K_E, float), np.asarray(ln_K_U, float),
+                        np.asarray(conc, float)[np.newaxis, :], tf_total,
+                        xp=np)
 
 
 def build_calc_df(model, manual_titrant_df):
@@ -240,7 +176,7 @@ def build_calc_df(model, manual_titrant_df):
     calc_df : pd.DataFrame
     internal_cols : list of str
     extra_kwargs : dict
-        ``tf_total`` and ``op_total`` scalars (in M).
+        ``tf_total`` (M, monomer units) and ``conc_unit_scale``.
     """
     import tfscreen.util.dataframe
 
@@ -281,12 +217,13 @@ def build_calc_df(model, manual_titrant_df):
 
     extra = {
         "tf_total": float(model.priors.theta.theta_tf_total_M),
-        "op_total": float(model.priors.theta.theta_op_total_M),
+        "conc_unit_scale": float(model.priors.theta.theta_conc_unit_scale),
     }
     return calc_df, ["genotype_idx", "titrant_name_idx"], extra
 
 
-def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total):
+def compute_theta_samples(calc_df, param_posteriors, *, tf_total,
+                          conc_unit_scale=1.0):
     """
     Compute posterior theta samples for the lac_dimer_unfolded model.
 
@@ -298,7 +235,9 @@ def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total):
     param_posteriors : dict-like
         Posterior samples keyed by parameter name (with ``theta_`` prefix).
     tf_total : float
-    op_total : float
+        Total TF concentration in M, monomer units.
+    conc_unit_scale : float, optional
+        Multiply ``titrant_conc`` by this factor to get M (1e-3 for mM data).
 
     Returns
     -------
@@ -309,7 +248,7 @@ def compute_theta_samples(calc_df, param_posteriors, *, tf_total, op_total):
     geno_indices    = calc_df["genotype_idx"].values.astype(int)
     titrant_indices = calc_df["titrant_name_idx"].values.astype(int)
 
-    conc = calc_df["titrant_conc"].values.copy().astype(float)
+    conc = calc_df["titrant_conc"].values.copy().astype(float) * conc_unit_scale
     conc[conc == 0] = _ZERO_CONC_VALUE
 
     def _load(key):
@@ -329,4 +268,4 @@ def _load(key):
     ln_K_U_pts  = ln_K_U_all[:, geno_indices]
 
     return _solve_theta_np(ln_K_op_pts, ln_K_HL_pts, ln_K_E_pts, ln_K_U_pts,
-                           conc, tf_total, op_total)
+                           conc, tf_total)
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/horseshoe.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/horseshoe.py
index 1c3fa1c9..97ee68e7 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/horseshoe.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/horseshoe.py
@@ -23,6 +23,23 @@
 The guide (in lnK_nn_prior.py) must provide matching ``pyro.sample`` sites
 for all variables sampled here.
 
+Unlike the per-mutation effects, which ``prior.py`` puts on the constants
+Δln K, the pairwise epistasis stays per state: epi[p, s] is sampled here
+and projected to the constants by the model.  In the U0 models (no unfolded
+state) the common-mode direction of epi[p, :] (an equal shift of every
+state) changes no constant, so the data do not constrain it; only the
+horseshoe shrinkage regularizes it, and the per-pair ``epi_ddG`` site is
+meaningful only up to that shift.  The ``epi_ln_K_*`` constants are what
+the data identify.
+
+The horseshoe stays non-centered.  Its
+scales τ, λ and c² are sample sites with proper priors, not free
+``pyro.param`` values, so under MAP raising them to shrink the offsets
+costs their heavy-tailed log priors and has a finite optimum.  A centered
+horseshoe, epi ~ Normal(0, τ·λ̃), would instead put an unbounded joint
+density at τ·λ̃ → 0 with epi = 0, so MAP would zero every weakly measured
+pair.
+
 Reference
 ---------
 Piironen & Vehtari, 2017. "Sparsity information and regularization in the
@@ -32,6 +49,7 @@
 import jax.numpy as jnp
 import numpyro as pyro
 import numpyro.distributions as dist
+from tfscreen.tfmodel.generative.components._horseshoe import regularized_scale, HalfCauchy
 
 _DEFAULT_D0 = 8.0  # Å — residues within ~8 Å Cα-Cα are considered contacts
 
@@ -70,6 +88,12 @@ def sample_pair_ddG(name, struct_names, contact_distances,
     -------
     jnp.ndarray, shape (P, S)
         Sampled epistasis ΔΔG per pair per structure.
+
+    Notes
+    -----
+    In the U0 models a shift of epi[p, :] equal across every structure
+    changes no constant, so the data leave that direction free and only
+    the horseshoe shrinkage regularizes it (see the module docstring).
     """
     S = len(struct_names)
     dists = jnp.array(contact_distances)   # (P, S)
@@ -77,7 +101,7 @@ def sample_pair_ddG(name, struct_names, contact_distances,
 
     # Global shrinkage scale and slab variance (shared across all structures)
     tau = pyro.sample(f"{name}_epi_tau",
-                      dist.HalfCauchy(tau_scale))
+                      HalfCauchy(tau_scale))
     c2  = pyro.sample(
         f"{name}_epi_c2",
         dist.InverseGamma(slab_df / 2.0,
@@ -95,7 +119,7 @@ def sample_pair_ddG(name, struct_names, contact_distances,
         with pyro.plate(f"{name}_pair_plate", P, dim=-1):
             lam    = pyro.sample(
                 f"{name}_epi_lambda",
-                dist.HalfCauchy(lam_scale.T),   # (S, P)
+                HalfCauchy(lam_scale.T),   # (S, P)
             )
             offset = pyro.sample(
                 f"{name}_epi_offset",
@@ -103,7 +127,7 @@ def sample_pair_ddG(name, struct_names, contact_distances,
             )
 
     # Regularised horseshoe: clamp variance contribution from τ
-    lam_tilde = jnp.sqrt(c2 * lam ** 2 / (c2 + tau ** 2 * lam ** 2))  # (S, P)
+    lam_tilde = regularized_scale(lam, tau, c2)  # (S, P)
     epi_SP    = offset * tau * lam_tilde                                  # (S, P)
 
     pyro.deterministic(f"{name}_epi_ddG", epi_SP.T)
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/io.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/io.py
index 6f1425bf..cd888f38 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/io.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/io.py
@@ -8,11 +8,16 @@
 matrix and contact-distance arrays, and returns a dict whose keys map
 directly to the ``struct_*`` fields of ``GrowthData`` / ``BindingData``.
 
-``load_ddG_prior_csv()`` — reads a CSV file with columns ``mut`` and one
-column per structure (e.g. ``H``, ``HO``, ``L``, ``LO``, ``HE2``, ``LE2``).
-Returns the same dict shape but with the (M, S) ddG values stored in
-``struct_features``; ``struct_n_chains`` and the contact arrays are None.
-Used by ``mwc_dimer_lnK_ddG_prior``.
+``load_ddG_prior_csv()`` — reads a CSV file with a mutation column (``mut``,
+``Mutation`` or ``mutation``) and one column per structure (e.g. ``H``,
+``HO``, ``L``, ``LO``, ``HE2``, ``LE2``).  Returns the same dict shape but
+with the (M, S) ddG values, in kT, stored in ``struct_features``;
+``struct_n_chains`` and the contact arrays are None.  Used by the PddG
+models.  Energy units, the sign convention, column mapping, residue
+numbering and coverage checks are described in its docstring and in
+``normalize_thermo_options()``.
+
+``load_thermo_data()`` picks the loader from the theta component name.
 
 HDF5 schema (written by generate_struct_ensemble.py):
     structure_names          str dataset: names of all structures in the file
@@ -20,12 +25,15 @@
     {name}/residue_nums      int32   (L,):   PDB residue numbers (sorted)
     {name}/dist_matrix       float32 (L, L): min Cα-Cα distances in Å
     {name}/n_chains_bearing_mut  int32 scalar: chains carrying the mutated residue
+    {name}/wt_aa             str     (L,):   optional one-letter wt residues
 """
 
+import warnings
+
 import numpy as np
 import h5py
 import pandas as pd
-from .features import build_feature_matrix, parse_mutation
+from .features import build_feature_matrix, parse_mutation, _MUT_PATTERN
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -47,12 +55,63 @@ def _read_h5_structure(hf, name):
             f"Structure group {name!r} is missing required dataset(s): "
             f"{sorted(missing)}. Found: {sorted(grp.keys())}."
         )
-    return {
+    out = {
         'logP':                 grp['logP'][:].astype(np.float32),
         'residue_nums':         grp['residue_nums'][:].astype(np.int32),
         'dist_matrix':          grp['dist_matrix'][:].astype(np.float32),
         'n_chains_bearing_mut': int(grp['n_chains_bearing_mut'][()]),
     }
+    # Optional (L,) one-letter wt residues, written by newer
+    # generate_struct_ensemble.py; used to check the residue numbering.
+    if 'wt_aa' in grp:
+        out['wt_aa'] = [a.decode() if isinstance(a, bytes) else str(a)
+                        for a in grp['wt_aa'][:]]
+    return out
+
+
+def _check_h5_coverage(h5_path, struct_names, struct_data, mut_labels, offset):
+    """
+    Check that every library mutation's residue is in every structure and
+    that the wt residues (when the file stores ``wt_aa``) agree.
+
+    Unlike the ΔΔG CSV there is no neutral value for a missing residue's
+    LigandMPNN features, so any missing residue is an error.
+    """
+    M = len(mut_labels)
+    if M == 0:
+        return
+    source = f"Structural ensemble {h5_path!r}"
+    lib_parsed = _parse_library_labels(mut_labels)
+    for sname, sdata in zip(struct_names, struct_data):
+        res_nums = sdata['residue_nums']
+        present = set(res_nums.tolist())
+        file_wt_by_pos = {}
+        if 'wt_aa' in sdata:
+            file_wt_by_pos = {int(r) - offset: a
+                              for r, a in zip(res_nums, sdata['wt_aa'])
+                              if a and a != 'X'}
+            _check_wt_identity(lib_parsed, mut_labels, file_wt_by_pos, offset,
+                               f"{source}, structure {sname!r}")
+        missing = [lbl for lbl, (_, pos) in zip(mut_labels, lib_parsed)
+                   if pos is not None and pos not in present]
+        if missing:
+            n_cov = M - len(missing)
+            span = (f"{int(res_nums.min())}-{int(res_nums.max())}"
+                    if res_nums.size else "none")
+            raise ValueError(
+                f"{source}, structure {sname!r}: residues for {len(missing)} "
+                f"of {M} library mutations are absent ({n_cov}/{M} covered; "
+                f"structure residues {span} after residue_offset {offset}): "
+                f"{_truncated(missing)}. Every library residue needs "
+                f"LigandMPNN features; check the residue numbering "
+                f"(thermo_options residue_offset) or rebuild the ensemble."
+                + _offset_hint(lib_parsed, file_wt_by_pos, offset)
+            )
+    if not any('wt_aa' in d for d in struct_data):
+        print(f"{source}: no 'wt_aa' datasets, so wild-type identity is not "
+              f"checked against the library.", flush=True)
+    print(f"{source}: all {M} library mutations covered in "
+          f"{len(struct_names)} structure(s).", flush=True)
 
 
 def _build_contact_arrays(struct_data_list, mut_labels, pair_labels):
@@ -126,7 +185,8 @@ def _build_contact_arrays(struct_data_list, mut_labels, pair_labels):
 # Public API
 # ──────────────────────────────────────────────────────────────────────────────
 
-def load_struct_ensemble(h5_path, mut_labels, pair_labels=None):
+def load_struct_ensemble(h5_path, mut_labels, pair_labels=None,
+                         thermo_options=None):
     """
     Load a structural ensemble HDF5 file and build all arrays for DataClass
     struct fields.
@@ -145,6 +205,11 @@ def load_struct_ensemble(h5_path, mut_labels, pair_labels=None):
         Pair labels in "A42G+C18R" or "A42G/C18R" format.  If None or
         empty, contact arrays are not built and the corresponding return
         values are None.
+    thermo_options : dict, optional
+        Only ``residue_offset`` applies (see :func:`normalize_thermo_options`);
+        it is added to every structure's ``residue_nums``.  Every library
+        residue must be present in every structure, and when the file stores
+        ``wt_aa`` its wt residues must match the library's.
 
     Returns
     -------
@@ -167,6 +232,12 @@ def load_struct_ensemble(h5_path, mut_labels, pair_labels=None):
         ]
         struct_data = [_read_h5_structure(hf, name) for name in struct_names]
 
+    offset = normalize_thermo_options(thermo_options)["residue_offset"]
+    if offset:
+        for d in struct_data:
+            d['residue_nums'] = (d['residue_nums'] + offset).astype(np.int32)
+    _check_h5_coverage(h5_path, struct_names, struct_data, mut_labels, offset)
+
     features = build_feature_matrix(struct_data, struct_names, mut_labels)
     n_chains  = np.array(
         [d['n_chains_bearing_mut'] for d in struct_data], dtype=np.int32
@@ -189,59 +260,496 @@ def load_struct_ensemble(h5_path, mut_labels, pair_labels=None):
     }
 
 
-def load_ddG_prior_csv(csv_path, mut_labels):
+# ──────────────────────────────────────────────────────────────────────────────
+# Loader options: energy units, column mapping, numbering, coverage
+# ──────────────────────────────────────────────────────────────────────────────
+
+# Gas constant in the two supported molar energy units.
+_R_KCAL = 1.987204259e-3   # kcal/(mol·K)
+_R_KJ   = 8.314462618e-3   # kJ/(mol·K)
+
+_UNIT_ALIASES = {
+    "kt": "kT",
+    "kcal/mol": "kcal/mol", "kcal": "kcal/mol", "kcal_per_mol": "kcal/mol",
+    "kj/mol": "kJ/mol", "kj": "kJ/mol", "kj_per_mol": "kJ/mol",
+}
+
+# Defaults reproduce the behaviour before these options existed: ΔΔG values
+# are used as-is (already in units of kT), file residue numbers are the
+# library's, and the structure columns carry the model's names.
+THERMO_OPTION_DEFAULTS = {
+    "ddG_units": "kT",
+    "temperature_K": 298.15,
+    "column_map": None,
+    "residue_offset": 0,
+    "max_missing_fraction": 0.1,
+}
+
+# Valid mutation-column names in a ΔΔG CSV, in lookup order.
+_MUT_COLUMN_ALIASES = ("mut", "Mutation", "mutation")
+
+_MAX_LISTED = 10   # longest list of labels printed in one message
+
+
+def _parse_column_map(value):
+    """Turn a ``{model_name: csv_column}`` dict or ``"HD=HO,LE2=LOE2"`` string into a dict."""
+    if value is None:
+        return None
+    if isinstance(value, dict):
+        out = {str(k): str(v) for k, v in value.items()}
+    elif isinstance(value, str):
+        out = {}
+        for item in value.split(","):
+            item = item.strip()
+            if not item:
+                continue
+            if "=" not in item:
+                raise ValueError(
+                    f"column_map entry {item!r} is not of the form "
+                    f"'model_name=csv_column'."
+                )
+            k, v = item.split("=", 1)
+            out[k.strip()] = v.strip()
+    else:
+        raise ValueError(
+            f"column_map must be a dict or a 'model_name=csv_column,...' "
+            f"string; got {type(value).__name__}."
+        )
+    return out or None
+
+
+def normalize_thermo_options(options=None):
     """
-    Load per-mutation per-structure ΔΔG prior means from a CSV file.
+    Validate a ``thermo_options`` mapping and fill in defaults.
+
+    Keys (all optional):
+
+    ``ddG_units``
+        Energy unit of the ΔΔG values in a PddG CSV: ``kT`` (default; values
+        are used as-is), ``kcal/mol`` or ``kJ/mol``.  Molar values are divided
+        by RT once at load, so the model always works in kT (natural-log K).
+    ``temperature_K``
+        Temperature for that conversion (default 298.15 K).
+    ``column_map``
+        ``{model_structure_name: file_column}`` (or the string
+        ``"HD=HO,LE2=LOE2"``) naming the CSV column that supplies a model
+        structure.  ``mut`` may be mapped too.  Unmapped names are looked up
+        under their own name.
+    ``residue_offset``
+        Integer added to every residue number in the file (CSV mutation labels
+        or HDF5 ``residue_nums``) to give the library's numbering, e.g. 1 for a
+        0-based file against a 1-based library.
+    ``max_missing_fraction``
+        Warn when more than this fraction of library mutations has no ΔΔG in
+        the CSV (default 0.1).  Zero coverage is always an error.
 
-    Expected CSV format: a ``mut`` column plus one column per structure.
-    The structure columns must contain exactly the names required by the
-    target theta model (e.g. ``H``, ``HO``, ``L``, ``LO``, ``HE2``,
-    ``LE2`` for ``mwc_dimer_lnK_ddG_prior``).  Column order in the CSV
-    does not matter; rows need not be sorted.
+    Returns
+    -------
+    dict
+        A new dict with every key present and ``ddG_units`` canonicalised.
+    """
+    if options is None:
+        options = {}
+    if not isinstance(options, dict):
+        raise ValueError(
+            f"thermo_options must be a dict; got {type(options).__name__}."
+        )
+    unknown = set(options) - set(THERMO_OPTION_DEFAULTS)
+    if unknown:
+        raise ValueError(
+            f"Unknown thermo_options key(s) {sorted(unknown)}; "
+            f"valid keys are {sorted(THERMO_OPTION_DEFAULTS)}."
+        )
+    out = dict(THERMO_OPTION_DEFAULTS)
+    out.update({k: v for k, v in options.items() if v is not None})
+
+    units = _UNIT_ALIASES.get(str(out["ddG_units"]).strip().lower())
+    if units is None:
+        raise ValueError(
+            f"ddG_units {out['ddG_units']!r} not recognised; use 'kT', "
+            f"'kcal/mol' or 'kJ/mol'."
+        )
+    out["ddG_units"] = units
+
+    temperature = float(out["temperature_K"])
+    if not np.isfinite(temperature) or temperature <= 0:
+        raise ValueError(f"temperature_K must be > 0 (Kelvin); got {temperature}.")
+    out["temperature_K"] = temperature
+
+    out["column_map"] = _parse_column_map(out["column_map"])
 
-    Mutations present in ``mut_labels`` but absent from the CSV receive a
-    prior mean of 0.0 (equivalent to no structural prediction for that
-    mutation).
+    offset = out["residue_offset"]
+    if isinstance(offset, bool) or int(offset) != offset:
+        raise ValueError(f"residue_offset must be an integer; got {offset!r}.")
+    out["residue_offset"] = int(offset)
+
+    frac = float(out["max_missing_fraction"])
+    if not 0.0 <= frac <= 1.0:
+        raise ValueError(f"max_missing_fraction must be in [0, 1]; got {frac}.")
+    out["max_missing_fraction"] = frac
+    return out
+
+
+def ddG_to_kT_factor(ddG_units="kT", temperature_K=298.15):
+    """
+    Multiplier converting ΔΔG in ``ddG_units`` to units of kT (1/RT).
+
+    ``kT`` gives exactly 1.0; ``kcal/mol`` at 298.15 K gives about 1.688.
+    """
+    units = normalize_thermo_options({"ddG_units": ddG_units,
+                                      "temperature_K": temperature_K})
+    if units["ddG_units"] == "kT":
+        return 1.0
+    R = _R_KCAL if units["ddG_units"] == "kcal/mol" else _R_KJ
+    return 1.0 / (R * units["temperature_K"])
+
+
+def _truncated(labels, n=_MAX_LISTED):
+    labels = list(labels)
+    text = ", ".join(str(x) for x in labels[:n])
+    if len(labels) > n:
+        text += f", ... ({len(labels) - n} more)"
+    return text
+
+
+def _parse_library_labels(mut_labels):
+    """(wt_aa, resnum) per library label; (None, None) for an unparseable one."""
+    out = []
+    for lbl in mut_labels:
+        m = _MUT_PATTERN.match(str(lbl))
+        out.append((m.group(1), int(m.group(2))) if m else (None, None))
+    return out
+
+
+def _offset_hint(lib_parsed, file_wt_by_pos, current_offset, search=50):
+    """
+    Look for a residue offset under which every library wt residue matches the
+    file.  ``file_wt_by_pos`` maps file-native residue number → wt letter.
+    Returns a sentence (or "") suggesting ``residue_offset``.
+    """
+    lib = [(wt, pos) for wt, pos in lib_parsed if wt is not None]
+    if not lib or not file_wt_by_pos:
+        return ""
+    best = None
+    for k in range(-search, search + 1):
+        compared = matched = 0
+        for wt, pos in lib:
+            fwt = file_wt_by_pos.get(pos - k)
+            if fwt is None:
+                continue
+            compared += 1
+            matched += (fwt == wt)
+        if compared and matched == compared:
+            if best is None or matched > best[1]:
+                best = (k, matched)
+    if best is None or best[0] == current_offset:
+        return ""
+    return (f" The library's wt residues all match the file if residue_offset "
+            f"is {best[0]} (file residue + {best[0]} = library residue; "
+            f"{best[1]} library mutations then line up); set "
+            f"thermo_options residue_offset: {best[0]} if that numbering is "
+            f"right.")
+
+
+def _check_wt_identity(lib_parsed, mut_labels, file_wt_by_pos, offset, source):
+    """Error if any library wt residue disagrees with the file at its position."""
+    mismatches = []
+    for lbl, (wt, pos) in zip(mut_labels, lib_parsed):
+        if wt is None:
+            continue
+        fwt = file_wt_by_pos.get(pos - offset)
+        if fwt is not None and fwt != wt:
+            mismatches.append(f"{lbl} (file wt {fwt}{pos - offset})")
+    if mismatches:
+        raise ValueError(
+            f"{source}: wild-type residues disagree with the library at "
+            f"{len(mismatches)} of {len(mut_labels)} library mutations "
+            f"({_truncated(mismatches)}). This usually means the file and the "
+            f"library number residues differently."
+            + _offset_hint(lib_parsed, file_wt_by_pos, offset)
+        )
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Dispatch from the theta model name
+# ──────────────────────────────────────────────────────────────────────────────
+
+def required_struct_names(theta_name):
+    """
+    Structure names a thermo PddG/PnnC theta component needs, or None.
+
+    Read from the component's ``STRUCTURE_NAMES`` so there is one source.
+    """
+    if theta_name is None:
+        return None
+    from tfscreen.tfmodel.generative.registry import model_registry
+    module = model_registry["theta"].get(theta_name)
+    names = getattr(module, "STRUCTURE_NAMES", None)
+    return tuple(names) if names is not None else None
+
+
+def load_thermo_data(path, mut_labels, pair_labels=None, theta_name=None,
+                     thermo_options=None):
+    """
+    Load ``thermo_data`` for a thermo theta component.
+
+    A ``PddG`` component reads a ΔΔG CSV via :func:`load_ddG_prior_csv`
+    (keeping only its structures) and a ``PnnC`` component an HDF5 ensemble
+    via :func:`load_struct_ensemble`.  For any other (or no) component the
+    file extension decides: ``.csv`` is a ΔΔG CSV, anything else HDF5.
+    """
+    if theta_name is not None and theta_name.endswith(".PddG"):
+        is_csv = True
+    elif theta_name is not None and theta_name.endswith(".PnnC"):
+        is_csv = False
+    else:
+        is_csv = str(path).lower().endswith(".csv")
+    if is_csv:
+        return load_ddG_prior_csv(path, mut_labels,
+                                  struct_names=required_struct_names(theta_name),
+                                  thermo_options=thermo_options)
+    return load_struct_ensemble(path, mut_labels, pair_labels,
+                                thermo_options=thermo_options)
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# ΔΔG CSV
+# ──────────────────────────────────────────────────────────────────────────────
+
+def load_ddG_prior_csv(csv_path, mut_labels, struct_names=None,
+                       thermo_options=None):
+    """
+    Load per-mutation per-structure ΔΔG prior means from a CSV file.
+
+    Format
+    ------
+    One row per single mutation.  The mutation column is ``mut`` (or
+    ``Mutation`` / ``mutation``), with labels like ``A42G``.  Every other
+    column is a structure.  An unnamed pandas index column (``Unnamed: 0``)
+    is dropped.  When ``struct_names`` is given, each required name must be a
+    column (or be mapped to one with ``column_map``); further columns are
+    ignored with a note.  Without it, every remaining column is kept, in file
+    order.  Rows whose label is not a single mutation are ignored.
+
+    Units and sign convention
+    -------------------------
+    ``ddG[m, X]`` is the change mutation ``m`` makes to the free energy of
+    structure ``X`` relative to the reference state (U, at ΔΔG_U = 0), so a
+    positive value destabilises X.  For an equilibrium constant
+    ``K_{A→B} = [B]/[A]`` the models use
+    ``Δln K_{A→B} = ΔΔG_A − ΔΔG_B``, which needs ΔΔG in units of kT.  Values
+    in ``kcal/mol`` or ``kJ/mol`` (``thermo_options['ddG_units']``) are
+    divided by RT once here; the default ``kT`` uses them as-is.  A predictor
+    that reports stability as ΔG_fold (negative = more stable) or reports
+    ΔΔG with the opposite sign must be flipped before use.
+
+    Coverage checks
+    ---------------
+    File labels are matched to ``mut_labels`` after adding
+    ``residue_offset`` to the file's residue numbers.  The loader prints how
+    many library mutations the file covers and raises if it covers none, or
+    if the file's wild-type residue at any library position disagrees with
+    the library (a numbering offset).  A library mutation missing from the
+    file gets a prior mean of 0.0 (no structural prediction); when more than
+    ``max_missing_fraction`` of them are missing a warning lists them.
 
     Parameters
     ----------
     csv_path : str
-        Path to the CSV file.
     mut_labels : list of str
         Mutation labels in the order expected by the model (e.g. "A42G").
+    struct_names : sequence of str, optional
+        Structures the theta model needs, returned in this order.
+    thermo_options : dict, optional
+        See :func:`normalize_thermo_options`.
 
     Returns
     -------
     dict with keys:
-        struct_names            : tuple of str — structure column names in CSV order
-        struct_features         : np.ndarray (M, S) float32 — ddG prior means
+        struct_names            : tuple of str
+        struct_features         : np.ndarray (M, S) float32, ΔΔG in kT
         struct_n_chains         : None
         struct_contact_pair_idx : None
         struct_contact_distances: None
     """
+    opts = normalize_thermo_options(thermo_options)
+    column_map = dict(opts["column_map"] or {})
+    offset = opts["residue_offset"]
+    source = f"ddG CSV {csv_path!r}"
+
     df = pd.read_csv(csv_path)
-    if 'mut' not in df.columns:
+    unnamed = [c for c in df.columns
+               if str(c).startswith("Unnamed:") or str(c).strip() == ""]
+    df = df.drop(columns=unnamed)
+
+    # ── Mutation column ──────────────────────────────────────────────────────
+    if "mut" in column_map:
+        mut_col = column_map.pop("mut")
+        if mut_col not in df.columns:
+            raise ValueError(
+                f"{source}: column_map names mutation column {mut_col!r}, "
+                f"which is not in the file; found columns: {list(df.columns)}."
+            )
+    else:
+        present = [c for c in _MUT_COLUMN_ALIASES if c in df.columns]
+        if not present:
+            raise ValueError(
+                f"{source} must have a 'mut' column (or 'Mutation'/'mutation'); "
+                f"found columns: {list(df.columns)}."
+            )
+        if len(present) > 1:
+            raise ValueError(
+                f"{source} has more than one mutation column {present}; "
+                f"keep one, or name it with column_map: {{mut: <column>}}."
+            )
+        mut_col = present[0]
+    other_cols = [c for c in df.columns if c != mut_col]
+
+    # ── Structure columns ────────────────────────────────────────────────────
+    if struct_names is None:
+        # No model given: keep every column; a column_map entry adds (or
+        # re-sources) a structure from the named column.
+        if not other_cols:
+            raise ValueError(
+                f"{source} has no structure columns (only {mut_col!r})."
+            )
+        out_names = list(other_cols)
+        src_cols = list(other_cols)
+        for s, c in column_map.items():
+            if c not in df.columns:
+                raise ValueError(
+                    f"{source}: column_map maps {s!r} to column {c!r}, which "
+                    f"is not in the file; found columns: {other_cols}."
+                )
+            if s in out_names:
+                src_cols[out_names.index(s)] = c
+            else:
+                out_names.append(s)
+                src_cols.append(c)
+    else:
+        out_names = list(struct_names)
+        unknown_map = set(column_map) - set(out_names)
+        if unknown_map:
+            raise ValueError(
+                f"{source}: column_map key(s) {sorted(unknown_map)} are not "
+                f"structures of this model {tuple(out_names)}."
+            )
+        src_cols = [column_map.get(s, s) for s in out_names]
+        missing = [f"{s}" + (f" (mapped to {c!r})" if c != s else "")
+                   for s, c in zip(out_names, src_cols) if c not in df.columns]
+        if missing:
+            raise ValueError(
+                f"{source} is missing structure column(s) {missing} needed by "
+                f"this model, which uses {tuple(out_names)}. Found columns: "
+                f"{other_cols}. Name the file column that supplies a structure "
+                f"with thermo_options column_map (e.g. {{HD: HO}})."
+            )
+        ignored = [c for c in other_cols if c not in src_cols]
+        if ignored:
+            print(f"{source}: ignoring column(s) not used by this model: "
+                  f"{ignored}.", flush=True)
+        mapped = {s: c for s, c in zip(out_names, src_cols) if s != c}
+        if mapped:
+            print(f"{source}: reading structure(s) from mapped column(s) "
+                  f"{mapped}.", flush=True)
+
+    # A column can supply two structures (column_map {HD: H}, or a column
+    # kept as its own structure and copied to another), so check each source
+    # column once, then take one copy per structure.
+    unique_cols = list(dict.fromkeys(src_cols))
+    numeric = df[unique_cols].apply(pd.to_numeric, errors="coerce")
+    bad = [c for c in unique_cols
+           if numeric[c].isna().all() and df[c].notna().any()]
+    if bad:
         raise ValueError(
-            f"CSV {csv_path!r} must have a 'mut' column; "
-            f"found columns: {list(df.columns)}."
+            f"{source}: structure column(s) {bad} are not numeric."
         )
-    struct_cols = [c for c in df.columns if c != 'mut']
-    if not struct_cols:
+
+    # ── Parse file labels, apply the residue offset ──────────────────────────
+    labels = df[mut_col].astype(str).str.strip()
+    file_wt_by_pos = {}
+    row_of = {}
+    n_unparsed = 0
+    duplicates = set()
+    for i, lbl in enumerate(labels):
+        m = _MUT_PATTERN.match(lbl)
+        if m is None:
+            n_unparsed += 1
+            continue
+        wt, pos, mt = m.group(1), int(m.group(2)), m.group(3)
+        prev = file_wt_by_pos.setdefault(pos, wt)
+        if prev != wt:
+            raise ValueError(
+                f"{source}: residue {pos} has two wild-type residues in the "
+                f"file ({prev} and {wt}); every label at a position must share "
+                f"its wt residue."
+            )
+        key = f"{wt}{pos + offset}{mt}"
+        if key in row_of:
+            duplicates.add(lbl)
+        row_of[key] = i
+    if duplicates:
         raise ValueError(
-            f"CSV {csv_path!r} has no structure columns (only 'mut')."
+            f"{source} lists mutation(s) more than once: {_truncated(sorted(duplicates))}."
         )
+    if n_unparsed:
+        print(f"{source}: ignoring {n_unparsed} row(s) whose label is not a "
+              f"single mutation like 'A42G'.", flush=True)
 
-    df = df.set_index('mut')
+    # ── Assemble (M, S) and check coverage ───────────────────────────────────
     M = len(mut_labels)
-    S = len(struct_cols)
-
+    S = len(src_cols)
+    scale = ddG_to_kT_factor(opts["ddG_units"], opts["temperature_K"])
+    vals = numeric[src_cols].to_numpy(dtype=np.float64)
     features = np.zeros((M, S), dtype=np.float32)
+    missing_labels = []
+    nan_labels = []
     for m_idx, mut in enumerate(mut_labels):
-        if mut in df.index:
-            features[m_idx] = df.loc[mut, struct_cols].values.astype(np.float32)
+        r = row_of.get(mut)
+        if r is None:
+            missing_labels.append(mut)
+            continue
+        row = vals[r]
+        if np.isnan(row).any():
+            nan_labels.append(mut)
+            continue
+        features[m_idx] = (row * scale if scale != 1.0 else row).astype(np.float32)
+
+    if M > 0:
+        lib_parsed = _parse_library_labels(mut_labels)
+        _check_wt_identity(lib_parsed, mut_labels, file_wt_by_pos, offset, source)
+        uncovered = missing_labels + nan_labels
+        n_cov = M - len(uncovered)
+        if n_cov == 0:
+            raise ValueError(
+                f"{source} covers none of the {M} library mutations "
+                f"(library e.g. {_truncated(mut_labels, 3)}; file e.g. "
+                f"{_truncated(labels.head(3).tolist(), 3)}). Check the residue "
+                f"numbering and the mutation-label format."
+                + _offset_hint(lib_parsed, file_wt_by_pos, offset)
+            )
+        unit_note = ("" if opts["ddG_units"] == "kT" else
+                     f"; converted {opts['ddG_units']} to kT at "
+                     f"{opts['temperature_K']:g} K (x{scale:.4f})")
+        print(f"{source}: ΔΔG for {n_cov}/{M} library mutations "
+              f"({100.0 * n_cov / M:.1f}%){unit_note}.", flush=True)
+        frac_missing = len(uncovered) / M
+        if frac_missing > opts["max_missing_fraction"]:
+            parts = []
+            if missing_labels:
+                parts.append(f"absent: {_truncated(missing_labels)}")
+            if nan_labels:
+                parts.append(f"blank values: {_truncated(nan_labels)}")
+            warnings.warn(
+                f"{source} has no ΔΔG for {len(uncovered)} of {M} library "
+                f"mutations ({100.0 * frac_missing:.1f}% > max_missing_fraction "
+                f"{opts['max_missing_fraction']:g}); they get a prior mean of "
+                f"0 ({'; '.join(parts)}).",
+                stacklevel=2,
+            )
 
     return {
-        'struct_names':             tuple(struct_cols),
+        'struct_names':             tuple(out_names),
         'struct_features':          features,
         'struct_n_chains':          None,
         'struct_contact_pair_idx':  None,
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/mwc.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/mwc.py
new file mode 100644
index 00000000..2fafbdab
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/mwc.py
@@ -0,0 +1,592 @@
+"""
+Standard MWC dimer equations shared by every O2_C4 and O2_C12 theta model.
+
+Both families are one physical model, the Monod-Wyman-Changeux (MWC)
+two-conformation lac repressor dimer. The dimer is in a high operator
+affinity conformation H or a low affinity conformation L, optionally with a
+globally unfolded state U. Each dimer has two identical, independent
+effector sites and one operator site. The constants are
+
+    K_h_l  [L]/[H], dimensionless
+    K_h_o  H + O → HO, M⁻¹
+    K_l_o  L + O → LO, M⁻¹
+    K_h_e  per-site (microscopic) effector association constant of H, M⁻¹
+    K_l_e  per-site (microscopic) effector association constant of L, M⁻¹
+    K_u    [U]/[H], dimensionless (U1 models only; 0 in U0 models)
+
+The O2_C12 models (M3) fit all five. The O2_C4 models (M2) are the exact
+restriction K_h_e = 0 (H does not bind effector) and K_l_o = 0 (L does not
+bind operator). Their three constants are K_op = K_h_o, K_HL = K_h_l and
+K_E = K_l_e, and they call :func:`mwc_theta` with the two restricted constants
+fixed at 0, so M2 is M3 with those constants removed by construction.
+
+Effector polynomial
+-------------------
+With two independent, equivalent sites the effector binding polynomial of a
+conformation is
+
+    P_H(e) = (1 + K_h_e·e)² = 1 + 2·K_h_e·e + K_h_e²·e²
+    P_L(e) = (1 + K_l_e·e)² = 1 + 2·K_l_e·e + K_l_e²·e²
+
+The 2 counts the two ways to fill one site. Within a conformation the two
+sites are independent, so the macroscopic constants are K1 = 2·K and
+K2 = K/2, with K2/K1 = 1/4. Cooperativity comes only from the conformational
+equilibrium: effector pulls H toward L when K_l_e > K_h_e and K_h_l > 1.
+
+Before 2026-10-11 these models used Sochor's (2014, PeerJ 2:e498) polynomial
+P = 1 + K·e + K²·e², which has no factor of 2 on the singly bound term. That
+gives K1 = K and K2 = K, so K2/K1 = 1, a fixed fourfold excess over the
+independent-site value 1/4. It builds a within-conformation cooperativity
+into every genotype that no constant can turn off. The O2_C4 models used a
+third form, L + 2E → LE2 in one step with an M⁻² constant and no singly bound
+state. Both forms are replaced by the standard one here.
+
+Bookkeeping
+-----------
+TF is in large excess over operator (tf_total = 1e-5 M monomer against about
+2.5e-8 M operator, about 400-fold), so operator-bound TF is left out of the
+TF mass balance and operator total does not enter the model. With
+r = tf_total/2 the dimer concentration (tf_total is in MONOMER units) and
+h the free apo H dimer, the TF mass balance over the states that are not
+bound to operator is
+
+    r = h·F(e),    F(e) = P_H(e) + K_h_l·P_L(e) + K_u = B0 + B1·e + B2·e²
+
+    B0 = 1 + K_h_l + K_u
+    B1 = 2·(K_h_e + K_h_l·K_l_e)
+    B2 = K_h_e² + K_h_l·K_l_e²
+
+Operator occupancy is the Langmuir isotherm of the operator for the TF
+species that bind it (operator binding does not change effector binding):
+
+    W = h·(K_h_o·P_H(e) + K_h_l·K_l_o·P_L(e))
+    θ = W / (1 + W)
+
+With K_h_e = K_l_o = 0 this is W = h·K_op, h = r / (1 + K_HL·(1 + K_E·e)² + K_U).
+
+Free effector
+-------------
+Bound effector per dimer is e·dF/de / F, summed over the weighted states
+(e·dP/de counts the occupied sites of each species). The effector mass
+balance e_total = e + r·e·F'(e)/F(e) with e·F'(e) = B1·e + 2·B2·e² gives,
+after multiplying through by F(e),
+
+    (e_total − e)·(B0 + B1·e + B2·e²) = r·(B1·e + 2·B2·e²)
+
+    B2·e³ + (B1 + B2·(2r − e_total))·e² + (B0 + B1·(r − e_total))·e
+          − B0·e_total = 0
+
+The cubic is negative at e = 0 and positive at e = e_total.
+
+Log space
+---------
+Every quantity is computed from the log constants (``theta_c4``,
+``theta_c12``, ``log_W``): partition-function sums are log-sum-exps,
+log P = 2·softplus(ln K + log e), and θ = sigmoid(log W). No constant is
+exponentiated, so any finite ln K gives a finite θ in [0, 1] with the right
+limits, in float32. Before 2026-10-11 θ was computed from exp(ln K): Laplace
+draws of the PddG pilot fits with ln K beyond ~44 overflowed K² in float32
+and wrote NaN θ for whole genotypes. The free effector is solved for
+x = log e by safeguarded Newton (``_solve_log_free_effector``) on the mass
+balance written so that it stays well conditioned on both sides of the
+equivalence point e_total = 2r (``_mass_balance``). Its derivatives come
+from the implicit function theorem (a custom JVP), not from the iterations,
+so gradients and Hessians are exact and finite. ``mwc_theta`` and
+``free_effector`` keep their linear-constant signatures and log their
+inputs.
+
+Projection of structural ΔΔG
+-----------------------------
+ΔΔG_X is the shift a mutation makes to the free energy of state X (kT,
+positive destabilizes X). For K_{A→B} = [B]/[A], Δln K_{A→B} = ΔΔG_A − ΔΔG_B.
+A doubly liganded structure XE2 has weight K_x_e²·e² relative to X, so
+ΔΔG_X − ΔΔG_XE2 = 2·Δln K_x_e and the per-site row carries 1/2. Mutations
+do not shift U (ΔΔG_U = 0), so Δln K_u = ΔΔG_H. The projection rows for each
+family are :data:`PROJ_C4`, :data:`PROJ_U_C4`, :data:`PROJ_C12` and
+:data:`PROJ_U_C12`.
+
+Default wild type
+-----------------
+The default wt constants put strong repression and near-complete induction
+at the PI's TF estimate, tf_total = 1e-5 M monomer (r = 5e-6 M dimer). The
+O2_C12 defaults keep Sochor's K_h_l = 6.3, K_h_o = 4.2e8 M⁻¹ and
+K_l_o = 0.1 M⁻¹ and set the effector selectivity ρ = K_l_e/K_h_e from the
+saturating limit. As e → ∞ only the doubly liganded HE2 and LE2 remain, so
+
+    W_sat = r·(K_h_o + K_h_l·K_l_o·ρ²) / (1 + K_h_l·ρ²)
+
+and θ_sat = W_sat/(1 + W_sat) depends on ρ alone. Asking for θ_sat = 0.05
+(W_sat = 1/19) and solving for ρ gives
+
+    ρ² = (r·K_h_o − W_sat) / (K_h_l·(W_sat − r·K_l_o))
+
+so ρ ≈ 79.6 (:data:`MWC_EFFECTOR_SELECTIVITY`). This departs from Sochor's
+fitted ρ = 7.6e5/5.6e4 ≈ 13.6. With that ρ the HE2 state still repressed:
+W_sat = r·K_h_o/(1 + K_h_l·ρ²) ≈ 1.8 and θ_sat ≈ 0.64 at 1e-5 M TF, which is
+implausible for lac. Sochor fitted ρ at a far lower TF level, where the
+residual HE2 repression did not show.
+
+The two effector constants then share one scale. K_h_e and K_l_e start at
+(SOCHOR_K_L_E/ρ, SOCHOR_K_L_E) and are multiplied by a common factor,
+:data:`MWC_EFFECTOR_SCALE` ≈ 0.393, chosen so that the midpoint is 80 µM
+IPTG. The midpoint is the total effector at which θ is halfway between θ(0)
+and θ_sat, with effector depletion at 1e-5 M TF. That gives
+K_h_e ≈ 3.7e3 M⁻¹ and K_l_e ≈ 3.0e5 M⁻¹ per site. Scaling both leaves ρ and
+so θ_sat unchanged.
+
+The O2_C4 defaults use the same K_h_l and K_h_o with K_h_e = K_l_o = 0, so
+θ_sat = 0, and calibrate K_E = K_l_e alone to the same 80 µM midpoint
+(:data:`DEFAULT_LN_K_E_C4`).
+
+Units
+-----
+Data concentrations (``titrant_conc``) are in mM. The components multiply
+them by ``theta_conc_unit_scale`` (1e-3) so that every concentration here is
+in M and every effector and operator constant in M⁻¹.
+"""
+
+import math
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+
+# Iterations of the safeguarded Newton solve for log free effector
+# (_solve_log_free_effector). Over 20,000 draws each of broad constants
+# (ln K SD 2 about the defaults), wide ones (SD 15) and extreme ones (every
+# ln K uniform on [-150, 150]), with total effector 1e-12 to 1 M (30% of them
+# within 0.1% of the equivalence point 2r) and TF 1e-12 to 1e-3 M, 12
+# iterations matched a 400-iteration solve exactly in float64, and float32
+# matched it to 7e-5 in log e. 16 leaves a margin. The old linear-space
+# Newton solve also took 16 steps, but overflowed in float32 and needed more
+# than 30 at large K_E.
+NEWTON_ITERATIONS = 16
+
+# Data concentrations are mM; this converts them to M.
+DEFAULT_CONC_UNIT_SCALE = 1e-3
+
+# Total TF, MONOMER units (M); r = tf_total/2 = 5e-6 M dimer. The PI's
+# current in vivo estimate.
+DEFAULT_TF_TOTAL_M = 1e-5
+
+# Sochor 2014 (PeerJ 2:e498) Table 1, in M.
+SOCHOR_K_H_L = 6.3
+SOCHOR_K_H_O = 4.2e8
+SOCHOR_K_L_O = 0.1
+SOCHOR_K_H_E = 5.6e4
+SOCHOR_K_L_E = 7.6e5
+
+# Calibrated wt defaults (see the module docstring). O2_C12 keeps Sochor's
+# K_h_l, K_h_o and K_l_o, widens the effector selectivity rho = K_l_e/K_h_e so
+# that theta_saturated = CALIBRATION_THETA_SATURATED at DEFAULT_TF_TOTAL_M, and
+# scales K_h_e and K_l_e by one common factor, MWC_EFFECTOR_SCALE, so that the
+# midpoint (theta halfway between theta(0) and theta_saturated, with effector
+# depletion at DEFAULT_TF_TOTAL_M) is at 80 µM. O2_C4 uses the same K_h_l and
+# K_h_o with K_h_e = K_l_o = 0 and calibrates K_l_e alone to the same
+# midpoint. Regenerate with test_mwc.py::_calibrate_* if the equations change.
+CALIBRATION_MIDPOINT_M = 80e-6
+CALIBRATION_THETA_SATURATED = 0.05
+
+
+def effector_selectivity_for_theta_saturated(theta_sat, K_h_l, K_h_o, K_l_o,
+                                             tf_total):
+    """
+    ρ = K_l_e/K_h_e that gives θ_sat at saturating effector.
+
+    Solves W_sat = r·(K_h_o + K_h_l·K_l_o·ρ²)/(1 + K_h_l·ρ²) for ρ, with
+    W_sat = θ_sat/(1 − θ_sat) and r = tf_total/2 (see module docstring).
+    Requires r·K_l_o < W_sat < r·K_h_o.
+    """
+    r = tf_total / 2.0
+    W = theta_sat / (1.0 - theta_sat)
+    return math.sqrt((r * K_h_o - W) / (K_h_l * (W - r * K_l_o)))
+
+
+MWC_EFFECTOR_SELECTIVITY = effector_selectivity_for_theta_saturated(
+    CALIBRATION_THETA_SATURATED, SOCHOR_K_H_L, SOCHOR_K_H_O, SOCHOR_K_L_O,
+    DEFAULT_TF_TOTAL_M)                                # ≈ 79.6
+MWC_EFFECTOR_SCALE = 0.39263690389004985
+DEFAULT_LN_K_H_L = math.log(SOCHOR_K_H_L)
+DEFAULT_LN_K_H_O = math.log(SOCHOR_K_H_O)
+DEFAULT_LN_K_L_O = math.log(SOCHOR_K_L_O)
+DEFAULT_LN_K_L_E = math.log(SOCHOR_K_L_E * MWC_EFFECTOR_SCALE)   # ≈ 3.0e5 M⁻¹
+DEFAULT_LN_K_H_E = DEFAULT_LN_K_L_E - math.log(MWC_EFFECTOR_SELECTIVITY)  # ≈ 3.7e3
+DEFAULT_LN_K_E_C4 = 12.410839287464412
+
+# Structure bases and projection rows (see module docstring).
+STRUCTURE_NAMES_C4 = ('H', 'HD', 'L', 'LE2')
+# rows = (K_op, K_HL, K_E), cols = (H, HD, L, LE2)
+PROJ_C4 = np.array([[1.0, -1.0, 0.0,  0.0],    # Δln K_op = ΔΔG_H − ΔΔG_HD
+                    [1.0,  0.0, -1.0, 0.0],    # Δln K_HL = ΔΔG_H − ΔΔG_L
+                    [0.0,  0.0, 0.5, -0.5]])   # Δln K_E  = (ΔΔG_L − ΔΔG_LE2)/2
+PROJ_U_C4 = np.array([1.0, 0.0, 0.0, 0.0])     # Δln K_U = ΔΔG_H
+
+STRUCTURE_NAMES_C12 = ('H', 'HO', 'L', 'LO', 'HE2', 'LE2')
+# rows = (K_h_l, K_h_o, K_h_e, K_l_o, K_l_e), cols = (H, HO, L, LO, HE2, LE2)
+PROJ_C12 = np.array([
+    [1.0,  0.0, -1.0,  0.0,  0.0,  0.0],   # Δln K_h_l = ΔΔG_H − ΔΔG_L
+    [1.0, -1.0,  0.0,  0.0,  0.0,  0.0],   # Δln K_h_o = ΔΔG_H − ΔΔG_HO
+    [0.5,  0.0,  0.0,  0.0, -0.5,  0.0],   # Δln K_h_e = (ΔΔG_H − ΔΔG_HE2)/2
+    [0.0,  0.0,  1.0, -1.0,  0.0,  0.0],   # Δln K_l_o = ΔΔG_L − ΔΔG_LO
+    [0.0,  0.0,  0.5,  0.0,  0.0, -0.5],   # Δln K_l_e = (ΔΔG_L − ΔΔG_LE2)/2
+])
+PROJ_U_C12 = np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0])   # Δln K_u = ΔΔG_H
+
+
+_LOG2 = math.log(2.0)
+_LOG4 = math.log(4.0)
+_LOG8 = math.log(8.0)
+
+
+def _lae3(a, b, c, xp):
+    """log(exp(a) + exp(b) + exp(c))."""
+    return xp.logaddexp(xp.logaddexp(a, b), c)
+
+
+def _log(x, xp):
+    """log, with log(0) = -inf and no numpy warning."""
+    if xp is np:
+        with np.errstate(divide="ignore"):
+            return np.log(x)
+    return xp.log(x)
+
+
+def _poly_coeffs(K_h_l, K_h_e, K_l_e, K_u):
+    """F(e) = B0 + B1·e + B2·e² over the states not bound to operator."""
+    B0 = 1.0 + K_h_l + K_u
+    B1 = 2.0 * (K_h_e + K_h_l * K_l_e)
+    B2 = K_h_e ** 2 + K_h_l * K_l_e ** 2
+    return B0, B1, B2
+
+
+def _log_poly_coeffs(ln_K_h_l, ln_K_h_e, ln_K_l_e, ln_K_u, xp=jnp):
+    """log B0, log B1, log B2 of F(e) from log constants (-inf for a 0)."""
+    lB0 = _lae3(0.0, ln_K_h_l, ln_K_u, xp)
+    lB1 = _LOG2 + xp.logaddexp(ln_K_h_e, ln_K_h_l + ln_K_l_e)
+    lB2 = xp.logaddexp(2.0 * ln_K_h_e, ln_K_h_l + 2.0 * ln_K_l_e)
+    return lB0, lB1, lB2
+
+
+def _effector_moments(lB0, lB1, lB2, x, xp):
+    """
+    Moments of n ∈ {0, 1, 2}, the number of effector sites filled on a dimer
+    not bound to operator, at x = log e. The weights are B0, B1·e and B2·e²
+    (n̄ = e·F'/F, and d n̄/d log e = Var(n)).
+
+    Returns log F(e), log n̄, log Var(n) and log m with m = 2 − n̄ (the empty
+    sites per dimer), each from log weights only.
+    """
+    t1 = lB1 + x
+    t2 = lB2 + 2.0 * x
+    lF = _lae3(lB0, t1, t2, xp)
+    lw0, lw1, lw2 = lB0 - lF, t1 - lF, t2 - lF
+    log_nbar = xp.logaddexp(lw1, _LOG2 + lw2)
+    log_m = xp.logaddexp(_LOG2 + lw0, lw1)
+    # Var(n) = Σ_{i<j} w_i w_j (i − j)², a sum of positive terms
+    log_var = _lae3(lw0 + lw1, _LOG4 + lw0 + lw2, lw1 + lw2, xp)
+    return lF, log_nbar, log_var, log_m
+
+
+def _log_excess(e_total, r, xp):
+    """
+    The pieces of the mass balance that depend only on the totals.
+
+    Returns log(max(c, 0)), log(max(−c, 0)) with c = e_total − 2r (the
+    effector left over once every TF site is filled; ``-inf`` for a zero),
+    and ``free_form``, 1.0 where e_total ≤ r and 0.0 elsewhere (which
+    residual :func:`_mass_balance` uses). c comes from the linear inputs, so
+    e_total at the equivalence point 2r gives exactly 0.
+    """
+    c = e_total - 2.0 * r
+
+    def log_pos(v):
+        ok = v > 0
+        return xp.where(ok, _log(xp.where(ok, v, 1.0), xp), -np.inf)
+
+    free_form = xp.where(e_total <= r, 1.0, 0.0)
+    return log_pos(c), log_pos(-c), free_form
+
+
+def _mass_balance(x, lB0, lB1, lB2, lt, lr, lc_pos, lc_neg, free_form, xp):
+    """
+    The effector mass balance e_total = e + r·n̄(e) as a residual φ(x) at
+    x = log e, and dφ/dx. Both forms rise monotonically through the same
+    root, the free effector.
+
+    - e_total ≤ r (TF sites well in excess, so n̄ ≤ 1 at the root):
+      φ = log(e + r·n̄) − log e_total, slope (e + r·Var)/(e + r·n̄).
+    - e_total > r: φ = log(e + c⁻) − log(c⁺ + r·m), m = 2 − n̄ the empty
+      sites per dimer, c = e_total − 2r, c⁺ = max(c, 0), c⁻ = max(−c, 0).
+      This is e = e_total − r·n̄ with e_total − r·n̄ = c + r·m written
+      without a difference of large terms. Slope
+      e/(e + c⁻) + r·Var/(c⁺ + r·m). The first form fails here: near
+      saturation e is a vanishing part of e + r·n̄, so φ is flat in x to
+      float precision and the root is lost (float32 put it 0.05-50 in
+      log e off at e_total ≈ 2r).
+    """
+    _, log_nbar, log_var, log_m = _effector_moments(lB0, lB1, lB2, x, xp)
+
+    log_bal = xp.logaddexp(x, lr + log_nbar)
+    phi_a = log_bal - lt
+    dphi_a = xp.exp(xp.logaddexp(x, lr + log_var) - log_bal)
+
+    log_left = xp.logaddexp(x, lc_neg)               # log(e + c⁻)
+    log_right = xp.logaddexp(lc_pos, lr + log_m)     # log(c⁺ + r·m)
+    phi_b = log_left - log_right
+    dphi_b = (xp.exp(x - log_left)
+              + xp.exp(lr + log_var - log_right))
+
+    use_a = free_form > 0
+    return (xp.where(use_a, phi_a, phi_b),
+            xp.where(use_a, dphi_a, dphi_b))
+
+
+def _solve_log_free_effector(lB0, lB1, lB2, lt, lr, lc_pos, lc_neg, free_form,
+                             xp):
+    """
+    Safeguarded Newton solve of φ(x) = 0 (see :func:`_mass_balance`).
+
+    The root is bracketed by [x_lo, log e_total]: φ(log e_total) ≥ 0, and
+    at e_lo = min(e_t/2, e_t·B0/(8r·B1), sqrt(e_t·B0/(8r·B2))) the bound
+    n̄ ≤ 2·(B1·e + B2·e²)/B0 gives e + r·n̄ ≤ e_total. Newton steps start at
+    log e_total (as the old linear solve did). A step that leaves the
+    current bracket, or does not at least halve the previous step, is
+    replaced by bisection (Numerical Recipes' rtsafe), so the bracket
+    shrinks at least as fast as bisection.
+    """
+    L = lt - _LOG8 - lr + lB0
+    x_lo = xp.minimum(xp.minimum(lt - _LOG2, L - lB1), 0.5 * (L - lB2))
+    lo = x_lo
+    hi = lt
+    x = hi
+    dx_old = 2.0 * (hi - lo)
+
+    def step(_, carry):
+        x, lo, hi, dx_old = carry
+        phi, dphi = _mass_balance(x, lB0, lB1, lB2, lt, lr, lc_pos, lc_neg,
+                                  free_form, xp)
+        lo = xp.where(phi < 0, x, lo)
+        hi = xp.where(phi > 0, x, hi)
+        dx = phi / dphi
+        x_newton = x - dx
+        ok = ((x_newton >= lo) & (x_newton <= hi)
+              & (xp.abs(dx) <= 0.5 * xp.abs(dx_old)))
+        x_bisect = 0.5 * (lo + hi)
+        x_next = xp.where(ok, x_newton, x_bisect)
+        dx_next = xp.where(ok, dx, x - x_bisect)
+        # Converged to float precision: stay. A Newton step there is
+        # rounding noise and need not halve, and bisecting would leave the
+        # root. φ is a difference of logs of size about |log e_total|.
+        done = ((xp.abs(phi) <= 4.0 * tol * xp.maximum(1.0, xp.abs(lt)))
+                | (xp.abs(dx) <= tol * xp.maximum(1.0, xp.abs(x))))
+        return (xp.where(done, x, x_next), lo, hi,
+                xp.where(done, 0.0, dx_next))
+
+    tol = 4.0 * float(xp.finfo(xp.result_type(x)).eps)
+    carry = (x, lo, hi, dx_old)
+    if xp is jnp:
+        carry = jax.lax.fori_loop(0, NEWTON_ITERATIONS, step, carry)
+    else:
+        with np.errstate(invalid="ignore", over="ignore", divide="ignore"):
+            for i in range(NEWTON_ITERATIONS):
+                carry = step(i, carry)
+    return carry[0]
+
+
+@jax.custom_jvp
+def _log_free_effector_jax(lB0, lB1, lB2, lt, lr, lc_pos, lc_neg, free_form):
+    return _solve_log_free_effector(lB0, lB1, lB2, lt, lr, lc_pos, lc_neg,
+                                    free_form, jnp)
+
+
+@_log_free_effector_jax.defjvp
+def _log_free_effector_jvp(primals, tangents):
+    """
+    Implicit derivative of the root, dx = −(∂φ/∂p·dp)/(∂φ/∂x), in place of
+    differentiating through the Newton iterations. It is written in JAX ops,
+    so it differentiates again (the Laplace Hessian).
+    """
+    x = _log_free_effector_jax(*primals)
+
+    def phi(x_, *p):
+        return _mass_balance(x_, *p, jnp)[0]
+
+    _, dphi_p = jax.jvp(lambda *p: phi(x, *p), primals, tangents)
+    _, dphi_dx = jax.jvp(lambda x_: phi(x_, *primals), (x,),
+                         (jnp.ones_like(x),))
+    return x, -dphi_p / dphi_dx
+
+
+def log_free_effector(lB0, lB1, lB2, e_total, r, xp=jnp):
+    """
+    log of the free effector from the mass balance, in log space.
+
+    Parameters
+    ----------
+    lB0, lB1, lB2 : array-like
+        log of the coefficients of F(e) (``_log_poly_coeffs``).
+    e_total : array-like
+        Total effector (M), ≥ 0.
+    r : float or array-like
+        Total TF dimer (M), tf_total/2.
+    xp : module
+        ``jax.numpy`` (default) or ``numpy``.
+
+    Returns
+    -------
+    x : array
+        log free effector in the broadcast shape; ``-inf`` where
+        ``e_total`` is 0.
+    pos : array of bool
+        ``e_total > 0``.
+    """
+    e_total = xp.asarray(e_total)
+    r = xp.asarray(r)
+    pos = e_total > 0
+    lt = _log(xp.where(pos, e_total, 1.0), xp)
+    lr = _log(r, xp)
+    lc_pos, lc_neg, free_form = _log_excess(xp.where(pos, e_total, 1.0), r,
+                                            xp)
+    args = xp.broadcast_arrays(*[xp.asarray(v, dtype=float) for v in
+                                 (lB0, lB1, lB2, lt, lr, lc_pos, lc_neg,
+                                  free_form)])
+    if xp is jnp:
+        x = _log_free_effector_jax(*args)
+    else:
+        x = _solve_log_free_effector(*args, np)
+    return xp.where(pos, x, -np.inf), pos
+
+
+def free_effector(B0, B1, B2, e_total, r, xp=jnp):
+    """
+    Free effector from the cubic mass balance (see module docstring).
+
+    Solved in log space by :func:`log_free_effector`, so the coefficients
+    can span any range.
+
+    Parameters
+    ----------
+    B0, B1, B2 : array-like
+        Coefficients of F(e), broadcastable against ``e_total``.
+    e_total : array-like
+        Total effector (M).
+    r : float
+        Total TF dimer (M), tf_total/2.
+    xp : module
+        ``jax.numpy`` (default) or ``numpy``.
+
+    Returns
+    -------
+    array
+        Free effector, in [0, e_total], in the broadcast shape.
+    """
+    x, pos = log_free_effector(_log(B0, xp), _log(B1, xp), _log(B2, xp),
+                               e_total, r, xp=xp)
+    return xp.where(pos, xp.exp(xp.where(pos, x, 0.0)), 0.0)
+
+
+def log_W(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e, ln_K_u, e_total,
+          tf_total, xp=jnp):
+    """
+    log W, the operator's log odds of being bound (θ = sigmoid(log W)),
+    computed in log space from log constants.
+
+    ``-inf`` stands for a constant the model does not have (K_h_e and K_l_o
+    in O2_C4, K_u in U0 models). Every sum of partition-function terms is a
+    log-sum-exp, so no constant is ever exponentiated and any finite log
+    constant gives a finite log W.
+    """
+    lr = _log(tf_total / 2.0, xp)
+    lB0, lB1, lB2 = _log_poly_coeffs(ln_K_h_l, ln_K_h_e, ln_K_l_e, ln_K_u,
+                                     xp=xp)
+    x, pos = log_free_effector(lB0, lB1, lB2, e_total, tf_total / 2.0, xp=xp)
+    x = xp.where(pos, x, 0.0)
+
+    # e > 0: W = r·(K_h_o·P_H + K_h_l·K_l_o·P_L)/F, log P = 2·softplus(ln K + x)
+    lP_H = 2.0 * xp.logaddexp(0.0, ln_K_h_e + x)
+    lP_L = 2.0 * xp.logaddexp(0.0, ln_K_l_e + x)
+    lF = _lae3(lB0, lB1 + x, lB2 + 2.0 * x, xp)
+    lW_e = lr + xp.logaddexp(ln_K_h_o + lP_H, ln_K_h_l + ln_K_l_o + lP_L) - lF
+
+    # e = 0: P_H = P_L = 1, F = B0
+    lW_0 = lr + xp.logaddexp(ln_K_h_o, ln_K_h_l + ln_K_l_o) - lB0
+    return xp.where(pos, lW_e, lW_0)
+
+
+def _sigmoid(z, xp):
+    """1/(1 + e^-z) without overflow; exactly 0 or 1 only past float range."""
+    return xp.exp(-xp.logaddexp(0.0, -z))
+
+
+def mwc_theta_log(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e, ln_K_u,
+                  e_total, tf_total, xp=jnp):
+    """θ = sigmoid(log W) from log constants (see :func:`log_W`)."""
+    return _sigmoid(log_W(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e,
+                          ln_K_u, e_total, tf_total, xp=xp), xp)
+
+
+def mwc_theta(K_h_l, K_h_o, K_h_e, K_l_o, K_l_e, K_u, e_total, tf_total,
+              xp=jnp):
+    """
+    Operator occupancy of the standard MWC dimer (see module docstring).
+
+    Every argument broadcasts against the others. Pass 0 for a constant the
+    model does not have (K_h_e and K_l_o in O2_C4, K_u in U0 models). The
+    constants are logged and θ computed by :func:`mwc_theta_log`.
+
+    Parameters
+    ----------
+    K_h_l, K_h_o, K_h_e, K_l_o, K_l_e, K_u : array-like
+        Equilibrium constants (linear, not log); effector constants per site.
+    e_total : array-like
+        Total effector (M).
+    tf_total : float
+        Total TF in MONOMER units (M).
+    xp : module
+        ``jax.numpy`` (default) or ``numpy``.
+
+    Returns
+    -------
+    array
+        θ in the broadcast shape.
+    """
+    ln = [_log(xp.asarray(k, dtype=float), xp)
+          for k in (K_h_l, K_h_o, K_h_e, K_l_o, K_l_e, K_u)]
+    return mwc_theta_log(*ln, e_total, tf_total, xp=xp)
+
+
+def theta_saturated(K_h_l, K_h_o, K_h_e, K_l_o, K_l_e, K_u, tf_total,
+                    xp=jnp):
+    """
+    θ in the limit of saturating effector (e → ∞, so depletion vanishes).
+
+    F(e), P_H and P_L are dominated by their e² terms, so
+    W → r·(K_h_o·K_h_e² + K_h_l·K_l_o·K_l_e²) / (K_h_e² + K_h_l·K_l_e²).
+    It is 0 for M2 (K_h_e = K_l_o = 0). Computed from the log constants.
+    """
+    hl, ho, he, lo, le = [_log(xp.asarray(k, dtype=float), xp)
+                          for k in (K_h_l, K_h_o, K_h_e, K_l_o, K_l_e)]
+    num = xp.logaddexp(ho + 2.0 * he, hl + lo + 2.0 * le)
+    den = xp.logaddexp(2.0 * he, hl + 2.0 * le)
+    lW = _log(tf_total / 2.0, xp) + num - den
+    return _sigmoid(xp.where(num == -np.inf, -np.inf, lW), xp)
+
+
+def theta_c12(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e, ln_K_u,
+              e_total, tf_total, xp=jnp):
+    """M3 (O2_C12) θ from log constants; ``ln_K_u=None`` for U0."""
+    ln_K_u = -np.inf if ln_K_u is None else ln_K_u
+    return mwc_theta_log(ln_K_h_l, ln_K_h_o, ln_K_h_e, ln_K_l_o, ln_K_l_e,
+                         ln_K_u, e_total, tf_total, xp=xp)
+
+
+def theta_c4(ln_K_op, ln_K_HL, ln_K_E, ln_K_U, e_total, tf_total, xp=jnp):
+    """
+    M2 (O2_C4) θ from log constants; ``ln_K_U=None`` for U0.
+
+    M3 with K_h_o = K_op, K_h_l = K_HL, K_l_e = K_E (per site, M⁻¹) and
+    K_h_e = K_l_o = 0 (log constants -inf).
+    """
+    ln_K_U = -np.inf if ln_K_U is None else ln_K_U
+    return mwc_theta_log(ln_K_HL, ln_K_op, -np.inf, -np.inf, ln_K_E, ln_K_U,
+                         e_total, tf_total, xp=xp)
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/nn.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/nn.py
index 54c56c6e..a331daf8 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/nn.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/nn.py
@@ -7,9 +7,24 @@
 and the data update those latent variables away from the NN prediction via
 a per-structure trust scale σ_s (see ``prior.py``).
 
-Zero-initialising all weights ensures the model starts in the perturbative
-(WT) regime — all mutation effects are zero at initialization and the data
-drive them away from zero as needed.
+Initialization
+--------------
+W1 starts at small He-scaled normal values; b1, W2 and b2 start at zero.
+With W2 = 0 every NN output starts at b2 = 0, so the prior means start in
+the perturbative (WT) regime, as before.  W1 must not be zero: with ReLU
+hidden units an all-zero network has h = relu(0) = 0, so dL/dW2 = h·g = 0,
+and dL/dW1, dL/db1 carry a factor of W2 = 0.  Only b2 would ever move and
+each MLP would collapse to a constant per structure.  With random W1 the
+hidden activations are nonzero, so W2 gets a gradient at the first step and
+W1, b1 get one as soon as W2 leaves zero.
+
+The W1 draw is deterministic: its PRNG seed is a CRC32 of the ``pyro.param``
+site name, so the model and the guide register identical values, a run is
+reproducible, and a structure keeps its initial weights when the structure
+order changes.  The init value is a concrete array, as ``pyro.param``
+requires outside a seed handler.  It is used only when the param store has
+no value for the site; a restored checkpoint or explicit ``init_params``
+win.
 
 The raw scalar output for structure s is multiplied by n_chains[s] to
 account for chain stoichiometry: in a symmetric homodimer the same mutation
@@ -28,13 +43,28 @@
 dense mutation-genotype matrix is also live at the same time.
 """
 
+import zlib
+
 import jax
 import jax.numpy as jnp
+import numpy as np
 import numpyro as pyro
 
 _DEFAULT_HIDDEN_SIZE = 16
 
 
+def _he_normal_init(site_name, shape, fan_in):
+    """
+    He-scaled normal init, N(0, 2/fan_in), seeded by the site name.
+
+    Returns a concrete float32 array that is the same for a given site name
+    on every call (model, guide, every run).
+    """
+    rng = np.random.default_rng(zlib.crc32(site_name.encode("utf-8")))
+    w = rng.standard_normal(shape) * np.sqrt(2.0 / fan_in)
+    return jnp.asarray(w, dtype=jnp.float32)
+
+
 # ---------------------------------------------------------------------------
 # Checkpointed pure forward pass (no side effects — safe to checkpoint)
 # ---------------------------------------------------------------------------
@@ -118,9 +148,13 @@ def compute_nn_predictions(name, features, struct_names, n_chains,
     # pyro.param calls are SIDE EFFECTS and must remain OUTSIDE the
     # checkpointed function.  We collect them into stacked arrays so
     # that _nn_forward can vmap over the structure dimension cleanly.
+    # W1 must start nonzero or no gradient reaches W1, b1 or W2 (see the
+    # module docstring); W2 = 0 keeps the initial output at b2 = 0.
     # ------------------------------------------------------------------
     W1_all = jnp.stack(
-        [pyro.param(f"{name}_nn_{sname}_W1", jnp.zeros((feat_dim, H)))
+        [pyro.param(f"{name}_nn_{sname}_W1",
+                    _he_normal_init(f"{name}_nn_{sname}_W1",
+                                    (feat_dim, H), feat_dim))
          for sname in struct_names], axis=0)   # (S, feat_dim, H)
     b1_all = jnp.stack(
         [pyro.param(f"{name}_nn_{sname}_b1", jnp.zeros(H))
diff --git a/src/tfscreen/tfmodel/generative/components/theta/thermo/prior.py b/src/tfscreen/tfmodel/generative/components/theta/thermo/prior.py
index 31fed14d..2233f926 100644
--- a/src/tfscreen/tfmodel/generative/components/theta/thermo/prior.py
+++ b/src/tfscreen/tfmodel/generative/components/theta/thermo/prior.py
@@ -1,80 +1,494 @@
 """
-Per-mutation per-structure ΔΔG sampling with NN-predicted prior means.
+Structural prior on the per-mutation equilibrium-constant shifts Δln K.
 
-This module provides the MODEL side of the ΔΔG prior.  Each ΔΔG[m, s] is
-a latent variable sampled as:
+This module provides the MODEL side of the PddG/PnnC structural prior.  The
+structural input is a per-state ΔΔG prior mean (the ΔΔG table for PddG, the
+structure MLP's output for PnnC), and the error model is per state: the true
+ΔΔG of state s is its prior mean plus Normal(0, σ_s).  The data see only the
+constants, Δln K = P · ΔΔG, with P the model's fixed projection (C × S; rows
+are constants, columns are states, ``thermo/mwc.py``).  So the prior is put
+on the constants, as the distribution that per-state model induces:
 
-    ΔΔG[m, s] = nn_means[m, s]  +  σ_s[s] · offset[m, s]
-    offset[m, s] ~ Normal(0, 1)
+    σ_s[s]     ~ LogNormal(sigma_loc, sigma_scale)            (one per state)
+    Δln K[m,:] ~ MultivariateNormal(P · prior_mean[m,:], P · diag(σ_s²) · Pᵀ)
 
-where σ_s is a per-structure trust scale learned via ``pyro.param``.
+``Δln K`` is the sampled per-mutation latent (site ``{name}_dlnK``, shape
+M × C, constants in the model's order).  P includes every row the model
+uses: the per-site effector rows (ΔΔG_X − ΔΔG_XE2)/2 and, in the U1 models,
+the unfolding row Δln K_U = ΔΔG_H (ΔΔG_U = 0).
 
-Interpretation of σ_s
+Why the constants and not the states
+------------------------------------
+From 2026-10-11 (commit c4f03d42) until this change the models sampled the
+per-state ΔΔG[m, s] ~ Normal(prior_mean, σ_s) and projected it.  In the U0
+models (no unfolded state) every constant is a difference of states, so a
+shift equal across all states of one mutation changes no observable: P has
+the null vector (1, …, 1).  Joint MAP used that direction to zero one
+state's residuals and collapse its σ_s toward 0 (an unbounded density): on
+a simulated C4_U0 MAP with true σ_s = 0.3 kT, σ_LE2 went to 0.001 and the
+ΔΔG error got worse than the old non-centered form.  With the prior on
+Δln K there is no per-state latent, so there is no unidentified direction
+to exploit, and one σ_s → 0 leaves P · diag(σ²) · Pᵀ positive definite (the
+null vector has no zero entry, so P minus any one column keeps full row
+rank), so the density stays bounded in that direction.
+
+In the U1 models P is square and invertible (the K_U row anchors the common
+shift), so this is a pure change of variables from the per-state model.
+Its Jacobian |det P| is constant, so the posterior and the MAP point,
+mapped back by P⁻¹, are the same as the per-state model's
+(``test_ddG_constant_prior.py`` checks it numerically).
+
+Identifiability of σ_s
+----------------------
+σ_s enters only through the C × C covariance P · diag(σ²) · Pᵀ.  Its
+entries are linear in σ², with coefficient P[i, s]·P[j, s] for state s, so
+every σ_s is identified (from enough mutations) when the S rank-one
+matrices p_s p_sᵀ (p_s the column of state s) are linearly independent.
+They are in all four families: C4_U0 (H alone has a K_op × K_HL term, L
+alone a K_HL × K_E term, HD and LE2 then from the K_op and K_E variances:
+σ_H² = cov(K_op, K_HL), σ_L² = −2·cov(K_HL, K_E)); C12_U0 (H alone couples
+K_h_l, K_h_o and K_h_e, L alone couples K_h_l, K_l_o and K_l_e, and HO, LO,
+HE2, LE2 each sit in one variance); and the U1 families, where P is
+invertible.  :func:`identifiable_sigma` computes the rank.
+
+Post-hoc per-state ΔΔG
 ----------------------
-σ_s controls how tightly the prior is centred on the NN prediction:
-  σ_s → 0  :  ΔΔG collapses to the deterministic NN output
-  σ_s → ∞  :  prior becomes flat; posterior is driven by the likelihood
-              alone (like lnK_mut with flat sigma prior)
+The deterministic site ``{name}_ddG`` (M × S) keeps the per-state tables
+meaningful:
+
+* U1 (square P): ΔΔG = P⁻¹ · Δln K, exact, with ΔΔG_U = 0.
+* U0: the conditional mean under the per-state prior,
+  ΔΔG = μ + Σ Pᵀ (P Σ Pᵀ)⁻¹ (Δln K − P μ), Σ = diag(σ_s²), μ the prior
+  mean.  The component along the null direction (a common shift of all
+  states) is set by the prior, i.e. by the table, not by the data.  Score
+  per-state ΔΔG only for U1 models; ``tfs-summarize-fit`` flags the U0
+  tables and leaves them out of calibration.
+
+When Δln K equals P · μ (the data agree with the table) both give ΔΔG = μ.
+
+Prior on σ_s
+------------
+σ_s is a positive scale whose plausible range spans two decades (a
+structural prediction good to 0.1 kT, or one off by 10 kT), so a prior
+symmetric in log σ_s is the natural weak choice.  The defaults
+(``theta_ddG_sigma_loc = 0``, median 1 kT, and ``theta_ddG_sigma_scale =
+1.2``) put 95% of the mass on 0.095-10.5 kT.  With hundreds of mutations the
+likelihood of the Δln K about their prior means outweighs this prior, so it
+mainly matters for states the data barely inform.  The prior is centered:
+the density of Δln K carries −M·log|det L| (L the Cholesky factor of the
+covariance), which pins σ_s near the scatter of the fitted Δln K about the
+prior means.  Until 2026-10-11 σ_s was a ``pyro.param`` with no prior in a
+non-centered form, which MAP drove to 4-37 kT whatever the truth.
 
-Initialising σ_s = 1.0 places the model between these extremes and lets
-the data learn how predictive each structure's LigandMPNN features are.
+Known MAP limit.  The density is still unbounded where the covariance turns
+singular with every residual inside the remaining subspace, so with weak
+data (true σ_s below about √3 times the standard error of the direction
+that measures it) a σ_s can drift toward 0 and the fit goes to the prior
+means.  A σ_s near 0 from MAP means "not resolved", not "no noise".
 
-The guide (in lnK_nn_prior.py) must provide matching ``pyro.sample`` sites
-for all ``{name}_ddG_offset`` variables sampled here.
+Implementation
+--------------
+The Cholesky factor of P · diag(σ²) · Pᵀ is computed once per evaluation
+from the QR decomposition of (P · diag(σ))ᵀ (no squaring of the condition
+number), and the per-mutation MultivariateNormal shares that ``scale_tril``
+across the M mutations (batch shape M, event shape C).  The cost is one
+small QR plus M triangular solves of size C ≤ 6.  No non-centered option is
+offered; for NUTS on weakly informed problems a reparameterization of
+``{name}_dlnK`` can be added without changing this module.
+
+Holding σ_s
+-----------
+The priors field ``theta_ddG_sigma_fixed`` (kT, default 0) holds every
+state's σ_s at one known value instead of sampling it, e.g. as an oracle
+arm of a simulation study where the truth noise is known.  With
+``sigma_fixed`` > 0 :func:`sample_dlnK` records ``{name}_ddG_sigma`` as a
+deterministic site (S,) filled with the held value, :func:`guide_dlnK`
+registers no σ_s parameters and samples no σ_s, and the Laplace (which
+sees only sample sites) leaves it out.  Posteriors still carry the
+deterministic site, so the ``ddG_sigma`` table is still extracted, with
+the held value in every row.  The value is a Python float read once per
+trace (``ddG_sigma_fixed``; a ``pytree_node=False`` field of every
+ModelPriors), so under jit it is static.  One scalar applies to all
+states; there is no per-state hold.
+
+Recorded sites
+--------------
+``{name}_ddG_sigma`` (S,) and ``{name}_dlnK`` (M, C) are sample sites
+(``{name}_ddG_sigma`` is deterministic when held);
+``{name}_ddG_prior_mean`` (M, S) and ``{name}_ddG`` (M, S, the post-hoc
+ΔΔG) are deterministic.  The state axis is in the order of the
+``struct_names`` passed in (every model passes its ``STRUCTURE_NAMES``).
+:func:`guide_dlnK` is the matching component-guide block,
+:func:`dlnK_guesses` the starting point, and :func:`ddG_extract_specs` turns
+the sites into ``tfs-extract-params`` tables.  The simulator draws its truth
+per state and conditions ``{name}_dlnK`` on its projection
+(:func:`per_state_truth`; ``simulate/sample_theta.py``).
 """
 
+import numpy as np
+import jax
 import jax.numpy as jnp
+import jax.scipy.linalg as jsl
 import numpyro as pyro
 import numpyro.distributions as dist
 import numpyro.distributions.constraints as constraints
 
+# Default LogNormal prior on σ_s (kT): median exp(0) = 1 kT, 95% of the mass
+# on exp(±1.96 · 1.2) = 0.095-10.5 kT.  The models' get_hyperparameters
+# expose these as theta_ddG_sigma_loc / theta_ddG_sigma_scale.
+DEFAULT_DDG_SIGMA_LOC = 0.0
+DEFAULT_DDG_SIGMA_SCALE = 1.2
+
+# Starting SD of the component guide's LogNormal on σ_s (log units).
+_GUIDE_SIGMA_SCALE_INIT = 0.1
+
+
+def ddG_sigma_fixed(priors):
+    """The held σ_s (kT) from a ModelPriors, or 0.0 when σ_s is sampled."""
+    return float(getattr(priors, "theta_ddG_sigma_fixed", 0.0))
+
+
+def ddG_sigma_site(name="theta"):
+    """Name of the per-state σ_s sample site."""
+    return f"{name}_ddG_sigma"
 
-def sample_ddG(name, struct_names, num_mut, nn_means):
+
+def dlnK_site(name="theta"):
+    """Name of the per-mutation Δln K sample site (M × C)."""
+    return f"{name}_dlnK"
+
+
+def projection(proj, proj_u=None):
     """
-    Sample per-mutation per-structure ΔΔG values with NN-predicted prior means.
+    The full projection from per-state ΔΔG to the sampled constants.
 
-    The per-structure trust scale σ_s is learned via ``pyro.param`` and is
-    shared across all mutations within a structure (but differs between
-    structures).
+    Parameters
+    ----------
+    proj : array, shape (C0, S)
+        The folded-state rows (``mwc.PROJ_C4`` or ``mwc.PROJ_C12``).
+    proj_u : array, shape (S,), optional
+        The unfolding row of a U1 model (``mwc.PROJ_U_*``), appended last.
+
+    Returns
+    -------
+    np.ndarray, shape (C, S)
+    """
+    P = np.asarray(proj, dtype=float)
+    if proj_u is not None:
+        P = np.vstack([P, np.asarray(proj_u, dtype=float).reshape(1, -1)])
+    return P
+
+
+def ddG_per_state_identified(proj):
+    """True when per-state ΔΔG follows from Δln K exactly (P square, invertible)."""
+    P = np.asarray(proj, dtype=float)
+    return bool(P.shape[0] == P.shape[1]
+                and np.linalg.matrix_rank(P) == P.shape[0])
+
+
+def identifiable_sigma(proj):
+    """
+    Whether every σ_s is identified by the covariance P · diag(σ²) · Pᵀ.
+
+    The covariance's upper-triangle entries are linear in σ², with
+    coefficient P[i, s]·P[j, s]; σ is identified when that (C(C+1)/2 × S)
+    matrix has rank S.
+
+    Returns
+    -------
+    bool
+    """
+    P = np.asarray(proj, dtype=float)
+    C, S = P.shape
+    iu = np.triu_indices(C)
+    design = np.stack([np.outer(P[:, s], P[:, s])[iu] for s in range(S)],
+                      axis=1)
+    return bool(np.linalg.matrix_rank(design) == S)
+
+
+def constant_scale_tril(proj, sigma):
+    """
+    Lower Cholesky factor of P · diag(σ²) · Pᵀ.
+
+    Computed from the QR decomposition of A = (P · diag(σ))ᵀ (S × C,
+    S ≥ C): A = Q R gives Aᵀ A = Rᵀ R, so L = Rᵀ with the signs of R's rows
+    made positive.  This avoids forming the covariance and squaring its
+    condition number.
+
+    Parameters
+    ----------
+    proj : array, shape (C, S)
+    sigma : array, shape (S,)
+
+    Returns
+    -------
+    jnp.ndarray, shape (C, C)
+    """
+    A = (jnp.asarray(proj) * sigma[None, :]).T          # (S, C)
+    _, r = jnp.linalg.qr(A)                             # r: (C, C)
+    d = jnp.diagonal(r)
+    sign = jnp.where(d < 0, -1.0, 1.0)
+    return (r * sign[:, None]).T
+
+
+def posthoc_ddG(dlnK, prior_means, sigma, proj):
+    """
+    Per-state ΔΔG implied by Δln K.
+
+    U1 (square P): ΔΔG = P⁻¹ · Δln K exactly.  U0: the conditional mean
+    under the per-state prior, μ + Σ Pᵀ (P Σ Pᵀ)⁻¹ (Δln K − P μ) with
+    Σ = diag(σ²); its common-mode component comes from the prior means.
+
+    Parameters
+    ----------
+    dlnK : array, shape (M, C)
+    prior_means : array, shape (M, S)
+    sigma : array, shape (S,)
+    proj : np.ndarray, shape (C, S)
+        A concrete (non-traced) array.
+
+    Returns
+    -------
+    jnp.ndarray, shape (M, S)
+    """
+    P = np.asarray(proj, dtype=float)
+    dlnK = jnp.asarray(dlnK)
+    if ddG_per_state_identified(P):
+        return dlnK @ jnp.asarray(np.linalg.inv(P).T, dtype=dlnK.dtype)
+    Pj = jnp.asarray(P, dtype=dlnK.dtype)
+    resid = dlnK - prior_means @ Pj.T                   # (M, C)
+    L = constant_scale_tril(Pj, sigma)
+    x = jsl.cho_solve((L, True), resid.T).T             # (M, C)
+    return prior_means + (x @ Pj) * (sigma ** 2)[None, :]
+
+
+def sample_dlnK(name, struct_names, num_mut, prior_means, proj,
+                sigma_loc=DEFAULT_DDG_SIGMA_LOC,
+                sigma_scale=DEFAULT_DDG_SIGMA_SCALE, sigma_fixed=0.0):
+    """
+    Sample σ_s per state and the per-mutation Δln K under the structural prior.
 
     Parameters
     ----------
     name : str
-        Prefix for all Numpyro sites and params.
+        Prefix for all Numpyro sites.
     struct_names : sequence of str, length S
-        Structure identifiers (used only to determine S).
+        State names (only S is used).
     num_mut : int
         Number of mutations M.
-    nn_means : jnp.ndarray, shape (M, S)
-        NN-predicted prior means for each mutation and structure.
+    prior_means : jnp.ndarray, shape (M, S)
+        Per-state ΔΔG prior means (the ΔΔG table or the MLP output).
+    proj : array, shape (C, S)
+        Projection from per-state ΔΔG to the constants (:func:`projection`).
+    sigma_loc, sigma_scale : float
+        LogNormal prior on σ_s, in log kT (the models' priors
+        ``theta_ddG_sigma_loc`` and ``theta_ddG_sigma_scale``).
+    sigma_fixed : float, default 0.0
+        If > 0, every σ_s is held at this value (kT): ``{name}_ddG_sigma``
+        is a deterministic site and the LogNormal prior is unused.  0
+        samples σ_s.  A Python float (static under jit).
 
     Returns
     -------
-    jnp.ndarray, shape (M, S)
-        Sampled ΔΔG values.
+    jnp.ndarray, shape (M, C)
+        Sampled Δln K, constants in the row order of ``proj``.
     """
     S = len(struct_names)
+    proj = np.asarray(proj, dtype=float)    # concrete, also under jit
+    P = jnp.asarray(proj, dtype=jnp.float32)
+    C = P.shape[0]
+
+    sigma_fixed = float(sigma_fixed)
+    if sigma_fixed > 0:
+        sigma_s = pyro.deterministic(ddG_sigma_site(name),
+                                     jnp.full(S, sigma_fixed, dtype=P.dtype))
+    else:
+        with pyro.plate(f"{name}_struct_plate", S, dim=-1):
+            sigma_s = pyro.sample(ddG_sigma_site(name),
+                                  dist.LogNormal(sigma_loc, sigma_scale))   # (S,)
+
+    pyro.deterministic(f"{name}_ddG_prior_mean", prior_means)
+
+    # A wt-only library (num_mut == 0, e.g. a stratified binding pool)
+    # skips the site, since numpyro.plate needs size > 0; the guides skip
+    # it the same way.
+    if num_mut == 0:
+        pyro.deterministic(f"{name}_ddG", jnp.zeros((0, S)))
+        return jnp.zeros((0, C))
+
+    loc = prior_means @ P.T                                   # (M, C)
+    scale_tril = constant_scale_tril(P, sigma_s)              # (C, C)
+    # validate_args=False lets the simulator condition σ_s to exactly 0
+    # (truth = table), where scale_tril is singular; a fit only ever sees
+    # σ_s > 0 from the LogNormal.
+    with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
+        dlnK = pyro.sample(dlnK_site(name),
+                           dist.MultivariateNormal(loc, scale_tril=scale_tril,
+                                                   validate_args=False))  # (M, C)
+
+    pyro.deterministic(f"{name}_ddG",
+                       posthoc_ddG(dlnK, prior_means, sigma_s, proj))
+    return dlnK
+
+
+def guide_dlnK(name, num_mut, num_struct, proj, prior_means_init,
+               sigma_fixed=0.0):
+    """
+    Component-guide block matching :func:`sample_dlnK`.
+
+    Mean field: σ_s ~ LogNormal(``{name}_ddG_sigma_locs``,
+    ``{name}_ddG_sigma_scales``) per state, and Δln K ~
+    Normal(``{name}_dlnK_locs``, ``{name}_dlnK_scales``) per mutation and
+    constant (an event of size C, matching the model's MultivariateNormal).
+    The names follow the ``{site}_loc(s)``/``{site}_scale(s)`` convention
+    that ``RunInference.component_guide_start`` maps a MAP onto.
+
+    Parameters
+    ----------
+    name : str
+        Site-name prefix.
+    num_mut, num_struct : int
+        M and S.
+    proj : array, shape (C, S)
+    prior_means_init : array, shape (M, S)
+        Per-state prior means at the start of the fit; the Δln K locations
+        start at P · prior_means_init.
+    sigma_fixed : float, default 0.0
+        If > 0 (σ_s held in the model), no σ_s parameters or site.
+
+    Returns
+    -------
+    jnp.ndarray, shape (M, C)
+        The guide's Δln K draw.
+    """
+    S = num_struct
+    P = jnp.asarray(proj, dtype=jnp.float32)
+    C = P.shape[0]
+    if not float(sigma_fixed) > 0:
+        sigma_locs = pyro.param(f"{name}_ddG_sigma_locs", jnp.zeros(S))
+        sigma_scales = pyro.param(f"{name}_ddG_sigma_scales",
+                                  jnp.full(S, _GUIDE_SIGMA_SCALE_INIT),
+                                  constraint=constraints.positive)
+        with pyro.plate(f"{name}_struct_plate", S, dim=-1):
+            pyro.sample(ddG_sigma_site(name),
+                        dist.LogNormal(sigma_locs, sigma_scales))
+    if num_mut == 0:
+        return jnp.zeros((0, C))
+    loc_init = jnp.asarray(prior_means_init, dtype=jnp.float32) @ P.T
+    dlnK_locs = pyro.param(f"{name}_dlnK_locs", loc_init)
+    dlnK_scales = pyro.param(f"{name}_dlnK_scales", jnp.ones((num_mut, C)),
+                             constraint=constraints.positive)
+    with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
+        return pyro.sample(dlnK_site(name),
+                           dist.Normal(dlnK_locs, dlnK_scales).to_event(1))
+
+
+def dlnK_guesses(name, prior_means, proj):
+    """
+    Starting values: Δln K at P · prior means, σ_s at 1 kT.
+
+    The σ_s entry is unused when σ_s is held (no sample site matches it).
+
+    Parameters
+    ----------
+    name : str
+        Site-name prefix.
+    prior_means : array, shape (M, S)
+        The per-state prior means at the start of the fit (the ΔΔG table for
+        PddG; zeros for PnnC, whose MLP output starts at 0).
+    proj : array, shape (C, S)
+    """
+    prior_means = jnp.asarray(prior_means, dtype=jnp.float32)
+    P = jnp.asarray(proj, dtype=jnp.float32)
+    return {dlnK_site(name): prior_means @ P.T,
+            ddG_sigma_site(name): jnp.ones(prior_means.shape[1])}
+
+
+def per_state_truth(prior_means, sigma, proj, rng_key):
+    """
+    Draw per-state ΔΔG truth and its projection (simulator).
+
+    ΔΔG = prior_mean + σ_s · z, z ~ Normal(0, 1) per mutation and state, and
+    Δln K = P · ΔΔG, which is distributed exactly as the model's
+    MultivariateNormal on Δln K.  The simulator conditions ``{name}_dlnK``
+    on the projection, so the per-state truth it writes is the draw itself
+    (not the post-hoc ΔΔG, which in U0 cannot recover the common shift).
+
+    Parameters
+    ----------
+    prior_means : array, shape (M, S)
+    sigma : array, shape (S,)
+        σ_s; 0 gives the prior means exactly.
+    proj : array, shape (C, S)
+    rng_key : jax.random.PRNGKey
+
+    Returns
+    -------
+    ddG : np.ndarray, shape (M, S)
+    dlnK : np.ndarray, shape (M, C)
+    """
+    prior_means = np.asarray(prior_means, dtype=float)
+    z = np.asarray(jax.random.normal(rng_key, prior_means.shape))
+    ddG = prior_means + np.asarray(sigma, dtype=float)[None, :] * z
+    dlnK = ddG @ np.asarray(proj, dtype=float).T
+    return ddG.astype(np.float32), dlnK.astype(np.float32)
+
+
+def ddG_extract_specs(ctx, struct_names, name="theta"):
+    """
+    ``get_extract_specs`` entries for the per-state ΔΔG sites.
+
+    The (M, S) deterministic sites ``{name}_ddG`` (post-hoc ΔΔG; see the
+    module docstring) and ``{name}_ddG_prior_mean`` become one table per
+    state, ``ddG_{structure}`` and ``ddG_prior_mean_{structure}``
+    (``tfs-extract-params`` writes ``{out_prefix}_ddG_H.csv`` and so on),
+    keyed by ``mutation``.  The names match the simulator's
+    ``ddG_{structure}`` truth columns in ``*_sim_mutation_parameters.csv``,
+    which ``tfs-summarize-fit`` joins on ``mutation`` (and flags for U0
+    models, whose per-state ΔΔG common mode comes from the prior).  The (S,)
+    site ``{name}_ddG_sigma`` becomes one table, ``ddG_sigma``, with one row
+    per state keyed by ``structure``; it matches the simulator's
+    ``*_sim_ddG_sigma.csv`` truth.  With σ_s held the site is deterministic
+    and the table gives the held value.
+
+    Parameters
+    ----------
+    ctx : ExtractionContext
+        Supplies ``mut_labels``.
+    struct_names : sequence of str
+        The state axis of the sites, i.e. the model's ``STRUCTURE_NAMES``.
+    name : str, default "theta"
+        Site-name prefix.
+    """
+    import pandas as pd
 
-    # Per-structure trust scale — learned, not sampled.
-    # Initialised to 1.0 so the model starts between deterministic-NN and flat.
-    sigma_s = pyro.param(
-        f"{name}_ddG_sigma_s",
-        jnp.ones(S),
-        constraint=constraints.positive,
-    )  # (S,)
-
-    # Sample offsets in nested plates → shape (S, M)
-    with pyro.plate(f"{name}_struct_plate", S, dim=-2):
-        with pyro.plate(f"{name}_mut_plate", num_mut, dim=-1):
-            offsets = pyro.sample(
-                f"{name}_ddG_offset",
-                dist.Normal(0.0, 1.0),
-            )  # (S, M)
-
-    # Apply per-structure scale and add NN prior means
-    # sigma_s[:, None] broadcasts (S,1) over (S, M)
-    # nn_means.T converts (M, S) → (S, M) for consistent (S, M) arithmetic
-    ddG_SM = nn_means.T + sigma_s[:, None] * offsets   # (S, M)
-
-    pyro.deterministic(f"{name}_ddG", ddG_SM.T)
-    return ddG_SM.T   # (M, S)
+    mut_labels = list(ctx.mut_labels)
+    num_mut, S = len(mut_labels), len(struct_names)
+    specs = []
+    for s, struct in enumerate(struct_names):
+        # Row-major flattening of (M, S): element (m, s) is m * S + s.
+        mut_df = pd.DataFrame({
+            "mutation": mut_labels,
+            "map_ddG": [m * S + s for m in range(num_mut)],
+        })
+        # Separate specs, so a posterior file without one of the sites
+        # still gives the other's tables.
+        for site in ("ddG", "ddG_prior_mean"):
+            specs.append(dict(
+                input_df=mut_df,
+                params_to_get=[site],
+                out_names=[f"{site}_{struct}"],
+                map_column="map_ddG",
+                get_columns=["mutation"],
+                in_run_prefix=f"{name}_",
+            ))
+    specs.append(dict(
+        input_df=pd.DataFrame({"structure": list(struct_names),
+                               "map_struct": range(S)}),
+        params_to_get=["ddG_sigma"],
+        out_names=["ddG_sigma"],
+        map_column="map_struct",
+        get_columns=["structure"],
+        in_run_prefix=f"{name}_",
+    ))
+    return specs
diff --git a/src/tfscreen/tfmodel/generative/components/theta_rescale/logit.py b/src/tfscreen/tfmodel/generative/components/theta_rescale/logit.py
index ffe506a2..f7072b67 100644
--- a/src/tfscreen/tfmodel/generative/components/theta_rescale/logit.py
+++ b/src/tfscreen/tfmodel/generative/components/theta_rescale/logit.py
@@ -9,6 +9,6 @@ def rescale(theta: jnp.ndarray) -> jnp.ndarray:
 
     Clips theta to [_EPS, 1 - _EPS] before applying the logit so that the
     output is always finite.  The same clipping pattern is used throughout
-    the rest of the theta pipeline (e.g. _congression.py, hill.py).
+    the rest of the theta pipeline (e.g. hill.py).
     """
     return jax.scipy.special.logit(jnp.clip(theta, _EPS, 1.0 - _EPS))
diff --git a/src/tfscreen/tfmodel/generative/components/transformation/_classes.py b/src/tfscreen/tfmodel/generative/components/transformation/_classes.py
new file mode 100644
index 00000000..67af7d4b
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/components/transformation/_classes.py
@@ -0,0 +1,43 @@
+"""
+The cell classes a transformation component hands to ``jax_model``.
+"""
+
+from typing import NamedTuple
+
+import jax.numpy as jnp
+
+
+class CellClasses(NamedTuple):
+    """
+    A genotype's cells split into classes that each grow at their own rate.
+
+    Every field has a leading class axis of size C (the clean class first);
+    the remaining axes follow the growth layout and broadcast against
+    ``(replicate, time, condition_pre, condition_sel, titrant_name,
+    titrant_conc, batch)``.
+
+    Attributes
+    ----------
+    theta : jnp.ndarray
+        Cell-level operator occupancy, before ``theta_rescale``.
+    activity : jnp.ndarray
+        Cell-level TF activity.
+    dk_geno : jnp.ndarray
+        Cell-level pleiotropic growth effect.
+    log_weight : jnp.ndarray
+        Log fraction of the genotype's cells in each class; sums (in linear
+        space) to 1 over the class axis.
+    """
+
+    theta: jnp.ndarray
+    activity: jnp.ndarray
+    dk_geno: jnp.ndarray
+    log_weight: jnp.ndarray
+
+
+def single_class(theta, activity, dk_geno) -> CellClasses:
+    """One class holding every cell (no congression)."""
+    return CellClasses(theta=theta[None],
+                       activity=jnp.asarray(activity)[None],
+                       dk_geno=jnp.asarray(dk_geno)[None],
+                       log_weight=jnp.zeros((1,) + (1,) * jnp.ndim(theta)))
diff --git a/src/tfscreen/tfmodel/generative/components/transformation/_congression.py b/src/tfscreen/tfmodel/generative/components/transformation/_congression.py
deleted file mode 100644
index f17c9236..00000000
--- a/src/tfscreen/tfmodel/generative/components/transformation/_congression.py
+++ /dev/null
@@ -1,554 +0,0 @@
-import math
-
-import jax
-import jax.numpy as jnp
-import numpyro as pyro
-import numpyro.distributions as dist
-from flax.struct import (
-    dataclass,
-    field
-)
-from tfscreen.tfmodel.data_class import GrowthData
-
-def _logit_normal_cdf(x, mu, sigma):
-    """
-    Cumulative Distribution Function for Logit-Normal distribution:
-    F(x) = Phi((logit(x) - mu) / sigma)
-    
-    Parameters
-    ----------
-    x : jax.array
-        occupancy value between 0 and 1
-    mu : float
-        Mean of the underlying Normal distribution in logit-space.
-    sigma : float
-        Standard deviation of the underlying Normal distribution in logit-space.
-
-    Returns
-    -------
-    jax.array
-        cdf array
-    """
-    # Clip x to [eps, 1-eps] for stability in logit
-    eps = 1e-6
-    x_safe = jnp.clip(x, eps, 1.0 - eps)
-    
-    # Calculate logit(x)
-    logit_x = jax.scipy.special.logit(x_safe)
-    
-    # Return Phi((logit(x) - mu) / sigma)
-    return jax.scipy.stats.norm.cdf(logit_x, loc=mu, scale=sigma)
-
-
-def _empirical_cdf(theta, t_grid):
-    """
-    Cumulative Distribution Function estimated from the observed population
-    of theta values.
-    """
-    n = theta.shape[-1]
-    sorted_theta = jnp.sort(theta, axis=-1)
-    
-    # Empirical CDF values at the sorted points.
-    # We use (i + 0.5) / n to be unbiased for a continuous distribution.
-    y = (jnp.arange(n) + 0.5) / n
-    
-    # Interpolate to the integration grid
-    shape = theta.shape[:-1]
-    flat_sorted = sorted_theta.reshape(-1, n)
-    
-    def get_cdf(s):
-        return jnp.interp(t_grid, s, y)
-    
-    flat_cdf = jax.vmap(get_cdf)(flat_sorted)
-    return flat_cdf.reshape(shape + (len(t_grid),))
-
-
-def update_thetas(theta, params, theta_dist=None, mask=None, n_grid=256,
-                  population_theta=None):
-    """
-    Corrects theta values for co-transformation using the method of
-    re-sampling from the background distribution.
-
-    Parameters
-    ----------
-    theta : jnp.array
-        Array of theta values to correct.
-        Expected shape: (..., num_genotype)
-    params : tuple
-        Tuple of parameters defining the background distribution.
-        If theta_dist is "logit_norm" (default): (lam, mu, sigma)
-        If theta_dist is "empirical": (lam,)
-    theta_dist : str, optional
-        One of "logit_norm" or "empirical".
-        If None, the distribution is inferred from the length of params.
-    mask : jnp.array, optional
-        Boolean array of shape (num_genotype,) where True indicates the
-        genotype should be corrected for congression. If None, all genotypes
-        are corrected.
-    n_grid : int, optional
-        Number of points for grid-based numerical integration (default 256).
-    population_theta : jnp.array, optional
-        Array of theta values used to build the background (empirical) CDF,
-        expected shape (..., num_population_genotype), broadcastable against
-        ``theta`` on every axis except the last.  Only consulted when
-        ``theta_dist == "empirical"``.  If None (default), ``theta`` itself is
-        used as the population sample, which is only statistically valid when
-        ``theta`` already spans the full genotype population — callers that
-        only ever see a genotype subset or minibatch (e.g. batched training or
-        single-genotype prediction) must pass the true population sample here
-        or the empirical CDF silently degenerates to whatever subset is
-        present, biasing the correction. Ignored for "logit_norm", which uses
-        the smooth analytic CDF in ``params`` instead of raw samples.
-
-    Returns
-    -------
-    jnp.array
-        Corrected theta array with shape broadcasted from inputs.
-    """
-    # Extract parameters. We assume the first is always lam.
-    lam = params[0]
-    bg_params = params[1:]
-
-    # Infer theta_dist if not provided
-    if theta_dist is None:
-        if len(bg_params) == 2:
-            theta_dist = "logit_norm"
-        elif len(bg_params) == 0:
-            theta_dist = "empirical"
-        else:
-            raise ValueError(f"Ambiguous parameter count: {len(params)}. Please specify theta_dist.")
-
-    # Integration grid
-    t_grid = jnp.linspace(0.0, 1.0, n_grid)
-
-    if theta_dist == "logit_norm":
-        # (lam, mu, sigma)
-        b_arrays = jnp.broadcast_arrays(*params[:3])
-        flat_params = [jnp.reshape(a, -1) for a in b_arrays]
-        num_p_batches = flat_params[0].shape[0]
-        integration_shape = b_arrays[0].shape
-        flat_lam = flat_params[0]
-        
-        Ft_grid = jax.vmap(lambda m, s: _logit_normal_cdf(t_grid, m, s))(flat_params[1], flat_params[2])
-        
-    elif theta_dist == "empirical":
-        # Build the background CDF from population_theta when supplied (the
-        # full genotype population) rather than theta itself (which may only
-        # cover a training minibatch or a handful of requested genotypes).
-        # See the population_theta docstring above.
-        pop_theta = theta if population_theta is None else population_theta
-
-        Ft_grid_raw = _empirical_cdf(pop_theta, t_grid)
-        num_p_batches = Ft_grid_raw.reshape(-1, n_grid).shape[0]
-
-        # Broadcast lambda to match the population's leading (non-genotype)
-        # dimensions, which must agree with theta's leading dimensions.
-        lam_b = jnp.broadcast_to(lam, pop_theta.shape[:-1])
-        flat_lam = lam_b.reshape(-1)
-
-        Ft_grid = Ft_grid_raw.reshape(-1, n_grid)
-        integration_shape = pop_theta.shape[:-1]
-
-    else:
-        raise ValueError(f"Unsupported theta_dist: {theta_dist}")
-
-    # Calculate integrand: exp(lam * (F(t) - 1))
-    # integrand_grid shape: (num_p_batches, n_grid)
-    integrand_grid = jnp.exp(flat_lam[:, None] * (Ft_grid - 1.0))
-    
-    # Cumulative integration using trapezoidal rule
-    # J(x) = integrate_0^x I(t) dt
-    h = 1.0 / (n_grid - 1)
-    f_mid = (integrand_grid[:, :-1] + integrand_grid[:, 1:]) * h / 2.0
-    J_grid = jnp.concatenate([jnp.zeros((num_p_batches, 1)), jnp.cumsum(f_mid, axis=1)], axis=1)
-    
-    # Expected value calculation part 1: G(x) = integrate_x^1 I(t) dt
-    # G(x) = J(1) - J(x)
-    G1 = J_grid[:, -1:]
-    Gx_grid_p = G1 - J_grid
-    
-    # Reshape Gx_grid back to broadcasting-ready shape
-    # If integration_shape has a trailing 1 (genotype dim), remove it so we can broadcast
-    # to (target_shape + n_grid) correctly.
-    if len(integration_shape) > 0 and integration_shape[-1] == 1:
-        res_batch_shape = integration_shape[:-1]
-    else:
-        res_batch_shape = integration_shape
-        
-    Gx_grid_final = Gx_grid_p.reshape(res_batch_shape + (n_grid,))
-    
-    # 3. Broadcast integration results to match theta's batch dimensions
-    target_shape = theta.shape[:-1]
-    num_genotypes = theta.shape[-1]
-    
-    # Gx_grid_final is already (integration_shape, n_grid)
-    # If integration_shape matches target_shape, we are good.
-    Gx_grid_b = jnp.broadcast_to(Gx_grid_final, target_shape + (n_grid,))
-    
-    # 4. Interpolate G(x) for each genotype in the full batch
-    flat_theta = theta.reshape(-1, num_genotypes)
-    flat_Gx = Gx_grid_b.reshape(-1, n_grid)
-    
-    def interp_row(g_row, th_row):
-        return jnp.interp(th_row, t_grid, g_row)
-    
-    integral_vals = jax.vmap(interp_row)(flat_Gx, flat_theta)
-    
-    # Expected observed value E[max(x, M)] = 1 - G(x)
-    corrected_flat = 1.0 - integral_vals
-    
-    # Reshape back to original dimensions
-    corrected_theta = corrected_flat.reshape(target_shape + (num_genotypes,))
-
-    # 5. Apply mask if provided
-    if mask is not None:
-        corrected_theta = jnp.where(mask, corrected_theta, theta)
-    
-    return corrected_theta
-
-def calculate_expected_observed_max(x_val, mu, sigma, lam, n_grid=100):
-    """
-    Calculate E[max(x, M)] where M is the maximum of a Poisson(lam) number
-    of samples from the background Logit-Normal(mu, sigma) distribution.
-    
-    Use the stable formula:
-    E[max(x, M)] = 1 - integrate_{x}^1 exp(lam * (F(t) - 1)) dt
-
-    Parameters
-    ----------
-    x_val : float
-        the true value of the object (float)
-    mu, sigma : float, float
-        shape parameters of the background population
-    lam : float
-        poisson parameter lambda
-    n_grid : int
-        number of grid points for integration
-
-    Returns
-    -------
-    float
-        expected observed value of x
-    """
-    x_safe = jnp.clip(x_val, 0.0, 1.0)
-    
-    # 1. Integration part: integrate_{x}^1 exp(lam * (F(t) - 1)) dt
-    def integrand(t):
-        Ft = _logit_normal_cdf(t, mu, sigma)
-        return jnp.exp(lam * (Ft - 1.0))
-
-    t_grid = jnp.linspace(x_safe, 1.0, n_grid)
-    y_vals = integrand(t_grid)
-    integral_val = jnp.trapezoid(y_vals, t_grid)
-    
-    return 1.0 - integral_val
-
-
-def calculate_expected_observed_min(x_val, mu, sigma, lam, n_grid=100):
-    """
-    Calculate E[min(x, M)] where M is the minimum of a Poisson(lam) number
-    of samples from the background Logit-Normal(mu, sigma) distribution.
-    
-    Use the stable formula:
-    E[min(x, M)] = integrate_{0}^x exp(-lam * F(t)) dt
-
-    Parameters
-    ----------
-    x_val : float
-        the true value of the object (float)
-    mu, sigma : float, float
-        shape parameters of the background population
-    lam : float
-        poisson parameter lambda
-    n_grid : int
-        number of grid points for integration
-
-    Returns
-    -------
-    float
-        expected observed value of x
-    """
-    x_safe = jnp.clip(x_val, 0.0, 1.0)
-    
-    # 1. Integration part: integrate_{0}^x exp(-lam * F(t)) dt
-    def integrand(t):
-        Ft = _logit_normal_cdf(t, mu, sigma)
-        return jnp.exp(-lam * Ft)
-
-    t_grid = jnp.linspace(0.0, x_safe, n_grid)
-    y_vals = integrand(t_grid)
-    integral_val = jnp.trapezoid(y_vals, t_grid)
-    
-    return integral_val
-
-
-@dataclass(frozen=True)
-class ModelPriors:
-    """
-    JAX Pytree holding data needed to specify model priors.
-
-    Attributes
-    ----------
-    lam_loc : float
-        Mean of the LogNormal prior for lambda.
-    lam_scale : float
-        Scale (sigma) of the LogNormal prior for lambda.
-    mu_anchoring_scale : float
-        Scale factor for the consistency prior anchoring mu_bg to prior_mu.
-    sigma_anchoring_scale : float
-        Scale factor for the consistency prior anchoring sigma_bg to prior_sigma.
-    """
-
-    lam_loc: float
-    lam_scale: float
-    mu_anchoring_scale: float
-    sigma_anchoring_scale: float
-    mode: str = field(default="logit_norm", pytree_node=False)
-    
-def define_model(name: str, 
-                 data: GrowthData, 
-                 priors: ModelPriors,
-                 anchors: tuple = None) -> tuple:
-    """
-    Defines the hierarchical model for the co-transformation parameter (lambda)
-    and background parameters (mu, sigma, etc.).
-
-    Parameters
-    ----------
-    name : str
-        The prefix for the Numpyro sample sites.
-    data : GrowthData
-        A data object containing metadata.
-    priors : ModelPriors
-        A Pytree containing the hyperparameters.
-    anchors : tuple, optional
-        (prior_mu, prior_sigma) expected population moments from the theta model.
-        Shape: (num_titrant_name, num_titrant_conc, 1)
-
-    Returns
-    -------
-    tuple
-        (lam, ...) sampled values depending on priors.mode.
-    """
-    
-    # Global shared lambda
-    lam = pyro.sample(
-        f"{name}_lam",
-        dist.LogNormal(priors.lam_loc, priors.lam_scale)
-    )
-    pyro.deterministic(f"{name}_lam_value", lam)
-
-    if priors.mode == "empirical":
-        return (lam,)
-
-    if anchors is None:
-        anc_mu = 0.0
-        anc_sigma = 1.0
-    else:
-        anc_mu, anc_sigma = anchors
-
-    if priors.mode == "logit_norm":
-        # Nested plates for titrant_name and titrant_conc
-        with pyro.plate(f"{name}_titrant_name_plate", data.num_titrant_name, dim=-3):
-            with pyro.plate(f"{name}_titrant_conc_plate", data.num_titrant_conc, dim=-2):
-        
-                mu = pyro.sample(
-                    f"{name}_mu",
-                    dist.Normal(anc_mu, priors.mu_anchoring_scale)
-                )
-                pyro.deterministic(f"{name}_mu_value", mu)
-
-                sigma = pyro.sample(
-                    f"{name}_sigma",
-                    dist.LogNormal(jnp.log(anc_sigma), priors.sigma_anchoring_scale)
-                )
-                pyro.deterministic(f"{name}_sigma_value", sigma)
-        
-    return lam, mu, sigma
-
-def guide(name: str, 
-          data: GrowthData, 
-          priors: ModelPriors,
-          anchors: tuple = None) -> tuple:
-    """
-    Guide corresponding to the co-transformation model.
-    Learns variational posteriors for lam, mu, and sigma.
-
-    Parameters
-    ----------
-    name : str
-        The prefix for the Numpyro sample sites.
-    data : GrowthData
-        A data object containing metadata.
-    priors : ModelPriors
-        A Pytree containing the hyperparameters for initialization.
-    anchors : tuple, optional
-        Population anchors (dummy here, but needed for interface).
-
-    Returns
-    -------
-    tuple
-        (lam, ...) sampled values from guide.
-    """
-    
-    # Variational approximation for lambda
-    h_lam_loc = pyro.param(f"{name}_lam_loc", jnp.array(priors.lam_loc))
-    h_lam_scale = pyro.param(f"{name}_lam_scale", jnp.array(priors.lam_scale),
-                             constraint=dist.constraints.positive)
-    lam = pyro.sample(f"{name}_lam", dist.LogNormal(h_lam_loc, h_lam_scale))
-
-    if priors.mode == "empirical":
-        return (lam,)
-
-    # Variational approximation for mu and sigma (plated)
-    local_shape = (data.num_titrant_name, data.num_titrant_conc, 1)
-
-    if anchors is None:
-        init_mu = 0.0
-        init_sigma = 1.0
-    else:
-        init_mu, init_sigma = anchors
-
-    if priors.mode == "logit_norm":
-        h_mu_loc = pyro.param(f"{name}_mu_loc", jnp.full(local_shape, init_mu))
-        h_mu_scale = pyro.param(f"{name}_mu_scale", jnp.full(local_shape, 0.1),
-                                constraint=dist.constraints.positive)
-        
-        h_sigma_loc = pyro.param(f"{name}_sigma_loc", jnp.full(local_shape, jnp.log(init_sigma)))
-        h_sigma_scale = pyro.param(f"{name}_sigma_scale", jnp.full(local_shape, 0.1),
-                                   constraint=dist.constraints.positive)
-
-        with pyro.plate(f"{name}_titrant_name_plate", data.num_titrant_name, dim=-3):
-            with pyro.plate(f"{name}_titrant_conc_plate", data.num_titrant_conc, dim=-2):
-                
-                mu = pyro.sample(f"{name}_mu", dist.Normal(h_mu_loc, h_mu_scale))
-                sigma = pyro.sample(f"{name}_sigma", dist.LogNormal(h_sigma_loc, h_sigma_scale))
-
-        return lam, mu, sigma
-
-    raise ValueError(f"Unsupported mode: {priors.mode}")
-
-
-def get_hyperparameters(lam_mean=None, lam_std=None):
-    """
-    Gets default values for the model hyperparameters.
-
-    Parameters
-    ----------
-    lam_mean : float, optional
-        Experimentally measured mean of the congression Poisson rate
-        lambda, in linear (arithmetic) space. Must be provided together
-        with ``lam_std``.
-    lam_std : float, optional
-        Experimentally measured standard deviation of lambda, in linear
-        (arithmetic) space. Must be provided together with ``lam_mean``.
-
-    Returns
-    -------
-    dict
-        A dictionary of hyperparameter names and their default values.
-
-    Raises
-    ------
-    ValueError
-        If exactly one of ``lam_mean``/``lam_std`` is given, or either is
-        not strictly positive.
-    """
-
-    if (lam_mean is None) != (lam_std is None):
-        raise ValueError(
-            "lam_mean and lam_std must be provided together (or neither)."
-        )
-
-    parameters = {}
-
-    if lam_mean is not None:
-        if lam_mean <= 0:
-            raise ValueError(f"lam_mean must be > 0; got {lam_mean}")
-        if lam_std <= 0:
-            raise ValueError(f"lam_std must be > 0; got {lam_std}")
-
-        # Moment-match the experimentally measured (mean, std) -- in linear
-        # space, as an experimentalist would report them -- onto the
-        # LogNormal prior's underlying-Normal parameters.
-        sigma2 = math.log(1.0 + (lam_std / lam_mean) ** 2)
-        parameters["lam_loc"] = math.log(lam_mean) - sigma2 / 2.0
-        parameters["lam_scale"] = math.sqrt(sigma2)
-    else:
-        # Placeholder only -- never used by tfs-configure-model /
-        # tfs-fit-model, which require an experimentally measured lam_mean/
-        # lam_std for this component (see ModelOrchestrator/configure_model).
-        # Exists so this module stays independently constructible/testable,
-        # mirroring dk_geno.pinned's placeholder convention.
-        parameters["lam_loc"] = 0.0
-        parameters["lam_scale"] = 1.0
-
-    # Anchoring scales (tightening this makes the background track the prior more closely)
-    parameters["mu_anchoring_scale"] = 0.5
-    parameters["sigma_anchoring_scale"] = 0.2
-
-    # Background model mode: 'logit_normal' (default) or 'empirical'
-    parameters["mode"] = "logit_norm"
-
-    return parameters
-
-
-def get_guesses(name, data, lam_mean=None):
-    """
-    Gets initial guess values for model parameters.
-
-    These are used to initialize the MCMC sampler (e.g., via
-    ``numpyro.infer.init_to_value``).
-
-    Parameters
-    ----------
-    name : str
-        The prefix for the parameter names (e.g., "theta").
-    data : DataClass
-        A data object containing metadata, primarily:
-        - ``data.num_genotype`` : (int) number of non-wt genotypes
-    lam_mean : float, optional
-        Experimentally measured mean of lambda (linear space); used
-        directly as the initial guess. If omitted, a placeholder guess is
-        used (see ``get_hyperparameters``).
-
-    Returns
-    -------
-    dict
-        A dictionary mapping parameter names to their initial
-        guess values.
-    """
-
-
-    guesses = {}
-    guesses[f"{name}_lam"] = lam_mean if lam_mean is not None else 1.0
-
-    # Only add these if we are not in empirical mode.
-    # NOTE: get_guesses currently doesn't know the mode from the config easily
-    # during initialization in model_orchestrator.py unless we pass it.
-    # For now, we'll keep them as guesses; they just won't be used if not
-    # in the model/guide.
-    guesses[f"{name}_mu"] = 0.0
-    guesses[f"{name}_sigma"] = 1.0
-
-    return guesses
-
-def get_priors(lam_mean=None, lam_std=None):
-    """
-    Utility function to create a populated ModelPriors object.
-
-    Parameters
-    ----------
-    lam_mean : float, optional
-        Experimentally measured mean of lambda (linear space). See
-        ``get_hyperparameters``.
-    lam_std : float, optional
-        Experimentally measured standard deviation of lambda (linear
-        space). See ``get_hyperparameters``.
-
-    Returns
-    -------
-    ModelPriors
-        A populated Pytree (Flax dataclass) of hyperparameters.
-    """
-    return ModelPriors(**get_hyperparameters(lam_mean=lam_mean, lam_std=lam_std))
diff --git a/src/tfscreen/tfmodel/generative/components/transformation/empirical.py b/src/tfscreen/tfmodel/generative/components/transformation/empirical.py
deleted file mode 100644
index 548bc4d2..00000000
--- a/src/tfscreen/tfmodel/generative/components/transformation/empirical.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from functools import partial
-import pandas as pd
-from . import _congression as congression
-
-def get_hyperparameters(lam_mean=None, lam_std=None):
-    """
-    Gets default values for the model hyperparameters, forced to empirical mode.
-
-    Parameters
-    ----------
-    lam_mean, lam_std : float, optional
-        Experimentally measured mean/std of lambda (linear space). See
-        ``_congression.get_hyperparameters``.
-    """
-    parameters = congression.get_hyperparameters(lam_mean=lam_mean, lam_std=lam_std)
-    parameters["mode"] = "empirical"
-    return parameters
-
-def get_priors(lam_mean=None, lam_std=None):
-    """
-    Gets model priors initialized to empirical mode.
-
-    Parameters
-    ----------
-    lam_mean, lam_std : float, optional
-        Experimentally measured mean/std of lambda (linear space). See
-        ``_congression.get_hyperparameters``.
-    """
-    return congression.ModelPriors(**get_hyperparameters(lam_mean=lam_mean, lam_std=lam_std))
-
-# These are mode-agnostic or depend on priors.mode
-get_guesses = congression.get_guesses
-define_model = congression.define_model
-guide = congression.guide
-
-# Bake the theta_dist for the update function
-update_thetas = partial(congression.update_thetas, theta_dist="empirical")
-
-# The empirical background CDF is estimated from raw per-genotype theta
-# samples (see _congression._empirical_cdf), so it is only well-calibrated
-# when built from the full genotype population rather than a training
-# minibatch or a handful of requested genotypes.  jax_model uses this flag to
-# decide whether to compute (or expect an externally supplied)
-# population-wide theta reference before calling update_thetas.
-NEEDS_FULL_POPULATION_THETA = True
-
-
-def get_extract_specs(ctx):
-    lam_df = pd.DataFrame({"parameter": ["lam"], "map_all": [0]})
-    return [dict(
-        input_df=lam_df,
-        params_to_get=["lam"],
-        map_column="map_all",
-        get_columns=["parameter"],
-        in_run_prefix="transformation_",
-    )]
diff --git a/src/tfscreen/tfmodel/generative/components/transformation/logit_norm.py b/src/tfscreen/tfmodel/generative/components/transformation/logit_norm.py
deleted file mode 100644
index 40bd3faa..00000000
--- a/src/tfscreen/tfmodel/generative/components/transformation/logit_norm.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from functools import partial
-import numpy as np
-import pandas as pd
-from . import _congression as congression
-
-def get_hyperparameters(lam_mean=None, lam_std=None):
-    """
-    Gets default values for the model hyperparameters, forced to logit_norm mode.
-
-    Parameters
-    ----------
-    lam_mean, lam_std : float, optional
-        Experimentally measured mean/std of lambda (linear space). See
-        ``_congression.get_hyperparameters``.
-    """
-    parameters = congression.get_hyperparameters(lam_mean=lam_mean, lam_std=lam_std)
-    parameters["mode"] = "logit_norm"
-    return parameters
-
-def get_priors(lam_mean=None, lam_std=None):
-    """
-    Gets model priors initialized to logit_norm mode.
-
-    Parameters
-    ----------
-    lam_mean, lam_std : float, optional
-        Experimentally measured mean/std of lambda (linear space). See
-        ``_congression.get_hyperparameters``.
-    """
-    return congression.ModelPriors(**get_hyperparameters(lam_mean=lam_mean, lam_std=lam_std))
-
-# These are mode-agnostic or depend on priors.mode
-get_guesses = congression.get_guesses
-define_model = congression.define_model
-guide = congression.guide
-
-# Bake the theta_dist for the update function
-update_thetas = partial(congression.update_thetas, theta_dist="logit_norm")
-
-# logit_norm's background CDF is the smooth analytic Logit-Normal(mu, sigma),
-# not a raw-sample empirical CDF, so it needs no population-wide theta
-# reference — mu/sigma already update smoothly with SVI regardless of which
-# genotypes are in the current batch. See empirical.py for the contrasting case.
-NEEDS_FULL_POPULATION_THETA = False
-
-
-def get_extract_specs(ctx):
-    lam_df = pd.DataFrame({"parameter": ["lam"], "map_all": [0]})
-    specs = [dict(
-        input_df=lam_df,
-        params_to_get=["lam"],
-        map_column="map_all",
-        get_columns=["parameter"],
-        in_run_prefix="transformation_",
-    )]
-
-    trans_df = (ctx.growth_tm.df[["titrant_name", "titrant_conc",
-                                   "titrant_name_idx", "titrant_conc_idx"]]
-                .drop_duplicates().copy())
-    conc_dim = np.where(np.array(ctx.growth_tm.tensor_dim_names) == "titrant_conc")[0][0]
-    num_titrant_conc = len(ctx.growth_tm.tensor_dim_labels[conc_dim])
-    trans_df["map_trans"] = (trans_df["titrant_name_idx"] * num_titrant_conc
-                             + trans_df["titrant_conc_idx"])
-    specs.append(dict(
-        input_df=trans_df,
-        params_to_get=["mu", "sigma"],
-        map_column="map_trans",
-        get_columns=["titrant_name", "titrant_conc"],
-        in_run_prefix="transformation_",
-    ))
-
-    return specs
diff --git a/src/tfscreen/tfmodel/generative/components/transformation/mixture.py b/src/tfscreen/tfmodel/generative/components/transformation/mixture.py
new file mode 100644
index 00000000..42ebdd94
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/components/transformation/mixture.py
@@ -0,0 +1,337 @@
+"""
+Congression as an observable-level mixture of clean and congressed cells.
+
+A genotype's cells are a mixture: most carry only its plasmid ("clean"),
+and the rest also carry co-resident plasmids drawn from the bulk library.
+Transformants carry M ~ zero-truncated Poisson(lambda) plasmids (cells that
+took up none are never seen: they do not survive selection), and a cell's
+plasmid copies are shared among the variants it carries, so a cell of M
+plasmids gives each 1/M of its abundance. A genotype's abundance therefore
+comes from cells with n co-residents in proportion to P(M = n + 1), and its
+congressed fraction is
+
+    w_cong = f_g (1 - P(M = 1)),   P(M = 1) = lambda exp(-lambda) / (1 - exp(-lambda))
+
+``lambda`` is the simulator's ``transformation_poisson_lambda``. Each class grows at its own rate, so the classes are mixed at the level of
+``exp(ln_cfu)``, not rates or theta:
+
+    ln_cfu_g(t) = ln_cfu0_g + log sum_c w_c exp(G_c(t))
+
+The congressed term ``E_S exp(G_{g,S}(t))`` over random co-resident sets S is
+evaluated by a fixed quadrature: K co-resident sets per genotype, drawn once
+by ``ModelOrchestrator._draw_coresident_sets`` (``data.coresident_idx``,
+``data.coresident_n``) and stratified by co-resident count. A congressed
+cell's plasmids have equal shares ``x_g = 1/M`` for a cell of M plasmids.
+Its theta follows ``data.congression_theta_rule``, per titrant
+concentration, with ``l_g = logit(theta_g)``:
+
+- ``homodimer`` (default): ``logit(theta_cell) = log sum_g x_g exp(l_g)``;
+- ``heterodimer``: ``logit(theta_cell) = 2 log sum_g x_g exp(l_g / 2)``;
+- ``max``: the highest-theta plasmid sets theta and TF activity.
+
+The homodimer and heterodimer rules require TF activity 1 (checked by
+``ModelOrchestrator``). Its dk_geno follows ``data.congression_dk_rule``, the
+soft-min family ``dk_cell = -(1/alpha) log sum_g x_g exp(-alpha dk_g)``:
+
+- ``dilution`` (default; ``alpha -> 0``): the share-weighted mean;
+- ``softmin``: finite ``alpha = data.congression_dk_alpha > 0`` (in float32
+  its absolute error is ~1e-7 / alpha, so use ``dilution`` for tiny alpha);
+- ``min`` (``alpha -> inf``): the worst variant sets the cell's cost.
+
+All rules match ``simulate/cell_rules.py``.
+
+See ``planning/congression-physics-plan.md`` ("Fit design (step 3)") and
+``planning/studies/congression-estimator/`` for why this estimator.
+"""
+
+import math
+
+import jax
+import jax.numpy as jnp
+import numpyro as pyro
+import numpyro.distributions as dist
+import pandas as pd
+from flax.struct import dataclass
+
+from tfscreen.tfmodel.data_class import GrowthData
+from tfscreen.tfmodel.generative.components.transformation._classes import (
+    CellClasses,
+)
+
+
+# Theta is clipped to [THETA_EPS, 1 - THETA_EPS] before its logit in the
+# partition-function rules (same value as simulate/cell_rules.py).
+THETA_EPS = 1e-6
+
+# power p in logit(theta_cell) = (1/p) log sum_g x_g exp(p l_g)
+_PARTITION_POWER = {"homodimer": 1.0, "heterodimer": 0.5}
+
+
+# The mixture's congressed cells draw co-residents' theta, activity and
+# dk_geno from the whole library, so jax_model must supply library-ordered
+# populations of all three (computed locally in training, or passed in as
+# data.external_*_population by prediction code).
+NEEDS_POPULATION = True
+
+
+@dataclass(frozen=True)
+class ModelPriors:
+    """
+    LogNormal prior on the congression rate lambda (the zero-truncated
+    Poisson rate of plasmids per transformant) (the log-space
+    location and scale).
+    """
+
+    lam_loc: float
+    lam_scale: float
+
+
+def define_model(name: str,
+                 data: GrowthData,
+                 priors: ModelPriors) -> jnp.ndarray:
+    """
+    Sample the global congression rate lambda.
+
+    Returns
+    -------
+    jnp.ndarray
+        lambda (scalar).
+    """
+    lam = pyro.sample(f"{name}_lam",
+                      dist.LogNormal(priors.lam_loc, priors.lam_scale))
+    pyro.deterministic(f"{name}_lam_value", lam)
+    return lam
+
+
+def guide(name: str,
+          data: GrowthData,
+          priors: ModelPriors) -> jnp.ndarray:
+    """
+    LogNormal variational posterior for lambda.
+    """
+    lam_loc = pyro.param(f"{name}_lam_loc", jnp.array(priors.lam_loc))
+    lam_scale = pyro.param(f"{name}_lam_scale", jnp.array(priors.lam_scale),
+                           constraint=dist.constraints.positive)
+    return pyro.sample(f"{name}_lam", dist.LogNormal(lam_loc, lam_scale))
+
+
+def _log_p_congressed(lam):
+    """
+    log(1 - P(M = 1)) for M ~ zero-truncated Poisson(lambda).
+
+    ``1 - P(M = 1) = (e^lam - 1 - lam) / (e^lam - 1)``. The numerator
+    cancels catastrophically for small lambda, so it switches to its series
+    there (both branches are kept finite so the gradient is too).
+    """
+    small = lam < 1e-2
+    lam_big = jnp.where(small, 1.0, lam)
+    numer_big = jnp.expm1(lam_big) - lam_big
+    numer_small = 0.5 * lam**2 * (1.0 + lam / 3.0 + lam**2 / 12.0
+                                   + lam**3 / 60.0)
+    numer = jnp.where(small, numer_small, numer_big)
+    return jnp.log(numer) - jnp.log(jnp.expm1(lam))
+
+
+def _log_class_weights(lam, bulk_fraction, coresident_n):
+    """
+    Log mixture weights, shape (1 + K, B): clean first, then the K sets.
+
+    ``w_cong = f (1 - P(M = 1))`` with M ~ zero-truncated Poisson(lambda);
+    set k (with n_k co-residents, so M = n_k + 1) gets
+    ``w_cong * P(M = n_k + 1 | M - 1 in strata) / K_{n_k}``, the stratum
+    probabilities renormalized over the counts that have sets. Written in
+    log space so the lambda gradient stays finite when ``f = 0`` (those
+    congressed weights are -inf and drop out of the logsumexp).
+    """
+    n = coresident_n.astype(float)                                  # (K,)
+    num_in_stratum = jnp.sum(coresident_n[None, :] == coresident_n[:, None],
+                             axis=1).astype(float)                  # (K,)
+
+    # Unnormalized log P(M = n + 1); constant factors cancel.
+    log_pmf = n * jnp.log(lam) - jax.scipy.special.gammaln(n + 2.0)
+    # Each stratum appears K_n times; divide by K_n so it counts once.
+    log_norm = jax.scipy.special.logsumexp(log_pmf - jnp.log(num_in_stratum))
+    log_stratum = log_pmf - log_norm - jnp.log(num_in_stratum)     # (K,)
+
+    log_p_cong = _log_p_congressed(lam)
+    log_f = jnp.log(bulk_fraction)                                  # (B,)
+
+    log_w_sets = log_f[None, :] + log_p_cong + log_stratum[:, None]  # (K, B)
+    log_w_clean = jnp.log1p(-bulk_fraction * jnp.exp(log_p_cong))    # (B,)
+
+    return jnp.concatenate([log_w_clean[None, :], log_w_sets], axis=0)
+
+
+def cell_classes(focal, population, lam, data: GrowthData) -> CellClasses:
+    """
+    Build the clean and congressed cell classes for the batch's genotypes.
+
+    Parameters
+    ----------
+    focal : tuple of jnp.ndarray
+        ``(theta, activity, dk_geno)`` for the batch. ``theta`` has the
+        growth layout ``(1, 1, 1, 1, num_titrant_name, num_titrant_conc,
+        batch)``; ``activity`` and ``dk_geno`` are ``(1, ..., 1, batch)``.
+    population : tuple of jnp.ndarray
+        Library-ordered ``(theta, activity, dk_geno)`` for every genotype:
+        ``theta`` ``(1, 1, 1, 1, tn, tc, num_genotype)``; ``activity`` and
+        ``dk_geno`` ``(num_genotype,)``.
+    lam : jnp.ndarray
+        Congression Poisson rate.
+    data : GrowthData
+        Uses ``batch_idx``, ``bulk_fraction``, ``coresident_idx``,
+        ``coresident_n``, ``congression_theta_rule``, ``congression_dk_rule``
+        and ``congression_dk_alpha``.
+
+    Returns
+    -------
+    CellClasses
+        Leading class axis of size 1 + K (clean first).
+    """
+    theta, activity, dk_geno = focal
+    theta_pop, activity_pop, dk_pop = population
+
+    idx = data.coresident_idx[data.batch_idx]                  # (B, K, N)
+    valid = idx >= 0
+    safe = jnp.where(valid, idx, 0)
+
+    # --- theta and activity -----------------------------------------------
+    # Slots: the focal plasmid, then the set's co-residents.
+    co_theta = theta_pop[..., safe]                             # (..., B, K, N)
+    focal_theta = jnp.broadcast_to(theta[..., None, None],
+                                   co_theta.shape[:-1] + (1,))
+    slot_theta = jnp.concatenate([focal_theta, co_theta], axis=-1)
+    slot_valid = jnp.concatenate(
+        [jnp.ones(valid.shape[:-1] + (1,), dtype=bool), valid], axis=-1)
+    act_focal = jnp.broadcast_to(activity, theta.shape)
+
+    rule = data.congression_theta_rule
+    if rule == "max":
+        # The highest-theta plasmid sets both theta and activity.
+        slot_theta = jnp.where(slot_valid, slot_theta, -jnp.inf)
+        best = jnp.argmax(slot_theta, axis=-1)[..., None]       # (..., B, K, 1)
+        cell_theta = jnp.take_along_axis(slot_theta, best, axis=-1)[..., 0]
+        slot_act = jnp.concatenate(
+            [jnp.broadcast_to(act_focal[..., None, None], focal_theta.shape),
+             jnp.broadcast_to(activity_pop[safe], co_theta.shape)], axis=-1)
+        cell_act = jnp.take_along_axis(slot_act, best, axis=-1)[..., 0]
+    elif rule in _PARTITION_POWER:
+        # Partition function over equal shares: logit(theta_cell) =
+        # (1/p) log sum_g x_g exp(p l_g). Activity is 1 (orchestrator check).
+        power = _PARTITION_POWER[rule]
+        clipped = jnp.clip(slot_theta, THETA_EPS, 1.0 - THETA_EPS)
+        logit = jnp.log(clipped) - jnp.log1p(-clipped)
+        num_slots = jnp.sum(slot_valid, axis=-1, keepdims=True)
+        log_share = jnp.where(slot_valid, -jnp.log(num_slots.astype(float)),
+                              -jnp.inf)
+        cell_logit = jax.scipy.special.logsumexp(
+            log_share + power * jnp.where(slot_valid, logit, 0.0),
+            axis=-1) / power
+        cell_theta = jax.nn.sigmoid(cell_logit)
+        cell_act = jnp.broadcast_to(act_focal[..., None], cell_theta.shape)
+    else:
+        raise ValueError(f"unknown congression_theta_rule {rule!r}")
+
+    # --- dk_geno: soft-min family over equal shares ------------------------
+    co_dk = jnp.where(valid, dk_pop[safe], 0.0)                 # (B, K, N)
+    num_plasmids = 1.0 + jnp.sum(valid, axis=-1)                # (B, K)
+    dk_rule = data.congression_dk_rule
+    if dk_rule == "dilution":
+        cell_dk = (dk_geno[..., None] + jnp.sum(co_dk, axis=-1)) / num_plasmids
+    else:
+        focal_dk = jnp.broadcast_to(dk_geno[..., None, None],
+                                    dk_geno.shape + valid.shape[-2:-1] + (1,))
+        co_dk = jnp.broadcast_to(co_dk, focal_dk.shape[:-1] + co_dk.shape[-1:])
+        slot_dk = jnp.concatenate([focal_dk, co_dk], axis=-1)   # (..., B, K, 1+N)
+        dk_valid = jnp.broadcast_to(
+            jnp.concatenate([jnp.ones(valid.shape[:-1] + (1,), dtype=bool),
+                             valid], axis=-1), slot_dk.shape)
+        if dk_rule == "min":
+            cell_dk = jnp.min(jnp.where(dk_valid, slot_dk, jnp.inf), axis=-1)
+        elif dk_rule == "softmin":
+            alpha = data.congression_dk_alpha
+            log_share = jnp.where(dk_valid, -jnp.log(num_plasmids)[..., None],
+                                  -jnp.inf)
+            cell_dk = -jax.scipy.special.logsumexp(
+                log_share - alpha * jnp.where(dk_valid, slot_dk, 0.0),
+                axis=-1) / alpha
+        else:
+            raise ValueError(f"unknown congression_dk_rule {dk_rule!r}")
+
+    # --- stack classes on a leading axis (clean first) --------------------
+    def stack(clean, congressed):
+        return jnp.concatenate([clean[None],
+                                jnp.moveaxis(congressed, -1, 0)], axis=0)
+
+    classes_theta = stack(theta, cell_theta)
+    classes_act = stack(act_focal, cell_act)
+    classes_dk = stack(dk_geno, cell_dk)
+
+    bulk_fraction = data.bulk_fraction[data.batch_idx]
+    log_w = _log_class_weights(lam, bulk_fraction, data.coresident_n)
+    log_w = log_w.reshape((log_w.shape[0],) + (1,) * (theta.ndim - 1)
+                          + (log_w.shape[-1],))
+
+    return CellClasses(theta=classes_theta, activity=classes_act,
+                       dk_geno=classes_dk, log_weight=log_w)
+
+
+def get_hyperparameters(lam_mean=None, lam_std=None):
+    """
+    Default hyperparameters: the LogNormal prior on lambda.
+
+    Parameters
+    ----------
+    lam_mean, lam_std : float, optional
+        Experimentally measured mean and standard deviation of lambda, in
+        linear space, moment-matched onto the LogNormal. Provide both or
+        neither; without them a placeholder prior is used (tfs-configure-model
+        requires a measured value for this component).
+
+    Raises
+    ------
+    ValueError
+        If exactly one of ``lam_mean``/``lam_std`` is given, or either is
+        not strictly positive.
+    """
+    if (lam_mean is None) != (lam_std is None):
+        raise ValueError(
+            "lam_mean and lam_std must be provided together (or neither)."
+        )
+
+    parameters = {}
+    if lam_mean is not None:
+        if lam_mean <= 0:
+            raise ValueError(f"lam_mean must be > 0; got {lam_mean}")
+        if lam_std <= 0:
+            raise ValueError(f"lam_std must be > 0; got {lam_std}")
+        sigma2 = math.log(1.0 + (lam_std / lam_mean) ** 2)
+        parameters["lam_loc"] = math.log(lam_mean) - sigma2 / 2.0
+        parameters["lam_scale"] = math.sqrt(sigma2)
+    else:
+        parameters["lam_loc"] = 0.0
+        parameters["lam_scale"] = 1.0
+
+    return parameters
+
+
+def get_guesses(name, data, lam_mean=None):
+    """Initial guess for lambda: the measured mean if given, else 1.0."""
+    return {f"{name}_lam": lam_mean if lam_mean is not None else 1.0}
+
+
+def get_priors(lam_mean=None, lam_std=None):
+    """Build the ModelPriors from ``get_hyperparameters``."""
+    return ModelPriors(**get_hyperparameters(lam_mean=lam_mean,
+                                             lam_std=lam_std))
+
+
+def get_extract_specs(ctx):
+    lam_df = pd.DataFrame({"parameter": ["lam"], "map_all": [0]})
+    return [dict(
+        input_df=lam_df,
+        params_to_get=["lam"],
+        map_column="map_all",
+        get_columns=["parameter"],
+        in_run_prefix="transformation_",
+    )]
diff --git a/src/tfscreen/tfmodel/generative/components/transformation/single.py b/src/tfscreen/tfmodel/generative/components/transformation/single.py
index 00ef8c1e..ee2b6fa6 100644
--- a/src/tfscreen/tfmodel/generative/components/transformation/single.py
+++ b/src/tfscreen/tfmodel/generative/components/transformation/single.py
@@ -1,46 +1,52 @@
-import jax.numpy as jnp
+"""
+No congression: every cell of a genotype carries only its own plasmid.
+"""
+
 from flax.struct import dataclass
+
 from tfscreen.tfmodel.data_class import GrowthData
+from tfscreen.tfmodel.generative.components.transformation._classes import (
+    CellClasses,
+    single_class,
+)
+
+
+# One class built from the genotype's own values; no population needed.
+NEEDS_POPULATION = False
+
 
 @dataclass(frozen=True)
 class ModelPriors:
     """
     JAX Pytree holding data needed to specify model priors.
-    
+
     No priors needed for single transformation (dummy).
     """
     pass
 
-def define_model(name: str, 
-                 data: GrowthData, 
-                 priors: ModelPriors,
-                 anchors: tuple = None) -> jnp.ndarray:
-    """
-    Dummy model definition for single transformation.
-    """
-    return 1.0, 1.0, 1.0 # Dummy lambda, a, b
 
-def guide(name: str, 
-          data: GrowthData, 
-          priors: ModelPriors,
-          anchors: tuple = None) -> jnp.ndarray:
-    """
-    Dummy guide for single transformation.
-    """
-    return 1.0, 1.0, 1.0
+def define_model(name: str,
+                 data: GrowthData,
+                 priors: ModelPriors):
+    """No latent parameters."""
+    return None
 
-def update_thetas(theta, params, mask=None):
-    """
-    Pass-through update_thetas for single transformation.
 
-    Returns theta unchanged.
-    """
-    return theta
+def guide(name: str,
+          data: GrowthData,
+          priors: ModelPriors):
+    """No latent parameters."""
+    return None
+
 
+def cell_classes(focal, population, params, data: GrowthData) -> CellClasses:
+    """
+    One class, weight 1, built from the genotype's own theta, activity and
+    dk_geno. ``population`` and ``params`` are ignored.
+    """
+    theta, activity, dk_geno = focal
+    return single_class(theta, activity, dk_geno)
 
-# No congression correction is applied at all in single-plasmid mode, so
-# there is nothing that needs a population-wide theta reference.
-NEEDS_FULL_POPULATION_THETA = False
 
 def get_hyperparameters():
     """
@@ -48,12 +54,14 @@ def get_hyperparameters():
     """
     return {}
 
+
 def get_guesses(name, data):
     """
     No guesses needed.
     """
     return {}
 
+
 def get_priors():
     return ModelPriors(**get_hyperparameters())
 
diff --git a/src/tfscreen/tfmodel/generative/model.py b/src/tfscreen/tfmodel/generative/model.py
index 2585f25d..29a1ccbc 100644
--- a/src/tfscreen/tfmodel/generative/model.py
+++ b/src/tfscreen/tfmodel/generative/model.py
@@ -5,6 +5,7 @@
     PriorsClass,
 )
 
+import jax
 import jax.numpy as jnp
 import numpyro as pyro
 
@@ -34,12 +35,14 @@ def jax_model(data: DataClass,
         - growth_noise
         - sample_offset
         - calculate_growth
-        - theta_binding_noise
+        - theta_binding_noise (absent in a growth-only model)
         - theta_growth_noise
-        - observe_binding
+        - observe_binding (absent in a growth-only model)
         - observe_growth
         - is_guide
-        The dictionary can also optionally have `observe_presplit` and/or
+        A growth-only model has ``data.binding = None``; the binding
+        prediction and likelihood are then skipped (static structure, so this
+        is jit-safe). The dictionary can also optionally have `observe_presplit` and/or
         `observe_base_growth` (the side-channel observers, present only when
         their data was supplied), plus `batch_idx` (which overrides whatever
         is in `batch_size`) or `batch_size`.
@@ -50,9 +53,9 @@ def jax_model(data: DataClass,
 
     binding_only = control.get("binding_only", False)
     is_guide = control["is_guide"]
-    theta_model, calc_theta, get_moments = control["theta"]
-    theta_binding_noise_model = control["theta_binding_noise"]
-    binding_observer = control["observe_binding"]
+    theta_model, calc_theta, _ = control["theta"]
+    theta_binding_noise_model = control.get("theta_binding_noise")
+    binding_observer = control.get("observe_binding")
 
     # -------------------------------------------------------------------------
     # Binding-only mode: theta is inferred directly from observed theta values.
@@ -64,7 +67,8 @@ def jax_model(data: DataClass,
         pyro.deterministic("theta_binding_pred", theta_binding)
         binding_pred = theta_binding_noise_model("theta_binding_noise",
                                                  theta_binding,
-                                                 priors.binding.theta_binding_noise)
+                                                 priors.binding.theta_binding_noise,
+                                                 data=data.binding)
         if is_guide:
             binding_observer("binding", data.binding, None)
         else:
@@ -79,7 +83,7 @@ def jax_model(data: DataClass,
     ln_cfu0_model = control["ln_cfu0"]
     activity_model = control["activity"]
     dk_geno_model = control["dk_geno"]
-    transformation_model, transformation_update, transformation_needs_population = \
+    transformation_model, cell_classes, transformation_needs_population = \
         control["transformation"]
     theta_growth_noise_model = control["theta_growth_noise"]
     theta_rescale = control["theta_rescale"]
@@ -104,17 +108,17 @@ def jax_model(data: DataClass,
                         data.growth,
                         priors.theta)
 
-    # Get population moments as anchors for the transformation model
-    anchors = get_moments(theta, data.growth)
-
     # -------------------------------------------------------------------------
-    # Make prediction for the binding experiment
+    # Make prediction for the binding experiment (none in a growth-only model)
 
-    theta_binding = calc_theta(theta,data.binding)
-    pyro.deterministic(f"theta_binding_pred",theta_binding)
-    binding_pred = theta_binding_noise_model("theta_binding_noise",
-                                             theta_binding,
-                                             priors.binding.theta_binding_noise)
+    has_binding = data.binding is not None
+    if has_binding:
+        theta_binding = calc_theta(theta,data.binding)
+        pyro.deterministic(f"theta_binding_pred",theta_binding)
+        binding_pred = theta_binding_noise_model("theta_binding_noise",
+                                                 theta_binding,
+                                                 priors.binding.theta_binding_noise,
+                                                 data=data.binding)
 
     # -------------------------------------------------------------------------
     # Make prediction for the growth experiment
@@ -129,75 +133,43 @@ def jax_model(data: DataClass,
                             data.growth,
                             priors.growth.ln_cfu0)
 
-    # # pleiotropic effect of mutation
-    dk_geno = dk_geno_model("dk_geno",
-                            data.growth,
-                            priors.growth.dk_geno)
-
-    # activity
-    activity = activity_model("activity",
-                              data.growth,
-                              priors.growth.activity)
-
-    # theta
-    theta_growth = calc_theta(theta,data.growth)
+    # pleiotropic effect of mutation and TF activity. When the congression
+    # mixture needs them, the components also return every genotype's value
+    # in library order (None if their latents arrived batch-sized).
+    if transformation_needs_population:
+        dk_geno, dk_population = dk_geno_model("dk_geno",
+                                               data.growth,
+                                               priors.growth.dk_geno,
+                                               return_population=True)
+        activity, activity_population = activity_model("activity",
+                                                       data.growth,
+                                                       priors.growth.activity,
+                                                       return_population=True)
+    else:
+        dk_geno = dk_geno_model("dk_geno",
+                                data.growth,
+                                priors.growth.dk_geno)
+        activity = activity_model("activity",
+                                  data.growth,
+                                  priors.growth.activity)
+
+    # theta (the genotype's own, before congression), at the titrant the
+    # growth sees (s times nominal, if the theta component has a shift)
+    growth_theta_data = growth_titrant(theta, data.growth)
+    theta_growth = calc_theta(theta, growth_theta_data)
     pyro.deterministic(f"theta_growth_pred",theta_growth)
 
-    # Transformation parameters (lam, mu, sigma)
+    # Transformation parameters (lambda for the mixture; none for single)
     trans_params = transformation_model("transformation",
                                         data.growth,
-                                        priors.growth.transformation,
-                                        anchors=anchors)
-
-    # Correct theta for transformation
-    # theta_growth shape: (..., titrant_name, titrant_conc, geno) or scattered
-    # Result broadcasts to interaction of (rep, pre) and (titrant)
-    # Parameters passed as tuple
-    if transformation_needs_population:
-        # The congression correction's background CDF must be estimated from
-        # the full genotype population, not whatever subset of genotypes is
-        # active in this particular forward pass (a training minibatch, or a
-        # handful of genotypes requested at prediction time) — see
-        # transformation/_congression.py::update_thetas.  When the caller
-        # hasn't supplied one explicitly (data.growth.external_theta_population),
-        # compute it locally by re-running calc_theta over every genotype.
-        # This is only correct when data.growth already spans the full
-        # population, which holds during SVI training (genotype minibatching
-        # never shrinks data.growth.num_genotype — see tensors/batch.py) but
-        # NOT for prediction code paths that subset genotypes; those must
-        # supply external_theta_population themselves.
-        if data.growth.external_theta_population is not None:
-            population_theta_growth = data.growth.external_theta_population
-        else:
-            # Deliberately leave scatter_theta untouched (rather than forcing
-            # it to 0) so population_theta_growth's leading (non-genotype)
-            # dimensions match theta_growth's exactly -- update_thetas relies
-            # on that alignment when broadcasting the correction back onto
-            # theta_growth's shape.
-            num_genotype = data.growth.num_genotype
-            full_population_idx = jnp.arange(num_genotype)
-            full_population_data = data.growth.replace(
-                batch_idx=full_population_idx,
-                geno_theta_idx=full_population_idx,
-            )
-            population_theta_growth = calc_theta(theta, full_population_data)
-
-        corr_theta_growth = transformation_update(
-            theta_growth,
-            params=trans_params,
-            mask=data.growth.congression_mask,
-            population_theta=population_theta_growth,
-        )
-    else:
-        corr_theta_growth = transformation_update(theta_growth,
-                                                  params=trans_params,
-                                                  mask=data.growth.congression_mask)
+                                        priors.growth.transformation)
 
+    # Noise acts on the genotype's own theta, before the cell classes are
+    # built; co-residents use noiseless population values.
     noisy_theta_growth = theta_growth_noise_model("theta_growth_noise",
-                                                  corr_theta_growth,
-                                                  priors.growth.theta_growth_noise)
-
-    rescaled_theta = theta_rescale(noisy_theta_growth)
+                                                  theta_growth,
+                                                  priors.growth.theta_growth_noise,
+                                                  data=data.growth)
 
     # -------------------------------------------------------------------------
     # finalize
@@ -207,6 +179,8 @@ def jax_model(data: DataClass,
     # variables (e.g. memory k1/tau0/k2) get guide sample sites registered.
     if is_guide:
 
+        rescaled_theta = theta_rescale(noisy_theta_growth)
+
         growth_transition_model("growth_transition",
                                 data.growth,
                                 priors.growth.growth_transition,
@@ -226,7 +200,8 @@ def jax_model(data: DataClass,
 
         growth_observer("growth", data.growth, None,
                         priors=priors.growth.growth_obs)
-        binding_observer("binding", data.binding, None)
+        if has_binding:
+            binding_observer("binding", data.binding, None)
 
         # Register side-channel guide sites. presplit.guide is a no-op (it
         # introduces no latents); base_growth.guide registers the k_ref
@@ -255,13 +230,26 @@ def jax_model(data: DataClass,
                                  growth=data.growth,
                                  priors=priors.growth.base_growth)
 
-        # calculate observable (all tensors have correct dimensions)
+        # Congression: split each genotype's cells into classes (clean, and
+        # congressed cells carrying co-resident plasmids), each with its own
+        # cell-level theta, activity and dk_geno and a mixture weight. See
+        # transformation/mixture.py.
+        population = None
+        if transformation_needs_population:
+            population = _population(growth_theta_data, theta, calc_theta,
+                                     dk_population, activity_population)
+        classes = cell_classes((noisy_theta_growth, activity, dk_geno),
+                               population, trans_params, data.growth)
+
+        # Grow every class. The class axis leads the growth layout and every
+        # growth component is elementwise, so each is called once.
+        rescaled_theta = theta_rescale(classes.theta)
         g_pre, g_sel = calculate_growth(params=growth_params,
-                                        dk_geno=dk_geno,
-                                        activity=activity,
+                                        dk_geno=classes.dk_geno,
+                                        activity=classes.activity,
                                         theta=rescaled_theta)
 
-        total_growth = growth_transition_model("growth_transition",
+        class_growth = growth_transition_model("growth_transition",
                                                data.growth,
                                                priors.growth.growth_transition,
                                                g_pre=g_pre,
@@ -270,6 +258,11 @@ def jax_model(data: DataClass,
                                                t_sel=data.growth.t_sel,
                                                theta=rescaled_theta)
 
+        # Mix the classes at the observable level: exp(ln_cfu) adds, rates
+        # do not.
+        total_growth = jax.scipy.special.logsumexp(
+            classes.log_weight + class_growth, axis=0)
+
         sigma_k = growth_noise_model("growth_noise",
                                      data.growth,
                                      priors.growth.growth_noise)
@@ -281,12 +274,70 @@ def jax_model(data: DataClass,
         ln_cfu_pred = ln_cfu0 + total_growth + delta_sample
 
         # Register results
-        pyro.deterministic(f"binding_pred", binding_pred)
+        if has_binding:
+            pyro.deterministic(f"binding_pred", binding_pred)
         pyro.deterministic(f"growth_pred", ln_cfu_pred)
 
         # Calculate likelihood
         growth_observer("growth", data.growth, ln_cfu_pred, sigma_k=sigma_k,
                         priors=priors.growth.growth_obs)
-        binding_observer("binding", data.binding, binding_pred)
+        if has_binding:
+            binding_observer("binding", data.binding, binding_pred)
+
+
+def growth_titrant(theta, growth):
+    """
+    The growth data as the theta component should see it: concentrations
+    multiplied by s when the component's parameters carry a growth titrant
+    shift log s (``growth_log_conc_shift``, hill_geno), else unchanged.
+    Binding data are never shifted, so the curve is in binding units.
+    A zero concentration stays zero (its log is a large negative sentinel).
+    """
+    shift = getattr(theta, "growth_log_conc_shift", None)
+    if shift is None:
+        return growth
+    return growth.replace(log_titrant_conc=growth.log_titrant_conc + shift,
+                          titrant_conc=growth.titrant_conc * jnp.exp(shift))
 
 
+def _population(growth, theta, calc_theta, dk_population, activity_population):
+    """
+    Library-ordered theta, activity and dk_geno for every genotype.
+
+    Each comes from ``growth.external_*_population`` when supplied (prediction
+    code that runs a genotype subset, or whose latents are batch-sized
+    substitutions), else is computed here: theta by evaluating the theta
+    component over the full library (valid because genotype mini-batching
+    never shrinks ``num_genotype``; see tensors/batch.py), activity and
+    dk_geno from their components' ``return_population`` values.
+
+    Raises
+    ------
+    ValueError
+        If activity or dk_geno is needed but neither the component nor the
+        data supplies it.
+    """
+    if growth.external_theta_population is not None:
+        theta_population = growth.external_theta_population
+    else:
+        full = jnp.arange(growth.num_genotype)
+        theta_population = calc_theta(
+            theta, growth.replace(batch_idx=full, geno_theta_idx=full))
+
+    if growth.external_dk_population is not None:
+        dk_population = growth.external_dk_population
+    if growth.external_activity_population is not None:
+        activity_population = growth.external_activity_population
+
+    missing = [name for name, value in (("dk_geno", dk_population),
+                                        ("activity", activity_population))
+               if value is None]
+    if missing:
+        raise ValueError(
+            f"The congression mixture needs every genotype's {missing}, but "
+            f"the latents were not library-sized and no "
+            f"external_*_population was supplied. Prediction code that runs "
+            f"a genotype subset must pass them (see analysis/prediction.py)."
+        )
+
+    return theta_population, activity_population, dk_population
diff --git a/src/tfscreen/tfmodel/generative/observe/growth_counts.py b/src/tfscreen/tfmodel/generative/observe/growth_counts.py
new file mode 100644
index 00000000..ac96801c
--- /dev/null
+++ b/src/tfscreen/tfmodel/generative/observe/growth_counts.py
@@ -0,0 +1,264 @@
+"""
+Growth observer on read counts (``growth_likelihood: counts``).
+
+Roadmap step 7 (planning/analysis-roadmap.md). Instead of observing
+``ln_cfu`` (a pseudocount-adjusted log frequency plus the tube total), each
+genotype's reads in each tube are observed directly:
+
+    reads[g, s] ~ NegBin(mean = mu[g, s], var = mu (1 + phi) + mu^2 / r)
+    log mu[g, s] = ln(depth_s) + ln_cfu_pred[g, s] - ln(N_s)
+
+``depth_s`` is the tube's total reads (including ``__unknown__``), ``N_s``
+the tube's total cells as supplied (``sample_ln_cfu``), and ``ln_cfu_pred``
+the model's cells of genotype g in tube s (with any per-tube
+``sample_offset``). So ``mu / depth`` is the predicted frequency. There is
+no pseudocount: a zero is an observation.
+
+The variance has two learned parts (study 0b, planning/studies/noise-anatomy/):
+
+- ``phi`` (``{name}_phi``): variance proportional to the mean, as from a
+  bottleneck upstream of the reads (template molecules into PCR, founder
+  cells per tube). Real data: about 3-10.
+- ``1/r`` (``{name}_inv_r``): a constant coefficient of variation, as from
+  jackpotting that scales with abundance.
+
+Both reduce to Poisson as they go to zero. The distribution is a
+negative binomial with ``mean = mu`` and concentration
+``1 / (phi / mu + 1 / r)``, evaluated in log space. Its log-pmf uses Loader's
+algorithm (``CountNegativeBinomial``): numpyro's own negative binomial loses
+several nats per observation to float32 cancellation at 10^4-10^6 reads.
+
+The per-row observation weights, masking and genotype mini-batching are
+identical to the ``ln_cfu`` observer (``observe/growth.py``).
+"""
+
+import jax
+import jax.numpy as jnp
+import numpyro as pyro
+import numpyro.distributions as dist
+from flax.struct import dataclass
+
+from tfscreen.tfmodel.data_class import GrowthData
+
+
+@dataclass(frozen=True)
+class GrowthCountsObsPriors:
+    """
+    LogNormal priors (in log space: loc, scale) on the count observer's
+    dispersion: ``phi`` (variance proportional to the mean) and ``inv_r``
+    (1/r, the squared coefficient of variation that does not shrink with
+    depth).
+    """
+    phi_loc: float
+    phi_scale: float
+    inv_r_loc: float
+    inv_r_scale: float
+
+
+def log_mean(data: GrowthData, ln_cfu_pred: jnp.ndarray) -> jnp.ndarray:
+    """``log mu = ln(depth) + ln_cfu_pred - ln(N)`` per growth-tensor cell."""
+    return data.ln_sample_reads + ln_cfu_pred - data.sample_ln_cfu
+
+
+# --- A negative binomial log-pmf that stays accurate in float32 -----------
+#
+# numpyro's NegativeBinomial computes lgamma(k + c) - lgamma(c) - lgamma(k + 1)
+# + c log p + k log q directly. At the read depths here (10^4-10^6 reads for
+# spiked genotypes) those terms are ~10^7 and cancel to a few nats, so float32
+# rounding alone costs several nats per observation. Loader's algorithm (C.
+# Loader, "Fast and accurate computation of binomial probabilities", 2000;
+# R's dbinom_raw / dnbinom_mu) writes the same probability through a deviance
+# term (bd0) and Stirling-series errors (stirlerr), none of which cancel.
+
+_HALF_LOG_2PI = 0.9189385332046727
+_BD0_TERMS = 10
+
+
+def _stirlerr(n):
+    """lgamma(n + 1) - (n + 1/2) log n + n - log(2 pi)/2, for n > 0."""
+    n = jnp.asarray(n)
+    # Each branch sees only inputs where it is valid, so the unused one
+    # cannot overflow and leak NaN into the gradient through jnp.where.
+    big_n = jnp.maximum(n, 15.0)
+    nn = big_n * big_n
+    large = (1/12 - (1/360 - (1/1260 - (1/1680 - (1/1188)/nn)/nn)/nn)/nn)/big_n
+    small_n = jnp.minimum(n, 15.0)
+    small = (jax.scipy.special.gammaln(small_n + 1.0)
+             - (small_n + 0.5)*jnp.log(small_n) + small_n - _HALF_LOG_2PI)
+    return jnp.where(n > 15.0, large, small)
+
+
+def _bd0(x, log_m):
+    """
+    Deviance ``x log(x / m) + m - x`` for x > 0, m = exp(log_m), without
+    cancellation when x is close to m (series in v = (x - m) / (x + m)).
+    """
+    m = jnp.exp(log_m)
+    direct = x*(jnp.log(x) - log_m) + m - x
+    v = (x - m)/(x + m)
+    near = jnp.abs(x - m) < 0.1*(x + m)
+    v = jnp.where(near, v, 0.0)
+    s = (x - m)*v
+    ej = 2.0*x*v
+    v2 = v*v
+    for j in range(1, _BD0_TERMS + 1):
+        ej = ej*v2
+        s = s + ej/(2*j + 1)
+    return jnp.where(near, s, direct)
+
+
+def _nb_log_prob(k, log_mu, log_c):
+    """
+    log NB(k; mean mu, concentration c), with the variance mu + mu^2 / c.
+    R's dnbinom_mu: log(c / (c + k)) + dbinom_raw(c, k + c, p, q) with
+    p = c / (c + mu) and q = mu / (c + mu); k = 0 is c log p.
+    """
+    c = jnp.exp(log_c)
+    log_p = -jax.nn.softplus(log_mu - log_c)
+    log_q = -jax.nn.softplus(log_c - log_mu)
+
+    zero = c*log_p
+
+    k_safe = jnp.maximum(k, 1.0)
+    n = k_safe + c
+    log_n = jnp.log(n)
+    lc = (_stirlerr(n) - _stirlerr(c) - _stirlerr(k_safe)
+          - _bd0(c, log_n + log_p) - _bd0(k_safe, log_n + log_q))
+    # log(2 pi c k / n), the binomial's normalizing term
+    lf = 2.0*_HALF_LOG_2PI + log_c + jnp.log(k_safe) - log_n
+    positive = (log_c - log_n) + lc - 0.5*lf
+
+    return jnp.where(k > 0, positive, zero)
+
+
+class CountNegativeBinomial(dist.Distribution):
+    """
+    Negative binomial on counts, parameterized by ``log_mu`` (log mean) and
+    ``log_c`` (log concentration; variance ``mu + mu^2 / c``), with a
+    float32-accurate log-pmf (see ``_nb_log_prob``). Sampling is Gamma-Poisson.
+    """
+    arg_constraints = {"log_mu": dist.constraints.real,
+                       "log_c": dist.constraints.real}
+    support = dist.constraints.nonnegative_integer
+
+    def __init__(self, log_mu, log_c, *, validate_args=None):
+        self.log_mu, self.log_c = jnp.broadcast_arrays(log_mu, log_c)
+        super().__init__(batch_shape=jnp.shape(self.log_mu),
+                         validate_args=validate_args)
+
+    def sample(self, key, sample_shape=()):
+        c = jnp.exp(self.log_c)
+        return dist.GammaPoisson(c, c*jnp.exp(-self.log_mu)).sample(key, sample_shape)
+
+    def log_prob(self, value):
+        return _nb_log_prob(value, self.log_mu, self.log_c)
+
+    @property
+    def mean(self):
+        return jnp.exp(self.log_mu)
+
+    @property
+    def variance(self):
+        mu = jnp.exp(self.log_mu)
+        return mu + mu*mu*jnp.exp(-self.log_c)
+
+
+# Floor on the concentration, c + exp(_LOG_C_FLOOR). Without it c ~ mu / phi
+# underflows float32 for genotypes predicted near extinction (log mu below
+# about -40, which the MAP reaches for genotypes with zero reads everywhere:
+# P(0) keeps rising as mu falls), and the gradient and Hessian of the log-pmf
+# go to NaN (count-likelihood grid, 2026-09-27). It changes only rows whose
+# expected reads are below ~phi * 1e-13, where P(0) is 1 either way and a
+# nonzero count still pushes mu up.
+_LOG_C_FLOOR = -30.0
+
+
+def count_distribution(log_mu, phi, inv_r):
+    """
+    Negative binomial with mean ``exp(log_mu)`` and variance
+    ``mu (1 + phi) + mu^2 inv_r``: concentration ``1 / (phi / mu + inv_r)``
+    (plus a floor of ``exp(_LOG_C_FLOOR)``), in log space so that very small
+    means do not underflow.
+    """
+    log_c = -jnp.logaddexp(jnp.log(phi) - log_mu, jnp.log(inv_r))
+    log_c = jnp.logaddexp(log_c, _LOG_C_FLOOR)
+    return CountNegativeBinomial(log_mu, log_c)
+
+
+def observe(name: str,
+            data: GrowthData,
+            ln_cfu_pred: jnp.ndarray,
+            sigma_k: jnp.ndarray = 0.0,
+            *,
+            priors: GrowthCountsObsPriors):
+    """
+    Observation site for the growth read counts.
+
+    Parameters
+    ----------
+    name : str
+        Prefix for the sample sites (``{name}_phi``, ``{name}_inv_r``,
+        ``{name}_obs``).
+    data : GrowthData
+        Needs ``counts``, ``ln_sample_reads`` and ``sample_ln_cfu`` (built by
+        ``ModelOrchestrator`` when ``growth_likelihood='counts'``) besides
+        the usual mask, sizes and mini-batch scale vector.
+    ln_cfu_pred : jnp.ndarray
+        The model's ln cells of each genotype in each tube.
+    sigma_k : jnp.ndarray, optional
+        Accepted for signature compatibility with the ``ln_cfu`` observer and
+        ignored: ``growth_noise`` must be ``zero`` with this observer (the
+        orchestrator refuses anything else), because extra per-row noise is
+        what ``phi`` and ``inv_r`` describe.
+    priors : GrowthCountsObsPriors
+    """
+    phi = pyro.sample(f"{name}_phi",
+                      dist.LogNormal(priors.phi_loc, priors.phi_scale))
+    inv_r = pyro.sample(f"{name}_inv_r",
+                        dist.LogNormal(priors.inv_r_loc, priors.inv_r_scale))
+
+    log_mu = log_mean(data, ln_cfu_pred)
+
+    with pyro.plate(f"{name}_replicate", size=data.num_replicate, dim=-7):
+        with pyro.plate(f"{name}_time", size=data.num_time, dim=-6):
+            with pyro.plate(f"{name}_condition_pre", size=data.num_condition_pre, dim=-5):
+                with pyro.plate(f"{name}_condition_sel", size=data.num_condition_sel, dim=-4):
+                    with pyro.plate(f"{name}_titrant_name", size=data.num_titrant_name, dim=-3):
+                        with pyro.plate(f"{name}_titrant_conc", size=data.num_titrant_conc, dim=-2):
+                            with pyro.plate("shared_genotype_plate", size=data.batch_size, dim=-1):
+                                with pyro.handlers.scale(scale=data.scale_vector):
+                                    with pyro.handlers.mask(mask=data.good_mask):
+                                        pyro.sample(f"{name}_obs",
+                                                    count_distribution(log_mu, phi, inv_r),
+                                                    obs=data.counts)
+
+
+def guide(name: str,
+          data: GrowthData,
+          ln_cfu_pred: jnp.ndarray,
+          sigma_k: jnp.ndarray = 0.0,
+          *,
+          priors: GrowthCountsObsPriors):
+    """LogNormal guides for ``phi`` and ``inv_r``, started at their prior
+    medians."""
+    for site, loc in (("phi", priors.phi_loc), ("inv_r", priors.inv_r_loc)):
+        site_loc = pyro.param(f"{name}_{site}_loc", jnp.asarray(loc))
+        site_scale = pyro.param(f"{name}_{site}_scale", jnp.array(0.1),
+                                constraint=dist.constraints.positive)
+        pyro.sample(f"{name}_{site}", dist.LogNormal(site_loc, site_scale))
+
+
+def get_hyperparameters():
+    """
+    Default priors. ``phi``: median 5, 95% interval about 0.7-35 (study 0b
+    found 3-10 on real data; a simulation without a bottleneck has about
+    0). ``inv_r``: median 0.01 (a 10% coefficient-of-variation floor), 95%
+    interval about 5e-4 to 0.2.
+    """
+    return {"phi_loc": float(jnp.log(5.0)), "phi_scale": 1.0,
+            "inv_r_loc": float(jnp.log(0.01)), "inv_r_scale": 1.5}
+
+
+def get_priors():
+    """Build GrowthCountsObsPriors from get_hyperparameters()."""
+    return GrowthCountsObsPriors(**get_hyperparameters())
diff --git a/src/tfscreen/tfmodel/generative/registry.py b/src/tfscreen/tfmodel/generative/registry.py
index 991cc951..a281a469 100644
--- a/src/tfscreen/tfmodel/generative/registry.py
+++ b/src/tfscreen/tfmodel/generative/registry.py
@@ -25,6 +25,7 @@
 from .components.theta import _simple as theta_simple
 from .components.theta import categorical_geno as theta_cat
 from .components.theta import hill_geno as theta_hill
+from .components.theta import hill_relative as theta_hill_relative
 from .components.theta import hill_mut as theta_hill_mut
 from .components.theta.thermo.O2_C4_K3_U0_a import PK as theta_lac_dimer_lnK_mut
 from .components.theta.thermo.O2_C4_K3_U0_a import PnnC as theta_lac_dimer_lnK_nn_prior
@@ -38,8 +39,7 @@
 from .components.theta.thermo.O2_C12_K5_U1_a import PK as theta_mwc_dimer_unfolded_lnK_mut
 from .components.theta.thermo.O2_C12_K5_U1_a import PnnC as theta_mwc_dimer_unfolded_lnK_nn_prior
 from .components.theta.thermo.O2_C12_K5_U1_a import PddG as theta_mwc_dimer_unfolded_lnK_ddG_prior
-from .components.transformation import empirical as transformation_empirical
-from .components.transformation import logit_norm as transformation_logit_norm
+from .components.transformation import mixture as transformation_mixture
 from .components.transformation import single as transformation_single
 
 from .components.theta_rescale import passthrough as theta_rescale_passthrough
@@ -47,6 +47,7 @@
 
 from .components.noise import zero as no_noise
 from .components.noise import beta as beta_noise
+from .components.noise import genotype_offset as genotype_offset_noise
 from .components.noise import logit_normal as logit_normal_noise
 
 from .components.growth_noise import zero as growth_noise_zero
@@ -54,9 +55,11 @@
 
 from .components.sample_offset import zero as sample_offset_zero
 from .components.sample_offset import normal as sample_offset_normal
+from .components.sample_offset import level as sample_offset_level
 
 from .observe import binding
 from .observe import growth
+from .observe import growth_counts
 from .observe import presplit
 from .observe import base_growth
 
@@ -83,8 +86,7 @@
         "horseshoe_mut":activity_horseshoe_mut,
     },
     "transformation":{
-        "empirical": transformation_empirical,
-        "logit_norm": transformation_logit_norm,
+        "mixture": transformation_mixture,
         "single": transformation_single,
     },
     "theta_rescale":{
@@ -95,6 +97,7 @@
         "_simple":theta_simple,
         "categorical_geno":theta_cat,
         "hill_geno":theta_hill,
+        "hill_relative":theta_hill_relative,
         "hill_mut":theta_hill_mut,
         "thermo.O2_C4_K3_U0_a.PK":theta_lac_dimer_lnK_mut,
         "thermo.O2_C4_K3_U0_a.PnnC":theta_lac_dimer_lnK_nn_prior,
@@ -117,6 +120,7 @@
     "theta_binding_noise":{
         "zero":no_noise,
         "beta":beta_noise,
+        "genotype_offset":genotype_offset_noise,
     },
     "growth_noise":{
         "zero":growth_noise_zero,
@@ -125,6 +129,7 @@
     "sample_offset":{
         "zero":sample_offset_zero,
         "normal":sample_offset_normal,
+        "level":sample_offset_level,
     },
     "growth_transition":{
         "instant":growth_transition_instant,
@@ -136,6 +141,7 @@
     },
     "observe_binding":binding,
     "observe_growth":growth,
+    "observe_growth_counts":growth_counts,
     "observe_presplit":presplit,
     "observe_base_growth":base_growth,
 }
diff --git a/src/tfscreen/tfmodel/genotype_fit/__init__.py b/src/tfscreen/tfmodel/genotype_fit/__init__.py
index a6f3a1e6..2867de45 100644
--- a/src/tfscreen/tfmodel/genotype_fit/__init__.py
+++ b/src/tfscreen/tfmodel/genotype_fit/__init__.py
@@ -3,7 +3,7 @@
 
 A non-Bayesian, per-genotype inference engine for the same growth model that
 ``tfmodel`` fits jointly.  Exposed by ``tfs-fit-genotypes`` and reused as
-Stages 1 / 1.5 of the empirical-phenotype simulation pipeline.
+Stage 1 of the empirical-phenotype simulation pipeline.
 """
 
 from tfscreen.tfmodel.genotype_fit.fit import (  # noqa: F401
@@ -17,7 +17,3 @@
     hill_theta_from_fit,
     read_calibration,
 )
-from tfscreen.tfmodel.genotype_fit.congression import (  # noqa: F401
-    correct_theta_matrix,
-    deattenuate_congression,
-)
diff --git a/src/tfscreen/tfmodel/genotype_fit/congression.py b/src/tfscreen/tfmodel/genotype_fit/congression.py
deleted file mode 100644
index e7fa640c..00000000
--- a/src/tfscreen/tfmodel/genotype_fit/congression.py
+++ /dev/null
@@ -1,231 +0,0 @@
-"""
-Congression de-attenuation of per-genotype theta curves (co-transformation).
-
-A barcode observed in the bulk is really a cell that received a
-zero-truncated-Poisson(lambda) number of plasmids.  Under **dominant-max
-occupancy** — the tightest-bound operator in a co-transformed cell sets the
-effective theta, regardless of marker — the theta a monoclonal Hill fit
-recovers for genotype ``g`` is inflated toward the population's high-occupancy
-envelope:
-
-    theta_obs  =  E[ max(theta_g, M) ],   M = max of Poisson(lambda) co-residents
-
-drawn from the population theta distribution.  This is exactly the inference's
-own congression operator (``transformation._congression.update_thetas``, the
-``E[max(x, M)]`` map with an empirical background CDF), and because a focal
-barcode is size-biased into its cell, the co-resident count is Poisson at the
-same ``lambda`` the simulator's ``transformation_poisson_lambda`` uses — so no
-lambda conversion is needed.
-
-We want ``theta_true`` such that pushing it through that forward map (with the
-background built from ``theta_true`` itself) reproduces ``theta_obs``.  That is
-a fixed point because the background *is* the corrected population, which
-changes every pass.  Per titrant concentration is an independent 1-D
-correction; the Hill refit afterward re-links the concentrations into a curve.
-
-This is the standalone (``tfs-fit-genotypes``) and Stage-1.5 (empirical
-simulation pipeline) de-attenuation layer; ``simulate/empirical/congression``
-re-exports it.
-
-Scope / assumptions
--------------------
-* **Dominant-max occupancy only** (single ``E[max]`` operator; no min variant).
-* Corrects only **bulk** genotypes; spiked genotypes are congression-free, so
-  they are excluded from both the correction and the background CDF and pass
-  through unchanged.
-* Operates on the genotype × concentration theta *point estimates* (pooled over
-  replicate/library by Stage 1); ``dk_geno`` is untouched (it is a growth
-  parameter, not part of the theta curve).  Estimation-noise deconvolution
-  stays in Stage 2 — this stage only removes the congression *bias*, and keeps
-  each genotype's Stage-1 covariance (a deliberate approximation: the bias
-  shift barely changes estimation precision).
-
-This is the θ-level analogue of the simulator's growth-level congression; the
-two agree to first order (exact at ``lambda -> 0``), and the spiked-only
-distribution is the external check on the residual.
-"""
-
-import numpy as np
-import jax.numpy as jnp
-import pandas as pd
-
-from tfscreen.util.io import read_dataframe
-from tfscreen.mle.fitters.least_squares import run_least_squares
-from tfscreen.tfmodel.genotype_fit.fit import (
-    _hill_theta, hill_theta_from_fit, _POWER_CLIP, _LOGIT_BOUND,
-)
-from tfscreen.tfmodel.generative.components.transformation._congression import (
-    update_thetas,
-)
-from scipy.special import expit  # noqa: F401  (kept for backward-compat imports)
-
-# Positions within the 5-element pheno block (PHENO_PARAMS_TRANSFORMED) of the
-# theta-curve params: logit_theta_low, logit_theta_high, log_hill_K, log_hill_n.
-# Index 0 (dk_geno) is a growth parameter and is left untouched.
-_THETA_PARAM_IDX = np.array([1, 2, 3, 4])
-
-_THETA_EPS = 1e-6
-
-
-def _theta_from_fit(fit, concs):
-    """Backward-compatible alias for :func:`fit.hill_theta_from_fit`."""
-    return hill_theta_from_fit(fit, concs)
-
-
-def correct_theta_matrix(theta_obs, lam, gain=1.0, tol=1e-4, max_iter=50,
-                         n_grid=256, return_history=False):
-    """Fixed-point de-attenuation of an observed-theta matrix.
-
-    Parameters
-    ----------
-    theta_obs : np.ndarray, shape (n_conc, n_geno)
-        Observed theta per concentration (rows) and genotype (cols).  Each row
-        is corrected against its own population CDF.
-    lam : float
-        Poisson co-resident rate (== the zero-truncated ``transformation_poisson_lambda``).
-    gain, tol, max_iter, n_grid : see module notes.
-    return_history : bool
-        If True, also return the list of intermediate ``theta_true`` matrices,
-        one per iteration (index 0 is the initial clamped ``theta_obs``, the
-        last entry is the converged estimate).  Lets callers report the
-        convergence trajectory per concentration.
-
-    Returns
-    -------
-    theta_true : np.ndarray, shape (n_conc, n_geno)
-    n_iter : int
-    history : list of np.ndarray
-        Only when ``return_history`` is True.
-    """
-    theta_obs = np.asarray(theta_obs, dtype=float)
-    theta_true = np.clip(theta_obs.copy(), _THETA_EPS, 1.0 - _THETA_EPS)
-    history = [theta_true.copy()]
-
-    if lam is None or float(lam) <= 0:
-        out = theta_obs.copy()
-        return (out, 0, history) if return_history else (out, 0)
-
-    n_iter = 0
-    for n_iter in range(1, max_iter + 1):
-        theta_pred = np.asarray(update_thetas(
-            jnp.asarray(theta_true), (float(lam),), theta_dist="empirical",
-            population_theta=jnp.asarray(theta_true), n_grid=n_grid))
-        resid = theta_obs - theta_pred
-        if np.nanmax(np.abs(resid)) < tol:
-            break
-        theta_true = np.clip(theta_true + gain * resid, _THETA_EPS, 1.0 - _THETA_EPS)
-        history.append(theta_true.copy())
-
-    return (theta_true, n_iter, history) if return_history else (theta_true, n_iter)
-
-
-def _refit_hill_theta(concs, theta, guess_pheno):
-    """Refit the 4 transformed theta-Hill params to a corrected theta curve.
-
-    ``guess_pheno`` is the genotype's current pheno block (used to seed).
-    Returns ``(logit_low, logit_high, log_K, log_n)``.
-    """
-    def model(p_t, x):
-        low = expit(p_t[0])
-        high = expit(p_t[1])
-        log_K = p_t[2]
-        n = np.exp(p_t[3])
-        return _hill_theta(x, low, high, log_K, n)
-
-    guess = np.asarray(guess_pheno)[_THETA_PARAM_IDX].astype(float)
-    lower = np.array([-_LOGIT_BOUND, -_LOGIT_BOUND, np.log(1e-12), np.log(0.05)])
-    upper = np.array([_LOGIT_BOUND, _LOGIT_BOUND, np.log(1e6), np.log(_POWER_CLIP)])
-    est, _std, _cov, _fit = run_least_squares(
-        some_model=model,
-        obs=np.asarray(theta, dtype=float),
-        obs_std=np.ones_like(theta, dtype=float),
-        guesses=guess, lower_bounds=lower, upper_bounds=upper,
-        args=(np.asarray(concs, dtype=float),))
-    return est
-
-
-def deattenuate_congression(fits, growth_df, lam, spiked=None,
-                            gain=1.0, tol=1e-4, max_iter=50, n_grid=256,
-                            return_theta_history=False):
-    """Congression-correct the bulk theta curves in a Stage-1 ``fits`` dict.
-
-    Parameters
-    ----------
-    fits : dict
-        ``{(genotype, titrant_name): GenotypeFit}`` from ``fit_phenotypes``.
-    growth_df : pandas.DataFrame or str
-        The real ln_cfu data (supplies the per-titrant concentration grid the
-        theta curves are corrected on).
-    lam : float or None
-        Zero-truncated Poisson congression rate.  ``None``/``<=0`` -> no-op.
-    spiked : iterable of str or None
-        Congression-free genotypes to exclude from correction and background.
-    gain, tol, max_iter, n_grid : fixed-point controls.
-    return_theta_history : bool
-        If True, also return a long-form DataFrame of the fixed-point theta
-        trajectory (columns ``genotype, titrant_name, titrant_conc, iter,
-        theta``), for diagnosing the convergence.
-
-    Returns
-    -------
-    corrected : dict
-        A new fits dict: bulk genotypes' theta-Hill params de-attenuated (Hill
-        refit; dk_geno and covariance unchanged); spiked genotypes untouched.
-    history_df : pandas.DataFrame
-        Only when ``return_theta_history`` is True.
-    """
-    empty_hist = pd.DataFrame(
-        columns=["genotype", "titrant_name", "titrant_conc", "iter", "theta"])
-
-    if lam is None or float(lam) <= 0:
-        out = dict(fits)
-        return (out, empty_hist) if return_theta_history else out
-
-    spiked = set(spiked or [])
-    growth_df = read_dataframe(growth_df)
-    corrected = dict(fits)
-    hist_rows = []
-
-    for titrant_name, sub in growth_df.groupby("titrant_name", observed=True):
-        concs = np.sort(sub["titrant_conc"].unique().astype(float))
-
-        keys = [k for k in fits
-                if k[1] == titrant_name and k[0] not in spiked
-                and np.all(np.isfinite(fits[k].est_t))]
-        if len(keys) < 2:
-            continue   # need a population to build a background CDF
-
-        # (n_conc, n_geno) observed theta.
-        theta_obs = np.stack([hill_theta_from_fit(fits[k], concs) for k in keys],
-                             axis=1)
-        result = correct_theta_matrix(
-            theta_obs, float(lam), gain=gain, tol=tol, max_iter=max_iter,
-            n_grid=n_grid, return_history=return_theta_history)
-
-        if return_theta_history:
-            theta_true, _, history = result
-            for it, mat in enumerate(history):
-                for i_c, c in enumerate(concs):
-                    for j, k in enumerate(keys):
-                        hist_rows.append({
-                            "genotype": k[0], "titrant_name": titrant_name,
-                            "titrant_conc": float(c), "iter": it,
-                            "theta": float(mat[i_c, j])})
-        else:
-            theta_true, _ = result
-
-        for j, k in enumerate(keys):
-            fit = fits[k]
-            new_theta_t = _refit_hill_theta(
-                concs, theta_true[:, j], fit.est_t[fit.pheno_slice])
-            est_t = np.array(fit.est_t, dtype=float)
-            pheno = est_t[fit.pheno_slice].copy()
-            pheno[_THETA_PARAM_IDX] = new_theta_t
-            est_t[fit.pheno_slice] = pheno
-            corrected[k] = fit._replace(est_t=est_t)
-
-    if return_theta_history:
-        history_df = (pd.DataFrame(hist_rows, columns=empty_hist.columns)
-                      if hist_rows else empty_hist)
-        return corrected, history_df
-    return corrected
diff --git a/src/tfscreen/tfmodel/genotype_fit/fit.py b/src/tfscreen/tfmodel/genotype_fit/fit.py
index c2c85de8..a9234501 100644
--- a/src/tfscreen/tfmodel/genotype_fit/fit.py
+++ b/src/tfscreen/tfmodel/genotype_fit/fit.py
@@ -151,8 +151,7 @@ def hill_theta_from_fit(fit, concs):
     """Evaluate a genotype's fitted Hill curve at ``concs`` (natural conc).
 
     Reads the theta-curve params out of a :class:`GenotypeFit`'s phenotype
-    block and evaluates :func:`_hill_theta`.  Used by :func:`predict_theta` and
-    by the congression de-attenuation stage.
+    block and evaluates :func:`_hill_theta`.  Used by :func:`predict_theta`.
     """
     pheno = np.asarray(fit.est_t)[fit.pheno_slice]
     theta_low = expit(pheno[1])
@@ -440,15 +439,13 @@ def fits_to_results_df(fits):
     """Assemble the per-genotype results table from a ``fits`` dict.
 
     ``fits`` is ``{(genotype, titrant_name): GenotypeFit}`` (as returned by
-    :func:`fit_phenotypes` or produced by the congression de-attenuation
-    stage).  Returns one row per fit with ``n_obs``, ``converged``, the five
+    :func:`fit_phenotypes`).  Returns one row per fit with ``n_obs``, ``converged``, the five
     natural-space phenotype parameters, and their transformed-space
     estimate/std (``<param>_t``, ``<param>_t_std``).  Dict insertion order is
     preserved.
 
-    Because this reads only ``est_t``/``cov_t``, it produces the same schema
-    whether the fits are raw (Stage 1) or de-attenuated (Stage 1.5), which is
-    how ``tfs-fit-genotypes`` emits the raw and corrected parameter tables.
+    Because this reads only ``est_t``/``cov_t``, it gives the same schema for
+    any fits dict with those fields.
     """
     rows = []
     for (geno, titr), gf in fits.items():
@@ -488,7 +485,7 @@ def predict_theta(fits, growth_df, theta_col="theta"):
         The ln_cfu data (supplies the per-titrant concentration grid).
     theta_col : str
         Name of the output theta column (default ``"theta"``).  Pass a
-        distinct name to concatenate/merge raw and de-attenuated predictions.
+        distinct name to merge predictions from different fits.
 
     Returns
     -------
diff --git a/src/tfscreen/tfmodel/inference/batch_safety.py b/src/tfscreen/tfmodel/inference/batch_safety.py
new file mode 100644
index 00000000..ed955d55
--- /dev/null
+++ b/src/tfscreen/tfmodel/inference/batch_safety.py
@@ -0,0 +1,301 @@
+"""
+Detect latent sites whose shape depends on the genotype mini-batch.
+
+Genotype mini-batching (``tensors/batch.py::get_batch``) slices the growth
+tensors down to ``batch_size`` genotypes but leaves ``num_genotype`` at the
+full library size.  A per-genotype latent is only safe under mini-batching if
+it is sampled at the full library size (a ``num_genotype``-sized plate) and
+then sliced to the batch with ``batch_idx`` -- the ``hill_geno`` pattern.  A
+latent sampled in a plate sized ``data.batch_size`` instead has one entry per
+batch *position*, not per genotype.  Component guides can hide this by
+holding full-size variational params and slicing them, but any numpyro
+autoguide (``AutoDelta`` for MAP included) sizes its params from a traced
+batch, so every step writes whichever genotypes are in that batch onto the
+same positions.  The result is silently wrong for every non-binding genotype.
+
+:func:`find_batch_dependent_latents` finds such sites by tracing the model at
+two different batch sizes: a correctly written latent has the same shape in
+both traces, while a batch-sized one changes shape.
+
+A library-sized latent can still be read in the wrong *order*: the full-batch
+training index is binding-first and reshuffled every step (see
+``ModelOrchestrator.get_random_idx``), so a component that returns
+library-ordered values without slicing them by ``batch_idx`` (for example by
+slicing only when ``batch_size < num_genotype``) pairs each batch position
+with the wrong genotype. Shapes are right, so the shape check cannot see it.
+:func:`find_batch_order_mismatches` catches it by tracing the model under two
+orderings of the same genotypes, with the latents held fixed, and checking
+that the batch-positional predictions permute with the index.
+"""
+
+import math
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+from numpyro.handlers import seed, substitute, trace
+
+
+def _latent_shapes(model_fn, priors, data):
+    """
+    Return {site_name: shape} for every unobserved sample site.
+
+    Traces under ``jax.eval_shape`` (abstract evaluation: no FLOPs, no
+    allocation), so the check is free even on a full-size library, falling
+    back to a concrete forward pass if a component cannot be traced
+    abstractly.
+    """
+
+    box = {}
+
+    def _traced(d, p):
+        box["trace"] = trace(seed(model_fn, rng_seed=0)).get_trace(data=d,
+                                                                   priors=p)
+        return jnp.zeros(())
+
+    try:
+        jax.eval_shape(_traced, data, priors)
+    except Exception:
+        box.clear()
+        _traced(data, priors)
+
+    return {
+        name: tuple(jnp.shape(site["value"]))
+        for name, site in box["trace"].items()
+        if site["type"] == "sample" and not site.get("is_observed", False)
+    }
+
+
+def orchestrator_latent_dimension(orchestrator):
+    """
+    Total number of scalar latents in a ``ModelOrchestrator``'s model.
+
+    This is the dimension of the flattened latent vector an
+    ``AutoContinuous`` guide (e.g. ``AutoMultivariateNormal``) works in.
+    """
+
+    num_genotype = orchestrator.data.num_genotype
+    data = orchestrator.get_batch(orchestrator.data, jnp.arange(num_genotype))
+    shapes = _latent_shapes(orchestrator.jax_model, orchestrator.priors, data)
+    return sum(math.prod(s) for s in shapes.values())
+
+
+def find_batch_dependent_latents(model_fn, priors, full_data, get_batch,
+                                 idx_a, idx_b):
+    """
+    Find latent sample sites whose shape changes with the batch size.
+
+    Parameters
+    ----------
+    model_fn : callable
+        Numpyro model taking ``data`` and ``priors`` keyword arguments.
+    priors : Any
+        Priors pytree passed through to ``model_fn``.
+    full_data : Any
+        Full (un-batched) data pytree.
+    get_batch : callable
+        ``get_batch(full_data, idx) -> batch_data``.
+    idx_a, idx_b : array-like of int
+        Two genotype index sets of *different* lengths.
+
+    Returns
+    -------
+    dict
+        Maps each batch-dependent site name to its ``(shape_a, shape_b)``
+        pair.  Empty when every latent is safe to mini-batch.
+
+    Raises
+    ------
+    ValueError
+        If ``idx_a`` and ``idx_b`` have the same length (the comparison
+        would be meaningless).
+    """
+
+    idx_a = jnp.asarray(idx_a)
+    idx_b = jnp.asarray(idx_b)
+    if idx_a.shape[0] == idx_b.shape[0]:
+        raise ValueError(
+            "idx_a and idx_b must have different lengths to expose "
+            "batch-size-dependent latent shapes."
+        )
+
+    shapes_a = _latent_shapes(model_fn, priors, get_batch(full_data, idx_a))
+    shapes_b = _latent_shapes(model_fn, priors, get_batch(full_data, idx_b))
+
+    return {
+        name: (shapes_a[name], shapes_b[name])
+        for name in shapes_a
+        if name in shapes_b and shapes_a[name] != shapes_b[name]
+    }
+
+
+def find_orchestrator_batch_dependent_latents(orchestrator):
+    """
+    Run :func:`find_batch_dependent_latents` on a ``ModelOrchestrator``.
+
+    Traces the model at the full library (``arange(num_genotype)``) and at
+    one fewer genotype.  Both index sets are prefixes of the library, so the
+    check does not depend on the configured ``batch_size``.
+
+    Parameters
+    ----------
+    orchestrator : ModelOrchestrator
+        Any object exposing ``jax_model``, ``priors``, ``data`` (with
+        ``num_genotype``) and ``get_batch``.
+
+    Returns
+    -------
+    dict
+        See :func:`find_batch_dependent_latents`.
+    """
+
+    num_genotype = orchestrator.data.num_genotype
+    if num_genotype < 2:
+        return {}
+
+    return find_batch_dependent_latents(
+        orchestrator.jax_model,
+        orchestrator.priors,
+        orchestrator.data,
+        orchestrator.get_batch,
+        idx_a=jnp.arange(num_genotype),
+        idx_b=jnp.arange(num_genotype - 1),
+    )
+
+
+# Deterministic sites whose last axis is the batch's genotype positions.
+_ORDER_SITES = ("growth_pred", "theta_growth_pred",
+                "binding_pred", "theta_binding_pred")
+
+
+def find_batch_order_mismatches(model_fn, priors, full_data, get_batch,
+                                idx_a, idx_b, sites=_ORDER_SITES,
+                                rtol=1e-5, atol=1e-6):
+    """
+    Find batch-positional predictions that do not follow the batch order.
+
+    Traces the model with the genotype index ``idx_a``, then again with
+    ``idx_b`` (a reordering of the same genotypes) while substituting every
+    latent sampled in the first trace. With the latents fixed, the value a
+    genotype gets must not depend on where it sits in the batch: for each
+    site in ``sites`` whose last axis has one entry per batch position, the
+    ``idx_b`` trace must equal the ``idx_a`` trace reordered to ``idx_b``.
+
+    Parameters
+    ----------
+    model_fn : callable
+        Numpyro model taking ``data`` and ``priors`` keyword arguments.
+    priors : Any
+        Priors pytree passed through to ``model_fn``.
+    full_data : Any
+        Full (un-batched) data pytree.
+    get_batch : callable
+        ``get_batch(full_data, idx) -> batch_data``.
+    idx_a, idx_b : array-like of int
+        Two orderings of the same genotype indices.
+    sites : tuple of str, optional
+        Deterministic sites to compare.
+    rtol, atol : float, optional
+        Tolerances for the comparison.
+
+    Returns
+    -------
+    dict
+        Maps each mismatching site to the largest absolute difference.
+        Empty when every compared prediction follows the batch order.
+
+    Raises
+    ------
+    ValueError
+        If ``idx_a`` and ``idx_b`` are not reorderings of the same indices.
+
+    Notes
+    -----
+    A latent that is itself batch-positional (``noise/beta``'s
+    ``{name}_dist``) is substituted in ``idx_a`` order and will show up here
+    as a mismatch; it is the documented exception to batch safety.
+    """
+
+    idx_a = np.asarray(idx_a, dtype=int)
+    idx_b = np.asarray(idx_b, dtype=int)
+    if (idx_a.shape != idx_b.shape
+            or not np.array_equal(np.sort(idx_a), np.sort(idx_b))):
+        raise ValueError("idx_a and idx_b must be reorderings of the same "
+                         "genotype indices.")
+
+    data_a = get_batch(full_data, jnp.asarray(idx_a))
+    data_b = get_batch(full_data, jnp.asarray(idx_b))
+
+    tr_a = trace(seed(model_fn, rng_seed=0)).get_trace(data=data_a,
+                                                       priors=priors)
+    latents = {name: site["value"] for name, site in tr_a.items()
+               if site["type"] == "sample"
+               and not site.get("is_observed", False)}
+    tr_b = trace(substitute(seed(model_fn, rng_seed=0),
+                            data=latents)).get_trace(data=data_b,
+                                                     priors=priors)
+
+    position_a = {int(g): i for i, g in enumerate(idx_a)}
+    order = np.array([position_a[int(g)] for g in idx_b])
+
+    found = {}
+    for name in sites:
+        if name not in tr_a or name not in tr_b:
+            continue
+        value_a = np.asarray(tr_a[name]["value"])
+        value_b = np.asarray(tr_b[name]["value"])
+        if value_a.ndim == 0 or value_a.shape[-1] != len(idx_a):
+            continue
+        expected = value_a[..., order]
+        if expected.shape != value_b.shape:
+            found[name] = float("inf")
+            continue
+        if not np.allclose(expected, value_b, rtol=rtol, atol=atol,
+                           equal_nan=True):
+            diff = np.abs(np.nan_to_num(expected) - np.nan_to_num(value_b))
+            found[name] = float(np.max(diff))
+    return found
+
+
+def find_orchestrator_batch_order_mismatches(orchestrator, seed_value=0):
+    """
+    Run :func:`find_batch_order_mismatches` on a ``ModelOrchestrator``.
+
+    Uses the full-batch training layout: the pinned binding genotypes first
+    (``batch_idx[:num_binding]``), then every other genotype. ``idx_a`` has
+    the others in library order, ``idx_b`` in a random order, as
+    ``get_random_idx`` produces each step.
+
+    Parameters
+    ----------
+    orchestrator : ModelOrchestrator
+        Any object exposing ``jax_model``, ``priors``, ``data`` (with
+        ``batch_idx``, ``num_binding`` and ``not_binding_idx``) and
+        ``get_batch``.
+    seed_value : int, optional
+        Seed for the reordering.
+
+    Returns
+    -------
+    dict
+        See :func:`find_batch_order_mismatches`.
+    """
+
+    data = orchestrator.data
+    pinned = np.asarray(data.batch_idx)[:data.num_binding]
+    rest = np.asarray(data.not_binding_idx)
+    if len(rest) < 2:
+        return {}
+
+    shuffled = np.random.default_rng(seed_value).permutation(rest)
+    if np.array_equal(shuffled, rest):
+        shuffled = rest[::-1]
+
+    return find_batch_order_mismatches(
+        orchestrator.jax_model,
+        orchestrator.priors,
+        orchestrator.data,
+        orchestrator.get_batch,
+        idx_a=np.concatenate([pinned, rest]),
+        idx_b=np.concatenate([pinned, shuffled]),
+    )
diff --git a/src/tfscreen/tfmodel/inference/convergence.py b/src/tfscreen/tfmodel/inference/convergence.py
new file mode 100644
index 00000000..fb36f0a8
--- /dev/null
+++ b/src/tfscreen/tfmodel/inference/convergence.py
@@ -0,0 +1,679 @@
+"""
+Convergence detection for the SVI/MAP optimization loop.
+
+Optimization runs in tumbling *windows* of ``window_steps`` optimizer steps.
+At the end of each window two tests decide whether the fit is still moving:
+
+**Loss test.**  The window's per-step losses are split into
+``loss_blocks`` blocks.  A least-squares line through the block medians gives
+the decrease in loss projected over one window (``drop``) and its standard
+error (``drop_se``, from the scatter of the block medians about the line).
+The loss is *improving* when ``drop`` exceeds both ``z * drop_se`` (the
+decrease is larger than the loss's own noise) and ``loss_rtol * |level|`` (a
+floor for deterministic losses, whose noise is ~0).  A loss that rises, or
+oscillates without net decrease, is not improving: that is the signature of
+a step size too large for the current stage, which a cut fixes.  Block
+medians keep the right-skewed noise of a stochastic ELBO from biasing the
+trend, and blocks of many steps make the standard error robust to
+short-range autocorrelation.
+
+**Loss skew.**  The loss being minimized is the *mean* of the per-step
+losses, which the block medians can hide: an ELBO estimate that is usually
+near its typical value but occasionally carries a huge penalty (a rare guide
+draw that violates a razor-sharp likelihood) has a median that ignores the
+penalties however much of the mean they make up.  The window's ``skew`` is
+``(mean - median) / (1.4826 * MAD)`` of the losses after removing the fitted
+trend line -- how many robust standard deviations the mean sits above the
+median.  It is roughly the penalized fraction of steps times the penalty, in
+per-step noise SDs.  Benign Monte Carlo noise, including the right skew of an
+ELBO estimate, stays below ~1; penalties that move the mean by more than
+``max_loss_skew`` noise SDs are flagged (on the congression-calibration runs,
+5-30% of steps carrying ~200 noise SDs gave 20-40).  The statistic only sees
+penalties that are a minority of steps: once most steps carry one, the median
+(and the robust spread) follow them, but then the loss level itself shows the
+problem.
+
+**Parameter test.**  Each tracked parameter is averaged over ``param_blocks``
+blocks of the window.  The same line fit through the block means gives each
+element's projected movement over one window and its standard error, both
+divided by a *normalizer*: the parameter's posterior SD (its guide scale) for
+a variational location, the prior SD for a MAP location, and 1 for a
+parameter already in log or logit units (positive or bounded parameters, so
+the movement is relative).  A posterior SD is floored at ``PRIOR_SD_FLOOR``
+times the site's prior SD (both in the location's own, unconstrained units):
+a mean-field guide can collapse a scale far below any honest posterior width
+(a hierarchical scale's guide SD reaching 1e-4 of its prior SD), and in those
+units a location creeping at a negligible absolute rate reads as moving
+indefinitely.  An element's *excess* is the part of its movement
+not explained by noise or by the optimizer's resolution,
+``max(|drift| - z * se - floor, 0)``, where ``floor`` is
+``MIN_STEP_COHERENCE * step_size * window_steps`` (normalized the same way).
+Adam moves a parameter by about the step size per step whatever the gradient's
+magnitude, so a parameter still descending covers a sizeable fraction of
+``step_size * window_steps`` per window, while one jittering about its optimum
+covers only a few step sizes; the floor keeps that jitter -- which can be many
+posterior SDs when a guide scale is smaller than the step size -- from reading
+as drift.  Movement below the floor means a gradient signal-to-noise ratio
+under ``MIN_STEP_COHERENCE``, which the current step size cannot resolve.  A
+parameter array is summarized by its maximum excess (small arrays) or a high
+quantile of it (per-genotype arrays, where a handful of elements always sit in
+the noise tail).  The parameters are *moving* when any summary exceeds
+``param_tolerance``.
+
+**Decisions.**  The two tests play different roles:
+
+- A window in which the loss is not improving is a *stall*.  After
+  ``patience`` consecutive stalls the step size is cut (by ``step_size_cut``,
+  floored at ``final_step_size``), whatever the parameters are doing --
+  unless the stalls, pooled, are a descent: one line through the block
+  medians of all ``patience`` windows (``pooled_loss_trend``) has a
+  significant drop per window (same ``z`` and ``loss_rtol``), and the stall
+  count starts over.  Each window alone can miss a slow, noisy descent
+  (count-likelihood run 0012: medians falling 1.5-1.9e5 per window at a
+  per-window t of 1.9, 3.0 and 1.0, then cut into a wrong optimum); pooling
+  three windows shrinks the slope's SE about fivefold, while a real plateau
+  stays at t ~ 0.  This is
+  the usual reduce-on-plateau rule: the loss alone decides when the current
+  step size has done what it can.  Parameters do not block a cut because some
+  directions never settle under a given objective (a MAP of a horseshoe local
+  scale drifts toward zero forever, at almost no change in loss), and holding
+  the step size high for them helps nothing.
+- Once the step size is at its floor, a window that is a stall, has no
+  moving parameter *and* has a loss skew within ``max_loss_skew`` is a
+  *plateau*, and ``patience`` consecutive plateaus are convergence.  A
+  parameter that is still moving therefore keeps the run going (and is named
+  in the log) until it stops or ``max_num_epochs`` is reached, so a slow slide
+  or a degenerate direction is reported, never mistaken for convergence; so
+  does a skewed loss, whose median plateau says nothing about the objective.
+  Skew never forces a cut: a smaller step size does not remove rare
+  penalties.
+
+**Exact loss (MAP).**  A MAP's objective is a deterministic function of the
+current point, so the noise the loss test guards against is only the
+mini-batch's.  On a full library that noise is large: at batch size 4096 of
+218,000 genotypes the per-window SE of the block-median trend was about 5e4
+nats, so a descent of 1e5 nats per window read as a stall and the step size
+was cut to its floor while the fit was still descending (staged-map study,
+2026-10-04).  When the caller passes the full-batch loss at the end of each
+window (``end_window(..., exact_loss=...)``), the loss test uses it instead.
+What noise remains is the optimizer's own jitter at the current step size:
+the point moves about the optimum, so a flat stretch of exact losses wobbles
+up and down. A least-squares line through the last ``patience + 1`` exact
+losses gives the drop per window and its SE (from the scatter about the
+line), and the window is *improving* when the drop exceeds both ``z * SE``
+and ``loss_rtol * |loss|``. A steady descent passes even when each window's
+change is small; jitter about a level does not. With fewer than three exact
+losses the drop since the last window decides. The line already pools the
+recent windows, so the separate pooled check is not run, and the skew check
+is skipped, since the exact loss is the objective itself.
+
+Because a parameter's systematic drift and its step-to-step jitter both scale
+with the step size, the significance part of the tests does not depend on the
+step size; each cut lowers the noise floor so the next stage resolves the
+estimates more finely.
+
+Everything here is host-side numpy (``param_drift`` also works on JAX arrays),
+so the logic can be tested on synthetic traces without a model.
+"""
+
+import numpy as np
+
+# Movement slower than this fraction of the step size per step is below the
+# optimizer's resolution at that step size (see the module docstring).
+MIN_STEP_COHERENCE = 0.01
+
+# Arrays with at most this many elements are summarized by their maximum
+# excess; larger (per-genotype, per-mutation) arrays by a quantile.
+SMALL_ARRAY_SIZE = 100
+
+# A posterior SD used as a normalizer is floored at this fraction of the
+# site's prior SD (see the module docstring).
+PRIOR_SD_FLOOR = 0.01
+
+# Largest loss skew, (mean - median) / robust SD, of a window that can count
+# as a plateau at the floor step size.  Clean traces (recorded real-data and
+# simulated SVI, MAP, synthetic exponential/lognormal noise) stay below ~1;
+# windows with rare large penalties reach 17-150.
+MAX_LOSS_SKEW = 3.0
+
+# A window whose loss (block median) falls below -RUNAWAY_LOSS_FACTOR times
+# the magnitude of the run's first block median is a runaway: the posterior
+# density is unbounded along some direction (a hierarchical scale collapsing
+# onto its offsets), so there is no optimum to converge to. A full-covariance
+# guide on a centred tube offset reached -8e23 from +4e4 and was called
+# converged (planning/studies/svi-overconfidence/, 2026-09-28).
+RUNAWAY_LOSS_FACTOR = 1e3
+
+# Convergence decisions returned by ConvergenceMonitor.end_window.
+CONTINUE = "continue"
+CUT = "cut"
+CONVERGED = "converged"
+DIVERGED = "diverged"
+
+
+def _line_fit(y, t):
+    """
+    Least-squares slope of ``y`` against ``t`` along axis 0, and its SE.
+
+    Parameters
+    ----------
+    y : array, shape (n, ...)
+        Values at each of ``n`` time points.
+    t : array, shape (n,)
+        Time points.
+
+    Returns
+    -------
+    slope, se : arrays with the trailing shape of ``y``
+    """
+
+    n = t.shape[0]
+    tc = t - t.mean()
+    sxx = float(np.sum(tc * tc))
+    tc = tc.reshape((n,) + (1,) * (y.ndim - 1))
+    ybar = y.mean(axis=0)
+    slope = (tc * (y - ybar)).sum(axis=0) / sxx
+    resid = y - ybar - slope * tc
+    s2 = (resid * resid).sum(axis=0) / (n - 2)
+    se = (s2 / sxx) ** 0.5
+    return slope, se
+
+
+def loss_trend(losses, num_blocks=20):
+    """
+    Projected change in loss over a window, with its standard error.
+
+    Parameters
+    ----------
+    losses : 1-D array
+        Per-step losses of one window, in step order.
+    num_blocks : int, optional
+        Number of blocks the window is split into (default 20).  Trailing
+        steps that do not fill a block are dropped.
+
+    Returns
+    -------
+    dict
+        ``level`` (median of the last block), ``mean`` (mean of the last
+        block), ``drop`` (loss decrease over the window predicted by the
+        fitted line; positive when the loss falls), ``drop_se``, ``t``
+        (``drop / drop_se``; ``inf`` when the block medians lie exactly on the
+        line) and ``skew`` (``loss_skew`` of the window about the line).
+    """
+
+    losses = np.asarray(losses, dtype=float).ravel()
+    num_blocks = min(num_blocks, losses.size)
+    if num_blocks < 3:
+        raise ValueError(
+            f"loss_trend needs at least 3 losses (got {losses.size})."
+        )
+    block = losses.size // num_blocks
+    used = losses[losses.size - num_blocks * block:]
+    blocks = used.reshape(num_blocks, block)
+    medians = np.median(blocks, axis=1)
+    centers = (np.arange(num_blocks) + 0.5) * block
+
+    slope, se = _line_fit(medians, centers)
+    width = float(used.size)
+    drop = -float(slope) * width
+    drop_se = float(se) * width
+    if drop_se > 0:
+        t = drop / drop_se
+    else:
+        t = np.inf if drop != 0 else 0.0
+
+    # Residuals about the fitted line, so a falling loss is not read as skew.
+    line = (medians.mean()
+            + float(slope) * (np.arange(used.size) + 0.5 - centers.mean()))
+
+    return {"level": float(medians[-1]),
+            "mean": float(blocks[-1].mean()),
+            "drop": drop,
+            "drop_se": drop_se,
+            "t": float(t),
+            "skew": loss_skew(used - line, level=medians[-1]),
+            "medians": medians,
+            "width": width}
+
+
+def pooled_loss_trend(windows):
+    """
+    One ``loss_trend``-style line through the block medians of consecutive
+    windows.
+
+    Parameters
+    ----------
+    windows : list of dict
+        ``loss_trend`` results of consecutive windows (oldest first), each
+        with ``medians`` and ``width``.
+
+    Returns
+    -------
+    dict
+        ``level`` (last block median of the last window), ``drop`` (loss
+        decrease per window, at the last window's width, predicted by the
+        pooled line), ``drop_se`` and ``t``.
+    """
+    ys, ts = [], []
+    offset = 0.0
+    for w in windows:
+        medians = np.asarray(w["medians"], dtype=float)
+        width = float(w["width"])
+        block = width / medians.size
+        ys.append(medians)
+        ts.append(offset + (np.arange(medians.size) + 0.5) * block)
+        offset += width
+    y = np.concatenate(ys)
+    t_all = np.concatenate(ts)
+    slope, se = _line_fit(y, t_all)
+    width = float(windows[-1]["width"])
+    drop = -float(slope) * width
+    drop_se = float(se) * width
+    if drop_se > 0:
+        t = drop / drop_se
+    else:
+        t = np.inf if drop != 0 else 0.0
+    return {"level": float(ys[-1][-1]), "drop": drop, "drop_se": drop_se,
+            "t": float(t)}
+
+
+def loss_skew(residuals, level=0.0, rtol=1e-6):
+    """
+    How far the mean of ``residuals`` sits above their median, in robust SDs.
+
+    Parameters
+    ----------
+    residuals : 1-D array
+        Per-step losses with any trend removed.
+    level : float, optional
+        Typical loss; ``rtol * |level|`` floors the robust SD so an exactly
+        constant (deterministic) loss has skew 0 rather than 0/0.
+    rtol : float, optional
+        Relative floor on the robust SD (default 1e-6, the default
+        ``loss_rtol``: differences below it are not resolved anyway).
+
+    Returns
+    -------
+    float
+        ``(mean - median) / max(1.4826 * MAD, rtol * |level|)``; 0 when the
+        spread and the floor are both zero.
+    """
+
+    r = np.asarray(residuals, dtype=float).ravel()
+    if r.size == 0:
+        return 0.0
+    med = np.median(r)
+    sd = max(1.4826 * float(np.median(np.abs(r - med))),
+             rtol * abs(float(level)))
+    gap = float(np.mean(r) - med)
+    if sd <= 0:
+        return 0.0
+    return gap / sd
+
+
+def param_drift(block_means, block_centers, window_steps, normalizer, z,
+                floor=0.0):
+    """
+    Per-element movement of a parameter over one window, in normalizer units.
+
+    Parameters
+    ----------
+    block_means : array, shape (num_blocks, ...)
+        The parameter averaged over each block of the window (unconstrained
+        space).
+    block_centers : array, shape (num_blocks,)
+        Step at the center of each block.
+    window_steps : int
+        Window length; the fitted slope is projected over this many steps.
+    normalizer : array or float
+        Divides the movement: posterior SD, prior SD, or 1.  Broadcast against
+        the parameter's shape.
+    z : float
+        Number of standard errors of movement attributed to noise.
+    floor : float, optional
+        Movement (in parameter units, before normalizing) attributed to the
+        optimizer's resolution (default 0).
+
+    Returns
+    -------
+    drift, excess : arrays with the parameter's shape
+        ``drift`` is the projected movement (signed); ``excess`` is
+        ``max(|drift| - z * se - floor, 0)``, all in normalizer units.
+    """
+
+    # Works for numpy and jax arrays alike.
+    xp = np
+    if type(block_means).__module__.startswith("jax"):
+        import jax.numpy as xp
+
+    t = np.asarray(block_centers, dtype=float)
+    slope, se = _line_fit(block_means, t)
+    drift = slope * window_steps / normalizer
+    se = se * window_steps / normalizer
+    excess = xp.maximum(xp.abs(drift) - z * se - floor / normalizer, 0.0)
+    return drift, excess
+
+
+def summarize_excess(excess, quantile=0.99):
+    """
+    One number per parameter array: max excess for small arrays, else a
+    quantile.  Non-finite elements count as infinitely moving.
+    """
+
+    excess = np.asarray(excess, dtype=float).ravel()
+    if excess.size == 0:
+        return 0.0
+    excess = np.where(np.isfinite(excess), excess, np.inf)
+    if excess.size <= SMALL_ARRAY_SIZE:
+        return float(np.max(excess))
+    return float(np.quantile(excess, quantile))
+
+
+class ConvergenceMonitor:
+    """
+    Decide, window by window, whether to continue, cut the step size, or stop.
+
+    Parameters
+    ----------
+    step_size : float
+        Current (initial) optimizer step size.
+    final_step_size : float or None, optional
+        Floor for the step size.  None, or a value >= ``step_size``, disables
+        cuts: the run is at its floor from the start.
+    step_size_cut : float, optional
+        Factor applied to the step size at each cut (default 0.1).
+    patience : int, optional
+        Consecutive windows without loss improvement required for a cut, and
+        (at the floor) without loss improvement or parameter movement
+        required for a stop (default 3).
+    z : float, optional
+        Standard errors of change attributed to noise (default 3).
+    loss_rtol : float, optional
+        Relative floor on a meaningful loss change per window (default 1e-6).
+    param_tolerance : float, optional
+        Largest allowed parameter excess per window, in normalizer units
+        (default 0.05).
+    max_loss_skew : float, optional
+        Largest loss skew (``loss_skew``) of a window that can be a plateau at
+        the floor step size (default ``MAX_LOSS_SKEW``).
+    """
+
+    def __init__(self,
+                 step_size,
+                 final_step_size=None,
+                 step_size_cut=0.1,
+                 patience=3,
+                 z=3.0,
+                 loss_rtol=1e-6,
+                 param_tolerance=0.05,
+                 max_loss_skew=MAX_LOSS_SKEW):
+
+        if not 0 < step_size_cut < 1:
+            raise ValueError(
+                f"step_size_cut must be between 0 and 1 (got {step_size_cut})."
+            )
+        if patience < 1:
+            raise ValueError(f"patience must be >= 1 (got {patience}).")
+
+        self.step_size = float(step_size)
+        if final_step_size is None:
+            final_step_size = self.step_size
+        self.final_step_size = float(final_step_size)
+        self.step_size_cut = float(step_size_cut)
+        self.patience = int(patience)
+        self.z = float(z)
+        self.loss_rtol = float(loss_rtol)
+        self.param_tolerance = float(param_tolerance)
+        self.max_loss_skew = float(max_loss_skew)
+
+        self.plateau_count = 0
+        self.num_cuts = 0
+        self.converged = False
+        self.diverged = False
+        # |loss| at the start of the run (first block median), the scale
+        # for the runaway check.
+        self.reference_loss = None
+        self.last = None
+        # loss_trend results of the current run of stalled windows, for the
+        # pooled check before a cut or a stop.
+        self._stalls = []
+        # Exact (full-batch) losses of recent windows, newest last, when the
+        # caller supplies them (MAP).
+        self._exact = []
+
+    @property
+    def at_floor(self):
+        """True when no further step-size cut is possible."""
+        return self.step_size <= self.final_step_size * (1 + 1e-9)
+
+    def loss_improving(self, stats):
+        """Whether a ``loss_trend`` result counts as a real improvement."""
+        floor = self.loss_rtol * abs(stats["level"])
+        return (stats["drop"] > self.z * stats["drop_se"]
+                and stats["drop"] > floor)
+
+    def drift_floor(self, window_steps):
+        """Movement per window below the optimizer's resolution."""
+        if not np.isfinite(self.step_size):
+            return 0.0
+        return MIN_STEP_COHERENCE * self.step_size * window_steps
+
+    def _exact_stats(self, exact_loss, loss_stats):
+        """Loss-test statistics from the exact loss (see the module docstring)."""
+        exact_loss = float(exact_loss)
+        prev = self._exact[-1] if self._exact else None
+        self._exact = (self._exact + [exact_loss])[-max(self.patience + 1, 3):]
+        y = np.asarray(self._exact, dtype=float)
+        if y.size >= 3:
+            slope, se = _line_fit(y, np.arange(y.size, dtype=float))
+            drop, drop_se = -float(slope), float(se)
+        elif prev is None or not np.isfinite(prev):
+            # First window: nothing to compare with yet; count it as a
+            # descent so the run does not start with a stall.
+            drop, drop_se = np.inf, 0.0
+        else:
+            drop, drop_se = prev - exact_loss, 0.0
+        if drop_se > 0:
+            t = drop / drop_se
+        else:
+            t = np.inf if drop > 0 else (-np.inf if drop < 0 else 0.0)
+        return {"level": exact_loss,
+                "mean": float(loss_stats.get("mean", np.nan)),
+                "drop": float(drop),
+                "drop_se": float(drop_se),
+                "t": float(t),
+                "skew": float(loss_stats.get("skew", 0.0))}
+
+    def end_window(self, step, loss_stats, param_excess=None, param_drift=None,
+                   exact_loss=None):
+        """
+        Record one finished window and return the decision.
+
+        Parameters
+        ----------
+        step : int
+            Optimizer step at the end of the window.
+        loss_stats : dict
+            Output of ``loss_trend`` for the window.
+        param_excess : dict or None, optional
+            ``{parameter name: summarized excess}`` (``summarize_excess``).
+        param_drift : dict or None, optional
+            ``{parameter name: summarized |drift|}``, reported only.
+        exact_loss : float or None, optional
+            The full-batch loss at the end of the window (MAP). When given,
+            the loss test and the runaway check use it instead of
+            ``loss_stats`` (see the module docstring).
+
+        Returns
+        -------
+        str
+            ``CONTINUE``, ``CUT`` (the step size has been cut; the caller
+            must apply ``self.step_size``) or ``CONVERGED``.
+        """
+
+        param_excess = dict(param_excess or {})
+        param_drift = dict(param_drift or {})
+        minibatch_level = float(loss_stats["level"])
+        exact = exact_loss is not None
+        if exact:
+            loss_stats = self._exact_stats(exact_loss, loss_stats)
+
+        if self.reference_loss is None:
+            medians = loss_stats.get("medians")
+            start = (float(np.asarray(medians)[0])
+                     if medians is not None and np.size(medians)
+                     else float(loss_stats["level"]))
+            self.reference_loss = abs(start)
+        runaway = (float(loss_stats["level"])
+                   < -RUNAWAY_LOSS_FACTOR * max(self.reference_loss, 1.0))
+
+        loss_improving = self.loss_improving(loss_stats)
+
+        worst_param, worst_excess = None, 0.0
+        for name, value in param_excess.items():
+            if worst_param is None or value > worst_excess:
+                worst_param, worst_excess = name, value
+        params_moving = worst_excess > self.param_tolerance
+
+        # A median plateau says nothing about the mean when rare penalties
+        # dominate it (see the module docstring).
+        loss_skew = float(loss_stats.get("skew", 0.0))
+        loss_skewed = (not exact) and not loss_skew <= self.max_loss_skew
+
+        # Cuts follow the loss alone; the stop also needs the parameters and
+        # an unskewed loss.
+        at_floor = self.at_floor
+        if at_floor:
+            plateau = not (loss_improving or params_moving or loss_skewed)
+        else:
+            plateau = not loss_improving
+        self.plateau_count = self.plateau_count + 1 if plateau else 0
+        if plateau and "medians" in loss_stats:
+            self._stalls = (self._stalls + [
+                {"medians": np.asarray(loss_stats["medians"], dtype=float),
+                 "width": float(loss_stats["width"])}])[-self.patience:]
+        else:
+            self._stalls = []
+
+        # Before acting on `patience` stalls, check whether they are, pooled,
+        # a slow descent rather than a plateau (see the module docstring).
+        pooled_t = np.nan
+        if exact:
+            pooled = None  # the exact loss test already pools (see above)
+        elif (self.plateau_count >= self.patience
+                and len(self._stalls) == self.patience and self.patience > 1):
+            pooled = pooled_loss_trend(self._stalls)
+        else:
+            pooled = None
+        if pooled is not None:
+            pooled_t = pooled["t"]
+            if self.loss_improving(pooled):
+                self.plateau_count = 0
+                self._stalls = []
+                plateau = False
+
+        decision = CONTINUE
+        step_size = self.step_size
+        if runaway:
+            decision = DIVERGED
+            self.diverged = True
+        elif self.plateau_count >= self.patience:
+            if at_floor:
+                decision = CONVERGED
+                self.converged = True
+            else:
+                decision = CUT
+                self.step_size = max(self.step_size * self.step_size_cut,
+                                     self.final_step_size)
+                self.num_cuts += 1
+                self.plateau_count = 0
+                self._stalls = []
+
+        self.last = {
+            "step": int(step),
+            "step_size": step_size,
+            "loss": minibatch_level,
+            "loss_exact": float(exact_loss) if exact else np.nan,
+            "loss_mean": float(loss_stats.get("mean", np.nan)),
+            "loss_drop": loss_stats["drop"],
+            "loss_drop_se": loss_stats["drop_se"],
+            "loss_t": loss_stats["t"],
+            "loss_improving": bool(loss_improving),
+            "loss_skew": loss_skew,
+            "loss_skewed": bool(loss_skewed),
+            "worst_param": worst_param,
+            "worst_param_excess": float(worst_excess),
+            "worst_param_drift": float(param_drift.get(worst_param, np.nan))
+                                 if worst_param is not None else np.nan,
+            "params_moving": bool(params_moving),
+            "plateau": bool(plateau),
+            "plateau_count": self.plateau_count,
+            "pooled_loss_t": float(pooled_t),
+            "decision": decision,
+        }
+        return decision
+
+    def describe(self):
+        """One-line summary of the last window, for logs."""
+        if self.last is None:
+            return "no complete convergence window yet"
+        r = self.last
+        exact = r.get("loss_exact", np.nan)
+        if exact is not None and np.isfinite(exact):
+            loss = (f"exact loss {exact:.10g} (mini-batch {r['loss']:.6g}), "
+                    f"drop/window {r['loss_drop']:.6g} +/- "
+                    f"{r['loss_drop_se']:.3g} (t={r['loss_t']:.3g})")
+        else:
+            loss = (f"loss {r['loss']:.6g}, drop/window {r['loss_drop']:.4g} "
+                    f"+/- {r['loss_drop_se']:.3g} (t={r['loss_t']:.3g})")
+        skew = r.get("loss_skew", 0.0)
+        if r.get("loss_skewed"):
+            loss += (f"; loss skewed ({skew:.3g} robust SDs of mean above "
+                     f"median: rare large penalties)")
+        pooled_t = r.get("pooled_loss_t", np.nan)
+        if np.isfinite(pooled_t):
+            verdict = ("a slow descent, not a plateau" if r["plateau_count"] == 0
+                       else "a plateau")
+            loss += (f"; pooled over {self.patience} stalled windows "
+                     f"t={pooled_t:.3g} ({verdict})")
+        if r["worst_param"] is None:
+            params = "no parameters tracked"
+        else:
+            params = (f"largest parameter movement {r['worst_param']} "
+                      f"(excess {r['worst_param_excess']:.3g}, "
+                      f"drift {r['worst_param_drift']:.3g})")
+        return (f"step {r['step']}: {loss}; {params}; step size "
+                f"{r['step_size']:.3g}; plateau {r['plateau_count']}/"
+                f"{self.patience}; {r['decision']}")
+
+    def state_dict(self):
+        """Serializable state for checkpoints."""
+        return {"step_size": self.step_size,
+                "final_step_size": self.final_step_size,
+                "plateau_count": self.plateau_count,
+                "num_cuts": self.num_cuts,
+                "converged": self.converged,
+                "diverged": self.diverged,
+                "reference_loss": self.reference_loss,
+                "last": self.last,
+                "stalls": [{"medians": w["medians"].tolist(),
+                            "width": w["width"]} for w in self._stalls],
+                "exact": list(self._exact)}
+
+    def load_state_dict(self, state):
+        """
+        Restore the stage (step size, cuts, plateau count) from a checkpoint.
+
+        The tolerances and floor stay as configured for the resumed run.
+        """
+        self.step_size = float(state["step_size"])
+        self.plateau_count = int(state.get("plateau_count", 0))
+        self.num_cuts = int(state.get("num_cuts", 0))
+        self.converged = bool(state.get("converged", False))
+        self.diverged = bool(state.get("diverged", False))
+        self.reference_loss = state.get("reference_loss")
+        self.last = state.get("last")
+        self._stalls = [{"medians": np.asarray(w["medians"], dtype=float),
+                         "width": float(w["width"])}
+                        for w in state.get("stalls", [])]
+        self._exact = [float(v) for v in state.get("exact", [])]
diff --git a/src/tfscreen/tfmodel/inference/initialization.py b/src/tfscreen/tfmodel/inference/initialization.py
new file mode 100644
index 00000000..f2137367
--- /dev/null
+++ b/src/tfscreen/tfmodel/inference/initialization.py
@@ -0,0 +1,440 @@
+"""
+Starting points for optimization: translating between site values and the
+parameters of each guide.
+
+Three kinds of dictionary describe a point in parameter space:
+
+- **site values**, keyed by model sample-site name, in constrained space
+  (what ``init_to_value`` takes, and what most configured guesses are keyed
+  by);
+- **AutoDelta parameters**, keyed ``{site}_auto_loc`` (a MAP point, also in
+  constrained space);
+- **component-guide parameters**, keyed by the guide's ``pyro.param`` names.
+  By convention a site ``s`` guided by a Normal or LogNormal has its location
+  in ``s_loc``/``s_locs`` and its scale in ``s_scale``/``s_scales``.
+
+``site_values`` pulls site values out of any mixture of the three, and
+``component_guide_init`` turns site values into component-guide parameters.
+The guide map behind both is built by tracing the guide and checking that each
+candidate parameter really is the location (or scale) of its site's
+distribution, so a component that does not follow the convention is reported
+rather than mis-initialized.
+"""
+
+import numpy as np
+import jax.numpy as jnp
+import numpyro.distributions as dist
+from numpyro import handlers
+
+_WRAPPERS = (dist.ExpandedDistribution, dist.Independent,
+             dist.MaskedDistribution)
+
+AUTO_LOC_SUFFIX = "_auto_loc"
+
+
+def _unwrap(fn):
+    """Strip batch-shape wrappers (expand, to_event, mask) off a distribution."""
+    while isinstance(fn, _WRAPPERS):
+        fn = fn.base_dist
+    return fn
+
+
+def structure_trace(model, seed=0, substitutions=None, substitute_fn=None,
+                    **model_kwargs):
+    """
+    Trace ``model`` once to read its structure: site shapes, supports and
+    distributions.
+
+    Unsubstituted latent sites are drawn from the prior, and with a wide
+    prior (the thermo ``PddG`` ddG prior with a large learned ``sigma_s``
+    gives ln K draws of +/-60) the draw can predict a non-finite ``ln_cfu``.
+    numpyro then refuses to construct the observation distribution
+    (``CountNegativeBinomial ... invalid log_mu``), although nothing that
+    reads a structure trace evaluates the observation. Distribution argument
+    validation is therefore switched off for the trace, so a structure
+    lookup never depends on the observation model accepting a prior draw.
+    Under ``jit`` the checks never run anyway (the values are tracers).
+
+    Parameters
+    ----------
+    model : callable
+        Numpyro model.
+    seed : int, optional
+        PRNG seed for the trace.
+    substitutions : dict or None, optional
+        Constrained site values to substitute.
+    substitute_fn : callable or None, optional
+        ``numpyro.handlers.substitute`` ``substitute_fn`` (site -> value or
+        None), applied after ``substitutions``.
+    **model_kwargs
+        Passed to the model.
+
+    Returns
+    -------
+    collections.OrderedDict
+        The numpyro trace.
+    """
+
+    from numpyro import validation_enabled
+
+    fn = handlers.seed(model, seed)
+    if substitutions:
+        fn = handlers.substitute(fn, data=substitutions)
+    if substitute_fn is not None:
+        fn = handlers.substitute(fn, substitute_fn=substitute_fn)
+    with validation_enabled(False):
+        return handlers.trace(fn).get_trace(**model_kwargs)
+
+
+def unconstrained_substitute_fn(unconstrained):
+    """
+    ``substitute_fn`` that sets each named latent site from its unconstrained
+    value, mapped through ``biject_to`` of the site's own support.
+
+    Every listed site takes its value without a prior draw, and a
+    hierarchical site's support is that of its distribution given the
+    substituted parents (what ``numpyro.infer.util.constrain_fn`` does).
+    """
+
+    from numpyro.distributions.transforms import biject_to
+
+    def fn(site):
+        name = site["name"]
+        if (site["type"] != "sample" or site.get("is_observed", False)
+                or name not in unconstrained):
+            return None
+        return biject_to(site["fn"].support)(unconstrained[name])
+
+    return fn
+
+
+def trace_model_sites(model, priors, data, substitutions=None, seed=0,
+                      unconstrained=None):
+    """
+    Trace ``model`` once and return its latent sample sites.
+
+    The trace is a ``structure_trace``: argument validation is off, so a
+    prior draw that makes the observation model's parameters non-finite does
+    not fail it.
+
+    Parameters
+    ----------
+    model : callable
+        Numpyro model taking ``priors=`` and ``data=``.
+    priors, data
+        Passed to the model.  ``data`` should be a full (library-ordered)
+        batch so per-genotype sites have library shape.
+    substitutions : dict or None, optional
+        Constrained site values to substitute while tracing.
+    seed : int, optional
+        PRNG seed for the trace.
+    unconstrained : dict or None, optional
+        Unconstrained site values (a MAP point) to substitute, each mapped
+        through its own site's support (``unconstrained_substitute_fn``).
+
+    Returns
+    -------
+    dict
+        ``{site name: numpyro trace site}`` for unobserved sample sites.
+    """
+
+    sub_fn = (None if unconstrained is None
+              else unconstrained_substitute_fn(unconstrained))
+    tr = structure_trace(model, seed=seed, substitutions=substitutions,
+                         substitute_fn=sub_fn, priors=priors, data=data)
+    return {name: site for name, site in tr.items()
+            if site["type"] == "sample" and not site.get("is_observed", False)}
+
+
+def _matching_param(site_name, suffixes, params, target, shape):
+    """Name of the param in ``params`` that equals ``target`` for this site."""
+    for suffix in suffixes:
+        name = site_name + suffix
+        if name not in params:
+            continue
+        value = jnp.asarray(params[name])
+        if value.shape != shape:
+            continue
+        try:
+            same = bool(jnp.allclose(jnp.broadcast_to(value, target.shape),
+                                     target))
+        except ValueError:
+            same = False
+        if same:
+            return name
+    return None
+
+
+def component_guide_map(guide, priors, data, seed=0):
+    """
+    Map each guide sample site to its location and scale parameters.
+
+    Parameters
+    ----------
+    guide : callable
+        The component guide (``orchestrator.jax_model_guide``).
+    priors, data
+        Passed to the guide; ``data`` should be a full batch.
+    seed : int, optional
+        PRNG seed for the trace.
+
+    Returns
+    -------
+    mapping : dict
+        ``{site: {"kind": "normal"|"lognormal", "loc": param name,
+        "scale": param name or None, "shape": site shape,
+        "scale_lower_bound": the scale param's constraint lower bound or
+        None}}``.
+    unmatched : list of str
+        Guide sample sites whose location parameter could not be identified
+        (not Normal/LogNormal, or not following the naming convention).
+    defaults : dict
+        ``{param name: initial value}`` for every guide param.
+    """
+
+    tr = handlers.trace(handlers.seed(guide, seed)).get_trace(priors=priors,
+                                                              data=data)
+    params = {name: site["value"] for name, site in tr.items()
+              if site["type"] == "param"}
+    lower_bounds = {}
+    for name, site in tr.items():
+        if site["type"] != "param":
+            continue
+        bound = getattr(site.get("kwargs", {}).get("constraint"),
+                        "lower_bound", None)
+        if bound is not None:
+            lower_bounds[name] = bound
+
+    mapping = {}
+    unmatched = []
+    for name, site in tr.items():
+        if site["type"] != "sample" or site.get("is_observed", False):
+            continue
+        fn = _unwrap(site["fn"])
+        if isinstance(fn, dist.LogNormal):
+            kind = "lognormal"
+        elif isinstance(fn, dist.Normal):
+            kind = "normal"
+        else:
+            unmatched.append(name)
+            continue
+
+        shape = jnp.shape(site["value"])
+        loc = _matching_param(name, ("_loc", "_locs"), params,
+                              jnp.asarray(fn.loc), shape)
+        if loc is None:
+            unmatched.append(name)
+            continue
+        scale = _matching_param(name, ("_scale", "_scales"), params,
+                                jnp.asarray(fn.scale), shape)
+        mapping[name] = {"kind": kind, "loc": loc, "scale": scale,
+                         "shape": shape,
+                         "scale_lower_bound": lower_bounds.get(scale)}
+
+    return mapping, sorted(unmatched), params
+
+
+def site_values(values, model_sites, guide_map=None):
+    """
+    Collect constrained site values from a mixed-key dictionary.
+
+    Recognized keys, in increasing priority: component-guide location params
+    (via ``guide_map``; a LogNormal location is exponentiated), bare site
+    names, and AutoDelta ``{site}_auto_loc`` params.  Values that cannot be
+    broadcast to the site's shape are skipped.
+
+    Parameters
+    ----------
+    values : dict
+        Guesses, a MAP result, or both.
+    model_sites : dict
+        ``trace_model_sites`` output.
+    guide_map : dict or None, optional
+        ``component_guide_map`` mapping.
+
+    Returns
+    -------
+    dict
+        ``{site: array}``.
+    """
+
+    out = {}
+
+    def _put(site, value):
+        shape = jnp.shape(model_sites[site]["value"])
+        try:
+            out[site] = jnp.broadcast_to(jnp.asarray(value, dtype=float),
+                                         shape)
+        except ValueError:
+            pass
+
+    if guide_map:
+        for site, entry in guide_map.items():
+            if site in model_sites and entry["loc"] in values:
+                value = jnp.asarray(values[entry["loc"]], dtype=float)
+                if entry["kind"] == "lognormal":
+                    value = jnp.exp(value)
+                _put(site, value)
+
+    for key, value in values.items():
+        if key in model_sites:
+            _put(key, value)
+
+    for key, value in values.items():
+        if key.endswith(AUTO_LOC_SUFFIX):
+            site = key[:-len(AUTO_LOC_SUFFIX)]
+            if site in model_sites:
+                _put(site, value)
+
+    return out
+
+
+def component_guide_init(values, guide_map, defaults, init_scale=None):
+    """
+    Component-guide parameters that start at the given site values.
+
+    Parameters
+    ----------
+    values : dict
+        Constrained site values (``site_values`` output).
+    guide_map : dict
+        ``component_guide_map`` mapping.
+    defaults : dict
+        Starting param values to fall back on (the guide's own initial values,
+        overlaid with any param-keyed guesses).
+    init_scale : float or None, optional
+        Upper bound for every mapped scale param: each starts at
+        ``min(default, init_scale)``.  None leaves scales at their defaults.
+        A scale whose constraint has a lower bound (``greater_than(1e-4)``
+        on the hyperparameter scales) starts at least ``init_scale`` above
+        that bound: a param starting on its bound is ``-inf`` in
+        unconstrained space and never moves.
+
+    Returns
+    -------
+    params : dict
+        ``{param name: value}`` for mapped locations and scales.
+    skipped : list of str
+        Sites with a value that could not be used (a non-positive value for a
+        LogNormal site).
+    """
+
+    params = {}
+    skipped = []
+    for site, entry in guide_map.items():
+        if site in values:
+            value = jnp.asarray(values[site], dtype=float)
+            if entry["kind"] == "lognormal":
+                if not bool(jnp.all(value > 0)):
+                    skipped.append(site)
+                    value = None
+                else:
+                    value = jnp.log(value)
+            if value is not None:
+                params[entry["loc"]] = jnp.broadcast_to(value, entry["shape"])
+
+        scale = entry["scale"]
+        if init_scale is not None and scale is not None and scale in defaults:
+            start = jnp.minimum(jnp.asarray(defaults[scale], dtype=float),
+                                init_scale)
+            bound = entry.get("scale_lower_bound")
+            if bound is not None:
+                start = jnp.maximum(start, jnp.asarray(bound) + init_scale)
+            params[scale] = start
+
+    return params, sorted(skipped)
+
+
+def site_prior_sds(model_sites):
+    """
+    Prior standard deviation of each site, broadcast to the site's shape.
+
+    Parameters
+    ----------
+    model_sites : dict
+        ``trace_model_sites`` output (ideally traced with the current site
+        values substituted, so hierarchical priors reflect them).
+
+    Returns
+    -------
+    dict
+        ``{site: array}`` for sites whose prior has a finite, positive
+        variance.  Other sites are left out.
+    """
+
+    out = {}
+    for name, site in model_sites.items():
+        try:
+            sd = jnp.sqrt(jnp.asarray(site["fn"].variance, dtype=float))
+            sd = jnp.broadcast_to(sd, jnp.shape(site["value"]))
+        except (NotImplementedError, ValueError, TypeError, AttributeError,
+                ArithmeticError):
+            # ArithmeticError: e.g. an InverseGamma with Python-float
+            # concentration 2 divides by zero in its variance.
+            continue
+        sd = np.asarray(sd)
+        if sd.size == 0 or not np.all(np.isfinite(sd)) or not np.all(sd > 0):
+            continue
+        out[name] = sd
+    return out
+
+
+def site_unconstrained_prior_sds(model_sites, seed=0, num_draws=64):
+    """
+    Prior spread of each site in its unconstrained coordinates.
+
+    Guide locations live in unconstrained space (a LogNormal guide's location
+    is ``log`` of a positive site, an AutoNormal location is the site mapped
+    through ``biject_to(support).inv``), so this is the prior width in the
+    same units.  A real-valued site with a finite prior variance uses the
+    exact SD; every other site uses a robust Monte Carlo estimate, ``IQR /
+    1.349`` of ``num_draws`` prior draws mapped to unconstrained space (the SD
+    for a normal, finite for heavy-tailed priors such as a half-Cauchy).
+
+    Parameters
+    ----------
+    model_sites : dict
+        ``trace_model_sites`` output (ideally traced with the current site
+        values substituted, so hierarchical priors reflect them).
+    seed : int, optional
+        PRNG seed for the Monte Carlo draws.
+    num_draws : int, optional
+        Draws per site for the Monte Carlo estimate (default 64).
+
+    Returns
+    -------
+    dict
+        ``{site: array}`` broadcast to the site's shape, for sites whose
+        spread is finite and positive everywhere.  Other sites are left out.
+    """
+
+    import jax
+    from numpyro.distributions.transforms import biject_to
+
+    exact = site_prior_sds(model_sites)
+    key = jax.random.PRNGKey(seed)
+
+    out = {}
+    for name, site in model_sites.items():
+        fn = site["fn"]
+        shape = jnp.shape(site["value"])
+        try:
+            real = _unwrap(fn).support is dist.constraints.real
+        except (NotImplementedError, AttributeError):
+            real = False
+        if real and name in exact:
+            out[name] = exact[name]
+            continue
+        try:
+            key, sub = jax.random.split(key)
+            draws = fn.sample(sub, (num_draws,))
+            u = biject_to(fn.support).inv(draws)
+            q25, q75 = jnp.quantile(u, jnp.array([0.25, 0.75]), axis=0)
+            sd = np.asarray(jnp.broadcast_to((q75 - q25) / 1.349, shape))
+        except (NotImplementedError, ValueError, TypeError, AttributeError,
+                ArithmeticError):
+            continue
+        if sd.size == 0 or not np.all(np.isfinite(sd)) or not np.all(sd > 0):
+            continue
+        out[name] = sd
+    return out
diff --git a/src/tfscreen/tfmodel/inference/model_params.py b/src/tfscreen/tfmodel/inference/model_params.py
new file mode 100644
index 00000000..87ce5306
--- /dev/null
+++ b/src/tfscreen/tfmodel/inference/model_params.py
@@ -0,0 +1,135 @@
+"""
+Learned parameters that the model itself registers with ``pyro.param``.
+
+A few components register point-estimated parameters inside the *model*, not
+only in a guide: the thermodynamic PnnC structure MLP weights
+(``theta/thermo/nn.py``).  (The PddG/PnnC per-structure ΔΔG scale σ_s was
+one until 2026-10-11; it is now the sample site ``theta_ddG_sigma``.)  They
+are empirical-Bayes estimates: the optimizer fits them alongside the guide, and they live in the
+same param store (``svi.get_params`` constrained, the optimizer state
+unconstrained), keyed by their bare names.
+
+Any code that runs the model after the fit (posterior forward passes, the
+Laplace Hessian, predictions, init hand-offs) must give the model these
+learned values. Without them every ``pyro.param`` falls back to its init
+value (NN = 0), and the output no longer matches the fit.
+
+``model_param_constraints`` finds the model's param sites by tracing the
+model; ``model_params`` picks their values out of any param dict;
+``hold_model_params`` wraps a model so they are fixed constants. The Laplace
+treats them as fixed (they are not part of the Hessian), and posterior files
+carry them in the ``model_params`` group (``write_model_params`` /
+``read_model_params``). A model without model-side params gets an empty dict
+everywhere and runs unchanged.
+"""
+
+import numpy as np
+import jax.numpy as jnp
+from numpyro import handlers
+from numpyro.distributions import constraints
+from numpyro.distributions.transforms import biject_to
+
+# HDF5 group of a posterior file that holds the model-side params.
+MODEL_PARAMS_GROUP = "model_params"
+
+
+def model_param_constraints(model, priors, data, seed=0):
+    """
+    ``{name: constraint}`` for every ``pyro.param`` site the model registers.
+
+    Parameters
+    ----------
+    model : callable
+        Numpyro model taking ``priors=`` and ``data=``.
+    priors, data
+        Passed to the model.
+    seed : int, optional
+        PRNG seed for the trace.
+    """
+    from tfscreen.tfmodel.inference.initialization import structure_trace
+
+    # a structure trace: a prior draw the observation refuses must not stop it
+    tr = structure_trace(model, seed=seed, priors=priors, data=data)
+    return {name: site["kwargs"].get("constraint", constraints.real)
+            for name, site in tr.items() if site["type"] == "param"}
+
+
+def model_params(params, param_constraints, unconstrained=False):
+    """
+    The model-side params found in ``params``, in constrained space.
+
+    Parameters
+    ----------
+    params : mapping
+        Any param dict keyed by param name: ``svi.get_params`` (constrained),
+        the optimizer state ``svi.optim.get_params`` (unconstrained), a MAP
+        ``_params.npz``, or an h5py group.
+    param_constraints : dict
+        ``model_param_constraints`` of the model.
+    unconstrained : bool, optional
+        True if ``params`` holds unconstrained values (an optimizer state);
+        they are mapped through each param's constraint.
+
+    Returns
+    -------
+    dict
+        ``{name: jnp array}``, constrained. Empty if the model has no
+        model-side params or ``params`` holds none of them.
+    """
+    out = {}
+    for name, constraint in param_constraints.items():
+        if name not in params:
+            continue
+        value = jnp.asarray(np.asarray(params[name]))
+        out[name] = biject_to(constraint)(value) if unconstrained else value
+    return out
+
+
+def hold_model_params(model, params):
+    """
+    ``model`` with its model-side params fixed at ``params``.
+
+    The values are substituted and the param sites are hidden from outer
+    handlers, so neither an outer ``substitute`` (``Predictive``,
+    ``potential_energy``) nor an SVI param store can replace them. Returns
+    ``model`` itself when ``params`` is empty.
+    """
+    if not params:
+        return model
+    params = {k: jnp.asarray(v) for k, v in params.items()}
+    return handlers.block(handlers.substitute(model, data=params),
+                          hide=list(params))
+
+
+def write_model_params(hf, params):
+    """Write ``params`` to the ``model_params`` group of an open h5 file."""
+    if not params:
+        return
+    group = hf.create_group(MODEL_PARAMS_GROUP)
+    for name, value in params.items():
+        group.create_dataset(name, data=np.asarray(value))
+
+
+def read_model_params(posteriors, names=()):
+    """
+    The model-side params stored with a posterior.
+
+    A posterior file (``tfs-sample-posterior``) keeps them in its
+    ``model_params`` group. A raw MAP ``_params.npz`` keeps them as top-level
+    arrays under their own names; pass the model's param names (``names``) to
+    pick those up.
+
+    Returns
+    -------
+    dict
+        ``{name: numpy array}``; empty if none are stored.
+    """
+    if MODEL_PARAMS_GROUP in posteriors:
+        group = posteriors[MODEL_PARAMS_GROUP]
+        return {k: np.asarray(group[k]) for k in group.keys()}
+    return {k: np.asarray(posteriors[k]) for k in names if k in posteriors}
+
+
+def sample_keys(posteriors):
+    """The keys of a posterior that hold samples (not the params group)."""
+    return [k for k in posteriors.keys() if k != MODEL_PARAMS_GROUP]
diff --git a/src/tfscreen/tfmodel/inference/run_inference.py b/src/tfscreen/tfmodel/inference/run_inference.py
index fc35309b..505bb9b5 100644
--- a/src/tfscreen/tfmodel/inference/run_inference.py
+++ b/src/tfscreen/tfmodel/inference/run_inference.py
@@ -11,11 +11,194 @@
     Trace_ELBO,
     Predictive,
 )
-from numpyro.optim import ClippedAdam
+from numpyro.infer import autoguide
+from numpyro.infer.initialization import (
+    init_to_median,
+    init_to_uniform,
+)
+from numpyro.distributions.transforms import IdentityTransform
+from numpyro.optim import Adam, ClippedAdam
 import numpy as np
+import pandas as pd
+
+from tfscreen.util.provenance import get_provenance
 import dill
+from functools import partial
 from tqdm.auto import tqdm
 
+from tfscreen.tfmodel.inference.batch_safety import (
+    find_orchestrator_batch_dependent_latents,
+    orchestrator_latent_dimension,
+)
+from tfscreen.tfmodel.inference import convergence as conv
+from tfscreen.tfmodel.inference.initialization import (
+    AUTO_LOC_SUFFIX,
+    component_guide_init,
+    component_guide_map,
+    site_prior_sds,
+    site_unconstrained_prior_sds,
+    structure_trace,
+    site_values,
+    trace_model_sites,
+)
+from tfscreen.tfmodel.inference.model_params import (
+    hold_model_params,
+    model_param_constraints,
+    model_params,
+    write_model_params,
+)
+
+# Smallest Hessian eigenvalue the Laplace posterior keeps, for elements with
+# no usable prior SD (see laplace_eigenvalue_floor).
+LAPLACE_MIN_EIGENVALUE = 1e-3
+
+
+def laplace_eigenvalue_floor(eigenvalues, eigenvectors, prior_precision):
+    """
+    Floor Hessian eigenvalues at the prior's curvature along each eigenvector.
+
+    At an optimum the Hessian of the negative log joint is the prior's
+    curvature plus the likelihood's, and a log-concave likelihood only adds
+    to it, so no direction should be flatter than the prior alone. An
+    eigenvalue below ``v^T diag(prior_precision) v`` (negative ones
+    included, from a MAP stopped short of its optimum) is raised to it:
+    the posterior is never wider than the prior along that direction.
+
+    Parameters
+    ----------
+    eigenvalues : numpy.ndarray
+        Shape ``(D,)``.
+    eigenvectors : numpy.ndarray
+        Shape ``(D, D)``, columns are eigenvectors (``numpy.linalg.eigh``).
+    prior_precision : numpy.ndarray
+        Shape ``(D,)``, positive prior precision per element.
+
+    Returns
+    -------
+    floored : numpy.ndarray
+        Eigenvalues, each at least its direction's prior curvature and at
+        least ``LAPLACE_MIN_EIGENVALUE``.
+    num_floored : int
+        How many eigenvalues were raised.
+    """
+
+    curvature = np.einsum("ij,i,ij->j", eigenvectors, prior_precision,
+                          eigenvectors)
+    floor = np.maximum(curvature, LAPLACE_MIN_EIGENVALUE)
+    floored = np.maximum(eigenvalues, floor)
+    return floored, int(np.sum(eigenvalues < floor))
+
+
+# The convergence window is split into this many blocks for the parameter
+# test (each block's parameter mean is accumulated on the device), and the
+# loop hands control back to the host once per block.
+PARAM_BLOCKS = 8
+
+# Every window spans at least this many epochs, so with mini-batching each
+# genotype is visited several times per window.
+MIN_WINDOW_EPOCHS = 10
+
+# Parameters never tracked for movement: dense covariance factors (their
+# diagonal is tracked through the derived posterior SD instead).
+_UNTRACKED_PARAM_TAGS = ("scale_tril", "cov_factor")
+
+# Location/scale suffix pairs, checked in order (``_auto_loc`` also ends in
+# ``_loc``).
+_LOC_SCALE_SUFFIXES = (("_auto_loc", "_auto_scale"),
+                       ("_locs", "_scales"),
+                       ("_loc", "_scale"))
+
+# Autoguides selectable through setup_svi(guide_type=...), keyed by the
+# snake_case form of the numpyro class name ('delta' keeps its historical short
+# name).  'component' -- the guide assembled from the model components -- is
+# handled separately.
+AUTOGUIDES = {
+    "delta": autoguide.AutoDelta,
+    "auto_normal": autoguide.AutoNormal,
+    "auto_diagonal_normal": autoguide.AutoDiagonalNormal,
+    "auto_multivariate_normal": autoguide.AutoMultivariateNormal,
+    "auto_low_rank_multivariate_normal": autoguide.AutoLowRankMultivariateNormal,
+}
+GUIDE_TYPES = ("component",) + tuple(AUTOGUIDES)
+
+# guide_kwargs each guide accepts (init_loc_fn is set through init_values).
+_AUTOGUIDE_KWARGS = {
+    "component": set(),
+    "delta": set(),
+    "auto_normal": {"init_scale"},
+    "auto_diagonal_normal": {"init_scale"},
+    "auto_multivariate_normal": {"init_scale"},
+    "auto_low_rank_multivariate_normal": {"init_scale", "rank"},
+}
+
+# Case-insensitive aliases: numpyro class names, plus 'auto_delta'.
+_GUIDE_ALIASES = {cls.__name__.lower(): name for name, cls in AUTOGUIDES.items()}
+_GUIDE_ALIASES["auto_delta"] = "delta"
+
+# Warn when the dense auto_multivariate_normal covariance exceeds this (GB).
+_DENSE_GUIDE_WARN_GB = 4.0
+
+
+def adam_optimizer(step_size, clip_norm=None):
+    """
+    The optimizer for SVI/MAP: plain Adam, or ClippedAdam when ``clip_norm``
+    is given.
+
+    ClippedAdam clips every gradient *element* to ``[-clip_norm,
+    clip_norm]``.  On a loss whose gradients are far larger than the clip
+    (thousands of nats per unit parameter), nearly every element is clipped
+    every step, so the update follows the sign of each draw's gradient: the
+    optimizer then settles where the per-step signs balance, not where the
+    expected gradient vanishes.  With a heavy-tailed ELBO estimate (rare
+    draws carrying a huge penalty) that point is biased toward the penalties,
+    so clipping is off by default.  Adam's own normalization already bounds
+    each step to a few times the step size.  Both optimizers keep the same
+    state, so a checkpoint from either resumes under the other.
+    """
+    if clip_norm is None:
+        return Adam(step_size=step_size)
+    return ClippedAdam(step_size=step_size, clip_norm=clip_norm)
+
+
+def _init_to_value_or(site=None, values=None, fallback=init_to_uniform):
+    """
+    ``init_to_value`` with a choice of strategy for sites missing from
+    ``values`` (numpyro's always falls back to ``init_to_uniform``).
+    """
+    if site is None:
+        return partial(_init_to_value_or, values=values, fallback=fallback)
+    if site["type"] == "sample" and not site["is_observed"] \
+            and site["name"] in (values or {}):
+        return values[site["name"]]
+    return fallback(site)
+
+
+def resolve_guide_type(guide_type):
+    """Return the canonical ``GUIDE_TYPES`` name for ``guide_type``."""
+
+    key = str(guide_type).lower()
+    key = _GUIDE_ALIASES.get(key, key)
+    if key not in GUIDE_TYPES:
+        raise ValueError(
+            f"guide_type '{guide_type}' not recognized. It should be one of "
+            f"{list(GUIDE_TYPES)} (numpyro class names such as 'AutoNormal' "
+            f"are also accepted)."
+        )
+    return key
+
+
+def check_guide_kwargs(guide_type, guide_kwargs):
+    """Raise ValueError if ``guide_kwargs`` has keys ``guide_type`` rejects."""
+
+    guide_type = resolve_guide_type(guide_type)
+    accepted = _AUTOGUIDE_KWARGS[guide_type]
+    unknown = set(guide_kwargs or {}) - accepted
+    if unknown:
+        raise ValueError(
+            f"guide option(s) {sorted(unknown)} not accepted by guide_type "
+            f"'{guide_type}' (accepted: {sorted(accepted) or 'none'})."
+        )
+
 _HDF5_MAX_CHUNK_BYTES = 1 << 30  # 1 GiB; HDF5 hard-limit is 4 GiB
 
 def _safe_chunks(first_dim, trailing_shape, dtype):
@@ -25,9 +208,8 @@ def _safe_chunks(first_dim, trailing_shape, dtype):
     safe_first = max(1, _HDF5_MAX_CHUNK_BYTES // (trailing * item))
     return (min(first_dim, safe_first),) + trailing_shape
 
-from collections import deque
-from functools import partial
 import os
+import warnings
 import h5py
 class RunInference:
     """
@@ -67,9 +249,17 @@ def __init__(self,model,seed):
         self._seed = seed
         self._main_key = random.PRNGKey(self._seed)
         self._current_step = 0
-        self._relative_change = np.inf
-        self._loss_start = None
-        self._loss_best = np.inf
+
+        # Step size and gradient clip of the optimizer built by setup_svi.
+        # run_optimization rebuilds the optimizer when it cuts the step size.
+        self._step_size = None
+        self._adam_clip_norm = None
+
+        # Convergence-monitor state: restored from a checkpoint (resume) and
+        # written back into checkpoints; the monitor itself lives in
+        # run_optimization.
+        self._monitor_state = None
+        self._monitor = None
 
         # Calculate iterations per epoch
         num_genotypes = self.model.data.num_genotype
@@ -80,108 +270,245 @@ def __init__(self,model,seed):
         batch_size = len(test_idx.flatten())
 
         self._iterations_per_epoch = int(np.ceil(num_genotypes / batch_size))
+        self._batch_size = batch_size
 
-        self._patience_counter = 0
+        # Set by setup_svi and written into checkpoints.
+        self._guide_type = None
+        self._guide_kwargs = {}
+
+        # Learned values of the model's own pyro.param sites (thermo PnnC
+        # NN weights; see inference/model_params.py), held fixed
+        # whenever the model runs after a fit. Set by the posterior and
+        # Hessian entry points from the params they are given; empty for a
+        # model without such params, which then runs unchanged.
+        self.fixed_model_params = {}
+        self._model_param_constraints = None
 
 
     def setup_svi(self,
                   adam_step_size=1e-6,
-                  adam_clip_norm=1.0,
+                  adam_clip_norm=None,
                   elbo_num_particles=2,
-                  guide_type="delta"):
+                  guide_type="delta",
+                  guide_kwargs=None,
+                  init_values=None):
         """
-        Set up SVI. 
+        Set up SVI.
 
         Parameters
         ----------
         adam_step_size : float or callable, optional
-            Step size for the ClippedAdam optimizer. Can be a fixed float or 
-             a callable (e.g., an optax schedule).
-        adam_clip_norm : float, optional
-            Gradient clipping norm for the ClippedAdam optimizer.
+            Step size for the Adam optimizer. Can be a fixed float or a
+            callable (e.g., an optax schedule).
+        adam_clip_norm : float or None, optional
+            Clip each gradient element to ``[-adam_clip_norm,
+            adam_clip_norm]`` (numpyro ``ClippedAdam``).  None (default)
+            disables clipping; see ``adam_optimizer`` for why.
         elbo_num_particles : int, optional
             Number of particles for ELBO estimation.
         guide_type : str, optional
-            Type of guide to use. 
-            - 'component' (default): Use the guide defined in the model components.
-            - 'delta': Use numpyro.infer.autoguide.AutoDelta. Sets up a MAP estimator. 
+            Variational family (default 'delta'); one of ``GUIDE_TYPES``.
+
+            - 'component': the guide assembled from the model components.
+            - 'delta': numpyro ``AutoDelta`` (MAP estimation).
+            - 'auto_normal', 'auto_diagonal_normal',
+              'auto_multivariate_normal', 'auto_low_rank_multivariate_normal':
+              the numpyro autoguide of the same name.
+
+            numpyro class names (e.g. 'AutoNormal') are also accepted,
+            case-insensitively.
+        guide_kwargs : dict or None, optional
+            Extra keyword arguments for an autoguide: ``init_scale`` (every
+            autoguide except 'delta') and ``rank``
+            ('auto_low_rank_multivariate_normal' only).
+        init_values : dict or None, optional
+            Constrained values keyed by model site name, used to initialize an
+            autoguide's location.  Sites not present fall
+            back to numpyro's uniform initialization.  Autoguides only.
 
         Returns
         -------
         numpyro.infer.SVI
             An SVI object
+
+        Raises
+        ------
+        ValueError
+            If ``guide_type`` is unknown, or if ``guide_kwargs``/``init_values``
+            are given for a guide that does not take them.  Whether the model
+            can be fit with an autoguide at all is checked when fitting starts
+            (``run_optimization``).
         """
 
-        if guide_type == "delta":
-            guide = numpyro.infer.autoguide.AutoDelta(self.model.jax_model)
-        elif guide_type == "component":
+        guide_type = resolve_guide_type(guide_type)
+        guide_kwargs = dict(guide_kwargs or {})
+        check_guide_kwargs(guide_type, guide_kwargs)
+
+        if guide_type == "component":
+            if init_values is not None:
+                raise ValueError(
+                    "init_values applies only to autoguides, not "
+                    "guide_type='component'."
+                )
             guide = self.model.jax_model_guide
         else:
-            raise ValueError(f"guide_type '{guide_type}' not recognized.")
-
-        optimizer = ClippedAdam(step_size=adam_step_size,
-                                clip_norm=adam_clip_norm)   
+            ctor_kwargs = dict(guide_kwargs)
+            if init_values is not None:
+                # Sites without a value fall back to the autoguide's own
+                # default initialization (median for AutoDelta, uniform for
+                # the others).
+                fallback = (init_to_median if guide_type == "delta"
+                            else init_to_uniform)
+                ctor_kwargs["init_loc_fn"] = partial(_init_to_value_or,
+                                                     values=init_values,
+                                                     fallback=fallback)
+            guide = AUTOGUIDES[guide_type](self.model.jax_model, **ctor_kwargs)
+
+        # Recorded in checkpoints so the same guide can be rebuilt on restore.
+        self._guide_type = guide_type
+        self._guide_kwargs = guide_kwargs
+
+        self._step_size = adam_step_size
+        self._adam_clip_norm = adam_clip_norm
+        optimizer = adam_optimizer(adam_step_size, adam_clip_norm)
         
         svi = SVI(self.model.jax_model,
                   guide,
                   optimizer,
                   loss=Trace_ELBO(num_particles=elbo_num_particles))
-        
+
         return svi
 
+    def _check_autoguide(self, guide):
+        """
+        Refuse an autoguide the model cannot support; report dense-guide size.
+
+        Autoguides size their parameters from a traced batch, so a latent whose
+        shape follows the genotype batch would get one parameter per batch
+        position instead of per genotype -- at any batch size, because the
+        full-batch index is reshuffled every step.  The component guide is
+        exempt: it holds library-sized parameters and slices them itself.
+        Called when fitting starts, since fitting is what creates the aliasing.
+        """
+
+        guide_type = self._guide_type or type(guide).__name__
+
+        found = find_orchestrator_batch_dependent_latents(self.model)
+        if found:
+            raise ValueError(
+                f"guide_type '{guide_type}' cannot fit latent site(s) "
+                f"{sorted(found)}: their shape follows the genotype mini-batch "
+                f"rather than the library, so an autoguide would assign one "
+                f"genotype's value to another. Use guide_type='component', or "
+                f"a component whose latents are library-sized (e.g. "
+                f"theta_growth_noise='logit_normal' instead of 'beta'). See "
+                f"inference/batch_safety.py."
+            )
+
+        if isinstance(guide, autoguide.AutoMultivariateNormal):
+            dim = orchestrator_latent_dimension(self.model)
+            # scale_tril plus Adam's two moment estimates, float32.
+            gigabytes = 3 * dim * dim * 4 / 1e9
+            msg = (f"auto_multivariate_normal: {dim} latent dimensions; the "
+                   f"dense covariance needs ~{gigabytes:.3g} GB (parameters "
+                   f"plus optimizer state).")
+            if gigabytes > _DENSE_GUIDE_WARN_GB:
+                warnings.warn(msg + " Consider "
+                              "'auto_low_rank_multivariate_normal'.")
+            else:
+                print(msg, flush=True)
+
     def run_optimization(self,
                          svi,
                          svi_state=None,
                          init_params=None,
                          out_prefix="tfs",
-                         convergence_tolerance=1e-5,
-                         convergence_window=10,
-                         patience=10,
-                         convergence_check_interval=2,
+                         convergence_window_steps=2000,
+                         patience=3,
+                         convergence_z=3.0,
+                         loss_rtol=1e-6,
+                         param_tolerance=0.05,
+                         final_step_size=None,
+                         step_size_cut=0.1,
                          checkpoint_interval=10,
                          max_num_epochs=10000000,
-                         init_param_jitter=0.1,
-                         epoch_checkpoint_interval=1000):
+                         init_param_jitter=0.0,
+                         epoch_checkpoint_interval=1000,
+                         exact_loss=None):
         """
-        Run the SVI optimization loop.
-
-        This method iterates, updates SVI state, calculates loss, checks for
-        convergence, and writes checkpoints.
+        Run the optimization loop until convergence or ``max_num_epochs``.
+
+        The loop runs in tumbling windows of optimizer steps.  At the end of
+        each window ``inference/convergence.py`` tests whether the loss is
+        still improving (relative to its own noise) and whether any parameter
+        is still moving (relative to its posterior or prior width).  After
+        ``patience`` consecutive windows without loss improvement the step
+        size is cut by ``step_size_cut``; once it has reached
+        ``final_step_size``, ``patience`` consecutive windows with no loss
+        improvement, no parameter movement and no loss skew (a mean well above
+        the median: rare large penalties the median hides) are convergence.
+        Every window is recorded in ``{out_prefix}_convergence.csv``.
 
         Parameters
         ----------
         svi : numpyro.infer.SVI
-            The SVI object from `setup_svi`.
+            The SVI object from `setup_svi`.  Its optimizer is replaced (same
+            state, smaller step size) at each step-size cut.
         svi_state : Any, optional
-            An existing SVI state to resume from. If None, a new state is
-            initialized. If a checkpoint file, restore from the checkpoint.
+            An existing SVI state to continue from, or the path of a checkpoint
+            to resume (restoring the step count, step size and convergence
+            stage).  If None, a new state is initialized and the step count
+            starts at zero.
         init_params : dict, optional
             Initial parameters.
         out_prefix : str, optional
             Root name for output files (checkpoints, losses).
-        convergence_tolerance : float, optional
-            Convergence criterion: stop when the window-to-window change in
-            smoothed loss is less than this fraction of the total improvement
-            seen since warm-up ended (i.e., since the loss deque first filled).
-            Default 1e-5.
-        convergence_window : int, optional
-            Number of epochs to average over for convergence check.
+        convergence_window_steps : int, optional
+            Length of a convergence window in optimizer steps (default 2000).
+            Raised to at least ``MIN_WINDOW_EPOCHS`` epochs and rounded up to a
+            multiple of ``PARAM_BLOCKS``.
         patience : int, optional
-            Number of consecutive convergence checks that must meet tolerance
-            to declare convergence.
-        convergence_check_interval : int, optional
-            Frequency (in epochs) to check for convergence.
+            Consecutive windows without loss improvement required for a
+            step-size cut, and (at ``final_step_size``) without loss
+            improvement or parameter movement required for convergence
+            (default 3).
+        convergence_z : float, optional
+            Standard errors of change attributed to noise (default 3).
+        loss_rtol : float, optional
+            Loss changes smaller than this fraction of the loss per window
+            count as a plateau even when significant (default 1e-6); matters
+            only for (near-)deterministic losses.
+        param_tolerance : float, optional
+            Parameter movement per window, beyond noise, that still counts as
+            a plateau (default 0.05), in posterior SDs (guide scale, floored
+            at ``PRIOR_SD_FLOOR`` of the prior SD), prior SDs (MAP locations),
+            or log/logit units (positive or bounded parameters).
+        final_step_size : float or None, optional
+            Smallest step size.  None (default) or a value >= the current step
+            size disables cuts: the first sustained plateau is convergence.
+        step_size_cut : float, optional
+            Factor applied to the step size at each cut (default 0.1).
         checkpoint_interval : int, optional
             Frequency (in epochs) to write checkpoints.
         max_num_epochs : int, optional
             Maximum number of optimization epochs to run.
         init_param_jitter : float, optional
-            amount of jitter to add to init_params. To turn off, set to 0.
+            Multiplicative log-normal jitter on ``init_params`` (default 0,
+            none).  It scales with each value, so it moves large locations
+            (ln_cfu0 near 15) by whole ln units; keep it off when starting
+            from a MAP point.
         epoch_checkpoint_interval : int or None, optional
             Frequency (in epochs) to write numbered epoch checkpoints to a
             ``checkpoints/`` subdirectory alongside ``out_prefix``. Files are
             named ``{epoch:07d}_checkpoint.pkl``. Set to None or 0 to
             disable (default 1000).
+        exact_loss : bool or None, optional
+            Judge each window by the exact full-batch loss at its end
+            (``full_batch_loss``) instead of the mini-batch losses: one
+            forward pass over the library per window, no sampling noise. None
+            (default) does so for a MAP (``AutoDelta``), whose loss is a
+            deterministic function of the point, and not for a variational
+            guide, whose ELBO is noisy anyway.
 
         Returns
         -------
@@ -190,7 +517,7 @@ def run_optimization(self,
         params : dict
             The final optimized parameters.
         converged : bool
-            True if the optimization converged based on the tolerance.
+            True if the run stopped because it converged (see above).
 
         Raises
         ------
@@ -198,10 +525,20 @@ def run_optimization(self,
             If parameters explode to NaN during optimization.
         FileExistsError
             If a numbered epoch checkpoint file already exists.
+        ValueError
+            If ``svi`` uses a numpyro autoguide (AutoDelta included) and the
+            model has latents whose shape follows the genotype mini-batch
+            (see ``inference/batch_safety.py``), or if step-size cuts are
+            requested for an optimizer built with a step-size schedule.
         """
 
-        # Set up update function (triggers jit)
-        update_function = jax.jit(svi.update)
+        # Refuse an autoguide the model cannot support before any fitting.
+        if isinstance(svi.guide, autoguide.AutoGuide):
+            self._check_autoguide(svi.guide)
+
+        if exact_loss is None:
+            exact_loss = isinstance(svi.guide, autoguide.AutoDelta)
+        self._use_exact_loss = bool(exact_loss)
 
         # Add jitter to the input parameters if they are specified
         if init_params is not None:
@@ -211,20 +548,6 @@ def run_optimization(self,
         # Put the data on to the gpu
         data_on_gpu = jax.device_put(self.model.data)
 
-        # JAX-optimized update function for use with lax.scan
-        def scan_fn(data_on_gpu, carry, indices):
-            svi_state = carry
-            batch = self.model.get_batch(data_on_gpu, indices)
-            new_svi_state, loss = update_function(svi_state,
-                                                  priors=self.model.priors,
-                                                  data=batch)
-            return new_svi_state, loss
-
-        # JIT the scan function. We pass data_on_gpu as a formal argument to 
-        # the jitted function to avoid capturing it as a constant in the 
-        # closure (which triggers expensive constant folding for large datasets)
-        fast_scan = jax.jit(lambda data, state, indices: jax.lax.scan(partial(scan_fn, data), state, indices))
-
         # Create an initial batch to initialize SVI
         gpu_batch_idx = jax.device_put(self.model.get_random_idx())
         batch_data = self.model.get_batch(data_on_gpu, gpu_batch_idx)
@@ -235,10 +558,11 @@ def scan_fn(data_on_gpu, carry, indices):
                                      init_params=init_params,
                                      priors=self.model.priors,
                                      data=batch_data)
-            
+
+        self._monitor_state = None
         if svi_state is None:
             svi_state = initial_svi_state
-            
+            self._current_step = 0
         elif isinstance(svi_state,str):
             if os.path.isfile(svi_state):
                 svi_state = self._restore_checkpoint(svi_state)
@@ -246,23 +570,84 @@ def scan_fn(data_on_gpu, carry, indices):
                 raise ValueError(
                     f"svi_state '{svi_state}' is not valid"
                 )
+
+        # --- Step size and convergence monitor ---
+
+        step_size = self._step_size
+        if step_size is None:
+            step_size = getattr(svi.optim, "step_size", None)
+        if callable(step_size):
+            if final_step_size is not None:
+                raise ValueError(
+                    "step-size cuts (final_step_size) need a constant step "
+                    "size from setup_svi, not a schedule."
+                )
+            monitor_step_size = np.nan
         else:
-            svi_state = svi_state
-            
-        # Initialize loss file
-        self._write_losses(np.array([]), out_prefix) 
-
-        # loss deque holds loss values for smoothing to check for convergence
-        # The window represents epochs, so we multiply by iterations_per_epoch.
-        # We need two such windows (one old, one new) to compare.
-        deque_maxlen = 2 * convergence_window * self._iterations_per_epoch
-        self._loss_deque = deque(maxlen=deque_maxlen)
+            monitor_step_size = float(step_size) if step_size is not None else np.nan
+
+        monitor = conv.ConvergenceMonitor(
+            step_size=monitor_step_size,
+            final_step_size=final_step_size,
+            step_size_cut=step_size_cut,
+            patience=patience,
+            z=convergence_z,
+            loss_rtol=loss_rtol,
+            param_tolerance=param_tolerance,
+        )
+        if self._monitor_state is not None:
+            monitor.load_state_dict(self._monitor_state)
+            if monitor.step_size != monitor_step_size:
+                print(f"Resuming at the checkpoint's step size "
+                      f"{monitor.step_size:.3g}.", flush=True)
+                self._set_step_size(svi, monitor.step_size)
+        self._monitor = monitor
+
+        # --- Window geometry ---
+
+        ipe = self._iterations_per_epoch
+        window_steps = max(int(convergence_window_steps),
+                           MIN_WINDOW_EPOCHS * ipe, 3 * PARAM_BLOCKS)
+        block_steps = int(np.ceil(window_steps / PARAM_BLOCKS))
+        window_steps = block_steps * PARAM_BLOCKS
+
+        # --- Parameters tracked for movement ---
+
+        unconstrained = svi.optim.get_params(svi_state.optim_state)
+        specs = self._param_normalizer_specs(svi, svi_state, unconstrained)
+        zero_acc = {k: jnp.zeros_like(unconstrained[k]) for k in specs}
+
+        # JAX-optimized update function for use with lax.scan.  Each step also
+        # adds the tracked (unconstrained) parameters to an accumulator, so
+        # the host gets per-block parameter means without per-step transfers.
+        def scan_fn(data_on_gpu, carry, indices):
+            state, acc = carry
+            batch = self.model.get_batch(data_on_gpu, indices)
+            new_state, loss = svi.update(state,
+                                         priors=self.model.priors,
+                                         data=batch)
+            current = svi.optim.get_params(new_state.optim_state)
+            acc = {k: acc[k] + current[k] for k in acc}
+            return (new_state, acc), loss
+
+        # Built as a fresh closure so a step-size cut (which swaps svi.optim)
+        # re-traces rather than hitting a cached compilation. The data are a
+        # formal argument to avoid capturing them as a constant (expensive
+        # constant folding for large datasets).
+        def build_scan():
+            return jax.jit(lambda data, state, acc, indices: jax.lax.scan(
+                partial(scan_fn, data), (state, acc), indices))
+
+        fast_scan = build_scan()
+
+        # Initialize loss and convergence files
+        self._write_losses(np.array([]), out_prefix)
+        self._write_convergence(None, out_prefix)
+
         converged = False
-        
-        # Convert intervals from epochs to iterations
-        check_interval_steps = convergence_check_interval * self._iterations_per_epoch
-        checkpoint_interval_steps = checkpoint_interval * self._iterations_per_epoch
-        total_steps = max_num_epochs * self._iterations_per_epoch
+        total_steps = max_num_epochs * ipe
+
+        checkpoint_interval_steps = checkpoint_interval * ipe
 
         # Track next checkpoint in steps
         # If resuming, we want to write checkpoint at the next multiple of
@@ -271,7 +656,7 @@ def scan_fn(data_on_gpu, carry, indices):
 
         # Set up epoch checkpoint tracking
         if epoch_checkpoint_interval:
-            epoch_checkpoint_interval_steps = epoch_checkpoint_interval * self._iterations_per_epoch
+            epoch_checkpoint_interval_steps = epoch_checkpoint_interval * ipe
             self._next_epoch_checkpoint_step = (
                 (self._current_step // epoch_checkpoint_interval_steps) + 1
             ) * epoch_checkpoint_interval_steps
@@ -281,17 +666,24 @@ def scan_fn(data_on_gpu, carry, indices):
         else:
             epoch_checkpoint_interval_steps = None
 
-        # Reset convergence state
-        self._patience_counter = 0
-        self._loss_start = None
-        self._loss_best = np.inf
-        
-        # Loop over steps in chunks of check_interval_steps
+        print(f"Convergence window: {window_steps} steps "
+              f"({window_steps / ipe:.4g} epochs); patience {patience} "
+              f"windows; step size {monitor.step_size:.3g}"
+              + (f" -> {monitor.final_step_size:.3g}"
+                 if not monitor.at_floor else "")
+              + f"; tracking {len(specs)} parameter arrays.", flush=True)
+
+        # Current window
+        window_losses = []
+        block_means = []
+        block_centers = []
+        window_filled = 0
+
         current_optimization_step = 0
         while current_optimization_step < total_steps:
 
             # Determine size of this block
-            block_size = min(check_interval_steps, total_steps - current_optimization_step)
+            block_size = min(block_steps, total_steps - current_optimization_step)
 
             # Generate a block of random indices (using NumPy/Python)
             block_idx = self.model.get_random_idx(num_batches=block_size)
@@ -300,36 +692,62 @@ def scan_fn(data_on_gpu, carry, indices):
             gpu_block_idx = jax.device_put(block_idx)
 
             # Run the block of updates using lax.scan (entirely on GPU)
-            svi_state, block_losses = fast_scan(data_on_gpu, svi_state, gpu_block_idx)
-            
+            (svi_state, acc), block_losses = fast_scan(data_on_gpu, svi_state,
+                                                       zero_acc, gpu_block_idx)
+
             # Convert JAX array to NumPy for host-side metadata management
-            # Ensure it is at least 1D for deque/IO
+            # Ensure it is at least 1D for IO
             interval_losses = np.atleast_1d(np.array(block_losses))
-            
+
             # Update counters
             current_optimization_step += block_size
             self._current_step += block_size
 
-            # Update loss deque for convergence check
-            self._update_loss_deque(interval_losses)
+            # Accumulate the window
+            window_losses.append(interval_losses)
+            block_means.append({k: v / block_size for k, v in acc.items()})
+            block_centers.append(window_filled + block_size / 2)
+            window_filled += block_size
 
-            # stdout
-            # Print status using the last loss in the block
-            print(f"Step: {self._current_step:10d}, Loss: {interval_losses[-1]:10.5e}, Change: {self._relative_change:10.5e}, Patience: {self._patience_counter:3d}", flush=True)
+            # stdout. Plateau counts from 1: the window in progress is the
+            # (plateau_count + 1)-th of the `patience` stalled windows that
+            # would cut the step size or stop, so 3/3 is the deciding one.
+            print(f"Step: {self._current_step:10d}, "
+                  f"Loss: {np.median(interval_losses):10.5e}, "
+                  f"Step size: {monitor.step_size:9.3e}, "
+                  f"Plateau: {monitor.plateau_count + 1}/{patience}", flush=True)
 
             # Check for explosion in parameters
             params = svi.get_params(svi_state)
             for k in params:
                 if np.any(np.isnan(params[k])):
-                    
+
                     nan_params = [(k,params[k]) for k in params]
                     raise RuntimeError(
                         f"model exploded (observed at step {self._current_step}). "
                         f"NaN params: {nan_params}."
                     )
-                
+
             # Write outputs (checkpoints and losses)
-            self._write_losses(interval_losses, out_prefix) 
+            self._write_losses(interval_losses, out_prefix)
+
+            # End of a convergence window
+            decision = None
+            if window_filled >= window_steps:
+                decision = self._end_window(svi, svi_state, monitor, specs,
+                                            np.concatenate(window_losses),
+                                            block_means, block_centers,
+                                            window_filled)
+                self._write_convergence(monitor.last, out_prefix)
+                print("Convergence window, " + monitor.describe(), flush=True)
+                window_losses = []
+                block_means = []
+                block_centers = []
+                window_filled = 0
+
+                if decision == conv.CUT:
+                    self._set_step_size(svi, monitor.step_size)
+                    fast_scan = build_scan()
 
             # Check if we should write a checkpoint
             if self._current_step >= self._next_checkpoint_step:
@@ -339,34 +757,429 @@ def scan_fn(data_on_gpu, carry, indices):
             # Check if we should write a numbered epoch checkpoint
             if epoch_checkpoint_interval_steps is not None:
                 if self._current_step >= self._next_epoch_checkpoint_step:
-                    current_epoch = self._current_step // self._iterations_per_epoch
+                    current_epoch = self._current_step // ipe
                     self._write_epoch_checkpoint(svi_state, current_epoch)
                     self._next_epoch_checkpoint_step += epoch_checkpoint_interval_steps
 
-            # Check for convergence               
-            if convergence_tolerance is not None: 
-                
-                if self._relative_change < convergence_tolerance:
-                    self._patience_counter += 1
-                else:
-                    self._patience_counter = 0
-
-                if self._patience_counter >= patience:
-                    converged = True
-                    # Final checkpoint before exiting
-                    self._write_checkpoint(svi_state, out_prefix)
-                    break
+            if decision == conv.CONVERGED:
+                converged = True
+                # Final checkpoint before exiting
+                self._write_checkpoint(svi_state, out_prefix)
+                break
+            if decision == conv.DIVERGED:
+                self._write_checkpoint(svi_state, out_prefix)
+                break
 
         # Write a final checkpoint when the loop exits by reaching max_num_epochs
         # (convergence already writes its own checkpoint via the break path above).
         if not converged and total_steps > 0:
             self._write_checkpoint(svi_state, out_prefix)
 
+        if total_steps > 0:
+            if converged:
+                print(f"Converged at step {self._current_step} (step size "
+                      f"{monitor.step_size:.3g}, {monitor.num_cuts} cut(s)): "
+                      f"no significant loss improvement, parameter movement "
+                      f"or loss skew for {patience} windows.", flush=True)
+            elif monitor.diverged:
+                print(f"Stopped at step {self._current_step}: the loss ran "
+                      f"away (below -{conv.RUNAWAY_LOSS_FACTOR:g} x its "
+                      f"starting magnitude {monitor.reference_loss:.4g}), so "
+                      f"the posterior density is unbounded along some "
+                      f"direction and there is no optimum. Not converged. "
+                      f"Last window: {monitor.describe()}", flush=True)
+            else:
+                print(f"Stopped at step {self._current_step} "
+                      f"(max_num_epochs) without converging. Last window: "
+                      f"{monitor.describe()}", flush=True)
+
         # Get final parameters
         params = svi.get_params(svi_state)
 
         return svi_state, params, converged
 
+    def _set_step_size(self, svi, step_size):
+        """
+        Give ``svi`` the same kind of optimizer (clipped or not) with a new
+        constant step size.
+
+        The optimizer state (step count, parameters, Adam moments) does not
+        depend on the step size, so the existing state carries over.
+        """
+
+        clip_norm = getattr(svi.optim, "clip_norm", None)
+        svi.optim = adam_optimizer(step_size, clip_norm)
+        self._step_size = step_size
+
+    @staticmethod
+    def _param_transforms(svi):
+        """``{param name: transform}`` recorded by ``svi.init``, if exposed."""
+        args = getattr(getattr(svi, "constrain_fn", None), "args", None)
+        if args and isinstance(args[0], dict):
+            return args[0]
+        return {}
+
+    def _param_normalizer_specs(self, svi, svi_state, unconstrained):
+        """
+        How to normalize each tracked parameter's movement.
+
+        Returns ``{param name: spec}`` where spec is one of
+
+        - ``("unit",)``: movement taken as is -- parameters on a positive or
+          bounded support (tracked in log/logit units, so relative), and real
+          parameters with no better reference;
+        - ``("scale", scale_name, floor)``: a guide location divided by its
+          paired guide scale (posterior SD);
+        - ``("auto_continuous", floor)``: an AutoContinuous ``auto_loc``
+          divided by the posterior SD derived from its scale parameters;
+        - ``("prior", sd)``: a MAP (AutoDelta) location divided by the prior
+          SD of its site.
+
+        ``floor`` (an array broadcastable to the location, or None) is
+        ``conv.PRIOR_SD_FLOOR`` times the site's prior SD in unconstrained
+        units; the posterior SD is never taken to be smaller.
+        """
+
+        transforms = self._param_transforms(svi)
+        shapes = {k: jnp.shape(v) for k, v in unconstrained.items()}
+
+        prior_sds = {}
+        floors = {}
+        if self._guide_type == "delta":
+            prior_sds = self._delta_prior_sds(svi, svi_state)
+        else:
+            floors = self._posterior_sd_floors(svi, svi_state, shapes)
+
+        specs = {}
+        for name in unconstrained:
+            if any(tag in name for tag in _UNTRACKED_PARAM_TAGS):
+                continue
+            transform = transforms.get(name)
+            if transform is not None and not isinstance(transform,
+                                                        IdentityTransform):
+                specs[name] = ("unit",)
+                continue
+
+            if name == "auto_loc":
+                specs[name] = ("auto_continuous", floors.get(name))
+                continue
+
+            partner = None
+            for loc_suffix, scale_suffix in _LOC_SCALE_SUFFIXES:
+                if name.endswith(loc_suffix):
+                    candidate = name[:-len(loc_suffix)] + scale_suffix
+                    if shapes.get(candidate) == shapes[name]:
+                        partner = candidate
+                    break
+
+            site = (name[:-len(AUTO_LOC_SUFFIX)]
+                    if name.endswith(AUTO_LOC_SUFFIX) else None)
+            if partner is not None:
+                specs[name] = ("scale", partner, floors.get(name))
+            elif site is not None and site in prior_sds \
+                    and jnp.shape(prior_sds[site]) == shapes[name]:
+                specs[name] = ("prior", prior_sds[site])
+            else:
+                specs[name] = ("unit",)
+        return specs
+
+    def _posterior_sd_floors(self, svi, svi_state, shapes):
+        """
+        ``{location param: PRIOR_SD_FLOOR * prior SD}`` for variational
+        guides, in the location's unconstrained units ({} on failure).
+
+        Component-guide locations are matched to their sites through the
+        ``{site}_loc(s)`` convention, AutoNormal's through ``{site}_auto_loc``,
+        and an AutoContinuous ``auto_loc`` is assembled from the per-site
+        floors in the guide's own latent order (sites without a finite prior
+        spread get no floor).
+        """
+
+        try:
+            constrained = svi.get_params(svi_state)
+            batch = self._trace_batch()
+            sites = trace_model_sites(self.model.jax_model, self.model.priors,
+                                      batch)
+            guide = svi.guide
+            if isinstance(guide, autoguide.AutoGuide):
+                guide_map = None
+                current = guide.median(constrained)
+            else:
+                guide_map, _, _ = component_guide_map(guide,
+                                                      self.model.priors,
+                                                      batch)
+                current = site_values(constrained, sites, guide_map)
+            sites = trace_model_sites(self.model.jax_model, self.model.priors,
+                                      batch, substitutions=current)
+            sds = site_unconstrained_prior_sds(sites)
+        except Exception as err:  # pragma: no cover - defensive
+            print(f"Could not compute prior SDs to floor posterior SDs for "
+                  f"parameter movement ({err}); guide scales are used as "
+                  f"they are.", flush=True)
+            return {}
+
+        frac = conv.PRIOR_SD_FLOOR
+        floors = {}
+        if guide_map is not None:
+            for site, entry in guide_map.items():
+                loc = entry["loc"]
+                if site in sds and loc in shapes:
+                    try:
+                        floors[loc] = frac * jnp.broadcast_to(sds[site],
+                                                              shapes[loc])
+                    except ValueError:
+                        pass
+        elif isinstance(guide, autoguide.AutoContinuous):
+            init_locs = getattr(guide, "_init_locs", None)
+            if init_locs and "auto_loc" in shapes:
+                from numpyro.infer.autoguide import _ravel_dict
+                parts = {}
+                for site, value in init_locs.items():
+                    shape = jnp.shape(value)
+                    if site in sds and jnp.size(sds[site]) == jnp.size(value):
+                        parts[site] = frac * jnp.reshape(
+                            jnp.asarray(sds[site], dtype=float), shape)
+                    else:
+                        parts[site] = jnp.zeros(shape)
+                flat, _ = _ravel_dict(parts)
+                if flat.shape == shapes["auto_loc"]:
+                    floors["auto_loc"] = flat
+        else:
+            for site, sd in sds.items():
+                loc = site + AUTO_LOC_SUFFIX
+                if loc in shapes:
+                    try:
+                        floors[loc] = frac * jnp.broadcast_to(sd, shapes[loc])
+                    except ValueError:
+                        pass
+        return floors
+
+    def _delta_prior_sds(self, svi, svi_state):
+        """Prior SD of each site at the current MAP point ({} on failure)."""
+        try:
+            constrained = svi.get_params(svi_state)
+            substitutions = {k[:-len(AUTO_LOC_SUFFIX)]: v
+                             for k, v in constrained.items()
+                             if k.endswith(AUTO_LOC_SUFFIX)}
+            sites = trace_model_sites(self.model.jax_model,
+                                      self.model.priors,
+                                      self._trace_batch(),
+                                      substitutions=substitutions)
+            return site_prior_sds(sites)
+        except Exception as err:  # pragma: no cover - defensive
+            print(f"Could not compute prior SDs for MAP parameter movement "
+                  f"({err}); movement is measured in unconstrained units.",
+                  flush=True)
+            return {}
+
+    def _trace_batch(self):
+        """
+        A deterministic batch for tracing: the first ``batch_size`` genotypes
+        of the full binding-first index (latent sites are library-sized
+        whatever the batch).
+        """
+        data = jax.device_put(self.model.data)
+        idx = jnp.asarray(self.model.data.batch_idx)[:self._batch_size]
+        return self.model.get_batch(data, idx)
+
+    def model_params(self, params, unconstrained=False):
+        """
+        The learned values of the model's own ``pyro.param`` sites found in
+        ``params`` (a param store, optimizer state or MAP ``_params.npz``),
+        constrained. Empty for a model without such params. See
+        ``inference/model_params.py``.
+        """
+        if self._model_param_constraints is None:
+            data = jax.device_put(self.model.data)
+            full = self.model.get_batch(
+                data, jnp.arange(self.model.data.num_genotype))
+            self._model_param_constraints = model_param_constraints(
+                self.model.jax_model, self.model.priors, full)
+        return model_params(params, self._model_param_constraints,
+                            unconstrained=unconstrained)
+
+    def _fixed_model(self):
+        """The model with ``fixed_model_params`` held fixed."""
+        return hold_model_params(self.model.jax_model, self.fixed_model_params)
+
+    def site_values(self, values):
+        """
+        Constrained site values found in ``values`` (guesses, a MAP result or
+        both), keyed by model site name.  See ``inference/initialization.py``.
+        """
+        batch = self._trace_batch()
+        sites = trace_model_sites(self.model.jax_model, self.model.priors,
+                                  batch)
+        guide_map, _, _ = component_guide_map(self.model.jax_model_guide,
+                                              self.model.priors, batch)
+        return site_values(values, sites, guide_map)
+
+    def component_guide_start(self, values, guesses=None, init_scale=None):
+        """
+        Initial component-guide parameters starting at the given site values.
+
+        Parameters
+        ----------
+        values : dict
+            Constrained site values (``site_values``).
+        guesses : dict or None, optional
+            Configured guesses.  Those keyed by component-guide param name are
+            kept (and overridden where ``values`` gives the same site).
+        init_scale : float or None, optional
+            Upper bound on every mapped guide scale at the start.
+
+        Returns
+        -------
+        dict
+            ``init_params`` for ``run_optimization`` with the component guide.
+        """
+
+        guesses = dict(guesses or {})
+        guide_map, unmatched, defaults = component_guide_map(
+            self.model.jax_model_guide, self.model.priors, self._trace_batch())
+
+        init_params = {k: v for k, v in guesses.items() if k in defaults}
+        start_defaults = dict(defaults)
+        start_defaults.update(init_params)
+
+        translated, skipped = component_guide_init(values, guide_map,
+                                                   start_defaults,
+                                                   init_scale=init_scale)
+        init_params.update(translated)
+
+        n_loc = sum(1 for e in guide_map.values() if e["loc"] in translated)
+        print(f"Guide start: {n_loc} of {len(guide_map) + len(unmatched)} "
+              f"guide sites start at the given values"
+              + (f"; scales capped at {init_scale:g}"
+                 if init_scale is not None else "")
+              + ".", flush=True)
+        if unmatched:
+            print(f"  guide sites without a recognized location parameter "
+                  f"(left at their defaults): {unmatched}", flush=True)
+        if skipped:
+            print(f"  non-positive values for LogNormal-guided sites (left "
+                  f"at their defaults): {skipped}", flush=True)
+        return init_params
+
+    @staticmethod
+    def _normalizer(spec, constrained):
+        """Evaluate one normalizer spec at the current constrained params."""
+        kind = spec[0]
+        floor = None
+        if kind == "scale":
+            norm = constrained[spec[1]]
+            floor = spec[2] if len(spec) > 2 else None
+        elif kind == "auto_continuous":
+            floor = spec[1] if len(spec) > 1 else None
+            if "auto_scale_tril" in constrained:
+                tril = constrained["auto_scale_tril"]
+                norm = jnp.sqrt(jnp.sum(tril * tril, axis=-1))
+            elif "auto_cov_factor" in constrained:
+                factor = constrained["auto_cov_factor"]
+                norm = jnp.sqrt(jnp.sum(factor * factor, axis=-1)
+                                + constrained["auto_scale"] ** 2)
+            elif "auto_scale" in constrained:
+                norm = constrained["auto_scale"]
+            else:
+                norm = 1.0
+        elif kind == "prior":
+            norm = spec[1]
+        else:
+            norm = 1.0
+        norm = jnp.asarray(norm, dtype=float)
+        if floor is not None:
+            norm = jnp.maximum(norm, floor)
+        return jnp.maximum(norm, 1e-12)
+
+    def _end_window(self, svi, svi_state, monitor, specs, losses,
+                    block_means, block_centers, window_steps):
+        """Run both convergence tests on a finished window; return the decision."""
+
+        loss_stats = conv.loss_trend(losses)
+
+        constrained = svi.get_params(svi_state)
+        exact = None
+        if getattr(self, "_use_exact_loss", False):
+            # the model's own params enter at their current values
+            exact = self.full_batch_loss(
+                {**{k[:-len("_auto_loc")]: v for k, v in constrained.items()
+                    if k.endswith("_auto_loc")},
+                 **self.model_params(constrained)})
+        centers = np.asarray(block_centers, dtype=float)
+        floor = monitor.drift_floor(window_steps)
+        excess_summary = {}
+        drift_summary = {}
+        for name, spec in specs.items():
+            stacked = jnp.stack([b[name] for b in block_means])
+            drift, excess = conv.param_drift(stacked, centers, window_steps,
+                                             self._normalizer(spec, constrained),
+                                             monitor.z, floor=floor)
+            excess_summary[name] = conv.summarize_excess(excess)
+            drift_summary[name] = conv.summarize_excess(jnp.abs(drift))
+
+        return monitor.end_window(self._current_step, loss_stats,
+                                  excess_summary, drift_summary,
+                                  exact_loss=exact)
+
+    def full_batch_loss(self, values, chunk_size=None):
+        """
+        The exact negative log joint at a point, over the whole library.
+
+        The model is evaluated on genotype chunks of the full batch with the
+        mini-batch scale removed. Each chunk's log density holds that
+        chunk's genotype-sliced terms (the growth observations) plus terms
+        every chunk repeats: the library-sized latents' priors and any data
+        not sliced by genotype (binding). The repeated part is measured as
+        ``f(a) + f(b) - f(a, b)`` on two one-genotype batches and counted
+        once, as in the arrowhead Laplace.
+
+        Parameters
+        ----------
+        values : dict
+            Constrained value of every latent site, keyed by site name (a
+            MAP's ``svi.get_params`` with ``_auto_loc`` stripped).
+        chunk_size : int or None, optional
+            Genotypes per chunk (default: the training batch size).
+
+        Returns
+        -------
+        float
+            ``-log p(data, values)``, the quantity a MAP minimizes, without
+            mini-batch noise.
+        """
+        from numpyro.infer.util import log_density
+
+        G = int(self.model.data.num_genotype)
+        if chunk_size is None:
+            chunk_size = int(np.size(self.model.get_random_idx()))
+        chunk_size = max(1, min(int(chunk_size), G))
+
+        fn = getattr(self, "_full_batch_lp", None)
+        if fn is None:
+            model, priors = self.model.jax_model, self.model.priors
+
+            def lp(batch, params):
+                return log_density(model, (), {"data": batch, "priors": priors},
+                                   params)[0]
+
+            fn = self._full_batch_lp = jax.jit(lp)
+
+        data = getattr(self, "_full_batch_data", None)
+        if data is None:
+            data = self._full_batch_data = jax.device_put(self.model.data)
+        values = {k: jnp.asarray(v) for k, v in values.items()}
+
+        def chunk_lp(idx):
+            return float(fn(self._unscaled_batch(data, jnp.asarray(idx)), values))
+
+        starts = list(range(0, G, chunk_size))
+        total = sum(chunk_lp(np.arange(s, min(s + chunk_size, G)))
+                    for s in starts)
+        if len(starts) > 1:
+            repeated = chunk_lp([0]) + chunk_lp([1]) - chunk_lp([0, 1])
+            total -= (len(starts) - 1) * repeated
+        return -total
+
     def _get_genotype_dim_map(self):
         """
         Identify which sites are in the genotype plate and what that dimension is.
@@ -387,10 +1200,8 @@ def _get_genotype_dim_map(self):
         probe_idx = jnp.arange(probe_size)
         probe_data = self.model.get_batch(data_on_gpu, probe_idx)
 
-        seeded_model = seed(self.model.jax_model, rng_seed=0)
-        traced_model = trace(seeded_model)
-        model_trace = traced_model.get_trace(data=probe_data,
-                                             priors=self.model.priors)
+        model_trace = structure_trace(self.model.jax_model, data=probe_data,
+                                      priors=self.model.priors)
 
         dim_map = {}
         genotype_dim = -1 # default fallback
@@ -496,6 +1307,26 @@ def _concat_genotype_chunks(chunk_list, axis, total_size):
         slices[pos] = slice(0, total_size)
         return merged[tuple(slices)]
 
+    def _batch_positional_latents(self):
+        """
+        Latent sites whose value has one entry per batch *position* rather
+        than per genotype (e.g. ``noise/beta``'s ``{name}_dist``).
+
+        Every other genotype-indexed latent is sampled at library size and
+        sliced to the batch inside its component with ``batch_idx``, so a
+        forward pass over a genotype chunk must hand it the full,
+        library-ordered value -- exactly as training does. Slicing it to the
+        chunk beforehand breaks components that index their parameters by
+        library position (``hill_geno``, ``thermo.*``, ``categorical_geno``):
+        every chunk after the first reads past the sliced array, and JAX
+        clamps the index instead of raising. Only the batch-positional
+        latents found here are sliced to the chunk.
+        """
+        if not hasattr(self, "_batch_positional_cache"):
+            self._batch_positional_cache = frozenset(
+                find_orchestrator_batch_dependent_latents(self.model))
+        return self._batch_positional_cache
+
     def _build_genotype_chunk_scanner(self, dim_map, sites_to_save):
         """
         Build a JIT-compiled forward-pass function for a single genotype chunk.
@@ -525,20 +1356,28 @@ def _build_genotype_chunk_scanner(self, dim_map, sites_to_save):
             ``chunk_fn(data, latents, key, batch_indices)
             -> (new_key, chunk_dict)``
         """
-        model_fn = self.model.jax_model
+        model_fn = self._fixed_model()
         get_batch = self.model.get_batch
         priors = self.model.priors
+        batch_positional = self._batch_positional_latents()
 
         @jax.jit
         def chunk_fn(data, latents, key, batch_indices):
+            # Output: every genotype-indexed site, sliced to this chunk.
             batch_latents = {
                 k: jnp.take(v, batch_indices, axis=dim_map[k]) if k in dim_map else v
                 for k, v in latents.items()
             }
+            # Model input: library-sized latents stay whole (components slice
+            # them with batch_idx); only batch-positional ones are sliced.
+            model_latents = {
+                k: batch_latents[k] if k in batch_positional else v
+                for k, v in latents.items()
+            }
             batch_data = get_batch(data, batch_indices)
 
             key, subkey = jax.random.split(key)
-            forward_sampler = Predictive(model_fn, posterior_samples=batch_latents)
+            forward_sampler = Predictive(model_fn, posterior_samples=model_latents)
             batch_pred = forward_sampler(subkey, priors=priors, data=batch_data)
 
             # Predictions take precedence over latents of the same name.
@@ -594,6 +1433,9 @@ def get_posteriors(self,
         guide = svi.guide
         params = jax.device_put(svi.get_params(svi_state))
         data_on_gpu = jax.device_put(self.model.data)
+        # The model's own learned params (in the same store as the guide's)
+        # are held at their fitted values in the forward pass.
+        self.fixed_model_params = self.model_params(params)
 
         # Get the mapping of site names to genotype dimension
         dim_map = self._get_genotype_dim_map()
@@ -632,6 +1474,12 @@ def get_posteriors(self,
                                                 priors=self.model.priors,
                                                 data=full_data)
 
+                # Drop autoguide auxiliary sites (e.g. AutoContinuous's
+                # flattened "_auto_latent", shape (samples, D)); they are not
+                # model sites.
+                latent_samples = {k: v for k, v in latent_samples.items()
+                                  if not k.startswith("_")}
+
                 # Forward pass: iterate over genotype chunks with the JIT-compiled
                 # chunk_fn.  Each chunk is computed, transferred to CPU, and
                 # discarded from GPU before the next chunk runs, keeping GPU
@@ -684,6 +1532,7 @@ def get_posteriors(self,
 
             # Add metadata to HDF5 file
             hf.attrs["num_samples"] = samples_written
+            write_model_params(hf, self.fixed_model_params)
 
             # Force flush to disk to avoid read issues on cluster file systems
             hf.flush()
@@ -772,8 +1621,12 @@ def _write_checkpoint(self,svi_state,out_prefix):
         out_dict = {"main_key":self._main_key,
                     "svi_state":host_svi_state,
                     "current_step":self._current_step,
-                    "loss_start":self._loss_start,
-                    "loss_best":self._loss_best}
+                    "step_size": self._checkpoint_step_size(),
+                    "convergence": self._convergence_state(),
+                    "guide_type":self._guide_type,
+                    "guide_kwargs":self._guide_kwargs,
+                    "adam_clip_norm":self._adam_clip_norm,
+                    "provenance": get_provenance()}
 
         tmp_checkpoint_file = f"{out_prefix}_checkpoint.tmp.pkl"
 
@@ -815,8 +1668,12 @@ def _write_epoch_checkpoint(self, svi_state, epoch):
         out_dict = {"main_key": self._main_key,
                     "svi_state": host_svi_state,
                     "current_step": self._current_step,
-                    "loss_start": self._loss_start,
-                    "loss_best": self._loss_best}
+                    "step_size": self._checkpoint_step_size(),
+                    "convergence": self._convergence_state(),
+                    "guide_type": self._guide_type,
+                    "guide_kwargs": self._guide_kwargs,
+                    "adam_clip_norm": self._adam_clip_norm,
+                    "provenance": get_provenance()}
 
         tmp_file = f"{epoch_file}.tmp"
         with open(tmp_file, "wb") as f:
@@ -825,9 +1682,13 @@ def _write_epoch_checkpoint(self, svi_state, epoch):
 
     def restore_svi_from_checkpoint(self, checkpoint_file, init_params=None):
         """
-        Rebuild a component SVI object and restore its state from a checkpoint,
+        Rebuild the SVI object recorded in a checkpoint and restore its state,
         without running any optimization steps or convergence checks.
 
+        The guide is rebuilt from the checkpoint's ``guide_type`` and
+        ``guide_kwargs``.  Checkpoints written before those were recorded are
+        treated as component-guide checkpoints (the only SVI guide then).
+
         ``svi.init()`` must be called at least once to wire up ``constrain_fn``
         on the SVI object before ``svi.get_params()`` can be used.  The state
         produced by ``init()`` is immediately discarded and replaced with the
@@ -838,9 +1699,11 @@ def restore_svi_from_checkpoint(self, checkpoint_file, init_params=None):
         checkpoint_file : str
             Path to the checkpoint .pkl file produced by tfs-fit-model.
         init_params : dict or None, optional
-            Initial parameter values forwarded to ``svi.init()``.  The values
-            are never used in inference (the checkpoint overwrites them), but
-            they must be structurally valid for the model.
+            Initial parameter values forwarded to ``svi.init()`` for a
+            component guide.  The values are never used in inference (the
+            checkpoint overwrites them), but they must be structurally valid
+            for the model.  Ignored for autoguides, whose parameter names
+            differ from the component guide's.
 
         Returns
         -------
@@ -849,7 +1712,14 @@ def restore_svi_from_checkpoint(self, checkpoint_file, init_params=None):
         svi_state : numpyro.infer.svi.SVIState
             Optimizer state restored from the checkpoint.
         """
-        svi = self.setup_svi(guide_type="component")
+        with open(checkpoint_file, "rb") as f:
+            checkpoint_data = dill.load(f)
+        guide_type = checkpoint_data.get("guide_type") or "component"
+        guide_kwargs = checkpoint_data.get("guide_kwargs") or None
+
+        svi = self.setup_svi(guide_type=guide_type, guide_kwargs=guide_kwargs)
+        if self._guide_type != "component":
+            init_params = None
 
         # init() is required to populate svi.constrain_fn; the resulting state
         # is thrown away — the checkpoint state is used instead.
@@ -885,10 +1755,10 @@ def _restore_checkpoint(self,checkpoint_file):
         self._main_key = checkpoint_data['main_key']
         if 'current_step' in checkpoint_data:
             self._current_step = checkpoint_data['current_step']
-        if 'loss_start' in checkpoint_data:
-            self._loss_start = checkpoint_data['loss_start']
-        if 'loss_best' in checkpoint_data:
-            self._loss_best = checkpoint_data['loss_best']
+        # Convergence stage (step size, plateau count); absent from
+        # checkpoints written before step-size cuts, which resume at the
+        # configured step size.
+        self._monitor_state = checkpoint_data.get('convergence')
 
         if not isinstance(svi_state,numpyro.infer.svi.SVIState):
             raise ValueError(
@@ -923,7 +1793,7 @@ def _write_losses(self,losses,out_prefix):
                 os.remove(readable_losses_file)
 
             with open(readable_losses_file, "w") as f:
-                f.write("epoch,loss,relative_change\n")
+                f.write("epoch,loss,step,step_size\n")
                 f.flush()
                 os.fsync(f.fileno())
 
@@ -939,55 +1809,86 @@ def _write_losses(self,losses,out_prefix):
             f.flush()
             os.fsync(f.fileno())
 
-        # Write a human-readable losses file: epoch,loss,relative_change
+        # Write a human-readable losses file: epoch, median loss of the block,
+        # step, step size.
+        step_size = self._checkpoint_step_size()
         with open(readable_losses_file, "a") as f:
-            f.write(f"{epoch},{losses[-1]},{self._relative_change}\n")
+            f.write(f"{epoch},{np.median(losses)},{self._current_step},"
+                    f"{step_size}\n")
             f.flush()
             os.fsync(f.fileno())
 
+    _CONVERGENCE_COLUMNS = ("step", "epoch", "step_size", "loss", "loss_exact",
+                            "loss_drop",
+                            "loss_drop_se", "loss_t", "loss_improving",
+                            "loss_mean", "loss_skew", "loss_skewed",
+                            "worst_param", "worst_param_excess",
+                            "worst_param_drift", "params_moving", "plateau",
+                            "plateau_count", "pooled_loss_t", "decision")
 
-
-
-    def _update_loss_deque(self, losses):
+    def _write_convergence(self, record, out_prefix):
         """
-        Update the loss deque and calculate the convergence metric.
+        Append one convergence-window record to ``{out_prefix}_convergence.csv``.
 
-        The metric is the window-to-window change in smoothed loss, normalized
-        by the total improvement seen since the deque first filled (warm-up):
+        With ``record=None`` (start of a run) the file is created with its
+        header -- replacing any old file -- on a fresh run, and left alone on
+        a resume.
+        """
 
-            |mean_old - mean_new| / max(|loss_start - loss_best|, 1e-6)
+        path = f"{out_prefix}_convergence.csv"
+        if record is None:
+            if self._current_step == 0 or not os.path.exists(path):
+                with open(path, "w") as f:
+                    f.write(",".join(self._CONVERGENCE_COLUMNS) + "\n")
+            else:
+                self._migrate_convergence_columns(path)
+            return
 
-        loss_start is captured once, when the deque fills for the first time.
-        loss_best is the running minimum of mean_new across all checks.
+        row = dict(record)
+        row["epoch"] = row["step"] // self._iterations_per_epoch
+        with open(path, "a") as f:
+            f.write(",".join(str(row.get(c, "")) for c in
+                             self._CONVERGENCE_COLUMNS) + "\n")
+            f.flush()
+            os.fsync(f.fileno())
 
-        Parameters
-        ----------
-        losses : list
-            List of new losses to add to the deque.
+    def _migrate_convergence_columns(self, path):
+        """
+        Rewrite a resumed run's convergence file under the current columns
+        (a file written by an older version lacks some), keeping every value
+        under its column name and leaving new columns empty.
         """
 
-        self._loss_deque.extend(list(losses))
-
-        # Deque must be full before any check (natural warm-up period)
-        if len(self._loss_deque) < self._loss_deque.maxlen:
-            self._relative_change = np.inf
+        import csv
+        with open(path, newline="") as f:
+            rows = list(csv.DictReader(f))
+            f.seek(0)
+            header = next(csv.reader(f), [])
+        if tuple(header) == self._CONVERGENCE_COLUMNS:
             return
+        with open(path, "w", newline="") as f:
+            writer = csv.DictWriter(f, fieldnames=self._CONVERGENCE_COLUMNS,
+                                    extrasaction="ignore", restval="",
+                                    lineterminator="\n")
+            writer.writeheader()
+            writer.writerows(rows)
+
+    def _checkpoint_step_size(self):
+        """Current constant step size, or None for a schedule/unknown."""
+        if self._monitor is not None and np.isfinite(self._monitor.step_size):
+            return self._monitor.step_size
+        if self._step_size is not None and not callable(self._step_size):
+            return float(self._step_size)
+        return None
 
-        history = np.array(self._loss_deque)
-        half = len(history) // 2
-        mean_old = np.mean(history[:half])
-        mean_new = np.mean(history[half:])
+    def _convergence_state(self):
+        """Monitor state for checkpoints (None before any optimization)."""
+        if self._monitor is None:
+            return self._monitor_state
+        return self._monitor.state_dict()
 
-        # Capture loss_start once, at the end of warm-up
-        if self._loss_start is None:
-            self._loss_start = mean_old
 
-        # Track the best (lowest) smoothed loss seen so far
-        self._loss_best = min(self._loss_best, mean_new)
 
-        # Denominator: total improvement since warm-up, with floor for robustness
-        denom = max(abs(self._loss_start - self._loss_best), 1e-6)
-        self._relative_change = abs(mean_old - mean_new) / denom
 
     def write_params(self,params,out_prefix):
         """
@@ -1019,8 +1920,10 @@ def _map_params_to_constrained(self, map_params):
         Parameters
         ----------
         map_params : dict
-            Parameter dict from an AutoDelta guide state, keys follow the
-            ``{site}_auto_loc`` convention, values are in unconstrained space.
+            AutoDelta parameters in *unconstrained* space, keys following the
+            ``{site}_auto_loc`` convention: the optimizer state
+            (``svi.optim.get_params(svi_state.optim_state)``), not
+            ``svi.get_params``, which returns constrained values.
 
         Returns
         -------
@@ -1042,9 +1945,7 @@ def _map_params_to_constrained(self, map_params):
             if k.endswith("_auto_loc")
         }
 
-        seeded_model = seed(self.model.jax_model, rng_seed=0)
-        traced_model = trace(seeded_model)
-        model_trace = traced_model.get_trace(**model_kwargs)
+        model_trace = structure_trace(self.model.jax_model, **model_kwargs)
 
         site_transforms = {
             name: biject_to(site["fn"].support)
@@ -1057,6 +1958,28 @@ def _map_params_to_constrained(self, map_params):
             for k, v in unconstrained.items()
         }
 
+    @staticmethod
+    def _check_library_sized_latents(latents, dim_map, total_num_genotypes):
+        """
+        Raise if any genotype-indexed MAP latent is not library-sized.
+
+        A batch-sized latent (a MAP checkpoint written before per-genotype
+        latents were sampled at library size, or a component that cannot be
+        -- see inference/batch_safety.py) holds values fit per mini-batch
+        position, aliased across genotypes.  Refuse it rather than reuse one
+        genotype's value for another.  ``dim_map`` axes are negative, so
+        ``latents`` may or may not carry a leading sample axis.
+        """
+        for k, v in latents.items():
+            if k in dim_map and v.shape[dim_map[k]] != total_num_genotypes:
+                raise ValueError(
+                    f"MAP latent '{k}' has {v.shape[dim_map[k]]} entries along "
+                    f"the genotype axis but the library has "
+                    f"{total_num_genotypes} genotypes. Its values were fit per "
+                    f"mini-batch position, not per genotype, so they cannot "
+                    f"be mapped back onto genotypes. Refit the model."
+                )
+
     def get_map_posteriors(self,
                            map_params,
                            out_prefix,
@@ -1080,7 +2003,8 @@ def get_map_posteriors(self,
         ----------
         map_params : dict
             Parameter dict from a MAP (AutoDelta) optimizer state, as
-            returned by ``svi.get_params(svi_state)``.  Keys follow the
+            returned by ``svi.optim.get_params(svi_state.optim_state)`` (not
+            ``svi.get_params``, which constrains them).  Keys follow the
             ``{site}_auto_loc`` convention; values are in unconstrained space.
         out_prefix : str
             Root name for the output file (written as
@@ -1097,9 +2021,14 @@ def get_map_posteriors(self,
         dim_map = self._get_genotype_dim_map()
 
         constrained = self._map_params_to_constrained(map_params)
+        self.fixed_model_params = self.model_params(map_params,
+                                                    unconstrained=True)
         # Add a leading sample dimension: (*shape) → (1, *shape)
         latent_samples = {k: jnp.expand_dims(v, 0) for k, v in constrained.items()}
 
+        self._check_library_sized_latents(latent_samples, dim_map,
+                                          total_num_genotypes)
+
         h5_file = f"{out_prefix}_posterior.h5"
 
         batch_collector = {}
@@ -1108,24 +2037,22 @@ def get_map_posteriors(self,
             end_idx = min(start_idx + forward_batch_size, total_num_genotypes)
             batch_indices = jnp.arange(start_idx, end_idx)
 
-            # Clip indices to the valid range for each latent before calling
-            # jnp.take.  On GPU, out-of-bounds jnp.take returns NaN; on CPU it
-            # usually clips silently.  MAP checkpoints trained with
-            # batch_size < N_geno have latent parameters sized to batch_size,
-            # so indices beyond that range must be clipped explicitly.
-            def _safe_take(v, idx, axis):
-                n = v.shape[axis if axis >= 0 else len(v.shape) + axis]
-                return jnp.take(v, jnp.clip(idx, 0, n - 1), axis=axis)
-
             batch_latents = {
-                k: _safe_take(v, batch_indices, dim_map[k])
+                k: jnp.take(v, batch_indices, axis=dim_map[k])
                 if k in dim_map else v
                 for k, v in latent_samples.items()
             }
+            # Library-sized latents stay whole for the model (see
+            # _batch_positional_latents); batch_latents is only the output.
+            batch_positional = self._batch_positional_latents()
+            model_latents = {
+                k: batch_latents[k] if k in batch_positional else v
+                for k, v in latent_samples.items()
+            }
 
             batch_data = self.model.get_batch(data_on_gpu, batch_indices)
-            forward_sampler = Predictive(self.model.jax_model,
-                                         posterior_samples=batch_latents)
+            forward_sampler = Predictive(self._fixed_model(),
+                                         posterior_samples=model_latents)
             pred_key = self.get_key()
             batch_pred = forward_sampler(pred_key,
                                          priors=self.model.priors,
@@ -1160,6 +2087,7 @@ def _safe_take(v, idx, axis):
                 hf.create_dataset(k, data=v, chunks=chunks,
                                   compression="gzip", compression_opts=4)
             hf.attrs["num_samples"] = 1
+            write_model_params(hf, self.fixed_model_params)
             hf.flush()
 
     def _chunked_hessian(self, pe_fn, flat_map, chunk_size):
@@ -1228,10 +2156,16 @@ def compute_hessian_sigmas(self, map_params, hessian_chunk_size=64):
         Parameters
         ----------
         map_params : dict
-            Parameter dict from a MAP (AutoDelta) optimizer state, as
-            returned by ``svi.get_params(svi_state)``.  Keys follow the
-            ``{site}_auto_loc`` convention; values are in unconstrained
-            space.
+            MAP (AutoDelta) parameters as returned by
+            ``svi.get_params(svi_state)`` (what ``run_optimization`` returns):
+            keys follow the ``{site}_auto_loc`` convention and values are in
+            *constrained* space (AutoDelta's ``_auto_loc`` params carry the
+            site's support, so ``get_params`` constrains them). They are
+            mapped to unconstrained space here, where the Hessian is taken.
+            (Until 2026-09-27 they were taken as already unconstrained, so
+            every positive site was evaluated at ``exp(value)``: the Hessian
+            of the pre-fit was taken at the wrong point, and overflowed on
+            the count likelihood's ``growth_phi``.)
         hessian_chunk_size : int, optional
             Number of Hessian rows to compute per device batch (default 64).
             Reduce if you hit device OOM during the Hessian computation.
@@ -1256,25 +2190,44 @@ def compute_hessian_sigmas(self, map_params, hessian_chunk_size=64):
         full_data = self.model.get_batch(data_on_gpu, all_indices)
         model_kwargs = {"priors": self.model.priors, "data": full_data}
 
-        # Strip _auto_loc suffix → unconstrained site-level param dict
-        unconstrained = {
+        # Strip _auto_loc suffix → constrained site-level param dict
+        constrained = {
             k[: -len("_auto_loc")]: jnp.array(v)
             for k, v in map_params.items()
             if k.endswith("_auto_loc")
         }
 
-        if len(unconstrained) == 0:
+        if len(constrained) == 0:
             return {}
 
+        # The model's own params are held at the MAP (empirical Bayes).
+        self.fixed_model_params = self.model_params(map_params)
+
+        # Per-site bijection (unconstrained -> constrained) from a model
+        # trace; identity for real-valued supports.
+        model_trace = structure_trace(self.model.jax_model, **model_kwargs)
+        site_transforms = {
+            name: biject_to(site["fn"].support)
+            for name, site in model_trace.items()
+            if site["type"] == "sample" and not site.get("is_observed", False)
+        }
+        unconstrained = {
+            name: (site_transforms[name].inv(v) if name in site_transforms
+                   else v)
+            for name, v in constrained.items()
+        }
+
         # Flatten to a single vector for Hessian computation.  We need to
         # remember the per-site shapes / offsets so we can pull the
         # diagonal back into per-site sigma arrays.
         flat_map, unravel = jax.flatten_util.ravel_pytree(unconstrained)
         D = flat_map.shape[0]
 
+        model_fn = self._fixed_model()
+
         def pe_fn(flat_p):
             return potential_energy(
-                self.model.jax_model, [], model_kwargs, unravel(flat_p)
+                model_fn, [], model_kwargs, unravel(flat_p)
             )
 
         print(f"Computing Hessian for {D} parameters "
@@ -1297,17 +2250,6 @@ def pe_fn(flat_p):
         sigma_unconstrained = unravel(jnp.array(sigma_unconstrained_flat,
                                                  dtype=flat_map.dtype))
 
-        # Get the per-site bijection from a model trace.  For unconstrained
-        # supports (Normal) this is the identity transform.
-        seeded_model = seed(self.model.jax_model, rng_seed=0)
-        traced_model = trace(seeded_model)
-        model_trace = traced_model.get_trace(**model_kwargs)
-        site_transforms = {
-            name: biject_to(site["fn"].support)
-            for name, site in model_trace.items()
-            if site["type"] == "sample" and not site.get("is_observed", False)
-        }
-
         out = {}
         for name, x_unc in unconstrained.items():
             transform = site_transforms.get(name)
@@ -1372,6 +2314,411 @@ def _get_site_names(self,target_sites="deterministic"):
         
         return site_names
 
+    def _laplace_prior_precision(self, unconstrained, model_kwargs, D):
+        """
+        Prior precision of each flattened unconstrained MAP element.
+
+        The prior SD comes from ``site_unconstrained_prior_sds`` on a trace
+        with every latent site set to its MAP value (``trace_model_sites``
+        with ``unconstrained=``), so a hierarchical latent's prior is
+        conditional on the MAP's hyperparameters and no site is drawn from
+        the prior. The trace is a structure trace, so the observation model
+        never has to accept the values.
+
+        Until 2026-10-11 the supports came from a first trace with every
+        site drawn from the prior. On the PddG fits that draw (ddG prior
+        widened by the learned ``sigma_s``, ln K_op from -38 to 61) gave a
+        non-finite ``ln_cfu``, the count likelihood refused it, and with only
+        a printed note the floor fell back to ``LAPLACE_MIN_EIGENVALUE``:
+        16-38 negative eigenvalues became directions with SD ~30.
+
+        A failure is an error unless ``self.allow_flat_laplace_floor`` (set
+        by ``get_laplace_posteriors(allow_flat_floor=True)``), which falls
+        back to ``LAPLACE_MIN_EIGENVALUE`` for every element with a warning.
+        Elements without a finite, positive prior SD get
+        ``LAPLACE_MIN_EIGENVALUE``, named by site in a warning. Returns a
+        length-``D`` numpy array in the order of
+        ``ravel_pytree(unconstrained)``.
+        """
+        import jax.flatten_util
+
+        allow_flat = getattr(self, "allow_flat_laplace_floor", False)
+        try:
+            sites = trace_model_sites(self._fixed_model(),
+                                      model_kwargs["priors"],
+                                      model_kwargs["data"],
+                                      unconstrained=unconstrained)
+            sds = site_unconstrained_prior_sds(sites)
+        except Exception as err:
+            msg = (f"Could not compute prior SDs for the Laplace floor "
+                   f"({type(err).__name__}: {err}).")
+            if not allow_flat:
+                raise RuntimeError(
+                    msg + " Without them every Hessian eigenvalue is floored "
+                    f"at a flat {LAPLACE_MIN_EIGENVALUE}, which turns a "
+                    "negative eigenvalue into a direction with SD "
+                    f"{LAPLACE_MIN_EIGENVALUE ** -0.5:.0f}. Pass "
+                    "allow_flat_floor=True (tfs-sample-posterior "
+                    "--allow_flat_laplace_floor) to accept that.") from err
+            warnings.warn(msg + f" Using a flat floor of "
+                          f"{LAPLACE_MIN_EIGENVALUE} for every element "
+                          "(allow_flat_floor).", RuntimeWarning, stacklevel=2)
+            print(f"  WARNING: {msg} Using a flat floor of "
+                  f"{LAPLACE_MIN_EIGENVALUE}.", flush=True)
+            return np.full(D, LAPLACE_MIN_EIGENVALUE)
+
+        prec = {}
+        missing = {}
+        for k, v in unconstrained.items():
+            p = np.full(np.shape(v), np.nan)
+            if k in sds:
+                try:
+                    p = 1.0 / np.broadcast_to(np.asarray(sds[k], float),
+                                              np.shape(v)) ** 2
+                except ValueError:
+                    pass
+            bad_k = ~np.isfinite(p) | (p <= 0)
+            if np.any(bad_k):
+                missing[k] = int(np.sum(bad_k))
+            prec[k] = jnp.asarray(p)
+        flat, _ = jax.flatten_util.ravel_pytree(prec)
+        flat = np.asarray(flat, dtype=float)
+        if flat.shape[0] != D:  # pragma: no cover - defensive
+            raise RuntimeError(f"Laplace prior precision has {flat.shape[0]} "
+                               f"elements; the MAP has {D}.")
+        bad = ~np.isfinite(flat) | (flat <= 0)
+        flat[bad] = LAPLACE_MIN_EIGENVALUE
+        if missing:
+            listed = ", ".join(f"{k} ({n})" for k, n in sorted(missing.items()))
+            msg = (f"No usable prior SD for {int(bad.sum())} of {D} MAP "
+                   f"elements; their Laplace floor is the flat "
+                   f"{LAPLACE_MIN_EIGENVALUE}: {listed}.")
+            warnings.warn(msg, RuntimeWarning, stacklevel=2)
+            print(f"  WARNING: {msg}", flush=True)
+        return flat
+
+    def _unscaled_batch(self, data, idx):
+        """
+        ``get_batch`` with the mini-batch likelihood scale removed.
+
+        ``scale_vector`` is built for the configured batch size, so a batch
+        taken at full size from a model configured with ``batch_size`` below
+        the library still weights every growth likelihood by
+        ``num_genotype / batch_size`` (53x on the dev-data screen at 4096).
+        A Hessian must see each observation once.
+        """
+        batch = self.model.get_batch(data, idx)
+        if getattr(batch, "growth", None) is not None:
+            batch = batch.replace(growth=batch.growth.replace(
+                scale_vector=jnp.ones_like(batch.growth.scale_vector)))
+        return batch
+
+    def _genotype_hessian_parts(self, unconstrained, dim_map, data, flat_map,
+                                unravel, genotype_chunk_size=None,
+                                leak_tolerance=1e-5, include_shared=False):
+        """
+        The pieces of the Hessian the block Laplaces need, from
+        Hessian-vector products over genotype chunks.
+
+        Given the shared parameters, genotypes do not couple, so the Hessian
+        of the negative log joint is an arrowhead: block-diagonal over
+        genotypes (``B x B`` each), plus each genotype's coupling to the
+        shared parameters and the shared-shared block. Block column ``j``
+        comes from one product whose probe is 1 on slot ``j`` of every
+        genotype in a chunk. With ``include_shared``, every shared element
+        gets its own probe: its response on a chunk's genotype rows is their
+        coupling (each genotype's prior and observations sit in its own
+        chunk), and its response on the shared rows is summed over chunks.
+        Every chunk's potential also carries the terms no chunk slices (the
+        priors, and binding observations in a joint model), so the sum
+        counts them once per chunk; those are measured as
+        ``H(a) + H(b) - H(a + b)`` on two one-genotype batches and the
+        surplus removed.
+
+        Returns
+        -------
+        elem_idx : numpy.ndarray, shape (G, B)
+            Flat positions of each genotype's parameters.
+        blocks : numpy.ndarray, shape (G, B, B)
+            Symmetrized genotype blocks.
+        shared_idx : numpy.ndarray, shape (S,)
+            Flat positions of the shared parameters.
+        coupling : numpy.ndarray or None, shape (G, B, S)
+            Genotype-shared entries (``include_shared`` only; float32).
+        shared_block : numpy.ndarray or None, shape (S, S)
+            Shared-shared block (``include_shared`` only).
+        """
+        import jax.flatten_util
+        from numpyro.infer.util import potential_energy
+
+        G = self.model.data.num_genotype
+        model_fn = self._fixed_model()
+        gid = {}
+        for k, v in unconstrained.items():
+            a = np.full(np.shape(v), -1.0)
+            if k in dim_map and np.ndim(v) > 0:
+                ax = dim_map[k] % np.ndim(v)
+                if np.shape(v)[ax] == G:
+                    shape = [1] * np.ndim(v)
+                    shape[ax] = G
+                    a = np.broadcast_to(np.arange(G).reshape(shape),
+                                        np.shape(v)).astype(float)
+            gid[k] = jnp.asarray(a)
+        flat_gid = np.rint(np.asarray(
+            jax.flatten_util.ravel_pytree(gid)[0])).astype(int)
+        per = np.bincount(flat_gid[flat_gid >= 0], minlength=G)
+        if per.size == 0 or not np.all(per == per[0]) or per[0] == 0:
+            raise ValueError("block Laplace needs the same number of "
+                             "parameters for every genotype")
+        B = int(per[0])
+        order = np.argsort(np.where(flat_gid >= 0, flat_gid, G), kind="stable")
+        elem_idx = order[:G * B].reshape(G, B)
+        shared_idx = np.flatnonzero(flat_gid < 0)
+        D = flat_map.shape[0]
+        S = shared_idx.size
+        print(f"Block Laplace: {G} genotypes x {B} parameters; {S} shared "
+              f"parameters {'included' if include_shared else 'held at the MAP'}",
+              flush=True)
+
+        priors = self.model.priors
+
+        @jax.jit
+        def hvp(batch, v):
+            def pe(f):
+                return potential_energy(model_fn, [],
+                                        {"priors": priors, "data": batch},
+                                        unravel(f))
+            return jax.jvp(jax.grad(pe), (flat_map,), (v,))[1]
+
+        def probe(positions):
+            return jnp.zeros(D).at[positions].set(1.0)
+
+        chunk = G if not genotype_chunk_size else int(genotype_chunk_size)
+        blocks = np.zeros((G, B, B))
+        coupling = shared_block = None
+        if include_shared:
+            coupling = np.zeros((G, B, S), dtype=np.float32)
+            shared_block = np.zeros((S, S))
+        num_chunks = 0
+        for start in range(0, G, chunk):
+            num_chunks += 1
+            idx = np.arange(start, min(start + chunk, G))
+            batch = self._unscaled_batch(data, jnp.asarray(idx))
+            if start == 0 and idx.size > 1:
+                probed, other = idx[::2], idx[1::2]
+                hv = np.asarray(hvp(batch, probe(elem_idx[probed].ravel())))
+                on = np.max(np.abs(hv[elem_idx[probed]]))
+                leak = np.max(np.abs(hv[elem_idx[other]]))
+                if on > 0 and leak / on > leak_tolerance:
+                    raise ValueError(
+                        f"genotype blocks are coupled (response on unprobed "
+                        f"genotypes {leak / on:.2e} of the probed ones); the "
+                        f"block Laplace does not apply to this model")
+            for j in range(B):
+                hv = np.asarray(hvp(batch, probe(elem_idx[idx, j])), dtype=float)
+                blocks[idx, :, j] = hv[elem_idx[idx]]
+            if include_shared:
+                for s_i, pos in enumerate(shared_idx):
+                    hv = np.asarray(hvp(batch, probe(pos)), dtype=float)
+                    coupling[idx, :, s_i] = hv[elem_idx[idx]]
+                    shared_block[:, s_i] += hv[shared_idx]
+
+        if include_shared and num_chunks > 1:
+            one = [self._unscaled_batch(data, jnp.asarray([g])) for g in (0, 1)]
+            both = self._unscaled_batch(data, jnp.asarray([0, 1]))
+            for s_i, pos in enumerate(shared_idx):
+                v = probe(pos)
+                invariant = (np.asarray(hvp(one[0], v), dtype=float)
+                             + np.asarray(hvp(one[1], v), dtype=float)
+                             - np.asarray(hvp(both, v), dtype=float))
+                shared_block[:, s_i] -= (num_chunks - 1) * invariant[shared_idx]
+
+        blocks = 0.5 * (blocks + np.transpose(blocks, (0, 2, 1)))
+        if include_shared:
+            shared_block = 0.5 * (shared_block + shared_block.T)
+        return elem_idx, blocks, shared_idx, coupling, shared_block
+
+    @staticmethod
+    def _floored_block_factors(blocks, prec_b, return_kept=False):
+        """
+        ``L_g`` with ``L_g L_g^T`` the inverse of each block after flooring
+        its eigenvalues at the prior's curvature (as the full Laplace).
+        With ``return_kept``, also ``L_g`` with the floored eigenvectors'
+        columns zeroed (``arrowhead_laplace_factors``).
+        """
+        G, B, _ = blocks.shape
+        w, V = np.linalg.eigh(blocks)
+        curvature = np.einsum("gij,gi,gij->gj", V, prec_b, V)
+        floor = np.maximum(curvature, LAPLACE_MIN_EIGENVALUE)
+        w_f = np.maximum(w, floor)
+        print(f"  {int(np.sum(np.any(w < 0, axis=1)))} of {G} blocks had "
+              f"negative eigenvalues; {int(np.sum(w < floor))} of {G * B} "
+              f"eigenvalues raised to the prior's curvature", flush=True)
+        L = V / np.sqrt(w_f)[:, None, :]
+        if return_kept:
+            return L, L * (w >= floor)[:, None, :]
+        return L
+
+    def block_laplace_factors(self, unconstrained, dim_map, data, flat_map,
+                              unravel, model_kwargs, genotype_chunk_size=None,
+                              leak_tolerance=1e-5):
+        """
+        Per-genotype Laplace factors with the shared parameters held at the MAP.
+
+        Given the shared parameters, each genotype's own unconstrained
+        parameters (every element of a genotype-plate site at its index)
+        appear only in that genotype's prior and observations, so the
+        Hessian of the negative log joint restricted to them is
+        block-diagonal: one ``B x B`` block per genotype. Column ``j`` of
+        every block comes from one Hessian-vector product whose probe is 1
+        on slot ``j`` of every genotype at once, so the whole factorization
+        costs ``B`` products per genotype chunk. Each block's eigenvalues
+        are floored at the prior's curvature along their eigenvectors
+        (``laplace_eigenvalue_floor``), as in the full Laplace.
+
+        Parameters
+        ----------
+        unconstrained : dict
+            Unconstrained MAP values by site.
+        dim_map : dict
+            Genotype axis of each genotype-plate site
+            (``_get_genotype_dim_map``).
+        data : DataClass
+            The full data (on device).
+        flat_map, unravel
+            ``ravel_pytree(unconstrained)``.
+        model_kwargs : dict
+            ``priors`` and the unscaled full-batch ``data`` (for the prior
+            precisions).
+        genotype_chunk_size : int or None, optional
+            Genotypes per pass (default all).
+        leak_tolerance : float, optional
+            Largest allowed response on other genotypes' parameters to a
+            probe on one set of genotypes, relative to the response on the
+            probed ones; above it the blocks are not separable and this
+            raises.
+
+        Returns
+        -------
+        elem_idx : numpy.ndarray, shape (num_genotype, B)
+            Flat positions of each genotype's parameters.
+        L_blocks : numpy.ndarray, shape (num_genotype, B, B)
+            Factors with ``L_g L_g^T`` = genotype g's conditional covariance.
+        """
+        elem_idx, blocks, _, _, _ = self._genotype_hessian_parts(
+            unconstrained, dim_map, data, flat_map, unravel,
+            genotype_chunk_size=genotype_chunk_size,
+            leak_tolerance=leak_tolerance)
+        prec = self._laplace_prior_precision(unconstrained, model_kwargs,
+                                             flat_map.shape[0])
+        return elem_idx, self._floored_block_factors(blocks, prec[elem_idx])
+
+    def arrowhead_laplace_factors(self, unconstrained, dim_map, data, flat_map,
+                                  unravel, model_kwargs,
+                                  genotype_chunk_size=None,
+                                  leak_tolerance=1e-5):
+        """
+        Block Laplace that keeps the shared parameters' uncertainty.
+
+        The Hessian is an arrowhead (``_genotype_hessian_parts``): genotype
+        blocks ``A_g``, couplings ``C_g`` to the shared parameters and the
+        shared block ``H_ss``. Its Gaussian factorizes exactly: the shared
+        parameters have precision ``H_ss - sum_g C_g^T A_g^-1 C_g`` (the
+        Schur complement), and given them genotype g is
+        ``N(x_g* - A_g^-1 C_g (s - s*), A_g^-1)``. So this is the full
+        Laplace, at one Hessian-vector product per shared parameter beyond
+        the block Laplace's. Each ``A_g`` and the Schur complement are
+        floored at the prior's curvature, as in the full Laplace, except
+        that a negative direction of the Schur complement is held at the
+        MAP (zero variance; named in the log). The MAP is a saddle there,
+        and every genotype follows a shared direction, so the prior's width
+        along it swamps every interval: on the relative-fit run 4 MAPs the
+        k/dk_geno slide (``dk_geno_hyper_shift`` with k, eigenvalue about
+        -1e7) floored at the prior gave k its prior SD (0.01) and X 95%
+        widths of 1-3 at every depth, and floored hyperscale directions
+        pulled high-depth X coverage to 0.53.
+
+        A floored direction of ``A_g`` carries no coupling: ``C_g`` is
+        projected off it. The floor marks a direction where the MAP is not
+        at an optimum (a negative or prior-flat eigenvalue), so its coupling
+        is not a curvature either, and kept it is ruinous: with the floored
+        curvature near the prior's (about 1) and a coupling to the growth
+        slope m near 1e5, ``C^T A^-1 C`` subtracted 6e10 from m's
+        curvature of 1e10 on a short dev-data MAP (167 of 308 blocks
+        floored) and m's draws spread to its prior. Those genotypes then
+        neither inform the shared parameters nor follow them along the
+        floored directions, which widens the shared intervals a little.
+
+        Parameters and the first two returns as ``block_laplace_factors``.
+
+        Returns
+        -------
+        elem_idx, L_blocks
+            As ``block_laplace_factors``.
+        shared_idx : numpy.ndarray, shape (S,)
+            Flat positions of the shared parameters.
+        L_shared : numpy.ndarray, shape (S, S)
+            ``L_shared L_shared^T`` = the shared parameters' covariance.
+        response : numpy.ndarray, shape (G, B, S), float32
+            ``A_g^-1 C_g``: how far each genotype's conditional mean moves per
+            unit change in the shared parameters (subtracted).
+        """
+        elem_idx, blocks, shared_idx, coupling, shared_block = \
+            self._genotype_hessian_parts(
+                unconstrained, dim_map, data, flat_map, unravel,
+                genotype_chunk_size=genotype_chunk_size,
+                leak_tolerance=leak_tolerance, include_shared=True)
+        G, B, S = coupling.shape
+        prec = self._laplace_prior_precision(unconstrained, model_kwargs,
+                                             flat_map.shape[0])
+        L_blocks, L_kept = self._floored_block_factors(
+            blocks, prec[elem_idx], return_kept=True)
+
+        response = np.zeros((G, B, S), dtype=np.float32)
+        schur = shared_block.copy()
+        step = 4096
+        for start in range(0, G, step):
+            sl = slice(start, min(start + step, G))
+            L = L_kept[sl]
+            C = coupling[sl].astype(float)
+            M = np.einsum("gij,gkj,gks->gis", L, L, C)
+            response[sl] = M
+            schur -= np.einsum("gbs,gbt->st", C, M)
+        schur = 0.5 * (schur + schur.T)
+
+        w, V = np.linalg.eigh(schur)
+        w_f, _ = laplace_eigenvalue_floor(w, V, prec[shared_idx])
+        negative = w < 0
+        w_f = np.where(negative, np.inf, w_f)
+        print(f"  shared parameters: {int(np.sum(negative))} of {S} "
+              f"directions of the Schur complement negative, held at the MAP; "
+              f"{int(np.sum(~negative & (w_f > w)))} raised to the prior's "
+              f"curvature", flush=True)
+        # name the parameters in the negative directions (held: their
+        # uncertainty is left out)
+        names = np.array([f"{k}[{i}]" for k in sorted(unconstrained)
+                          for i in range(int(np.size(unconstrained[k])))])
+        held_rows = []
+        if names.size == flat_map.shape[0]:
+            names = names[shared_idx]
+            for rank, j in enumerate(np.argsort(w)[:int(np.sum(negative))]):
+                top = np.argsort(-np.abs(V[:, j]))[:5]
+                if rank < 10:
+                    print(f"    eigenvalue {w[j]:.2e}: " + ", ".join(
+                        f"{names[t]} {V[t, j]:+.2f}" for t in top[:3]),
+                        flush=True)
+                for t in top:
+                    held_rows.append({"direction": rank,
+                                      "eigenvalue": float(w[j]),
+                                      "parameter": str(names[t]),
+                                      "loading": float(V[t, j])})
+        # The held directions have no interval; tfs-sample-posterior writes
+        # them to {out_prefix}_held_directions.csv.
+        self.held_shared_directions = pd.DataFrame(
+            held_rows, columns=["direction", "eigenvalue", "parameter", "loading"])
+        L_shared = V / np.sqrt(w_f)[None, :]
+        return elem_idx, L_blocks, shared_idx, L_shared, response
+
     def get_laplace_posteriors(self,
                                map_params,
                                out_prefix,
@@ -1379,7 +2726,11 @@ def get_laplace_posteriors(self,
                                sampling_batch_size=100,
                                forward_batch_size=512,
                                hessian_chunk_size=64,
-                               sites_to_save=None):
+                               sites_to_save=None,
+                               block_genotypes=False,
+                               genotype_chunk_size=None,
+                               block_shared=False,
+                               allow_flat_floor=False):
         """
         Generate posterior samples from a MAP solution using the Laplace approximation.
 
@@ -1393,7 +2744,8 @@ def get_laplace_posteriors(self,
         ----------
         map_params : dict
             Parameter dict from a MAP (AutoDelta) optimizer state, as returned
-            by ``svi.get_params(svi_state)``. Keys follow the
+            by ``svi.optim.get_params(svi_state.optim_state)`` (not
+            ``svi.get_params``, which constrains them). Keys follow the
             ``{site}_auto_loc`` convention used by AutoDelta; values are in
             the unconstrained parameter space.
         out_prefix : str
@@ -1411,9 +2763,40 @@ def get_laplace_posteriors(self,
         sites_to_save : list of str or None, optional
             If given, only these site names are written to the HDF5 file.
             If None (default), all sites are saved.
+        block_genotypes : bool, optional
+            Per-genotype (block-diagonal) Laplace instead of the full one
+            (default False). The shared parameters (growth k and m,
+            hyperparameters, tube offsets) are held at the MAP, and each
+            genotype's own parameters get the Laplace of their conditional
+            posterior: given the shared parameters they couple to no other
+            genotype, so the Hessian splits into one small block per
+            genotype (``block_laplace_factors``). Memory and time scale with
+            the library, not its square, so it runs on a full library where
+            the full Hessian cannot. It leaves out the shared parameters'
+            uncertainty unless ``block_shared``.
+        genotype_chunk_size : int or None, optional
+            Genotypes per Hessian-vector-product pass in block mode (default
+            None, the whole library in one pass). Lower it if the pass runs
+            out of device memory.
+        block_shared : bool, optional
+            With ``block_genotypes``, keep the shared parameters' uncertainty
+            (``arrowhead_laplace_factors``; default False): the shared
+            parameters are drawn from their Laplace marginal and each
+            genotype from its conditional given that draw, which is the full
+            Laplace. Costs one more Hessian-vector product per shared
+            parameter per genotype chunk.
+        allow_flat_floor : bool, optional
+            If the prior SDs behind the eigenvalue floor cannot be computed,
+            fall back to the flat ``LAPLACE_MIN_EIGENVALUE`` floor with a
+            warning instead of raising (default False). See
+            ``_laplace_prior_precision``.
 
         Notes
         -----
+        The model's own learned ``pyro.param`` sites (thermo PnnC NN
+        weights; see ``inference/model_params.py``) are empirical-Bayes point
+        estimates: they are held at the MAP and are not part of the Hessian.
+
         Hessian inversion is O(D²) in memory and O(D³) in compute, where D is
         the total number of unconstrained parameters.  The Hessian is computed
         in row-chunks (see ``hessian_chunk_size``) to bound peak device memory
@@ -1424,12 +2807,14 @@ def get_laplace_posteriors(self,
         from numpyro.distributions.transforms import biject_to
         import jax.flatten_util
 
+        self.allow_flat_laplace_floor = bool(allow_flat_floor)
         data_on_gpu = jax.device_put(self.model.data)
         total_num_genotypes = self.model.data.num_genotype
         dim_map = self._get_genotype_dim_map()
 
         all_indices = jnp.arange(total_num_genotypes)
-        full_data = self.model.get_batch(data_on_gpu, all_indices)
+        # unscaled: at full batch every genotype's likelihood counts once
+        full_data = self._unscaled_batch(data_on_gpu, all_indices)
         model_kwargs = {"priors": self.model.priors, "data": full_data}
 
         # Strip _auto_loc suffix → unconstrained site-level param dict
@@ -1439,51 +2824,120 @@ def get_laplace_posteriors(self,
             if k.endswith("_auto_loc")
         }
 
+        # Refuse aliased (batch-sized) latents before the costly Hessian.
+        self._check_library_sized_latents(unconstrained, dim_map,
+                                          total_num_genotypes)
+
+        # The model's own learned params (thermo PnnC NN weights) are
+        # empirical-Bayes point estimates: held at the MAP, outside the
+        # Hessian, in every model evaluation below.
+        self.fixed_model_params = self.model_params(map_params,
+                                                    unconstrained=True)
+
         # Flatten to a single vector for Hessian computation
         flat_map, unravel = jax.flatten_util.ravel_pytree(unconstrained)
         D = flat_map.shape[0]
-        print(f"Computing Hessian for {D} parameters "
-              f"(chunk_size={hessian_chunk_size}) ...", flush=True)
-
-        def pe_fn(flat_p):
-            return potential_energy(
-                self.model.jax_model, [], model_kwargs, unravel(flat_p)
-            )
+        if block_shared and not block_genotypes:
+            raise ValueError("block_shared needs block_genotypes")
+        if block_genotypes and block_shared:
+            elem_idx, L_blocks, shared_idx, L_shared, response = \
+                self.arrowhead_laplace_factors(
+                    unconstrained, dim_map, data_on_gpu, flat_map, unravel,
+                    model_kwargs, genotype_chunk_size=genotype_chunk_size)
+            elem_flat = jnp.asarray(elem_idx.reshape(-1))
+            shared_flat = jnp.asarray(shared_idx)
+            L_blocks = jnp.asarray(L_blocks, dtype=jnp.float32)
+            L_shared = jnp.asarray(L_shared, dtype=jnp.float32)
+            response = jnp.asarray(response)
+
+            def draw(key, n):
+                # shared parameters from their marginal; each genotype from
+                # its conditional given them
+                k_s, k_g = jax.random.split(key)
+                d_shared = jax.random.normal(
+                    k_s, shape=(n, L_shared.shape[1])) @ L_shared.T
+                z = jax.random.normal(k_g, shape=(n,) + L_blocks.shape[:2])
+                delta = (jnp.einsum("gij,ngj->ngi", L_blocks, z)
+                         - jnp.einsum("gbs,ns->ngb", response, d_shared))
+                base = jnp.broadcast_to(flat_map, (n, D))
+                base = base.at[:, shared_flat].add(d_shared)
+                return base.at[:, elem_flat].add(delta.reshape(n, -1))
+        elif block_genotypes:
+            elem_idx, L_blocks = self.block_laplace_factors(
+                unconstrained, dim_map, data_on_gpu, flat_map, unravel,
+                model_kwargs, genotype_chunk_size=genotype_chunk_size)
+            elem_flat = jnp.asarray(elem_idx.reshape(-1))
+            L_blocks = jnp.asarray(L_blocks, dtype=jnp.float32)
+
+            def draw(key, n):
+                # shared parameters stay at the MAP; each genotype's block
+                # gets mean + L_g z_g
+                z = jax.random.normal(key, shape=(n,) + L_blocks.shape[:2])
+                delta = jnp.einsum("gij,ngj->ngi", L_blocks, z)
+                base = jnp.broadcast_to(flat_map, (n, D))
+                return base.at[:, elem_flat].add(delta.reshape(n, -1))
+        else:
+            # The prior SDs first: a failure there stops the run before the
+            # Hessian, not after it.
+            prior_precision = self._laplace_prior_precision(unconstrained,
+                                                            model_kwargs, D)
+            print(f"Computing Hessian for {D} parameters "
+                  f"(chunk_size={hessian_chunk_size}) ...", flush=True)
+
+            model_fn = self._fixed_model()
+
+            def pe_fn(flat_p):
+                return potential_energy(
+                    model_fn, [], model_kwargs, unravel(flat_p)
+                )
 
-        H_np = self._chunked_hessian(pe_fn, flat_map, hessian_chunk_size)
-
-        # Project Hessian to the PD cone and compute the Cholesky factor of the
-        # covariance in float64 (via numpy) so that sampling is numerically
-        # stable even when JAX_ENABLE_X64 is not set.
-        #
-        # Three sources of instability in the naive float32 approach:
-        #  1. Saddle-point MAP → negative Hessian eigenvalues → non-PD covariance
-        #  2. Very large eigenvalues (e.g. 2.6e8) → covariance condition number
-        #     ~1e11, which exceeds float32 precision (~1e7) and reintroduces
-        #     negative eigenvalues after the V @ diag(1/λ) @ V^T reconstruction.
-        #  3. jax.random.multivariate_normal uses Cholesky internally; if the
-        #     covariance is not PD the Cholesky fails → NaN samples.
-        #
-        # Fix: do the eigendecomposition and Cholesky in numpy float64, clamp
-        # negative eigenvalues to 1e-3 (caps max variance per direction at 1000),
-        # then sample as mean + L @ z where z ~ N(0,I) in float32.
-        print("Projecting Hessian to PD cone and computing Cholesky ...", flush=True)
-        eigenvalues_np, eigenvectors_np = np.linalg.eigh(H_np)
-        n_negative = int(np.sum(eigenvalues_np < 0))
-        if n_negative > 0:
-            print(f"  Warning: {n_negative} negative Hessian eigenvalues "
-                  f"(min={eigenvalues_np.min():.3e}); clamping to 1e-3.",
-                  flush=True)
-        eigenvalues_pd = np.maximum(eigenvalues_np, 1e-3)
-        cov_np = eigenvectors_np @ np.diag(1.0 / eigenvalues_pd) @ eigenvectors_np.T
-        # Cholesky in float64; cast factor to float32 for the forward pass
-        L_np = np.linalg.cholesky(cov_np)
-        L = jnp.array(L_np, dtype=jnp.float32)
+            H_np = self._chunked_hessian(pe_fn, flat_map, hessian_chunk_size)
+
+            # Project Hessian to the PD cone and compute the Cholesky factor of the
+            # covariance in float64 (via numpy) so that sampling is numerically
+            # stable even when JAX_ENABLE_X64 is not set.
+            #
+            # Three sources of instability in the naive float32 approach:
+            #  1. Saddle-point MAP → negative Hessian eigenvalues → non-PD covariance
+            #  2. Very large eigenvalues (e.g. 2.6e8) → covariance condition number
+            #     ~1e11, which exceeds float32 precision (~1e7) and reintroduces
+            #     negative eigenvalues after the V @ diag(1/λ) @ V^T reconstruction.
+            #  3. jax.random.multivariate_normal uses Cholesky internally; if the
+            #     covariance is not PD the Cholesky fails → NaN samples.
+            #
+            # Fix: do the eigendecomposition and Cholesky in numpy float64, floor
+            # each eigenvalue at the prior's curvature along its eigenvector
+            # (``laplace_eigenvalue_floor``), then sample as mean + L @ z where
+            # z ~ N(0,I) in float32. The old flat floor of 1e-3 gave a direction
+            # with a negative eigenvalue a variance of 1000 in unconstrained
+            # units; a MAP stopped short of its optimum always has a few, and
+            # where they touched the growth slopes m, Laplace draws of m spread
+            # 50x wider than its posterior (SVI-overconfidence two-stage grid,
+            # seed 8: m from -0.049 to +0.017, truth -0.010).
+            print("Projecting Hessian to PD cone and computing Cholesky ...", flush=True)
+            eigenvalues_np, eigenvectors_np = np.linalg.eigh(H_np)
+            n_negative = int(np.sum(eigenvalues_np < 0))
+            eigenvalues_pd, n_floored = laplace_eigenvalue_floor(
+                eigenvalues_np, eigenvectors_np, prior_precision)
+            if n_negative > 0:
+                print(f"  Warning: {n_negative} negative Hessian eigenvalues "
+                      f"(min={eigenvalues_np.min():.3e}); the MAP is not at an "
+                      f"optimum.", flush=True)
+            print(f"  {n_floored} of {D} eigenvalues raised to the prior's "
+                  f"curvature along their eigenvector.", flush=True)
+            cov_np = eigenvectors_np @ np.diag(1.0 / eigenvalues_pd) @ eigenvectors_np.T
+            # Cholesky in float64; cast factor to float32 for the forward pass
+            L_np = np.linalg.cholesky(cov_np)
+            L = jnp.array(L_np, dtype=jnp.float32)
+
+            def draw(key, n):
+                # mean + z @ L^T (z ~ N(0,I)) avoids a second Cholesky
+                # inside jax.random.multivariate_normal
+                z = jax.random.normal(key, shape=(n, D))
+                return flat_map + z @ L.T
 
         # Get unconstrained → constrained transform for each latent site
-        seeded_model = seed(self.model.jax_model, rng_seed=0)
-        traced_model = trace(seeded_model)
-        model_trace = traced_model.get_trace(**model_kwargs)
+        model_trace = structure_trace(self.model.jax_model, **model_kwargs)
 
         site_transforms = {
             name: biject_to(site["fn"].support)
@@ -1514,8 +2968,7 @@ def pe_fn(flat_p):
                 # Use mean + z @ L^T (z ~ N(0,I)) to avoid a second Cholesky
                 # inside jax.random.multivariate_normal.
                 sample_key = self.get_key()
-                z = jax.random.normal(sample_key, shape=(this_batch_size, D))
-                flat_samples = flat_map + z @ L.T
+                flat_samples = draw(sample_key, this_batch_size)
 
                 # Unravel each sample and transform to constrained space.
                 # jax.vmap(unravel) maps (N, D) → pytree of (N, *shape) arrays.
@@ -1558,6 +3011,7 @@ def pe_fn(flat_p):
                 samples_written += batch_size_actual
 
             hf.attrs["num_samples"] = samples_written
+            write_model_params(hf, self.fixed_model_params)
             hf.flush()
 
     def get_nuts_posteriors(self,
@@ -1631,7 +3085,9 @@ def run_nuts(self,
                  num_warmup=500,
                  num_samples=500,
                  num_chains=1,
-                 target_accept_prob=0.9):
+                 target_accept_prob=0.9,
+                 init_values=None,
+                 dense_mass=False):
         """
         Run NUTS (No-U-Turn Sampler) MCMC on the full dataset.
 
@@ -1645,6 +3101,15 @@ def run_nuts(self,
             Number of MCMC chains.
         target_accept_prob : float
             Target acceptance probability for step-size adaptation.
+        init_values : dict or None, optional
+            Constrained site values to start every chain at (``site_values``,
+            e.g. from a MAP warm-up).  Sites without a value start at their
+            prior median.
+        dense_mass : bool, optional
+            Adapt a dense mass matrix instead of a diagonal one.  It costs
+            O(D^2) memory and O(D^3) per adaptation window but follows
+            correlated posteriors (the relative fit's m*X ridge), where a
+            diagonal mass matrix leaves NUTS at its maximum tree depth.
 
         Returns
         -------
@@ -1652,30 +3117,30 @@ def run_nuts(self,
             The MCMC object after sampling. Call `.get_samples()` to get
             posterior samples as a dict of {site_name: jnp.array}.
         """
-        from numpyro.infer import MCMC, NUTS, init_to_value, init_to_median
-        import numpyro.infer.util
+        from numpyro.infer import MCMC, NUTS
 
         main_key = random.PRNGKey(self._seed)
 
+        # The full data set as one batch, in library order (the path the
+        # Laplace posterior uses).
+        full_data = self.model.get_batch(self.model.data,
+                                         jnp.arange(self.model.data.num_genotype))
         jax_model_kwargs = {
             "priors": self.model.priors,
-            "data": self.model.data,
+            "data": full_data,
         }
 
-        # Initialise from prior median — more robust than random for
-        # hierarchical models with constrained parameters.
-        init_params, _, _, _ = numpyro.infer.util.initialize_model(
-            main_key,
-            self.model.jax_model,
-            model_args=[],
-            model_kwargs=jax_model_kwargs,
-            init_strategy=init_to_median,
-        )
-        init_strategy = init_to_value(values=init_params)
+        # Start at the given constrained site values, else at prior medians.
+        # (This used to hand initialize_model's ParamInfo tuple to
+        # init_to_value, which matched no site, so every chain started at
+        # init_to_uniform's draw in [-2, 2] on the unconstrained scale.)
+        init_strategy = _init_to_value_or(values=init_values,
+                                          fallback=init_to_median)
 
         kernel = NUTS(self.model.jax_model,
                       init_strategy=init_strategy,
-                      target_accept_prob=target_accept_prob)
+                      target_accept_prob=target_accept_prob,
+                      dense_mass=dense_mass)
 
         mcmc = MCMC(kernel,
                     num_warmup=num_warmup,
@@ -1692,4 +3157,31 @@ def run_nuts(self,
             total = num_samples * num_chains
             print(f"NUTS: {num_div} divergences out of {total} samples")
 
+        self._report_nuts_diagnostics(mcmc)
+
         return mcmc
+
+    @staticmethod
+    def _report_nuts_diagnostics(mcmc):
+        """Print the worst split R-hat and smallest effective sample size."""
+        from numpyro.diagnostics import summary
+
+        try:
+            stats = summary(mcmc.get_samples(group_by_chain=True))
+        except Exception as err:  # diagnostics must never sink a finished run
+            print(f"NUTS: diagnostics unavailable ({err})")
+            return
+        worst_rhat, worst_rhat_site = -np.inf, None
+        least_neff, least_neff_site = np.inf, None
+        for site, st in stats.items():
+            # Constant deterministic sites (fixed values) give inf/NaN.
+            r_hat = np.asarray(st.get("r_hat", np.nan), dtype=float)
+            r_hat = r_hat[np.isfinite(r_hat)]
+            n_eff = np.asarray(st.get("n_eff", np.nan), dtype=float)
+            n_eff = n_eff[np.isfinite(n_eff)]
+            if r_hat.size and r_hat.max() > worst_rhat:
+                worst_rhat, worst_rhat_site = float(r_hat.max()), site
+            if n_eff.size and n_eff.min() < least_neff:
+                least_neff, least_neff_site = float(n_eff.min()), site
+        print(f"NUTS: max split R-hat {worst_rhat:.3f} ({worst_rhat_site}); "
+              f"min n_eff {least_neff:.0f} ({least_neff_site})", flush=True)
diff --git a/src/tfscreen/tfmodel/inference/staged_map.py b/src/tfscreen/tfmodel/inference/staged_map.py
new file mode 100644
index 00000000..cbb8cde8
--- /dev/null
+++ b/src/tfscreen/tfmodel/inference/staged_map.py
@@ -0,0 +1,255 @@
+"""
+A MAP fit with level tube offsets, run in three stages.
+
+A cold MAP with ``sample_offset: level`` can settle in a mode where the tube
+offsets carry the population's growth: Adam moves every parameter by about
+one step size per step, so in the first window it moves the offsets and the
+growth rates k and m (prior SD 0.002 per minute) by whole units, and the
+step-size cuts then freeze that mode. On the dev-data fit the offsets landed
+near ±2.8; starting from a MAP without offsets plus each tube's best offset
+was 1.2e5 nats better (``planning/dev-data/real_fit``, 2026-10-01). The
+stages do that start automatically:
+
+1. **No offsets.** The MAP with every tube offset held at 0.
+2. **Offsets alone.** Every other site held at stage 1's MAP; the offsets
+   are the only latents. Given the rest, the tubes do not couple, so this is
+   each tube's best offset (what ``small_offsets.py`` computed by hand).
+3. **Joint.** The full MAP from stage 1's point plus stage 2's offsets, at a
+   small step size so the first window cannot carry the start away.
+
+While the offsets are held, a learned offset SD (``sigma_fixed`` = 0) is
+held too, at the prior's scale: with every offset at 0 the SD's MAP is 0 and
+the density is unbounded.
+
+The model's own learned params (the thermo PnnC NN weights; see
+``model_params.py``) follow the sites: stage 2 holds them at stage 1's
+values, and stage 3 starts from them.
+
+Each stage is an ordinary MAP on a model conditioned on its held values
+(``HeldModel``), so it writes its own checkpoint, convergence record and
+``_params.npz`` under ``{out_prefix}_stage1`` and ``{out_prefix}_stage2``;
+stage 3 writes the run's own ``{out_prefix}_*`` files. A finished stage
+(its ``_params.npz`` exists) is reused; an interrupted one resumes from its
+checkpoint.
+"""
+
+import os
+
+import jax.numpy as jnp
+import numpy as np
+from numpyro import handlers
+
+from tfscreen.tfmodel.inference.model_params import hold_model_params
+
+STAGE_CHOICES = ("auto", "on", "off")
+
+# Stage 3's starting step size: about one step size of movement per Adam
+# step, small against the offsets' SD (0.17) and the growth rates' prior SD.
+DEFAULT_STAGED_STEP_SIZE = 1e-4
+
+
+class HeldModel:
+    """
+    A model with some sample sites held at fixed values.
+
+    Wraps a ``ModelOrchestrator`` (or anything ``RunInference`` accepts):
+    ``jax_model`` is the wrapped model conditioned on ``held``, so an
+    autoguide built on it has no parameters for those sites and the loss
+    counts their (constant) log density at the held values. Every other
+    attribute is the wrapped model's, including ``jax_model_guide``, which
+    ``RunInference.site_values`` reads only for parameter names: fit a
+    HeldModel with an autoguide (MAP), never with the component guide.
+
+    Parameters
+    ----------
+    model : object
+        The model to wrap.
+    held : dict
+        Site name to constrained value. Per-genotype values must be
+        library-sized, as a MAP's ``_params.npz`` stores them.
+    params : dict or None, optional
+        The model's own ``pyro.param`` values to hold fixed (constrained).
+    """
+
+    def __init__(self, model, held, params=None):
+        self._model = model
+        # jax arrays: the model indexes per-genotype values with traced
+        # batch indices, which a numpy array cannot take
+        self._held = {k: jnp.asarray(v) for k, v in held.items()}
+        self._jax_model = hold_model_params(
+            handlers.condition(model.jax_model, data=self._held), params)
+
+    @property
+    def held(self):
+        return dict(self._held)
+
+    @property
+    def jax_model(self):
+        return self._jax_model
+
+    def __getattr__(self, name):
+        return getattr(self._model, name)
+
+
+def offset_sites(orchestrator):
+    """
+    The level offset's site names and the value its SD is held at.
+
+    Returns
+    -------
+    offset_site, sigma_site : str
+    sigma_hold : float or None
+        The SD to hold while the offsets are held, or None if the SD is
+        already fixed (``sigma_fixed`` > 0, a deterministic site).
+    """
+    priors = orchestrator.priors.growth.sample_offset
+    sigma_hold = None if float(priors.sigma_fixed) > 0 else float(priors.sigma_prior_scale)
+    return "sample_offset_offset", "sample_offset_sigma", sigma_hold
+
+
+def use_stages(stage_offsets, orchestrator, analysis_method,
+               checkpoint_file=None, init_from=None):
+    """
+    Whether ``tfs-fit-model`` runs the staged MAP.
+
+    ``auto`` stages a fresh MAP (no checkpoint to resume, no ``init_from``
+    start) of a model with level tube offsets. ``on`` requires such a fit;
+    ``off`` never stages.
+    """
+    if stage_offsets not in STAGE_CHOICES:
+        raise ValueError(f"stage_offsets must be one of {STAGE_CHOICES}; got "
+                         f"{stage_offsets!r}.")
+    if stage_offsets == "off":
+        return False
+    level = orchestrator.settings.get("sample_offset") == "level"
+    fresh_map = (analysis_method == "map" and checkpoint_file is None
+                 and init_from is None)
+    if stage_offsets == "auto":
+        return level and fresh_map
+    if not level:
+        raise ValueError("stage_offsets='on' needs sample_offset 'level'.")
+    if not fresh_map:
+        raise ValueError("stage_offsets='on' needs analysis_method 'map' with "
+                         "no checkpoint_file and no init_from.")
+    return True
+
+
+def read_params(path):
+    """A MAP ``_params.npz`` as {site: constrained value}."""
+    with np.load(path) as z:
+        return {k[:-len("_auto_loc")]: np.asarray(z[k])
+                for k in z.files if k.endswith("_auto_loc")}
+
+
+def read_model_params(path):
+    """
+    The model's own learned params in a MAP ``_params.npz``: every array but
+    the AutoDelta ``{site}_auto_loc`` ones (constrained).
+    """
+    with np.load(path) as z:
+        return {k: np.asarray(z[k]) for k in z.files
+                if not k.endswith("_auto_loc")}
+
+
+def _stage(run_map, make_ri, model, held, init_values, prefix, label,
+           params=None, **kwargs):
+    """Run one stage, or reuse it if its params file is already there."""
+    params_file = f"{prefix}_params.npz"
+    if os.path.exists(params_file):
+        print(f"{label}: reusing {params_file}", flush=True)
+        return read_params(params_file)
+    checkpoint = f"{prefix}_checkpoint.pkl"
+    resume = checkpoint if os.path.exists(checkpoint) else None
+    print(f"{label}: {'resuming ' + checkpoint if resume else 'starting'} "
+          f"({len(held)} site(s) held)", flush=True)
+    ri = make_ri(HeldModel(model, held, params=params))
+    run_map(ri,
+            init_values=None if resume else ri.site_values(init_values),
+            checkpoint_file=resume,
+            out_prefix=prefix,
+            label=label,
+            **kwargs)
+    return read_params(params_file)
+
+
+def run_staged_map(orchestrator, make_ri, run_map, guesses, out_prefix,
+                   map_kwargs, stage_map_kwargs=None,
+                   staged_step_size=DEFAULT_STAGED_STEP_SIZE):
+    """
+    Run the three-stage MAP (see the module docstring).
+
+    Parameters
+    ----------
+    orchestrator : ModelOrchestrator
+        The model, with ``sample_offset: level``.
+    make_ri : callable
+        ``make_ri(model) -> RunInference`` for a (possibly held) model.
+    run_map : callable
+        ``tfs-fit-model``'s ``_run_map``.
+    guesses : dict
+        The configured guesses (any form ``RunInference.site_values`` takes).
+    out_prefix : str
+        The run's prefix. Stages 1 and 2 write ``{out_prefix}_stage1_*`` and
+        ``{out_prefix}_stage2_*``; stage 3 writes ``{out_prefix}_*``.
+    map_kwargs : dict
+        ``_run_map`` keyword arguments for stage 3 (optimizer and
+        convergence settings); its ``adam_step_size`` is replaced by
+        ``staged_step_size``.
+    stage_map_kwargs : dict or None
+        ``_run_map`` keyword arguments for stages 1 and 2 (default
+        ``map_kwargs``).
+    staged_step_size : float
+        Stage 3's starting step size.
+
+    Returns
+    -------
+    The stage-3 ``_run_map`` result: ``(svi_state, params, converged)``.
+    """
+    if stage_map_kwargs is None:
+        stage_map_kwargs = map_kwargs
+    offset_site, sigma_site, sigma_hold = offset_sites(orchestrator)
+
+    # Stage 1: offsets (and a learned SD) held.
+    held1 = {offset_site: _offset_zeros(orchestrator)}
+    if sigma_hold is not None:
+        held1[sigma_site] = np.asarray(sigma_hold, dtype=float)
+    stage1 = _stage(run_map, make_ri, orchestrator, held1, guesses,
+                    f"{out_prefix}_stage1", "Stage 1 (no tube offsets)",
+                    **stage_map_kwargs)
+
+    # Stage 2: every other site held at stage 1; the offsets start at 0.
+    held2 = {k: v for k, v in stage1.items() if k != offset_site}
+    if sigma_hold is not None:
+        held2[sigma_site] = np.asarray(sigma_hold, dtype=float)
+    # The model's own params (thermo PnnC NN) are held at stage 1's too.
+    stage1_params = read_model_params(f"{out_prefix}_stage1_params.npz")
+    stage2 = _stage(run_map, make_ri, orchestrator, held2,
+                    {offset_site: held1[offset_site]},
+                    f"{out_prefix}_stage2", "Stage 2 (tube offsets alone)",
+                    params=stage1_params, **stage_map_kwargs)
+    off = np.asarray(stage2[offset_site])
+    print(f"  stage 2 offsets: SD {off.std():.3f}, range {off.min():+.2f} to "
+          f"{off.max():+.2f}", flush=True)
+
+    # Stage 3: joint, from stage 1 + stage 2's offsets.
+    start = {**guesses, **{f"{k}_auto_loc": v for k, v in stage1.items()},
+             f"{offset_site}_auto_loc": off}
+    ri = make_ri(orchestrator)
+    kwargs = dict(map_kwargs)
+    kwargs["adam_step_size"] = staged_step_size
+    if stage1_params:
+        kwargs["init_params"] = stage1_params
+    print(f"Stage 3 (joint, step size {staged_step_size:g})", flush=True)
+    return run_map(ri,
+                   init_values=ri.site_values(start),
+                   out_prefix=out_prefix,
+                   label="Staged MAP",
+                   **kwargs)
+
+
+def _offset_zeros(orchestrator):
+    """Zeros of the per-tube offset's shape (one per tube of the grid)."""
+    d = orchestrator.data.growth
+    n = (d.num_replicate * d.num_time * d.num_condition_pre
+         * d.num_condition_sel * d.num_titrant_name * d.num_titrant_conc)
+    return np.zeros(n, dtype=float)
diff --git a/src/tfscreen/tfmodel/model_orchestrator.py b/src/tfscreen/tfmodel/model_orchestrator.py
index 50a6e144..7a231e54 100644
--- a/src/tfscreen/tfmodel/model_orchestrator.py
+++ b/src/tfscreen/tfmodel/model_orchestrator.py
@@ -38,7 +38,8 @@
 
 def _read_growth_df(growth_df,
                     theta_group_cols=None,
-                    treatment_cols=None):
+                    treatment_cols=None,
+                    growth_likelihood="lncfu"):
     """
     Reads and preprocesses a DataFrame containing growth curve data.
 
@@ -64,6 +65,10 @@ def _read_growth_df(growth_df,
         Column names used to define unique growth treatment conditions.
         If not specified, defaults to ["condition_pre", "condition_sel",
         "titrant_name", "titrant_conc"].
+    growth_likelihood : str, optional
+        "lncfu" (default) or "counts". For "counts" the frame also needs
+        ``counts``, each tube's total reads and total cells; see
+        :func:`_add_count_columns`.
 
     Returns
     -------
@@ -105,6 +110,9 @@ def _read_growth_df(growth_df,
     required.extend(["ln_cfu","ln_cfu_std","replicate","t_pre","t_sel"])
     tfscreen.util.dataframe.check_columns(growth_df,required_columns=required)
 
+    if growth_likelihood == "counts":
+        growth_df = _add_count_columns(growth_df)
+
     # These two maps are used to look up parameters after sampling posteriors
     growth_df = add_group_columns(target_df=growth_df,
                                   group_cols=treatment_cols,
@@ -124,6 +132,62 @@ def _read_growth_df(growth_df,
         
     return growth_df
 
+def _add_count_columns(growth_df):
+    """
+    Columns the count likelihood needs (``growth_likelihood='counts'``):
+    ``counts`` (reads of the genotype in the tube), ``ln_sample_reads`` (ln of
+    the tube's total reads, ``__unknown__`` included) and ``sample_ln_cfu``
+    (ln of the tube's total cells).
+
+    The tube's total reads come from ``sample_reads`` (written by
+    ``counts_to_lncfu``) when present, else from ``adjusted_counts /
+    frequency`` (the frequency denominator of older processed files, which
+    also counts one pseudocount per genotype: about 1% more than the reads,
+    the same for every genotype in a tube, so a per-tube offset absorbs
+    it). ``sample_ln_cfu`` is used as given, else derived from
+    ``sample_cfu`` as ``counts_to_lncfu`` does.
+
+    Raises
+    ------
+    ValueError
+        If a column cannot be found or derived, or a tube's reads or
+        ``counts`` are not finite and non-negative.
+    """
+    from tfscreen.process_raw.counts_to_lncfu import get_sample_ln_cfu
+
+    if "counts" not in growth_df.columns:
+        raise ValueError("growth_likelihood='counts' needs a 'counts' column "
+                         "in growth_df (reads per genotype per tube).")
+    counts = growth_df["counts"].to_numpy(dtype=float)
+    if not np.all(np.isfinite(counts) & (counts >= 0)):
+        raise ValueError("growth_df 'counts' must be finite and >= 0.")
+
+    if "sample_reads" in growth_df.columns:
+        reads = growth_df["sample_reads"].to_numpy(dtype=float)
+    elif {"adjusted_counts", "frequency"} <= set(growth_df.columns):
+        with np.errstate(divide="ignore", invalid="ignore"):
+            reads = (growth_df["adjusted_counts"].to_numpy(dtype=float)
+                     / growth_df["frequency"].to_numpy(dtype=float))
+    else:
+        raise ValueError(
+            "growth_likelihood='counts' needs each tube's total reads: a "
+            "'sample_reads' column (tfs-process-counts writes it), or "
+            "'adjusted_counts' and 'frequency' to derive it from.")
+    if not np.all(np.isfinite(reads) & (reads > 0)):
+        raise ValueError("Every tube's total reads must be finite and > 0.")
+    growth_df["ln_sample_reads"] = np.log(reads)
+
+    if "sample_ln_cfu" not in growth_df.columns:
+        try:
+            growth_df = get_sample_ln_cfu(growth_df)
+        except ValueError as e:
+            raise ValueError(
+                f"growth_likelihood='counts' needs each tube's total cells: "
+                f"{e}") from e
+
+    return growth_df
+
+
 def _infer_is_selection(growth_df, per_library=False):
     """
     Infer which growth conditions are selective from the data structure.
@@ -167,7 +231,8 @@ def _infer_is_selection(growth_df, per_library=False):
         return {cond: bool(cond not in pre_set) for cond in sorted(all_conds)}
 
 
-def _build_growth_tm(growth_df, growth_shares_replicates=False):
+def _build_growth_tm(growth_df, growth_shares_replicates=False,
+                     growth_likelihood="lncfu"):
     """
     Builds a TensorManager for the main growth data.
 
@@ -186,6 +251,9 @@ def _build_growth_tm(growth_df, growth_shares_replicates=False):
         Must contain all columns required for pivots and maps.
     growth_shares_replicates : bool, optional
         Whether to exclude replicate when mapping identical parameters.
+    growth_likelihood : str, optional
+        "lncfu" (default) or "counts". "counts" also registers the
+        ``counts``, ``ln_sample_reads`` and ``sample_ln_cfu`` tensors.
 
     Returns
     -------
@@ -222,6 +290,9 @@ def _build_growth_tm(growth_df, growth_shares_replicates=False):
     growth_tm.add_data_tensor("ln_cfu_std",dtype=FLOAT_DTYPE)
     growth_tm.add_data_tensor("t_pre",dtype=FLOAT_DTYPE)
     growth_tm.add_data_tensor("t_sel",dtype=FLOAT_DTYPE)
+    if growth_likelihood == "counts":
+        for col in ("counts", "ln_sample_reads", "sample_ln_cfu"):
+            growth_tm.add_data_tensor(col, dtype=FLOAT_DTYPE)
 
     # This creates a full-dimension map tensor that lets us look up the growth
     # conditions (pre and sel) for each element in the tensor. 
@@ -691,64 +762,178 @@ def _setup_batching(growth_genotypes,
     return out
 
 
-# Theta components that cannot yet supply a full-population theta reference
-# for the "empirical" transformation's congression correction.  Unlike
-# hill_mut/hill_geno/thermo.*, which already assemble per-genotype parameters
-# for the full library on every forward pass (batching only gathers a subset
-# of an already-full-size ThetaParam), categorical_geno only ever samples
-# batch_size-worth of per-genotype offsets per call.  Wiring it up to
-# "empirical" would silently reproduce the population-CDF bug the
-# NEEDS_FULL_POPULATION_THETA mechanism exists to fix (see
-# transformation/_congression.py and generative/model.py), rather than
-# raising — so it's blocked explicitly here until categorical_geno gets its
-# own full-population code path.
-_THETA_MODELS_INCOMPATIBLE_WITH_EMPIRICAL = frozenset({"categorical_geno"})
-
+# Transformation components that were removed or renamed, with the reason.
+# Refused by name so an old config fails loudly instead of silently meaning
+# something new.
+# Growth observation models: Student-t on ln_cfu, or negative binomial on
+# read counts (roadmap step 7).
+_GROWTH_LIKELIHOODS = {"lncfu": "observe_growth",
+                       "counts": "observe_growth_counts"}
+
+# Rules for a congressed cell's theta (transformation/mixture.py).
+_CONGRESSION_THETA_RULES = ("homodimer", "heterodimer", "max")
+# Rules for a congressed cell's dk_geno (the soft-min family; only
+# "softmin" takes congression_dk_alpha).
+_CONGRESSION_DK_RULES = ("dilution", "softmin", "min")
+
+# Theta components on the wt-relative X scale (roadmap step 5): real-valued,
+# defined up to an affine map, gauged on wt at two concentrations.
+_RELATIVE_THETA = ("hill_relative",)
+
+_RETIRED_TRANSFORMATIONS = {
+    "empirical": (
+        "transformation 'empirical' has been replaced by 'mixture'. The old "
+        "component corrected each genotype's theta to the expected maximum "
+        "over co-resident plasmids and grew one trajectory with the "
+        "genotype's own dk_geno; averaging theta cannot represent a mixture "
+        "of growth rates. 'mixture' instead mixes clean and congressed cells "
+        "at the level of exp(ln_cfu), with cell-level theta, activity and "
+        "dk_geno (see planning/congression-physics-plan.md). Set "
+        "transformation: mixture (fits are not comparable with the old "
+        "component)."
+    ),
+    "logit_norm": (
+        "transformation 'logit_norm' has been removed: it modeled the "
+        "co-resident background as a parametric theta distribution with no "
+        "dk_geno counterpart. Use 'mixture' (or 'single' for no congression)."
+    ),
+}
+
+
+def _check_relative_theta(theta, has_binding, activity, theta_rescale,
+                          condition_growth, theta_growth_noise,
+                          transformation, congression_theta_rule,
+                          theta_gauge_conc):
+    """
+    Refuse settings that assume theta is an absolute occupancy when the theta
+    component is on the relative X scale, and check ``theta_gauge_conc``.
 
-def _check_theta_transformation_compatibility(theta, transformation, binding_only=False):
+    Returns ``theta_gauge_conc`` as a list of two floats, or None.
     """
-    Raise a clear error for known theta/transformation combinations that
-    would silently produce incorrect results rather than failing loudly.
+    if theta not in _RELATIVE_THETA:
+        if theta_gauge_conc is not None:
+            raise ValueError(
+                f"theta_gauge_conc={theta_gauge_conc!r} was given but theta "
+                f"{theta!r} is not a relative theta component "
+                f"({list(_RELATIVE_THETA)}); only those have a gauge.")
+        return None
+
+    why = (f"theta={theta!r} infers a wt-relative growth variable X, not an "
+           f"absolute occupancy (roadmap step 5, "
+           f"planning/analysis-roadmap.md)")
+    if has_binding:
+        raise ValueError(
+            f"{why}, so binding data (an absolute occupancy) cannot be fit "
+            f"with it. Leave out binding_df.")
+    fixed = {"activity": (activity, "fixed"),
+             "theta_rescale": (theta_rescale, "passthrough"),
+             "condition_growth": (condition_growth, "linear"),
+             "theta_growth_noise": (theta_growth_noise, "zero")}
+    for key, (value, required) in fixed.items():
+        if value != required:
+            raise ValueError(
+                f"{why}; it requires {key}={required!r} (got {value!r}). "
+                f"X's scale absorbs activity, and rescaling, nonlinear "
+                f"growth and theta noise all assume theta in (0, 1).")
+    if transformation == "mixture" and congression_theta_rule != "max":
+        raise ValueError(
+            f"{why}; under the congression mixture only "
+            f"congression_theta_rule='max' is invariant to X's affine gauge "
+            f"(the partition-function rules take logit(theta)). Got "
+            f"{congression_theta_rule!r}.")
+
+    if theta_gauge_conc is None:
+        return None
+    gauge = [float(c) for c in theta_gauge_conc]
+    if len(gauge) != 2 or not (0.0 <= gauge[0] < gauge[1]):
+        raise ValueError(
+            f"theta_gauge_conc must be two concentrations (c_lo, c_hi) with "
+            f"0 <= c_lo < c_hi; got {theta_gauge_conc!r}.")
+    return gauge
 
-    Parameters
-    ----------
-    theta : str
-        Name of the requested theta component (model_registry["theta"] key).
-    transformation : str
-        Name of the requested transformation component
-        (model_registry["transformation"] key).
-    binding_only : bool, optional
-        Whether the model is being built in binding-only mode. In that mode
-        jax_model returns before ever touching the transformation/congression
-        code path (see generative/model.py's early `if binding_only: ...
-        return`), so the transformation setting is inert and this check does
-        not apply. Default False.
 
-    Raises
-    ------
-    ValueError
-        If `transformation == "empirical"` and `theta` is a component that
-        cannot supply a full-population theta reference for the congression
-        correction, and the model is not binding-only.
+def _check_factored_ln_cfu0(ln_cfu0, growth_df):
+    """
+    Refuse ``ln_cfu0: hierarchical_factored`` when it would share a
+    genotype's starting abundance across separately grown libraries.
+
+    The factored model gives each genotype one baseline per replicate, shared
+    by every ``condition_pre``, plus one offset per tube. That holds when the
+    pre-conditions are split from one culture. When a replicate's
+    pre-conditions come from different libraries (kanR and pheS are
+    transformed and grown up separately, user 2026-09-28), each genotype's
+    starting abundance differs between them and the fit pushes the
+    difference into its growth rates: on simulations with separate libraries
+    it gave a confident per-genotype theta error, 95% coverage 0.09 above
+    1000 reads against 0.80 with ``hierarchical``
+    (planning/studies/svi-overconfidence/). Data without a ``library``
+    column are not checked.
     """
-    if binding_only:
+    if ln_cfu0 != "hierarchical_factored" or growth_df is None:
+        return
+    if "library" not in growth_df.columns:
         return
+    per_rep = (growth_df[["replicate", "condition_pre", "library"]]
+               .drop_duplicates()
+               .groupby("replicate", observed=True)["library"].nunique())
+    mixed = per_rep[per_rep > 1]
+    if len(mixed):
+        pairs = (growth_df[["condition_pre", "library"]].drop_duplicates()
+                 .astype(str).agg(" from ".join, axis=1).tolist())
+        raise ValueError(
+            "ln_cfu0='hierarchical_factored' shares each genotype's starting "
+            "abundance across every condition_pre of a replicate, but these "
+            f"pre-conditions come from different libraries ({', '.join(pairs)}), "
+            "which are grown up separately. Use ln_cfu0='hierarchical' (one "
+            "starting abundance per replicate, condition_pre and genotype).")
+
+
+def _check_congression_sets(congression_sets):
+    """
+    Validate ``congression_sets``: a non-empty sequence of non-negative
+    integers with a positive sum. Returns it as a tuple of ints.
+    """
+    try:
+        values = [int(v) for v in congression_sets]
+        exact = all(float(v) == int(v) for v in congression_sets)
+    except (TypeError, ValueError):
+        values, exact = None, False
+    if (not values or not exact or any(v < 0 for v in values)
+            or sum(values) == 0):
+        raise ValueError(
+            f"congression_sets must be a non-empty list of non-negative "
+            f"integers with a positive sum (entry n-1 = number of sets with "
+            f"n co-residents); got {congression_sets!r}."
+        )
+    return tuple(values)
 
-    if (transformation == "empirical"
-            and theta in _THETA_MODELS_INCOMPATIBLE_WITH_EMPIRICAL):
+
+def _check_congression_dk_alpha(congression_dk_rule, congression_dk_alpha):
+    """
+    Validate ``congression_dk_alpha`` against the dk rule: ``"softmin"``
+    needs a finite alpha > 0 (returned as a float); every other rule takes
+    none (returns None).
+    """
+    if congression_dk_rule != "softmin":
+        if congression_dk_alpha is not None:
+            raise ValueError(
+                f"congression_dk_alpha is only used by "
+                f"congression_dk_rule='softmin'; got "
+                f"congression_dk_rule={congression_dk_rule!r} with "
+                f"congression_dk_alpha={congression_dk_alpha!r}."
+            )
+        return None
+    try:
+        alpha = float(congression_dk_alpha)
+    except (TypeError, ValueError):
+        alpha = None
+    if alpha is None or not np.isfinite(alpha) or alpha <= 0:
         raise ValueError(
-            f"theta='{theta}' is not compatible with transformation='empirical'. "
-            "The empirical congression correction requires a theta reference "
-            f"covering the full genotype population, but '{theta}' only ever "
-            "assembles theta for the genotypes in the current batch/request "
-            "(see categorical_geno.define_model's batch_size-scoped "
-            "logit_theta_offset plate). Using it with transformation='empirical' "
-            "would silently reproduce the population-CDF bug this check exists "
-            "to prevent, rather than raise. Use transformation='single' or "
-            "'logit_norm' with this theta model, or choose a theta component "
-            "that already assembles full-population parameters (hill_geno, "
-            "hill_mut, or a thermo.* component)."
+            f"congression_dk_rule='softmin' needs a finite "
+            f"congression_dk_alpha > 0; got {congression_dk_alpha!r}. Use "
+            f"'dilution' or 'min' for the alpha -> 0 and alpha -> inf limits."
         )
+    return alpha
 
 
 class ModelOrchestrator:
@@ -764,9 +949,13 @@ class ModelOrchestrator:
     Parameters
     ----------
     growth_df : pd.DataFrame or str
-        DataFrame or path to file with growth data.
-    binding_df : pd.DataFrame or str
-        DataFrame or path to file with binding data.
+        DataFrame or path to file with growth data. None for a binding-only
+        model (``binding_only=True``).
+    binding_df : pd.DataFrame or str or None
+        DataFrame or path to file with binding data. None for a growth-only
+        model: theta is then inferred from growth alone, with no binding
+        likelihood, no ``theta_binding_noise`` component and no binding
+        weight (both of which are refused unless left at their defaults).
     condition_growth : str, optional
         Model name for condition-specific growth. Allowed values are 'linear'
         (default), 'linear_independent', 'power', or 'saturation'.
@@ -779,27 +968,28 @@ class ModelOrchestrator:
         Model name for genotype-specific death rate. Allowed values are 'hierarchical' 
         (default) or 'fixed'.
     activity : str, optional
-        Model name for genotype activity. Allowed values are "horseshoe" (default), 
-        "hierarchical", or "fixed".
+        Model name for genotype activity: "fixed" (default; activity 1 for
+        every genotype), "hierarchical_geno", "horseshoe_geno",
+        "hierarchical_mut" or "horseshoe_mut".
     theta : str, optional
         Model name for theta calculation (e.g., "hill").
     transformation : str, optional
-        Model name for transformation correction. Allowed values are 'single'
-        (default), 'empirical', or 'logit_norm'.
+        Model name for congression. 'single' (default; every cell carries one
+        plasmid) or 'mixture' (clean and congressed cells mixed at the
+        observable level; see ``transformation/mixture.py``).
     transformation_lambda : tuple, optional
         ``(mean, std)`` -- the experimentally measured congression lambda,
         in linear space -- used to anchor the ``transformation`` prior when
-        it is 'empirical' or 'logit_norm'. Forbidden when
-        ``transformation == 'single'`` (which has no lambda parameter). If
-        omitted for 'empirical'/'logit_norm', a weakly-informative
-        placeholder prior is used (see
-        ``generative/components/transformation/_congression.get_hyperparameters``);
+        it is 'mixture'. Forbidden when ``transformation == 'single'``
+        (which has no lambda parameter). If omitted for 'mixture', a
+        weakly-informative placeholder prior is used (see
+        ``generative/components/transformation/mixture.get_hyperparameters``);
         ``configure_model`` (the ``tfs-configure-model`` entry point)
         requires it explicitly rather than silently falling back to the
         placeholder.
     theta_growth_noise : str, optional
         Model name for noise on theta in the growth model ('zero', 'beta',
-        or 'logit_normal'). Default 'logit_normal'.
+        or 'logit_normal'). Default 'zero', as in tfs-configure-model.
     theta_binding_noise : str, optional
         Model name for noise on theta in the binding model ('zero' or 'beta').
     spiked_genotypes : list or str, optional
@@ -814,6 +1004,37 @@ class ModelOrchestrator:
         ``spiked_genotypes``.
     batch_size : int, optional
         The batch size for SVI. If None (default), use full batch.
+    congression_sets : sequence of int, optional
+        Number of fixed co-resident sets drawn per genotype for the
+        congression mixture, by co-resident count: entry n-1 is the number of
+        sets with n co-resident plasmids. Default (12, 3, 1): 16 sets, with
+        1, 2 or 3 co-residents. See ``_draw_coresident_sets``.
+    congression_seed : int, optional
+        Seed for drawing the co-resident sets (default 0). Recorded in the
+        config, so a refit reproduces the same draws.
+    congression_theta_rule : str, optional
+        How the congression mixture builds a congressed cell's theta from its
+        plasmids' thetas: ``"homodimer"`` (default; ``logit theta_cell = log
+        sum_g x_g exp(l_g)``), ``"heterodimer"`` (``2 log sum_g x_g exp(l_g /
+        2)``) or ``"max"`` (the highest-theta plasmid sets theta and
+        activity). The homodimer and heterodimer rules require
+        ``activity='fixed'`` (activity 1).
+    congression_dk_rule : str, optional
+        How the congression mixture builds a congressed cell's dk_geno from
+        its plasmids', the soft-min family ``dk_cell = -(1/alpha) log sum_g
+        x_g exp(-alpha dk_g)``: ``"dilution"`` (default; alpha -> 0, the
+        share-weighted mean), ``"softmin"`` (finite alpha, from
+        ``congression_dk_alpha``) or ``"min"`` (alpha -> inf, the worst
+        variant sets the cost).
+    congression_dk_alpha : float, optional
+        alpha for ``congression_dk_rule="softmin"`` (> 0, in units of 1/dk);
+        required by that rule and refused by the others.
+    growth_likelihood : str, optional
+        How growth is observed: ``"lncfu"`` (default; Student-t on ``ln_cfu``,
+        ``observe/growth.py``) or ``"counts"`` (negative binomial on the read
+        counts, ``observe/growth_counts.py``; needs ``counts``, each tube's
+        total reads and total cells, see ``_add_count_columns``, and
+        ``growth_noise='zero'``).
 
     Attributes
     ----------
@@ -841,12 +1062,12 @@ def __init__(self,
                  ln_cfu0="hierarchical",
                  dk_geno="hierarchical_geno",
                  dk_geno_pins_file=None,
-                 activity="horseshoe_geno",
+                 activity="fixed",
                  theta="hill_geno",
                  transformation="single",
                  transformation_lambda=None,
                  theta_rescale="passthrough",
-                 theta_growth_noise="logit_normal",
+                 theta_growth_noise="zero",
                  theta_binding_noise="zero",
                  growth_noise="zero",
                  sample_offset="zero",
@@ -855,9 +1076,28 @@ def __init__(self,
                  growth_shares_replicates=False,
                  epistasis=False,
                  thermo_data=None,
+                 thermo_options=None,
                  binding_weight=None,
                  presplit_df=None,
-                 base_growth_df=None):
+                 base_growth_df=None,
+                 congression_sets=(12, 3, 1),
+                 congression_seed=0,
+                 congression_theta_rule="homodimer",
+                 congression_dk_rule="dilution",
+                 congression_dk_alpha=None,
+                 growth_likelihood="lncfu",
+                 theta_gauge_conc=None,
+                 mut_labels=None,
+                 pair_labels=None):
+
+        # Fixed mutation/pair index space (not a setting: never written to a
+        # configuration). copy_orchestrator passes the fitted model's labels
+        # so a model rebuilt on a genotype subset indexes per-mutation and
+        # per-pair parameters exactly as the fit did.
+        self._fixed_mut_labels = (None if mut_labels is None
+                                  else list(mut_labels))
+        self._fixed_pair_labels = (None if pair_labels is None
+                                   else list(pair_labels))
 
         self._ln_cfu_df = growth_df
         self._binding_df = binding_df
@@ -888,11 +1128,86 @@ def __init__(self,
         self._growth_shares_replicates = growth_shares_replicates
         self._epistasis = epistasis
         self._thermo_data = thermo_data
+        # Validated up front so a bad option fails at configure time.  Stored
+        # as given (None stays None) so old configs round-trip unchanged.
+        if thermo_options is not None:
+            from tfscreen.tfmodel.generative.components.theta.thermo.io import (
+                normalize_thermo_options,
+            )
+            normalize_thermo_options(thermo_options)
+            thermo_options = dict(thermo_options)
+        self._thermo_options = thermo_options
         self._binding_weight = binding_weight
+        self._congression_sets = _check_congression_sets(congression_sets)
+        self._congression_seed = int(congression_seed)
+        self._congression_theta_rule = congression_theta_rule
+        self._congression_dk_rule = congression_dk_rule
+        self._congression_dk_alpha = _check_congression_dk_alpha(
+            congression_dk_rule, congression_dk_alpha)
+        self._growth_likelihood = growth_likelihood
+
+        if self._growth_likelihood not in _GROWTH_LIKELIHOODS:
+            raise ValueError(
+                f"growth_likelihood must be one of "
+                f"{list(_GROWTH_LIKELIHOODS)}; got {self._growth_likelihood!r}.")
+        if self._growth_likelihood == "counts" and growth_noise != "zero":
+            raise ValueError(
+                f"growth_likelihood='counts' describes extra per-row noise "
+                f"through its own dispersion (phi, inv_r), so growth_noise "
+                f"must be 'zero'; got {growth_noise!r}.")
+
+        if self._transformation in _RETIRED_TRANSFORMATIONS:
+            raise ValueError(_RETIRED_TRANSFORMATIONS[self._transformation])
+
+        self._theta_gauge_conc = _check_relative_theta(
+            theta, binding_df is not None, activity, theta_rescale,
+            condition_growth, theta_growth_noise, transformation,
+            congression_theta_rule, theta_gauge_conc)
+
+        # A growth-only model has no binding data at all: no binding tensors,
+        # no binding likelihood, no binding noise component.
+        self._has_binding = binding_df is not None
+        if self._binding_only and not self._has_binding:
+            raise ValueError("binding_only=True requires binding_df.")
+        if not self._binding_only and growth_df is None:
+            raise ValueError(
+                "growth_df is required unless binding_only=True.")
+        if not self._has_binding:
+            if self._binding_weight is not None:
+                raise ValueError(
+                    f"binding_weight={self._binding_weight!r} was given but "
+                    f"there is no binding_df; a growth-only model has no "
+                    f"binding likelihood to weight.")
+            if self._theta_binding_noise != "zero":
+                raise ValueError(
+                    f"theta_binding_noise={self._theta_binding_noise!r} was "
+                    f"given but there is no binding_df; a growth-only model "
+                    f"has no binding observations to add noise to. Leave it "
+                    f"at 'zero'.")
 
-        _check_theta_transformation_compatibility(
-            self._theta, self._transformation, binding_only=self._binding_only
-        )
+        if self._congression_theta_rule not in _CONGRESSION_THETA_RULES:
+            raise ValueError(
+                f"congression_theta_rule must be one of "
+                f"{list(_CONGRESSION_THETA_RULES)}; got "
+                f"{self._congression_theta_rule!r}."
+            )
+        if self._congression_dk_rule not in _CONGRESSION_DK_RULES:
+            raise ValueError(
+                f"congression_dk_rule must be one of "
+                f"{list(_CONGRESSION_DK_RULES)}; got "
+                f"{self._congression_dk_rule!r}."
+            )
+        if (self._transformation == "mixture"
+                and self._congression_theta_rule != "max"
+                and self._activity != "fixed"):
+            raise ValueError(
+                f"congression_theta_rule={self._congression_theta_rule!r} "
+                f"mixes the occupancies of a cell's variants and requires TF "
+                f"activity 1 (activity='fixed'); got "
+                f"activity={self._activity!r}. Use activity='fixed', or "
+                f"congression_theta_rule='max'. See the congression physics "
+                f"plan, step 4."
+            )
 
         if self._library_file is not None:
             if self._spiked_genotypes is not None:
@@ -955,8 +1270,12 @@ def _initialize_data(self):
         # Load in growth data, creating two blocks of tensors. One holds the
         # growth (replicate,time,condition,genotype) data. The other holds
         # the theta (titrant_conc,theta_group) tensor.
-        self.growth_df = _read_growth_df(self._ln_cfu_df)
-        self.growth_tm = _build_growth_tm(self.growth_df, self._growth_shares_replicates)
+        self.growth_df = _read_growth_df(self._ln_cfu_df,
+                                         growth_likelihood=self._growth_likelihood)
+        _check_factored_ln_cfu0(self._ln_cfu0, self.growth_df)
+        self.growth_tm = _build_growth_tm(self.growth_df,
+                                          self._growth_shares_replicates,
+                                          growth_likelihood=self._growth_likelihood)
                    
         # Assemble tensors. 
         tensors = {}
@@ -964,6 +1283,9 @@ def _initialize_data(self):
         from_growth_tm = ["ln_cfu","ln_cfu_std","t_pre","t_sel",
                           "map_condition_pre","map_condition_sel","good_mask"]
                           
+        if self._growth_likelihood == "counts":
+            from_growth_tm += ["counts", "ln_sample_reads", "sample_ln_cfu"]
+
         for k in from_growth_tm:
             tensors[k] = self.growth_tm.tensors[k]
 
@@ -982,11 +1304,11 @@ def _initialize_data(self):
         wt_loc = np.where(self.growth_tm.tensor_dim_labels[genotype_idx] == "wt")
         wt_info = {"wt_indexes":jnp.array(wt_loc[0], dtype=jnp.int32)}
 
-         # scatter_theta tells the theta model caller to return a full-sized
-        # growth_tm tensor instead of the smaller growth_theta_tm-sized tensor
-        # congression_mask is a boolean array of shape (num_genotype,) that 
-        # tells the model which genotypes should be corrected for congression.
-        # Initialize to all True (no masking).
+        # scatter_theta tells the theta model caller to return a full-sized
+        # growth_tm tensor instead of the smaller growth_theta_tm-sized tensor.
+        # mask is a boolean array of shape (num_genotype,): False for spiked
+        # genotypes. It sets the ln_cfu0 prior class and, on the legacy path,
+        # the binary bulk_fraction. Initialize to all True (nothing spiked).
         mask = np.ones(sizes["num_genotype"],dtype=bool)
 
         # A library composition table (the current interface) names the spiked
@@ -1039,6 +1361,21 @@ def _initialize_data(self):
             spiked_idx = np.where(np.isin(genotype_names,self._spiked_genotypes))[0]
             mask[spiked_idx] = False
 
+        bulk_fraction = self._build_bulk_fraction(mask)
+        coresident_idx, coresident_n = self._draw_coresident_sets(mask)
+
+        if (self._transformation == "mixture"
+                and np.any(bulk_fraction > 0)
+                and np.all(coresident_idx < 0)):
+            raise ValueError(
+                "transformation='mixture' needs co-resident plasmids to draw, "
+                "but no genotype with growth data can be one (the co-resident "
+                "pool is empty: every genotype is spiked-only or has zero "
+                "bulk share) while some genotypes have bulk_fraction > 0. "
+                "Check the library composition table, or use "
+                "transformation='single'."
+            )
+
         wt_mask = np.zeros(sizes["num_genotype"], dtype=bool)
         wt_mask[wt_loc[0]] = True
 
@@ -1059,10 +1396,16 @@ def _initialize_data(self):
         sizes["num_ln_cfu0_library_classes"] = 2
 
         other_data = {"scatter_theta":1,
-                      "congression_mask":jnp.array(mask,dtype=bool),
                       "ln_cfu0_spiked_mask":jnp.array(~mask,dtype=bool),
                       "ln_cfu0_wt_mask":jnp.array(wt_mask,dtype=bool),
-                      "ln_cfu0_library_masks":jnp.array(_library_masks,dtype=bool)}
+                      "ln_cfu0_library_masks":jnp.array(_library_masks,dtype=bool),
+                      "bulk_fraction":jnp.array(bulk_fraction,dtype=float),
+                      "coresident_idx":jnp.array(coresident_idx,dtype=jnp.int32),
+                      "coresident_n":jnp.array(coresident_n,dtype=jnp.int32),
+                      "congression_theta_rule":self._congression_theta_rule,
+                      "congression_dk_rule":self._congression_dk_rule,
+                      "congression_dk_alpha":self._congression_dk_alpha,
+                      "growth_likelihood":self._growth_likelihood}
 
         # Grab the titrant concentration and log_titrant_conc (1D array from 
         # the tensor labels along dimension 6)
@@ -1074,6 +1417,23 @@ def _initialize_data(self):
         
         other_data["titrant_conc"] = titrant_conc
         other_data["log_titrant_conc"] = log_titrant_conc
+
+        # The relative theta component's gauge: wt's X is 1 at c_lo and 0 at
+        # c_hi, by default the lowest and highest measured concentration.
+        # The resolved pair goes into settings, so the config records it.
+        if self._theta in _RELATIVE_THETA:
+            if len(wt_loc[0]) != 1:
+                raise ValueError(
+                    f"theta={self._theta!r} is gauged on wt, so the growth "
+                    f"data must contain the genotype 'wt' (found "
+                    f"{len(wt_loc[0])}).")
+            if self._theta_gauge_conc is None:
+                self._theta_gauge_conc = [float(np.min(titrant_conc)),
+                                          float(np.max(titrant_conc))]
+            gauge = np.array(self._theta_gauge_conc, dtype=float)
+            gauge[gauge == 0] = ZERO_CONC_VALUE
+            other_data["theta_gauge_log_conc"] = jnp.asarray(
+                np.log(gauge), dtype=FLOAT_DTYPE)
         other_data["growth_shares_replicates"] = bool(self._growth_shares_replicates)
 
         # Resolve pinned dk_geno values (dk_geno == "pinned") from an
@@ -1123,7 +1483,9 @@ def _initialize_data(self):
              mut_geno_matrix,
              pair_nnz_pair_idx,
              pair_nnz_geno_idx) = build_mut_geno_matrix(
-                _genotypes, skip_pairs=not self._epistasis
+                _genotypes, skip_pairs=not self._epistasis,
+                mut_labels=self._fixed_mut_labels,
+                pair_labels=self._fixed_pair_labels,
             )
             mut_nnz_mut_idx, mut_nnz_geno_idx = build_mut_sparse_indices(mut_geno_matrix)
             # Expose labels as model attributes for downstream interpretation
@@ -1161,21 +1523,18 @@ def _initialize_data(self):
                 else:
                     raise ValueError(
                         f"theta='{self._theta}' requires --thermo_data "
-                        f"(path to a CSV with columns 'mut' and one per structure)."
+                        f"(path to a CSV with a 'mut' column and one per structure)."
                     )
             from tfscreen.tfmodel.generative.components.theta.thermo.io import (
-                load_struct_ensemble,
-                load_ddG_prior_csv,
+                load_thermo_data,
+            )
+            _pair_labels_for_struct = self.pair_labels if self._epistasis else None
+            _struct_data = load_thermo_data(
+                self._thermo_data, mut_labels,
+                pair_labels=_pair_labels_for_struct,
+                theta_name=self._theta,
+                thermo_options=self._thermo_options,
             )
-            if self._theta in _ddG_prior_models:
-                _struct_data = load_ddG_prior_csv(
-                    self._thermo_data, mut_labels
-                )
-            else:
-                _pair_labels_for_struct = self.pair_labels if self._epistasis else None
-                _struct_data = load_struct_ensemble(
-                    self._thermo_data, mut_labels, _pair_labels_for_struct
-                )
             _struct_names_tuple = _struct_data["struct_names"]   # tuple; set via .replace()
             growth_data_sources.append({
                 "num_struct":               len(_struct_names_tuple),
@@ -1186,40 +1545,47 @@ def _initialize_data(self):
             })
 
         # ---------------------------------------------------------------------
-        # binding dataclass
-  
-        # Load in the binding data, creating one block of tensors. This is
-        # only (titrant_conc,theta_group). Use the growth tensor manager to
-        # make sure that the mapping to parameters matches between the growth 
-        # and the binding data. 
-        self.binding_df = _read_binding_df(self._binding_df,self.growth_tm.df)
-        self.binding_tm = _build_binding_tm(self.binding_df)
-
-        # Grab the sizes
-        sizes = {"num_titrant_name":self.binding_tm.tensor_shape[0],
-                 "num_titrant_conc":self.binding_tm.tensor_shape[1],
-                 "num_genotype":self.binding_tm.tensor_shape[2]}
-        other_data = {"scatter_theta":0}
-
-        # Grab the titrant concentration and log_titrant_conc (1D array from 
-        # the tensor labels along dimension 6)
-        idx = np.where(np.array(self.binding_tm.tensor_dim_names) == "titrant_conc")[0][0]
-        titrant_conc = np.array(self.binding_tm.tensor_dim_labels[idx])
-        log_titrant_conc = titrant_conc.copy()
-        log_titrant_conc[log_titrant_conc == 0] = ZERO_CONC_VALUE
-        log_titrant_conc = np.log(log_titrant_conc)
-
-        other_data["titrant_conc"] = titrant_conc
-        other_data["log_titrant_conc"] = log_titrant_conc
-
-        binding_data_sources = [self.binding_tm.tensors,sizes,other_data]
+        # binding dataclass (absent in a growth-only model)
+
+        if self._has_binding:
+
+            # Load in the binding data, creating one block of tensors. This is
+            # only (titrant_conc,theta_group). Use the growth tensor manager to
+            # make sure that the mapping to parameters matches between the
+            # growth and the binding data.
+            self.binding_df = _read_binding_df(self._binding_df,self.growth_tm.df)
+            self.binding_tm = _build_binding_tm(self.binding_df)
+
+            # Grab the sizes
+            sizes = {"num_titrant_name":self.binding_tm.tensor_shape[0],
+                     "num_titrant_conc":self.binding_tm.tensor_shape[1],
+                     "num_genotype":self.binding_tm.tensor_shape[2]}
+            other_data = {"scatter_theta":0}
+
+            # Grab the titrant concentration and log_titrant_conc (1D array
+            # from the tensor labels along dimension 6)
+            idx = np.where(np.array(self.binding_tm.tensor_dim_names) == "titrant_conc")[0][0]
+            titrant_conc = np.array(self.binding_tm.tensor_dim_labels[idx])
+            log_titrant_conc = titrant_conc.copy()
+            log_titrant_conc[log_titrant_conc == 0] = ZERO_CONC_VALUE
+            log_titrant_conc = np.log(log_titrant_conc)
+
+            other_data["titrant_conc"] = titrant_conc
+            other_data["log_titrant_conc"] = log_titrant_conc
+
+            binding_data_sources = [self.binding_tm.tensors,sizes,other_data]
+            binding_genotypes = self.binding_tm.tensor_dim_labels[-1]
+        else:
+            self.binding_df = None
+            self.binding_tm = None
+            binding_genotypes = np.array([], dtype=object)
 
         # ---------------------------------------------------------------------
         # Create full-batch data (source of truth for indices and shapes) 
        
         # Pass None as batch_size to get full indices and scale factors of 1.0
         full_batch_data = _setup_batching(self.growth_tm.tensor_dim_labels[-1],
-                                          self.binding_tm.tensor_dim_labels[-1],
+                                          binding_genotypes,
                                           batch_size=None)
         
         # If mini-batching is requested, pre-calculate the scale vector that 
@@ -1228,7 +1594,7 @@ def _initialize_data(self):
             
             # Use the helper to find indices and scale factor for the target batch size
             scaling_info = _setup_batching(self.growth_tm.tensor_dim_labels[-1],
-                                           self.binding_tm.tensor_dim_labels[-1],
+                                           binding_genotypes,
                                            self._batch_size)
             
             # Create a full-sized vector with these scale factors
@@ -1250,22 +1616,29 @@ def _initialize_data(self):
         growth_batch_data["geno_theta_idx"] = np.arange(full_batch_data["batch_size"],dtype=int)
         growth_data_sources.append(growth_batch_data)
         
-        # Record relevant batch data for the binding dataset
-        binding_batch_data = {}
-        binding_num_binding = full_batch_data["num_binding"]
-        binding_batch_data["batch_idx"] = full_batch_data["batch_idx"][:binding_num_binding]
-        binding_batch_data["batch_size"] = binding_num_binding
-        binding_batch_data["scale_vector"] = full_batch_data["scale_vector"][:binding_num_binding]
-        binding_batch_data["geno_theta_idx"] = np.arange(binding_num_binding,dtype=int)
-
-        # Apply binding weight: upscale the binding likelihood to compete with
-        # the (typically much larger) growth dataset.  None → auto-compute as
-        # N_growth_rows / N_binding_rows so each binding observation contributes
-        # the same weight as the average growth observation.
-        if self._binding_weight is None:
-            self._binding_weight = len(self.growth_tm.df) / max(len(self.binding_tm.df), 1)
-        binding_batch_data["scale_vector"] = binding_batch_data["scale_vector"] * self._binding_weight
-        binding_data_sources.append(binding_batch_data)
+        if self._has_binding:
+
+            # Record relevant batch data for the binding dataset
+            binding_batch_data = {}
+            binding_num_binding = full_batch_data["num_binding"]
+            binding_batch_data["batch_idx"] = full_batch_data["batch_idx"][:binding_num_binding]
+            binding_batch_data["batch_size"] = binding_num_binding
+            binding_batch_data["scale_vector"] = full_batch_data["scale_vector"][:binding_num_binding]
+            binding_batch_data["geno_theta_idx"] = np.arange(binding_num_binding,dtype=int)
+            binding_batch_data["binding_ordinal"] = np.arange(binding_num_binding,dtype=int)
+
+            # Apply binding weight. None -> 1: each binding observation
+            # counts once, as in the full-batch log density (binding
+            # genotypes ride in every batch at scale 1). Until 2026-10-16
+            # None meant N_growth_rows / N_binding_rows, which made the
+            # binding data count as much as the whole growth dataset, so any
+            # growth/binding mismatch distorted growth
+            # (planning/joint-fit-return.md). A config written before then
+            # stores its resolved weight and keeps it.
+            if self._binding_weight is None:
+                self._binding_weight = 1.0
+            binding_batch_data["scale_vector"] = binding_batch_data["scale_vector"] * self._binding_weight
+            binding_data_sources.append(binding_batch_data)
 
         # ---------------------------------------------------------------------
         # Populate dataclasses
@@ -1280,8 +1653,11 @@ def _initialize_data(self):
             growth_dataclass = growth_dataclass.replace(struct_names=_struct_names_tuple)
 
         # Populate a BindingData flax dataclass with all keys in `sources`
-        binding_dataclass = populate_dataclass(BindingData,
-                                               sources=binding_data_sources)
+        if self._has_binding:
+            binding_dataclass = populate_dataclass(BindingData,
+                                                   sources=binding_data_sources)
+        else:
+            binding_dataclass = None
 
         # ---------------------------------------------------------------------
         # presplit dataclass (optional)
@@ -1412,7 +1788,9 @@ def _initialize_binding_only_data(self):
              mut_geno_matrix,
              pair_nnz_pair_idx,
              pair_nnz_geno_idx) = build_mut_geno_matrix(
-                _genotypes, skip_pairs=not self._epistasis
+                _genotypes, skip_pairs=not self._epistasis,
+                mut_labels=self._fixed_mut_labels,
+                pair_labels=self._fixed_pair_labels,
             )
             mut_nnz_mut_idx, mut_nnz_geno_idx = build_mut_sparse_indices(mut_geno_matrix)
             self.mut_labels = mut_labels
@@ -1443,21 +1821,18 @@ def _initialize_binding_only_data(self):
                 else:
                     raise ValueError(
                         f"theta='{self._theta}' requires --thermo_data "
-                        f"(path to a CSV with columns 'mut' and one per structure)."
+                        f"(path to a CSV with a 'mut' column and one per structure)."
                     )
             from tfscreen.tfmodel.generative.components.theta.thermo.io import (
-                load_struct_ensemble,
-                load_ddG_prior_csv,
+                load_thermo_data,
+            )
+            _pair_labels_for_struct = self.pair_labels if self._epistasis else None
+            _struct_data = load_thermo_data(
+                self._thermo_data, self.mut_labels,
+                pair_labels=_pair_labels_for_struct,
+                theta_name=self._theta,
+                thermo_options=self._thermo_options,
             )
-            if self._theta in _ddG_prior_models:
-                _struct_data = load_ddG_prior_csv(
-                    self._thermo_data, self.mut_labels
-                )
-            else:
-                _pair_labels_for_struct = self.pair_labels if self._epistasis else None
-                _struct_data = load_struct_ensemble(
-                    self._thermo_data, self.mut_labels, _pair_labels_for_struct
-                )
             _struct_names_tuple = _struct_data["struct_names"]
             binding_data_sources.append({
                 "num_struct":               len(_struct_names_tuple),
@@ -1486,6 +1861,7 @@ def _initialize_binding_only_data(self):
             "batch_size":     actual_batch_size,   # static mini-batch target
             "scale_vector":   scale_vector,        # full N; get_batch slices
             "geno_theta_idx": batch_idx,           # full N; get_batch slices
+            "binding_ordinal": batch_idx,          # full N; get_batch slices
         })
 
         binding_dataclass = populate_dataclass(BindingData, sources=binding_data_sources)
@@ -1545,8 +1921,10 @@ def _initialize_classes(self):
                         ("transformation", self._transformation, "growth"),
                         ("theta_growth_noise", self._theta_growth_noise, "growth"),
                         ("growth_noise", self._growth_noise, "growth"),
-                        ("sample_offset", self._sample_offset, "growth"),
-                        ("theta_binding_noise", self._theta_binding_noise, "binding")]
+                        ("sample_offset", self._sample_offset, "growth")]
+            if self._has_binding:
+                load_map.append(("theta_binding_noise",
+                                 self._theta_binding_noise, "binding"))
 
         main_control_kwargs = {"is_guide":False}
         guide_control_kwargs = {"is_guide":True}
@@ -1646,15 +2024,14 @@ def _initialize_classes(self):
                                              component_module.run_model,
                                              component_module.get_population_moments)
             elif key == "transformation":
-                needs_full_population = getattr(
-                    component_module, "NEEDS_FULL_POPULATION_THETA", False
-                )
+                needs_population = getattr(component_module,
+                                           "NEEDS_POPULATION", False)
                 main_control_kwargs[key] = (component_module.define_model,
-                                            component_module.update_thetas,
-                                            needs_full_population)
+                                            component_module.cell_classes,
+                                            needs_population)
                 guide_control_kwargs[key] = (component_module.guide,
-                                             component_module.update_thetas,
-                                             needs_full_population)
+                                             component_module.cell_classes,
+                                             needs_population)
             elif key == "condition_growth":
                 main_control_kwargs[key] = component_module.define_model
                 guide_control_kwargs[key] = component_module.guide
@@ -1678,12 +2055,15 @@ def _initialize_classes(self):
         main_control_kwargs["theta_rescale"] = rescale_fn
         guide_control_kwargs["theta_rescale"] = rescale_fn
 
-        # Set the observables; growth observer is only needed in the full model
-        main_control_kwargs["observe_binding"] = model_registry["observe_binding"].observe
-        guide_control_kwargs["observe_binding"] = model_registry["observe_binding"].guide
+        # Set the observables; the binding observer only when there is binding
+        # data, the growth observer only in the full model
+        if self._has_binding:
+            main_control_kwargs["observe_binding"] = model_registry["observe_binding"].observe
+            guide_control_kwargs["observe_binding"] = model_registry["observe_binding"].guide
         if not self._binding_only:
-            main_control_kwargs["observe_growth"] = model_registry["observe_growth"].observe
-            guide_control_kwargs["observe_growth"] = model_registry["observe_growth"].guide
+            growth_observer = model_registry[_GROWTH_LIKELIHOODS[self._growth_likelihood]]
+            main_control_kwargs["observe_growth"] = growth_observer.observe
+            guide_control_kwargs["observe_growth"] = growth_observer.guide
 
             # Optional side-channel observers -- wired only when their data was
             # supplied. Both borrow growth's genotype batch state and a latent
@@ -1730,7 +2110,7 @@ def _initialize_classes(self):
             )
         else:
             priors_class_kwargs["growth"]["growth_obs"] = \
-                model_registry["observe_growth"].get_priors()
+                model_registry[_GROWTH_LIKELIHOODS[self._growth_likelihood]].get_priors()
             if self._base_growth_df is not None:
                 priors_class_kwargs["growth"]["base_growth"] = \
                     model_registry["observe_base_growth"].get_priors(
@@ -1743,8 +2123,11 @@ def _initialize_classes(self):
                 )
             growth_priors = populate_dataclass(GrowthPriors,
                                                sources=priors_class_kwargs["growth"])
-        binding_priors = populate_dataclass(BindingPriors,
-                                            sources=priors_class_kwargs["binding"])
+        if self._has_binding:
+            binding_priors = populate_dataclass(BindingPriors,
+                                                sources=priors_class_kwargs["binding"])
+        else:
+            binding_priors = None
         priors = populate_dataclass(PriorsClass,
                                     sources=dict(theta=priors_class_kwargs["theta"]["theta"],
                                                  growth=growth_priors,
@@ -1859,6 +2242,143 @@ def priors(self):
         """The PriorsClass Pytree holding all model priors."""
         return self._priors
 
+    def _build_bulk_fraction(self, spiked_free_mask):
+        """
+        Per-genotype bulk fraction (congression purity), aligned to the
+        growth tensor's genotype axis.
+
+        With a library composition table, each genotype takes the table's
+        ``bulk_fraction``. The ``__unknown__`` bucket (reads that match no
+        designed genotype) is treated as bulk (1.0), as the congression mask
+        treats it; any other genotype missing from the table is an error.
+        Without a table, the legacy binary applies: 0 for spiked genotypes,
+        1 otherwise.
+
+        Parameters
+        ----------
+        spiked_free_mask : numpy.ndarray
+            Boolean, shape (num_genotype,); False for spiked genotypes (the
+            legacy congression mask).
+
+        Returns
+        -------
+        numpy.ndarray
+            Float, shape (num_genotype,), values in [0, 1].
+        """
+
+        if self._library_df is None:
+            return np.where(spiked_free_mask, 1.0, 0.0)
+
+        genotype_idx = self.growth_tm.tensor_dim_names.index("genotype")
+        genotype_names = [str(g) for g in
+                          self.growth_tm.tensor_dim_labels[genotype_idx]]
+
+        table = self._library_df.set_index("genotype")["bulk_fraction"]
+        missing = [g for g in genotype_names
+                   if g not in table.index and g != "__unknown__"]
+        if len(missing) > 0:
+            raise ValueError(
+                f"{len(missing)} genotypes in the growth data are missing from "
+                f"the library composition table ({self._library_file}), e.g. "
+                f"{missing[:5]}. Regenerate it with tfs-configure-model "
+                f"--library_config."
+            )
+
+        values = np.array([1.0 if g == "__unknown__" else float(table[g])
+                           for g in genotype_names])
+        if np.any(~np.isfinite(values)) or np.any((values < 0) | (values > 1)):
+            raise ValueError(
+                f"bulk_fraction in {self._library_file} must be finite and in "
+                f"[0, 1] for every genotype with growth data."
+            )
+        return values
+
+    def _coresident_pool(self, spiked_free_mask):
+        """
+        Probability of each genotype (growth-tensor order) being drawn as a
+        co-resident plasmid.
+
+        Co-residents come from the bulk sub-libraries, so with a library
+        composition table a genotype's weight is its bulk share of the pool,
+        ``pool_fraction * bulk_fraction``. Only genotypes with growth data
+        can be drawn (they are the only ones with theta and dk_geno in the
+        model); library genotypes with no data, mostly dropouts, are left
+        out, which biases the background slightly toward survivors. The
+        ``__unknown__`` bucket is never drawn. Without a table (legacy
+        path), the pool is uniform over non-spiked genotypes.
+
+        Returns
+        -------
+        numpy.ndarray
+            Float, shape (num_genotype,), summing to 1; all zeros when no
+            genotype can be drawn.
+        """
+
+        genotype_idx = self.growth_tm.tensor_dim_names.index("genotype")
+        genotype_names = [str(g) for g in
+                          self.growth_tm.tensor_dim_labels[genotype_idx]]
+
+        if self._library_df is None:
+            weights = np.where(spiked_free_mask, 1.0, 0.0)
+        else:
+            table = self._library_df.set_index("genotype")
+            weights = np.array([
+                float(table.loc[g, "pool_fraction"] * table.loc[g, "bulk_fraction"])
+                if g in table.index else 0.0
+                for g in genotype_names
+            ])
+
+        weights[np.array(genotype_names) == "__unknown__"] = 0.0
+        weights = np.where(np.isfinite(weights) & (weights > 0), weights, 0.0)
+
+        total = weights.sum()
+        return weights / total if total > 0 else weights
+
+    def _draw_coresident_sets(self, spiked_free_mask):
+        """
+        Draw each genotype's fixed co-resident plasmid sets.
+
+        The congression mixture approximates a congressed cell's expected
+        growth by a fixed quadrature over co-resident sets (see
+        planning/congression-physics-plan.md, "Fit design (step 3)"). Sets
+        are stratified by co-resident count: ``congression_sets[n-1]`` sets
+        of ``n`` co-residents each. Every co-resident is an independent draw
+        from ``_coresident_pool`` (a genotype can co-reside with itself).
+        Drawn once, with ``congression_seed``, and never redrawn.
+
+        Returns
+        -------
+        coresident_idx : numpy.ndarray
+            Int, shape (num_genotype, K, N_max), growth-tensor genotype
+            indices; -1 pads sets with fewer than N_max co-residents, and
+            every entry is -1 when the pool is empty. K is the total number
+            of sets and N_max = len(congression_sets).
+        coresident_n : numpy.ndarray
+            Int, shape (K,): the number of co-residents in each set.
+        """
+
+        pool = self._coresident_pool(spiked_free_mask)
+        num_genotype = len(pool)
+        n_max = len(self._congression_sets)
+
+        coresident_n = np.concatenate([
+            np.full(k, n, dtype=int)
+            for n, k in enumerate(self._congression_sets, start=1)
+        ])
+        num_sets = len(coresident_n)
+
+        coresident_idx = np.full((num_genotype, num_sets, n_max), -1, dtype=int)
+        if pool.sum() <= 0 or num_sets == 0:
+            return coresident_idx, coresident_n
+
+        rng = np.random.default_rng(self._congression_seed)
+        draws = rng.choice(num_genotype, size=(num_genotype, num_sets, n_max),
+                           p=pool)
+        slot = np.arange(n_max)[None, None, :]
+        coresident_idx = np.where(slot < coresident_n[None, :, None], draws, -1)
+
+        return coresident_idx, coresident_n
+
     @property
     def library_df(self):
         """
@@ -1897,7 +2417,15 @@ def settings(self):
             "growth_shares_replicates": self._growth_shares_replicates,
             "epistasis": self._epistasis,
             "thermo_data": self._thermo_data,
+            "thermo_options": getattr(self, "_thermo_options", None),
             "binding_weight": self._binding_weight,
             "presplit_df": getattr(self, "_presplit_df", None),
             "base_growth_df": getattr(self, "_base_growth_df", None),
+            "congression_sets": list(self._congression_sets),
+            "congression_seed": self._congression_seed,
+            "congression_theta_rule": self._congression_theta_rule,
+            "congression_dk_rule": self._congression_dk_rule,
+            "congression_dk_alpha": self._congression_dk_alpha,
+            "growth_likelihood": self._growth_likelihood,
+            "theta_gauge_conc": self._theta_gauge_conc,
         }
diff --git a/src/tfscreen/tfmodel/priors_edit.py b/src/tfscreen/tfmodel/priors_edit.py
new file mode 100644
index 00000000..e966ec05
--- /dev/null
+++ b/src/tfscreen/tfmodel/priors_edit.py
@@ -0,0 +1,410 @@
+"""
+Edits to a configured model's priors CSV.
+
+``tfs-configure-model`` writes ``{out_prefix}_priors.csv`` with every prior at
+its component default. This module changes it in the documented ways, so
+nobody edits the file by hand:
+
+- ``apply_prior_overrides``: set scalar priors by name
+  (``tfs-configure-model --set_priors name=value ...``), such as a held
+  population SD (``theta_log_hill_n_hyper_scale_fixed``), the tube-offset
+  SD (``sigma_fixed``) or the thermo models' ΔΔG scale
+  (``theta_ddG_sigma_fixed``).
+- ``growth_prior_updates``: per-condition growth k/m priors from a table
+  (``--growth_priors``), for models the pre-fit does not calibrate.
+- ``growth_priors_from_wt_rates``: such a table from wt monoculture growth
+  rates on the relative-X gauge (``--growth_priors_wt_rates``).
+- ``apply_priors_updates``: write scalar or per-condition updates into the
+  CSV (shared with ``tfs-prefit-calibration``).
+"""
+
+import shutil
+import sys
+
+import numpy as np
+import pandas as pd
+
+# Columns of a --growth_priors table and the linear-growth prior rows each
+# one sets. m_scale sets both of linear's m scales: a condition reads the one
+# its +/- flag names, so they carry the same per-condition values.
+GROWTH_PRIOR_FIELDS = {
+    "k_loc": ("k_loc",),
+    "k_scale": ("k_scale",),
+    "m_loc": ("m_loc",),
+    "m_scale": ("m_scale_plus", "m_scale_minus"),
+    "k_pinned": ("k_pinned",),
+    "m_pinned": ("m_pinned",),
+    "m_warp": ("m_warp",),
+}
+
+# Growth-priors columns that are 0/1 flags (linear's per-condition clamps).
+GROWTH_PIN_FIELDS = ("k_pinned", "m_pinned", "m_warp")
+
+GROWTH_PRIOR_PREFIX = "growth.condition_growth."
+
+WT_RATE_COLUMNS = ("condition_sel", "titrant_conc", "rate_mean", "rate_sd",
+                   "num_replicates")
+
+
+def condition_rep_labels(orchestrator):
+    """
+    Per-condition labels (``condition_rep`` and, when present, ``replicate``)
+    in the model's condition order (``map_condition_rep``); None without a
+    growth model.
+    """
+    growth_tm = getattr(orchestrator, "growth_tm", None)
+    if growth_tm is None:
+        return None
+    crm = growth_tm.map_groups.get("condition_rep")
+    if crm is None or getattr(crm, "empty", True):
+        return None
+    sorted_map = crm.sort_values("map_condition_rep").reset_index(drop=True)
+    cols = [c for c in ("replicate", "condition_rep") if c in sorted_map.columns]
+    if not cols:
+        return None
+    return sorted_map[cols].reset_index(drop=True)
+
+
+def apply_priors_updates(priors_path, prior_updates, cond_rep_labels=None,
+                         backup=True):
+    """
+    Apply prior updates to a priors CSV.
+
+    Rows whose ``parameter`` is not in ``prior_updates`` are preserved.
+
+    * **scalar** (float): overwrites the ``value`` of the matching scalar row.
+      A warning is printed if no row matches.
+    * **array** (1-D ``np.ndarray``): a per-condition prior (e.g.
+      ``growth.condition_growth.k_loc``). Existing rows for that parameter are
+      replaced with one indexed row per condition, tagged with the
+      ``condition_rep`` (and ``replicate``) labels from ``cond_rep_labels``,
+      so the loader can name-join them to the model's condition order.
+
+    Parameters
+    ----------
+    priors_path : str
+        Path to the priors CSV.
+    prior_updates : dict[str, float | np.ndarray]
+        Update values keyed by dotted parameter name.
+    cond_rep_labels : pandas.DataFrame or None
+        Per-condition labels in the order of the array updates (see
+        ``condition_rep_labels``). Without them array rows carry only
+        ``flat_index``.
+    backup : bool
+        Copy the file to ``{priors_path}.bak`` before writing.
+
+    Returns
+    -------
+    set of str
+        The parameter names that were updated.
+    """
+    if not prior_updates:
+        return set()
+    # The value column mixes numbers and text (bool flags), so pandas >= 3
+    # reads it as a string dtype that refuses a float; keep it object.
+    df = pd.read_csv(priors_path, dtype={"value": object})
+    if "parameter" not in df.columns or "value" not in df.columns:
+        raise ValueError(
+            f"Priors CSV {priors_path} is missing required 'parameter' / "
+            "'value' columns."
+        )
+
+    scalar_updates = {}
+    array_updates = {}
+    for row_name, new_val in prior_updates.items():
+        arr = np.asarray(new_val)
+        if arr.ndim == 0:
+            scalar_updates[row_name] = float(arr)
+        else:
+            array_updates[row_name] = arr
+
+    matched = set()
+
+    for row_name, new_val in scalar_updates.items():
+        mask = df["parameter"] == row_name
+        if mask.any():
+            df.loc[mask, "value"] = new_val
+            matched.add(row_name)
+
+    missing = sorted(set(scalar_updates) - matched)
+    if missing:
+        print(
+            f"  warning: {len(missing)} prior update(s) had no matching row "
+            f"in {priors_path}: {missing}",
+            file=sys.stderr,
+        )
+
+    if array_updates:
+        new_frames = []
+        for row_name, arr in array_updates.items():
+            flat_val = np.asarray(arr).flatten()
+            df = df[df["parameter"] != row_name]
+            row_df = pd.DataFrame({"parameter": row_name,
+                                   "value": flat_val,
+                                   "flat_index": range(len(flat_val))})
+            if (cond_rep_labels is not None
+                    and len(cond_rep_labels) == len(flat_val)):
+                for col in ("replicate", "condition_rep"):
+                    if col in cond_rep_labels.columns:
+                        row_df[col] = cond_rep_labels[col].to_numpy()
+            new_frames.append(row_df)
+            matched.add(row_name)
+        df = pd.concat([df] + new_frames, ignore_index=True)
+
+    if backup:
+        shutil.copy2(priors_path, priors_path + ".bak")
+    df.to_csv(priors_path, index=False)
+    print(f"Updated {len(matched)} priors row(s) in {priors_path}")
+    return matched
+
+
+def parse_prior_overrides(items):
+    """
+    Parse ``name=value`` strings into a dict of floats.
+
+    Raises
+    ------
+    ValueError
+        On an item without ``=``, a non-numeric value or a repeated name.
+    """
+    out = {}
+    for item in items or []:
+        if "=" not in item:
+            raise ValueError(f"Prior override '{item}' is not of the form "
+                             "name=value.")
+        name, value = item.split("=", 1)
+        name = name.strip()
+        try:
+            value = float(value)
+        except ValueError as e:
+            raise ValueError(f"Prior override '{item}': value is not a "
+                             "number.") from e
+        if name in out:
+            raise ValueError(f"Prior override '{name}' is given twice.")
+        out[name] = value
+    return out
+
+
+def resolve_prior_name(name, parameters):
+    """
+    Resolve a prior name to the full dotted row name in a priors CSV.
+
+    ``name`` may be the full row name (``theta.theta_log_hill_n_hyper_scale_fixed``)
+    or any dotted suffix of exactly one row (``theta_log_hill_n_hyper_scale_fixed``,
+    ``sample_offset.sigma_fixed``, ``sigma_fixed`` when only one component
+    has it).
+
+    Raises
+    ------
+    ValueError
+        If no row or more than one row matches.
+    """
+    parameters = list(dict.fromkeys(parameters))
+    if name in parameters:
+        return name
+    hits = [p for p in parameters if p.endswith("." + name)]
+    if len(hits) == 1:
+        return hits[0]
+    if not hits:
+        close = [p for p in parameters if name.split(".")[-1] in p][:10]
+        hint = f" Similar: {close}." if close else ""
+        raise ValueError(f"No prior named '{name}' in this model.{hint}")
+    raise ValueError(f"Prior name '{name}' is ambiguous; it matches {hits}. "
+                     "Give the full name.")
+
+
+def apply_prior_overrides(priors_path, overrides):
+    """
+    Set scalar priors by name (``tfs-configure-model --set_priors``).
+
+    Parameters
+    ----------
+    priors_path : str
+        Path to the priors CSV.
+    overrides : dict[str, float]
+        Prior name (full or a unique suffix; see ``resolve_prior_name``) to
+        value.
+
+    Returns
+    -------
+    dict[str, float]
+        The full row names set, with their values.
+
+    Raises
+    ------
+    ValueError
+        If a name is unknown or ambiguous, or names a per-condition prior
+        (set those with ``--growth_priors``).
+    """
+    if not overrides:
+        return {}
+    df = pd.read_csv(priors_path)
+    if "flat_index" in df.columns:
+        indexed = set(df.loc[df["flat_index"].notna(), "parameter"])
+    else:
+        indexed = set()
+
+    resolved = {}
+    for name, value in overrides.items():
+        full = resolve_prior_name(name, df["parameter"])
+        if full in indexed:
+            raise ValueError(f"Prior '{full}' is per-condition (indexed rows); "
+                             "set it with --growth_priors, not --set_priors.")
+        if full in resolved:
+            raise ValueError(f"Prior '{full}' is set twice.")
+        resolved[full] = float(value)
+
+    apply_priors_updates(priors_path, resolved, backup=False)
+    return resolved
+
+
+def _labels_table(cond_rep_labels):
+    labels = cond_rep_labels.copy()
+    labels["condition_rep"] = labels["condition_rep"].astype(str)
+    if "replicate" in labels.columns:
+        labels["replicate"] = labels["replicate"].astype(str)
+    return labels
+
+
+def growth_prior_updates(table, cond_rep_labels, defaults):
+    """
+    Per-condition linear-growth prior arrays from a table.
+
+    Parameters
+    ----------
+    table : pandas.DataFrame
+        One row per condition: ``condition_rep``, optionally ``replicate``,
+        and any of ``k_loc``, ``k_scale``, ``m_loc``, ``m_scale``, and the
+        0/1 clamps ``k_pinned``, ``m_pinned`` (linear holds a pinned
+        condition's k or m at its loc and samples the rest). A table without
+        ``replicate`` applies each row to every replicate. A missing value
+        (NaN) keeps the default.
+    cond_rep_labels : pandas.DataFrame
+        The model's per-condition labels (``condition_rep_labels``).
+    defaults : dict[str, float]
+        Scalar default for each prior row (``k_loc``, ``k_scale``, ``m_loc``,
+        ``m_scale_plus``, ``m_scale_minus``, ``k_pinned``, ``m_pinned``),
+        used for conditions or values the table leaves out.
+
+    Returns
+    -------
+    dict[str, numpy.ndarray]
+        ``growth.condition_growth.<field>`` to a per-condition array in the
+        order of ``cond_rep_labels``, for ``apply_priors_updates``.
+
+    Raises
+    ------
+    ValueError
+        On an unknown column, a condition the model does not have, a
+        condition given twice, a non-positive scale or a pin flag other than
+        0 or 1.
+    """
+    if cond_rep_labels is None:
+        raise ValueError("Per-condition growth priors need a growth model.")
+    table = table.copy()
+    if "condition_rep" not in table.columns:
+        raise ValueError("A growth-priors table needs a 'condition_rep' column.")
+    value_cols = [c for c in table.columns if c not in ("condition_rep", "replicate")]
+    unknown = [c for c in value_cols if c not in GROWTH_PRIOR_FIELDS]
+    if unknown:
+        raise ValueError(f"Unknown growth-priors column(s) {unknown}; allowed: "
+                         f"{list(GROWTH_PRIOR_FIELDS)}.")
+
+    labels = _labels_table(cond_rep_labels)
+    table["condition_rep"] = table["condition_rep"].astype(str)
+    key_cols = ["condition_rep"]
+    if "replicate" in table.columns:
+        if "replicate" not in labels.columns:
+            raise ValueError("The growth-priors table has a 'replicate' column "
+                             "but the model shares conditions across replicates.")
+        table["replicate"] = table["replicate"].astype(str)
+        key_cols = ["replicate", "condition_rep"]
+
+    dup = table.duplicated(subset=key_cols, keep=False)
+    if dup.any():
+        raise ValueError("Growth-priors table gives a condition more than once: "
+                         f"{table.loc[dup, key_cols].drop_duplicates().values.tolist()}")
+    known = set(map(tuple, labels[key_cols].drop_duplicates().values.tolist()))
+    given = set(map(tuple, table[key_cols].values.tolist()))
+    extra = sorted(given - known)
+    if extra:
+        raise ValueError(f"Growth-priors table names condition(s) {extra} that "
+                         f"the model does not have; it has {sorted(known)}.")
+
+    for col in value_cols:
+        vals = pd.to_numeric(table[col], errors="coerce")
+        if col.endswith("_scale") and (vals.dropna() <= 0).any():
+            raise ValueError(f"Growth-priors column '{col}' must be > 0.")
+        if col in GROWTH_PIN_FIELDS and not vals.dropna().isin([0, 1]).all():
+            raise ValueError(f"Growth-priors column '{col}' must be 0 or 1.")
+
+    merged = labels.merge(table, on=key_cols, how="left")
+    updates = {}
+    for col in value_cols:
+        vals = pd.to_numeric(merged[col], errors="coerce").to_numpy(dtype=float)
+        for field in GROWTH_PRIOR_FIELDS[col]:
+            arr = np.where(np.isnan(vals), float(defaults[field]), vals)
+            updates[GROWTH_PRIOR_PREFIX + field] = arr
+    return updates
+
+
+def growth_priors_from_wt_rates(rates, gauge_conc, sd_floor=0.002):
+    """
+    A growth-priors table from wt monoculture growth rates (relative-X fit).
+
+    In the X gauge (``theta: hill_relative``) wt's X is 1 at the low gauge
+    concentration and 0 at the high one, and growth is ``k + dk + m X`` with
+    wt's dk pinned at 0. So wt grows at ``k`` at the high concentration and
+    ``k + m`` at the low one:
+
+        k = wt rate at c_hi,    m = wt rate at c_lo - wt rate at c_hi.
+
+    Each SD is the standard error of the replicate mean, floored at
+    ``sd_floor`` (day-to-day and library-versus-monoculture differences are
+    at least that).
+
+    Parameters
+    ----------
+    rates : pandas.DataFrame
+        Columns ``condition_sel`` (the condition, matched to the model's
+        ``condition_rep``), ``titrant_conc``, ``rate_mean``, ``rate_sd`` and
+        ``num_replicates``; rows at both gauge concentrations for each
+        condition. Only the conditions it lists get priors; give it only the
+        conditions where the monoculture represents the library's wt.
+    gauge_conc : sequence of float
+        ``(c_lo, c_hi)``, the model's ``theta_gauge_conc``.
+    sd_floor : float
+        Smallest SD for k and m, per minute.
+
+    Returns
+    -------
+    pandas.DataFrame
+        ``condition_rep``, ``k_loc``, ``k_scale``, ``m_loc``, ``m_scale``.
+    """
+    missing = [c for c in WT_RATE_COLUMNS if c not in rates.columns]
+    if missing:
+        raise ValueError(f"wt-rates table is missing column(s) {missing}; it "
+                         f"needs {list(WT_RATE_COLUMNS)}.")
+    c_lo, c_hi = (float(c) for c in gauge_conc)
+    rows = []
+    for cond, g in rates.groupby("condition_sel", sort=True):
+        conc = g["titrant_conc"].astype(float).to_numpy()
+
+        def _at(c):
+            hit = g[np.isclose(conc, c)]
+            if len(hit) != 1:
+                raise ValueError(f"wt-rates table needs exactly one row for "
+                                 f"condition '{cond}' at titrant_conc {c:g}; "
+                                 f"found {len(hit)}.")
+            r = hit.iloc[0]
+            return float(r["rate_mean"]), float(r["rate_sd"]) / np.sqrt(float(r["num_replicates"]))
+
+        lo, se_lo = _at(c_lo)
+        hi, se_hi = _at(c_hi)
+        rows.append({"condition_rep": str(cond),
+                     "k_loc": hi,
+                     "k_scale": max(se_hi, sd_floor),
+                     "m_loc": lo - hi,
+                     "m_scale": max(float(np.hypot(se_lo, se_hi)), sd_floor)})
+    return pd.DataFrame(rows, columns=["condition_rep", "k_loc", "k_scale",
+                                       "m_loc", "m_scale"])
diff --git a/src/tfscreen/tfmodel/scripts/configure_model_cli.py b/src/tfscreen/tfmodel/scripts/configure_model_cli.py
index 5eca4fbe..6803f281 100644
--- a/src/tfscreen/tfmodel/scripts/configure_model_cli.py
+++ b/src/tfscreen/tfmodel/scripts/configure_model_cli.py
@@ -1,5 +1,16 @@
 from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
 from tfscreen.tfmodel.configuration_io import write_configuration
+from tfscreen.tfmodel.priors_edit import (
+    GROWTH_PIN_FIELDS,
+    GROWTH_PRIOR_FIELDS,
+    GROWTH_PRIOR_PREFIX,
+    apply_prior_overrides,
+    apply_priors_updates,
+    condition_rep_labels,
+    growth_prior_updates,
+    growth_priors_from_wt_rates,
+    parse_prior_overrides,
+)
 from tfscreen.tfmodel.model_stats import (
     count_model_dimensions,
     format_model_stats,
@@ -13,6 +24,7 @@
     standardize_genotypes,
     write_library_composition,
 )
+from tfscreen.genetics.library_subsample import subsample_record
 
 import numpy as np
 import pandas as pd
@@ -121,7 +133,100 @@ def check_genotypes_in_library(library_genotypes, data_df, label,
     )
 
 
-def configure_model(binding_df,
+def check_spikes_in_data(composition, growth_df, allow_missing=False):
+    """
+    Fail unless every spiked genotype of the library has growth data.
+
+    A spike named in the library config but absent from the counts usually
+    means the reads were called with a different library config (a spike
+    list or codon that disagrees with the real spikes).
+
+    Parameters
+    ----------
+    composition : pandas.DataFrame
+        ``library_composition_table`` output (``genotype``, ``in_spiked_origin``).
+    growth_df : str or pandas.DataFrame
+        Growth data with a ``genotype`` column.
+    allow_missing : bool
+        Print the missing spikes instead of raising.
+    """
+    spikes = set(composition.loc[composition["in_spiked_origin"].astype(bool),
+                                 "genotype"])
+    if not spikes or growth_df is None:
+        return
+    df = read_dataframe(growth_df)
+    observed = set(pd.unique(np.asarray(standardize_genotypes(df["genotype"]))))
+    missing = sorted(spikes - observed)
+    if not missing:
+        return
+    msg = (f"{len(missing)} of {len(spikes)} spiked genotypes in "
+           f"library_config have no growth data: {missing}. Check that the "
+           f"counts were called with this library config "
+           f"(tfs-process-fastq).")
+    if not allow_missing:
+        raise ValueError(msg + " Pass --allow_missing_spikes if these spikes "
+                         "really dropped out.")
+    print(f"warning: {msg}", flush=True)
+
+
+def _edit_priors(orchestrator, priors_path, set_priors=None, growth_priors=None,
+                 growth_priors_wt_rates=None, growth_priors_sd_floor=0.002,
+                 condition_growth_model="linear", theta_model=None):
+    """Apply --growth_priors, --growth_priors_wt_rates and --set_priors."""
+    if growth_priors is not None or growth_priors_wt_rates is not None:
+        if orchestrator.growth_tm is None:
+            raise ValueError("growth_priors need growth data.")
+        if condition_growth_model != "linear":
+            raise ValueError("growth_priors and growth_priors_wt_rates support "
+                             "condition_growth_model 'linear' only.")
+        table = None
+        if growth_priors is not None:
+            table = read_dataframe(growth_priors)
+        if growth_priors_wt_rates is not None:
+            if theta_model != "hill_relative":
+                raise ValueError("growth_priors_wt_rates sets k and m on the "
+                                 "relative X gauge; it needs theta_model "
+                                 "'hill_relative'.")
+            gauge = orchestrator.settings["theta_gauge_conc"]
+            wt = growth_priors_from_wt_rates(read_dataframe(growth_priors_wt_rates),
+                                             gauge, sd_floor=growth_priors_sd_floor)
+            if table is None:
+                table = wt
+            else:
+                if "replicate" in table.columns:
+                    raise ValueError("growth_priors_wt_rates cannot be combined "
+                                     "with a per-replicate growth_priors table.")
+                table = table.set_index("condition_rep")
+                wt = wt.set_index("condition_rep")
+                table = wt.combine_first(table).reset_index()
+
+        priors_df = pd.read_csv(priors_path)
+        scalar = priors_df[priors_df.get("flat_index", pd.Series(np.nan, index=priors_df.index)).isna()]
+        defaults = {}
+        for fields in GROWTH_PRIOR_FIELDS.values():
+            for f in fields:
+                row = scalar[scalar["parameter"] == GROWTH_PRIOR_PREFIX + f]
+                if row.empty:
+                    raise ValueError(f"Priors CSV has no '{GROWTH_PRIOR_PREFIX + f}' row.")
+                value = row["value"].iloc[0]
+                if f in GROWTH_PIN_FIELDS:
+                    # Flags may be written as 0/1 or True/False.
+                    value = str(value).strip().lower() in ("1", "1.0", "true")
+                defaults[f] = float(value)
+
+        labels = condition_rep_labels(orchestrator)
+        updates = growth_prior_updates(table, labels, defaults)
+        apply_priors_updates(priors_path, updates, cond_rep_labels=labels,
+                             backup=False)
+
+    if set_priors:
+        resolved = apply_prior_overrides(priors_path,
+                                         parse_prior_overrides(set_priors))
+        for name, value in resolved.items():
+            print(f"  prior {name} = {value:g}", flush=True)
+
+
+def configure_model(binding_df=None,
                     growth_df=None,
                     presplit_df=None,
                     base_growth_df=None,
@@ -130,20 +235,37 @@ def configure_model(binding_df,
                     growth_transition_model="instant",
                     ln_cfu0_model="hierarchical",
                     dk_geno_model="hierarchical_geno",
-                    activity_model="horseshoe_geno",
+                    activity_model="fixed",
                     theta_model="hill_geno",
                     transformation_model="single",
                     transformation_lambda=None,
+                    congression_theta_rule="homodimer",
+                    congression_dk_rule="dilution",
+                    congression_dk_alpha=None,
+                    congression_sets=None,
                     theta_rescale_model="passthrough",
                     theta_growth_noise_model="zero",
                     theta_binding_noise_model="zero",
                     growth_noise_model="zero",
+                    sample_offset_model="level",
+                    growth_likelihood="counts",
+                    theta_gauge_conc=None,
                     library_config=None,
                     growth_shares_replicates=False,
                     epistasis=False,
                     thermo_data=None,
+                    thermo_ddG_units=None,
+                    thermo_temperature_K=None,
+                    thermo_column_map=None,
+                    thermo_residue_offset=None,
+                    thermo_max_missing_fraction=None,
                     batch_size=1024,
                     binding_weight=None,
+                    set_priors=None,
+                    growth_priors=None,
+                    growth_priors_wt_rates=None,
+                    growth_priors_sd_floor=0.002,
+                    allow_missing_spikes=False,
                     skip_model_stats=False):
     """
     Build and write the YAML configuration files needed by tfs-fit-model.
@@ -155,15 +277,19 @@ def configure_model(binding_df,
     for a growth model, {out_prefix}_library.csv (the per-genotype library
     composition resolved from library_config).
 
-    When only binding_df is provided (no growth_df), a binding-only model is
-    configured that infers theta directly from observed binding measurements
-    rather than from bacterial growth data.
+    At least one of growth_df and binding_df is required. With both, the
+    joint growth + binding model is configured. With only binding_df, a
+    binding-only model infers theta directly from the binding measurements.
+    With only growth_df, a growth-only model infers theta from growth alone
+    (no binding likelihood, so theta_binding_noise_model must stay 'zero'
+    and binding_weight unset; tfs-prefit-calibration needs binding and
+    refuses such a config).
 
     Parameters
     ----------
-    binding_df : str
+    binding_df : str, optional
         Path to the binding data CSV file (theta vs. titrant measurements per
-        genotype). Required.
+        genotype). When omitted, a growth-only model is configured.
     growth_df : str, optional
         Path to the growth data CSV file (ln_cfu measurements per genotype,
         replicate, and timepoint). When omitted, a binding-only model is
@@ -196,7 +322,9 @@ def configure_model(binding_df,
     ln_cfu0_model : str, optional
         Model to use to describe ln_cfu0, the initial populations of genotypes
         in each replicate. Allowed values are 'hierarchical' (default) or
-        'hierarchical_factored'.
+        'hierarchical_factored'. The latter shares each genotype's baseline
+        across pre-conditions and is refused when they come from different
+        libraries (kanR and pheS are grown up separately).
     dk_geno_model : str, optional
         Model to use to describe dk_geno, the pleiotropic effect of a genotype
         on growth, independent of occupancy. Allowed values are
@@ -205,8 +333,9 @@ def configure_model(binding_df,
     activity_model : str, optional
         Model to use to describe activity, a scalar multiplied against
         occupancy that defines how strongly a genotype alters transcription
-        given its occupancy. Allowed values are 'fixed', 'hierarchical_geno',
-        'horseshoe_geno' (default), 'hierarchical_mut', or 'horseshoe_mut'.
+        given its occupancy. Allowed values are 'fixed' (default; activity 1
+        for every genotype), 'hierarchical_geno', 'horseshoe_geno',
+        'hierarchical_mut', or 'horseshoe_mut'.
     theta_model : str, optional
         Model to use to describe theta, the fractional occupancy of a genotype
         on the transcription factor binding site. Allowed values are
@@ -216,16 +345,45 @@ def configure_model(binding_df,
         'thermo.O2_C4_K3_U0_a.PddG', 'thermo.O2_C12_K5_U0_a.PK',
         'thermo.O2_C12_K5_U0_a.PnnC', 'thermo.O2_C12_K5_U0_a.PddG', and
         their O2_C4_K3_U1_a / O2_C12_K5_U1_a unfolded equivalents).
+        'hill_relative' fits growth alone on a wt-relative scale X instead
+        of theta (wt's X is 1 at the low and 0 at the high gauge
+        concentration, theta_gauge_conc). It takes no binding_df, and
+        requires activity 'fixed', theta_rescale 'passthrough', linear
+        condition growth, theta_growth_noise 'zero' and, with the
+        'mixture' transformation, congression_theta_rule 'max'.
     transformation_model : str, optional
-        Model for transformation correction. Allowed values are 'single'
-        (default), 'empirical', or 'logit_norm'.
+        Model for congression. Allowed values are 'single' (default; one
+        plasmid per cell) or 'mixture' (clean and congressed cells mixed at
+        the observable level). 'empirical' and 'logit_norm' were removed.
     transformation_lambda : list or tuple, optional
         ``(mean, std)`` -- the experimentally measured congression lambda,
         in linear space (e.g. ``(0.36, 0.05)``). Required when
-        ``transformation_model`` is 'empirical' or 'logit_norm'; forbidden
+        ``transformation_model`` is 'mixture'; forbidden
         when it is 'single'. Used to moment-match a LogNormal prior for the
         transformation's lambda parameter, replacing the manual step of
         hand-editing the priors/guesses CSVs with rescaled log-space values.
+    congression_theta_rule : str, optional
+        How the 'mixture' transformation builds a congressed cell's theta
+        from its plasmids' thetas: 'homodimer' (default), 'heterodimer' or
+        'max'. The homodimer and heterodimer rules require activity_model
+        'fixed'. Ignored by 'single'.
+    congression_dk_rule : str, optional
+        How the 'mixture' transformation builds a congressed cell's dk_geno
+        from its plasmids': 'dilution' (default; the share-weighted mean),
+        'softmin' (with congression_dk_alpha) or 'min' (the worst variant
+        sets the cost). Ignored by 'single'.
+    congression_dk_alpha : float, optional
+        alpha for congression_dk_rule 'softmin' (> 0, in units of 1/dk):
+        ``dk_cell = -(1/alpha) log sum_g x_g exp(-alpha dk_g)``. Required by
+        'softmin', refused by the other rules.
+    congression_sets : list of int, optional
+        With the 'mixture' transformation, the number of fixed co-resident
+        sets drawn per genotype, by co-resident count: entry n-1 is the
+        number of sets with n co-resident plasmids (the mixture's
+        quadrature over a congressed cell's partners). None keeps the
+        orchestrator's default, 12 3 1 (16 sets). Each set is one cell class
+        grown through the whole model, so the cost of a fit grows with the
+        total. Written to the config.
     theta_rescale_model : str, optional
         Rescaling applied to theta before it enters the growth model. Allowed
         values are 'passthrough' (default, identity) or 'logit' (maps theta to
@@ -236,12 +394,37 @@ def configure_model(binding_df,
         'logit_normal'.
     theta_binding_noise_model : str, optional
         Model to use for stochastic experimental noise in theta measured by
-        binding. Allowed values are 'zero' (default) or 'beta'.
+        binding. Allowed values are 'zero' (default), 'beta' or
+        'genotype_offset' (one offset per binding genotype, the same at every
+        titrant concentration, with a learned scale: an in vivo/in vitro
+        difference or a curve's day-normalization error).
     growth_noise_model : str, optional
         Model for additive growth-rate noise. 'zero' (default) adds no noise;
         'normal_kt' learns a global sigma_k that inflates the observation scale
         in quadrature with ln_cfu_std, capturing biological variability in
         growth rates not explained by theta or dk_geno.
+    sample_offset_model : str, optional
+        Per-tube offset shared by every genotype in a tube. 'level'
+        (default) adds one ln_cfu offset per tube with a learned, constant
+        SD (the tube's composition offset and any error in its supplied
+        total, including the tube's own growth noise); 'zero' adds none;
+        'normal' adds a per-tube growth-rate offset scaled by elapsed time.
+        With growth_likelihood 'counts', an error in a tube's supplied total
+        otherwise moves every genotype in it.
+    growth_likelihood : str, optional
+        How growth data are observed: 'counts' (default; negative binomial
+        on the read counts, with a learned dispersion whose variance has a
+        part proportional to the mean and a quadratic part; no pseudocount)
+        or 'lncfu' (Student-t on ln_cfu). 'counts' needs a 'counts' column,
+        each tube's total reads ('sample_reads', or 'adjusted_counts' and
+        'frequency' to derive it) and total cells ('sample_ln_cfu', or
+        'sample_cfu' with its uncertainty), and growth_noise_model 'zero'.
+        It cut theta RMSE by 35-60% against 'lncfu' on simulations
+        (planning/studies/count-likelihood/).
+    theta_gauge_conc : list of float, optional
+        ``c_lo c_hi``: the titrant concentrations at which theta_model
+        'hill_relative' pins wt's X to 1 and 0. Default: the lowest and
+        highest concentration in growth_df. Refused by other theta models.
     library_config : str, optional
         Path to the library YAML describing the screened library -- the same
         file handed to ``tfs-process-fastq``.  Required whenever ``growth_df``
@@ -272,21 +455,80 @@ def configure_model(binding_df,
         structure containing pre-computed ΔΔG prior means; the required
         structure columns depend on the topology -- ``O2_C4_*`` models use
         (``H``, ``HD``, ``L``, ``LE2``) while ``O2_C12_*`` models use
-        (``H``, ``HO``, ``L``, ``LO``, ``HE2``, ``LE2``).
+        (``H``, ``HO``, ``L``, ``LO``, ``HE2``, ``LE2``).  ``Mutation`` or
+        ``mutation`` may name the mutation column, an unnamed index column
+        is dropped and other columns are ignored.  ΔΔG_X shifts state X's
+        free energy (positive destabilises X).  The loader prints how many
+        library mutations the file covers and fails on zero coverage or on
+        wt residues that disagree with the library.
+    thermo_ddG_units : str, optional
+        Unit of the PddG CSV's ΔΔG values: ``kT`` (default; used as-is),
+        ``kcal/mol`` or ``kJ/mol`` (divided by RT at load).
+    thermo_temperature_K : float, optional
+        Temperature for that conversion. Default 298.15.
+    thermo_column_map : str, optional
+        ``model_name=csv_column`` pairs, comma-separated (e.g. ``HD=HO``),
+        naming the CSV column that supplies a model structure.
+    thermo_residue_offset : int, optional
+        Added to the file's residue numbers (CSV labels or HDF5
+        ``residue_nums``) to give the library numbering, e.g. 1 for a
+        0-based file. Default 0.
+    thermo_max_missing_fraction : float, optional
+        Warn when more than this fraction of library mutations has no ΔΔG
+        in the CSV. Default 0.1.
+        These five are stored in the config as ``components.thermo_options``.
     batch_size : int, optional
         Mini-batch size for SVI. Defaults to 1024. Set to None to use the full
         dataset as a single batch.
     binding_weight : float, optional
-        Multiplicative scale applied to the binding log-likelihood at every SVI
-        step.  Because growth data typically outnumber binding observations by
-        several orders of magnitude, the binding likelihood contributes a
-        negligible gradient signal unless it is upweighted.  When None
-        (default), the weight is auto-computed as
-        ``N_growth_rows / N_binding_rows`` so that each binding observation
-        contributes the same total weight as the average growth observation.
-        Pass an explicit positive float to override this heuristic.  The
-        resolved value (never None) is saved in the YAML so that
-        ``tfs-fit-model`` applies the same weight without recomputing it.
+        Multiplicative scale applied to the binding log-likelihood. None
+        (default) is 1: each binding observation counts once, the
+        likelihood of the data as measured. Until 2026-10-16 the default was
+        ``N_growth_rows / N_binding_rows``, which made the binding data
+        count as much as the whole growth dataset; pass that ratio to
+        reproduce an older configuration. The resolved value (never None)
+        is saved in the YAML, so a config written before the change keeps
+        its weight.
+    set_priors : list of str, optional
+        ``name=value`` pairs that set scalar priors in
+        {out_prefix}_priors.csv, so the file is never edited by hand. A name
+        is a full row name (``theta.theta_log_hill_n_hyper_scale_fixed``) or
+        a unique dotted suffix of one (``theta_log_hill_n_hyper_scale_fixed``,
+        ``sample_offset.sigma_fixed``). An unknown or ambiguous name is an
+        error. Common uses: ``sigma_fixed=0.17`` holds the level tube
+        offsets' SD; ``theta_log_hill_n_hyper_scale_fixed=0.5`` holds the
+        population SD of log(hill_n) (and, for hill_relative,
+        ``theta_X_low_hyper_scale_fixed``, ``theta_X_delta_hyper_scale_fixed``,
+        ``theta_log_hill_K_hyper_scale_fixed``); for the PddG/PnnC thermo
+        models, ``theta_ddG_sigma_fixed=1.69`` holds every structure's
+        ΔΔG scale σ_s at 1.69 kT.
+    growth_priors : str, optional
+        CSV of per-condition growth priors for linear condition growth: a
+        ``condition_rep`` column (and ``replicate``, if conditions are not
+        shared across replicates) plus any of ``k_loc``, ``k_scale``,
+        ``m_loc``, ``m_scale``, and the 0/1 flags ``k_pinned``, ``m_pinned``,
+        which hold that condition's k or m at its loc instead of fitting it
+        (say, one marker's k and m held at an independent estimate while the
+        other's are fitted). Conditions or values it leaves out keep the
+        defaults. Use it where tfs-prefit-calibration does not run (a
+        growth-only model); the pre-fit overwrites k_loc and m_loc.
+    growth_priors_wt_rates : str, optional
+        CSV of wt monoculture growth rates (``condition_sel``,
+        ``titrant_conc``, ``rate_mean``, ``rate_sd``, ``num_replicates``),
+        for theta_model 'hill_relative' only. For each listed condition,
+        k = the rate at the high gauge concentration and m = the rate at the
+        low one minus k, each with the standard error of the mean as SD
+        (floored at growth_priors_sd_floor). List only the conditions where
+        the monoculture stands for the library's wt. Overrides
+        growth_priors for the conditions it lists.
+    growth_priors_sd_floor : float, optional
+        Smallest k and m prior SD (per minute) derived from
+        growth_priors_wt_rates. Default 0.002.
+    allow_missing_spikes : bool, optional
+        By default, a spiked genotype named in library_config with no growth
+        data is an error: it usually means the counts were called with a
+        different library config. Pass this to only report it (a spike can
+        drop out of a real experiment).
     skip_model_stats : bool, optional
         Skip the pre-fit parameter/observation accounting. By default a
         summary is printed to stdout and written to
@@ -300,13 +542,18 @@ def configure_model(binding_df,
     -------
     None
     """
-    if binding_df is None:
-        raise ValueError("binding_df must be provided")
+    if binding_df is None and growth_df is None:
+        raise ValueError("At least one of binding_df and growth_df must be "
+                         "provided.")
 
     binding_only = growth_df is None
     if not binding_only:
         check_component_compatibility(condition_growth_model, theta_rescale_model)
 
+    if congression_sets is not None and transformation_model == "single":
+        raise ValueError("congression_sets sets the congression mixture's "
+                         "co-resident draws; it needs transformation_model "
+                         "'mixture'.")
     if transformation_model != "single" and transformation_lambda is None:
         raise ValueError(
             f"transformation_model='{transformation_model}' requires "
@@ -343,14 +590,35 @@ def configure_model(binding_df,
             check_genotypes_in_library(set(composition["genotype"]),
                                        data_df, label)
 
+        check_spikes_in_data(composition, growth_df,
+                             allow_missing=allow_missing_spikes)
+
         library_file = f"{out_prefix}_library.csv"
         write_library_composition(composition, library_file)
         print(f"Wrote library composition to {library_file}", flush=True)
 
+        library_yaml = read_yaml(library_config)
         library_meta = {
             "source": library_config if isinstance(library_config, str) else "<dict>",
-            "library_mixture": read_yaml(library_config)["library_mixture"],
+            "library_mixture": library_yaml["library_mixture"],
         }
+        # A subsampled library (tfs-simulate's library_genotypes /
+        # library_subsample): the snapshot holds only the kept genotypes.
+        subsample = subsample_record(library_yaml)
+        if subsample is not None:
+            library_meta["subsample"] = subsample
+            print(f"Library subsample: {len(composition)} genotypes kept "
+                  f"({subsample})", flush=True)
+
+    # Structural-input loader options; None keeps the old behaviour and
+    # leaves the written config's thermo_options null.
+    thermo_options = {k: v for k, v in (
+        ("ddG_units", thermo_ddG_units),
+        ("temperature_K", thermo_temperature_K),
+        ("column_map", thermo_column_map),
+        ("residue_offset", thermo_residue_offset),
+        ("max_missing_fraction", thermo_max_missing_fraction),
+    ) if v is not None} or None
 
     # Initialize model to build mappings and get guesses
     orchestrator = ModelOrchestrator(growth_df,
@@ -366,14 +634,23 @@ def configure_model(binding_df,
                      theta=theta_model,
                      transformation=transformation_model,
                      transformation_lambda=transformation_lambda,
+                     congression_theta_rule=congression_theta_rule,
+                     congression_dk_rule=congression_dk_rule,
+                     congression_dk_alpha=congression_dk_alpha,
+                     **({} if congression_sets is None
+                        else {"congression_sets": [int(v) for v in congression_sets]}),
                      theta_rescale=theta_rescale_model,
                      theta_growth_noise=theta_growth_noise_model,
                      theta_binding_noise=theta_binding_noise_model,
                      growth_noise=growth_noise_model,
+                     sample_offset=sample_offset_model,
+                     growth_likelihood=growth_likelihood,
+                     theta_gauge_conc=theta_gauge_conc,
                      library_file=library_file,
                      growth_shares_replicates=growth_shares_replicates,
                      epistasis=epistasis,
                      thermo_data=thermo_data,
+                     thermo_options=thermo_options,
                      batch_size=batch_size,
                      binding_weight=binding_weight)
 
@@ -385,11 +662,21 @@ def configure_model(binding_df,
     write_configuration(orchestrator=orchestrator,
                         out_prefix=out_prefix,
                         growth_df_path=growth_path,
-                        binding_df_path=binding_df if isinstance(binding_df, str) else "binding.csv",
+                        binding_df_path=(None if binding_df is None else
+                                         binding_df if isinstance(binding_df, str)
+                                         else "binding.csv"),
                         presplit_df_path=presplit_path,
                         base_growth_df_path=base_growth_path,
                         library_meta=library_meta)
 
+    _edit_priors(orchestrator, f"{out_prefix}_priors.csv",
+                 set_priors=set_priors,
+                 growth_priors=growth_priors,
+                 growth_priors_wt_rates=growth_priors_wt_rates,
+                 growth_priors_sd_floor=growth_priors_sd_floor,
+                 condition_growth_model=condition_growth_model,
+                 theta_model=theta_model)
+
     # Pre-fit accounting: how many parameters, how many observations, and
     # which genotypes are individually under-determined.
     if not skip_model_stats:
@@ -400,15 +687,27 @@ def configure_model(binding_df,
 def main():
     return generalized_main(configure_model,
                             manual_arg_types={"binding_df":str,
+                                              "congression_dk_alpha":float,
                                               "growth_df":str,
                                               "presplit_df":str,
                                               "base_growth_df":str,
                                               "library_config":str,
                                               "thermo_data":str,
+                                              "thermo_ddG_units":str,
+                                              "thermo_temperature_K":float,
+                                              "thermo_column_map":str,
+                                              "thermo_residue_offset":int,
+                                              "thermo_max_missing_fraction":float,
                                               "batch_size":int,
                                               "binding_weight":float,
-                                              "transformation_lambda":float},
-                            manual_arg_nargs={"transformation_lambda":2})
+                                              "set_priors":str,
+                                              "transformation_lambda":float,
+                                              "congression_sets":int,
+                                              "theta_gauge_conc":float},
+                            manual_arg_nargs={"set_priors":"+",
+                                              "transformation_lambda":2,
+                                              "congression_sets":"+",
+                                              "theta_gauge_conc":2})
 
 if __name__ == "__main__":
     main()
diff --git a/src/tfscreen/tfmodel/scripts/diagnose_nan_cli.py b/src/tfscreen/tfmodel/scripts/diagnose_nan_cli.py
deleted file mode 100644
index 8846da9d..00000000
--- a/src/tfscreen/tfmodel/scripts/diagnose_nan_cli.py
+++ /dev/null
@@ -1,166 +0,0 @@
-"""
-diagnose_nan.py — NaN debugging tool for SVI optimization.
-
-This script helps identify which variational parameters are collapsing toward
-zero and causing NaN explosions during SVI. It loads a checkpoint, disables
-JIT compilation, enables JAX NaN trapping, and runs the optimizer step-by-step
-so that the exact operation that first produces a NaN raises an informative
-traceback rather than silently contaminating all parameters via lax.scan.
-
-Typical usage
--------------
-After observing a "model exploded" RuntimeError during a long SVI run, locate
-the most recent checkpoint file (e.g. ``tfs_checkpoint.pkl``) and run::
-
-    tfs-diagnose-nan tfs_config.yaml tfs_checkpoint.pkl --num_steps 200
-
-If a NaN occurs within num_steps, JAX will raise a FloatingPointError with a
-traceback pointing to the first offending operation. The per-step printout of
-near-zero scale parameters helps identify which hyperparameters are collapsing
-even before the NaN appears.
-
-Background
-----------
-The root cause of late-stage NaN explosions in SVI is typically a variational
-scale parameter (e.g. ``*_hyper_loc_scale`` or ``*_hyper_scale_scale``) drifting
-toward zero when the corresponding level of the hierarchy is poorly constrained
-by data (e.g., all genotypes are spiked and there is no free library to pool
-over). When a scale crosses float32 subnormal (~1e-38), ``1/scale`` in the
-gradient overflows to ``inf``, which propagates backward through the ELBO as
-``inf - inf = NaN``. The NaN typically surfaces in a different parameter than
-the one that caused it, because the backward pass has already accumulated NaN
-cotangents by the time it reaches the crash site.
-
-The fix is to add a floor to those constraints via
-``dist.constraints.greater_than(1e-4)`` instead of ``dist.constraints.positive``.
-"""
-
-import argparse
-
-import jax
-import dill
-
-from tfscreen.tfmodel.configuration_io import read_configuration
-from tfscreen.tfmodel.inference.run_inference import RunInference
-
-
-def diagnose_nan(config_file,
-                 checkpoint_file,
-                 seed=0,
-                 num_steps=200,
-                 near_zero_threshold=0.01):
-    """
-    Run SVI step-by-step from a checkpoint with JAX NaN trapping enabled.
-
-    Disables JIT compilation so that JAX raises a FloatingPointError with a
-    full traceback at the exact operation that first produces a NaN, rather
-    than silently propagating it through a compiled lax.scan block.
-
-    Each step prints the current loss and any variational scale parameters
-    whose constrained value is below ``near_zero_threshold``. These are the
-    likely culprits: a scale approaching zero causes ``1/scale`` to overflow
-    in the gradient computation.
-
-    Parameters
-    ----------
-    config_file : str
-        Path to the YAML configuration file for the model.
-    checkpoint_file : str
-        Path to the ``.pkl`` checkpoint file to resume from.
-    seed : int, optional
-        Random seed passed to RunInference (default 0). The seed only affects
-        parameter initialization; the checkpoint state overrides it.
-    num_steps : int, optional
-        Number of optimizer steps to run (default 200). Each step processes
-        one random mini-batch.
-    near_zero_threshold : float, optional
-        Scalar variational parameters whose constrained value falls below this
-        threshold are printed each step (default 0.01).
-    """
-
-    print("Enabling JAX NaN trapping and disabling JIT.", flush=True)
-    jax.config.update("jax_debug_nans", True)
-    jax.config.update("jax_disable_jit", True)
-
-    orchestrator, init_params = read_configuration(config_file)
-    ri = RunInference(orchestrator, seed=seed)
-
-    svi_obj = ri.setup_svi(adam_step_size=1e-6, guide_type="component")
-
-    # SVI requires init() to be called before update() so that constrain_fn
-    # is wired up. We call it here and then immediately replace the returned
-    # state with the checkpoint state.
-    data_on_gpu = jax.device_put(ri.model.data)
-    idx = ri.model.get_random_idx(num_batches=1)
-    batch_data = ri.model.get_batch(data_on_gpu, idx)
-    init_key = ri.get_key()
-    _ = svi_obj.init(init_key,
-                     init_params=init_params,
-                     priors=ri.model.priors,
-                     data=batch_data)
-
-    print(f"Loading checkpoint from: {checkpoint_file}", flush=True)
-    with open(checkpoint_file, "rb") as f:
-        chk = dill.load(f)
-    svi_state = chk["svi_state"]
-
-    update_fn = svi_obj.update  # not JIT-compiled (jax_disable_jit is on)
-
-    print(f"Running {num_steps} steps. Near-zero threshold: {near_zero_threshold}\n",
-          flush=True)
-
-    for i in range(num_steps):
-        idx = ri.model.get_random_idx(num_batches=1)
-        batch = ri.model.get_batch(data_on_gpu, idx)
-        svi_state, loss = update_fn(svi_state,
-                                    priors=ri.model.priors,
-                                    data=batch)
-
-        params = svi_obj.get_params(svi_state)
-
-        # Report scalar scale parameters that are near zero
-        near_zero = {k: float(v)
-                     for k, v in params.items()
-                     if v.ndim == 0 and float(v) < near_zero_threshold}
-
-        print(f"Step {i:4d}: loss={loss:.4e}  near-zero scales: {near_zero}",
-              flush=True)
-
-    print("\nCompleted all steps without a NaN.", flush=True)
-    print("If no NaN appeared, the checkpoint may already be past the point of",
-          flush=True)
-    print("collapse, or num_steps was too small. Try increasing num_steps or",
-          flush=True)
-    print("starting from an earlier checkpoint.", flush=True)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description=(
-            "Run SVI step-by-step from a checkpoint with JAX NaN trapping "
-            "enabled. Prints near-zero scale parameters each step and raises "
-            "a FloatingPointError with a full traceback at the first NaN."
-        )
-    )
-    parser.add_argument("config_file",
-                        help="Path to the YAML configuration file.")
-    parser.add_argument("checkpoint_file",
-                        help="Path to the .pkl SVI checkpoint file.")
-    parser.add_argument("--seed", type=int, default=0,
-                        help="Random seed for RunInference (default: 0).")
-    parser.add_argument("--num_steps", type=int, default=200,
-                        help="Number of optimizer steps to run (default: 200).")
-    parser.add_argument("--near_zero_threshold", type=float, default=0.01,
-                        help="Print scalar scale params below this value "
-                             "(default: 0.01).")
-    args = parser.parse_args()
-
-    diagnose_nan(config_file=args.config_file,
-                 checkpoint_file=args.checkpoint_file,
-                 seed=args.seed,
-                 num_steps=args.num_steps,
-                 near_zero_threshold=args.near_zero_threshold)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/tfscreen/tfmodel/scripts/extract_params_cli.py b/src/tfscreen/tfmodel/scripts/extract_params_cli.py
index cc8154df..460782e2 100644
--- a/src/tfscreen/tfmodel/scripts/extract_params_cli.py
+++ b/src/tfscreen/tfmodel/scripts/extract_params_cli.py
@@ -1,10 +1,12 @@
 import os
 import dill
 import numpy as np
+import pandas as pd
 import h5py
 from tfscreen.tfmodel.configuration_io import read_configuration
 from tfscreen.tfmodel.inference.run_inference import RunInference
 from tfscreen.tfmodel.analysis.extraction import extract_parameters
+from tfscreen.tfmodel.inference.model_params import read_model_params
 from tfscreen.util.cli import generalized_main
 
 
@@ -23,7 +25,15 @@ def extract_params(config_file, param_file, out_prefix="tfs_params"):
     ``q0.95``, ``q0.975``, ``q0.99``, ``q0.995``, ``q0.999``.
 
     Writes one CSV per parameter group named ``{out_prefix}_{param_name}.csv``
-    (e.g. tfs_params_activity.csv, tfs_params_theta.csv).
+    (e.g. tfs_params_activity.csv, tfs_params_theta.csv; thermo PddG/PnnC
+    also give one ``ddG_{structure}`` and ``ddG_prior_mean_{structure}``
+    table per structure, both deterministic sites that need a posterior
+    file, plus one ``ddG_sigma`` table of the per-structure SD σ_s keyed by
+    ``structure``; the ΔΔG is computed from the fitted constants,
+    ``thermo/prior.py``). A model with its own learned
+    ``pyro.param`` sites (the thermo PnnC NN weights) also gets
+    ``{out_prefix}_model_params.csv``: one row per element (``parameter``,
+    ``index``, ``value``).
 
     Parameters
     ----------
@@ -69,6 +79,8 @@ def _extract_from_checkpoint(orchestrator, ckpt_path, out_prefix):
 
     constrained = ri._map_params_to_constrained(map_params)
     posteriors = {k: np.expand_dims(np.asarray(v), 0) for k, v in constrained.items()}
+    _write_model_params(ri.model_params(map_params, unconstrained=True),
+                        out_prefix)
 
     print(f"Writing parameter CSVs to {out_prefix}_*.csv...", flush=True)
     param_dfs = extract_parameters(orchestrator, posteriors, q_to_get=[0.5])
@@ -103,6 +115,8 @@ def _extract_from_posteriors(orchestrator, posterior_file, out_prefix):
                   "the '_params.npz' file instead of the posterior .h5 file. "
                   "Extraction of natural parameters may fail.", flush=True)
 
+        _write_model_params(read_model_params(posteriors), out_prefix)
+
         print(f"Extracting parameters to {out_prefix}_*.csv...", flush=True)
         params = extract_parameters(orchestrator, posteriors)
         for p_name, p_df in params.items():
@@ -111,6 +125,25 @@ def _extract_from_posteriors(orchestrator, posterior_file, out_prefix):
     print("Done.", flush=True)
 
 
+def _write_model_params(params, out_prefix):
+    """
+    Write the model's own learned params (fixed point estimates, no
+    quantiles) to ``{out_prefix}_model_params.csv``, one row per element.
+    """
+    if not params:
+        return
+    rows = []
+    for name in sorted(params):
+        values = np.asarray(params[name])
+        for idx in np.ndindex(values.shape):
+            rows.append({"parameter": name,
+                         "index": ",".join(str(i) for i in idx),
+                         "value": float(values[idx])})
+    out_file = f"{out_prefix}_model_params.csv"
+    pd.DataFrame(rows).to_csv(out_file, index=False)
+    print(f"  Wrote {out_file}", flush=True)
+
+
 def main():
     generalized_main(extract_params)
 
diff --git a/src/tfscreen/tfmodel/scripts/fit_genotypes_cli.py b/src/tfscreen/tfmodel/scripts/fit_genotypes_cli.py
index 81777280..c1390cf4 100644
--- a/src/tfscreen/tfmodel/scripts/fit_genotypes_cli.py
+++ b/src/tfscreen/tfmodel/scripts/fit_genotypes_cli.py
@@ -7,69 +7,48 @@
 genotype's phenotype ``(dk_geno, theta_low, theta_high, log_hill_K, hill_n)``
 by nonlinear least squares (see :mod:`tfscreen.tfmodel.genotype_fit.fit`).
 
-An optional congression de-attenuation pass (``--congression_lambda``) removes
-the co-transformation bias from the *bulk* genotypes' theta curves via the
-iterative fixed point (predict theta -> de-attenuate against the population ->
-refit Hill).  It is inherently a population operation: the correction couples
-all bulk genotypes through their shared background occupancy distribution.
-Spiked genotypes (named by ``--library_config``) are congression-free and pass through
-untouched.
-
 Outputs (under ``--out_prefix``)
 --------------------------------
-* ``<prefix>_params.csv`` — one row per (genotype, titrant_name): the RAW MLE
+* ``<prefix>_params.csv`` — one row per (genotype, titrant_name): the MLE
   fit (natural + transformed params, transformed-space std).
-* ``<prefix>_params_deattenuated.csv`` — same schema for the de-attenuated
-  fits (only when ``--congression_lambda`` is given).
 * ``<prefix>_theta.csv`` — long form ``[genotype, titrant_name, titrant_conc,
-  theta_raw]`` (plus ``theta_deattenuated`` when congression ran): the fitted
-  Hill curves evaluated on each titrant's concentration grid.
-* ``<prefix>_theta_history.csv`` — long form ``[genotype, titrant_name,
-  titrant_conc, iter, theta]``: the congression fixed-point trajectory (only
-  with ``--congression_lambda`` and ``--save_theta_history``).
+  theta_raw]``: the fitted Hill curves evaluated on each titrant's
+  concentration grid.
+
+The fits are not corrected for congression (co-transformation): the bulk
+genotypes' theta curves carry that bias.  The old theta-only de-attenuation
+(``--congression_lambda``) was retired with the move to the observable-level
+congression mixture in ``tfs-fit-model``.
 """
 
 import os
 
 from tfscreen.tfmodel.genotype_fit.fit import (
-    fit_phenotypes, fits_to_results_df, predict_theta,
+    fit_phenotypes, predict_theta,
 )
 from tfscreen.util.io import read_dataframe
-from tfscreen.genetics import library_composition_table
 from tfscreen.util.cli.generalized_main import generalized_main
 
 
-def fit_genotypes(growth_file,
-                  calibration_file,
-                  out_prefix="tfs_mle",
-                  congression_lambda=None,
-                  library_config=None,
+def fit_genotypes(growth_df,
+                  growth_calibration_file,
+                  out_prefix="tfs_fit_genotypes",
                   intercept_cols="replicate",
                   dk_geno_prior_sd=1.0,
                   min_obs=None,
-                  num_workers=1,
-                  save_theta_history=False):
+                  num_workers=1):
     """
     Fit the growth model to each genotype independently (frozen calibration).
 
     Parameters
     ----------
-    growth_file : str
-        Processed ``ln_cfu`` CSV (``tfs-process-counts`` output).
-    calibration_file : str
+    growth_df : str
+        Processed growth CSV (``tfs-process-counts`` output).
+    growth_calibration_file : str
         Frozen per-condition growth calibration: a ``tfs-prefit-calibration``
         priors CSV, or a wide ``condition_rep,growth_k,growth_m`` CSV.
     out_prefix : str
         Output prefix (see the module docstring for the files written).
-    congression_lambda : float, optional
-        Zero-truncated Poisson congression rate (the same lambda as the
-        simulator's ``transformation_poisson_lambda``).  When given, run the
-        de-attenuation pass; omit for raw MLE fits only.
-    library_config : str, optional
-        Library YAML describing the screened library (the same file handed to
-        ``tfs-process-fastq``).  Genotypes encoded by a spiked sequence are
-        congression-free and are excluded from the de-attenuation correction
-        and its background.  Only relevant with ``--congression_lambda``.
     intercept_cols : str
         Comma-separated columns whose unique combinations each get a nuisance
         ``ln_cfu0`` (default ``"replicate"``; empty string -> single intercept).
@@ -80,17 +59,9 @@ def fit_genotypes(growth_file,
     num_workers : int
         Parallelize the per-genotype fits over a process pool: ``1`` (default)
         serial; ``-1`` uses ``os.cpu_count() - 1``; ``N`` uses ``N``.
-    save_theta_history : bool
-        Also write the congression fixed-point theta trajectory
-        (``<prefix>_theta_history.csv``).  Only meaningful with
-        ``--congression_lambda``.
     """
-    growth_df = read_dataframe(growth_file)
-    spiked = None
-    if library_config is not None:
-        composition = library_composition_table(library_config)
-        spiked = list(composition.loc[composition["in_spiked_origin"],
-                                      "genotype"])
+    growth_df = read_dataframe(growth_df)
+    calibration_file = growth_calibration_file
 
     icols = [c.strip() for c in str(intercept_cols).split(",") if c.strip()]
 
@@ -98,7 +69,7 @@ def fit_genotypes(growth_file,
     if dk_geno_prior_sd is not None and float(dk_geno_prior_sd) > 0:
         dk_prior = (0.0, float(dk_geno_prior_sd))
 
-    # --- Raw per-genotype MLE fits. ---------------------------------------
+    # --- Per-genotype MLE fits. -------------------------------------------
     print("Fitting each genotype independently (frozen calibration)...",
           flush=True)
     results_df, fits = fit_phenotypes(
@@ -107,32 +78,6 @@ def fit_genotypes(growth_file,
 
     theta_df = predict_theta(fits, growth_df, theta_col="theta_raw")
 
-    # --- Optional congression de-attenuation. -----------------------------
-    deatt_df = None
-    history_df = None
-    do_congression = (congression_lambda is not None
-                      and float(congression_lambda) > 0)
-    if do_congression:
-        from tfscreen.tfmodel.genotype_fit.congression import (
-            deattenuate_congression,
-        )
-        print(f"De-attenuating congression "
-              f"(lambda={float(congression_lambda):g})...", flush=True)
-        result = deattenuate_congression(
-            fits, growth_df, float(congression_lambda), spiked=spiked,
-            return_theta_history=save_theta_history)
-        if save_theta_history:
-            corrected_fits, history_df = result
-        else:
-            corrected_fits = result
-
-        deatt_df = fits_to_results_df(corrected_fits)
-        theta_deatt = predict_theta(corrected_fits, growth_df,
-                                    theta_col="theta_deattenuated")
-        theta_df = theta_df.merge(
-            theta_deatt, on=["genotype", "titrant_name", "titrant_conc"],
-            how="left")
-
     # --- Write outputs. ---------------------------------------------------
     params_path = os.path.abspath(f"{out_prefix}_params.csv")
     results_df.to_csv(params_path, index=False)
@@ -140,37 +85,20 @@ def fit_genotypes(growth_file,
     theta_path = os.path.abspath(f"{out_prefix}_theta.csv")
     theta_df.to_csv(theta_path, index=False)
 
-    deatt_path = None
-    if deatt_df is not None:
-        deatt_path = os.path.abspath(f"{out_prefix}_params_deattenuated.csv")
-        deatt_df.to_csv(deatt_path, index=False)
-
-    history_path = None
-    if history_df is not None:
-        history_path = os.path.abspath(f"{out_prefix}_theta_history.csv")
-        history_df.to_csv(history_path, index=False)
-
     bar = "=" * 72
     print(f"\n{bar}")
     print(f"Fit {len(fits)} (genotype, titrant_name) groups.")
-    print("\n  Per-genotype parameters (raw MLE fit):")
+    print("\n  Per-genotype parameters (MLE fit):")
     print(f"    {params_path}")
-    if deatt_path is not None:
-        print("  Per-genotype parameters (congression-de-attenuated):")
-        print(f"    {deatt_path}")
     print("  Predicted theta vs (genotype, titrant_name, titrant_conc):")
     print(f"    {theta_path}")
-    if history_path is not None:
-        print("  Congression fixed-point theta trajectory:")
-        print(f"    {history_path}")
     print(bar)
 
 
 def main():
     return generalized_main(
         fit_genotypes,
-        manual_arg_types={"library_config": str, "congression_lambda": float,
-                          "min_obs": int, "num_workers": int})
+        manual_arg_types={"min_obs": int, "num_workers": int})
 
 
 if __name__ == "__main__":
diff --git a/src/tfscreen/tfmodel/scripts/fit_model_cli.py b/src/tfscreen/tfmodel/scripts/fit_model_cli.py
index 5440ed13..369dba95 100644
--- a/src/tfscreen/tfmodel/scripts/fit_model_cli.py
+++ b/src/tfscreen/tfmodel/scripts/fit_model_cli.py
@@ -1,76 +1,98 @@
 import os
 import dill
-
-import optax
-
-from tfscreen.tfmodel.inference.run_inference import RunInference
-
+import numpy as np
+import jax.numpy as jnp
+
+from tfscreen.tfmodel.inference.run_inference import (
+    RunInference,
+    check_guide_kwargs,
+    resolve_guide_type,
+)
+
+from tfscreen.tfmodel.inference.staged_map import (
+    DEFAULT_STAGED_STEP_SIZE,
+    run_staged_map,
+    use_stages,
+)
 from tfscreen.util.cli.generalized_main import generalized_main
 
 from tfscreen.tfmodel.configuration_io import read_configuration
 
+# Starting scale of the variational guide, in each site's unconstrained units:
+# an upper bound on every component-guide scale, and the autoguides'
+# ``init_scale`` for a fresh fit. It has to be small because it is one number
+# for every site: 0.1 on a growth rate (per minute) is 17 ln units over a
+# selection, and at 0.1 SVI started ~1000x above the pre-MAP's loss and
+# re-descended into other optima (relative-fit grid, 2026-09-27). SVI widens
+# the scales itself; the entropy term raises each about e-fold per 1000 Adam
+# steps at step size 1e-3.
+DEFAULT_GUIDE_INIT_SCALE = 1e-4
+
+def _optimization_kwargs(convergence_window_steps=2000,
+                         patience=3,
+                         convergence_z=3.0,
+                         loss_rtol=1e-6,
+                         param_tolerance=0.05,
+                         adam_final_step_size=1e-6,
+                         adam_step_size_cut=0.1,
+                         checkpoint_interval=10,
+                         max_num_epochs=100000,
+                         init_param_jitter=0.0,
+                         epoch_checkpoint_interval=1000):
+    """``RunInference.run_optimization`` keyword arguments from CLI names."""
+    return dict(convergence_window_steps=convergence_window_steps,
+                patience=patience,
+                convergence_z=convergence_z,
+                loss_rtol=loss_rtol,
+                param_tolerance=param_tolerance,
+                final_step_size=adam_final_step_size,
+                step_size_cut=adam_step_size_cut,
+                checkpoint_interval=checkpoint_interval,
+                max_num_epochs=max_num_epochs,
+                init_param_jitter=init_param_jitter,
+                epoch_checkpoint_interval=epoch_checkpoint_interval)
+
 
 def _run_map(ri,
-             init_params,
+             init_values=None,
+             init_params=None,
              checkpoint_file=None,
              out_prefix="tfs",
              adam_step_size=1e-3,
-             adam_final_step_size=1e-6,
-             adam_clip_norm=1.0,
+             adam_clip_norm=None,
              elbo_num_particles=2,
-             convergence_tolerance=1e-5,
-             convergence_window=10,
-             patience=10,
-             convergence_check_interval=2,
-             checkpoint_interval=10,
-             max_num_epochs=100000,
-             init_param_jitter=0.0,
-             epoch_checkpoint_interval=1000):
+             label="MAP",
+             **optimization_kwargs):
     """
-    Run maximum a posteriori (MAP) optimization for hierarchical model inference.
-
-    This function sets up and runs MAP optimization using the provided RunInference
-    object and initial parameters. It manages checkpointing, convergence checking,
-    and writes parameter values to disk. Returns the final optimizer state, parameters,
-    and convergence status.
+    Run maximum a posteriori (MAP) optimization (an AutoDelta guide).
 
     Parameters
     ----------
     ri : RunInference
         RunInference object that manages model setup and optimization routines.
-    init_params : dict
-        Initial parameter values for MAP optimization.
+    init_values : dict or None, optional
+        Constrained site values the MAP starts from (``ri.site_values``).
+        Sites without a value start at the prior median.
+    init_params : dict or None, optional
+        Starting values of the model's own ``pyro.param`` sites (thermo PnnC NN
+        weights; ``ri.model_params``), e.g. an earlier fit's.
     checkpoint_file : str or None, optional
         Path to a checkpoint file to resume optimization from, or None to start fresh.
     out_prefix : str, optional
         Output file root for checkpoints and results (default "tfs").
     adam_step_size : float, optional
         Starting step size for the Adam optimizer (default 1e-3).
-    adam_final_step_size : float, optional
-        Final step size for the Adam optimizer (default 1e-6).
-    adam_clip_norm : float, optional
-        Gradient clipping norm for Adam optimizer (default 1.0).
+    adam_clip_norm : float or None, optional
+        Clip each gradient element to +/- this value (numpyro ClippedAdam).
+        None (default) disables clipping: when gradients are much larger
+        than the clip, an elementwise clip makes Adam follow the gradient's
+        sign, and its fixed point is biased by rare large ELBO penalties.
     elbo_num_particles : int, optional
         Number of particles for ELBO estimation during MAP (default 2).
-    max_num_epochs : int, optional
-        Maximum number of MAP optimization epochs (default 100000).
-    convergence_tolerance : float, optional
-        Relative change in smoothed loss to declare MAP convergence (default 1e-5).
-    convergence_window : int, optional
-        Number of epochs to average for convergence check (default 10).
-    patience : int, optional
-        Number of consecutive checks meeting tolerance to declare convergence
-        (default 10).
-    convergence_check_interval : int, optional
-        Frequency (in epochs) to check for convergence (default 2).
-    checkpoint_interval : int, optional
-        Steps between checkpoints and convergence checks (default 10).
-    init_param_jitter : float, optional
-        Amount of jitter to add to init_params (default 0.0).
-    epoch_checkpoint_interval : int or None, optional
-        Frequency (in epochs) to write numbered epoch checkpoints to a
-        ``checkpoints/`` subdirectory (default 1000). Set to 0 or None to
-        disable.
+    label : str, optional
+        Name of the run in the closing message (default "MAP").
+    **optimization_kwargs
+        Passed to ``ri.run_optimization`` (see ``_optimization_kwargs``).
 
     Notes
     -----
@@ -84,36 +106,24 @@ def _run_map(ri,
     params : dict
         Final optimized parameters from MAP.
     converged : bool
-        True if MAP converged according to the specified tolerance.
+        True if the run converged (see ``RunInference.run_optimization``).
     """
 
-    # Create a learning rate schedule
-    schedule = optax.exponential_decay(
-        init_value=adam_step_size,
-        transition_steps=float(max_num_epochs * ri._iterations_per_epoch),
-        decay_rate=adam_final_step_size / adam_step_size
-    )
-
-     # Create a maximum a posteriori svi object
-    map_obj = ri.setup_svi(adam_step_size=schedule,
+    # Create a maximum a posteriori svi object
+    map_obj = ri.setup_svi(adam_step_size=adam_step_size,
                            adam_clip_norm=adam_clip_norm,
                            elbo_num_particles=elbo_num_particles,
-                           guide_type="delta")
+                           guide_type="delta",
+                           init_values=init_values)
 
     # Run MAP
+    if init_params:
+        optimization_kwargs["init_params"] = init_params
     svi_state, params, converged = ri.run_optimization(
         map_obj,
-        init_params=init_params,
         out_prefix=out_prefix,
         svi_state=checkpoint_file,
-        convergence_tolerance=convergence_tolerance,
-        convergence_window=convergence_window,
-        patience=patience,
-        convergence_check_interval=convergence_check_interval,
-        checkpoint_interval=checkpoint_interval,
-        max_num_epochs=max_num_epochs,
-        init_param_jitter=init_param_jitter,
-        epoch_checkpoint_interval=epoch_checkpoint_interval
+        **optimization_kwargs
     )
 
     # Write the current parameter values
@@ -121,9 +131,9 @@ def _run_map(ri,
 
     # Write convergence information to stdout
     if converged:
-        print("MAP run converged.",flush=True)
+        print(f"{label} run converged.",flush=True)
     else:
-        print("MAP run has not yet converged.",flush=True)
+        print(f"{label} run has not yet converged.",flush=True)
 
     return svi_state, params, converged
 
@@ -132,62 +142,44 @@ def _run_svi(ri,
              checkpoint_file=None,
              out_prefix="tfs",
              adam_step_size=1e-3,
-             adam_final_step_size=1e-6,
-             adam_clip_norm=1.0,
+             adam_clip_norm=None,
              elbo_num_particles=2,
-             convergence_tolerance=1e-5,
-             convergence_window=10,
-             patience=10,
-             convergence_check_interval=2,
-             checkpoint_interval=10,
-             max_num_epochs=100000,
-             init_param_jitter=0.1,
-             epoch_checkpoint_interval=1000):
+             guide_type="component",
+             guide_kwargs=None,
+             init_values=None,
+             **optimization_kwargs):
     """
     Run stochastic variational inference (SVI) for hierarchical model inference.
 
-    This function sets up and runs SVI optimization using the provided RunInference
-    object and initial parameters. It manages checkpointing, convergence checking,
-    and optionally draws posterior samples after convergence.
-
     Parameters
     ----------
     ri : RunInference
         RunInference object that manages model setup and optimization routines.
     init_params : dict or None
-        Initial parameter values for SVI optimization. If None, uses parameters
-        captured during SVI object initialization.
+        Initial component-guide parameters (``ri.component_guide_start``),
+        plus the model's own params (``ri.model_params``). An autoguide,
+        whose parameter names differ, takes only the model's own params.
     checkpoint_file : str or None, optional
         Path to a checkpoint file to resume optimization from, or None to start fresh.
     out_prefix : str, optional
         Output file root for checkpoints and results (default "tfs").
     adam_step_size : float, optional
         Starting step size for the Adam optimizer (default 1e-3).
-    adam_final_step_size : float, optional
-        Final step size for the Adam optimizer (default 1e-6).
-    adam_clip_norm : float, optional
-        Gradient clipping norm for Adam optimizer (default 1.0).
+    adam_clip_norm : float or None, optional
+        Clip each gradient element to +/- this value (numpyro ClippedAdam).
+        None (default) disables clipping: when gradients are much larger
+        than the clip, an elementwise clip makes Adam follow the gradient's
+        sign, and its fixed point is biased by rare large ELBO penalties.
     elbo_num_particles : int, optional
         Number of particles for ELBO estimation during SVI (default 2).
-    convergence_tolerance : float, optional
-        Relative change in loss to declare SVI convergence (default 1e-5).
-    convergence_window : int, optional
-        Number of epochs to average for convergence check (default 10).
-    patience : int, optional
-        Number of consecutive checks meeting tolerance to declare convergence
-        (default 10).
-    convergence_check_interval : int, optional
-        Frequency (in epochs) to check for convergence (default 2).
-    checkpoint_interval : int, optional
-        Frequency (in epochs) between checkpoints (default 10).
-    max_num_epochs : int, optional
-        Maximum number of SVI optimization epochs (default 100000).
-    init_param_jitter : float, optional
-        Amount of jitter to add to init_params (default 0.1).
-    epoch_checkpoint_interval : int or None, optional
-        Frequency (in epochs) to write numbered epoch checkpoints to a
-        ``checkpoints/`` subdirectory (default 1000). Set to 0 or None to
-        disable.
+    guide_type : str, optional
+        Variational family passed to ``ri.setup_svi`` (default 'component').
+    guide_kwargs : dict or None, optional
+        Autoguide options (``rank``, ``init_scale``) passed to ``ri.setup_svi``.
+    init_values : dict or None, optional
+        Constrained site values an autoguide's location starts from.
+    **optimization_kwargs
+        Passed to ``ri.run_optimization`` (see ``_optimization_kwargs``).
 
     Notes
     -----
@@ -205,42 +197,35 @@ def _run_svi(ri,
     svi_params : dict
         Final optimized parameters from SVI.
     converged : bool
-        True if SVI converged according to the specified tolerance.
+        True if the run converged (see ``RunInference.run_optimization``).
     """
 
-    # Create a learning rate schedule
-    schedule = optax.exponential_decay(
-        init_value=adam_step_size,
-        transition_steps=float(max_num_epochs * ri._iterations_per_epoch),
-        decay_rate=adam_final_step_size / adam_step_size
-    )
-
     # Create an svi object
-    svi_obj = ri.setup_svi(adam_step_size=schedule,
+    svi_obj = ri.setup_svi(adam_step_size=adam_step_size,
                            adam_clip_norm=adam_clip_norm,
                            elbo_num_particles=elbo_num_particles,
-                           guide_type="component")
+                           guide_type=guide_type,
+                           guide_kwargs=guide_kwargs,
+                           init_values=init_values)
+
+    # init_params are substituted by parameter name, which only the component
+    # guide's names can match; an autoguide starts from init_values instead,
+    # and keeps only the model's own params (thermo PnnC NN weights).
+    if resolve_guide_type(guide_type) != "component":
+        init_params = ri.model_params(init_params) if init_params else None
+        init_params = init_params or None
 
-    # Run svi. Note that `init_params` is not used here, but is required by the
-    # run_optimization method.
     svi_state, params, converged = ri.run_optimization(
         svi_obj,
         init_params=init_params,
         out_prefix=f"{out_prefix}",
         svi_state=checkpoint_file,
-        convergence_tolerance=convergence_tolerance,
-        convergence_window=convergence_window,
-        patience=patience,
-        convergence_check_interval=convergence_check_interval,
-        checkpoint_interval=checkpoint_interval,
-        max_num_epochs=max_num_epochs,
-        init_param_jitter=init_param_jitter,
-        epoch_checkpoint_interval=epoch_checkpoint_interval
+        **optimization_kwargs
     )
 
     # Write convergence information to stdout (skip when restoring from
     # checkpoint with no additional epochs — convergence is not meaningful).
-    if max_num_epochs > 0:
+    if optimization_kwargs.get("max_num_epochs", 1) > 0:
         if converged:
             print("SVI run converged.",flush=True)
         else:
@@ -249,11 +234,13 @@ def _run_svi(ri,
     return svi_obj, svi_state, params, converged
 
 def _run_nuts(ri,
+              init_values=None,
               out_prefix="tfs",
               nuts_num_warmup=500,
               nuts_num_samples=500,
               nuts_num_chains=1,
               nuts_target_accept_prob=0.9,
+              nuts_dense_mass=False,
               forward_batch_size=512):
     """
     Run NUTS (No-U-Turn Sampler) MCMC inference.
@@ -262,6 +249,9 @@ def _run_nuts(ri,
     ----------
     ri : RunInference
         RunInference object that manages model setup and MCMC routines.
+    init_values : dict or None, optional
+        Constrained site values to start the chains at (default: prior
+        medians).
     out_prefix : str, optional
         Output file root for checkpoints and results (default "tfs").
     nuts_num_warmup : int, optional
@@ -272,6 +262,8 @@ def _run_nuts(ri,
         Number of MCMC chains (default 1).
     nuts_target_accept_prob : float, optional
         Target acceptance probability for NUTS step-size adaptation (default 0.9).
+    nuts_dense_mass : bool, optional
+        Adapt a dense mass matrix (default False, diagonal).
     forward_batch_size : int, optional
         Number of genotypes to process per forward-model batch when computing
         posteriors (default 512).
@@ -285,7 +277,9 @@ def _run_nuts(ri,
     mcmc = ri.run_nuts(num_warmup=nuts_num_warmup,
                        num_samples=nuts_num_samples,
                        num_chains=nuts_num_chains,
-                       target_accept_prob=nuts_target_accept_prob)
+                       target_accept_prob=nuts_target_accept_prob,
+                       init_values=init_values,
+                       dense_mass=nuts_dense_mass)
 
     mcmc_samples = mcmc.get_samples()
 
@@ -305,29 +299,80 @@ def _run_nuts(ri,
     return mcmc_samples
 
 
+def _check_checkpoint_guide(checkpoint_file, guide_type):
+    """Refuse to resume an SVI checkpoint with a different guide."""
+
+    # A missing file is reported by run_optimization when it tries to restore.
+    if not os.path.isfile(checkpoint_file):
+        return
+
+    with open(checkpoint_file, "rb") as f:
+        saved = dill.load(f).get("guide_type") or "component"
+    if saved != guide_type:
+        raise ValueError(
+            f"Checkpoint '{checkpoint_file}' was written with guide_type "
+            f"'{saved}', but guide_type '{guide_type}' was requested. Resume "
+            f"with guide_type='{saved}'."
+        )
+
+
+def _read_init_from(path):
+    """
+    A MAP ``*_params.npz`` (constrained values, as ``write_params`` saves
+    them) as a start.
+
+    Returns
+    -------
+    values : dict
+        The ``{site}_auto_loc`` arrays, for ``site_values``: they take
+        precedence over guesses keyed by site or by guide parameter.
+    others : dict
+        Every other array: the model's own learned params (thermo PnnC NN
+        weights), for ``ri.model_params``.
+    """
+    with np.load(path) as z:
+        values = {k: jnp.asarray(z[k]) for k in z.files if k.endswith("_auto_loc")}
+        others = {k: jnp.asarray(z[k]) for k in z.files
+                  if not k.endswith("_auto_loc")}
+    if not values:
+        raise ValueError(f"init_from '{path}' has no '*_auto_loc' arrays; it "
+                         "should be the *_params.npz of a MAP fit.")
+    print(f"Starting from {len(values)} MAP sites in {path}")
+    return values, others
+
+
 def fit_model(config_file,
               seed=None,
               checkpoint_file=None,
               analysis_method="svi",
+              guide_type="component",
+              guide_rank=None,
+              guide_init_scale=None,
               out_prefix="tfs_fit_model",
               adam_step_size=1e-3,
               adam_final_step_size=1e-6,
-              adam_clip_norm=1.0,
+              adam_step_size_cut=0.1,
+              adam_clip_norm=None,
               elbo_num_particles=2,
-              convergence_tolerance=1e-5,
-              convergence_window=10,
-              patience=10,
-              convergence_check_interval=2,
+              convergence_window_steps=2000,
+              patience=3,
+              convergence_z=3.0,
+              loss_rtol=1e-6,
+              param_tolerance=0.05,
               checkpoint_interval=10,
               max_num_epochs=100000,
               forward_batch_size=512,
-              pre_map_num_epoch=1000,
-              init_param_jitter=0.1,
+              pre_map_num_epoch=10000,
+              init_param_jitter=0.0,
               nuts_num_warmup=500,
               nuts_num_samples=500,
               nuts_num_chains=1,
               nuts_target_accept_prob=0.9,
-              epoch_checkpoint_interval=1000):
+              nuts_dense_mass=False,
+              epoch_checkpoint_interval=1000,
+              init_from=None,
+              stage_offsets="auto",
+              staged_step_size=DEFAULT_STAGED_STEP_SIZE):
     """
     Fit the joint hierarchical model using a previously generated configuration file.
 
@@ -335,6 +380,15 @@ def fit_model(config_file,
     and other latent parameters using Stochastic Variational Inference (SVI) or maximum a
     posteriori (MAP) approaches based on the config.
 
+    Optimization (SVI, MAP and the pre-MAP warm-up) runs in windows of
+    ``convergence_window_steps`` optimizer steps.  After ``patience``
+    consecutive windows without a significant improvement in the loss
+    (against its own noise), the step size is cut by ``adam_step_size_cut``.
+    Once it has reached ``adam_final_step_size``, the run stops after
+    ``patience`` consecutive windows in which the loss has not improved and no
+    parameter has moved (against its posterior or prior width).  Each window
+    is recorded in ``{out_prefix}_convergence.csv``.
+
     Parameters
     ----------
     config_file : str
@@ -343,44 +397,84 @@ def fit_model(config_file,
         Random seed for reproducibility. Must be provided if not loading from a checkpoint.
     checkpoint_file : str or None, optional
         Path to a checkpoint file to resume SVI from, or None to start fresh.
+        A resumed run continues at the checkpoint's step size and convergence
+        stage.
     analysis_method : str, optional
         Method for inference. Allowed values are 'svi' (default), 'map', or 'nuts'.
         Case-insensitive. Posterior sampling is not performed; call
         ``tfs-sample-posterior`` after fitting.
+    guide_type : str, optional
+        Variational family for analysis_method 'svi' (default 'component',
+        the guide assembled from the model components).  Also accepts the
+        numpyro autoguides 'auto_normal', 'auto_diagonal_normal',
+        'auto_multivariate_normal', 'auto_low_rank_multivariate_normal' and
+        'delta'; numpyro class names such as 'AutoNormal' work too.  Every
+        guide's location starts at the pre-MAP solution when
+        ``pre_map_num_epoch > 0``, otherwise at the configured guesses.
+        A resumed checkpoint must use the guide it was written with.
+    guide_rank : int, optional
+        Covariance rank for 'auto_low_rank_multivariate_normal' (numpyro's
+        default when omitted).
+    guide_init_scale : float, optional
+        Initial scale of the variational distribution, in each site's
+        unconstrained units (default 1e-4, ``DEFAULT_GUIDE_INIT_SCALE``).
+        For 'component' it caps every guide scale at the start; for an
+        autoguide it is numpyro's ``init_scale`` for a fresh fit (a resumed
+        fit keeps its checkpoint's).  SVI widens the scales itself; a large
+        start throws the pre-MAP point away.  Not accepted by 'delta'.
     out_prefix : str, optional
-        Prefix for all output files: checkpoints, parameter files, and the
-        posterior HDF5 (default 'tfs_fit_model'). Files are named
-        {out_prefix}_checkpoint.pkl, {out_prefix}_params.npz, etc.
+        Prefix for all output files (default 'tfs_fit_model'):
+        {out_prefix}_checkpoint.pkl, {out_prefix}_params.npz,
+        {out_prefix}_losses.txt, {out_prefix}_convergence.csv, the pre-MAP's
+        {out_prefix}_premap_* files, and, for NUTS only, the posterior HDF5.
     adam_step_size : float, optional
         Starting step size for the Adam optimizer (default 1e-3).
     adam_final_step_size : float, optional
-        Final step size for the Adam optimizer (default 1e-6).
-    adam_clip_norm : float, optional
-        Gradient clipping norm for Adam optimizer (default 1.0).
+        Smallest step size (default 1e-6).  Set equal to ``adam_step_size``
+        for a constant step size.
+    adam_step_size_cut : float, optional
+        Factor applied to the step size at each cut (default 0.1).
+    adam_clip_norm : float or None, optional
+        Clip each gradient element to +/- this value (numpyro ClippedAdam).
+        None (default) disables clipping: when gradients are much larger
+        than the clip, an elementwise clip makes Adam follow the gradient's
+        sign, and its fixed point is biased by rare large ELBO penalties.
     elbo_num_particles : int, optional
         Number of particles for ELBO estimation (default 2).
-    convergence_tolerance : float, optional
-        Relative change in loss to declare SVI convergence (default 1e-5).
-    convergence_window : int, optional
-        Number of epochs to average for convergence check (default 10).
+    convergence_window_steps : int, optional
+        Optimizer steps per convergence window (default 2000; at least 10
+        epochs, so with mini-batching every genotype is visited several times
+        per window).
     patience : int, optional
-        Number of consecutive checks meeting tolerance to declare convergence
-        (default 10).
-    convergence_check_interval : int, optional
-        Frequency (in epochs) to check for convergence (default 2).
+        Consecutive windows without loss improvement required for a step-size
+        cut, and (at the final step size) without loss improvement or
+        parameter movement required for stopping (default 3).
+    convergence_z : float, optional
+        Standard errors of change attributed to noise (default 3).
+    loss_rtol : float, optional
+        Loss changes per window below this fraction of the loss count as no
+        change (default 1e-6); matters only for (near-)deterministic losses.
+    param_tolerance : float, optional
+        Parameter movement per window, beyond noise, still counted as no
+        change (default 0.05), in posterior SDs (SVI), prior SDs (MAP), or
+        log/logit units (positive or bounded parameters).
     checkpoint_interval : int, optional
         Frequency (in epochs) between checkpoints (default 10).
     max_num_epochs : int, optional
-        Maximum number of SVI optimization epochs (default 100000).
+        Maximum number of SVI or MAP epochs (default 100000); a cap only.
     forward_batch_size : int, optional
         When getting NUTS posteriors, calculate forward predictions in batches
         of this size (default 512).
     pre_map_num_epoch : int, optional
-        Number of MAP iterations to run prior to SVI (default 1000). Only used
-        if analysis_method is 'svi'.
+        Maximum number of epochs of the MAP warm-up run before SVI or NUTS
+        (default 10000; 0 skips it).  The warm-up stops earlier when it
+        converges.  SVI's guide and NUTS's chains start at its point.  Not
+        used by 'map'.
     init_param_jitter : float, optional
-        Amount of jitter to add after the (optional) MAP run to break symmetry
-        (default 0.1).
+        Multiplicative jitter on the component guide's starting parameters
+        (default 0, none).  Not used by autoguides or MAP.  Leave it off when
+        starting from a pre-MAP: it scales with each value, so 0.1 moves an
+        ln_cfu0 location near 15 by about 1.5 ln units.
     nuts_num_warmup : int, optional
         Number of NUTS warmup steps (default 500). Only used if
         analysis_method is 'nuts'.
@@ -393,11 +487,39 @@ def fit_model(config_file,
     nuts_target_accept_prob : float, optional
         Target acceptance probability for NUTS step-size adaptation
         (default 0.9). Only used if analysis_method is 'nuts'.
+    nuts_dense_mass : bool, optional
+        Adapt a dense mass matrix for NUTS instead of a diagonal one
+        (default False).  Worth it for correlated posteriors of up to a few
+        thousand latents; with a diagonal one the relative fit ran at NUTS's
+        maximum tree depth.  Only used if analysis_method is 'nuts'.
     epoch_checkpoint_interval : int or None, optional
         Frequency (in epochs) to write numbered epoch checkpoints to a
         ``checkpoints/`` subdirectory alongside ``out_prefix`` (default 1000).
         Files are named ``{epoch:07d}_checkpoint.pkl``. Set to 0 or None to
         disable. Raises ``FileExistsError`` if a target file already exists.
+    init_from : str or None, optional
+        A ``*_params.npz`` written by a MAP fit (``{site}_auto_loc`` arrays)
+        to start from instead of the configured guesses, wherever it names a
+        site of this model; other sites start at their guesses. The fit may
+        be of a different model: starting a ``sample_offset: level`` fit from
+        a ``zero`` fit's point, say (the npz can carry
+        ``sample_offset_offset_auto_loc`` to set the offsets too). Refused
+        with ``checkpoint_file``, which sets the start itself.
+    stage_offsets : str, optional
+        'auto' (default), 'on' or 'off'. A MAP with level tube offsets
+        (``sample_offset: level``) started cold can settle in a mode where
+        the offsets carry the population's growth. Staged, it runs three
+        MAPs: the offsets held at 0 ({out_prefix}_stage1_*), the offsets
+        alone with everything else held there ({out_prefix}_stage2_*), and
+        the joint MAP from that point at ``staged_step_size``
+        ({out_prefix}_*). A finished stage is reused when the command is
+        rerun; an interrupted one resumes. 'auto' stages a fresh MAP of a
+        level-offset model (no checkpoint_file, no init_from); 'on' requires
+        one; 'off' fits it in one go.
+    staged_step_size : float, optional
+        Starting step size of the staged MAP's joint stage (default 1e-4):
+        Adam moves every parameter by about a step size per step, and a
+        larger step can carry the start back into the offset mode.
 
     Returns
     -------
@@ -414,6 +536,28 @@ def fit_model(config_file,
 
     analysis_method = analysis_method.lower()
 
+    # Validate guide options before any (slow) fitting starts.
+    guide_type = resolve_guide_type(guide_type)
+    guide_kwargs = {}
+    if guide_rank is not None:
+        guide_kwargs["rank"] = guide_rank
+    if guide_init_scale is not None and guide_type != "component":
+        guide_kwargs["init_scale"] = guide_init_scale
+    if analysis_method != "svi" and (guide_type != "component" or guide_kwargs
+                                     or guide_init_scale is not None):
+        raise ValueError(
+            "guide_type, guide_rank and guide_init_scale apply only to "
+            "analysis_method='svi' ('map' always uses 'delta'; 'nuts' uses no "
+            "guide)."
+        )
+    check_guide_kwargs(guide_type, guide_kwargs)
+    if guide_init_scale is not None and not guide_init_scale > 0:
+        raise ValueError(
+            f"guide_init_scale must be positive (got {guide_init_scale})."
+        )
+    if analysis_method == "svi" and checkpoint_file is not None:
+        _check_checkpoint_guide(checkpoint_file, guide_type)
+
     # Check for existing results to avoid overwriting unless resuming
     if checkpoint_file is None:
         checkpoint_path = f"{out_prefix}_checkpoint.pkl"
@@ -424,7 +568,7 @@ def fit_model(config_file,
                 "the file or change out_prefix."
             )
 
-        if analysis_method == "svi" and pre_map_num_epoch > 0:
+        if analysis_method in ("svi", "nuts") and pre_map_num_epoch > 0:
             premap_path = f"{out_prefix}_premap_checkpoint.pkl"
             if os.path.exists(premap_path):
                 raise FileExistsError(
@@ -432,7 +576,15 @@ def fit_model(config_file,
                     "overwrite, delete the file or change out_prefix."
                 )
 
-    orchestrator, init_params = read_configuration(config_file)
+    if init_from is not None and checkpoint_file is not None:
+        raise ValueError("init_from and checkpoint_file both set the starting "
+                         "point; give one.")
+
+    orchestrator, guesses = read_configuration(config_file)
+    init_from_params = {}
+    if init_from is not None:
+        init_from_values, init_from_params = _read_init_from(init_from)
+        guesses = {**guesses, **init_from_values}
 
     # For posterior mode the seed is optional: the checkpoint restores the PRNG
     # key for SVI checkpoints, and any valid key works for MAP/Laplace sampling.
@@ -441,66 +593,142 @@ def fit_model(config_file,
     # Run SVI / MAP
     ri = RunInference(orchestrator, effective_seed)
 
+    # The model's own learned params (thermo PnnC NN weights) start
+    # where an --init_from fit left them; sample-site values alone would
+    # restart them at their init values.
+    start_params = ri.model_params(init_from_params) if init_from_params else {}
+
+    optimizer_kwargs = dict(adam_step_size=adam_step_size,
+                            adam_clip_norm=adam_clip_norm,
+                            elbo_num_particles=elbo_num_particles)
+    convergence_kwargs = dict(convergence_window_steps=convergence_window_steps,
+                              patience=patience,
+                              convergence_z=convergence_z,
+                              loss_rtol=loss_rtol,
+                              param_tolerance=param_tolerance,
+                              adam_final_step_size=adam_final_step_size,
+                              adam_step_size_cut=adam_step_size_cut)
+
     if analysis_method == "svi":
-        if pre_map_num_epoch > 0 and checkpoint_file is None:
-            _, init_params, _ = _run_map(ri,
-                                         init_params=init_params,
-                                         checkpoint_file=None,
-                                         out_prefix=f"{out_prefix}_premap",
-                                         adam_step_size=adam_step_size,
-                                         adam_final_step_size=adam_final_step_size,
-                                         adam_clip_norm=adam_clip_norm,
-                                         elbo_num_particles=elbo_num_particles,
-                                         convergence_tolerance=convergence_tolerance,
-                                         convergence_window=convergence_window,
-                                         patience=patience,
-                                         checkpoint_interval=pre_map_num_epoch,
-                                         max_num_epochs=pre_map_num_epoch,
-                                         init_param_jitter=0.0,
-                                         epoch_checkpoint_interval=None)
+
+        # Resuming: the checkpoint overwrites the starting point.
+        if checkpoint_file is not None:
+            init_params = guesses
+            init_values = None
+
+        else:
+            # Configured guesses, as site values (they are keyed by site or by
+            # component-guide parameter name).
+            start_values = ri.site_values(guesses)
+
+            if pre_map_num_epoch > 0:
+                _, map_params, _ = _run_map(
+                    ri,
+                    init_values=start_values,
+                    init_params=start_params,
+                    out_prefix=f"{out_prefix}_premap",
+                    label="Pre-MAP",
+                    **optimizer_kwargs,
+                    **_optimization_kwargs(
+                        **convergence_kwargs,
+                        checkpoint_interval=pre_map_num_epoch,
+                        max_num_epochs=pre_map_num_epoch,
+                        epoch_checkpoint_interval=None))
+                # The MAP point replaces the guesses where it has a value,
+                # and its model-side params carry over too.
+                start_values = {**start_values, **ri.site_values(map_params)}
+                start_params = {**start_params, **ri.model_params(map_params)}
+
+            init_scale = (DEFAULT_GUIDE_INIT_SCALE
+                          if guide_init_scale is None else guide_init_scale)
+            if guide_type == "component":
+                init_params = ri.component_guide_start(start_values,
+                                                       guesses=guesses,
+                                                       init_scale=init_scale)
+                init_params = {**init_params, **start_params}
+                init_values = None
+            else:
+                init_params = start_params or None
+                init_values = start_values
+                if guide_type != "delta":
+                    guide_kwargs["init_scale"] = init_scale
 
         return _run_svi(ri,
                         init_params=init_params,
                         checkpoint_file=checkpoint_file,
+                        guide_type=guide_type,
+                        guide_kwargs=guide_kwargs,
+                        init_values=init_values,
                         out_prefix=out_prefix,
-                        adam_step_size=adam_step_size,
-                        adam_final_step_size=adam_final_step_size,
-                        adam_clip_norm=adam_clip_norm,
-                        elbo_num_particles=elbo_num_particles,
-                        convergence_tolerance=convergence_tolerance,
-                        convergence_window=convergence_window,
-                        patience=patience,
-                        convergence_check_interval=convergence_check_interval,
-                        checkpoint_interval=checkpoint_interval,
-                        max_num_epochs=max_num_epochs,
-                        init_param_jitter=init_param_jitter,
-                        epoch_checkpoint_interval=epoch_checkpoint_interval)
+                        **optimizer_kwargs,
+                        **_optimization_kwargs(
+                            **convergence_kwargs,
+                            checkpoint_interval=checkpoint_interval,
+                            max_num_epochs=max_num_epochs,
+                            init_param_jitter=init_param_jitter,
+                            epoch_checkpoint_interval=epoch_checkpoint_interval))
+
+    elif analysis_method == "map" and use_stages(stage_offsets, orchestrator,
+                                                 analysis_method,
+                                                 checkpoint_file=checkpoint_file,
+                                                 init_from=init_from):
+        map_kwargs = dict(**optimizer_kwargs,
+                          **_optimization_kwargs(
+                              **convergence_kwargs,
+                              checkpoint_interval=checkpoint_interval,
+                              max_num_epochs=max_num_epochs,
+                              epoch_checkpoint_interval=epoch_checkpoint_interval))
+        stage_kwargs = dict(map_kwargs, epoch_checkpoint_interval=None)
+        return run_staged_map(orchestrator,
+                              make_ri=lambda m: RunInference(m, effective_seed),
+                              run_map=_run_map,
+                              guesses=guesses,
+                              out_prefix=out_prefix,
+                              map_kwargs=map_kwargs,
+                              stage_map_kwargs=stage_kwargs,
+                              staged_step_size=staged_step_size)
 
     elif analysis_method == "map":
+        init_values = None
+        if checkpoint_file is None:
+            init_values = ri.site_values(guesses)
         return _run_map(ri,
-                        init_params=init_params,
+                        init_values=init_values,
+                        init_params=start_params,
                         checkpoint_file=checkpoint_file,
                         out_prefix=out_prefix,
-                        adam_step_size=adam_step_size,
-                        adam_final_step_size=adam_final_step_size,
-                        adam_clip_norm=adam_clip_norm,
-                        elbo_num_particles=elbo_num_particles,
-                        convergence_tolerance=convergence_tolerance,
-                        convergence_window=convergence_window,
-                        patience=patience,
-                        convergence_check_interval=convergence_check_interval,
-                        checkpoint_interval=checkpoint_interval,
-                        max_num_epochs=max_num_epochs,
-                        init_param_jitter=init_param_jitter,
-                        epoch_checkpoint_interval=epoch_checkpoint_interval)
+                        **optimizer_kwargs,
+                        **_optimization_kwargs(
+                            **convergence_kwargs,
+                            checkpoint_interval=checkpoint_interval,
+                            max_num_epochs=max_num_epochs,
+                            epoch_checkpoint_interval=epoch_checkpoint_interval))
 
     elif analysis_method == "nuts":
+        # Start the chains at the MAP warm-up's point, as SVI does: from
+        # prior medians warmup spends its adaptation getting to the mode.
+        init_values = ri.site_values(guesses)
+        if pre_map_num_epoch > 0:
+            _, map_params, _ = _run_map(
+                ri,
+                init_values=init_values,
+                out_prefix=f"{out_prefix}_premap",
+                label="Pre-MAP",
+                **optimizer_kwargs,
+                **_optimization_kwargs(
+                    **convergence_kwargs,
+                    checkpoint_interval=pre_map_num_epoch,
+                    max_num_epochs=pre_map_num_epoch,
+                    epoch_checkpoint_interval=None))
+            init_values = {**init_values, **ri.site_values(map_params)}
         mcmc_samples = _run_nuts(ri,
+                                 init_values=init_values,
                                  out_prefix=out_prefix,
                                  nuts_num_warmup=nuts_num_warmup,
                                  nuts_num_samples=nuts_num_samples,
                                  nuts_num_chains=nuts_num_chains,
                                  nuts_target_accept_prob=nuts_target_accept_prob,
+                                 nuts_dense_mass=nuts_dense_mass,
                                  forward_batch_size=forward_batch_size)
         return None, mcmc_samples, True
 
@@ -514,15 +742,19 @@ def fit_model(config_file,
 def main():
     return generalized_main(fit_model,
                             manual_arg_types={"config_file":str,
+                                              "adam_clip_norm":float,
                                               "seed":int,
                                               "checkpoint_file":str,
+                                              "guide_rank":int,
+                                              "guide_init_scale":float,
                                               "pre_map_num_epoch":int,
                                               "init_param_jitter":float,
                                               "nuts_num_warmup":int,
                                               "nuts_num_samples":int,
                                               "nuts_num_chains":int,
                                               "nuts_target_accept_prob":float,
-                                              "epoch_checkpoint_interval":int})
+                                              "epoch_checkpoint_interval":int,
+                                              "init_from":str})
 
 if __name__ == "__main__":
     main()
diff --git a/src/tfscreen/tfmodel/scripts/fit_two_stage_cli.py b/src/tfscreen/tfmodel/scripts/fit_two_stage_cli.py
new file mode 100644
index 00000000..82c1c20a
--- /dev/null
+++ b/src/tfscreen/tfmodel/scripts/fit_two_stage_cli.py
@@ -0,0 +1,691 @@
+"""
+tfs-fit-two-stage: the two-stage fit of a growth-only relative model, with
+X intervals that carry stage 1's k uncertainty (planning/two-stage-fit.md).
+"""
+
+import contextlib
+import inspect
+import json
+import os
+
+import pandas as pd
+import yaml
+
+from tfscreen.genetics.library_design import library_composition_table
+from tfscreen.tfmodel import two_stage as ts
+from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+from tfscreen.tfmodel.scripts.extract_params_cli import extract_params
+from tfscreen.tfmodel.scripts.fit_model_cli import fit_model
+from tfscreen.tfmodel.scripts.predict_epistasis_cli import predict_epistasis
+from tfscreen.tfmodel.scripts.predict_theta_cli import predict_theta
+from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+from tfscreen.util.cli.generalized_main import generalized_main
+from tfscreen.util.provenance import get_provenance
+
+STEPS = ("stage1", "jackknife", "stage2", "kshift", "predict", "consistency",
+         "summary")
+
+# tfs-configure-model arguments that name files (resolved against the YAML's
+# directory).
+PATH_KEYS = ("growth_df", "library_config", "growth_priors",
+             "growth_priors_wt_rates", "base_growth_df", "presplit_df",
+             "thermo_data", "binding_df")
+
+# The joint-fit pieces (planning/joint-fit-return.md, paused 2026-10-18)
+# and the marker warp are experimental and not part of the two-stage route:
+# the fit is growth-only, and stage 2's pins carry k and m but not a warp.
+EXPERIMENTAL_PRIORS = ("growth_log_conc_shift", "offset_scale_fixed",
+                       "offset_scale_scale", "warp_log_a", "warp_x0")
+
+# Set by the two-stage fit itself, so not allowed at the top level.
+RESERVED_KEYS = ("out_prefix", "transformation_model", "transformation_lambda",
+                 "congression_theta_rule")
+
+TWO_STAGE_DEFAULTS = {
+    "transformation_lambda": None,
+    "seed": 1,
+    "jackknife_subsamples": 30,
+    "jackknife_drop_frac": 0.2,
+    "num_posterior_samples": 500,
+    "consistency": True,
+    "consistency_library": None,
+    "predict_epistasis": False,
+    "kshift": False,
+    "kshift_step_size": 1e-5,
+    "kshift_control": True,
+    "fit": {},
+    "posterior": {},
+}
+
+FIT_RESERVED = ("config_file", "seed", "out_prefix", "analysis_method",
+                "init_from", "checkpoint_file")
+POSTERIOR_RESERVED = ("config_file", "checkpoint_file", "out_prefix",
+                      "num_posterior_samples")
+
+# The chained functions' arguments, read once (the YAML is checked against
+# them).
+CONFIGURE_ARGS = frozenset(inspect.signature(configure_model).parameters)
+FIT_ARGS = frozenset(inspect.signature(fit_model).parameters)
+POSTERIOR_ARGS = frozenset(inspect.signature(sample_posterior).parameters)
+
+CONFIG = "tfs_configure_config.yaml"
+CHECKPOINT = "tfs_fit_model_checkpoint.pkl"
+PARAMS = "tfs_fit_model_params.npz"
+POSTERIOR = "tfs_posterior.h5"
+PRED = "tfs_pred_theta.csv"
+PRED_MAP = "tfs_pred_theta_map.csv"
+SUMMARY = "tfs_x.csv"
+READS = "genotype_reads.csv"
+
+
+# ---------------------------------------------------------------------------
+# The YAML
+# ---------------------------------------------------------------------------
+
+def read_two_stage_config(config_file):
+    """
+    The configure arguments and two-stage settings from the YAML.
+
+    Top-level keys are ``tfs-configure-model``'s arguments, under the same
+    names; file paths are relative to the YAML's directory. The
+    ``two_stage`` block holds the two-stage settings
+    (``TWO_STAGE_DEFAULTS``).
+
+    Returns
+    -------
+    tuple of dict
+        ``(configure_kwargs, settings)``.
+
+    Raises
+    ------
+    ValueError
+        On an unknown or reserved key, a missing input, a model the
+        two-stage fit does not support (including binding data, theta
+        noise, the joint-fit priors and an m_warp column in
+        growth_priors), or a missing lambda prior.
+    """
+    with open(config_file) as fh:
+        raw = yaml.safe_load(fh) or {}
+    base = os.path.dirname(os.path.abspath(config_file))
+    settings = dict(TWO_STAGE_DEFAULTS)
+    block = raw.pop("two_stage", None) or {}
+    unknown = sorted(set(block) - set(TWO_STAGE_DEFAULTS))
+    if unknown:
+        raise ValueError(f"Unknown two_stage key(s) {unknown}; allowed: "
+                         f"{sorted(TWO_STAGE_DEFAULTS)}.")
+    settings.update(block)
+
+    allowed = CONFIGURE_ARGS
+    reserved = sorted(set(raw) & set(RESERVED_KEYS))
+    if reserved:
+        raise ValueError(f"{reserved} are set by the two-stage fit (stage 1 "
+                         "fits the congression mixture, stage 2 does not); "
+                         "put the lambda prior in the two_stage block.")
+    unknown = sorted(set(raw) - allowed)
+    if unknown:
+        raise ValueError(f"Unknown key(s) {unknown}: top-level keys are "
+                         "tfs-configure-model's arguments.")
+    for key in ("growth_df", "library_config"):
+        if not raw.get(key):
+            raise ValueError(f"The two-stage fit needs {key}.")
+    cfg = dict(raw)
+    for key in PATH_KEYS:
+        if cfg.get(key):
+            path = cfg[key]
+            if not os.path.isabs(path):
+                path = os.path.join(base, path)
+            if not os.path.exists(path):
+                raise ValueError(f"{key}: {path} does not exist")
+            cfg[key] = path
+
+    if cfg.get("theta_model") != "hill_relative":
+        raise ValueError("The two-stage fit is for the growth-only relative "
+                         "model: set theta_model: hill_relative.")
+    if cfg.get("condition_growth_model", "linear") != "linear":
+        raise ValueError("The two-stage fit pins linear growth's k and m: "
+                         "condition_growth_model must be linear.")
+    if not cfg.get("growth_shares_replicates"):
+        raise ValueError("The two-stage fit pins one k and m per condition: "
+                         "set growth_shares_replicates: true.")
+    for item in cfg.get("set_priors") or []:
+        name = str(item).split("=")[0].strip()
+        if name.endswith("_pinned"):
+            raise ValueError(f"set_priors {item!r}: stage 2 pins k and m per "
+                             "condition itself.")
+        if any(x in name for x in EXPERIMENTAL_PRIORS):
+            raise ValueError(f"set_priors {item!r} sets an experimental "
+                             "joint-fit or warp prior, which the two-stage "
+                             "fit does not support.")
+    _check_not_experimental(cfg)
+
+    lam = settings["transformation_lambda"]
+    if lam is None or len(lam) != 2:
+        raise ValueError("two_stage.transformation_lambda (mean, std) is "
+                         "required: stage 1 fits the congression mixture, "
+                         "with the measured lambda as its prior.")
+    settings["transformation_lambda"] = [float(x) for x in lam]
+    if int(settings["jackknife_subsamples"]) < 2:
+        raise ValueError("two_stage.jackknife_subsamples must be at least 2")
+    settings["kshift_step_size"] = float(settings["kshift_step_size"])
+    if not settings["kshift_step_size"] > 0:
+        raise ValueError("two_stage.kshift_step_size must be positive")
+    for name, res, sig in (("fit", FIT_RESERVED, FIT_ARGS),
+                           ("posterior", POSTERIOR_RESERVED, POSTERIOR_ARGS)):
+        extra = settings[name] or {}
+        bad = sorted(set(extra) - sig) + sorted(set(extra) & set(res))
+        if bad:
+            raise ValueError(f"two_stage.{name}: {bad} cannot be set here.")
+        settings[name] = dict(extra)
+    return cfg, settings
+
+
+def _check_not_experimental(cfg):
+    """Refuse the experimental joint-fit pieces and the marker warp."""
+    if cfg.get("binding_df"):
+        raise ValueError("The two-stage fit is growth-only: leave out "
+                         "binding_df and compare X with binding after the "
+                         "fit.")
+    for key in ("theta_growth_noise_model", "theta_binding_noise_model"):
+        if cfg.get(key, "zero") != "zero":
+            raise ValueError(f"{key} {cfg[key]!r}: the two-stage fit needs "
+                             "'zero' (the joint-fit noise models, such as "
+                             "genotype_offset, are experimental).")
+    path = cfg.get("growth_priors")
+    if path:
+        table = pd.read_csv(path)
+        if "m_warp" in table.columns and (table["m_warp"].fillna(0) != 0).any():
+            raise ValueError(f"growth_priors {path}: an m_warp column turns "
+                             "on the marker warp, which stage 2's pins do "
+                             "not carry, so stage 1 would fit it and stage 2 "
+                             "drop it. The warp is not part of the two-stage "
+                             "fit in this release.")
+
+
+# ---------------------------------------------------------------------------
+# Shared pieces of a step
+# ---------------------------------------------------------------------------
+
+@contextlib.contextmanager
+def _in_dir(path):
+    os.makedirs(path, exist_ok=True)
+    old = os.getcwd()
+    os.chdir(path)
+    try:
+        yield
+    finally:
+        os.chdir(old)
+
+
+def _say(msg):
+    print(f">>> {msg}", flush=True)
+
+
+def _stage1_kwargs(cfg, settings):
+    out = dict(cfg)
+    out.update(transformation_model="mixture",
+               transformation_lambda=settings["transformation_lambda"],
+               congression_theta_rule="max")
+    return out
+
+
+def _configure(kwargs, growth_df, growth_priors=None, drop_wt_rates=False):
+    if os.path.exists(CONFIG):
+        return
+    kw = dict(kwargs)
+    kw["growth_df"] = os.path.abspath(growth_df)
+    if growth_priors is not None:
+        kw["growth_priors"] = os.path.abspath(growth_priors)
+    if drop_wt_rates:
+        kw.pop("growth_priors_wt_rates", None)
+    configure_model(out_prefix="tfs_configure", **kw)
+
+
+def _fit(settings, init_from=None, **override):
+    if os.path.exists(PARAMS):
+        return
+    kw = {**settings["fit"], **override}
+    fit_model(CONFIG, seed=int(settings["seed"]), analysis_method="map",
+              init_from=init_from, **kw)
+
+
+def _km(path="km.csv"):
+    if not os.path.exists(path):
+        extract_params(CONFIG, CHECKPOINT, out_prefix="tfs_params")
+        if (os.path.exists("tfs_params_growth_k.csv")
+                and os.path.exists("tfs_params_growth_m.csv")):
+            km = ts.km_table(pd.read_csv("tfs_params_growth_k.csv"),
+                             pd.read_csv("tfs_params_growth_m.csv"))
+        else:
+            # a pinned k or m is a deterministic site, which a MAP
+            # checkpoint does not hold, so extraction skips both
+            km = ts.km_from_map(PARAMS, "tfs_configure_priors.csv")
+        km.to_csv(path)
+    return pd.read_csv(path, index_col="condition_rep")
+
+
+def _posterior(settings):
+    if os.path.exists(POSTERIOR):
+        return
+    kw = {"skip_growth_observations": True}
+    kw.update(settings["posterior"])
+    sample_posterior(CONFIG, CHECKPOINT, out_prefix="tfs_posterior",
+                     num_posterior_samples=int(settings["num_posterior_samples"]),
+                     **kw)
+
+
+# ---------------------------------------------------------------------------
+# The steps
+# ---------------------------------------------------------------------------
+
+def run_stage1(out_dir, cfg, settings):
+    """wt, spikes and singles with the congression mixture; writes km.csv."""
+    d = os.path.join(out_dir, "stage1")
+    if os.path.exists(os.path.join(d, "km.csv")):
+        _say("stage1: done, skipped")
+        return
+    _say("stage1")
+    with _in_dir(d):
+        if not os.path.exists("tfs_growth.csv"):
+            lib = library_composition_table(cfg["library_config"])
+            keep = ts.stage1_genotypes(lib, ts.growth_genotypes(cfg["growth_df"]))
+            ts.filter_growth_csv(cfg["growth_df"], "tfs_growth.csv", genotypes=keep)
+            print(f"stage 1: {len(keep)} genotypes (wt, spikes, singles)",
+                  flush=True)
+        _configure(_stage1_kwargs(cfg, settings), "tfs_growth.csv")
+        _fit(settings)
+        _km()
+
+
+def run_jackknife_task(out_dir, cfg, settings, task):
+    """One delete-d jackknife subsample of stage 1 (task numbered from 1)."""
+    n_tasks = int(settings["jackknife_subsamples"])
+    if not 1 <= task <= n_tasks:
+        raise ValueError(f"jackknife_task must be 1-{n_tasks}, got {task}")
+    src = os.path.join(out_dir, "stage1", "tfs_growth.csv")
+    if not os.path.exists(src):
+        raise RuntimeError("the jackknife needs stage 1's genotype table; run "
+                           "--step stage1 first")
+    d = os.path.join(out_dir, "jackknife", f"jk_{task:02d}")
+    if os.path.exists(os.path.join(d, "km.csv")):
+        _say(f"jackknife {task}: done, skipped")
+        return
+    _say(f"jackknife {task}")
+    with _in_dir(d):
+        if not os.path.exists("tfs_growth.csv"):
+            sub, n, nd, dropped = ts.jackknife_subsample(
+                pd.read_csv(src), float(settings["jackknife_drop_frac"]), task)
+            sub.to_csv("tfs_growth.csv", index=False)
+            with open("subsample.json", "w") as fh:
+                json.dump({"n": n, "d": nd, "dropped": dropped}, fh, indent=1)
+        _configure(_stage1_kwargs(cfg, settings), "tfs_growth.csv")
+        _fit(settings)
+        _km()
+
+
+def summarize_jackknife(out_dir, settings):
+    """k and m SEs and k's main direction from every subsample."""
+    d = os.path.join(out_dir, "jackknife")
+    if os.path.exists(os.path.join(d, "k_step.csv")):
+        return
+    n_tasks = int(settings["jackknife_subsamples"])
+    dirs = [os.path.join(d, f"jk_{i:02d}") for i in range(1, n_tasks + 1)]
+    missing = [x for x in dirs if not os.path.exists(os.path.join(x, "km.csv"))]
+    if missing:
+        raise RuntimeError(f"{len(missing)} of {n_tasks} jackknife subsamples "
+                           f"are not done, e.g. {missing[:3]}")
+    nd = set()
+    est = []
+    for x in dirs:
+        with open(os.path.join(x, "subsample.json")) as fh:
+            info = json.load(fh)
+        nd.add((info["n"], info["d"]))
+        est.append(pd.read_csv(os.path.join(x, "km.csv"), index_col="condition_rep"))
+    if len(nd) != 1:
+        raise RuntimeError(f"subsamples differ in tubes kept: {sorted(nd)}")
+    n, dd = nd.pop()
+    out = ts.jackknife_km(est, n, dd)
+    out["se"].to_csv(os.path.join(d, "jackknife_se.csv"))
+    out["k_cov"].to_csv(os.path.join(d, "k_cov.csv"))
+    out["k_step"].rename_axis("condition_rep").to_csv(os.path.join(d, "k_step.csv"))
+    with open(os.path.join(d, "jackknife.json"), "w") as fh:
+        json.dump({"n": n, "d": dd, "factor": out["factor"],
+                   "k_share": out["k_share"], "subsamples": n_tasks}, fh, indent=1)
+    print(f"jackknife: {n_tasks} subsamples, {dd} of {n} tubes dropped; k's "
+          f"main direction carries {out['k_share']:.3f} of its variance",
+          flush=True)
+
+
+def run_jackknife(out_dir, cfg, settings):
+    for task in range(1, int(settings["jackknife_subsamples"]) + 1):
+        run_jackknife_task(out_dir, cfg, settings, task)
+    summarize_jackknife(out_dir, settings)
+
+
+def run_stage2(out_dir, cfg, settings):
+    """The full library with every condition's k and m pinned at stage 1."""
+    d = os.path.join(out_dir, "stage2")
+    if os.path.exists(os.path.join(d, PRED)):
+        _say("stage2: done, skipped")
+        return
+    km_path = os.path.join(out_dir, "stage1", "km.csv")
+    if not os.path.exists(km_path):
+        raise RuntimeError("stage 2 needs stage 1's k and m; run --step stage1")
+    _say("stage2")
+    km = pd.read_csv(km_path, index_col="condition_rep")
+    with _in_dir(d):
+        ts.pinned_growth_priors(km).to_csv("growth_priors_pinned.csv", index=False)
+        _configure(cfg, cfg["growth_df"], "growth_priors_pinned.csv",
+                   drop_wt_rates=True)
+        _fit(settings)
+        _posterior(settings)
+        if not os.path.exists("tfs_params_growth_k.csv"):
+            extract_params(CONFIG, POSTERIOR, out_prefix="tfs_params")
+        predict_theta(CONFIG, POSTERIOR, out_prefix="tfs_pred_theta")
+
+
+def run_kshift(out_dir, cfg, settings):
+    """
+    Two stage 2 refits at k plus and minus one jackknife SD; the shift.
+
+    Each refit starts from stage 2's MAP at two_stage.kshift_step_size,
+    with the dk_geno population held at stage 2's MAP: at the default
+    step size (1e-3) and with it free, the refits of
+    planning/studies/two-stage-validation/ run 1 left the MAP and slid
+    every genotype's dk_geno against wt, so the shift measured where the
+    optimizer stopped. With two_stage.kshift_control a third refit keeps
+    k at stage 1's values; kshift/drift.csv says how far each refit moved
+    X from stage 2's MAP, and a warning is printed when the control moved
+    more than a quarter of the shift.
+    """
+    d = os.path.join(out_dir, "kshift")
+    if os.path.exists(os.path.join(d, "shift.csv")):
+        _say("kshift: done, skipped")
+        return
+    summarize_jackknife(out_dir, settings)
+    s2 = os.path.abspath(os.path.join(out_dir, "stage2"))
+    init = os.path.join(s2, PARAMS)
+    if not os.path.exists(init):
+        raise RuntimeError("the k-shift refits start from stage 2's MAP; run "
+                           "--step stage2")
+    _say("kshift")
+    with _in_dir(s2):
+        if not os.path.exists(PRED_MAP):
+            predict_theta(CONFIG, CHECKPOINT, out_prefix=PRED_MAP[:-4])
+        ref = pd.read_csv(PRED_MAP)
+    km = pd.read_csv(os.path.join(out_dir, "stage1", "km.csv"),
+                     index_col="condition_rep")
+    step = pd.read_csv(os.path.join(out_dir, "jackknife", "k_step.csv"),
+                       index_col="condition_rep")["k_step"]
+    plus, minus = ts.k_shift_priors(km, step)
+    refits = [("plus", plus), ("minus", minus)]
+    if settings["kshift_control"]:
+        refits.append(("zero", ts.pinned_growth_priors(km)))
+    held = ts.dk_geno_hypers(init)
+    step_size = settings["kshift_step_size"]
+    final = min(float(settings["fit"].get("adam_final_step_size", 1e-6)),
+                step_size)
+    preds = {}
+    for name, table in refits:
+        with _in_dir(os.path.join(d, name)):
+            table.to_csv("growth_priors_pinned.csv", index=False)
+            _configure(cfg, cfg["growth_df"], "growth_priors_pinned.csv",
+                       drop_wt_rates=True)
+            ts.pin_dk_geno_hypers("tfs_configure_priors.csv", held)
+            _fit(settings, init_from=init, adam_step_size=step_size,
+                 adam_final_step_size=final)
+            if not os.path.exists(PRED):
+                predict_theta(CONFIG, CHECKPOINT, out_prefix="tfs_pred_theta")
+            preds[name] = pd.read_csv(PRED)
+    shift = ts.shift_table(preds["plus"], preds["minus"])
+    drift = ts.refit_drift(ref, preds)
+    drift.to_csv(os.path.join(d, "drift.csv"), index=False)
+    shift.to_csv(os.path.join(d, "shift.csv"), index=False)
+    nw = shift["genotype"] != "wt"
+    size = float(shift.loc[nw, "shift"].abs().median())
+    print(f"kshift: median |shift| {size:.4f}", flush=True)
+    for _, r in drift.iterrows():
+        print(f"kshift: {r['refit']} moved X by {r['median_change']:+.4f} "
+              f"(q10 {r['q10_change']:+.4f}, q90 {r['q90_change']:+.4f})",
+              flush=True)
+    if settings["kshift_control"]:
+        zero = abs(float(drift.set_index("refit").loc["zero", "median_change"]))
+        if zero > 0.25 * size:
+            print(f"kshift: warning: the control refit (k unchanged) moved X "
+                  f"by {zero:.4f}, more than a quarter of the shift; the "
+                  "shift measures the optimizer, not k", flush=True)
+
+
+def run_predict(out_dir, cfg, settings):
+    """Stage 2's X (and epistasis), with the shared k shift if two_stage.kshift."""
+    d = os.path.abspath(os.path.join(out_dir, "predict"))
+    os.makedirs(d, exist_ok=True)
+    shift = None
+    if settings["kshift"]:
+        shift = os.path.abspath(os.path.join(out_dir, "kshift", "shift.csv"))
+        if not os.path.exists(shift):
+            raise RuntimeError("prediction with two_stage.kshift needs "
+                               "kshift/shift.csv; run --step kshift")
+    epi_done = (not settings["predict_epistasis"]
+                or os.path.exists(os.path.join(d, "tfs_pred_epistasis.csv")))
+    if os.path.exists(os.path.join(d, PRED)) and epi_done:
+        _say("predict: done, skipped")
+        return
+    _say("predict")
+    with _in_dir(os.path.join(out_dir, "stage2")):
+        if not os.path.exists(os.path.join(d, PRED)):
+            predict_theta(CONFIG, POSTERIOR, out_prefix=os.path.join(d, "tfs_pred_theta"),
+                          shared_shift=shift, shift_seed=int(settings["seed"]))
+        epi = os.path.join(d, "tfs_pred_epistasis.csv")
+        if settings["predict_epistasis"] and not os.path.exists(epi):
+            predict_epistasis(CONFIG, POSTERIOR,
+                              out_prefix=os.path.join(d, "tfs_pred_epistasis"),
+                              scale="add", shared_shift=shift,
+                              shift_seed=int(settings["seed"]))
+
+
+def _library_conditions(table, library):
+    rows = table[table["library"].astype(str) == str(library)]
+    return sorted(set(rows["condition_pre"].astype(str))
+                  | set(rows["condition_sel"].astype(str)))
+
+
+def run_consistency(out_dir, cfg, settings, name="consistency"):
+    """Stage 2 on one library's tubes alone, against the full stage 2.
+
+    Writes out_dir/name/ (another name keeps a second library's run apart).
+    """
+    d = os.path.join(out_dir, name)
+    if os.path.exists(os.path.join(d, "consistency.csv")):
+        _say(f"{name}: done, skipped")
+        return
+    both = os.path.join(out_dir, "stage2", PRED)
+    if not os.path.exists(both):
+        raise RuntimeError("the consistency check needs stage 2; run --step stage2")
+    s1 = pd.read_csv(os.path.join(out_dir, "stage1", "tfs_growth.csv"),
+                     usecols=["library", "condition_pre", "condition_sel"])
+    libraries = sorted(s1["library"].astype(str).unique())
+    lib = settings["consistency_library"] or libraries[0]
+    if lib not in libraries:
+        raise ValueError(f"consistency_library {lib!r} is not one of {libraries}")
+    if len(libraries) < 2:
+        raise ValueError("the consistency check needs two libraries")
+    _say(f"consistency ({lib} alone)")
+    km = pd.read_csv(os.path.join(out_dir, "stage1", "km.csv"),
+                     index_col="condition_rep")
+    conds = [c for c in _library_conditions(s1, lib) if c in km.index]
+    with _in_dir(d):
+        if not os.path.exists("tfs_growth.csv"):
+            ts.filter_growth_csv(cfg["growth_df"], "tfs_growth.csv", library=lib)
+        ts.pinned_growth_priors(km, conditions=conds).to_csv(
+            "growth_priors_pinned.csv", index=False)
+        _configure(cfg, "tfs_growth.csv", "growth_priors_pinned.csv",
+                   drop_wt_rates=True)
+        _fit(settings)
+        _posterior(settings)
+        if not os.path.exists(PRED):
+            predict_theta(CONFIG, POSTERIOR, out_prefix="tfs_pred_theta")
+        out = ts.marker_consistency(pd.read_csv(os.path.abspath(
+            os.path.join("..", "stage2", PRED))), pd.read_csv(PRED))
+        out.to_csv("consistency.csv")
+    print(f"consistency: {int(out['inconsistent'].sum())} of {len(out)} "
+          f"genotypes beyond |z| 2 ({lib} alone against both libraries)",
+          flush=True)
+
+
+def run_summary(out_dir, cfg, settings):
+    """
+    The users' table, out_dir/tfs_x.csv: X with the shift, read depth and
+    the consistency flag per genotype and concentration.
+
+    Rewritten every time (it is cheap once the reads are counted, which
+    are kept in out_dir/genotype_reads.csv), so it picks up a consistency
+    step run after it.
+    """
+    pred_path = os.path.join(out_dir, "predict", PRED)
+    if not os.path.exists(pred_path):
+        raise RuntimeError("the summary needs predict/tfs_pred_theta.csv; "
+                           "run --step predict")
+    _say("summary")
+    reads_path = os.path.join(out_dir, READS)
+    if not os.path.exists(reads_path):
+        ts.genotype_reads(cfg["growth_df"]).to_csv(reads_path)
+    reads = pd.read_csv(reads_path, index_col="genotype")
+    cons_path = os.path.join(out_dir, "consistency", "consistency.csv")
+    cons = (pd.read_csv(cons_path, index_col="genotype")
+            if os.path.exists(cons_path) else None)
+    shift_path = os.path.join(out_dir, "kshift", "shift.csv")
+    shift = (pd.read_csv(shift_path)
+             if settings["kshift"] and os.path.exists(shift_path) else None)
+    out = ts.x_summary(pd.read_csv(pred_path), reads, cons, shift)
+    out.to_csv(os.path.join(out_dir, SUMMARY), index=False)
+    n = out["genotype"].nunique()
+    flagged = (int(cons["inconsistent"].sum()) if cons is not None
+               else "no consistency check")
+    print(f"summary: {n} genotypes in {SUMMARY}; inconsistent: {flagged}",
+          flush=True)
+
+
+# The jackknife only feeds the k shift, which validation run 2 found too
+# small on simulations (planning/studies/two-stage-validation/), so both
+# are off by default.
+KSHIFT_STEPS = ("jackknife", "kshift")
+
+RUNNERS = {"stage1": run_stage1, "jackknife": run_jackknife,
+           "stage2": run_stage2, "kshift": run_kshift,
+           "predict": run_predict, "consistency": run_consistency,
+           "summary": run_summary}
+
+
+# ---------------------------------------------------------------------------
+# The command
+# ---------------------------------------------------------------------------
+
+def fit_two_stage(config_file,
+                  out_dir="two_stage",
+                  step="all",
+                  jackknife_task=None,
+                  consistency=True):
+    """
+    Fit a growth-only relative model in two stages.
+
+    The full-library MAP makes the growth slope m too steep (each double
+    mutant pulls it a little and the pulls add), and congression flattens a
+    marker's m in any stage 1 large enough to pin X's scale
+    (planning/studies/m-bias-factorial/). This command runs, in out_dir:
+    stage1 (wt, spikes and singles with the congression mixture, max rule,
+    lambda fitted with two_stage.transformation_lambda as its prior: every
+    condition's k and m); stage2 (the full library with every condition's
+    k and m pinned at stage 1; MAP, Laplace, X); predict (stage 2's X, and
+    with two_stage.predict_epistasis its epistasis, predict/); consistency
+    (stage 2 on one library's tubes alone against the full stage 2, per
+    genotype z-scores, consistency/consistency.csv); summary
+    (out_dir/tfs_x.csv: per genotype and concentration, X's quantiles, the
+    genotype's reads in total and per library, its number of mutations,
+    in_training_data and the consistency z and flag; the table to read
+    results from).
+
+    X's intervals leave out k's uncertainty, shared by every genotype; on
+    simulations the deep genotypes' 95% intervals covered 0.88-0.90 up to
+    1e5 reads and 0.76 above (planning/studies/two-stage-validation/).
+    two_stage.kshift (experimental, off by default) adds it: jackknife
+    (two_stage.jackknife_subsamples refits of stage 1, each dropping
+    two_stage.jackknife_drop_frac of the tubes, stratified by condition and
+    IPTG: k's SE and main direction), then kshift (two stage 2 refits at k
+    plus and minus one jackknife SD, from stage 2's MAP at
+    two_stage.kshift_step_size with the dk_geno population held there:
+    kshift/shift.csv, added to predict's draws as one shared multiple; with
+    two_stage.kshift_control a k-unchanged control refit and
+    kshift/drift.csv). Validation run 2 found that shift a third or less of
+    the true one on simulations.
+
+    Every step but summary skips work whose outputs exist, so a run resumes
+    and a step reruns by deleting its directory; summary is rewritten each
+    time. --step runs one step, the k-shift steps included whatever
+    two_stage.kshift says.
+
+    Parameters
+    ----------
+    config_file : str
+        YAML whose top-level keys are tfs-configure-model's arguments
+        (growth_df and library_config required; theta_model hill_relative,
+        growth_shares_replicates true; file paths relative to the YAML),
+        plus a two_stage block: transformation_lambda (mean, std; required),
+        seed (1), num_posterior_samples (500), consistency (true),
+        consistency_library (default the first library alphabetically),
+        predict_epistasis (false), kshift (false; experimental, see above),
+        jackknife_subsamples (30), jackknife_drop_frac (0.2),
+        kshift_step_size (1e-5, the k-shift refits' starting step size),
+        kshift_control (true, the k-unchanged control refit), fit (extra
+        tfs-fit-model arguments) and posterior (extra tfs-sample-posterior
+        arguments).
+    out_dir : str, optional
+        Directory for every step's outputs. Default 'two_stage'.
+    step : str, optional
+        'all' (default) or one of stage1, jackknife, stage2, kshift,
+        predict, consistency, summary.
+    jackknife_task : int or None, optional
+        Run only this jackknife subsample (1 to jackknife_subsamples), for
+        a cluster array; needs stage 1 done. The kshift step collects them.
+    consistency : bool, optional
+        Run the consistency step with 'all' (default True; --no_consistency
+        skips it, as does consistency: false in the YAML).
+    """
+    cfg, settings = read_two_stage_config(config_file)
+    if not consistency:
+        settings["consistency"] = False
+    if step != "all" and step not in STEPS:
+        raise ValueError(f"step must be 'all' or one of {STEPS}, got {step!r}")
+    out_dir = os.path.abspath(out_dir)
+    os.makedirs(out_dir, exist_ok=True)
+    resolved = os.path.join(out_dir, "two_stage_config.yaml")
+    with open(resolved, "w") as fh:
+        yaml.safe_dump({**cfg, "two_stage": settings}, fh, sort_keys=False)
+    with open(os.path.join(out_dir, "two_stage_provenance.json"), "w") as fh:
+        json.dump(get_provenance(), fh, indent=1, default=str)
+
+    if jackknife_task is not None:
+        if step not in ("all", "jackknife"):
+            raise ValueError("jackknife_task runs one jackknife subsample; "
+                             "use it with --step jackknife (or alone)")
+        run_jackknife_task(out_dir, cfg, settings, int(jackknife_task))
+        return
+
+    todo = STEPS if step == "all" else (step,)
+    for name in todo:
+        if name == "consistency" and step == "all" and not settings["consistency"]:
+            _say("consistency: off")
+            continue
+        if name in KSHIFT_STEPS and step == "all" and not settings["kshift"]:
+            _say(f"{name}: off (two_stage.kshift is false)")
+            continue
+        RUNNERS[name](out_dir, cfg, settings)
+    _say("done")
+
+
+def main():
+    generalized_main(fit_two_stage,
+                     manual_arg_types={"jackknife_task": int})
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/tfscreen/tfmodel/scripts/predict_epistasis_cli.py b/src/tfscreen/tfmodel/scripts/predict_epistasis_cli.py
index 9c514d3b..05bae0c0 100644
--- a/src/tfscreen/tfmodel/scripts/predict_epistasis_cli.py
+++ b/src/tfscreen/tfmodel/scripts/predict_epistasis_cli.py
@@ -15,7 +15,9 @@ def predict_epistasis(config_file,
                       scale="logit",
                       scale_constant=1.0,
                       regime_eps=0.01,
-                      regime_ci=0.95):
+                      regime_ci=0.95,
+                      shared_shift=None,
+                      shift_seed=0):
     """
     Predict second-order epistasis from the joint posterior of a fitted model.
 
@@ -56,7 +58,7 @@ def predict_epistasis(config_file,
     param_file : str
         Path to a posterior .h5 file produced by tfs-sample-posterior, or a MAP
         checkpoint .pkl file produced by tfs-fit-model.  A .pkl provides a
-        single point estimate, so every ep_<quantile> column collapses to that
+        single point estimate, so every q<level> column collapses to that
         value (no uncertainty); run tfs-sample-posterior first to obtain a
         Laplace posterior and real quantiles.
     out_prefix : str, optional
@@ -84,6 +86,8 @@ def predict_epistasis(config_file,
         Epistatic scale. 'logit' (default) computes additive epistasis of
         logit(theta) -- the natural scale for an occupancy in [0, 1]. 'add':
         (Y11 - Y10) - (Y01 - Y00). 'mult': (Y11 / Y10) / (Y01 / Y00).
+        A relative fit (theta 'hill_relative') gives X, not theta: it takes
+        only 'add', and its output has no 'in_regime' column.
     scale_constant : float, optional
         Constant applied to the transform before the difference-of-differences;
         multiplies the reported epistasis. Default 1.0. Mainly a unit
@@ -100,6 +104,16 @@ def predict_epistasis(config_file,
         Central posterior-mass fraction required inside the band for a corner to
         count as in-regime. Default 0.95 (checks the theta 2.5-97.5% interval).
         Must be in (0, 1).
+    shared_shift : str or None, optional
+        CSV of a shift shared by every non-wt genotype (genotype,
+        titrant_name, titrant_conc, shift), written by the two-stage fit: the
+        X change from one standard deviation of stage 1's k. Each joint draw
+        gets one standard-normal multiple of it before epistasis is
+        computed. The cycle's wt corner does not move, so an epistasis on X
+        (X_ab - X_a - X_b + X_wt) keeps one net copy of the shift. Needs a
+        posterior file with draws. Default None.
+    shift_seed : int, optional
+        Seed for the shared per-draw multiples. Default 0.
     """
     if (titrant_names_file is None) != (titrant_concs_file is None):
         raise ValueError(
@@ -179,6 +193,8 @@ def predict_epistasis(config_file,
         scale_constant=scale_constant,
         regime_eps=regime_eps,
         regime_ci=regime_ci,
+        shared_shift=shared_shift,
+        shift_seed=shift_seed,
     )
 
     # Restrict cycles to those whose double mutant is a requested genotype.
@@ -198,7 +214,8 @@ def main():
                                        "titrant_names_file": str,
                                        "titrant_concs_file": str,
                                        "only_files": bool,
-                                       "scale": str})
+                                       "scale": str,
+                                       "shared_shift": str})
 
 
 if __name__ == "__main__":
diff --git a/src/tfscreen/tfmodel/scripts/predict_growth_cli.py b/src/tfscreen/tfmodel/scripts/predict_growth_cli.py
index 556abf2a..828d36a9 100644
--- a/src/tfscreen/tfmodel/scripts/predict_growth_cli.py
+++ b/src/tfscreen/tfmodel/scripts/predict_growth_cli.py
@@ -3,6 +3,7 @@
 from tfscreen.tfmodel.configuration_io import read_configuration
 from tfscreen.tfmodel.inference.checkpoint_io import resolve_param_file
 from tfscreen.tfmodel.inference.posteriors import load_posteriors
+from tfscreen.tfmodel.inference.model_params import sample_keys
 from tfscreen.tfmodel.analysis.prediction import predict
 from tfscreen.tfmodel.analysis.batch_sizing import estimate_genotype_batch_size
 from tfscreen.util.cli import generalized_main, read_lines
@@ -73,7 +74,7 @@ def predict_growth(config_file,
                    num_marginal_samples=None,
                    genotype_batch_size=None,
                    subset_genotypes=False,
-                   subset_seed=None):
+                   seed=None):
     """
     Predict growth signal (ln_cfu) from a fitted hierarchical model.
 
@@ -99,14 +100,14 @@ def predict_growth(config_file,
         Path to a posterior .h5 file produced by tfs-sample-posterior, or a
         MAP checkpoint .pkl file produced by tfs-fit-model.
 
-        When a .pkl file is supplied the output contains a single ``point_est``
-        column with no uncertainty information.
+        When a .pkl file is supplied the output has a single ``q0.5`` column
+        (the MAP point) and no uncertainty.
 
         To obtain uncertainty estimates from a MAP fit, first run
-        tfs-sample-posterior on the .pkl checkpoint; it will construct a
-        Laplace (Hessian-based) posterior approximation and write a .h5 file.
-        Passing that .h5 here produces the full quantile columns (median,
-        lower_95, upper_95, etc.).
+        tfs-sample-posterior on the .pkl checkpoint; it builds a Laplace
+        approximation and writes a .h5 file. Passing that .h5 here gives the
+        full ladder of bare ``q<level>`` quantile columns (``q0.025``,
+        ``q0.5``, ``q0.975``, ...).
 
         NUTS and SVI checkpoints are not supported directly; run
         tfs-sample-posterior first.
@@ -130,8 +131,8 @@ def predict_growth(config_file,
         file arguments, ignoring training-data combinations. Default False.
     num_samples : int or None, optional
         Number of joint posterior samples to include as sample_0 … sample_N-1
-        columns alongside the quantile columns. Set to None for quantiles only.
-        Default 0.
+        columns alongside the quantile columns. 0 (default) writes quantiles
+        only.
     num_marginal_samples : int or None, optional
         Number of posterior samples to run through the model when computing
         quantiles. If None, all available samples are used.
@@ -150,7 +151,7 @@ def predict_growth(config_file,
         with the remainder of the block filled by a random sample of the other
         genotypes. Intended for quickly assessing the input/output ln_cfu
         correlation without paying for a full prediction sweep. Default False.
-    subset_seed : int or None, optional
+    seed : int or None, optional
         Seed for the random draw used by subset_genotypes, making the sampled
         block reproducible. Ignored unless subset_genotypes is True. Default
         None (non-deterministic draw).
@@ -187,7 +188,7 @@ def predict_growth(config_file,
 
     if genotype_batch_size is None:
         _, resolved_posteriors = load_posteriors(param_file, q_to_get=None)
-        first_key = next(iter(resolved_posteriors.keys()))
+        first_key = sample_keys(resolved_posteriors)[0]
         total_available = resolved_posteriors[first_key].shape[0]
         n_for_quantiles = (total_available if num_marginal_samples is None
                           else min(num_marginal_samples, total_available))
@@ -200,11 +201,12 @@ def predict_growth(config_file,
               f"genotypes based on available memory.", flush=True)
 
     # Binding genotypes (those with direct theta_obs measurements) must appear
-    # in every batch so the binding TensorManager is never empty.
-    try:
-        binding_genos = [str(g) for g in orchestrator.binding_df["genotype"].unique()]
-    except Exception:
+    # in every batch so the binding TensorManager is never empty. A
+    # growth-only model has none.
+    if orchestrator.binding_df is None:
         binding_genos = []
+    else:
+        binding_genos = [str(g) for g in orchestrator.binding_df["genotype"].unique()]
     binding_set = set(binding_genos)
 
     # Subset mode: predict a single memory-fit block of genotypes rather than
@@ -228,7 +230,7 @@ def predict_growth(config_file,
 
         block = genotype_batch_size
         n_random = max(0, block - len(keep))
-        rng = np.random.default_rng(subset_seed)
+        rng = np.random.default_rng(seed)
         if n_random < len(remaining):
             idx = rng.choice(len(remaining), size=n_random, replace=False)
             sampled = [remaining[i] for i in sorted(idx)]
@@ -309,7 +311,7 @@ def main():
                                        "titrant_concs_file": str,
                                        "num_marginal_samples": int,
                                        "genotype_batch_size": int,
-                                       "subset_seed": int,
+                                       "seed": int,
                                        "only_files": bool,
                                        "subset_genotypes": bool})
 
diff --git a/src/tfscreen/tfmodel/scripts/predict_theta_cli.py b/src/tfscreen/tfmodel/scripts/predict_theta_cli.py
index 5e7b04a2..411c7ba1 100644
--- a/src/tfscreen/tfmodel/scripts/predict_theta_cli.py
+++ b/src/tfscreen/tfmodel/scripts/predict_theta_cli.py
@@ -1,3 +1,6 @@
+import warnings
+
+import numpy as np
 import pandas as pd
 from tfscreen.tfmodel.configuration_io import read_configuration
 from tfscreen.tfmodel.inference.checkpoint_io import resolve_param_file
@@ -9,6 +12,38 @@
 from tfscreen.util.cli import generalized_main, read_lines
 
 
+_NON_VALUE_COLUMNS = ("titrant_conc", "in_training_data")
+
+
+def warn_nonfinite_theta(result_df, max_listed=10):
+    """
+    Warn if any predicted theta value is NaN or infinite.
+
+    A non-finite theta in any posterior draw makes every quantile of that
+    row NaN. Until 2026-10-11 the thermodynamic theta overflowed on extreme
+    Laplace draws and whole genotypes were written as NaN without a word.
+
+    Returns the number of rows with a non-finite value.
+    """
+    value_cols = [c for c in result_df.columns
+                  if c not in _NON_VALUE_COLUMNS
+                  and pd.api.types.is_numeric_dtype(result_df[c])]
+    if not value_cols:
+        return 0
+    bad = ~np.isfinite(result_df[value_cols].to_numpy(dtype=float)).all(axis=1)
+    n_bad = int(bad.sum())
+    if n_bad:
+        genos = pd.unique(result_df.loc[bad, "genotype"]).tolist()
+        listed = ", ".join(str(g) for g in genos[:max_listed])
+        more = (f" and {len(genos) - max_listed} more"
+                if len(genos) > max_listed else "")
+        warnings.warn(f"{n_bad} of {len(result_df)} predicted theta rows "
+                      f"({len(genos)} genotype(s): {listed}{more}) have NaN "
+                      f"or infinite values; they are written as they are.",
+                      RuntimeWarning, stacklevel=2)
+    return n_bad
+
+
 def predict_theta(config_file,
                   param_file,
                   out_prefix="tfs_pred_theta",
@@ -17,7 +52,9 @@ def predict_theta(config_file,
                   titrant_concs_file=None,
                   only_files=False,
                   num_samples=0,
-                  genotype_batch_size=2000):
+                  genotype_batch_size=2000,
+                  shared_shift=None,
+                  shift_seed=0):
     """
     Predict operator occupancy (theta) from a fitted hierarchical model.
 
@@ -39,6 +76,10 @@ def predict_theta(config_file,
     (genotype, titrant_name, titrant_conc) triple was in the training data,
     0 otherwise.
 
+    A relative theta component (``hill_relative``) predicts the wt-relative
+    growth variable X, not theta; its output gets a column ``theta_scale``
+    with the value ``X`` so nothing downstream takes it for an occupancy.
+
     Parameters
     ----------
     config_file : str
@@ -47,14 +88,14 @@ def predict_theta(config_file,
         Path to a posterior .h5 file produced by tfs-sample-posterior, or a
         MAP checkpoint .pkl file produced by tfs-fit-model.
 
-        When a .pkl file is supplied the output contains a single ``point_est``
-        column with no uncertainty information.
+        When a .pkl file is supplied the output has a single ``q0.5`` column
+        (the MAP point) and no uncertainty.
 
         To obtain uncertainty estimates from a MAP fit, first run
-        tfs-sample-posterior on the .pkl checkpoint; it will construct a
-        Laplace (Hessian-based) posterior approximation and write a .h5 file.
-        Passing that .h5 here produces the full quantile columns (median,
-        lower_95, upper_95, etc.).
+        tfs-sample-posterior on the .pkl checkpoint; it builds a Laplace
+        approximation and writes a .h5 file. Passing that .h5 here gives the
+        full ladder of bare ``q<level>`` quantile columns (``q0.025``,
+        ``q0.5``, ``q0.975``, ...).
 
         NUTS and SVI checkpoints are not supported directly; run
         tfs-sample-posterior first.
@@ -82,13 +123,25 @@ def predict_theta(config_file,
         Default False.
     num_samples : int or None, optional
         Number of joint posterior samples to include as sample_0 … sample_N-1
-        columns alongside the quantile columns. Set to None for quantiles only.
-        Default 0.
+        columns alongside the quantile columns. 0 (default) writes quantiles
+        only.
     genotype_batch_size : int, optional
         When predicting unmeasured genotypes, process this many at a time to
         cap the memory used by the epistasis pair-indicator matrix
         (batch_size × N_pair × 4 bytes).  Smaller values reduce peak memory
         at the cost of more iterations.  Default 2000.
+    shared_shift : str or None, optional
+        CSV of a shift shared by every non-wt genotype (columns genotype,
+        titrant_name, titrant_conc, shift), written by the two-stage fit:
+        the X change from one standard deviation of stage 1's k. Each
+        posterior draw gets one standard-normal multiple of it, the same
+        for every row, before quantiles are taken, so each genotype's
+        interval carries k's uncertainty while differences between
+        genotypes keep only what does not cancel. wt (fixed by the X gauge)
+        does not move. Needs a posterior file with draws (not a MAP
+        checkpoint) and training genotypes only. Default None.
+    shift_seed : int, optional
+        Seed for the shared per-draw multiples. Default 0.
     """
     if (titrant_names_file is None) != (titrant_concs_file is None):
         raise ValueError(
@@ -156,6 +209,12 @@ def predict_theta(config_file,
     # Decide extraction path.
     out_of_training = [g for g in requested_genotypes if g not in training_genotypes]
 
+    if out_of_training and shared_shift is not None:
+        raise ValueError(
+            "shared_shift applies to training genotypes only; "
+            f"{len(out_of_training)} requested genotype(s) were not in the "
+            "training data.")
+
     if out_of_training:
         module = model_registry.get("theta", {}).get(orchestrator._theta)
         if module is None or not hasattr(module, "predict_unmeasured"):
@@ -193,6 +252,8 @@ def predict_theta(config_file,
             manual_titrant_df=manual_titrant_df,
             num_samples=num_samples,
             q_to_get=q_to_get,
+            shared_shift=shared_shift,
+            shift_seed=shift_seed,
         )
         # extract_theta_curves returns all training genotypes when manual_titrant_df
         # has no 'genotype' column; filter to the requested subset.
@@ -205,6 +266,12 @@ def predict_theta(config_file,
         axis=1,
     )
 
+    theta_scale = getattr(model_registry["theta"].get(orchestrator._theta),
+                          "THETA_SCALE", "theta")
+    if theta_scale != "theta":
+        result_df["theta_scale"] = theta_scale
+
+    warn_nonfinite_theta(result_df)
     out_file = f"{out_prefix}.csv"
     result_df.to_csv(out_file, index=False)
     print(f"Wrote {len(result_df)} rows to {out_file}", flush=True)
@@ -217,7 +284,8 @@ def main():
                                        "titrant_concs_file": str,
                                        "only_files": bool,
                                        "num_samples": int,
-                                       "genotype_batch_size": int})
+                                       "genotype_batch_size": int,
+                                       "shared_shift": str})
 
 
 if __name__ == "__main__":
diff --git a/src/tfscreen/tfmodel/scripts/prefit_calibration_cli.py b/src/tfscreen/tfmodel/scripts/prefit_calibration_cli.py
index 046d43e4..0e51c8c2 100644
--- a/src/tfscreen/tfmodel/scripts/prefit_calibration_cli.py
+++ b/src/tfscreen/tfmodel/scripts/prefit_calibration_cli.py
@@ -63,13 +63,16 @@
 import numpy as np
 import pandas as pd
 import jax.numpy as jnp
-import optax
 
 import yaml
 
 from numpyro.handlers import seed, trace
 
 import tfscreen
+from tfscreen.tfmodel.priors_edit import (
+    apply_priors_updates,
+    condition_rep_labels,
+)
 from tfscreen.util.cli.generalized_main import generalized_main
 from tfscreen.tfmodel.inference.run_inference import RunInference
 from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
@@ -341,7 +344,7 @@ def _build_calibration_model(orchestrator_prod, growth_df_cal, binding_df_cal):
         settings[k] = v
     # transformation is forced to "single" above, which carries no lambda
     # parameter; drop any production transformation_lambda (set when the production
-    # transformation is "empirical"/"logit_norm") so it doesn't get passed
+    # transformation is "mixture") so it doesn't get passed
     # through to a "single" calibration model.
     settings["transformation_lambda"] = None
     settings["spiked_genotypes"] = None
@@ -349,9 +352,10 @@ def _build_calibration_model(orchestrator_prod, growth_df_cal, binding_df_cal):
     # it has to be dropped alongside spiked_genotypes.
     settings["library_file"] = None
     # Equal weighting: the calibration MAP must learn the binding→growth
-    # linkage from both data sources together.  The production binding_weight
-    # (N_growth_prod / N_binding_prod, often >> 1) would drown the binding
-    # signal, so we reset to 1.0 here.
+    # linkage from both data sources together.  A production config may
+    # store a large binding_weight (N_growth_prod / N_binding_prod, the
+    # default before 2026-10-16), which would drown the growth signal, so
+    # we reset to 1.0 here.
     settings["binding_weight"] = 1.0
 
     # dk_geno: the calibration MAP must not *learn* dk_geno from
@@ -890,111 +894,9 @@ def _resolve_scale_bounds(condition_growth_choice,
     return bounds
 
 
-def _condition_rep_labels(orchestrator):
-    """
-    Per-condition label frame (``condition_rep`` and, when present,
-    ``replicate``) ordered by ``map_condition_rep`` — i.e. the same order as
-    the per-condition MAP arrays.  Returns ``None`` when no condition_rep map
-    is available.  Used to tag per-condition prior rows so the loader can
-    name-join them back to the production condition order.
-    """
-    growth_tm = getattr(orchestrator, "growth_tm", None)
-    if growth_tm is None:
-        return None
-    crm = growth_tm.map_groups.get("condition_rep")
-    if crm is None or getattr(crm, "empty", True):
-        return None
-    sorted_map = crm.sort_values("map_condition_rep").reset_index(drop=True)
-    cols = [c for c in ("replicate", "condition_rep") if c in sorted_map.columns]
-    if not cols:
-        return None
-    return sorted_map[cols].reset_index(drop=True)
-
-
-def _apply_priors_updates(priors_path, prior_updates, cond_rep_labels=None):
-    """
-    Apply prior updates to the production priors CSV.  Writes a ``.bak`` copy
-    first.  Rows whose ``parameter`` is not in ``prior_updates`` are preserved.
-
-    Two kinds of update value are supported:
-
-    * **scalar** (float) — overwrites the ``value`` of the matching
-      ``parameter`` row in place (unchanged behaviour).  A warning is logged
-      if no row matches.
-    * **array** (1-D ``np.ndarray``) — a per-condition prior (e.g.
-      ``condition_growth.k_loc``).  Any existing rows for that parameter are
-      dropped and replaced with one indexed row per condition, tagged with the
-      ``condition_rep`` (and ``replicate``) labels from ``cond_rep_labels`` so
-      the loader can name-join them back to the model's condition order.
-
-    Parameters
-    ----------
-    priors_path : str
-        Path to the production priors CSV.
-    prior_updates : dict[str, float | np.ndarray]
-        Update values keyed by dotted parameter name.
-    cond_rep_labels : pandas.DataFrame or None
-        Per-condition label columns (``condition_rep`` and, when present,
-        ``replicate``) ordered by ``map_condition_rep`` — i.e. matching the
-        order of the per-condition MAP arrays.  Required to label array
-        updates; when absent, array rows carry only ``flat_index``.
-    """
-    if not prior_updates:
-        return
-    df = pd.read_csv(priors_path)
-    if "parameter" not in df.columns or "value" not in df.columns:
-        raise ValueError(
-            f"Priors CSV {priors_path} is missing required 'parameter' / "
-            "'value' columns."
-        )
-
-    scalar_updates = {}
-    array_updates = {}
-    for row_name, new_val in prior_updates.items():
-        arr = np.asarray(new_val)
-        if arr.ndim == 0:
-            scalar_updates[row_name] = float(arr)
-        else:
-            array_updates[row_name] = arr
-
-    matched = set()
-
-    # Scalar overwrites (in place).
-    for row_name, new_val in scalar_updates.items():
-        mask = df["parameter"] == row_name
-        if mask.any():
-            df.loc[mask, "value"] = new_val
-            matched.add(row_name)
-
-    missing = sorted(set(scalar_updates) - matched)
-    if missing:
-        print(
-            f"  warning: {len(missing)} prior update(s) had no matching row "
-            f"in {priors_path}: {missing}",
-            file=sys.stderr,
-        )
-
-    # Array updates: drop existing rows for the parameter, append indexed rows.
-    if array_updates:
-        new_frames = []
-        for row_name, arr in array_updates.items():
-            flat_val = np.asarray(arr).flatten()
-            df = df[df["parameter"] != row_name]
-            row_df = pd.DataFrame({"parameter": row_name,
-                                   "value": flat_val,
-                                   "flat_index": range(len(flat_val))})
-            if (cond_rep_labels is not None
-                    and len(cond_rep_labels) == len(flat_val)):
-                for col in ("replicate", "condition_rep"):
-                    if col in cond_rep_labels.columns:
-                        row_df[col] = cond_rep_labels[col].to_numpy()
-            new_frames.append(row_df)
-            matched.add(row_name)
-        df = pd.concat([df] + new_frames, ignore_index=True)
-
-    shutil.copy2(priors_path, priors_path + ".bak")
-    df.to_csv(priors_path, index=False)
-    print(f"Updated {len(matched)} priors row(s) in {priors_path}")
+# Shared with tfs-configure-model (--growth_priors, --set_priors).
+_condition_rep_labels = condition_rep_labels
+_apply_priors_updates = apply_priors_updates
 
 
 def _apply_guesses_updates(guesses_path, guess_updates):
@@ -1094,47 +996,45 @@ def _resolve_csv_paths(config_file):
 # ---------------------------------------------------------------------------
 
 def _run_calibration_map(ri,
-                         init_params,
+                         init_values,
                          out_prefix,
                          checkpoint_file,
                          adam_step_size,
                          adam_final_step_size,
+                         adam_step_size_cut,
                          adam_clip_norm,
                          elbo_num_particles,
-                         convergence_tolerance,
-                         convergence_window,
+                         convergence_window_steps,
                          patience,
-                         convergence_check_interval,
+                         convergence_z,
+                         loss_rtol,
+                         param_tolerance,
                          checkpoint_interval,
                          max_num_epochs,
-                         init_param_jitter,
                          epoch_checkpoint_interval):
     """Set up a MAP SVI optimizer and run it; return ``(svi_state, params,
     converged)``.  Behaviour mirrors ``fit_model._run_map`` but
     the ``always_get_posterior`` plumbing is dropped (the pre-fit only
     consumes MAP point estimates and Hessian-derived sigmas)."""
-    schedule = optax.exponential_decay(
-        init_value=adam_step_size,
-        transition_steps=float(max_num_epochs * ri._iterations_per_epoch),
-        decay_rate=adam_final_step_size / adam_step_size,
-    )
-    map_obj = ri.setup_svi(adam_step_size=schedule,
+    map_obj = ri.setup_svi(adam_step_size=adam_step_size,
                            adam_clip_norm=adam_clip_norm,
                            elbo_num_particles=elbo_num_particles,
-                           guide_type="delta")
+                           guide_type="delta",
+                           init_values=init_values)
 
     svi_state, params, converged = ri.run_optimization(
         map_obj,
-        init_params=init_params,
         out_prefix=out_prefix,
         svi_state=checkpoint_file,
-        convergence_tolerance=convergence_tolerance,
-        convergence_window=convergence_window,
+        convergence_window_steps=convergence_window_steps,
         patience=patience,
-        convergence_check_interval=convergence_check_interval,
+        convergence_z=convergence_z,
+        loss_rtol=loss_rtol,
+        param_tolerance=param_tolerance,
+        final_step_size=adam_final_step_size,
+        step_size_cut=adam_step_size_cut,
         checkpoint_interval=checkpoint_interval,
         max_num_epochs=max_num_epochs,
-        init_param_jitter=init_param_jitter,
         epoch_checkpoint_interval=epoch_checkpoint_interval
     )
 
@@ -1159,15 +1059,16 @@ def run_prefit_calibration(config_file,
                            out_prefix="tfs_prefit",
                            adam_step_size=1e-3,
                            adam_final_step_size=1e-6,
-                           adam_clip_norm=1.0,
+                           adam_step_size_cut=0.1,
+                           adam_clip_norm=None,
                            elbo_num_particles=2,
-                           convergence_tolerance=1e-5,
-                           convergence_window=10,
-                           patience=10,
-                           convergence_check_interval=2,
+                           convergence_window_steps=2000,
+                           patience=3,
+                           convergence_z=3.0,
+                           loss_rtol=1e-6,
+                           param_tolerance=0.05,
                            checkpoint_interval=10,
                            max_num_epochs=100000,
-                           init_param_jitter=0.0,
                            epoch_checkpoint_interval=0,
                            k_scale_floor=_DEFAULT_K_SCALE_FLOOR,
                            m_scale_floor=_DEFAULT_M_SCALE_FLOOR,
@@ -1215,28 +1116,38 @@ def run_prefit_calibration(config_file,
     adam_step_size : float, optional
         Starting step size for the Adam optimizer (default 1e-3).
     adam_final_step_size : float, optional
-        Final step size for the Adam optimizer (default 1e-6).
-    adam_clip_norm : float, optional
-        Gradient clipping norm for the Adam optimizer (default 1.0).
+        Smallest step size for the Adam optimizer (default 1e-6).  The step
+        size is cut by ``adam_step_size_cut`` each time the fit plateaus, and
+        the run converges at the first plateau at this size.
+    adam_step_size_cut : float, optional
+        Factor applied to the step size at each cut (default 0.1).
+    adam_clip_norm : float or None, optional
+        Clip each gradient element to +/- this value (numpyro ClippedAdam).
+        None (default) disables clipping: when gradients are much larger
+        than the clip, an elementwise clip makes Adam follow the gradient's
+        sign, and its fixed point is biased by rare large ELBO penalties.
     elbo_num_particles : int, optional
         Number of particles for ELBO estimation (default 2).
-    convergence_tolerance : float, optional
-        Relative change in loss to declare MAP convergence (default 1e-5).
-    convergence_window : int, optional
-        Number of epochs to average when checking convergence (default 10).
+    convergence_window_steps : int, optional
+        Optimizer steps per convergence window (default 2000; at least 10
+        epochs).
     patience : int, optional
-        Number of consecutive convergence checks that must pass before
-        declaring convergence (default 10).
-    convergence_check_interval : int, optional
-        Frequency (in epochs) at which convergence is checked (default 2).
+        Consecutive windows without loss improvement required for a step-size
+        cut, and (at the final step size) without loss improvement or
+        parameter movement required for convergence (default 3).
+    convergence_z : float, optional
+        Standard errors of change attributed to noise (default 3).
+    loss_rtol : float, optional
+        Loss changes per window below this fraction of the loss count as no
+        change (default 1e-6).
+    param_tolerance : float, optional
+        Parameter movement per window, beyond noise, still counted as no
+        change (default 0.05), in prior SDs (or log/logit units for positive
+        or bounded parameters).
     checkpoint_interval : int, optional
         Frequency (in epochs) between checkpoint writes (default 10).
     max_num_epochs : int, optional
-        Maximum number of MAP optimization epochs (default 100000).
-    init_param_jitter : float, optional
-        Jitter added to initial parameters to break symmetry (default 0.0).
-        The pre-fit benefits from determinism given a seed, so this is 0
-        by default (unlike tfs-fit-model which defaults to 0.1).
+        Maximum number of MAP optimization epochs (default 100000); a cap only.
     epoch_checkpoint_interval : int or None, optional
         Frequency (in epochs) to write numbered epoch checkpoints to a
         ``checkpoints/`` subdirectory (default 0). Set to 0 or None to
@@ -1285,6 +1196,17 @@ def run_prefit_calibration(config_file,
     if seed is None and checkpoint_file is None:
         raise ValueError("seed must be provided unless loading from a checkpoint.")
 
+    # The calibration fits the growth-theta link on genotypes that have both
+    # growth and binding data, so a growth-only model has nothing to fit.
+    with open(config_file, "r") as fh:
+        _data_paths = (yaml.safe_load(fh) or {}).get("data", {})
+    if not _data_paths.get("binding"):
+        raise ValueError(
+            f"{config_file} is a growth-only model (no binding data). "
+            f"tfs-prefit-calibration calibrates the growth-theta link on the "
+            f"genotypes that have binding measurements, so it needs binding "
+            f"data. Fit a growth-only model with tfs-fit-model directly.")
+
     # 1. Resolve production config and CSV targets.
     priors_path, guesses_path = _resolve_csv_paths(config_file)
     orchestrator_prod, _ = read_configuration(config_file)
@@ -1308,22 +1230,28 @@ def run_prefit_calibration(config_file,
     effective_seed = seed if seed is not None else 0
     ri = RunInference(orchestrator_cal, effective_seed)
 
+    # Start from the configured guesses (resuming: from the checkpoint).
+    init_values = None
+    if checkpoint_file is None:
+        init_values = ri.site_values(orchestrator_cal.init_params)
+
     svi_state, params, converged = _run_calibration_map(
         ri,
-        init_params=orchestrator_cal.init_params,
+        init_values=init_values,
         out_prefix=out_prefix,
         checkpoint_file=checkpoint_file,
         adam_step_size=adam_step_size,
         adam_final_step_size=adam_final_step_size,
+        adam_step_size_cut=adam_step_size_cut,
         adam_clip_norm=adam_clip_norm,
         elbo_num_particles=elbo_num_particles,
-        convergence_tolerance=convergence_tolerance,
-        convergence_window=convergence_window,
+        convergence_window_steps=convergence_window_steps,
         patience=patience,
-        convergence_check_interval=convergence_check_interval,
+        convergence_z=convergence_z,
+        loss_rtol=loss_rtol,
+        param_tolerance=param_tolerance,
         checkpoint_interval=checkpoint_interval,
         max_num_epochs=max_num_epochs,
-        init_param_jitter=init_param_jitter,
         epoch_checkpoint_interval=epoch_checkpoint_interval
     )
 
@@ -1393,9 +1321,9 @@ def main():
     return generalized_main(
         run_prefit_calibration,
         manual_arg_types={"config_file": str,
+                          "adam_clip_norm": float,
                           "seed": int,
                           "checkpoint_file": str,
-                          "init_param_jitter": float,
                           "k_scale_floor": float,
                           "m_scale_floor": float,
                           "k_scale_ceiling": float,
diff --git a/src/tfscreen/tfmodel/scripts/sample_posterior_cli.py b/src/tfscreen/tfmodel/scripts/sample_posterior_cli.py
index 11ef71bf..4ae8ce81 100644
--- a/src/tfscreen/tfmodel/scripts/sample_posterior_cli.py
+++ b/src/tfscreen/tfmodel/scripts/sample_posterior_cli.py
@@ -1,9 +1,58 @@
 import os
+
 import dill
+import numpy as np
 from tfscreen.tfmodel.configuration_io import read_configuration
 from tfscreen.tfmodel.inference.run_inference import RunInference
 from tfscreen.util.cli.generalized_main import generalized_main
 
+# Per-observation growth sites, dropped by skip_growth_observations.
+GROWTH_OBSERVATION_SITES = ("growth_pred", "growth_obs")
+
+LAPLACE_CHOICES = ("auto", "full", "arrowhead", "blocks", "point")
+
+# Above this many MAP parameters, --laplace auto uses the arrowhead Laplace.
+# The full Laplace's dense Hessian is O(D^2) memory (20,000 parameters is
+# 1.6 GB in float32 and 3.2 GB in float64) and O(D^3) to factor; the
+# arrowhead gives the same Gaussian when the genotypes do not couple.
+DEFAULT_LAPLACE_MAX_PARAMS = 20000
+
+
+def resolve_laplace(laplace, num_params, laplace_max_params):
+    """
+    Pick the Laplace variant for a MAP checkpoint.
+
+    Parameters
+    ----------
+    laplace : str
+        One of ``LAPLACE_CHOICES``.
+    num_params : int
+        Number of unconstrained MAP parameters.
+    laplace_max_params : int
+        Largest model for which ``auto`` uses the full Laplace.
+
+    Returns
+    -------
+    str
+        ``full``, ``arrowhead``, ``blocks`` or ``point``.
+    """
+    if laplace not in LAPLACE_CHOICES:
+        raise ValueError(f"laplace must be one of {LAPLACE_CHOICES}; got "
+                         f"{laplace!r}.")
+    if laplace != "auto":
+        return laplace
+    return "full" if num_params <= laplace_max_params else "arrowhead"
+
+
+class _AllSitesExcept:
+    """A ``sites_to_save`` that holds every site name except ``skip``."""
+
+    def __init__(self, skip):
+        self.skip = frozenset(skip)
+
+    def __contains__(self, site):
+        return site not in self.skip
+
 
 def sample_posterior(config_file,
                      checkpoint_file,
@@ -12,7 +61,12 @@ def sample_posterior(config_file,
                      num_posterior_samples=10000,
                      sampling_batch_size=100,
                      forward_batch_size=512,
-                     hessian_chunk_size=64):
+                     hessian_chunk_size=64,
+                     laplace="auto",
+                     laplace_max_params=DEFAULT_LAPLACE_MAX_PARAMS,
+                     skip_growth_observations=False,
+                     genotype_chunk_size=None,
+                     allow_flat_laplace_floor=False):
     """
     Draw posterior samples from an existing MAP, SVI, or NUTS checkpoint.
 
@@ -55,7 +109,48 @@ def sample_posterior(config_file,
     hessian_chunk_size : int, optional
         Number of Hessian rows computed per device batch for MAP checkpoints
         (default 64). Reduce if the Hessian computation hits device OOM.
+    laplace : str, optional
+        How a MAP checkpoint becomes a posterior (ignored for SVI and NUTS):
+
+        - ``auto`` (default): ``full`` up to ``laplace_max_params``
+          parameters, ``arrowhead`` above.
+        - ``full``: the Laplace from the dense Hessian, O(D^2) memory. Out of
+          reach on a full library (millions of parameters).
+        - ``arrowhead``: the full Laplace computed per genotype. The Hessian
+          is an arrowhead (per-genotype blocks, their coupling to the shared
+          parameters, the shared block); the shared parameters (growth k and
+          m, hyperparameters, tube offsets) are drawn from their marginal and
+          each genotype from its conditional. Runs on a full library. A
+          negative direction of the shared block's Schur complement is held
+          at the MAP; those directions are written to
+          ``{out_prefix}_held_directions.csv``, since they have no interval.
+        - ``blocks``: per-genotype blocks only, the shared parameters held at
+          the MAP. Leaves out the k/m uncertainty.
+        - ``point``: the MAP point itself, one sample, no Hessian.
+    laplace_max_params : int, optional
+        Largest number of MAP parameters for which ``auto`` picks the full
+        Laplace (default 20000).
+    skip_growth_observations : bool, optional
+        Leave the per-observation growth sites (``growth_pred``,
+        ``growth_obs``) out of the file (default False). They are most of its
+        size (94% on a 300-genotype subset; about 100 GB per site at 500
+        samples on a 200,000-genotype library), and ``tfs-predict-growth``
+        recomputes growth from the parameter samples rather than reading
+        them.
+    genotype_chunk_size : int or None, optional
+        Genotypes per Hessian-vector-product pass for the arrowhead and
+        blocks Laplace (default None, all at once). Lower it on device OOM.
+    allow_flat_laplace_floor : bool, optional
+        If the prior SDs that floor the Laplace's Hessian eigenvalues cannot
+        be computed, warn and floor every eigenvalue at a flat 1e-3 instead
+        of stopping with an error (default False). The flat floor turns a
+        negative eigenvalue into a direction with SD ~30 in unconstrained
+        units, so use it only to get a posterior out of a model whose prior
+        SDs fail for a reason you understand.
     """
+    if laplace not in LAPLACE_CHOICES:
+        raise ValueError(f"laplace must be one of {LAPLACE_CHOICES}; got "
+                         f"{laplace!r}.")
     if not os.path.isfile(checkpoint_file):
         raise FileNotFoundError(
             f"Checkpoint file not found: '{checkpoint_file}'. "
@@ -71,20 +166,44 @@ def sample_posterior(config_file,
     # RunInference methods write {out_prefix}_posterior.h5; rename to {out_prefix}.h5
     # after each call so the output matches the documented convention.
     ri_prefix = f"{out_prefix}_tmp_posterior"
+    sites_to_save = (_AllSitesExcept(GROWTH_OBSERVATION_SITES)
+                     if skip_growth_observations else None)
 
     if "mcmc_samples" in chk_data:
         # NUTS checkpoint: regenerate posteriors from saved samples.
         print("Detected NUTS checkpoint. Writing posterior predictives...", flush=True)
         ri.get_nuts_posteriors(chk_data["mcmc_samples"],
                                out_prefix=ri_prefix,
-                               forward_batch_size=forward_batch_size)
+                               forward_batch_size=forward_batch_size,
+                               sites_to_save=sites_to_save)
     else:
-        temp_svi = ri.setup_svi(guide_type="delta")
-        chk_params = temp_svi.optim.get_params(chk_data["svi_state"].optim_state)
+        # Checkpoints record the guide that wrote them.  Older ones do not;
+        # there the only autoguide was AutoDelta (MAP), recognizable by its
+        # "{site}_auto_loc" parameter names.
+        guide_type = chk_data.get("guide_type")
+        if guide_type is None or guide_type == "delta":
+            temp_svi = ri.setup_svi(guide_type="delta")
+            chk_params = temp_svi.optim.get_params(chk_data["svi_state"].optim_state)
+            is_map = (guide_type == "delta"
+                      or any("_auto_loc" in k for k in chk_params))
+        else:
+            is_map = False
+
+        if is_map:
+            num_params = sum(int(np.size(v)) for k, v in chk_params.items()
+                             if k.endswith("_auto_loc"))
+            method = resolve_laplace(laplace, num_params, laplace_max_params)
+            print(f"Detected MAP checkpoint ({num_params} parameters); "
+                  f"Laplace: {method}"
+                  + (f" (auto, threshold {laplace_max_params})"
+                     if laplace == "auto" else ""), flush=True)
 
-        if any("_auto_loc" in k for k in chk_params):
-            # MAP checkpoint: Hessian-based Laplace approximation.
-            print("Detected MAP checkpoint. Drawing Laplace posterior samples...", flush=True)
+        if is_map and method == "point":
+            ri.get_map_posteriors(map_params=chk_params,
+                                  out_prefix=ri_prefix,
+                                  forward_batch_size=forward_batch_size,
+                                  sites_to_save=sites_to_save)
+        elif is_map:
             ri.get_laplace_posteriors(
                 map_params=chk_params,
                 out_prefix=ri_prefix,
@@ -92,7 +211,18 @@ def sample_posterior(config_file,
                 sampling_batch_size=sampling_batch_size,
                 forward_batch_size=forward_batch_size,
                 hessian_chunk_size=hessian_chunk_size,
+                sites_to_save=sites_to_save,
+                block_genotypes=method in ("arrowhead", "blocks"),
+                genotype_chunk_size=genotype_chunk_size,
+                block_shared=method == "arrowhead",
+                allow_flat_floor=allow_flat_laplace_floor,
             )
+            held = getattr(ri, "held_shared_directions", None)
+            if method == "arrowhead" and held is not None:
+                held_file = f"{out_prefix}_held_directions.csv"
+                held.to_csv(held_file, index=False)
+                print(f"Held shared directions written to {held_file}",
+                      flush=True)
         else:
             # SVI checkpoint: rebuild the guide object then restore the saved
             # variational state directly — no optimization loop needed.
@@ -105,7 +235,8 @@ def sample_posterior(config_file,
                               out_prefix=ri_prefix,
                               num_posterior_samples=num_posterior_samples,
                               sampling_batch_size=sampling_batch_size,
-                              forward_batch_size=forward_batch_size)
+                              forward_batch_size=forward_batch_size,
+                              sites_to_save=sites_to_save)
 
     src = f"{ri_prefix}_posterior.h5"
     dst = f"{out_prefix}.h5"
@@ -114,12 +245,14 @@ def sample_posterior(config_file,
 
 
 def main():
-    generalized_main(sample_posterior,
-                     manual_arg_types={"seed": int,
-                                       "num_posterior_samples": int,
-                                       "sampling_batch_size": int,
-                                       "forward_batch_size": int,
-                                       "hessian_chunk_size": int})
+    return generalized_main(sample_posterior,
+                            manual_arg_types={"seed": int,
+                                              "num_posterior_samples": int,
+                                              "sampling_batch_size": int,
+                                              "forward_batch_size": int,
+                                              "hessian_chunk_size": int,
+                                              "laplace_max_params": int,
+                                              "genotype_chunk_size": int})
 
 
 if __name__ == "__main__":
diff --git a/src/tfscreen/tfmodel/scripts/sample_prior_cli.py b/src/tfscreen/tfmodel/scripts/sample_prior_cli.py
index 3572ee4d..04f53aba 100644
--- a/src/tfscreen/tfmodel/scripts/sample_prior_cli.py
+++ b/src/tfscreen/tfmodel/scripts/sample_prior_cli.py
@@ -34,9 +34,9 @@ def sample_prior(config_file,
 
     ``<out_prefix>_NNN_growth.csv``
         Synthetic growth DataFrame with ``ln_cfu`` replaced by prior
-        predictions.  When ``--noise`` is set (the default), observation
-        noise drawn from ``Normal(0, ln_cfu_std)`` is added.  This file
-        can be passed directly to ``tfs-fit-model`` as training data.
+        predictions.  Observation noise drawn from ``Normal(0, ln_cfu_std)``
+        is added unless ``--no_noise`` is given.  Pass it to
+        ``tfs-configure-model --growth_df`` to fit it.
 
     ``<out_prefix>_NNN_ground_truth.h5``
         The latent parameters used to generate that dataset, in the same
diff --git a/src/tfscreen/tfmodel/scripts/setup_grid_cli.py b/src/tfscreen/tfmodel/scripts/setup_grid_cli.py
index 5778de61..9b0d0c86 100644
--- a/src/tfscreen/tfmodel/scripts/setup_grid_cli.py
+++ b/src/tfscreen/tfmodel/scripts/setup_grid_cli.py
@@ -43,9 +43,36 @@
       run_name: "{{ binding_df | basename }}__{{ condition_growth }}"
 - Incompatible component combinations are skipped automatically and logged in
   ``grid_summary.json``.
-- Relative paths in configure_model blocks (binding_df, growth_df,
-  thermo_data) are resolved relative to the grid YAML's directory, then
-  re-expressed relative to each subdirectory in the written config file.
+
+INPUT FILES
+-----------
+The grid directory is self-contained and can be moved as a unit (on and off a
+cluster, onto another partition). Every input file a run needs is copied into
+``<out_dir>/inputs/`` once, and each run's config and rendered template
+refer to it as ``../inputs/<name>``. Nothing in a run points outside the grid.
+
+- The configure_model file arguments are the keys in ``_PATH_KEYS``
+  (``binding_df``, ``growth_df``, ``presplit_df``, ``base_growth_df``,
+  ``thermo_data``, ``library_config``). A relative value resolves against the
+  grid YAML's directory. tfs-configure-model reads the original file; the
+  written ``tfs_configure_config.yaml`` then names the copy wherever it
+  recorded that file (``data.*``, ``components.thermo_data``,
+  ``components.presplit_df``/``base_growth_df``, ``library.source``).
+- The priors, guesses and library-composition CSVs are per-run outputs written
+  next to the config (``priors_file``/``guesses_file``/``library_file``, read
+  relative to the config) and are left alone.
+- Data paths in the config are read relative to the working directory, so
+  each run is launched from its own directory (as the templates do).
+- A ``template`` variable that names an existing file (relative to the grid
+  YAML, or absolute) is copied the same way.
+- Two different files with the same name are kept apart by a numeric suffix
+  (``growth_2.csv``); the same file used by many runs is copied once.
+- Setup fails rather than write a run that depends on a location outside the
+  grid. Before anything is written: a missing input file, an input that is a
+  directory, a configure_model value outside ``_PATH_KEYS`` that names an
+  existing file, or a template that does not render. After tfs-configure-model
+  runs: any other path in the written config that names an existing file
+  outside the grid (add its argument to ``_PATH_KEYS``).
 """
 
 import itertools
@@ -58,10 +85,12 @@
 
 from tfscreen.util.cli import generalized_main
 from tfscreen.util.grid_utils import (
+    InputStager as _InputStager,
+    check_no_outside_paths as _check_no_outside_paths,
     make_jinja_env as _make_jinja_env,
     make_run_name as _make_run_name,
-    relativize_config_paths as _relativize_config_paths,
-    relativize_template_vars as _relativize_template_vars,
+    render_run_template as _render_run_template,
+    stage_template_vars as _stage_template_vars,
 )
 from tfscreen.tfmodel.generative.registry import model_registry
 from tfscreen.tfmodel.scripts.configure_model_cli import (
@@ -82,17 +111,28 @@
     "theta_growth_noise",
     "theta_binding_noise",
     "growth_noise",
+    "sample_offset",
 })
 
-# configure_model arguments that are file paths and need abs→rel rewriting.
+# configure_model arguments that are input file paths. Each is copied into
+# <out_dir>/inputs/ and the written config names the copy. Add any new
+# file-valued configure_model argument here; setup fails on a configure_model
+# value outside this set that names an existing file.
 _PATH_KEYS = frozenset({
     "binding_df", "growth_df", "presplit_df", "base_growth_df", "thermo_data",
     "library_config",
 })
 
+# Top-level keys of the written config naming files configure_model writes
+# into the run directory (read relative to the config): per-run outputs, not
+# inputs to copy.
+_RUN_OUTPUT_KEYS = (("priors_file",), ("guesses_file",), ("library_file",))
+
 # Fixed output prefix used inside every per-combination run.
 _CONFIGURE_OUT_PREFIX = "tfs_configure"
 
+_TOOL = "tfs-setup-grid"
+
 
 # ---------------------------------------------------------------------------
 # Block expansion
@@ -137,6 +177,60 @@ def _resolve_cm_paths(cm_vars, base_dir):
     return out
 
 
+def _input_paths(resolved_cm):
+    """Return ``{key: path}`` for the file arguments present in resolved_cm."""
+    return {k: resolved_cm[k] for k in sorted(_PATH_KEYS)
+            if isinstance(resolved_cm.get(k), str) and resolved_cm[k]}
+
+
+def _check_cm_vars(cm_vars, grid_yaml_dir, stager):
+    """Validate one combination's configure_model variables (writes nothing).
+
+    Fails if a value outside ``_PATH_KEYS`` names an existing file, or if a
+    ``_PATH_KEYS`` value is missing or a directory.
+    """
+    _check_no_outside_paths(
+        cm_vars, [(k,) for k in _PATH_KEYS], grid_yaml_dir,
+        hint=(f"If tfs-configure-model reads this file, add the argument to "
+              f"_PATH_KEYS in {__name__}; otherwise change the value."),
+    )
+    for key, path in _input_paths(_resolve_cm_paths(cm_vars, grid_yaml_dir)).items():
+        stager.stage(path, f"configure_model variable '{key}'")
+
+
+def _stage_written_config(cfg, path_map, subdir):
+    """Point a written config at the staged input copies (returns a new dict).
+
+    Every string value equal to an input path in ``path_map`` (original
+    absolute path → ``../inputs/<name>``) is replaced by its copy, wherever
+    tfs-configure-model recorded it. Any other value that names an existing
+    file outside the run directory is an error: the run would not be movable.
+    """
+    staged_keys = []
+
+    def rewrite(node, key_path):
+        if isinstance(node, dict):
+            return {k: rewrite(v, key_path + (k,)) for k, v in node.items()}
+        if isinstance(node, list):
+            return [rewrite(v, key_path + (i,)) for i, v in enumerate(node)]
+        if isinstance(node, str) and node in path_map:
+            staged_keys.append(key_path)
+            return path_map[node]
+        return node
+
+    out = rewrite(cfg, ())
+    # The provenance block records where configure ran (cwd, command line);
+    # it is a record, never read back as an input.
+    checked = {k: v for k, v in out.items() if k != "provenance"}
+    _check_no_outside_paths(
+        checked, staged_keys + list(_RUN_OUTPUT_KEYS), subdir,
+        hint=(f"tfs-configure-model recorded a path {_TOOL} does not know how "
+              f"to copy. If it is an input file, add the configure_model "
+              f"argument that supplies it to _PATH_KEYS in {__name__}."),
+    )
+    return out
+
+
 # ---------------------------------------------------------------------------
 # configure_model kwargs preparation
 # ---------------------------------------------------------------------------
@@ -161,14 +255,14 @@ def _cm_kwargs(cm_vars):
 # Public entry point
 # ---------------------------------------------------------------------------
 
-def setup_grid(grid_yaml, out_prefix="grid"):
+def setup_grid(grid_yaml, out_dir="grid"):
     """
     Set up a directory grid for model runs.
 
     Reads a grid YAML file, expands all configure_model and template blocks
     into their Cartesian product, and for each combination:
 
-    1. Creates a subdirectory under *out_prefix*.
+    1. Creates a subdirectory under *out_dir*.
     2. Calls tfs-configure-model (via its Python API) to generate
        ``tfs_configure_config.yaml``, ``tfs_configure_priors.csv``, and
        ``tfs_configure_guesses.csv`` inside that subdirectory.  Incompatible
@@ -177,14 +271,18 @@ def setup_grid(grid_yaml, out_prefix="grid"):
        variables and writes the result to the subdirectory.
     4. Writes ``combo.json`` recording the variable assignments for that run.
 
-    A ``grid_summary.json`` is written to *out_prefix* listing all created
-    runs and any skipped combinations.
+    A ``grid_summary.json`` is written to *out_dir* listing all created
+    runs and any skipped combinations. Input files named by the
+    configure_model or template variables are copied into
+    ``<out_dir>/inputs/`` and referenced as ``../inputs/<name>``, so the
+    *out_dir* directory can be moved as a unit. Every combination's inputs
+    and template are validated before anything is written.
 
     Parameters
     ----------
     grid_yaml : str
         Path to the grid YAML file.
-    out_prefix : str, optional
+    out_dir : str, optional
         Root directory for the grid.  Created if it does not exist.
         Default ``"grid"``.
 
@@ -243,17 +341,33 @@ def setup_grid(grid_yaml, out_prefix="grid"):
         except jinja2.TemplateSyntaxError as e:
             raise ValueError(f"Template syntax error in {output_file}: {e}") from e
 
-    os.makedirs(out_prefix, exist_ok=True)
+    # Validate every combination's input files and render its template before
+    # writing anything, so a bad input cannot leave a half-built grid.
+    # (Component incompatibilities only surface when configure_model runs; those
+    # combinations are skipped below, as before.)
+    runs_to_write = []
+    checker = _InputStager(out_dir, _TOOL, dry_run=True)
+    for i, (cm_vars, tmpl_vars) in enumerate(all_combos, start=1):
+        all_vars = {**cm_vars, **tmpl_vars}
+        run_name = _make_run_name(run_name_template, all_vars, i)
+        _check_cm_vars(cm_vars, grid_yaml_dir, checker)
+        if jinja_template is not None:
+            _render_run_template(jinja_template, _stage_template_vars(
+                tmpl_vars, grid_yaml_dir, checker), run_name)
+        runs_to_write.append((run_name, cm_vars, tmpl_vars))
+
+    os.makedirs(out_dir, exist_ok=True)
+    stager = _InputStager(out_dir, _TOOL)
 
     all_runs = []
     skipped = []
 
-    for i, (cm_vars, tmpl_vars) in enumerate(all_combos, start=1):
+    for run_name, cm_vars, tmpl_vars in runs_to_write:
         all_vars = {**cm_vars, **tmpl_vars}
-        run_name = _make_run_name(run_name_template, all_vars, i)
-        subdir = os.path.abspath(os.path.join(out_prefix, run_name))
+        subdir = os.path.abspath(os.path.join(out_dir, run_name))
 
-        # Resolve relative paths in cm_vars to absolute before calling configure_model.
+        # configure_model reads the original input files (relative paths
+        # resolved against the grid YAML's directory).
         resolved_cm = _resolve_cm_paths(cm_vars, grid_yaml_dir)
         cm_kw = _cm_kwargs(resolved_cm)
         cm_out_prefix = os.path.join(subdir, _CONFIGURE_OUT_PREFIX)
@@ -262,7 +376,6 @@ def setup_grid(grid_yaml, out_prefix="grid"):
 
         try:
             configure_model(out_prefix=cm_out_prefix, **cm_kw)
-            _relativize_config_paths(f"{cm_out_prefix}_config.yaml", subdir)
         except Exception as exc:
             reason = str(exc)
             skipped.append({"run": run_name, "reason": reason, "combo": all_vars})
@@ -270,15 +383,27 @@ def setup_grid(grid_yaml, out_prefix="grid"):
             print(f"  SKIP {run_name}: {reason}", flush=True)
             continue
 
+        # Copy the inputs into <out_dir>/inputs/ and point the written
+        # config at the copies (../inputs/<name>).
+        path_map = {
+            path: stager.stage(path, f"configure_model variable '{key}'")
+            for key, path in _input_paths(resolved_cm).items()
+        }
+        cfg_path = f"{cm_out_prefix}_config.yaml"
+        with open(cfg_path) as fh:
+            cfg = yaml.safe_load(fh)
+        try:
+            cfg = _stage_written_config(cfg, path_map, subdir)
+        except ValueError:
+            shutil.rmtree(subdir, ignore_errors=True)
+            raise
+        with open(cfg_path, "w") as fh:
+            yaml.dump(cfg, fh, default_flow_style=False, sort_keys=False)
+
         # Render Jinja2 template with template-section variables only.
         if jinja_template is not None:
-            rendered_tmpl_vars = _relativize_template_vars(tmpl_vars, grid_yaml_dir, subdir)
-            try:
-                rendered = jinja_template.render(**rendered_tmpl_vars)
-            except jinja2.UndefinedError as exc:
-                raise ValueError(
-                    f"Undefined template variable for run '{run_name}': {exc}"
-                ) from exc
+            rendered = _render_run_template(jinja_template, _stage_template_vars(
+                tmpl_vars, grid_yaml_dir, stager), run_name)
             out_filename = os.path.basename(output_file)
             with open(os.path.join(subdir, out_filename), "w") as fh:
                 fh.write(rendered)
@@ -295,11 +420,11 @@ def setup_grid(grid_yaml, out_prefix="grid"):
     # Write grid_summary.json.
     summary = {
         "grid_yaml": grid_yaml,
-        "out_prefix": os.path.abspath(out_prefix),
+        "out_dir": os.path.abspath(out_dir),
         "runs": all_runs,
         "skipped": skipped,
     }
-    summary_path = os.path.join(out_prefix, "grid_summary.json")
+    summary_path = os.path.join(out_dir, "grid_summary.json")
     with open(summary_path, "w") as fh:
         json.dump(summary, fh, indent=2)
         fh.write("\n")
@@ -307,7 +432,7 @@ def setup_grid(grid_yaml, out_prefix="grid"):
     n_ok = len(all_runs)
     n_skip = len(skipped)
     print(
-        f"\n{n_ok} run{'s' if n_ok != 1 else ''} created under {out_prefix}/",
+        f"\n{n_ok} run{'s' if n_ok != 1 else ''} created under {out_dir}/",
         flush=True,
     )
     if n_skip:
@@ -323,7 +448,7 @@ def setup_grid(grid_yaml, out_prefix="grid"):
 def main():
     generalized_main(
         setup_grid,
-        manual_arg_types={"grid_yaml": str, "out_prefix": str},
+        manual_arg_types={"grid_yaml": str, "out_dir": str},
     )
 
 
diff --git a/src/tfscreen/tfmodel/scripts/subset_genotypes_cli.py b/src/tfscreen/tfmodel/scripts/subset_genotypes_cli.py
deleted file mode 100644
index 2da39f26..00000000
--- a/src/tfscreen/tfmodel/scripts/subset_genotypes_cli.py
+++ /dev/null
@@ -1,299 +0,0 @@
-import numpy as np
-import pandas as pd
-
-from tfscreen.genetics.expand_genotype_columns import expand_genotype_columns
-from tfscreen.util.cli import generalized_main, read_lines
-
-
-def _get_num_muts(df):
-    """Return a Series with the number of mutations per genotype row."""
-    if "num_muts" not in df.columns:
-        df = expand_genotype_columns(df)
-    return df["num_muts"]
-
-
-def _mutations_in_genotype(genotype):
-    """Return a frozenset of individual mutation strings for a genotype."""
-    if genotype == "wt":
-        return frozenset()
-    return frozenset(genotype.split("/"))
-
-
-def _reconcile_doubles_and_singles(double_genos, single_genos, blacklist):
-    """
-    Return (reconciled_doubles, reconciled_singles).
-
-    Keeps only doubles whose constituent singles are both present in
-    single_genos and neither is blacklisted.  The reconciled singles set is
-    the collection of all singles that participate in at least one kept double.
-    """
-    reconciled_doubles = []
-    reconciled_singles = set()
-    for g in sorted(double_genos):
-        muts = _mutations_in_genotype(g)
-        if muts <= single_genos and not (muts & blacklist):
-            reconciled_doubles.append(g)
-            reconciled_singles.update(muts)
-    return reconciled_doubles, reconciled_singles
-
-
-def subset_genotypes(
-    data_file,
-    n_singles,
-    n_steps,
-    out_prefix="tfs_subset",
-    whitelist_file=None,
-    blacklist_file=None,
-    random_seed=42,
-):
-    """
-    Subset a genotype dataset into titrated training/left-out pairs for
-    cross-validation of double-mutant predictive power.
-
-    Selects a fixed set of single-mutant genotypes, finds all double mutants
-    constructible from those singles that are present in the dataset, then
-    generates n_steps output pairs that progressively include more doubles in
-    the training set. The complement doubles (not in training) are written to a
-    left-out file for evaluating predictive performance.
-
-    Wildtype rows are always included in every training set.
-
-    Parameters
-    ----------
-    data_file : str
-        Path to the input CSV (must contain a 'genotype' column).
-    n_singles : int
-        Target number of single-mutant genotypes to include. Whitelist
-        genotypes count toward this total.
-    n_steps : int
-        Number of training/left-out pairs to generate (M). The doubles
-        included in training are spaced linearly from 0 to all possible,
-        with duplicate counts collapsed.
-    out_prefix : str, optional
-        Prefix for all output files. Default 'tfs_subset'.
-    whitelist_file : str, optional
-        Path to a plain-text file (one genotype per line, # comments allowed)
-        of genotypes that must be included in the selected singles.
-    blacklist_file : str, optional
-        Path to a plain-text file of genotypes that must never be included.
-    random_seed : int, optional
-        Random seed for reproducible sampling of singles and doubles ordering.
-        Default 42.
-    """
-    n_singles = int(n_singles)
-    n_steps = int(n_steps)
-    rng = np.random.default_rng(random_seed)
-
-    df = pd.read_csv(data_file)
-    if "genotype" not in df.columns:
-        raise ValueError(f"data_file '{data_file}' has no 'genotype' column")
-
-    # Classify genotypes by mutation count
-    num_muts = _get_num_muts(df)
-    all_genotypes = set(df["genotype"].unique())
-
-    wt_genos = set(df.loc[num_muts == 0, "genotype"].unique())
-    single_genos = set(df.loc[num_muts == 1, "genotype"].unique())
-    double_genos = set(df.loc[num_muts == 2, "genotype"].unique())
-
-    # Load whitelist / blacklist
-    whitelist = set(read_lines(whitelist_file)) if whitelist_file else set()
-    blacklist = set(read_lines(blacklist_file)) if blacklist_file else set()
-
-    # wt is always included in every training set; drop it from the whitelist
-    # silently so users can include it without causing a validation error.
-    whitelist_no_wt = whitelist - wt_genos
-
-    # Split whitelist into singles and doubles by mutation count.
-    whitelist_singles = set()
-    whitelist_doubles = set()
-    for g in whitelist_no_wt:
-        n = len(_mutations_in_genotype(g))
-        if n == 1:
-            whitelist_singles.add(g)
-        elif n == 2:
-            whitelist_doubles.add(g)
-        else:
-            raise ValueError(
-                f"Whitelist genotype '{g}' has {n} mutations; "
-                f"only singles and doubles are supported in the whitelist."
-            )
-
-    bad_white_singles = whitelist_singles - single_genos
-    if bad_white_singles:
-        raise ValueError(
-            f"Whitelist singles not present in growth data: {sorted(bad_white_singles)}"
-        )
-    bad_white_doubles = whitelist_doubles - double_genos
-    if bad_white_doubles:
-        raise ValueError(
-            f"Whitelist doubles not present in growth data: {sorted(bad_white_doubles)}"
-        )
-
-    overlap = (whitelist_singles | whitelist_doubles) & blacklist
-    if overlap:
-        raise ValueError(
-            f"Genotypes appear in both whitelist and blacklist: {sorted(overlap)}"
-        )
-
-    # Constituent singles required by whitelist doubles must exist and not be blacklisted.
-    whitelist_double_singles = set()
-    for d in whitelist_doubles:
-        whitelist_double_singles.update(_mutations_in_genotype(d))
-
-    blacklisted_double_singles = whitelist_double_singles & blacklist
-    if blacklisted_double_singles:
-        raise ValueError(
-            f"Whitelist doubles require singles that are blacklisted: "
-            f"{sorted(blacklisted_double_singles)}"
-        )
-    missing_double_singles = whitelist_double_singles - single_genos
-    if missing_double_singles:
-        raise ValueError(
-            f"Whitelist doubles require singles not present in growth data: "
-            f"{sorted(missing_double_singles)}"
-        )
-
-    # All singles that are forced in (directly whitelisted or required by a whitelisted double).
-    forced_singles = whitelist_singles | whitelist_double_singles
-
-    # Reconcile: keep only doubles whose constituent singles both exist in the
-    # data and are not blacklisted.
-    reconciled_doubles, _ = _reconcile_doubles_and_singles(
-        double_genos, single_genos, blacklist
-    )
-
-    print(
-        f"Reconciled pool: {len(reconciled_doubles)} doubles with complete cycles.",
-        flush=True,
-    )
-
-    # Effective target: n_singles, but never below the number of forced singles.
-    effective_target = max(n_singles, len(forced_singles))
-    if effective_target > n_singles:
-        print(
-            f"NOTE: whitelist forces {len(forced_singles)} singles "
-            f"({len(whitelist_singles)} direct + {len(whitelist_double_singles)} via "
-            f"{len(whitelist_doubles)} whitelisted doubles), which exceeds "
-            f"n_singles={n_singles}. Using {effective_target} as the target.",
-            flush=True,
-        )
-
-    # Phase 1 — seed: add forced singles and all reconciled doubles that
-    # involve any forced single (pulling in their partners too).
-    selected_singles = set(forced_singles)
-    for d in reconciled_doubles:
-        muts = _mutations_in_genotype(d)
-        if muts & forced_singles:
-            selected_singles.update(muts)
-
-    if forced_singles:
-        print(
-            f"Whitelist seeded {len(forced_singles)} singles → "
-            f"{len(selected_singles)} after expanding their cycles.",
-            flush=True,
-        )
-
-    # Phase 2 — expand: iterate shuffled cycles until effective_target reached.
-    shuffled_doubles = list(reconciled_doubles)
-    rng.shuffle(shuffled_doubles)
-
-    for d in shuffled_doubles:
-        if len(selected_singles) >= effective_target:
-            break
-        selected_singles.update(_mutations_in_genotype(d))
-
-    # Phase 3 — trim: if seeding/expansion overshot, remove non-forced
-    # singles one at a time (randomly) until effective_target is reached.
-    if len(selected_singles) > effective_target:
-        trimmable = list(selected_singles - forced_singles)
-        rng.shuffle(trimmable)
-        while len(selected_singles) > effective_target:
-            selected_singles.discard(trimmable.pop())
-
-    if len(selected_singles) < n_singles:
-        print(
-            f"WARNING: only {len(selected_singles)} singles available "
-            f"(requested {n_singles}).",
-            flush=True,
-        )
-
-    print(f"Selected {len(selected_singles)} single-mutant genotypes.", flush=True)
-
-    # Build double universe: reconciled doubles whose constituent singles are
-    # both in the selected set, excluding whitelist doubles (always in training).
-    double_universe = [
-        g for g in reconciled_doubles
-        if _mutations_in_genotype(g) <= selected_singles and g not in whitelist_doubles
-    ]
-
-    rng.shuffle(double_universe)
-    n_doubles_total = len(double_universe)
-
-    print(
-        f"Double universe: {n_doubles_total} doubles constructible from "
-        f"selected singles and present in the dataset.",
-        flush=True,
-    )
-
-    # Generate n_steps linearly-spaced counts, then deduplicate
-    raw_counts = np.linspace(0, n_doubles_total, n_steps)
-    step_counts = sorted(set(int(round(c)) for c in raw_counts))
-    actual_steps = len(step_counts)
-
-    if actual_steps < n_steps:
-        print(
-            f"NOTE: deduplicated to {actual_steps} unique steps "
-            f"(double universe smaller than n_steps).",
-            flush=True,
-        )
-
-    # Base training genotype set: wt + selected singles + whitelisted doubles (always included).
-    base_training = wt_genos | selected_singles | whitelist_doubles
-    n_sing = len(selected_singles)
-    pad = max(len(str(n_sing)), len(str(n_doubles_total)))
-
-    for i, k in enumerate(step_counts, start=1):
-        train_doubles = set(double_universe[:k])
-        leftout_doubles = [g for g in double_universe[k:]]
-
-        train_genos = base_training | train_doubles
-        train_df = df[df["genotype"].isin(train_genos)].copy()
-
-        n_doub = len(train_doubles)
-
-        stem = f"{out_prefix}_{n_sing:0{pad}d}-singles_{n_doub:0{pad}d}-doubles"
-        growth_out = f"{stem}_growth.csv"
-        leftout_out = f"{stem}_leftout.txt"
-
-        train_df.to_csv(growth_out, index=False)
-
-        with open(leftout_out, "w") as fh:
-            fh.write("# Double mutants constructible from selected singles not in training set\n")
-            for g in leftout_doubles:
-                fh.write(f"{g}\n")
-
-        print(
-            f"  Step {i:02d}/{actual_steps}: {n_doub} doubles in training, "
-            f"{len(leftout_doubles)} left out → {growth_out}",
-            flush=True,
-        )
-
-    print("Done.", flush=True)
-
-
-def main():
-    generalized_main(
-        subset_genotypes,
-        manual_arg_types={
-            "n_singles": int,
-            "n_steps": int,
-            "whitelist_file": str,
-            "blacklist_file": str,
-            "random_seed": int,
-        },
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/tfscreen/tfmodel/scripts/summarize_fit_cli.py b/src/tfscreen/tfmodel/scripts/summarize_fit_cli.py
index 07e796fe..256c8dff 100644
--- a/src/tfscreen/tfmodel/scripts/summarize_fit_cli.py
+++ b/src/tfscreen/tfmodel/scripts/summarize_fit_cli.py
@@ -21,13 +21,21 @@
 def _find_params_or_posterior(run_dir):
     """Return (kind, path) for the best available prediction source in run_dir.
 
-    Preference order: ``*_posterior.h5`` first (richer uncertainty), then
-    ``*_params.npz`` (MAP fallback).  Returns ``(None, None)`` when neither
-    is found.  Warns when multiple files of the same type are present and
-    uses the first alphabetically.
+    Preference order: a posterior ``.h5`` first (richer uncertainty), then
+    ``*_params.npz`` (MAP fallback).  A posterior is ``*_posterior.h5`` or,
+    since ``tfs-sample-posterior`` writes ``{out_prefix}.h5``, any other
+    ``.h5`` that is not a ``tfs-sample-prior`` ground truth
+    (``*_ground_truth.h5``).  Returns ``(None, None)`` when nothing is found.
+    Warns when several candidates of the same kind are present and uses the
+    first alphabetically.
     """
-    for suffix, kind in (("_posterior.h5", "posterior"), ("_params.npz", "params")):
-        matches = sorted(glob.glob(os.path.join(run_dir, f"*{suffix}")))
+    h5 = sorted(glob.glob(os.path.join(run_dir, "*.h5")))
+    named = [p for p in h5 if p.endswith("_posterior.h5")]
+    other = [p for p in h5 if p not in named
+             and not p.endswith("_ground_truth.h5")]
+    candidates = (("posterior", named or other),
+                  ("params", sorted(glob.glob(os.path.join(run_dir, "*_params.npz")))))
+    for kind, matches in candidates:
         if not matches:
             continue
         if len(matches) > 1:
@@ -80,6 +88,31 @@ def _try_plot_theta_fits(binding_df, pred_df, out_prefix):
         warnings.warn(f"Could not generate theta fit plots: {exc}")
 
 
+# Number of randomly chosen extra genotypes whose trajectories are plotted
+# when the run has no binding genotypes to plot.
+_TRAJECTORY_RANDOM_GENOTYPES = 10
+
+
+def _default_trajectory_genotypes(orchestrator,
+                                  num_random=_TRAJECTORY_RANDOM_GENOTYPES,
+                                  seed=0):
+    """
+    Genotypes to plot when there is no binding data to choose them: wt, the
+    spiked genotypes, and a fixed-seed sample of ``num_random`` others (so
+    reruns plot the same ones). Without this a growth-only run would write a
+    trajectory CSV and PDF for every genotype in the library.
+    """
+    tm = orchestrator.growth_tm
+    all_genos = [str(g) for g in tm.tensor_dim_labels[tm.tensor_dim_names.index("genotype")]]
+    spiked = [str(g) for g in (orchestrator.settings.get("spiked_genotypes") or [])]
+    keep = [g for g in ["wt"] + spiked if g in all_genos]
+    keep = list(dict.fromkeys(keep))
+    rest = sorted(set(all_genos) - set(keep))
+    rng = np.random.default_rng(seed)
+    extra = rng.choice(rest, size=min(num_random, len(rest)), replace=False)
+    return keep + sorted(str(g) for g in extra)
+
+
 def _try_plot_trajectories(config_file, config_yaml, run_dir, out_prefix, binding_df):
     """Attempt to generate per-genotype growth trajectory plots.
 
@@ -92,7 +125,7 @@ def _try_plot_trajectories(config_file, config_yaml, run_dir, out_prefix, bindin
     kind, pred_path = _find_params_or_posterior(run_dir)
     if pred_path is None:
         warnings.warn(
-            "No *_posterior.h5 or *_params.npz found in "
+            "No posterior .h5 or *_params.npz found in "
             f"{run_dir}; skipping trajectory plots."
         )
         return
@@ -103,7 +136,10 @@ def _try_plot_trajectories(config_file, config_yaml, run_dir, out_prefix, bindin
 
         orchestrator, _ = read_configuration(config_file)
 
-        genotypes = list(binding_df["genotype"].unique()) if binding_df is not None else None
+        if binding_df is not None:
+            genotypes = list(binding_df["genotype"].unique())
+        else:
+            genotypes = _default_trajectory_genotypes(orchestrator)
 
         pred_df = predict_geno_trajectory_df(
             orchestrator,
@@ -139,6 +175,133 @@ def _find_unique(run_dir, suffix, label, warn_missing=True):
     return matches[0]
 
 
+def _is_relative_config(config_yaml):
+    """True when the fit's theta component predicts X, not theta."""
+    from tfscreen.tfmodel.generative.registry import model_registry
+
+    components = (config_yaml or {}).get("components", {}) or {}
+    module = model_registry["theta"].get(components.get("theta"))
+    return getattr(module, "THETA_SCALE", "theta") == "X"
+
+
+def _x_gauge(config_yaml, ref_theta_file, run_dir):
+    """
+    What is needed to put simulated truth on a relative fit's X scale.
+
+    Returns None unless the fit's theta component is on the X scale
+    (``hill_relative``). Otherwise a dict with ``gauge`` (c_lo, c_hi), ``wt``
+    ({titrant_name: (theta_wt(c_lo), theta_wt(c_hi))}, from the reference
+    theta file) and ``activity`` (genotype -> simulated activity, from
+    ``*_sim_parameters.csv``; empty when absent, meaning activity 1). Warns
+    and returns None when the truth cannot be mapped.
+    """
+    if not _is_relative_config(config_yaml):
+        return None
+    components = config_yaml.get("components", {}) or {}
+
+    gauge = components.get("theta_gauge_conc")
+    if gauge is None or ref_theta_file is None:
+        warnings.warn("Relative (X-scale) fit without a recorded gauge or a "
+                      "reference theta file; its truth cannot be put on the "
+                      "X scale.")
+        return None
+
+    gt = pd.read_csv(ref_theta_file)
+    theta_col = "theta_obs" if "theta_obs" in gt.columns else "theta"
+    wt = gt[gt["genotype"].astype(str) == "wt"]
+    wt_theta = {}
+    for titrant, rows in wt.groupby("titrant_name"):
+        by_conc = rows.set_index("titrant_conc")[theta_col]
+        values = []
+        for c in gauge:
+            match = by_conc[np.isclose(by_conc.index.values.astype(float), c)]
+            if len(match) == 0:
+                warnings.warn(f"The reference theta file has no wt value at "
+                              f"the gauge concentration {c} ({titrant}); the "
+                              f"truth cannot be put on the X scale.")
+                return None
+            values.append(float(match.iloc[0]))
+        wt_theta[titrant] = tuple(values)
+
+    activity = {}
+    params_file = _find_unique(run_dir, "_sim_parameters.csv",
+                               "sim parameters", warn_missing=False)
+    if params_file is not None:
+        params = pd.read_csv(params_file)
+        if "activity" in params.columns:
+            activity = dict(zip(params["genotype"].astype(str),
+                                params["activity"].astype(float)))
+
+    return {"gauge": gauge, "wt": wt_theta, "activity": activity}
+
+
+def _x_scale_ref(gt_df, x_gauge):
+    """The ``ref`` column of a reference theta table, on the X scale."""
+    from tfscreen.tfmodel.generative.components.theta.hill_relative import (
+        x_scale_truth,
+    )
+    act = x_gauge["activity"]
+    lo = gt_df["titrant_name"].map(lambda t: x_gauge["wt"][t][0])
+    hi = gt_df["titrant_name"].map(lambda t: x_gauge["wt"][t][1])
+    a = gt_df["genotype"].astype(str).map(lambda g: act.get(g, 1.0))
+    return x_scale_truth(gt_df["ref"].values, lo.values, hi.values,
+                         a.values, act.get("wt", 1.0))
+
+
+def _is_tfmodel_config(path):
+    """True when the YAML at ``path`` is a tfmodel config ('data' + 'components')."""
+    try:
+        with open(path) as fh:
+            cfg = yaml.safe_load(fh)
+    except Exception:
+        return False
+    return isinstance(cfg, dict) and "data" in cfg and "components" in cfg
+
+
+def _find_fit_config(run_dir):
+    """Return the tfmodel config in run_dir, or None.
+
+    Grid run directories also hold other ``*_config.yaml`` files (e.g. the
+    simulate config ``tfs_sim_config.yaml``), so a file counts only if it has
+    the 'data' and 'components' sections ``read_configuration`` requires.
+    """
+    matches = sorted(glob.glob(os.path.join(run_dir, "*_config.yaml")))
+    configs = [m for m in matches if _is_tfmodel_config(m)]
+    if not configs:
+        warnings.warn(
+            f"No tfmodel config (*_config.yaml with 'data' and 'components') "
+            f"found in {run_dir}"
+        )
+        return None
+    if len(configs) > 1:
+        warnings.warn(
+            f"Multiple tfmodel config files found in {run_dir}; "
+            f"using {os.path.basename(configs[0])}"
+        )
+    return configs[0]
+
+
+# Losses written by runs other than the main fit: tfs-prefit-calibration
+# ({prefix}_prefit_losses.txt) and the pre-MAP ahead of SVI
+# ({out_prefix}_premap_losses.txt).
+_AUX_LOSSES_RE = re.compile(r"(^|_)(prefit|premap)_losses\.txt$")
+
+
+def _find_fit_losses(run_dir):
+    """Return the main fit's ``*_losses.txt`` in run_dir, or None."""
+    matches = sorted(glob.glob(os.path.join(run_dir, "*_losses.txt")))
+    fit_losses = [m for m in matches
+                  if not _AUX_LOSSES_RE.search(os.path.basename(m))]
+    if not fit_losses:
+        return None
+    if len(fit_losses) > 1:
+        warnings.warn(
+            f"Multiple fit losses files found in {run_dir}; "
+            f"using {os.path.basename(fit_losses[0])}"
+        )
+    return fit_losses[0]
+
+
 def _resolve_path(path, run_dir):
     """Return an existing path given a raw path and a fallback base directory."""
     if path is None:
@@ -381,6 +544,47 @@ def _compute_epi_obs(params_df, ref_series, param_name):
     return ep_map.reindex(std_pairs).values
 
 
+def _read_keyed_truth(run_dir, name, key):
+    """
+    Read ``*_sim_{name}.csv`` (per-mutation or per-pair truth) indexed by key.
+
+    Returns None when the file is absent (real data, or a theta model with no
+    per-mutation truth) or unreadable (with a warning).  Pair labels are
+    standardized so they match ``standardize_genotypes`` of a fit's pairs.
+    """
+    path = _find_unique(run_dir, f"_sim_{name}.csv", f"sim {name}",
+                        warn_missing=False)
+    if path is None:
+        return None
+    try:
+        df = pd.read_csv(path)
+    except Exception as exc:
+        warnings.warn(f"Could not load {path}: {exc}")
+        return None
+    if key not in df.columns:
+        warnings.warn(f"{path} has no '{key}' column; ignoring it")
+        return None
+    if key == "pair":
+        from tfscreen.genetics import standardize_genotypes
+        df[key] = standardize_genotypes(df[key])
+    return df.set_index(key)
+
+
+def _compute_keyed_obs(params_df, truth_df, column, key):
+    """Return ref array for a params file joined to keyed truth on ``key``.
+
+    Returns ``None`` when params_df has no ``key`` column.  Rows whose key is
+    absent from the truth receive NaN.
+    """
+    if key not in params_df.columns:
+        return None
+    labels = params_df[key]
+    if key == "pair":
+        from tfscreen.genetics import standardize_genotypes
+        labels = pd.Series(standardize_genotypes(labels), index=params_df.index)
+    return truth_df[column].reindex(labels).values
+
+
 def _try_plot_params_corr(out_df, label, pdf_path):
     """Save a scatter correlation plot of ref vs median for a params CSV.
 
@@ -410,20 +614,66 @@ def _try_plot_params_corr(out_df, label, pdf_path):
         warnings.warn(f"Could not generate parameter correlation plot {pdf_path}: {exc}")
 
 
-def _summarize_params(run_dir, out_prefix):
+# Per-structure ddG truth columns (ddG_{structure}) and the fit's prior-mean
+# tables (ddG_prior_mean_{structure}); see theta/thermo/prior.py.
+_DDG_TRUTH = re.compile(r"ddG_(?!prior_mean_)(.+)")
+_DDG_PRIOR_MEAN = re.compile(r"ddG_prior_mean_(.+)")
+
+
+def _ddG_per_state_identified(config_yaml):
+    """
+    Whether the fit's per-state ΔΔG tables are identified by the data.
+
+    The PddG/PnnC models put their prior on Δln K (``thermo/prior.py``).
+    In the U1 models the projection ``DLNK_PROJ`` is square and the
+    per-state ΔΔG is Δln K mapped back exactly; in the U0 models it is the
+    conditional mean under the per-state prior, whose common-mode component
+    (an equal shift of every state) comes from the prior means, not the
+    data.  Returns True when the config is missing or the theta component
+    has no ``DLNK_PROJ``.
+    """
+    from tfscreen.tfmodel.generative.registry import model_registry
+    from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
+        ddG_per_state_identified,
+    )
+
+    components = (config_yaml or {}).get("components", {}) or {}
+    module = model_registry["theta"].get(components.get("theta"))
+    proj = getattr(module, "DLNK_PROJ", None)
+    return True if proj is None else ddG_per_state_identified(proj)
+
+
+def _summarize_params(run_dir, out_prefix, config_yaml=None):
     """Annotate *_params_*.csv files with ground-truth ref from tfs_sim_parameters.csv.
 
     Scans run_dir for a ``*_sim_parameters.csv`` file.  If absent the function
     returns silently (real-data runs have no ground truth).
 
-    For each non-genotype column in sim_parameters the function checks that a
-    matching ``*_params_{col}.csv`` exists and warns if it does not.
+    For each non-genotype column in sim_parameters (and each column of the
+    per-mutation ``*_sim_mutation_parameters.csv`` and per-pair
+    ``*_sim_pair_parameters.csv`` truth, when present) the function checks
+    that a matching ``*_params_{col}.csv`` exists and warns if it does not.
 
     All ``*_params_*.csv`` files in run_dir are then classified by their
     suffix as *direct* (genotype key), *diff* (``_d_`` infix, mutation key),
-    or *epi* (``_epi_`` infix, pair key).  A ``ref`` column is appended to
-    each file whose parameter name resolves via the transform registry and
-    written to the summary directory under ``{out_prefix}_params_*.csv``.
+    or *epi* (``_epi_`` infix, pair key).  A diff or epi file whose full name
+    (``d_ln_K_op``) is a column of the per-mutation or per-pair truth is
+    joined to it directly on ``mutation`` / ``pair``, as is a direct file
+    named for a per-mutation truth column (the thermo models' per-structure
+    ``ddG_H`` tables).  A prior-mean table ``ddG_prior_mean_H`` is joined to
+    the ``ddG_H`` truth and gets no calibration (it is a point).  When the
+    truth has ``ddG_*`` columns but the fit wrote no ddG tables (a PK fit),
+    a note replaces the per-structure warnings.  For a U0 PddG/PnnC fit
+    (``config_yaml``'s theta component; see :func:`_ddG_per_state_identified`)
+    the per-state ``ddG_H`` tables are still written, with the truth in
+    ``ref_unidentified`` instead of ``ref`` and ``per_state_identified`` =
+    False, and get no plot or calibration: their common mode comes from the
+    prior.  Score those fits on ``d_ln_K_*``.  Otherwise the
+    parameter name resolves via the transform registry: direct files by
+    genotype, diff files as sim[mutation] − sim[wt], epi files as the
+    additive epistasis of the per-genotype truth.  A ``ref`` column is
+    appended to each resolved file and written to the summary directory
+    under ``{out_prefix}_params_*.csv``.
     """
     sim_path = _find_unique(run_dir, "_sim_parameters.csv", "sim_parameters",
                             warn_missing=False)
@@ -445,10 +695,39 @@ def _summarize_params(run_dir, out_prefix):
 
     sim_cols = [c for c in sim_df.columns if c != "genotype"]
 
-    for col in sim_cols:
+    # Per-mutation / per-pair truth (thermodynamic models; tfs-simulate's
+    # mutation_parameters / pair_parameters outputs), keyed like the fit's
+    # d_* / epi_* tables.
+    keyed_truth = {
+        "diff": _read_keyed_truth(run_dir, "mutation_parameters", "mutation"),
+        "epi": _read_keyed_truth(run_dir, "pair_parameters", "pair"),
+    }
+    keyed_cols = [c for t in keyed_truth.values() if t is not None
+                  for c in t.columns]
+
+    # Per-structure ddG truth (PddG/PnnC simulations).  A fit with no ddG
+    # (PK parameterizes d_ln_K directly) writes no ddG tables; that is the
+    # "no structural prior" arm, scored on d_ln_K_*, so say so once instead
+    # of warning per structure.
+    ddG_cols = [c for c in keyed_cols if _DDG_TRUTH.fullmatch(c)]
+    if ddG_cols and not glob.glob(os.path.join(run_dir, "*_params_ddG_*.csv")):
+        print(f"Truth has per-structure ddG ({', '.join(ddG_cols)}) but the "
+              f"fit wrote no *_params_ddG_*.csv; expected for a model "
+              f"without ddG (PK). Its d_ln_K_* are scored instead.")
+        keyed_cols = [c for c in keyed_cols if c not in ddG_cols]
+
+    for col in sim_cols + keyed_cols:
         if not glob.glob(os.path.join(run_dir, f"*_params_{col}.csv")):
             warnings.warn(f"Expected *_params_{col}.csv in {run_dir} but none found")
 
+    # U0 structural fits: per-state ΔΔG is not identified by the data.
+    ddG_identified = _ddG_per_state_identified(config_yaml)
+    if ddG_cols and not ddG_identified:
+        print("The fit's theta component has no unfolded state (U0), so its "
+              "per-state ddG common mode comes from the prior means. The "
+              "ddG_* tables are written with the truth as ref_unidentified "
+              "and are not scored; score d_ln_K_* instead.")
+
     transform_registry = _build_transform_registry(sim_df)
 
     for param_file in sorted(glob.glob(os.path.join(run_dir, "*_params_*.csv"))):
@@ -467,10 +746,25 @@ def _summarize_params(run_dir, out_prefix):
         else:
             continue
 
-        if param_name not in transform_registry:
+        key = {"diff": "mutation", "epi": "pair"}.get(kind)
+        full_name = f"{'d' if kind == 'diff' else kind}_{param_name}"
+        truth_df = keyed_truth.get(kind)
+        is_prior_mean = False
+        if kind == "direct":
+            # Per-structure ddG tables (ddG_H, ...) are keyed by mutation and
+            # join the per-mutation truth by name.  A prior-mean table
+            # (ddG_prior_mean_H) is scored against the same truth (ddG_H):
+            # how well the structural prior alone predicts it.
+            m_prior = _DDG_PRIOR_MEAN.fullmatch(param_name)
+            key, truth_df = "mutation", keyed_truth["diff"]
+            full_name = f"ddG_{m_prior.group(1)}" if m_prior else param_name
+            is_prior_mean = m_prior is not None
+        use_keyed = truth_df is not None and full_name in truth_df.columns
+
+        if not use_keyed and param_name not in transform_registry:
             continue
 
-        ref_series = transform_registry[param_name]
+        ref_series = None if use_keyed else transform_registry[param_name]
 
         try:
             params_df = pd.read_csv(param_file)
@@ -480,7 +774,9 @@ def _summarize_params(run_dir, out_prefix):
 
         ref = None
         try:
-            if kind == "direct":
+            if use_keyed:
+                ref = _compute_keyed_obs(params_df, truth_df, full_name, key)
+            elif kind == "direct":
                 ref = _compute_direct_obs(params_df, ref_series)
             elif kind == "diff":
                 ref = _compute_diff_obs(params_df, ref_series)
@@ -494,7 +790,14 @@ def _summarize_params(run_dir, out_prefix):
             continue
 
         out_df = params_df.copy()
-        out_df["ref"] = ref
+        unidentified = (kind == "direct" and use_keyed and not is_prior_mean
+                        and not ddG_identified
+                        and _DDG_TRUTH.fullmatch(full_name) is not None)
+        if unidentified:
+            out_df["ref_unidentified"] = ref
+            out_df["per_state_identified"] = False
+        else:
+            out_df["ref"] = ref
 
         if kind == "epi":
             out_name = f"{out_prefix}_params_epi_{param_name}.csv"
@@ -513,9 +816,56 @@ def _summarize_params(run_dir, out_prefix):
             warnings.warn(f"Could not write {out_name}: {exc}")
             continue
 
+        if unidentified:
+            continue
         pdf_name = out_name.replace(".csv", ".pdf")
         _try_plot_params_corr(out_df, plot_label, pdf_name)
-        _try_calibration(out_df, "ref", out_name[:-4], plot_label)
+        # A prior mean is a point (its quantiles coincide), so it has no
+        # calibration; its r and RMSE against the truth are what count.
+        if not is_prior_mean:
+            _try_calibration(out_df, "ref", out_name[:-4], plot_label)
+
+
+def _summarize_ddG_sigma(run_dir, out_prefix):
+    """Annotate *_params_ddG_sigma.csv with the truth in *_sim_ddG_sigma.csv.
+
+    A PddG/PnnC simulation writes the true per-structure σ_s (the SD of the
+    true ΔΔG about the structural prior mean) as ``*_sim_ddG_sigma.csv``
+    (``structure``, ``ddG_sigma``); a PddG/PnnC fit extracts its σ_s as
+    ``*_params_ddG_sigma.csv``, one row per structure (``thermo/prior.py``).
+    They are joined on ``structure``.  Silently skips without the truth
+    (real data, other models) or when the fit wrote no ddG tables at all (a
+    PK fit; ``_summarize_params`` prints the note for that).  No
+    correlation plot: there are only a handful of structures.
+    """
+    sim_path = _find_unique(run_dir, "_sim_ddG_sigma.csv", "sim ddG_sigma",
+                            warn_missing=False)
+    if sim_path is None:
+        return
+    param_file = _find_unique(run_dir, "_params_ddG_sigma.csv",
+                              "ddG_sigma params", warn_missing=False)
+    if param_file is None:
+        if glob.glob(os.path.join(run_dir, "*_params_ddG_*.csv")):
+            warnings.warn(f"Expected *_params_ddG_sigma.csv in {run_dir} "
+                          f"but none found")
+        return
+    try:
+        truth = pd.read_csv(sim_path).set_index("structure")["ddG_sigma"]
+        params_df = pd.read_csv(param_file)
+        out_df = params_df.copy()
+        out_df["ref"] = truth.reindex(params_df["structure"].astype(str)).values
+    except Exception as exc:
+        warnings.warn(f"Could not join ddG_sigma truth: {exc}")
+        return
+
+    out_name = f"{out_prefix}_params_ddG_sigma.csv"
+    try:
+        out_df.to_csv(out_name, index=False)
+        print(f"Wrote parameter summary to {out_name}")
+    except Exception as exc:
+        warnings.warn(f"Could not write {out_name}: {exc}")
+        return
+    _try_calibration(out_df, "ref", out_name[:-4], "ddG_sigma")
 
 
 # Fit-side extract names for the condition_growth component (see
@@ -525,7 +875,33 @@ def _summarize_params(run_dir, out_prefix):
 _GROWTH_PARAM_NAMES = ["growth_k", "growth_m", "growth_n", "growth_min", "growth_max"]
 
 
-def _summarize_condition_growth_params(run_dir, out_prefix):
+def _x_scale_growth_ref(growth_ref_df, x_gauge):
+    """
+    ``growth_k``/``growth_m`` truth on a relative fit's X scale (roadmap C4:
+    k is wt's growth at c_hi, m its change between the gauge
+    concentrations). Needs a single titrant (the gauge is per titrant, k and
+    m per condition); otherwise warns and blanks both.
+    """
+    from tfscreen.tfmodel.generative.components.theta.hill_relative import (
+        x_scale_growth_truth,
+    )
+    out = growth_ref_df.copy()
+    if not {"growth_k", "growth_m"} <= set(out.columns):
+        return out
+    if len(x_gauge["wt"]) != 1:
+        warnings.warn("growth_k/growth_m truth is not mapped to the X scale "
+                      "with more than one titrant; left blank.")
+        out["growth_k"] = np.nan
+        out["growth_m"] = np.nan
+        return out
+    lo, hi = next(iter(x_gauge["wt"].values()))
+    out["growth_k"], out["growth_m"] = x_scale_growth_truth(
+        out["growth_k"].values, out["growth_m"].values, lo, hi,
+        x_gauge["activity"].get("wt", 1.0))
+    return out
+
+
+def _summarize_condition_growth_params(run_dir, out_prefix, x_gauge=None):
     """Annotate *_params_growth_{name}.csv with ground-truth ref from tfs_sim_growth_parameters.csv.
 
     Scans run_dir for a ``*_sim_growth_parameters.csv`` file (written by
@@ -538,7 +914,8 @@ def _summarize_condition_growth_params(run_dir, out_prefix):
     the configured growth model produces), the function looks for a
     matching ``*_params_{name}.csv`` file, warns if it is absent, and
     otherwise joins the two on ``condition_rep`` and writes the annotated
-    result to ``{out_prefix}_params_{name}.csv``.
+    result to ``{out_prefix}_params_{name}.csv``. For a relative (X-scale)
+    fit, ``x_gauge`` (from ``_x_gauge``) maps the k/m truth onto its scale.
     """
     sim_path = _find_unique(run_dir, "_sim_growth_parameters.csv",
                             "sim growth parameters", warn_missing=False)
@@ -558,6 +935,9 @@ def _summarize_condition_growth_params(run_dir, out_prefix):
         )
         return
 
+    if x_gauge is not None:
+        growth_ref_df = _x_scale_growth_ref(growth_ref_df, x_gauge)
+
     present_param_names = [c for c in _GROWTH_PARAM_NAMES if c in growth_ref_df.columns]
 
     for param_name in present_param_names:
@@ -700,7 +1080,7 @@ def _summarize_transformation_lam(run_dir, out_prefix):
     _try_calibration(merged, "ref", out_name[:-4], "transformation_lam")
 
 
-def _summarize_growth_params(run_dir, out_prefix):
+def _summarize_growth_params(run_dir, out_prefix, x_gauge=None):
     """Annotate growth-model parameter files with simulated ground truth.
 
     Handles two independent, optionally-present ground-truth sources
@@ -716,10 +1096,120 @@ def _summarize_growth_params(run_dir, out_prefix):
     base_growth_data); each is silently skipped when its sim-side file
     isn't found.
     """
-    _summarize_condition_growth_params(run_dir, out_prefix)
+    _summarize_condition_growth_params(run_dir, out_prefix, x_gauge)
     _summarize_k_ref(run_dir, out_prefix)
 
 
+# Per-tube offset sites and their SDs, by sample_offset component
+_TUBE_OFFSET_SITES = (("sample_offset_offset", "sample_offset_sigma"),
+                      ("sample_offset_delta_k", "sample_offset_sigma_env"))
+
+
+def _held_offset_sigma(config_yaml, config_file):
+    """The level offsets' held SD (``sigma_fixed`` > 0) from the priors CSV."""
+    if config_yaml is None or config_file is None:
+        return None
+    name = config_yaml.get("priors_file")
+    if not name:
+        return None
+    path = os.path.join(os.path.dirname(config_file), name)
+    if not os.path.exists(path):
+        return None
+    priors = pd.read_csv(path)
+    hit = priors[priors["parameter"].astype(str)
+                 .str.endswith("sample_offset.sigma_fixed")]
+    if len(hit) == 0:
+        return None
+    value = float(hit["value"].iloc[0])
+    return value if value > 0 else None
+
+
+def _summarize_tube_offsets(run_dir, out_prefix, config_yaml, config_file):
+    """
+    Size and structure of the per-tube sample offsets.
+
+    Reads the ``*_sample_offset_offset.csv`` (or ``_delta_k``) that
+    tfs-extract-params writes, takes the prior SD from the extracted sigma
+    or the held ``sigma_fixed``, and writes ``{out_prefix}_tube_offsets.csv``
+    (one row per tube, with ``z``), ``{out_prefix}_tube_offset_trends.csv``
+    (one row per condition) and ``{out_prefix}_tube_offsets.pdf``. Returns
+    the summary dict for the JSON, or None when the fit has no offsets.
+    """
+    from tfscreen.tfmodel.analysis.tube_offsets import (
+        format_tube_offset_summary, tube_offset_diagnostic)
+
+    offset_file = sigma_file = None
+    for site, sigma_site in _TUBE_OFFSET_SITES:
+        offset_file = _find_unique(run_dir, f"_{site}.csv", site,
+                                   warn_missing=False)
+        if offset_file is not None:
+            sigma_file = _find_unique(run_dir, f"_{sigma_site}.csv",
+                                      sigma_site, warn_missing=False)
+            break
+    if offset_file is None:
+        return None
+
+    sigma, sigma_source = None, None
+    if sigma_file is not None:
+        sigma = float(pd.read_csv(sigma_file)["q0.5"].iloc[0])
+        sigma_source = os.path.basename(sigma_file)
+    else:
+        sigma = _held_offset_sigma(config_yaml, config_file)
+        sigma_source = "sigma_fixed" if sigma is not None else None
+
+    tubes, trends, summary = tube_offset_diagnostic(pd.read_csv(offset_file),
+                                                    sigma=sigma)
+    summary["file"] = os.path.basename(offset_file)
+    summary["sigma_source"] = sigma_source
+    print(format_tube_offset_summary(summary, trends))
+
+    for df, suffix in ((tubes, "tube_offsets"), (trends, "tube_offset_trends")):
+        out_name = f"{out_prefix}_{suffix}.csv"
+        try:
+            df.to_csv(out_name, index=False)
+            print(f"Wrote {out_name}")
+        except Exception as exc:
+            warnings.warn(f"Could not write {out_name}: {exc}")
+
+    pdf_file = f"{out_prefix}_tube_offsets.pdf"
+    try:
+        _plot_tube_offsets(tubes, trends, pdf_file)
+        print(f"Wrote tube offset plot to {pdf_file}")
+    except Exception as exc:
+        warnings.warn(f"Could not plot tube offsets: {exc}")
+    return summary
+
+
+def _plot_tube_offsets(tubes, trends, pdf_file):
+    """Offset against titrant, one panel per selection condition, by time."""
+    conds = list(dict.fromkeys(tubes["condition_sel"].astype(str)))
+    ncol = min(3, len(conds))
+    nrow = int(np.ceil(len(conds) / ncol))
+    fig, axes = plt.subplots(nrow, ncol, figsize=(4.5 * ncol, 3.8 * nrow),
+                             squeeze=False)
+    conc = tubes["titrant_conc"].astype(float)
+    floor = conc[conc > 0].min() / 10 if (conc > 0).any() else 1.0
+    y = "z" if "z" in tubes.columns else "offset"
+    t_min, t_max = tubes["t_sel"].min(), tubes["t_sel"].max()
+    for ax, cond in zip(axes.flat, conds):
+        g = tubes[tubes["condition_sel"].astype(str) == cond]
+        sc = ax.scatter(np.maximum(g["titrant_conc"].astype(float), floor),
+                        g[y], c=g["t_sel"], cmap="viridis", vmin=t_min,
+                        vmax=t_max, s=18)
+        ax.axhline(0, color="gray", lw=0.8)
+        ax.set_xscale("log")
+        flag = trends.loc[trends["condition_sel"].astype(str) == cond,
+                          "structured"].any()
+        ax.set_title(cond + (" (trend)" if flag else ""))
+        ax.set_xlabel("titrant (0 drawn at min/10)")
+        ax.set_ylabel("offset / prior SD" if y == "z" else "offset (ln)")
+    for ax in list(axes.flat)[len(conds):]:
+        ax.set_visible(False)
+    fig.colorbar(sc, ax=axes, label="t_sel")
+    fig.savefig(pdf_file)
+    plt.close(fig)
+
+
 def summarize_fit(run_dir,
                   ref_theta_file=None,
                   out_prefix=None):
@@ -747,8 +1237,9 @@ def summarize_fit(run_dir,
       written when a ref theta file is resolved and has matching rows).
     - ``{out_prefix}_theta_corr.pdf`` — two-panel correlation plot for theta
       (training left, test right).
-    - ``{out_prefix}_growth_corr.csv`` — relative symlink to the
-      *_pred_growth.csv in run_dir (only created when that file is present).
+    - ``{out_prefix}_growth_corr.csv`` — a copy of the *_pred_growth.csv in
+      run_dir with ``ln_cfu``/``ln_cfu_std`` renamed ``ref``/``ref_std``
+      (only written when that file is present).
     - ``{out_prefix}_growth_corr.pdf`` — correlation plot for ln_cfu (only
       written when *_pred_growth.csv is present).
     - ``{out_prefix}_{genotype}_theta_fits.csv`` / ``.pdf`` — per-genotype
@@ -759,6 +1250,13 @@ def summarize_fit(run_dir,
       configured and a posterior/params file is found).
     - ``{out_prefix}_losses.pdf`` — training loss curve (only written when
       *_losses.txt is present).
+    - ``{out_prefix}_tube_offsets.csv`` / ``_tube_offset_trends.csv`` /
+      ``_tube_offsets.pdf`` — the per-tube sample offsets in prior SDs and,
+      per condition, their Spearman trend with titrant and with time (only
+      when tfs-extract-params wrote ``*_sample_offset_offset.csv``). A
+      condition whose trend has BH q < 0.05 is flagged ``structured``: the
+      offsets are carrying growth (the offset mode) rather than tube noise.
+      The JSON's ``tube_offsets`` key holds the summary.
 
     Parameters
     ----------
@@ -776,7 +1274,7 @@ def summarize_fit(run_dir,
         argument explicitly to use a theta reference from outside *run_dir*
         or with a non-standard name.
     out_prefix : str, optional
-        Prefix for output files.  Defaults to {run_dir}/tfs_summarize.
+        Prefix for output files.  Defaults to {run_dir}/summary/tfs_summarize.
     """
     run_dir = os.path.abspath(run_dir)
     if out_prefix is None:
@@ -801,9 +1299,9 @@ def summarize_fit(run_dir,
     growth_pred_df = None
 
     # --- Locate files in run_dir ---
-    config_file = _find_unique(run_dir, "_config.yaml", "config")
+    config_file = _find_fit_config(run_dir)
     theta_pred_file = _find_unique(run_dir, "_pred_theta.csv", "theta predictions")
-    losses_file = _find_unique(run_dir, "_losses.txt", "losses", warn_missing=False)
+    losses_file = _find_fit_losses(run_dir)
     growth_pred_file = _find_unique(run_dir, "_pred_growth.csv", "growth predictions",
                                     warn_missing=False)
 
@@ -892,8 +1390,20 @@ def summarize_fit(run_dir,
                                       "ref theta", warn_missing=False)
     metadata["ref_theta_file"] = ref_theta_file
 
+    # A relative fit predicts X, not theta: its truth is mapped onto the X
+    # gauge (roadmap C8).
+    x_gauge = None
+    try:
+        x_gauge = _x_gauge(config_yaml, ref_theta_file, run_dir)
+    except Exception as exc:
+        warnings.warn(f"Could not build the X-scale truth: {exc}")
+    relative = _is_relative_config(config_yaml)
+    metadata["theta_scale"] = "X" if relative else "theta"
+
     # --- Theta test statistics ---
-    if ref_theta_file is not None and pred_df is not None:
+    # (a relative fit whose truth could not be mapped to X is not compared)
+    if (ref_theta_file is not None and pred_df is not None
+            and (x_gauge is not None or not relative)):
         try:
             gt_df = pd.read_csv(ref_theta_file)
             join_cols = ["genotype", "titrant_name", "titrant_conc"]
@@ -909,6 +1419,8 @@ def summarize_fit(run_dir,
                 theta_col = None
             if theta_col is not None:
                 gt_df = gt_df.rename(columns={theta_col: "ref"})
+                if x_gauge is not None:
+                    gt_df["ref"] = _x_scale_ref(gt_df, x_gauge)
             test_merged = pred_df.merge(
                 gt_df[join_cols + ["ref"]],
                 on=join_cols,
@@ -943,6 +1455,14 @@ def summarize_fit(run_dir,
         except Exception as exc:
             warnings.warn(f"Could not compute growth training statistics: {exc}")
 
+    # --- Tube offsets ---
+    tube_offsets = None
+    try:
+        tube_offsets = _summarize_tube_offsets(run_dir, out_prefix,
+                                               config_yaml, config_file)
+    except Exception as exc:
+        warnings.warn(f"Could not summarize tube offsets: {exc}")
+
     # --- Trajectory plots ---
     _try_plot_trajectories(config_file, config_yaml, run_dir, out_prefix, binding_df)
 
@@ -959,6 +1479,7 @@ def summarize_fit(run_dir,
         "growth": {
             "training": growth_training_stats,
         },
+        "tube_offsets": tube_offsets,
     }
     json_file = f"{out_prefix}_fit_summary.json"
     try:
@@ -1078,8 +1599,9 @@ def summarize_fit(run_dir,
         warnings.warn(f"Could not generate loss curve plot: {exc}")
 
     # --- Sim-parameter correlation CSVs ---
-    _summarize_params(run_dir, out_prefix)
-    _summarize_growth_params(run_dir, out_prefix)
+    _summarize_params(run_dir, out_prefix, config_yaml)
+    _summarize_ddG_sigma(run_dir, out_prefix)
+    _summarize_growth_params(run_dir, out_prefix, x_gauge)
     _summarize_transformation_lam(run_dir, out_prefix)
 
     return results
diff --git a/src/tfscreen/tfmodel/scripts/summarize_grid_cli.py b/src/tfscreen/tfmodel/scripts/summarize_grid_cli.py
index 8e21d0e5..4a7c15fc 100644
--- a/src/tfscreen/tfmodel/scripts/summarize_grid_cli.py
+++ b/src/tfscreen/tfmodel/scripts/summarize_grid_cli.py
@@ -1,7 +1,8 @@
 """
 tfs-summarize-grid — summarize the results of a model grid created by tfs-setup-grid.
 
-Scans every immediate subdirectory of a grid directory for ``combo.json``.
+Scans every immediate subdirectory of a grid directory (tfs-setup-grid or
+tfs-setup-sim-grid) for ``combo.json``.
 For each completed run it reads the component settings and any output files
 present, then writes a flat summary CSV with one row per run.
 
@@ -59,7 +60,10 @@ def summarize_grid(grid_dir, out_prefix=None):
     Summarize a model grid created by tfs-setup-grid.
 
     Scans every immediate subdirectory of *grid_dir* for ``combo.json`` and
-    collects the per-run variable assignments into a summary CSV.
+    collects the per-run variable assignments (configure_model, simulate and
+    template variables) into a summary CSV, with the statistics of the run's
+    ``*_fit_summary.json`` (in the run directory or its ``summary/``, where
+    tfs-summarize-fit writes by default) when there is one.
 
     Parameters
     ----------
@@ -99,8 +103,9 @@ def summarize_grid(grid_dir, out_prefix=None):
 
         row = {"run": name}
 
-        # Flatten configure_model and template variable dicts into the row.
-        for section in ("configure_model", "template"):
+        # Flatten the grid variables (configure_model for a model grid,
+        # simulate for a simulation grid, template for both) into the row.
+        for section in ("configure_model", "simulate", "template"):
             for k, v in (combo.get(section) or {}).items():
                 row[k] = v
 
@@ -110,7 +115,10 @@ def summarize_grid(grid_dir, out_prefix=None):
         )
 
         # Merge fit summary statistics if present.
-        matches = sorted(glob.glob(os.path.join(subdir, "*_fit_summary.json")))
+        # tfs-summarize-fit writes into summary/ by default.
+        matches = (sorted(glob.glob(os.path.join(subdir, "*_fit_summary.json")))
+                   or sorted(glob.glob(os.path.join(subdir, "summary",
+                                                    "*_fit_summary.json"))))
         if matches:
             try:
                 with open(matches[0]) as fh:
diff --git a/src/tfscreen/tfmodel/scripts/summarize_sbc_cli.py b/src/tfscreen/tfmodel/scripts/summarize_sbc_cli.py
deleted file mode 100644
index d105d97a..00000000
--- a/src/tfscreen/tfmodel/scripts/summarize_sbc_cli.py
+++ /dev/null
@@ -1,21 +0,0 @@
-"""
-tfs-summarize-sbc — compute SBC rank statistics from a directory of runs.
-
-Scans *sbc_dir* for ``*_ground_truth.h5`` / ``*_posterior.h5`` pairs produced
-by ``tfs-sample-prior`` and ``tfs-sample-posterior``, computes posterior rank
-statistics, and writes summary outputs.
-"""
-
-from tfscreen.tfmodel.analysis.error_calibration import summarize_sbc
-from tfscreen.util.cli import generalized_main
-
-
-def main():
-    generalized_main(
-        summarize_sbc,
-        manual_arg_types={"sbc_dir": str, "out_prefix": str},
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/tfscreen/tfmodel/tensors/batch.py b/src/tfscreen/tfmodel/tensors/batch.py
index 2d6e342c..5ea00251 100644
--- a/src/tfscreen/tfmodel/tensors/batch.py
+++ b/src/tfscreen/tfmodel/tensors/batch.py
@@ -50,8 +50,14 @@ def get_batch(full_data: DataClass, idx: jnp.ndarray) -> DataClass:
             map_condition_pre=full_data.growth.map_condition_pre[...,idx],
             map_condition_sel=full_data.growth.map_condition_sel[...,idx],
             good_mask=full_data.growth.good_mask[...,idx],
-            congression_mask=full_data.growth.congression_mask[...,idx],
         )
+        # Count-likelihood tensors (growth_likelihood == "counts" only).
+        if getattr(full_data.growth, "counts", None) is not None:
+            new_growth = new_growth.replace(
+                counts=full_data.growth.counts[...,idx],
+                ln_sample_reads=full_data.growth.ln_sample_reads[...,idx],
+                sample_ln_cfu=full_data.growth.sample_ln_cfu[...,idx],
+            )
         return full_data.replace(growth=new_growth)
 
     # Binding-only mode: idx is a set of binding-genotype indices.
@@ -62,6 +68,7 @@ def get_batch(full_data: DataClass, idx: jnp.ndarray) -> DataClass:
             batch_idx=idx,
             scale_vector=full_data.binding.scale_vector[idx],
             geno_theta_idx=jnp.arange(batch_size, dtype=jnp.int32),
+            binding_ordinal=idx,
             theta_obs=full_data.binding.theta_obs[..., idx],
             theta_std=full_data.binding.theta_std[..., idx],
             good_mask=full_data.binding.good_mask[..., idx],
diff --git a/src/tfscreen/tfmodel/two_stage.py b/src/tfscreen/tfmodel/two_stage.py
new file mode 100644
index 00000000..b896f5d6
--- /dev/null
+++ b/src/tfscreen/tfmodel/two_stage.py
@@ -0,0 +1,675 @@
+"""
+Pieces of the two-stage fit (``planning/two-stage-fit.md``).
+
+The full-library MAP of a growth-only relative fit makes the growth slope
+m too steep (each double mutant pulls it a little, and the pulls add), and
+congression flattens a marker's m in any stage 1 large enough to pin X's
+scale. The two-stage fit estimates every condition's k and m from a
+subset (wt, the spikes and the singles) with the congression mixture,
+then fits the full library with them pinned
+(``planning/studies/m-bias-factorial/``). k's uncertainty comes from a
+delete-d jackknife of stage 1 and reaches X as one shift shared by every
+non-wt genotype (``analysis/shared_shift.py``).
+
+These functions work on DataFrames; the command that chains them (with
+configure, fit, sample and predict) is ``tfs-fit-two-stage``.
+"""
+
+import numpy as np
+import pandas as pd
+
+TUBE_COLUMNS = ["sample", "library", "condition_sel", "titrant_conc"]
+STRATA = ("condition_sel", "titrant_conc")
+PRED_KEY = ["genotype", "titrant_name", "titrant_conc"]
+
+
+# ---------------------------------------------------------------------------
+# Stage 1 genotypes
+# ---------------------------------------------------------------------------
+
+def is_single(genotype):
+    """True for a genotype with exactly one mutation (no '/', not wt)."""
+    g = str(genotype)
+    return g != "wt" and not g.startswith("__") and "/" not in g
+
+
+def stage1_genotypes(library_df, genotypes):
+    """
+    The stage-1 genotypes among those with growth data.
+
+    Stage 1 is wt, every genotype in the spiked origin and every single
+    mutant: about 950 genotypes in the dev-data screen, few enough that
+    their pull on m is small, and enough to pin X's scale (the spikes alone
+    are not).
+
+    Parameters
+    ----------
+    library_df : pandas.DataFrame
+        The library composition table (``genotype``, ``in_spiked_origin``;
+        ``genetics.library_composition_table`` or the configure snapshot).
+    genotypes : iterable of str
+        Genotypes with growth data.
+
+    Returns
+    -------
+    list of str
+        Sorted; always includes wt when it has data.
+    """
+    present = {str(g) for g in genotypes}
+    spiked = set(library_df.loc[library_df["in_spiked_origin"].astype(bool),
+                                "genotype"].astype(str))
+    keep = {g for g in present if g == "wt" or g in spiked or is_single(g)}
+    return sorted(keep)
+
+
+# ---------------------------------------------------------------------------
+# Jackknife subsamples of tubes
+# ---------------------------------------------------------------------------
+
+def tube_table(growth_df):
+    """One row per tube: sample, library, condition_sel, titrant_conc."""
+    missing = [c for c in TUBE_COLUMNS if c not in growth_df.columns]
+    if missing:
+        raise ValueError(f"growth table lacks tube column(s) {missing}")
+    return (growth_df[TUBE_COLUMNS].drop_duplicates("sample")
+            .reset_index(drop=True))
+
+
+def choose_tube_drops(tubes, drop_frac, seed, strata=STRATA):
+    """
+    Tubes to drop for one delete-d jackknife subsample.
+
+    Drops ``round(drop_frac * n)`` tubes at random from each stratum (by
+    default condition and IPTG level), always leaving at least one. Tubes
+    cannot be resampled with replacement: a duplicated tube lands on its
+    original's tensor cell.
+
+    Parameters
+    ----------
+    tubes : pandas.DataFrame
+        ``tube_table`` output.
+    drop_frac : float
+        Share of each stratum to drop, in [0, 1).
+    seed : int
+        Seed for this subsample.
+    strata : sequence of str
+        Columns defining the strata.
+
+    Returns
+    -------
+    set of str
+        The dropped tubes' ``sample`` names.
+    """
+    if not 0 <= drop_frac < 1:
+        raise ValueError(f"drop_frac must be in [0, 1), got {drop_frac}")
+    rng = np.random.default_rng(seed)
+    drop = []
+    for _, g in tubes.groupby(list(strata), sort=True):
+        k = min(int(round(drop_frac * len(g))), len(g) - 1)
+        if k > 0:
+            drop.extend(rng.choice(g["sample"].to_numpy(), size=k,
+                                   replace=False))
+    return set(drop)
+
+
+def jackknife_subsample(growth_df, drop_frac, seed, strata=STRATA):
+    """
+    A growth table with one jackknife subsample's tubes dropped.
+
+    Returns
+    -------
+    tuple
+        ``(subsample_df, n, d, dropped)``: the kept rows, the number of
+        tubes, the number dropped and their sorted names.
+    """
+    tubes = tube_table(growth_df)
+    dropped = choose_tube_drops(tubes, drop_frac, seed, strata)
+    keep = ~growth_df["sample"].isin(dropped)
+    return (growth_df[keep].reset_index(drop=True), len(tubes), len(dropped),
+            sorted(dropped))
+
+
+# ---------------------------------------------------------------------------
+# k and m from the jackknife
+# ---------------------------------------------------------------------------
+
+def km_table(k_df, m_df):
+    """
+    Per-condition k and m from extracted parameter tables.
+
+    Parameters
+    ----------
+    k_df, m_df : pandas.DataFrame
+        ``tfs-extract-params`` output for growth_k and growth_m
+        (``condition_rep``, ``q0.5``; one row per condition with
+        ``growth_shares_replicates``, otherwise averaged over replicates).
+
+    Returns
+    -------
+    pandas.DataFrame
+        Index ``condition_rep``, columns ``k`` and ``m``.
+    """
+    k = k_df.groupby("condition_rep")["q0.5"].mean()
+    m = m_df.groupby("condition_rep")["q0.5"].mean()
+    out = pd.DataFrame({"k": k, "m": m})
+    if out.isna().any().any():
+        raise ValueError("k and m tables name different conditions")
+    return out
+
+
+def _flag(v):
+    """A 0/1 pin flag as written in a priors CSV (0.0, 1, True, ...)."""
+    if isinstance(v, str):
+        return v.strip().lower() in ("1", "1.0", "true")
+    return bool(float(v))
+
+
+def km_from_map(params_npz, priors_csv, name="condition_growth"):
+    """
+    Per-condition k and m from a MAP's parameters and its priors CSV, for
+    when ``tfs-extract-params`` cannot give them: a MAP checkpoint holds no
+    deterministic sites, and a pinned k or m (all conditions, or some) is
+    one. A pinned condition takes its prior location.
+
+    Parameters
+    ----------
+    params_npz : str
+        ``tfs-fit-model``'s ``_params.npz`` (constrained ``{site}_auto_loc``).
+    priors_csv : str
+        The configuration's priors CSV, with per-condition (indexed) growth
+        rows naming ``condition_rep``.
+    name : str
+        The growth component's site prefix.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Index ``condition_rep``, columns ``k`` and ``m``.
+    """
+    z = np.load(params_npz)
+    p = pd.read_csv(priors_csv)
+    prefix = f"growth.{name}."
+    labels = None
+    for field in ("k_loc", "m_loc", "k_pinned", "m_pinned", "k_scale"):
+        r = p[(p["parameter"] == prefix + field) & p["flat_index"].notna()]
+        if len(r):
+            labels = r.sort_values("flat_index")["condition_rep"].astype(str).tolist()
+            break
+    if labels is None:
+        raise ValueError(f"{priors_csv} has no per-condition growth rows to "
+                         "name the conditions")
+    n = len(labels)
+
+    def per_condition(field, default):
+        r = p[p["parameter"] == prefix + field]
+        if r.empty:
+            return np.full(n, default, dtype=object)
+        if r["flat_index"].notna().any():
+            r = r.sort_values("flat_index")
+            return r["value"].to_numpy(dtype=object)
+        return np.full(n, r["value"].iloc[0], dtype=object)
+
+    out = {}
+    for param in ("k", "m"):
+        loc = per_condition(f"{param}_loc", 0.0).astype(float)
+        pinned = np.array([_flag(v) for v in per_condition(f"{param}_pinned", 0)])
+        full, free = f"{name}_{param}_auto_loc", f"{name}_{param}_free_auto_loc"
+        value = loc.copy()
+        if full in z.files:
+            value = np.asarray(z[full], dtype=float).reshape(n)
+        elif free in z.files:
+            value[~pinned] = np.asarray(z[free], dtype=float).ravel()
+        elif not pinned.all():
+            raise ValueError(f"{params_npz} has no {param} for the free "
+                             "conditions")
+        out[param] = value
+    return pd.DataFrame(out, index=pd.Index(labels, name="condition_rep"))
+
+
+def jackknife_km(estimates, n, d):
+    """
+    Jackknife standard errors of k and m, and k's main direction.
+
+    For n tubes, d dropped per subsample and subsample estimates t_i with
+    mean t_bar, the delete-d jackknife variance is
+    (n - d) / d * mean((t_i - t_bar)^2).
+
+    Parameters
+    ----------
+    estimates : sequence of pandas.DataFrame
+        ``km_table`` of each subsample.
+    n, d : int
+        Tubes per full table and tubes dropped per subsample.
+
+    Returns
+    -------
+    dict
+        ``se``: DataFrame (index ``condition_rep``) with ``k_mean``,
+        ``k_se``, ``m_mean``, ``m_se``; ``k_cov``: k's covariance across
+        conditions (jackknife-scaled); ``k_step``: one standard deviation
+        along its first principal component (a Series by condition, sign
+        fixed so its largest entry is positive); ``k_share``: that
+        component's share of k's variance; ``factor``: (n - d) / d.
+    """
+    if len(estimates) < 2:
+        raise ValueError("the jackknife needs at least two subsamples")
+    if not 0 < d < n:
+        raise ValueError(f"need 0 < d < n, got n={n}, d={d}")
+    conds = estimates[0].index
+    for e in estimates[1:]:
+        if set(e.index) != set(conds):
+            raise ValueError("subsamples name different conditions")
+    factor = (n - d) / d
+    k = pd.DataFrame([e["k"].reindex(conds) for e in estimates])
+    m = pd.DataFrame([e["m"].reindex(conds) for e in estimates])
+    se = pd.DataFrame({
+        "k_mean": k.mean(), "k_se": np.sqrt(factor * k.var(ddof=0)),
+        "m_mean": m.mean(), "m_se": np.sqrt(factor * m.var(ddof=0)),
+    })
+    se.index.name = "condition_rep"
+    dev = (k - k.mean()) * np.sqrt(factor)
+    cov = dev.T.to_numpy() @ dev.to_numpy() / len(k)
+    w, v = np.linalg.eigh(cov)
+    top = int(np.argmax(w))
+    vec = v[:, top] * np.sqrt(max(w[top], 0.0))
+    if vec[np.argmax(np.abs(vec))] < 0:
+        vec = -vec
+    share = float(w[top] / w.sum()) if w.sum() > 0 else float("nan")
+    return {"se": se,
+            "k_cov": pd.DataFrame(cov, index=conds, columns=conds),
+            "k_step": pd.Series(vec, index=conds, name="k_step"),
+            "k_share": share,
+            "factor": factor}
+
+
+# ---------------------------------------------------------------------------
+# Pinned growth priors for stage 2
+# ---------------------------------------------------------------------------
+
+def pinned_growth_priors(km, scale=0.002, conditions=None):
+    """
+    A ``--growth_priors`` table pinning every condition's k and m.
+
+    Parameters
+    ----------
+    km : pandas.DataFrame
+        Index ``condition_rep``, columns ``k`` and ``m`` (``km_table``).
+    scale : float
+        The prior scales written alongside (unused while pinned).
+    conditions : sequence of str or None
+        Keep only these conditions (e.g. one library's), else all.
+
+    Returns
+    -------
+    pandas.DataFrame
+        ``condition_rep``, ``k_loc``, ``k_scale``, ``m_loc``, ``m_scale``,
+        ``k_pinned`` and ``m_pinned`` (1), for
+        ``tfs-configure-model --growth_priors``.
+    """
+    sub = km if conditions is None else km.loc[list(conditions)]
+    return pd.DataFrame({
+        "condition_rep": sub.index.astype(str),
+        "k_loc": sub["k"].to_numpy(), "k_scale": scale,
+        "m_loc": sub["m"].to_numpy(), "m_scale": scale,
+        "k_pinned": 1, "m_pinned": 1,
+    })
+
+
+def k_shift_priors(km, k_step, scale=0.002):
+    """
+    Pinned tables with k one step up and down (m unchanged).
+
+    Returns
+    -------
+    tuple of pandas.DataFrame
+        ``(plus, minus)``, each a ``pinned_growth_priors`` table.
+    """
+    step = k_step.reindex(km.index)
+    if step.isna().any():
+        raise ValueError("k_step does not cover every condition")
+    plus = km.assign(k=km["k"] + step)
+    minus = km.assign(k=km["k"] - step)
+    return pinned_growth_priors(plus, scale), pinned_growth_priors(minus, scale)
+
+
+# ---------------------------------------------------------------------------
+# The k-shift refits: dk_geno's population held at stage 2's MAP
+# ---------------------------------------------------------------------------
+
+DK_GENO_HYPERS = ("hyper_loc", "hyper_scale", "hyper_shift")
+
+
+def dk_geno_hypers(params_npz, name="dk_geno"):
+    """
+    The dk_geno population hyperparameters at a MAP.
+
+    Run 1 of ``planning/studies/two-stage-validation/`` found the k-shift
+    refits sliding every non-wt genotype's dk_geno together against wt
+    through these (stage 2's Laplace held them as its one negative
+    direction), so the refits hold them at stage 2's values.
+
+    Parameters
+    ----------
+    params_npz : str
+        A MAP ``*_params.npz`` (constrained values).
+    name : str
+        The dk_geno component's site prefix.
+
+    Returns
+    -------
+    dict
+        Suffix (``hyper_loc``, ``hyper_scale``, ``hyper_shift``) to value,
+        for those the MAP has; empty for a dk_geno without a learned
+        population (``fixed``, ``pinned``).
+    """
+    out = {}
+    with np.load(params_npz) as z:
+        for h in DK_GENO_HYPERS:
+            key = f"{name}_{h}_auto_loc"
+            if key in z.files:
+                out[h] = float(np.asarray(z[key]).ravel()[0])
+    return out
+
+
+def pin_dk_geno_hypers(priors_csv, values, component="growth.dk_geno"):
+    """
+    Hold dk_geno's population hyperparameters in a priors CSV.
+
+    Writes one ``{component}.pinned.{suffix}`` row per value (replacing any
+    already there), which the dk_geno component reads as a pinned
+    (deterministic) hyperparameter.
+
+    Parameters
+    ----------
+    priors_csv : str
+        The ``tfs-configure-model`` priors CSV, rewritten in place.
+    values : dict
+        Suffix to value (``dk_geno_hypers``).
+    component : str
+        The dk_geno component's row prefix in the priors CSV.
+    """
+    if not values:
+        return
+    df = pd.read_csv(priors_csv, dtype={"value": object})
+    names = [f"{component}.pinned.{h}" for h in values]
+    df = df[~df["parameter"].isin(names)]
+    add = pd.DataFrame({"parameter": names, "value": list(values.values())})
+    pd.concat([df, add], ignore_index=True).to_csv(priors_csv, index=False)
+
+
+def refit_drift(reference_pred, refit_preds):
+    """
+    How far each k-shift refit moved X from stage 2's MAP.
+
+    Parameters
+    ----------
+    reference_pred : pandas.DataFrame
+        Stage 2's X at its MAP (``tfs-predict-theta`` output).
+    refit_preds : dict
+        Refit name (``plus``, ``minus``, ``zero``) to its X at its MAP.
+
+    Returns
+    -------
+    pandas.DataFrame
+        One row per refit: ``median_change`` and ``q10_change`` /
+        ``q90_change`` of (X_refit - X_stage2) over non-wt rows. A refit
+        with k unchanged (``zero``) should not move; the plus and minus
+        refits should move by about the same amount in opposite directions.
+    """
+    ref = reference_pred[PRED_KEY + ["q0.5"]].rename(columns={"q0.5": "x_ref"})
+    rows = []
+    for name, pred in refit_preds.items():
+        df = ref.merge(pred[PRED_KEY + ["q0.5"]], on=PRED_KEY, how="inner")
+        d = (df["q0.5"] - df["x_ref"])[df["genotype"].astype(str) != "wt"]
+        rows.append({"refit": name, "median_change": float(d.median()),
+                     "q10_change": float(d.quantile(0.1)),
+                     "q90_change": float(d.quantile(0.9))})
+    return pd.DataFrame(rows)
+
+
+# ---------------------------------------------------------------------------
+# The shared X shift
+# ---------------------------------------------------------------------------
+
+def shift_table(plus_pred, minus_pred):
+    """
+    X's change per standard deviation of k, from the two shifted refits.
+
+    Parameters
+    ----------
+    plus_pred, minus_pred : pandas.DataFrame
+        ``tfs-predict-theta`` output of the stage 2 refits with k one step
+        up and down (``genotype``, ``titrant_name``, ``titrant_conc``,
+        ``q0.5``).
+
+    Returns
+    -------
+    pandas.DataFrame
+        ``genotype``, ``titrant_name``, ``titrant_conc`` and ``shift`` =
+        (X_plus - X_minus) / 2 (signed; wt set to 0), for
+        ``--shared_shift``.
+    """
+    p = plus_pred[PRED_KEY + ["q0.5"]].rename(columns={"q0.5": "x_plus"})
+    m = minus_pred[PRED_KEY + ["q0.5"]].rename(columns={"q0.5": "x_minus"})
+    df = p.merge(m, on=PRED_KEY, how="inner")
+    if len(df) != len(p) or len(df) != len(m):
+        raise ValueError("the plus and minus predictions cover different rows")
+    df["shift"] = (df["x_plus"] - df["x_minus"]) / 2
+    df.loc[df["genotype"].astype(str) == "wt", "shift"] = 0.0
+    return df[PRED_KEY + ["shift"]]
+
+
+# ---------------------------------------------------------------------------
+# Growth tables too large to load (a full-size table is about 8 GB)
+# ---------------------------------------------------------------------------
+
+CHUNK = 5_000_000
+
+
+def growth_genotypes(path, chunksize=CHUNK):
+    """The genotypes in a growth table, read in chunks."""
+    seen = set()
+    for c in pd.read_csv(path, usecols=["genotype"], chunksize=chunksize):
+        seen.update(c["genotype"].astype(str).unique())
+    return seen
+
+
+def filter_growth_csv(src, dst, genotypes=None, library=None,
+                      chunksize=CHUNK):
+    """
+    Write the rows of a growth table for some genotypes or one library.
+
+    Parameters
+    ----------
+    src, dst : str
+        Input and output CSV paths.
+    genotypes : iterable of str or None
+        Keep only these genotypes.
+    library : str or None
+        Keep only this library's tubes.
+
+    Returns
+    -------
+    int
+        Rows written.
+
+    Raises
+    ------
+    ValueError
+        If nothing is kept.
+    """
+    keep_g = None if genotypes is None else {str(g) for g in genotypes}
+    n = 0
+    header = True
+    with open(dst, "w") as fh:
+        for c in pd.read_csv(src, chunksize=chunksize):
+            mask = pd.Series(True, index=c.index)
+            if keep_g is not None:
+                mask &= c["genotype"].astype(str).isin(keep_g)
+            if library is not None:
+                mask &= c["library"].astype(str) == str(library)
+            c = c[mask]
+            if len(c):
+                c.to_csv(fh, index=False, header=header)
+                header = False
+                n += len(c)
+    if n == 0:
+        raise ValueError(f"filtering {src} kept no rows "
+                         f"(library={library!r})")
+    return n
+
+
+# ---------------------------------------------------------------------------
+# Marker consistency
+# ---------------------------------------------------------------------------
+
+def marker_consistency(both_pred, one_pred, z_flag=2.0):
+    """
+    Per genotype, how far X from one library's data alone departs from X
+    from both libraries.
+
+    X from one library is less precise and somewhat shifted and stretched
+    (on the full-size simulation, dropping pheS stretched X about 1.08 and
+    shifted it about 0.13), so the two are compared after the best affine
+    map of one onto the other (weighted by the rows' combined variance,
+    non-wt rows). A genotype's level offset is its mean residual over
+    concentrations (the disagreement is 91-92% level), and its z is that
+    offset over the median of its rows' combined SD. On the simulation
+    2.5% of deep doubles' rows fell beyond 2 SD, on the real data 8.2%
+    (``planning/studies/m-bias-factorial/``).
+
+    Parameters
+    ----------
+    both_pred, one_pred : pandas.DataFrame
+        ``tfs-predict-theta`` output (``genotype``, ``titrant_name``,
+        ``titrant_conc``, ``q0.025``, ``q0.5``, ``q0.975``) from the stage 2
+        fit and from the one-library refit.
+    z_flag : float
+        |z| above which a genotype is flagged.
+
+    Returns
+    -------
+    pandas.DataFrame
+        One row per genotype: ``n_rows``, ``level_offset``, ``level_sd``,
+        ``z`` and ``inconsistent``.
+    """
+    cols = PRED_KEY + ["q0.025", "q0.5", "q0.975"]
+    m = both_pred[cols].merge(one_pred[cols], on=PRED_KEY,
+                              suffixes=("_both", "_one"))
+    m = m[m["genotype"].astype(str) != "wt"]
+    sd_b = (m["q0.975_both"] - m["q0.025_both"]) / 3.92
+    sd_o = (m["q0.975_one"] - m["q0.025_one"]) / 3.92
+    m = m.assign(sd=np.sqrt(sd_b ** 2 + sd_o ** 2))
+    ok = np.isfinite(m["q0.5_both"]) & np.isfinite(m["q0.5_one"]) & (m["sd"] > 0)
+    m = m[ok]
+    if m.empty:
+        raise ValueError("no non-wt rows in common between the two fits")
+    w = 1.0 / m["sd"] ** 2
+    slope, intercept = np.polyfit(m["q0.5_both"], m["q0.5_one"], 1,
+                                  w=np.sqrt(w))
+    m = m.assign(res=m["q0.5_one"] - (intercept + slope * m["q0.5_both"]))
+    g = m.groupby("genotype")
+    out = pd.DataFrame({"n_rows": g.size(),
+                        "level_offset": g["res"].mean(),
+                        "level_sd": g["sd"].median()})
+    out["z"] = out["level_offset"] / out["level_sd"]
+    out["inconsistent"] = out["z"].abs() > z_flag
+    out.attrs["affine"] = (float(intercept), float(slope))
+    return out
+
+
+# ---------------------------------------------------------------------------
+# The summary table
+# ---------------------------------------------------------------------------
+
+def genotype_reads(path, chunksize=CHUNK):
+    """
+    Each genotype's reads, in total and per library, from a growth table.
+
+    Parameters
+    ----------
+    path : str
+        A ``tfs-process-counts`` growth table (``genotype``, ``library``,
+        ``counts``), read in chunks.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Index ``genotype``; ``reads`` and one ``reads_{library}`` column per
+        library (0 where a genotype has no rows in a library).
+    """
+    parts = []
+    for c in pd.read_csv(path, usecols=["genotype", "library", "counts"],
+                         chunksize=chunksize):
+        c["genotype"] = c["genotype"].astype(str)
+        c["library"] = c["library"].astype(str)
+        parts.append(c.groupby(["genotype", "library"])["counts"].sum())
+    by = pd.concat(parts).groupby(level=[0, 1]).sum().unstack(fill_value=0)
+    by.columns = [f"reads_{lib}" for lib in by.columns]
+    by.insert(0, "reads", by.sum(axis=1))
+    by.index.name = "genotype"
+    return by.astype("int64")
+
+
+def num_mutations(genotype):
+    """0 for wt, else the number of mutations in a '/'-joined genotype."""
+    genotype = str(genotype)
+    return 0 if genotype == "wt" else genotype.count("/") + 1
+
+
+def x_summary(pred, reads, consistency=None, shift=None):
+    """
+    One table per genotype and concentration for the two-stage fit's users.
+
+    Parameters
+    ----------
+    pred : pandas.DataFrame
+        X with the shared k shift (``predict/tfs_pred_theta.csv``): the
+        prediction key, the ``q<level>`` ladder, ``in_training_data`` and
+        ``theta_scale``.
+    reads : pandas.DataFrame
+        ``genotype_reads`` output.
+    consistency : pandas.DataFrame or None
+        ``marker_consistency`` output (index ``genotype``), or None when the
+        consistency step did not run.
+    shift : pandas.DataFrame or None
+        ``shift_table`` output: X's change per SD of k, the shared part of
+        each interval.
+
+    Returns
+    -------
+    pandas.DataFrame
+        ``genotype``, ``num_mutations``, ``titrant_name``, ``titrant_conc``,
+        the quantiles, ``k_shift``, ``in_training_data``, ``theta_scale``,
+        ``reads`` and ``reads_{library}``, and ``consistency_z`` and
+        ``inconsistent`` (NaN for wt, for genotypes the check did not
+        cover and when it did not run), in the prediction's row order.
+    """
+    out = pred.copy()
+    out["genotype"] = out["genotype"].astype(str)
+    out.insert(1, "num_mutations", out["genotype"].map(num_mutations))
+    if shift is not None:
+        sh = shift[PRED_KEY + ["shift"]].rename(columns={"shift": "k_shift"})
+        sh = sh.assign(genotype=sh["genotype"].astype(str))
+        out = out.merge(sh, on=PRED_KEY, how="left")
+    else:
+        out["k_shift"] = np.nan
+    r = reads.reset_index()
+    r["genotype"] = r["genotype"].astype(str)
+    out = out.merge(r, on="genotype", how="left")
+    read_cols = [c for c in r.columns if c != "genotype"]
+    out[read_cols] = out[read_cols].fillna(0).astype("int64")
+    if consistency is not None:
+        c = consistency[["z", "inconsistent"]].rename(
+            columns={"z": "consistency_z"}).reset_index()
+        c["genotype"] = c["genotype"].astype(str)
+        out = out.merge(c, on="genotype", how="left")
+    else:
+        out["consistency_z"] = np.nan
+        out["inconsistent"] = np.nan
+    qs = [c for c in pred.columns if c.startswith("q")]
+    tail = [c for c in ("in_training_data", "theta_scale") if c in out.columns]
+    order = (["genotype", "num_mutations", "titrant_name", "titrant_conc"]
+             + qs + ["k_shift"] + tail + read_cols
+             + ["consistency_z", "inconsistent"])
+    return out[order]
diff --git a/src/tfscreen/util/cli/generalized_main.py b/src/tfscreen/util/cli/generalized_main.py
index 0ddf388f..6a1903b3 100644
--- a/src/tfscreen/util/cli/generalized_main.py
+++ b/src/tfscreen/util/cli/generalized_main.py
@@ -1,17 +1,135 @@
-import sys 
-import inspect
 import argparse
+import inspect
+import os
+import re
+import sys
+
+from tfscreen.util import provenance
+
+
+def _split_docstring(doc):
+    """
+    Split a numpy-style docstring into its description and per-parameter help.
+
+    Returns
+    -------
+    description : str
+        Everything before the ``Parameters`` section, dedented.
+    param_help : dict
+        Parameter name -> its description, joined onto one line.
+    """
+    if not doc:
+        return "", {}
+    doc = inspect.cleandoc(doc)
+    lines = doc.splitlines()
+
+    def _is_header(i, name=None):
+        if i + 1 >= len(lines):
+            return False
+        underline = lines[i + 1].strip()
+        if not underline or set(underline) != {"-"}:
+            return False
+        return name is None or lines[i].strip() == name
+
+    start = next((i for i in range(len(lines)) if _is_header(i, "Parameters")), None)
+    if start is None:
+        return doc.strip(), {}
+    description = "\n".join(lines[:start]).strip()
+
+    param_help = {}
+    current = None
+    for i in range(start + 2, len(lines)):
+        if _is_header(i):
+            break
+        line = lines[i]
+        if not line.strip():
+            continue
+        if not line.startswith((" ", "\t")):
+            # "name : type" or "name" (numpydoc allows several names "a, b")
+            names = line.split(":")[0]
+            current = [n.strip() for n in names.split(",") if n.strip()]
+            for n in current:
+                param_help[n] = ""
+        elif current is not None:
+            for n in current:
+                param_help[n] = (param_help[n] + " " + line.strip()).strip()
+    return description, param_help
+
+
+class _HelpFormatter(argparse.RawDescriptionHelpFormatter):
+    """Keep the description's layout; wrap argument help normally."""
+
+
+class ParserCaptured(Exception):
+    """Raised instead of running the command while ``capture_parsers`` is on."""
+
+    def __init__(self, parser):
+        super().__init__(parser.prog)
+        self.parser = parser
+
+
+# When True, generalized_main builds the parser and raises ParserCaptured
+# instead of parsing and running (used to generate the CLI reference).
+_CAPTURE_PARSER = False
+
+
+def capture_parser(main):
+    """
+    The argparse parser a ``tfs-*`` ``main()`` builds, without running it.
+
+    Parameters
+    ----------
+    main : callable
+        An entry point's ``main`` function (one that calls generalized_main).
+
+    Returns
+    -------
+    argparse.ArgumentParser
+    """
+    global _CAPTURE_PARSER
+    _CAPTURE_PARSER = True
+    try:
+        main()
+    except ParserCaptured as e:
+        return e.parser
+    finally:
+        _CAPTURE_PARSER = False
+    raise RuntimeError(f"{main} did not call generalized_main")
+
+
+def _help_text(param_help, name, default, show_default):
+    text = param_help.get(name, "")
+    # Reduce double-backtick literals to plain text for the terminal.
+    text = re.sub(r"``([^`]+)``", r"\1", text)
+    text = text.replace("%", "%%")
+    if show_default and "default" not in text.lower():
+        text = f"{text} (default: {default!r})" if text else f"(default: {default!r})"
+    return text or None
+
 
 def generalized_main(fcn,
                      argv=None,
                      manual_arg_defaults=None,
                      manual_arg_types=None,
-                     manual_arg_nargs=None):
+                     manual_arg_nargs=None,
+                     prog=None,
+                     write_provenance=True):
     """
-    A generalized main function that constructs a command line argument parser
-    and then parses arguments for any function. It infers argument defaults and
-    types from the function signature. These inferences can be overwritten by 
-    manual_arg_defaults and manual_arg_types. 
+    Build a command-line parser from a function's signature and run it.
+
+    Parameters without a default become positional arguments; parameters with
+    a default become ``--name`` flags of the default's type. A bool that
+    defaults to False is a ``--name`` switch; one that defaults to True takes
+    ``--no_name`` to turn it off (``--name`` is accepted and does nothing). A
+    flag whose default is None reads a string unless ``manual_arg_types``
+    gives its type. The function's numpy-style docstring supplies the help:
+    its text before ``Parameters`` is the description, and each parameter's
+    entry is that argument's help.
+
+    Before the function runs, the run's provenance (tfscreen version, git
+    commit, command line) is printed to stderr. If the function takes an
+    ``out_prefix`` argument, it is also written to
+    ``{out_prefix}_provenance.json``.
 
     Parameters
     ----------
@@ -21,90 +139,110 @@ def generalized_main(fcn,
         arguments to parse. if None, use sys.argv[1:]
     manual_arg_defaults : dict
         dictionary keying arguments to defaults that differ from the signature.
-        The argument type is set to the type of the value specified. 
+        The argument type is set to the type of the value specified.
     manual_arg_types: dict
         dictionary keying arguments to types. This overrides the types inferred
-        from the signature or manual_arg_defaults. 
+        from the signature or manual_arg_defaults.
     manual_arg_nargs: dict
         dictionary keying arguments to nargs. This overrides the nargs inferred
-        from the signature or manual_arg_defaults. 
+        from the signature or manual_arg_defaults.
+    prog : str, optional
+        program name shown in usage. Default: the name the command was run as
+        (``sys.argv[0]``) when ``argv`` is None, else the function name.
+    write_provenance : bool
+        print the provenance and write ``{out_prefix}_provenance.json``.
+
+    Returns
+    -------
+    None
+        Always None, whatever ``fcn`` returns: console scripts run
+        ``sys.exit(main())``, and any other value would end the process with
+        status 1 and print the value.
     """
 
-    # Get command line arguments
+    command_argv = list(sys.argv) if argv is None else [prog or fcn.__name__, *argv]
+    if prog is None:
+        prog = os.path.basename(sys.argv[0]) if argv is None else fcn.__name__
     if argv is None:
         argv = sys.argv[1:]
 
-    # Build dicts of manual arg types and defaults if not specified
-    if manual_arg_types is None:
-        manual_arg_types = {}
-
-    if manual_arg_defaults is None:
-        manual_arg_defaults = {}
+    manual_arg_types = manual_arg_types or {}
+    manual_arg_defaults = manual_arg_defaults or {}
+    manual_arg_nargs = manual_arg_nargs or {}
 
-    if manual_arg_nargs is None:
-        manual_arg_nargs = {}
-
-    # Build parser
-    description = dict(inspect.getmembers(fcn))["__doc__"]
-    parser = argparse.ArgumentParser(prog=f"{fcn.__name__}.py",
+    description, param_help = _split_docstring(fcn.__doc__)
+    description = re.sub(r"``([^`]+)``", r"\1", description)
+    parser = argparse.ArgumentParser(prog=prog,
                                      description=description,
-                                     formatter_class=argparse.RawTextHelpFormatter)
+                                     formatter_class=_HelpFormatter)
 
-    # Build parser arguments using signature of fcn
     param = inspect.signature(fcn).parameters
     for p in param:
 
-        # Grab default and type the default for the parameter. If no default 
-        # specified, make required. 
         if param[p].default is not param[p].empty:
-            arg_type = type(param[p].default)
             default = param[p].default
+            arg_type = type(default)
             required = False
         else:
-            arg_type = None
             default = None
+            arg_type = None
             required = True
-        
-        # If the argument is in manual defaults, override what we got from the
-        # function signature. 
+
         if p in manual_arg_defaults:
-            arg_type = type(manual_arg_defaults[p])
             default = manual_arg_defaults[p]
+            arg_type = type(default)
             required = False
-        
-        # manual_arg_type takes precedence over any types inferred above. 
+
         if p in manual_arg_types:
             arg_type = manual_arg_types[p]
+        elif arg_type is type(None):
+            arg_type = str
 
-        # assume nargs is None unless the user overrides explicitly
-        if p in manual_arg_nargs:
-            nargs = manual_arg_nargs[p]
-        else:
-            nargs = None
+        nargs = manual_arg_nargs.get(p, None)
 
-            # Add the appropriate argument to the parser. 
         if required:
-            parser.add_argument(p,type=arg_type,nargs=nargs)
-        else:
-            arg_name = f"--{p}"
-            if arg_type is bool:
-                if default is True:
-                    parser.add_argument(arg_name,action="store_false")
-                else:
-                    parser.add_argument(arg_name,action="store_true")
+            parser.add_argument(p, type=arg_type, nargs=nargs,
+                                help=_help_text(param_help, p, None, False))
+            continue
+
+        arg_name = f"--{p}"
+        if arg_type is bool:
+            if default is True:
+                text = _help_text(param_help, p, None, False)
+                parser.add_argument(f"--no_{p}", dest=p, action="store_false",
+                                    help=(f"Turn off (on by default): {text}"
+                                          if text else f"turn off {p}"))
+                parser.add_argument(arg_name, dest=p, action="store_true",
+                                    help=argparse.SUPPRESS)
+                parser.set_defaults(**{p: True})
             else:
-                # If nargs is specified, the type should be the type of the elements,
-                # not the list itself. 
-                if nargs is not None and arg_type is list:
-                    if default is not None and len(default) > 0:
-                        arg_type = type(default[0])
-                    else:
-                        arg_type = str
-
-                parser.add_argument(arg_name,type=arg_type,default=default,nargs=nargs)
-                
-    # Parse stats
+                parser.add_argument(arg_name, action="store_true",
+                                    help=_help_text(param_help, p, None, False))
+            continue
+
+        # With nargs, the type is the element type, not the list itself.
+        if nargs is not None and arg_type in (list, tuple):
+            if default is not None and len(default) > 0:
+                arg_type = type(default[0])
+            else:
+                arg_type = str
+
+        parser.add_argument(arg_name, type=arg_type, default=default, nargs=nargs,
+                            help=_help_text(param_help, p, default, True))
+
+    if _CAPTURE_PARSER:
+        raise ParserCaptured(parser)
+
     args = parser.parse_args(argv)
+    kwargs = args.__dict__
+
+    if write_provenance:
+        provenance.set_command(command_argv)
+        prov = provenance.get_provenance()
+        provenance.print_provenance(prov)
+        out_prefix = kwargs.get("out_prefix", None)
+        if isinstance(out_prefix, str) and out_prefix:
+            provenance.write_provenance(f"{out_prefix}_provenance.json", prov)
 
-    # Call function with kwargs
-    fcn(**args.__dict__)
+    fcn(**kwargs)
+    return None
diff --git a/src/tfscreen/util/grid_utils.py b/src/tfscreen/util/grid_utils.py
index 9ccc21c8..84b0acbf 100644
--- a/src/tfscreen/util/grid_utils.py
+++ b/src/tfscreen/util/grid_utils.py
@@ -1,16 +1,29 @@
 """
 Shared utilities for tfs-setup-grid and tfs-setup-sim-grid.
 
-Provides run-name generation, Jinja2 environment construction, and config-file
-path rewriting.  Both grid CLIs import from here; keep this module free of any
-growth-model- or simulate-specific logic.
+Provides run-name generation, Jinja2 environment construction and input-file
+staging.  Both grid CLIs import from here; keep
+this module free of any growth-model- or simulate-specific logic.
+
+Input staging
+-------------
+A grid output directory must be movable as a unit (on and off a cluster, onto
+another partition), so a run may not refer to anything outside it.
+``InputStager`` copies each input file into ``<out_dir>/inputs/`` once and
+returns the path a run subdirectory uses to reach it (``../inputs/<name>``);
+``check_no_outside_paths`` fails on a config value that names a file but is not
+one of the keys the caller stages.
 """
 
+import filecmp
 import os
 import re
+import shutil
 
 import jinja2
-import yaml
+
+# Directory under out_dir holding the input files shared by all runs.
+INPUTS_DIRNAME = "inputs"
 
 
 def sanitize(s):
@@ -43,41 +56,151 @@ def make_run_name(name_template, all_vars, index):
     return f"{prefix}_{suffix}" if suffix else prefix
 
 
-def relativize_node(node, subdir):
-    """Recursively rewrite absolute path strings in a config node to be relative to subdir."""
-    if isinstance(node, dict):
-        return {k: relativize_node(v, subdir) for k, v in node.items()}
-    if isinstance(node, list):
-        return [relativize_node(v, subdir) for v in node]
-    if isinstance(node, str) and node and os.path.isabs(node) and os.path.exists(node):
-        return os.path.relpath(node, subdir)
-    return node
+# ---------------------------------------------------------------------------
+# Input staging (self-contained, movable grid directories)
+# ---------------------------------------------------------------------------
 
+class InputStager:
+    """Copy input files into ``<out_dir>/inputs/`` once each.
 
-def relativize_config_paths(yaml_path, subdir):
-    """Rewrite absolute paths anywhere in a written config YAML to be relative to subdir."""
-    with open(yaml_path) as fh:
-        cfg = yaml.safe_load(fh)
-    updated = relativize_node(cfg, subdir)
-    if updated != cfg:
-        with open(yaml_path, "w") as fh:
-            yaml.dump(updated, fh, default_flow_style=False, sort_keys=False)
+    ``stage(path, what)`` returns the path a run subdirectory uses to reach the
+    copy (``../inputs/<name>``). The same source file always maps to the same
+    copy. A different file whose name is taken (by an earlier source in this
+    setup, or by a different file already in the directory) gets a numeric
+    suffix, so an existing run is never left pointing at changed contents.
 
+    With ``dry_run=True`` it only checks that each path is an existing file and
+    copies nothing, so a whole grid can be validated before anything is written.
 
-def relativize_template_vars(tmpl_vars, grid_yaml_dir, subdir):
-    """Return tmpl_vars with any path-like string values rewritten relative to subdir.
+    Parameters
+    ----------
+    out_dir : str
+        Root directory of the grid.
+    tool : str
+        Name of the calling CLI, used in error messages.
+    dry_run : bool, optional
+        Validate only; copy nothing. Default False.
+    """
 
-    A value is treated as a path when it resolves (relative to grid_yaml_dir, or
-    as-is if absolute) to a file or directory that exists on disk.
+    def __init__(self, out_dir, tool, dry_run=False):
+        self.inputs_dir = os.path.join(os.path.abspath(out_dir), INPUTS_DIRNAME)
+        self.tool = tool
+        self.dry_run = dry_run
+        self._by_source = {}
+        self._claimed = set()
+
+    def stage(self, path, what):
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"{what} names a file that does not exist: {path}")
+        if not os.path.isfile(path):
+            raise ValueError(
+                f"{what} names a directory ({path}). {self.tool} copies input "
+                f"files into the grid so it can be moved, and does not copy "
+                f"directories. Point it at a file."
+            )
+        source = os.path.realpath(path)
+        if self.dry_run:
+            return os.path.join("..", INPUTS_DIRNAME, os.path.basename(source))
+        if source in self._by_source:
+            return self._by_source[source]
+
+        os.makedirs(self.inputs_dir, exist_ok=True)
+        stem, ext = os.path.splitext(os.path.basename(source))
+        n = 1
+        while True:
+            name = f"{stem}{ext}" if n == 1 else f"{stem}_{n}{ext}"
+            dest = os.path.join(self.inputs_dir, name)
+            if name not in self._claimed:
+                if not os.path.exists(dest):
+                    shutil.copy2(source, dest)
+                    break
+                if os.path.isfile(dest) and filecmp.cmp(source, dest, shallow=False):
+                    break
+            n += 1
+
+        self._claimed.add(name)
+        rel = os.path.join("..", INPUTS_DIRNAME, name)
+        self._by_source[source] = rel
+        return rel
+
+
+def check_no_outside_paths(cfg, known_key_paths, source_dirs, hint):
+    """Fail if a config value outside ``known_key_paths`` names an existing file.
+
+    Such a value would be written as-is and so depend on a location outside the
+    grid. An absolute value is checked as-is (file or directory); a relative
+    value is checked as a file against the directory it came from.
+
+    Parameters
+    ----------
+    cfg : dict
+        Config (possibly nested) to walk.
+    known_key_paths : iterable of tuple
+        Key paths (tuples of keys into ``cfg``) the caller stages itself.
+    source_dirs : str or dict
+        Directory relative values resolve against: one directory for the whole
+        config, or a dict mapping each top-level key to its directory.
+    hint : str
+        Appended to the error message: what to do if the value really is a
+        file the run reads.
+    """
+    known = {tuple(k) for k in known_key_paths}
+
+    def base_dir(key_path):
+        if isinstance(source_dirs, dict):
+            return source_dirs[key_path[0]]
+        return source_dirs
+
+    def walk(node, key_path):
+        if isinstance(node, dict):
+            for k, v in node.items():
+                walk(v, key_path + (k,))
+        elif isinstance(node, list):
+            for i, v in enumerate(node):
+                walk(v, key_path + (i,))
+        elif isinstance(node, str) and node and key_path not in known:
+            if os.path.isabs(node):
+                target = node if os.path.exists(node) else None
+            else:
+                cand = os.path.join(base_dir(key_path), node)
+                target = cand if os.path.isfile(cand) else None
+            if target is not None:
+                dotted = ".".join(str(k) for k in key_path)
+                raise ValueError(
+                    f"Config key '{dotted}' = '{node}' names a file or "
+                    f"directory ({os.path.normpath(target)}), but '{dotted}' is "
+                    f"not one of the file-path keys copied into the grid. The "
+                    f"grid must be movable, so a run cannot refer to a location "
+                    f"outside it. {hint}"
+                )
+
+    walk(cfg, ())
+
+
+def stage_template_vars(tmpl_vars, grid_yaml_dir, stager):
+    """Return tmpl_vars with each value naming an existing file replaced by its copy.
+
+    A value is a file when it resolves (relative to grid_yaml_dir, or as-is if
+    absolute) to an existing file. A value naming an existing directory is an
+    error (``.`` excepted: it means the run directory).
     """
     out = {}
     for key, val in tmpl_vars.items():
-        if isinstance(val, str) and val:
-            abs_path = val if os.path.isabs(val) else os.path.normpath(
-                os.path.join(grid_yaml_dir, val)
-            )
-            if os.path.exists(abs_path):
-                out[key] = os.path.relpath(abs_path, subdir)
-                continue
+        if isinstance(val, str) and val and val != ".":
+            path = val if os.path.isabs(val) else os.path.join(grid_yaml_dir, val)
+            if os.path.isfile(path):
+                val = stager.stage(path, f"Template variable '{key}'")
+            elif os.path.isdir(path):
+                stager.stage(path, f"Template variable '{key}'")  # raises
         out[key] = val
     return out
+
+
+def render_run_template(jinja_template, tmpl_vars, run_name):
+    """Render the run template, naming the run on an undefined variable."""
+    try:
+        return jinja_template.render(**tmpl_vars)
+    except jinja2.UndefinedError as exc:
+        raise ValueError(
+            f"Undefined template variable for run '{run_name}': {exc}"
+        ) from exc
diff --git a/src/tfscreen/util/io/read_dataframe.py b/src/tfscreen/util/io/read_dataframe.py
index 0fc42539..fe0f5e7e 100644
--- a/src/tfscreen/util/io/read_dataframe.py
+++ b/src/tfscreen/util/io/read_dataframe.py
@@ -46,7 +46,12 @@ def read_dataframe(source, index_column=None):
     # 1. Handle different source types (path vs. DataFrame)
     if isinstance(source, str):
         path = source
-        ext = path.split(".")[-1].strip().lower()
+        parts = path.strip().lower().split(".")
+        # a compressed text table (.csv.gz, .tsv.bz2, ...) is read by its
+        # inner extension; pandas infers the compression from the path
+        if len(parts) > 2 and parts[-1] in ("gz", "bz2", "xz", "zst", "zip"):
+            parts = parts[:-1]
+        ext = parts[-1]
         try:
             if ext in ["xlsx", "xls"]:
                 df = pd.read_excel(path)
diff --git a/src/tfscreen/util/provenance.py b/src/tfscreen/util/provenance.py
new file mode 100644
index 00000000..8a6e2281
--- /dev/null
+++ b/src/tfscreen/util/provenance.py
@@ -0,0 +1,122 @@
+"""
+Provenance of a run: the tfscreen version, the git commit it ran from and
+the command line.
+
+Every ``tfs-*`` command prints this when it starts and writes it to
+``{out_prefix}_provenance.json`` (see ``generalized_main``). Writers that keep
+their own records (configs, checkpoints) embed ``get_provenance()``.
+"""
+
+import datetime
+import json
+import os
+import platform
+import shlex
+import subprocess
+import sys
+
+from tfscreen.__version__ import __version__
+
+_PACKAGE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+# Set by generalized_main for the run in progress; None outside a CLI run.
+_COMMAND = None
+
+# git_state() of this process, looked up once (checkpoints ask often).
+_GIT_STATE = None
+
+
+def _git(*args):
+    """Run git in the package directory; None if git or a repository is missing."""
+    try:
+        out = subprocess.run(["git", "-C", _PACKAGE_DIR, *args],
+                             capture_output=True, text=True, timeout=10)
+    except (OSError, subprocess.SubprocessError):
+        return None
+    if out.returncode != 0:
+        return None
+    return out.stdout.strip()
+
+
+def git_state():
+    """
+    The commit the package was imported from.
+
+    Returns
+    -------
+    dict
+        ``commit`` (full hash, or None when the package is not inside a git
+        checkout, as for an installed wheel) and ``dirty`` (True when tracked
+        files differ from that commit, None when unknown).
+    """
+    commit = _git("rev-parse", "HEAD")
+    if commit is None:
+        return {"commit": None, "dirty": None}
+    status = _git("status", "--porcelain", "--untracked-files=no")
+    return {"commit": commit, "dirty": None if status is None else bool(status)}
+
+
+def set_command(argv):
+    """Record the command line of the run in progress (called by generalized_main)."""
+    global _COMMAND
+    _COMMAND = " ".join(shlex.quote(a) for a in argv)
+
+
+def get_provenance():
+    """
+    Provenance of the current process.
+
+    Returns
+    -------
+    dict
+        ``tfscreen_version``, ``commit``, ``dirty``, ``command`` (None outside
+        a ``tfs-*`` run), ``python``, ``host``, ``cwd`` and ``time`` (local,
+        ISO 8601).
+    """
+    global _GIT_STATE
+    if _GIT_STATE is None:
+        _GIT_STATE = git_state()
+    prov = {"tfscreen_version": __version__}
+    prov.update(_GIT_STATE)
+    prov.update({
+        "command": _COMMAND,
+        "python": platform.python_version(),
+        "host": platform.node(),
+        "cwd": os.getcwd(),
+        "time": datetime.datetime.now().isoformat(timespec="seconds"),
+    })
+    return prov
+
+
+def format_provenance(prov):
+    """One line for a log: version, commit (with a dirty flag) and command."""
+    commit = prov.get("commit")
+    if commit is None:
+        where = "no git commit"
+    else:
+        where = f"commit {commit[:10]}" + (" (dirty)" if prov.get("dirty") else "")
+    line = f"tfscreen {prov['tfscreen_version']}, {where}"
+    if prov.get("command"):
+        line += f": {prov['command']}"
+    return line
+
+
+def write_provenance(path, prov=None):
+    """Write provenance (default: ``get_provenance()``) to a JSON file."""
+    if prov is None:
+        prov = get_provenance()
+    parent = os.path.dirname(os.path.abspath(path))
+    os.makedirs(parent, exist_ok=True)
+    with open(path, "w") as fh:
+        json.dump(prov, fh, indent=2)
+        fh.write("\n")
+    return path
+
+
+def print_provenance(prov=None, stream=None):
+    """Print the one-line provenance summary (to stderr by default)."""
+    if prov is None:
+        prov = get_provenance()
+    if stream is None:
+        stream = sys.stderr
+    print(format_provenance(prov), file=stream, flush=True)
diff --git a/tests/conftest.py b/tests/conftest.py
index 93467c4a..46b69c5f 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -6,6 +6,14 @@
 # Set before any tfscreen/numba imports happen during collection.
 os.environ.setdefault("NUMBA_DISABLE_JIT", "1")
 
+# pandas 3 (what CI installs) always copies on write, so to_numpy() can
+# return a read-only view; pandas 2 can opt in. Run the tests that way on
+# pandas 2 too, so code that writes into such a view fails locally as it
+# does on CI (shared_shift.row_shifts did, 2026-10-18).
+import pandas as pd
+if int(pd.__version__.split(".")[0]) < 3:
+    pd.options.mode.copy_on_write = True
+
 def get_files(base_dir):
     """
     Traverse base_dir and return a dictionary that keys all files and some
@@ -112,3 +120,26 @@ def pytest_collection_modifyitems(config, items):
     for item in items:
         if "slow" in item.keywords:
             item.add_marker(skip_slow)
+
+
+_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+
+
+@pytest.fixture(autouse=True)
+def _no_provenance_files_in_repo(monkeypatch):
+    """
+    Tests that call a command's main() with its default --out_prefix would
+    leave {out_prefix}_provenance.json in the working directory (usually the
+    repository). Skip provenance files that would land inside the
+    repository; ones under a test's tmp_path are written as usual.
+    """
+    from tfscreen.util import provenance
+
+    real = provenance.write_provenance
+
+    def guarded(path, prov=None):
+        if os.path.realpath(path).startswith(_REPO_ROOT + os.sep):
+            return path
+        return real(path, prov)
+
+    monkeypatch.setattr(provenance, "write_provenance", guarded)
diff --git a/tests/fixtures/generate_reference_posteriors.py b/tests/fixtures/generate_reference_posteriors.py
index 193d7b96..3b8fb674 100644
--- a/tests/fixtures/generate_reference_posteriors.py
+++ b/tests/fixtures/generate_reference_posteriors.py
@@ -52,7 +52,7 @@ def run():
         binding_df=BINDING_CSV,
         batch_size=BATCH_SIZE,
         condition_growth="linear",
-        transformation="empirical",
+        transformation="mixture",
         theta="hill_geno",
         growth_transition="instant",
         dk_geno="hierarchical_geno",
@@ -72,7 +72,6 @@ def run():
         svi=svi,
         max_num_epochs=NUM_STEPS,
         out_prefix=out_prefix,
-        convergence_check_interval=100,
         checkpoint_interval=500,
     )
     print(f"  converged={converged}")
diff --git a/tests/smoke-tests/test_configure_run_smoke.py b/tests/smoke-tests/test_configure_run_smoke.py
index 9cc2d76e..7c1d3b52 100644
--- a/tests/smoke-tests/test_configure_run_smoke.py
+++ b/tests/smoke-tests/test_configure_run_smoke.py
@@ -99,7 +99,9 @@ def test_configure_run_pipeline_smoke(tmpdir, library_smoke_yaml):
 
     # Run configuration
     configure_model(binding_path,
+                              growth_likelihood="lncfu", sample_offset_model="zero",
                               growth_df=growth_path,
+                              allow_missing_spikes=True,  # no wt in this toy table
                               library_config=library_smoke_yaml,
                               out_prefix=out_prefix)
 
@@ -143,18 +145,155 @@ def test_configure_run_pipeline_smoke(tmpdir, library_smoke_yaml):
     assert os.path.exists(os.path.join(tmpdir, "test_tfs_out_hill_n.csv"))
 
 
+@pytest.mark.slow
+def test_configure_run_growth_only_smoke(tmpdir, growth_smoke_csv,
+                                         library_smoke_yaml):
+    """
+    configure -> fit -> sample posterior -> extract params -> predict growth
+    for a growth-only model (growth data, no binding data).
+    """
+    from tfscreen.tfmodel.scripts.predict_growth_cli import predict_growth
+
+    cfg_prefix = os.path.join(tmpdir, "test_tfs_go")
+    configure_model(growth_df=growth_smoke_csv,
+                    library_config=library_smoke_yaml,
+                    out_prefix=cfg_prefix)
+    config_file = f"{cfg_prefix}_config.yaml"
+    with open(config_file, "r") as f:
+        config = yaml.safe_load(f)
+    assert "binding" not in config["data"]
+
+    out_prefix = os.path.join(tmpdir, "test_tfs_go_out")
+    fit_model(config_file=config_file,
+              seed=42,
+              max_num_epochs=1,
+              out_prefix=out_prefix)
+
+    sample_posterior(config_file=config_file,
+                     checkpoint_file=f"{out_prefix}_checkpoint.pkl",
+                     out_prefix=f"{out_prefix}_posterior",
+                     num_posterior_samples=10,
+                     sampling_batch_size=10)
+    posterior = f"{out_prefix}_posterior.h5"
+    assert os.path.exists(posterior)
+
+    summarize_posteriors(config_file=config_file,
+                         param_file=posterior,
+                         out_prefix=out_prefix)
+
+    predict_growth(config_file, posterior, out_prefix=f"{out_prefix}_pred")
+    pred = pd.read_csv(f"{out_prefix}_pred.csv")
+    assert len(pred) > 0
+
+
+@pytest.mark.slow
+def test_configure_run_count_likelihood_smoke(tmpdir, growth_smoke_csv,
+                                              binding_smoke_csv,
+                                              library_smoke_yaml):
+    """
+    configure -> fit -> sample posterior -> predict growth with the count
+    likelihood and a per-tube level offset.
+    """
+    from tfscreen.tfmodel.scripts.predict_growth_cli import predict_growth
+
+    cfg_prefix = os.path.join(tmpdir, "test_tfs_counts")
+    configure_model(binding_df=binding_smoke_csv,
+                    growth_df=growth_smoke_csv,
+                    library_config=library_smoke_yaml,
+                    growth_likelihood="counts",
+                    sample_offset_model="level",
+                    out_prefix=cfg_prefix)
+    config_file = f"{cfg_prefix}_config.yaml"
+
+    out_prefix = os.path.join(tmpdir, "test_tfs_counts_out")
+    fit_model(config_file=config_file, seed=42, max_num_epochs=1,
+              out_prefix=out_prefix)
+    sample_posterior(config_file=config_file,
+                     checkpoint_file=f"{out_prefix}_checkpoint.pkl",
+                     out_prefix=f"{out_prefix}_posterior",
+                     num_posterior_samples=10,
+                     sampling_batch_size=10)
+    posterior = f"{out_prefix}_posterior.h5"
+    predict_growth(config_file, posterior, out_prefix=f"{out_prefix}_pred")
+    assert len(pd.read_csv(f"{out_prefix}_pred.csv")) > 0
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("guide_type", ["auto_normal",
+                                        "auto_low_rank_multivariate_normal"])
+def test_configure_run_autoguide_smoke(tmpdir, guide_type, library_smoke_yaml):
+    """
+    configure -> fit-model --guide_type <autoguide> (warm-started from the
+    pre-MAP point) -> sample-posterior, restoring the guide from the
+    checkpoint's recorded guide_type.
+    """
+    import h5py
+
+    growth_df = pd.DataFrame({
+        "library": ["lib", "lib"],
+        "replicate": ["R1", "R1"],
+        "condition_pre": ["C1-", "C1-"],
+        "condition_sel": ["C2+", "C2+"],
+        "genotype": ["M42I", "L45P"],
+        "t": [0, 10],
+        "t_sel": [0, 10],
+        "t_pre": [12, 12],
+        "ln_cfu": [1.0, 2.0],
+        "ln_cfu_std": [0.1, 0.1],
+        "titrant_name": ["T1", "T1"],
+        "titrant_conc": [0.1, 0.1]
+    })
+    growth_path = os.path.join(tmpdir, "test_growth.csv")
+    growth_df.to_csv(growth_path, index=False)
+
+    binding_df = pd.DataFrame({
+        "genotype": ["M42I", "L45P"],
+        "titrant_name": ["T1", "T1"],
+        "titrant_conc": [0.1, 0.1],
+        "theta_obs": [0.5, 0.6],
+        "theta_std": [0.05, 0.05]
+    })
+    binding_path = os.path.join(tmpdir, "test_binding.csv")
+    binding_df.to_csv(binding_path, index=False)
+
+    cfg_prefix = os.path.join(tmpdir, "test_tfs")
+    configure_model(binding_path, growth_df=growth_path,
+                    allow_missing_spikes=True,  # no wt in this toy table
+                    growth_likelihood="lncfu", sample_offset_model="zero",
+                    library_config=library_smoke_yaml, out_prefix=cfg_prefix)
+    config_file = f"{cfg_prefix}_config.yaml"
+
+    out_prefix = os.path.join(tmpdir, "test_tfs_auto")
+    fit_model(config_file=config_file,
+              seed=42,
+              max_num_epochs=1,
+              pre_map_num_epoch=2,
+              guide_type=guide_type,
+              out_prefix=out_prefix)
+
+    sample_posterior(config_file=config_file,
+                     checkpoint_file=f"{out_prefix}_checkpoint.pkl",
+                     out_prefix=f"{out_prefix}_posterior",
+                     num_posterior_samples=10,
+                     sampling_batch_size=10)
+
+    with h5py.File(f"{out_prefix}_posterior.h5", "r") as hf:
+        assert hf.attrs["num_samples"] == 10
+        assert not any(k.startswith("_") for k in hf)
+
+
 @pytest.mark.slow
 def test_configure_run_binding_weight_smoke(tmpdir, library_smoke_yaml):
     """
     Smoke test for the binding_weight feature end-to-end.
 
     Verifies that:
-    1. binding_weight=None auto-computes N_growth / N_binding and saves it as
-       a concrete float in the YAML (not None).
+    1. binding_weight=None resolves to 1.0 and is saved as a concrete float
+       in the YAML (not None).
     2. An explicit binding_weight is stored exactly in the YAML.
     3. A run configured with the auto-weight completes without error.
     """
-    # Growth data: 20 rows  |  Binding data: 4 rows  →  expected auto-weight = 5.0
+    # Growth data: 20 rows  |  Binding data: 4 rows  →  default weight 1.0, not 5.0
     growth_df = pd.DataFrame({
         "library":       ["lib"] * 20,
         "replicate":     ["R1"] * 20,
@@ -183,7 +322,9 @@ def test_configure_run_binding_weight_smoke(tmpdir, library_smoke_yaml):
     # ── 1. Explicit binding_weight is preserved in the YAML ──────────────────
     out_explicit = os.path.join(tmpdir, "tfs_explicit")
     configure_model(binding_path,
+                    growth_likelihood="lncfu", sample_offset_model="zero",
                     growth_df=growth_path,
+                    allow_missing_spikes=True,  # no wt in this toy table
                     library_config=library_smoke_yaml,
                     out_prefix=out_explicit,
                     binding_weight=99.0)
@@ -197,7 +338,9 @@ def test_configure_run_binding_weight_smoke(tmpdir, library_smoke_yaml):
     # ── 2. Auto-computed binding_weight is a concrete float, not None ─────────
     out_auto = os.path.join(tmpdir, "tfs_auto")
     configure_model(binding_path,
+                    growth_likelihood="lncfu", sample_offset_model="zero",
                     growth_df=growth_path,
+                    allow_missing_spikes=True,  # no wt in this toy table
                     library_config=library_smoke_yaml,
                     out_prefix=out_auto)
 
@@ -207,8 +350,8 @@ def test_configure_run_binding_weight_smoke(tmpdir, library_smoke_yaml):
     auto_weight = cfg_auto["components"]["binding_weight"]
     assert auto_weight is not None, "Auto binding_weight must not be None in the YAML"
     assert isinstance(auto_weight, float), "Auto binding_weight should be a float"
-    # 20 growth rows / 4 binding rows = 5.0
-    assert auto_weight == pytest.approx(20.0 / 4.0)
+    # 1, not 20 growth rows / 4 binding rows (the default until 2026-10-16)
+    assert auto_weight == pytest.approx(1.0)
 
     # ── 3. A fit using the auto-weight config runs to completion ─────────────
     out_fit = os.path.join(tmpdir, "tfs_fit")
diff --git a/tests/smoke-tests/test_convergence_smoke.py b/tests/smoke-tests/test_convergence_smoke.py
new file mode 100644
index 00000000..456fd669
--- /dev/null
+++ b/tests/smoke-tests/test_convergence_smoke.py
@@ -0,0 +1,112 @@
+"""
+Convergence on a small real fit: configure -> prefit -> fit (pre-MAP + SVI,
+and MAP) on the smoke data, run until the monitor stops them.
+"""
+
+import os
+
+import dill
+import pandas as pd
+import pytest
+
+from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+from tfscreen.tfmodel.scripts.fit_model_cli import fit_model
+from tfscreen.tfmodel.scripts.prefit_calibration_cli import (
+    run_prefit_calibration,
+)
+
+# Small windows keep the smoke fits to about a minute.
+_FAST = dict(convergence_window_steps=1000, patience=2,
+             epoch_checkpoint_interval=None)
+
+
+@pytest.fixture
+def configured(tmpdir, growth_smoke_csv, binding_smoke_csv,
+               library_smoke_yaml):
+    out_prefix = os.path.join(tmpdir, "conv")
+    configure_model(binding_smoke_csv,
+                    growth_df=growth_smoke_csv,
+                    library_config=library_smoke_yaml,
+                    out_prefix=out_prefix,
+                    skip_model_stats=True)
+    return f"{out_prefix}_config.yaml", str(tmpdir)
+
+
+def _windows(prefix):
+    return pd.read_csv(f"{prefix}_convergence.csv")
+
+
+@pytest.mark.slow
+def test_prefit_then_svi_converges(configured):
+    config_file, tmpdir = configured
+
+    prefit = os.path.join(tmpdir, "conv_prefit")
+    _, _, prefit_converged = run_prefit_calibration(
+        config_file=config_file, seed=1, out_prefix=prefit,
+        max_num_epochs=60000, hessian_chunk_size=8, **_FAST)
+    prefit_windows = _windows(prefit)
+    assert len(prefit_windows) >= 2
+    if prefit_converged:
+        assert prefit_windows["decision"].iloc[-1] == "converged"
+
+    fit = os.path.join(tmpdir, "conv_fit")
+    *_, converged = fit_model(config_file=config_file, seed=42,
+                              out_prefix=fit, max_num_epochs=100000,
+                                pre_map_num_epoch=5000, **_FAST)
+    assert converged
+
+    windows = _windows(fit)
+    assert windows["decision"].iloc[-1] == "converged"
+    assert (windows["decision"] == "cut").sum() == 3  # 1e-3 -> 1e-6
+    last = windows.iloc[-1]
+    assert last["step_size"] == pytest.approx(1e-6)
+    assert not last["loss_improving"]
+    assert not last["params_moving"]
+
+    # the pre-MAP ran under the same monitor, in its own files
+    assert os.path.exists(f"{fit}_premap_convergence.csv")
+
+    with open(f"{fit}_checkpoint.pkl", "rb") as f:
+        ckpt = dill.load(f)
+    assert ckpt["convergence"]["converged"]
+    assert ckpt["step_size"] == pytest.approx(1e-6)
+
+
+@pytest.mark.slow
+def test_svi_resume_after_convergence_stays_converged(configured):
+    """Resuming a converged fit continues at the final step size and
+    converges again within patience windows, rather than restarting the
+    schedule or never stopping."""
+    config_file, tmpdir = configured
+    fit = os.path.join(tmpdir, "conv_fit")
+    fit_model(config_file=config_file, seed=42, out_prefix=fit,
+              max_num_epochs=100000, pre_map_num_epoch=0, **_FAST)
+    n_before = len(_windows(fit))
+
+    *_, converged = fit_model(config_file=config_file,
+                                checkpoint_file=f"{fit}_checkpoint.pkl",
+                                out_prefix=fit, max_num_epochs=20000,
+                                **_FAST)
+    windows = _windows(fit)
+    assert converged
+    resumed = windows.iloc[n_before:]
+    assert list(resumed["step_size"]) == pytest.approx([1e-6] * len(resumed))
+    assert len(resumed) <= 6
+
+
+@pytest.mark.slow
+def test_map_reports_honestly(configured):
+    """MAP of the default hierarchical model has unbounded directions (the
+    hyper-scale funnel); whatever it does, a stop must be backed by the last
+    window and a cap must be reported as not converged."""
+    config_file, tmpdir = configured
+    fit = os.path.join(tmpdir, "conv_map")
+    *_, converged = fit_model(config_file=config_file, seed=42,
+                                analysis_method="map", out_prefix=fit,
+                                max_num_epochs=20000, **_FAST)
+    last = _windows(fit).iloc[-1]
+    if converged:
+        assert last["decision"] == "converged"
+        assert not last["params_moving"]
+    else:
+        assert last["decision"] != "converged"
diff --git a/tests/smoke-tests/test_model_smoke.py b/tests/smoke-tests/test_model_smoke.py
index df387401..58b1393c 100644
--- a/tests/smoke-tests/test_model_smoke.py
+++ b/tests/smoke-tests/test_model_smoke.py
@@ -12,14 +12,14 @@
 # ModelOrchestrator arguments. We want to make sure every entry in the 
 # model_registry is tested at least once. 
 SMOKE_CONFIGS = [
-    # Default-ish config
+    # Default-ish config (homodimer congression rule, activity fixed at 1)
     {
         "condition_growth":"linear",
-        "transformation":"empirical",
+        "transformation":"mixture",
         "theta":"hill_geno",
         "growth_transition":"instant",
         "dk_geno":"hierarchical_geno",
-        "activity":"horseshoe_geno",
+        "activity":"fixed",
         "ln_cfu0":"hierarchical",
         "theta_growth_noise":"zero",
         "theta_binding_noise":"zero"
@@ -27,26 +27,29 @@
     # test other condition growth and transformation
     {
         "condition_growth":"linear",
-        "transformation":"logit_norm",
+        "transformation":"mixture",
         "theta":"categorical_geno",
         "growth_transition":"memory",
         "dk_geno":"fixed",
         "activity":"hierarchical_geno",
+        "congression_theta_rule":"max",   # learned activity needs the max rule
         "theta_growth_noise":"beta",
         "theta_binding_noise":"beta"
     },
     # test other transition and transformation
     {
         "condition_growth":"power",
-        "transformation":"logit_norm",
+        "transformation":"mixture",
         "theta":"hill_geno",
         "growth_transition":"baranyi",
         "activity":"fixed",
+        "congression_theta_rule":"heterodimer",
     },
-    # test power and saturation
+    # test power and saturation (and learned horseshoe activity)
     {
         "condition_growth":"power",
         "transformation":"single",
+        "activity":"horseshoe_geno",
     },
     {
         "condition_growth":"saturation",
@@ -120,10 +123,19 @@ def test_model_svi_smoke(growth_smoke_csv,
     initialized and a few steps of optimization can be performed without error.
     """
     out_prefix = os.path.join(tmpdir, "smoke_test")
-    
+
+    # hierarchical_factored shares a genotype's baseline across condition_pre,
+    # which the orchestrator refuses when they come from different libraries
+    # (the smoke data's kanR and pheS): run it on a one-library copy.
+    growth = growth_smoke_csv
+    if config.get("ln_cfu0") == "hierarchical_factored":
+        import pandas as pd
+        growth = pd.read_csv(growth_smoke_csv)
+        growth["library"] = "kanR"
+
     # Initialize ModelOrchestrator
     model = ModelOrchestrator(
-        growth_df=growth_smoke_csv,
+        growth_df=growth,
         binding_df=binding_smoke_csv,
         batch_size=None, # Use all data for smoke test
         **config
@@ -132,8 +144,12 @@ def test_model_svi_smoke(growth_smoke_csv,
     # Initialize RunInference
     inference = RunInference(model=model, seed=42)
     
-    # Setup SVI
-    svi = inference.setup_svi(adam_step_size=1e-3)
+    # Setup SVI. beta theta_growth_noise's per-observation latent is
+    # batch-shaped, which no autoguide (AutoDelta included) can fit -- see
+    # inference/batch_safety.py -- so exercise it through the component guide.
+    guide_type = ("component" if config.get("theta_growth_noise") == "beta"
+                  else "delta")
+    svi = inference.setup_svi(adam_step_size=1e-3, guide_type=guide_type)
     
     # Run a very short optimization
     # We only run for a few steps to verify it doesn't crash
diff --git a/tests/smoke-tests/test_posteriors_smoke.py b/tests/smoke-tests/test_posteriors_smoke.py
index a3a29d93..041f2afa 100644
--- a/tests/smoke-tests/test_posteriors_smoke.py
+++ b/tests/smoke-tests/test_posteriors_smoke.py
@@ -21,7 +21,7 @@ def test_checkpoint_and_posterior_smoke(growth_smoke_csv,
         growth_df=growth_smoke_csv,
         binding_df=binding_smoke_csv,
         condition_growth="linear",
-        transformation="logit_norm",
+        transformation="mixture",
         theta="hill_geno",
         batch_size=None
     )
@@ -82,7 +82,10 @@ def test_extract_parameters_smoke(growth_smoke_csv,
         growth_df=growth_smoke_csv,
         binding_df=binding_smoke_csv,
         theta="hill_geno",
-        transformation="logit_norm"
+        transformation="mixture",
+        # a learned activity (extracted below) needs the max congression rule
+        activity="horseshoe_geno",
+        congression_theta_rule="max",
     )
     
     inference = RunInference(model=model, seed=42)
@@ -106,7 +109,7 @@ def test_extract_parameters_smoke(growth_smoke_csv,
     param_dfs = extract_parameters(model, posterior_file)
     assert "hill_n" in param_dfs
     assert "activity" in param_dfs
-    assert "lam" in param_dfs # for logit_norm/empirical
+    assert "lam" in param_dfs # for mixture
     
     # Verify they are DataFrames
     import pandas as pd
diff --git a/tests/smoke-tests/test_prediction_smoke.py b/tests/smoke-tests/test_prediction_smoke.py
index f5951fd9..fc179792 100644
--- a/tests/smoke-tests/test_prediction_smoke.py
+++ b/tests/smoke-tests/test_prediction_smoke.py
@@ -21,7 +21,9 @@ def test_prediction_smoke(growth_smoke_csv,
         growth_df=growth_smoke_csv,
         binding_df=binding_smoke_csv,
         theta="hill_geno",
-        transformation="logit_norm"
+        transformation="mixture",
+        # plated on titrant_conc; the expansion check below relies on it
+        theta_growth_noise="logit_normal",
     )
     
     inference = RunInference(model=model, seed=42)
@@ -73,7 +75,8 @@ def test_prediction_smoke(growth_smoke_csv,
     assert "q0.975" in pred_df.columns
 
     # 3. Test expansion restriction for plated dimensions
-    # 'logit_norm' plates on titrant_conc, so expanding it should fail.
+    # theta_growth_noise logit_normal plates on titrant_conc, so expanding
+    # it should fail.
     with pytest.raises(ValueError, match="is plated on .* and cannot be expanded"):
         predict(
             model,
@@ -84,17 +87,12 @@ def test_prediction_smoke(growth_smoke_csv,
     with pytest.raises(ValueError, match="is plated on .* and cannot be expanded"):
         
         # 4. Test categorical theta restriction (as requested)
-        # Create model with categorical theta. transformation must be
-        # something other than the default "empirical": categorical_geno
-        # cannot supply the full-population theta reference "empirical"
-        # needs (see model_orchestrator._check_theta_transformation_compatibility),
-        # and that's a separate concern from the conc-expansion restriction
-        # this block is testing.
+        # Create model with categorical theta (plated on titrant_conc).
         model_cat = ModelOrchestrator(
             growth_df=growth_smoke_csv,
             binding_df=binding_smoke_csv,
             theta="categorical_geno",
-            transformation="logit_norm"
+            transformation="mixture"
         )
         
         # Generate a valid posterior file for model_cat
diff --git a/tests/smoke-tests/test_prefit_calibration_smoke.py b/tests/smoke-tests/test_prefit_calibration_smoke.py
index 67948529..652fc689 100644
--- a/tests/smoke-tests/test_prefit_calibration_smoke.py
+++ b/tests/smoke-tests/test_prefit_calibration_smoke.py
@@ -77,6 +77,7 @@ def test_prefit_writes_per_condition_baseline_prior(tmpdir, library_smoke_yaml,
     out_prefix = os.path.join(tmpdir, f"tfs_{cg_model}")
 
     configure_model(binding_path,
+                    growth_likelihood="lncfu", sample_offset_model="zero",
                     growth_df=growth_path,
                     library_config=library_smoke_yaml,
                     condition_growth_model=cg_model,
@@ -94,8 +95,8 @@ def test_prefit_writes_per_condition_baseline_prior(tmpdir, library_smoke_yaml,
 
     run_prefit_calibration(config_file=config_file,
                            seed=1,
+                           out_prefix=f"{out_prefix}_prefit",
                            max_num_epochs=3,
-                           convergence_check_interval=1,
                            patience=1,
                            hessian_chunk_size=8)
 
@@ -126,6 +127,7 @@ def test_configure_prefit_fit_full_loop_with_per_condition_priors(tmpdir,
     out_prefix = os.path.join(tmpdir, "tfs_full")
 
     configure_model(binding_path,
+                    growth_likelihood="lncfu", sample_offset_model="zero",
                     growth_df=growth_path,
                     library_config=library_smoke_yaml,
                     condition_growth_model="linear",
@@ -134,7 +136,8 @@ def test_configure_prefit_fit_full_loop_with_per_condition_priors(tmpdir,
     config_file = f"{out_prefix}_config.yaml"
 
     run_prefit_calibration(config_file=config_file, seed=1,
-                           max_num_epochs=3, convergence_check_interval=1,
+                           out_prefix=f"{out_prefix}_prefit",
+                           max_num_epochs=3,
                            patience=1, hessian_chunk_size=8)
 
     # Sanity: the priors CSV now carries per-condition k_loc rows.
diff --git a/tests/smoke-tests/test_run_growth_analysis_smoke.py b/tests/smoke-tests/test_run_growth_analysis_smoke.py
index e1d69499..ca4a15df 100644
--- a/tests/smoke-tests/test_run_growth_analysis_smoke.py
+++ b/tests/smoke-tests/test_run_growth_analysis_smoke.py
@@ -30,7 +30,7 @@ def test_run_growth_analysis_smoke(growth_smoke_csv, binding_smoke_csv, tmpdir):
         growth_df=growth_smoke_csv,
         binding_df=binding_smoke_csv,
         condition_growth="linear",
-        transformation="logit_norm",
+        transformation="mixture",
         theta="hill_geno",
         batch_size=None
     )
@@ -89,7 +89,7 @@ def test_run_growth_analysis_nuts_smoke(growth_smoke_csv, binding_smoke_csv, tmp
         growth_df=growth_smoke_csv,
         binding_df=binding_smoke_csv,
         condition_growth="linear",
-        transformation="logit_norm",
+        transformation="mixture",
         theta="hill_geno",
         batch_size=None,
     )
@@ -144,7 +144,7 @@ def test_run_growth_analysis_nuts_posterior_smoke(growth_smoke_csv, binding_smok
         growth_df=growth_smoke_csv,
         binding_df=binding_smoke_csv,
         condition_growth="linear",
-        transformation="logit_norm",
+        transformation="mixture",
         theta="hill_geno",
         batch_size=None,
     )
diff --git a/tests/smoke-tests/test_staged_map_smoke.py b/tests/smoke-tests/test_staged_map_smoke.py
new file mode 100644
index 00000000..d248cc4b
--- /dev/null
+++ b/tests/smoke-tests/test_staged_map_smoke.py
@@ -0,0 +1,52 @@
+"""The staged level-offset MAP on a real (small) model, end to end."""
+import os
+
+import numpy as np
+import pytest
+
+from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+from tfscreen.tfmodel.scripts.fit_model_cli import fit_model
+from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+
+OFF = "sample_offset_offset_auto_loc"
+
+
+def _keys(path):
+    with np.load(path) as z:
+        return set(z.files)
+
+
+@pytest.mark.slow
+def test_staged_map_smoke(tmp_path, growth_smoke_csv, library_smoke_yaml):
+    cfg = str(tmp_path / "cfg")
+    configure_model(growth_df=growth_smoke_csv, library_config=library_smoke_yaml,
+                    theta_model="hill_relative", growth_shares_replicates=True,
+                    out_prefix=cfg, skip_model_stats=True)
+    out = str(tmp_path / "fit")
+    fit_model(f"{cfg}_config.yaml", seed=1, analysis_method="map",
+              max_num_epochs=2, convergence_window_steps=20, out_prefix=out)
+
+    stage1, stage2, final = (_keys(f"{out}_stage1_params.npz"),
+                             _keys(f"{out}_stage2_params.npz"),
+                             _keys(f"{out}_params.npz"))
+    # stage 1 holds the offsets (and their learned SD); stage 2 fits only them
+    assert OFF not in stage1 and "sample_offset_sigma_auto_loc" not in stage1
+    assert stage2 == {OFF}
+    assert OFF in final and stage1 <= final
+    for stage in ("stage1", "stage2"):
+        assert os.path.exists(f"{out}_{stage}_convergence.csv")
+
+    # the final checkpoint is the full model's
+    sample_posterior(f"{cfg}_config.yaml", f"{out}_checkpoint.pkl",
+                     out_prefix=str(tmp_path / "post"),
+                     num_posterior_samples=10, skip_growth_observations=True)
+    assert os.path.exists(tmp_path / "post.h5")
+
+    # rerun with the final outputs gone: stages 1 and 2 are reused
+    for f in ("checkpoint.pkl", "params.npz"):
+        os.remove(f"{out}_{f}")
+    mtime = os.path.getmtime(f"{out}_stage1_params.npz")
+    fit_model(f"{cfg}_config.yaml", seed=1, analysis_method="map",
+              max_num_epochs=2, convergence_window_steps=20, out_prefix=out)
+    assert os.path.getmtime(f"{out}_stage1_params.npz") == mtime
+    assert os.path.exists(f"{out}_params.npz")
diff --git a/tests/tfscreen/analysis/test_calibration_grid.py b/tests/tfscreen/analysis/test_calibration_grid.py
new file mode 100644
index 00000000..006b8163
--- /dev/null
+++ b/tests/tfscreen/analysis/test_calibration_grid.py
@@ -0,0 +1,403 @@
+"""Tests for tfscreen.analysis.calibration_grid (tfs-summarize-calibration)."""
+
+import json
+import os
+
+import matplotlib
+matplotlib.use("Agg")
+
+import numpy as np
+import pandas as pd
+import pytest
+from scipy.stats import norm
+
+from tfscreen.analysis.calibration_grid import (
+    calibration_metrics,
+    genotype_strata,
+    paired_differences,
+    parse_baseline,
+    quantile_columns,
+    read_quantities,
+    summarize_calibration,
+    summarize_grid_runs,
+    summarize_run,
+)
+
+
+LEVELS = np.array([0.001, 0.01, 0.025, 0.05, 0.1, 0.159, 0.25, 0.5,
+                   0.75, 0.841, 0.9, 0.95, 0.975, 0.99, 0.999])
+
+
+def _posterior(rng, n, sigma, spread):
+    """Truth, and quantile ladders of a posterior with std sigma*spread.
+
+    With spread == 1 the intervals are calibrated: truth ~ N(center, sigma).
+    """
+    center = rng.uniform(0.2, 0.8, size=n)
+    ref = center + rng.normal(0.0, sigma, size=n)
+    qmat = center[:, None] + norm.ppf(LEVELS)[None, :] * sigma * spread
+    return ref, qmat
+
+
+# ---------------------------------------------------------------------------
+# calibration_metrics
+# ---------------------------------------------------------------------------
+
+def test_calibrated_posterior_has_nominal_coverage():
+    ref, qmat = _posterior(np.random.default_rng(0), 20000, 0.05, 1.0)
+    m = calibration_metrics(ref, qmat, LEVELS)
+    assert m["n"] == 20000
+    assert m["coverage_0.95"] == pytest.approx(0.95, abs=0.01)
+    assert m["coverage_0.5"] == pytest.approx(0.5, abs=0.015)
+    assert m["calibration_error"] < 0.015
+    assert m["width_0.95"] == pytest.approx(2 * 1.96 * 0.05, rel=0.01)
+    assert m["rmse"] == pytest.approx(0.05, rel=0.03)
+
+
+def test_overconfident_posterior_undercovers_with_negative_bias():
+    ref, qmat = _posterior(np.random.default_rng(1), 20000, 0.05, 0.25)
+    m = calibration_metrics(ref, qmat, LEVELS)
+    assert m["coverage_0.95"] < 0.5
+    assert m["calibration_bias"] < -0.3
+    assert m["ks_stat"] > 0.2
+
+
+def test_nonfinite_rows_are_dropped():
+    ref = np.array([0.5, np.nan, 0.4])
+    qmat = np.tile(np.linspace(0.0, 1.0, len(LEVELS)), (3, 1))
+    qmat[2, 3] = np.nan
+    assert calibration_metrics(ref, qmat, LEVELS)["n"] == 1
+
+
+def test_no_rows_gives_nan_metrics():
+    m = calibration_metrics(np.array([np.nan]),
+                            np.full((1, len(LEVELS)), 0.5), LEVELS)
+    assert m["n"] == 0 and np.isnan(m["coverage_0.95"])
+
+
+def test_quantile_columns_sorted_numerically():
+    df = pd.DataFrame(columns=["q0.975", "genotype", "q0.025", "q0.5", "ref"])
+    cols, levels = quantile_columns(df)
+    assert cols == ["q0.025", "q0.5", "q0.975"]
+    assert list(levels) == [0.025, 0.5, 0.975]
+
+
+# ---------------------------------------------------------------------------
+# A fake grid
+# ---------------------------------------------------------------------------
+
+GENOTYPES = [f"G{i}V" for i in range(40)]
+SPIKED = {"G0V", "G1V"}
+ALSO_BULK = {"G1V"}                       # G1V: spiked and in the bulk (mixed)
+BINDING = {"G0V", "G1V", "G2V", "G3V"}   # two spiked + two bulk with binding
+
+
+def _write_run(run_dir, combo, spread, seed, with_summary=True):
+    os.makedirs(run_dir, exist_ok=True)
+    with open(os.path.join(run_dir, "combo.json"), "w") as fh:
+        json.dump(combo, fh)
+
+    rows = ([("spiked", g) for g in GENOTYPES if g in SPIKED]
+            + [("double-1-2", g) for g in GENOTYPES
+               if g not in SPIKED or g in ALSO_BULK])
+    pd.DataFrame(rows, columns=["library_origin", "genotype"]).to_csv(
+        os.path.join(run_dir, "tfs_sim_library.csv"), index=False)
+    pd.DataFrame({"genotype": sorted(BINDING)}).to_csv(
+        os.path.join(run_dir, "tfs_sim_binding.csv"), index=False)
+
+    if not with_summary:
+        return
+    summary = os.path.join(run_dir, "summary")
+    os.makedirs(summary, exist_ok=True)
+    rng = np.random.default_rng(seed)
+    concs = np.arange(25)
+    genos = np.repeat(GENOTYPES, len(concs))
+    ref, qmat = _posterior(rng, len(genos), 0.05, spread)
+    ref[:len(concs)] = 0.999            # G0V saturated
+    theta = pd.DataFrame(qmat, columns=[f"q{lv:g}" for lv in LEVELS])
+    theta.insert(0, "genotype", genos)
+    theta.insert(1, "titrant_conc", np.tile(concs, len(GENOTYPES)))
+    theta["ref"] = ref
+    theta.to_csv(os.path.join(summary, "tfs_summarize_theta_corr_test.csv"), index=False)
+
+    ref_m, qmat_m = _posterior(rng, 30, 0.1, spread)
+    mut = pd.DataFrame(qmat_m, columns=[f"q{lv:g}" for lv in LEVELS])
+    mut.insert(0, "mutation", [f"M{i}" for i in range(30)])
+    mut["ref"] = ref_m
+    mut.to_csv(os.path.join(summary, "tfs_summarize_params_d_logit_low.csv"), index=False)
+    # Calibration by-products written next to params files must be ignored.
+    pd.DataFrame({"nominal": [0.5], "empirical": [0.5]}).to_csv(
+        os.path.join(summary, "tfs_summarize_params_d_logit_low_calibration_curve.csv"),
+        index=False)
+
+
+@pytest.fixture
+def grid_dir(tmp_path):
+    """component (overconfident) and auto_normal (calibrated) x 2 sim seeds."""
+    grid = tmp_path / "grid"
+    for guide, rank, spread in (("component", None, 0.3), ("auto_normal", None, 1.0)):
+        for sim_seed in (1, 2):
+            combo = {"simulate": {"seed": sim_seed},
+                     "template": {"guide_type": guide, "guide_rank": rank,
+                                  "batch_size": 160, "fit_seed": 1}}
+            _write_run(str(grid / f"run_{guide}_sim{sim_seed}"), combo, spread,
+                       seed=sim_seed * 10 + (spread == 1.0))
+    _write_run(str(grid / "run_unfinished"),
+               {"simulate": {"seed": 3},
+                "template": {"guide_type": "auto_normal", "guide_rank": None,
+                             "batch_size": 160, "fit_seed": 1}},
+               spread=1.0, seed=0, with_summary=False)
+    return str(grid)
+
+
+def test_genotype_strata(grid_dir):
+    strata = genotype_strata(os.path.join(grid_dir, "run_component_sim1")).set_index("genotype")
+    assert len(strata) == len(GENOTYPES)
+    assert strata.loc["G0V", "purity"] == "spike"
+    assert strata.loc["G1V", "purity"] == "mixed"
+    assert strata.loc["G2V", "purity"] == "bulk"
+    assert strata.loc["G2V", "has_binding"] == "yes"
+    assert strata.loc["G9V", "has_binding"] == "no"
+
+
+def test_read_quantities_skips_calibration_byproducts(grid_dir):
+    summary = os.path.join(grid_dir, "run_component_sim1", "summary")
+    assert [q for q, _ in read_quantities(summary)] == ["theta_test", "d_logit_low"]
+
+
+def test_summarize_run_strata_rows(grid_dir):
+    rows, problem = summarize_run(os.path.join(grid_dir, "run_auto_normal_sim1"))
+    assert problem is None
+    theta = pd.DataFrame([r for r in rows if r["quantity"] == "theta_test"])
+    pooled = theta[(theta.has_binding == "all") & (theta.purity == "all")
+                   & (theta.theta_regime == "all")]
+    assert pooled["n"].item() == 40 * 25
+    cross = theta[(theta.has_binding == "yes") & (theta.purity == "bulk")]
+    assert cross["n"].item() == 2 * 25        # G2V, G3V
+    saturated = theta[theta.theta_regime == "saturated"]
+    assert saturated["n"].item() >= 25        # at least G0V
+    # Mutation-level params have no genotype, so only a pooled row.
+    mut = [r for r in rows if r["quantity"] == "d_logit_low"]
+    assert len(mut) == 1 and mut[0]["has_binding"] == "all"
+
+
+def test_x_scale_rows_have_their_own_regime(grid_dir):
+    """A relative fit's theta_test (theta_scale X) is not split by the
+    theta band, which means nothing for X."""
+    run = os.path.join(grid_dir, "run_auto_normal_sim1")
+    path = os.path.join(run, "summary", "tfs_summarize_theta_corr_test.csv")
+    df = pd.read_csv(path)
+    df["theta_scale"] = "X"
+    df.to_csv(path, index=False)
+    rows, _ = summarize_run(run)
+    regimes = {r["theta_regime"] for r in rows if r["quantity"] == "theta_test"}
+    assert regimes == {"all", "X"}
+
+
+def test_unfinished_run_reported_in_status(grid_dir):
+    runs_df, status_df, grid_vars = summarize_grid_runs(grid_dir)
+    status = status_df.set_index("run")["status"]
+    assert status["run_unfinished"] != "ok"
+    assert (status.drop("run_unfinished") == "ok").all()
+    assert set(grid_vars) == {"seed", "guide_type", "guide_rank", "batch_size", "fit_seed"}
+
+
+@pytest.mark.parametrize("text,expected", [
+    (["guide_type=component", "guide_rank=None"], {"guide_type": "component", "guide_rank": None}),
+    (["batch_size=160", "eps=0.5"], {"batch_size": 160, "eps": 0.5}),
+])
+def test_parse_baseline(text, expected):
+    assert parse_baseline(text) == expected
+
+
+def test_parse_baseline_requires_key_value():
+    with pytest.raises(ValueError, match="key=value"):
+        parse_baseline(["component"])
+
+
+def test_paired_differences_match_same_simulation(grid_dir):
+    runs_df, _, grid_vars = summarize_grid_runs(grid_dir)
+    paired = paired_differences(runs_df, grid_vars,
+                                {"guide_type": "component", "guide_rank": None})
+    pooled = paired[(paired.quantity == "theta_test") & (paired.has_binding == "all")
+                    & (paired.purity == "all") & (paired.theta_regime == "all")]
+    assert sorted(pooled["seed"]) == [1, 2]            # one pair per simulation
+    assert (pooled["delta_calibration_error"] < 0).all()  # calibrated beats baseline
+    assert (pooled["delta_width_0.95"] > 0).all()
+
+
+def test_paired_differences_fail_fast(grid_dir):
+    runs_df, _, grid_vars = summarize_grid_runs(grid_dir)
+    with pytest.raises(ValueError, match="not grid variables"):
+        paired_differences(runs_df, grid_vars, {"nope": 1})
+    with pytest.raises(ValueError, match="No runs match"):
+        paired_differences(runs_df, grid_vars, {"guide_type": "missing"})
+
+
+def test_summarize_calibration_writes_outputs(grid_dir, tmp_path):
+    out_prefix = str(tmp_path / "out" / "calib")
+    summarize_calibration(grid_dir, out_prefix=out_prefix,
+                          baseline=["guide_type=component", "guide_rank=None"],
+                          facet_by=["batch_size"])
+    for suffix in ("_runs.csv", "_run_status.csv", "_arms.csv", "_paired.csv",
+                   "_paired_summary.csv", "_theta_test_calibration_curves.pdf",
+                   "_metadata.json"):
+        assert os.path.exists(out_prefix + suffix), suffix
+
+    arms = pd.read_csv(out_prefix + "_arms.csv")
+    pooled = arms[(arms.quantity == "theta_test") & (arms.has_binding == "all")
+                  & (arms.purity == "all") & (arms.theta_regime == "all")]
+    assert set(pooled["n_runs"]) == {2}               # 2 finished sims per arm
+    cov = pooled.set_index("guide_type")["coverage_0.95_mean"]
+    assert cov["auto_normal"] == pytest.approx(0.95, abs=0.03)
+    assert cov["component"] < 0.7
+
+    paired_summary = pd.read_csv(out_prefix + "_paired_summary.csv")
+    row = paired_summary[(paired_summary.quantity == "theta_test")
+                         & (paired_summary.has_binding == "all")
+                         & (paired_summary.purity == "all")
+                         & (paired_summary.theta_regime == "all")]
+    assert row["n_pairs"].item() == 2
+    assert row["delta_calibration_error_mean"].item() < 0
+
+    meta = json.load(open(out_prefix + "_metadata.json"))
+    assert meta["replicate_keys"] == ["seed", "fit_seed"]
+    assert meta["n_runs"] == 5 and meta["n_runs_ok"] == 4
+
+
+def test_unfinished_baseline_skips_paired_outputs(grid_dir, tmp_path):
+    """Mid-grid: the baseline exists but has not finished -> warn, no paired files."""
+    out_prefix = str(tmp_path / "mid")
+    with pytest.warns(UserWarning, match="No finished run matches baseline"):
+        summarize_calibration(grid_dir, out_prefix=out_prefix,
+                              baseline=["guide_type=auto_normal", "seed=3"])
+    assert os.path.exists(out_prefix + "_arms.csv")
+    assert not os.path.exists(out_prefix + "_paired.csv")
+
+
+def test_baseline_absent_from_grid_raises(grid_dir, tmp_path):
+    """A baseline matching no run at all (e.g. a typo) fails before writing."""
+    with pytest.raises(ValueError, match="No run in"):
+        summarize_calibration(grid_dir, out_prefix=str(tmp_path / "typo"),
+                              baseline=["guide_type=componet"])
+    with pytest.raises(ValueError, match="not grid variables"):
+        summarize_calibration(grid_dir, out_prefix=str(tmp_path / "typo"),
+                              baseline=["guide=component"])
+
+
+def test_facet_by_must_be_arm_variable(grid_dir, tmp_path):
+    with pytest.raises(ValueError, match="facet_by"):
+        summarize_calibration(grid_dir, out_prefix=str(tmp_path / "c"),
+                              facet_by=["seed"])
+
+
+def test_main_registers_list_flags():
+    from unittest.mock import patch
+    from tfscreen.analysis.scripts import summarize_calibration_cli as cli
+    with patch.object(cli, "generalized_main") as gm:
+        cli.main()
+    args, kwargs = gm.call_args
+    assert args[0] is summarize_calibration
+    assert kwargs["manual_arg_nargs"] == {"replicate_keys": "+", "baseline": "+",
+                                          "facet_by": "+"}
+
+
+# ---------------------------------------------------------------------------
+# Edge cases
+# ---------------------------------------------------------------------------
+
+def test_interp_quantile_outside_stored_levels_is_nan():
+    from tfscreen.analysis.calibration_grid import _interp_quantile
+    levels = np.array([0.1, 0.5, 0.9])
+    qmat = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+    assert np.all(np.isnan(_interp_quantile(qmat, levels, 0.05)))
+    assert np.all(np.isnan(_interp_quantile(qmat, levels, 0.95)))
+    np.testing.assert_allclose(_interp_quantile(qmat, levels, 0.5), [2.0, 5.0])
+    np.testing.assert_allclose(_interp_quantile(qmat, levels, 0.3), [1.5, 4.5])
+
+
+def test_read_combo_warns_on_shared_key(tmp_path):
+    from tfscreen.analysis.calibration_grid import read_combo
+    with open(tmp_path / "combo.json", "w") as fh:
+        json.dump({"simulate": {"seed": 1, "x": 2},
+                   "template": {"seed": 5}}, fh)
+    with pytest.warns(UserWarning, match="'seed' is both a simulate"):
+        flat = read_combo(str(tmp_path))
+    assert flat == {"seed": 5, "x": 2}
+
+
+def test_genotype_strata_none_without_library(tmp_path):
+    assert genotype_strata(str(tmp_path)) is None
+
+
+def test_summarize_run_empty_summary_dir(tmp_path):
+    os.makedirs(tmp_path / "summary")
+    rows, problem = summarize_run(str(tmp_path))
+    assert rows == [] and problem == "no calibration outputs"
+
+
+def test_summarize_run_warns_on_genotypes_missing_from_library(grid_dir):
+    run = os.path.join(grid_dir, "run_auto_normal_sim1")
+    lib = os.path.join(run, "tfs_sim_library.csv")
+    df = pd.read_csv(lib)
+    df[df["genotype"] != "G39V"].to_csv(lib, index=False)
+    with pytest.warns(UserWarning, match="missing from the sim library"):
+        rows, problem = summarize_run(run)
+    assert problem is None
+    pooled = [r for r in rows if r["quantity"] == "theta_test"
+              and r["purity"] == "all" and r["has_binding"] == "all"
+              and r["theta_regime"] == "all"]
+    assert pooled[0]["n"] == 40 * 25
+
+
+def test_summarize_grid_runs_requires_runs(tmp_path):
+    with pytest.raises(ValueError, match="No run directories"):
+        summarize_grid_runs(str(tmp_path))
+
+
+def test_label_skips_missing_and_integer_floats():
+    from tfscreen.analysis.calibration_grid import _label
+    row = {"a": None, "b": np.nan, "c": 160.0, "d": 0.5, "e": "x"}
+    assert _label(row, ["a", "b", "c", "d", "e"]) == "c=160, d=0.5, e=x"
+    assert _label({"a": None}, ["a"]) == "(all)"
+
+
+def test_plot_calibration_curves_nothing_to_plot(tmp_path):
+    from tfscreen.analysis.calibration_grid import plot_calibration_curves
+    arms = pd.DataFrame({"quantity": ["other"], "has_binding": ["all"],
+                         "purity": ["all"], "theta_regime": ["all"]})
+    out = str(tmp_path / "c.pdf")
+    assert plot_calibration_curves(arms, [], "theta_test", out) is None
+    assert not os.path.exists(out)
+
+
+def test_summarize_calibration_warns_on_unknown_replicate_key(grid_dir, tmp_path):
+    out_prefix = str(tmp_path / "rk")
+    with pytest.warns(UserWarning, match=r"replicate keys \['nope'\]"):
+        summarize_calibration(grid_dir, out_prefix=out_prefix,
+                              replicate_keys=["seed", "fit_seed", "nope"])
+    meta = json.load(open(out_prefix + "_metadata.json"))
+    assert meta["replicate_keys"] == ["seed", "fit_seed"]
+
+
+def test_summarize_calibration_no_finished_runs(tmp_path):
+    grid = tmp_path / "grid"
+    _write_run(str(grid / "run_a"), {"simulate": {"seed": 1}}, spread=1.0,
+               seed=0, with_summary=False)
+    out_prefix = str(tmp_path / "empty")
+    with pytest.warns(UserWarning, match="No run has calibration outputs"):
+        summarize_calibration(str(grid), out_prefix=out_prefix)
+    assert os.path.exists(out_prefix + "_run_status.csv")
+    assert not os.path.exists(out_prefix + "_runs.csv")
+    assert not os.path.exists(out_prefix + "_arms.csv")
+
+
+@pytest.mark.filterwarnings("ignore:.*found in sys.modules:RuntimeWarning")
+def test_cli_module_runs_as_script(monkeypatch):
+    import runpy
+    import sys
+    monkeypatch.setattr(sys, "argv", ["tfs-summarize-calibration", "--help"])
+    with pytest.raises(SystemExit) as exc:
+        runpy.run_module("tfscreen.analysis.scripts.summarize_calibration_cli",
+                         run_name="__main__")
+    assert exc.value.code == 0
diff --git a/tests/tfscreen/analysis/test_stats_test_suite.py b/tests/tfscreen/analysis/test_stats_test_suite.py
index 1f294ebd..e44bfad9 100644
--- a/tests/tfscreen/analysis/test_stats_test_suite.py
+++ b/tests/tfscreen/analysis/test_stats_test_suite.py
@@ -6,10 +6,9 @@
 def test_stats_test_suite_perfect_fit():
     param_real = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
     param_est = param_real.copy()
-    param_std = np.array([0.1, 0.1, 0.1, 0.1, 0.1]) # Small std
     
     # Perfect fit
-    res = stats_test_suite(param_est, param_real, param_std)
+    res = stats_test_suite(param_est, param_real)
     
     assert res["pct_success"] == 1.0
     assert res["rmse"] == 0.0
@@ -17,10 +16,7 @@ def test_stats_test_suite_perfect_fit():
     assert res["pearson_r"] == 1.0
     assert res["r_squared"] == 1.0
     assert res["mean_error"] == 0.0
-    # In perfect fit, estimates == real. 
-    # real >= estimate - 1.96*std ? real >= real - 1.96*std (True, since std > 0)
-    # real <= estimate + 1.96*std ? real <= real + 1.96*std (True)
-    assert res["coverage_prob"] == 1.0 
+    assert "coverage_prob" not in res
     
     # Residuals are all 0. Correlation undefined?
     # pearsonr of constant input issues warning and returns nan usually.
@@ -33,16 +29,13 @@ def test_stats_test_suite_with_noise():
     param_real = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
     # Add small bias
     param_est = param_real + 0.1
-    # param_std big enough to cover bias (0.1 < 1.96 * 0.1) -> 0.1 < 0.196. Yes.
-    param_std = np.ones(5) * 0.1
     
-    res = stats_test_suite(param_est, param_real, param_std)
+    res = stats_test_suite(param_est, param_real)
 
     assert res["pct_success"] == 1.0
     assert np.isclose(res["mean_error"], 0.1)
     # RMSE: sqrt(mean(0.1^2)) = 0.1
     assert np.isclose(res["rmse"], 0.1)
-    assert np.isclose(res["coverage_prob"], 1.0) # Bias small enough to be covered
     
     # Signal range: 97.5 percentile of [1..5] - 2.5 percentile of [1..5]
     # np.percentile([1,2,3,4,5], [2.5, 97.5])
@@ -53,9 +46,8 @@ def test_stats_test_suite_with_noise():
 def test_stats_test_suite_nans():
     param_real = np.array([1.0, 2.0, 3.0])
     param_est = np.array([1.0, np.nan, 3.0])
-    param_std = np.array([0.1, 0.1, 0.1])
     
-    res = stats_test_suite(param_est, param_real, param_std)
+    res = stats_test_suite(param_est, param_real)
 
     assert res["pct_success"] == 2/3
     # Metrics should be calculated on the 2 valid points
@@ -64,21 +56,18 @@ def test_stats_test_suite_nans():
 def test_zero_signal_range():
     param_real = np.array([1.0, 1.0, 1.0])
     param_est = np.array([1.1, 1.1, 1.1])
-    param_std = np.array([0.1, 0.1, 0.1])
     
-    res = stats_test_suite(param_est, param_real, param_std)
+    res = stats_test_suite(param_est, param_real)
 
     # Signal range is 0
     assert res["normalized_rmse"] == np.inf
 
-def test_stats_test_suite_no_std():
+def test_stats_test_suite_metrics():
     param_real = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
     param_est = param_real + 0.1
 
     res = stats_test_suite(param_est, param_real)
 
-    assert np.isnan(res["coverage_prob"])
-    # All other metrics should still be computed
     assert np.isclose(res["rmse"], 0.1)
     assert np.isclose(res["mean_error"], 0.1)
     assert np.isfinite(res["pearson_r"])
@@ -90,10 +79,9 @@ def test_het_breuschpagan_linalg_error(mocker):
     
     param_real = np.array([1.0, 2.0, 3.0])
     param_est = np.array([1.1, 2.2, 3.3])
-    param_std = np.array([0.1, 0.1, 0.1])
     
     with pytest.warns(UserWarning, match="het_breuschpagan test did not converge"):
-         res = stats_test_suite(param_est, param_real, param_std)
+         res = stats_test_suite(param_est, param_real)
     
     assert np.isnan(res["bp_p_value"])
 
diff --git a/tests/tfscreen/genetics/test_library_manager.py b/tests/tfscreen/genetics/test_library_manager.py
index 73d0b0f0..a09fe2ac 100644
--- a/tests/tfscreen/genetics/test_library_manager.py
+++ b/tests/tfscreen/genetics/test_library_manager.py
@@ -439,8 +439,10 @@ def test_prepare_indexes():
     lm.libraries_seen = {'1', '2'}
     lm.lib_lookup = ['.', '1', '.', '2', '2', '.', '1']
     lm.first_amplicon_residue = 42
-    # The contents of wt_blocks don't matter, only its length
-    lm.wt_blocks = [[] for _ in range(len(lm.lib_lookup))]
+    # Tile blocks are sites only where mut_blocks differs from wt_blocks
+    lm.wt_blocks = [['a'], ['gct'], ['c'], ['gat'], ['cgt'], ['g'], ['aaa']]
+    lm.mut_blocks = [['a'], ['gct', 'gat'], ['c'], ['gat', 'gct'], ['cgt', 'cct'],
+                     ['g'], ['tta']]
 
     # 2. Call the method under test
     lm._prepare_indexes()
@@ -814,8 +816,9 @@ def test_get_libraries_dispatcher(mock_get_singles,
     # should cause to call _get_spiked
     lm.run_config = {"spiked_seqs":"spike_call"} 
 
-    # 3. Call the method under test
-    dna_libs, aa_libs = lm.get_libraries()
+    # 3. Call the method under test (the mocked genotypes include no wt)
+    with pytest.warns(UserWarning, match="no wildtype sequence"):
+        dna_libs, aa_libs = lm.get_libraries()
 
     # 4. Assert that each helper was called correctly
     mock_get_singles.assert_called_once_with('1')
@@ -837,4 +840,131 @@ def test_get_libraries_dispatcher(mock_get_singles,
         "spiked": ["spiked_aa"]
     }
     assert dna_libs == expected_dna
-    assert aa_libs == expected_aa
\ No newline at end of file
+    assert aa_libs == expected_aa
+
+def test_prepare_indexes_skips_non_site_blocks():
+    """
+    Tile blocks identical to wt (non-degenerate codons, flanks) are not sites.
+    """
+    lm = LibraryManager.__new__(LibraryManager)
+    lm.libraries_seen = {'1'}
+    lm.lib_lookup = ['.', '1', '1', '1', '1']
+    lm.first_amplicon_residue = 1
+    lm.wt_blocks = [['a'], ['tg'], ['gct'], ['aaa'], ['c']]
+    lm.mut_blocks = [['a'], ['tg'], ['gct', 'gat'], ['aaa'], ['c']]
+
+    lm._prepare_indexes()
+
+    assert lm.indexers == {'1': [2]}
+
+
+def test_prepare_indexes_tile_without_sites():
+    """A tile with no codon differing from wt is a config error."""
+    lm = LibraryManager.__new__(LibraryManager)
+    lm.libraries_seen = {'1'}
+    lm.lib_lookup = ['.', '1']
+    lm.first_amplicon_residue = 1
+    lm.wt_blocks = [['a'], ['gct']]
+    lm.mut_blocks = [['a'], ['gct']]
+
+    with pytest.raises(ValueError, match="no library sites"):
+        lm._prepare_indexes()
+
+
+# ----------------------------------------------------------------------------
+# Library enumeration end to end: which codons count as sites
+# ----------------------------------------------------------------------------
+
+# wt: atg gct aaa | gat cgt  ->  M A K | D R  (residues 10-14)
+# tile 1 = codons 10-12, NNT at A11 only (GCT is an NNT codon: 1 wt entry)
+# tile 2 = codons 13-14, NNT at D13 only (GAT is an NNT codon: 1 wt entry)
+@pytest.fixture
+def partial_tile_config() -> dict:
+    return {
+        "reading_frame": 0,
+        "first_amplicon_residue": 10,
+        "wt_seq":      "atggctaaagatcgt",
+        "degen_sites": "...nnt...nnt...",
+        "tiles":       "111111111222222",
+        "tile_combos": ["single-1", "single-2", "double-1-2"],
+    }
+
+
+def _origin_counts(config):
+    """Total and wt degeneracy per library origin."""
+    df = LibraryManager(config).build_library_df()
+    df["library_origin"] = df["library_origin"].astype(str)
+    df["genotype"] = df["genotype"].astype(str)
+    total = df.groupby("library_origin")["degeneracy"].sum().to_dict()
+    wt = (df[df["genotype"] == "wt"]
+          .set_index("library_origin")["degeneracy"].to_dict())
+    return total, wt
+
+
+def test_enumeration_partial_tiles_count_only_sites(partial_tile_config):
+    total, wt = _origin_counts(partial_tile_config)
+    assert total == {"single-1": 16, "single-2": 16, "double-1-2": 256}
+    assert wt == {"single-1": 1, "single-2": 1, "double-1-2": 1}
+
+
+def test_enumeration_fully_degenerate_tiles(partial_tile_config):
+    partial_tile_config["degen_sites"] = "nntnntnntnntnnt"
+    total, wt = _origin_counts(partial_tile_config)
+    # 3 and 2 NNT sites; inter-tile doubles: 3 * 2 site pairs * 16 * 16
+    assert total == {"single-1": 48, "single-2": 32, "double-1-2": 1536}
+    # wt codons that are NNT: gct (A11), gat (D13), cgt (R14); atg/aaa are not
+    assert wt == {"single-1": 1, "single-2": 2, "double-1-2": 2}
+
+
+def test_enumeration_explicit_mutant_codon_is_a_site(partial_tile_config):
+    partial_tile_config["degen_sites"] = "...gat...nnt..."
+    partial_tile_config["tile_combos"] = ["single-1"]
+    # The only designed sequence is A11D, so the library has no wt
+    with pytest.warns(UserWarning, match="no wildtype sequence"):
+        df = LibraryManager(partial_tile_config).build_library_df()
+    assert list(df["genotype"].astype(str)) == ["A11D"]
+    assert list(df["degeneracy"]) == [1]
+
+
+def test_enumeration_flank_is_not_a_site(partial_tile_config):
+    # tile 1 starts one base into codon 10, leaving a 2-base left flank
+    partial_tile_config["tiles"] = ".11111111222222"
+    partial_tile_config["tile_combos"] = ["single-1"]
+    total, wt = _origin_counts(partial_tile_config)
+    assert total == {"single-1": 16}
+    assert wt == {"single-1": 1}
+
+
+def test_enumeration_library_composition_wt_share(partial_tile_config):
+    from tfscreen.genetics.library_design import library_composition_table
+
+    partial_tile_config["library_mixture"] = {"single-1": 1, "single-2": 1,
+                                              "double-1-2": 1}
+    comp = library_composition_table(partial_tile_config)
+    wt = comp.set_index("genotype").loc["wt"]
+    assert wt["pool_fraction"] == pytest.approx((1/16 + 1/16 + 1/256) / 3)
+    assert wt["bulk_fraction"] == pytest.approx(1.0)
+
+
+def test_enumeration_warns_without_wt(partial_tile_config):
+    # atg (M10) and aaa (K12) are not NNT codons, so no site encodes wt
+    partial_tile_config["degen_sites"] = "nnt...nnt......"
+    partial_tile_config["tiles"] = "111111111......"
+    partial_tile_config["tile_combos"] = ["single-1"]
+    with pytest.warns(UserWarning, match="no wildtype sequence"):
+        df = LibraryManager(partial_tile_config).build_library_df()
+    assert "wt" not in set(df["genotype"].astype(str))
+
+
+def test_enumeration_wt_spike_suppresses_warning(partial_tile_config):
+    import warnings
+
+    partial_tile_config["degen_sites"] = "nnt...nnt......"
+    partial_tile_config["tiles"] = "111111111......"
+    partial_tile_config["tile_combos"] = ["single-1"]
+    partial_tile_config["spiked_seqs"] = ["..............."]
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        df = LibraryManager(partial_tile_config).build_library_df()
+    wt = df[df["genotype"].astype(str) == "wt"]
+    assert list(wt["library_origin"].astype(str)) == ["spiked"]
diff --git a/tests/tfscreen/genetics/test_library_subsample.py b/tests/tfscreen/genetics/test_library_subsample.py
new file mode 100644
index 00000000..521237ef
--- /dev/null
+++ b/tests/tfscreen/genetics/test_library_subsample.py
@@ -0,0 +1,297 @@
+"""Tests for tfscreen.genetics.library_subsample (library_genotypes and
+library_subsample in the library YAML)."""
+
+import numpy as np
+import pandas as pd
+import pytest
+import yaml
+
+from tfscreen.genetics import LibraryManager, library_composition_table
+from tfscreen.genetics.library_subsample import (
+    build_library,
+    format_subsample_summary,
+    subsample_design,
+    subsample_library_df,
+    subsample_record,
+)
+
+
+@pytest.fixture
+def config():
+    # Two NNT sites per tile; tile 1 can encode wt (Ala, gct), tile 2 cannot
+    # (Glu). Spikes: wt and A2L (also bulk members of single-1).
+    return {
+        "reading_frame": 0,
+        "first_amplicon_residue": 1,
+        "wt_seq":      "atggcaaaaccggaatgcggc",
+        "degen_sites": "...nntnnt...nnt......",
+        "tiles":       "...111111...222......",
+        "tile_combos": ["single-1", "single-2", "double-1-2"],
+        "spiked_seqs": [".....................",
+                        "...ctt..............."],
+        "library_mixture": {"single-1": 10, "single-2": 10,
+                            "double-1-2": 100, "spiked": 1},
+        "transform_sizes": {"single-1": 1000, "single-2": 1000,
+                            "double-1-2": 100000, "spiked": 10},
+        "total_num_reads": 1_000_000,
+        "pcr_template_molecules": 50_000,
+    }
+
+
+def _full(config):
+    df = LibraryManager(config).build_library_df()
+    df["genotype"] = df["genotype"].astype(str)
+    return df
+
+
+def _kept(df, origin):
+    return set(df.loc[df["library_origin"] == origin, "genotype"].astype(str))
+
+
+def test_no_subsample_returns_input(config):
+    df = _full(config)
+    out, summary = subsample_library_df(df, config)
+    assert out is df and summary is None
+    assert subsample_design(config, summary) == {}
+    assert subsample_record(config) is None
+
+
+# ---------------------------------------------------------------------------
+# library_genotypes
+# ---------------------------------------------------------------------------
+
+def test_list_keeps_listed_plus_wt_and_spikes(config, tmp_path):
+    full = _full(config)
+    doubles = sorted(g for g in _kept(full, "double-1-2") if g.count("/") == 1)
+    singles = sorted(g for g in _kept(full, "single-2"))[:3]
+    listed = doubles[:5] + singles
+    # unsorted mutations and comments are fine
+    lines = ["# kept genotypes"] + ["/".join(reversed(listed[0].split("/")))] \
+        + listed[1:]
+    (tmp_path / "keep.txt").write_text("\n".join(lines) + "\n")
+    config["library_genotypes"] = "keep.txt"
+    out, summary = subsample_library_df(full, config, base_dir=str(tmp_path))
+
+    protected = {"wt", "A2L"}
+    assert set(out["genotype"].astype(str)) == set(listed) | protected
+    # A2L is kept in every origin that encodes it, spiked and bulk
+    assert set(out.loc[out["genotype"] == "A2L", "library_origin"]) == \
+        {"single-1", "spiked"}
+    # weights renormalized within each origin
+    np.testing.assert_allclose(
+        out.groupby("library_origin", observed=True)["weight"].sum(), 1.0)
+    s = summary.set_index("library_origin")
+    assert s.loc["spiked", "num_genotypes_kept"] == 2
+    assert s.loc["single-2", "num_genotypes_kept"] == 3
+
+
+def test_list_path_relative_to_yaml(config, tmp_path):
+    sub = tmp_path / "cfg"
+    sub.mkdir()
+    full = _full(config)
+    keep = sorted(_kept(full, "single-2"))[:2]
+    (sub / "keep.txt").write_text("\n".join(keep))
+    config["library_genotypes"] = "keep.txt"
+    path = sub / "library.yaml"
+    path.write_text(yaml.safe_dump(config))
+    df, summary = build_library(str(path))
+    assert set(keep) <= set(df["genotype"].astype(str))
+    assert summary is not None
+
+
+def test_list_genotype_outside_library_is_an_error(config, tmp_path):
+    (tmp_path / "keep.txt").write_text("A2L\nQ99W\n")
+    config["library_genotypes"] = str(tmp_path / "keep.txt")
+    with pytest.raises(ValueError, match="not in the described library"):
+        subsample_library_df(_full(config), config)
+
+
+def test_list_missing_or_empty_file(config, tmp_path):
+    config["library_genotypes"] = str(tmp_path / "nope.txt")
+    with pytest.raises(FileNotFoundError):
+        subsample_library_df(_full(config), config)
+    (tmp_path / "empty.txt").write_text("# nothing\n")
+    config["library_genotypes"] = str(tmp_path / "empty.txt")
+    with pytest.raises(ValueError, match="lists no genotypes"):
+        subsample_library_df(_full(config), config)
+
+
+def test_list_that_empties_an_origin_is_an_error(config, tmp_path):
+    full = _full(config)
+    # only tile-1 singles: single-2 has no wt and nothing listed
+    (tmp_path / "keep.txt").write_text("\n".join(_kept(full, "single-1")))
+    config["library_genotypes"] = str(tmp_path / "keep.txt")
+    with pytest.raises(ValueError, match="no genotypes"):
+        subsample_library_df(full, config)
+
+
+# ---------------------------------------------------------------------------
+# library_subsample
+# ---------------------------------------------------------------------------
+
+def test_subsample_keeps_n_per_origin(config):
+    full = _full(config)
+    config.update(library_subsample={"double-1-2": 20},
+                  library_subsample_seed=1)
+    out, summary = subsample_library_df(full, config)
+    dbl = _kept(out, "double-1-2")
+    assert len(dbl - {"wt", "A2L"}) == 20
+    # other origins untouched
+    for o in ("single-1", "single-2", "spiked"):
+        assert _kept(out, o) == _kept(full, o)
+    s = summary.set_index("library_origin")
+    assert s.loc["double-1-2", "num_genotypes"] == \
+        len(_kept(full, "double-1-2"))
+
+
+def test_subsample_seed_reproducible_and_nested(config):
+    full = _full(config)
+
+    def draw(n, seed, extra=None):
+        cf = dict(config, library_subsample={"double-1-2": n, **(extra or {})},
+                  library_subsample_seed=seed)
+        return _kept(subsample_library_df(full, cf)[0], "double-1-2")
+
+    assert draw(30, 5) == draw(30, 5)
+    assert draw(30, 5) != draw(30, 6)
+    # a smaller N keeps a subset of a larger N's genotypes
+    assert draw(10, 5) <= draw(30, 5)
+    # subsampling a second origin leaves the first origin's draw alone
+    assert draw(30, 5) == draw(30, 5, {"single-2": 4})
+
+
+@pytest.mark.parametrize("update,match", [
+    ({"library_subsample": {"double-1-2": 5}}, "library_subsample_seed"),
+    ({"library_subsample": {"double-1-2": 5},
+      "library_subsample_seed": -1}, "non-negative integer"),
+    ({"library_subsample": {"double-1-2": 5},
+      "library_subsample_seed": 1.5}, "non-negative integer"),
+    ({"library_subsample": {"double-9-9": 5},
+      "library_subsample_seed": 1}, "not in the library"),
+    ({"library_subsample": {"spiked": 1},
+      "library_subsample_seed": 1}, "spiked origin"),
+    ({"library_subsample": {"double-1-2": 0},
+      "library_subsample_seed": 1}, "integer >= 1"),
+    ({"library_subsample": {"double-1-2": True},
+      "library_subsample_seed": 1}, "integer >= 1"),
+    ({"library_subsample": {"double-1-2": 10_000},
+      "library_subsample_seed": 1}, "only"),
+    ({"library_subsample": [5], "library_subsample_seed": 1},
+     "must map library origins"),
+    ({"library_subsample": {"double-1-2": 5}, "library_subsample_seed": 1,
+      "library_subsample_depth": "per_genotype"}, "library_subsample_depth"),
+])
+def test_subsample_errors(config, update, match):
+    config.update(update)
+    with pytest.raises(ValueError, match=match):
+        subsample_library_df(_full(config), config)
+
+
+def test_list_then_subsample(config, tmp_path):
+    full = _full(config)
+    doubles = sorted(g for g in _kept(full, "double-1-2") if "/" in g)[:40]
+    keep = doubles + sorted(_kept(full, "single-1") | _kept(full, "single-2"))
+    (tmp_path / "keep.txt").write_text("\n".join(keep))
+    config.update(library_genotypes=str(tmp_path / "keep.txt"),
+                  library_subsample={"double-1-2": 10},
+                  library_subsample_seed=2)
+    out, _ = subsample_library_df(full, config)
+    dbl = _kept(out, "double-1-2") - {"wt", "A2L"}
+    # (double-1-2 also encodes some singles, which the list keeps)
+    assert len(dbl) == 10 and dbl <= set(keep)
+
+
+# ---------------------------------------------------------------------------
+# Depth (subsample_design) and the fit-side composition
+# ---------------------------------------------------------------------------
+
+def test_full_library_depth_keeps_per_sequence_quantities(config):
+    full = _full(config)
+    config.update(library_subsample={"double-1-2": 20},
+                  library_subsample_seed=1)
+    out, summary = subsample_library_df(full, config)
+    design = subsample_design(config, summary)
+
+    s = summary.set_index("library_origin")
+    frac = s["num_sequences_kept"] / s["num_sequences"]
+    mix = config["library_mixture"]
+    for o in mix:
+        assert design["library_mixture"][o] == pytest.approx(mix[o] * frac[o])
+        assert design["transform_sizes"][o] == \
+            max(1, round(config["transform_sizes"][o] * frac[o]))
+    share = sum(design["library_mixture"].values()) / sum(mix.values())
+    assert design["kept_pool_share"] == pytest.approx(share)
+    assert design["total_num_reads"] == round(1_000_000 * share)
+    assert design["pcr_template_molecules"] == round(50_000 * share)
+    assert "cfu0" not in design
+    # a kept double's share of the pool relative to a single is the same as
+    # in the full library
+    text = format_subsample_summary(config, summary, design)
+    assert "double-1-2" in text and "cfu0 unchanged" in text
+
+
+def test_mixture_missing_an_origin_is_an_error(config):
+    full = _full(config)
+    config.update(library_subsample={"double-1-2": 20},
+                  library_subsample_seed=1)
+    _, summary = subsample_library_df(full, config)
+    del config["library_mixture"]["single-2"]
+    with pytest.raises(ValueError,
+                       match=r"library_mixture has no value.*single-2"):
+        subsample_design(config, summary)
+
+
+def test_as_written_depth_changes_nothing(config):
+    config.update(library_subsample={"double-1-2": 20},
+                  library_subsample_seed=1,
+                  library_subsample_depth="as_written")
+    _, summary = subsample_library_df(_full(config), config)
+    assert subsample_design(config, summary) == {}
+
+
+def test_composition_of_a_subsample(config):
+    full_table = library_composition_table(config).set_index("genotype")
+    config.update(library_subsample={"double-1-2": 20},
+                  library_subsample_seed=1)
+    sub = library_composition_table(config).set_index("genotype")
+    sub_df, _ = build_library(config)
+    assert set(sub.index) == set(sub_df["genotype"].astype(str))
+    assert sub["pool_fraction"].sum() == pytest.approx(1.0)
+    # full_library depth: a kept genotype all of whose sequences are kept
+    # (here those encoded by one origin) keeps its abundance relative to
+    # every other such genotype, and every kept spike its bulk fraction
+    one = [g for g in sub.index if "|" not in full_table.loc[g, "origins"]]
+    assert any("/" in g for g in one) and any("/" not in g for g in one)
+    ratio = (sub.loc[one, "pool_fraction"]
+             / full_table.loc[one, "pool_fraction"])
+    np.testing.assert_allclose(ratio, ratio.iloc[0], rtol=1e-10)
+    np.testing.assert_allclose(sub.loc[["wt", "A2L"], "bulk_fraction"],
+                               full_table.loc[["wt", "A2L"], "bulk_fraction"],
+                               rtol=1e-10)
+
+    config["library_subsample_depth"] = "as_written"
+    written = library_composition_table(config).set_index("genotype")
+    ratio = (written.loc[one, "pool_fraction"]
+             / full_table.loc[one, "pool_fraction"])
+    assert not np.allclose(ratio, ratio.iloc[0])
+
+
+def test_subsample_record(config, tmp_path):
+    (tmp_path / "keep.txt").write_text("A2L\n")
+    config.update(library_genotypes=str(tmp_path / "keep.txt"),
+                  library_subsample={"double-1-2": 3},
+                  library_subsample_seed=4)
+    rec = subsample_record(config)
+    assert rec == {"library_genotypes": "keep.txt",
+                   "library_subsample": {"double-1-2": 3},
+                   "library_subsample_seed": 4,
+                   "library_subsample_depth": "full_library"}
+
+
+def test_summary_columns(config):
+    config.update(library_subsample={"double-1-2": 3}, library_subsample_seed=4)
+    _, summary = subsample_library_df(_full(config), config)
+    assert isinstance(summary, pd.DataFrame)
+    assert list(summary.columns) == ["library_origin", "num_genotypes",
+                                     "num_genotypes_kept", "num_sequences",
+                                     "num_sequences_kept"]
diff --git a/tests/tfscreen/process_raw/test_binding.py b/tests/tfscreen/process_raw/test_binding.py
new file mode 100644
index 00000000..a626d6c8
--- /dev/null
+++ b/tests/tfscreen/process_raw/test_binding.py
@@ -0,0 +1,118 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from tfscreen.process_raw.binding import (
+    BINDING_COLUMNS,
+    _round_sig,
+    binding_table,
+    format_info,
+)
+
+
+def _wells():
+    """
+    wt measured on two days (two bioreps) at 2 uM, with floating-point
+    residue in one day's IPTG; M42I once at 2 uM; wt also at 20 uM; and a
+    row with no theta.
+    """
+    rows = []
+    for rep, date, iptg, offset in ((1, "d1", [0.0, 1e-6, 6e-8], 0.0),
+                                    (2, "d2", [0.0, 1e-6, 6.000000000000001e-08], 0.1)):
+        for c, base in zip(iptg, (0.9, 0.5, 0.8)):
+            rows.append(dict(genotype="wt", biorep=rep, date=date,
+                             protein_dimer_conc_molar=2e-6,
+                             iptg_conc_molar=c, theta=base + offset))
+    for c, t in ((0.0, 0.3), (1e-6, 0.2)):
+        rows.append(dict(genotype="M42I", biorep=1, date="d3",
+                         protein_dimer_conc_molar=2.0000000000000003e-06,
+                         iptg_conc_molar=c, theta=t))
+    rows.append(dict(genotype="M42I", biorep=2, date="d4",
+                     protein_dimer_conc_molar=2e-6, iptg_conc_molar=0.0,
+                     theta=np.nan))
+    rows.append(dict(genotype="wt", biorep=1, date="d5",
+                     protein_dimer_conc_molar=2e-5, iptg_conc_molar=0.0,
+                     theta=0.99))
+    return pd.DataFrame(rows)
+
+
+def test_round_sig():
+    out = _round_sig([0.0, 6.000000000000001e-08, 6e-08, 123456789.0], 6)
+    assert out[0] == 0.0
+    assert out[1] == out[2]
+    assert out[3] == 123457000.0
+
+
+def test_binding_table_averages_and_pools():
+    df, summary, info = binding_table(_wells(), 2.0)
+    assert list(df.columns) == BINDING_COLUMNS
+    assert info["rows_in"] == 10
+    assert info["rows_no_theta"] == 1
+    assert info["rows_kept"] == 8
+
+    wt = df[df["genotype"] == "wt"].set_index("titrant_conc")
+    # The rounded 6e-8 M lines up across days: three wt points, in mM.
+    assert sorted(wt.index) == [0.0, 6e-05, 0.001]
+    assert wt.loc[0.0, "theta_obs"] == pytest.approx(0.95)
+
+    # Every wt pair differs by 0.1: residuals +-0.05, 3 dof.
+    assert info["pooled_dof"] == 3
+    assert info["pooled_sd"] == pytest.approx(np.sqrt(6 * 0.05 ** 2 / 3))
+    s = info["pooled_sd"]
+    assert wt.loc[0.0, "theta_std"] == pytest.approx(s / np.sqrt(2))
+    m = df[df["genotype"] == "M42I"]
+    assert np.allclose(m["theta_std"], s)
+    assert (df["titrant_name"] == "iptg").all()
+
+    sm = summary.set_index(["genotype", "titrant_conc"])
+    assert sm.loc[("wt", 0.0), "n_replicates"] == 2
+    assert np.isnan(sm.loc[("M42I", 0.0), "replicate_sd"])
+
+
+def test_technical_repeats_count_once():
+    w = _wells()
+    w = pd.concat([w, w.iloc[[0]].assign(theta=0.7)], ignore_index=True)
+    df, summary, _ = binding_table(w, 2.0)
+    sm = summary.set_index(["genotype", "titrant_conc"])
+    assert sm.loc[("wt", 0.0), "n_replicates"] == 2
+    assert sm.loc[("wt", 0.0), "n_measurements"] == 3
+    # Day 1 averages its two wells (0.9, 0.7) to 0.8 before day 2's 1.0.
+    assert sm.loc[("wt", 0.0), "theta_obs"] == pytest.approx(0.9)
+
+
+def test_fixed_theta_std_and_units():
+    df, _, info = binding_table(_wells(), 2000.0, protein_conc_units="nM",
+                                theta_std=0.2)
+    assert info["replicate_sd_used"] == 0.2
+    wt0 = df[(df["genotype"] == "wt") & (df["titrant_conc"] == 0.0)]
+    assert wt0["theta_std"].iloc[0] == pytest.approx(0.2 / np.sqrt(2))
+    assert "0.2" in format_info(info)
+
+
+def test_unreplicated_needs_theta_std():
+    with pytest.raises(ValueError, match="give theta_std"):
+        binding_table(_wells(), 20.0)
+    df, _, _ = binding_table(_wells(), 20.0, theta_std=0.05)
+    assert len(df) == 1
+
+
+def test_refusals():
+    with pytest.raises(ValueError, match="no measurements at protein"):
+        binding_table(_wells(), 5.0)
+    with pytest.raises(ValueError, match="missing column"):
+        binding_table(_wells().drop(columns="date"), 2.0)
+    with pytest.raises(ValueError, match="not one of"):
+        binding_table(_wells(), 2.0, protein_conc_units="pM")
+    with pytest.raises(ValueError, match="theta_std must be positive"):
+        binding_table(_wells(), 2.0, theta_std=0.0)
+    with pytest.raises(ValueError, match="sig_digits must be at least 1"):
+        binding_table(_wells(), 2.0, sig_digits=0)
+    for bad in (0.0, -1.0, np.nan):
+        with pytest.raises(ValueError, match="protein_conc must be positive"):
+            binding_table(_wells(), bad)
+
+
+def test_no_replicate_cols_makes_each_row_a_replicate():
+    df, summary, _ = binding_table(_wells(), 2.0, replicate_cols=())
+    sm = summary.set_index(["genotype", "titrant_conc"])
+    assert sm.loc[("wt", 0.0), "n_replicates"] == 2
diff --git a/tests/tfscreen/process_raw/test_calibrate_od600_cli.py b/tests/tfscreen/process_raw/test_calibrate_od600_cli.py
new file mode 100644
index 00000000..2465fadf
--- /dev/null
+++ b/tests/tfscreen/process_raw/test_calibrate_od600_cli.py
@@ -0,0 +1,49 @@
+"""Tests for tfs-calibrate-od600."""
+import os
+
+import pytest
+
+from tfscreen.process_raw import od600 as O
+from tfscreen.process_raw.scripts.calibrate_od600_cli import (
+    calibrate_od600,
+    main,
+)
+
+EXAMPLE = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                       "examples", "od600")
+
+
+def test_writes_all_outputs(tmp_path):
+    out = str(tmp_path / "sub" / "cal")
+    calibrate_od600(os.path.join(EXAMPLE, "replicates.csv"),
+                    os.path.join(EXAMPLE, "plate_counts.csv"),
+                    out_prefix=out)
+    for suffix in (".yaml", "_replicates.csv", "_plate_counts.csv", ".pdf"):
+        assert os.path.exists(out + suffix)
+    cal = O.read_calibration(out + ".yaml")
+    assert cal["source"]["plate_count_file"] == "plate_counts.csv"
+
+
+def test_main_parses_flags(tmp_path):
+    out = str(tmp_path / "cal")
+    main_args = [os.path.join(EXAMPLE, "replicates.csv"),
+                 os.path.join(EXAMPLE, "plate_counts.csv"),
+                 "--out_prefix", out, "--degree", "1",
+                 "--detection_threshold", "0.1"]
+    import sys
+    from unittest.mock import patch
+    with patch.object(sys, "argv", ["tfs-calibrate-od600"] + main_args):
+        main()
+    cal = O.read_calibration(out + ".yaml")
+    assert cal["degree"] == 1 and cal["detection_threshold"] == 0.1
+
+
+@pytest.mark.filterwarnings("ignore:.*found in sys.modules:RuntimeWarning")
+def test_cli_module_runs_as_script(monkeypatch):
+    import runpy
+    import sys
+    monkeypatch.setattr(sys, "argv", ["tfs-calibrate-od600", "--help"])
+    with pytest.raises(SystemExit) as exc:
+        runpy.run_module("tfscreen.process_raw.scripts.calibrate_od600_cli",
+                         run_name="__main__")
+    assert exc.value.code == 0
diff --git a/tests/tfscreen/process_raw/test_od600_calibration.py b/tests/tfscreen/process_raw/test_od600_calibration.py
new file mode 100644
index 00000000..423716cc
--- /dev/null
+++ b/tests/tfscreen/process_raw/test_od600_calibration.py
@@ -0,0 +1,243 @@
+"""Tests for tfscreen.process_raw.od600 (OD600-to-CFU calibration)."""
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+import yaml
+from scipy.optimize import curve_fit
+
+from tfscreen.process_raw import od600 as O
+
+EXAMPLE = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                       "examples", "od600")
+
+
+def _plates(od, colonies, volume=0.1, dilution=1e5):
+    n = len(od)
+    return pd.DataFrame({"od600": od, "colonies": colonies,
+                         "dilution": dilution, "plated_volume_mL": volume,
+                         "num_dilutions": 5, "plating_steps": 1})
+
+
+def _replicates():
+    rows = []
+    for d, m in [(1.0, 0.6), (1 / 3, 0.27), (1 / 9, 0.15), (1 / 27, 0.10),
+                 (1 / 81, 0.092)]:
+        for k in (-1, 1):
+            rows.append({"dilution": d, "od600": m * (1 + 0.01 * k)})
+    return pd.DataFrame(rows)
+
+
+@pytest.fixture(scope="module")
+def example():
+    rep = pd.read_csv(os.path.join(EXAMPLE, "replicates.csv"))
+    plate = pd.read_csv(os.path.join(EXAMPLE, "plate_counts.csv"))
+    return O.calibrate(rep, plate)
+
+
+# ---------------------------------------------------------------------------
+# the two experiments
+# ---------------------------------------------------------------------------
+
+def test_reading_noise_table():
+    t = O.reading_noise(_replicates())
+    assert list(t["dilution"]) == sorted(t["dilution"], reverse=True)
+    assert np.allclose(t["rel_sd"], 0.01)
+    assert (t["n"] == 2).all()
+
+
+def test_detection_threshold_is_midway_between_two_most_dilute():
+    t = O.reading_noise(_replicates())
+    assert O.detection_threshold(t) == pytest.approx((0.10 + 0.092) / 2)
+
+
+def test_reading_noise_needs_columns():
+    with pytest.raises(ValueError, match="dilution"):
+        O.reading_noise(pd.DataFrame({"od600": [0.1, 0.2]}))
+
+
+def test_plate_counts_divide_by_plated_volume():
+    df = O.plate_counts_to_cfu(_plates([0.5], [400]), pipette_rel_error=0.02)
+    assert df["cfu_per_mL"].iloc[0] == pytest.approx(400 * 1e5 / 0.1)
+    rel = np.sqrt(1 / 400 + 6 * 0.02 ** 2)
+    assert df["cfu_per_mL_std"].iloc[0] == pytest.approx(4e8 * rel)
+
+
+def test_plate_counts_refuse_empty_plates():
+    with pytest.raises(ValueError, match="no colonies"):
+        O.plate_counts_to_cfu(_plates([0.5, 0.3], [400, 0]))
+
+
+def test_fit_recovers_exact_polynomial():
+    od = np.linspace(0.1, 0.6, 8)
+    true = np.array([1e6, 8e8, 2e8])
+    y = true[0] + true[1] * od + true[2] * od ** 2
+    coef, cov, chi2 = O.fit_polynomial(od, y, 0.05 * y)
+    assert np.allclose(coef, true, rtol=1e-8)
+
+
+def test_fit_covariance_matches_curve_fit():
+    rng = np.random.default_rng(1)
+    od = np.linspace(0.1, 0.6, 12)
+    y = 8e8 * od + 2e8 * od ** 2
+    sd = 0.08 * y
+    y_obs = y + sd * rng.normal(size=y.size)
+    coef, cov, chi2 = O.fit_polynomial(od, y_obs, sd)
+    p, pcov = curve_fit(lambda x, a, b, c: a + b * x + c * x ** 2, od, y_obs,
+                        sigma=sd, absolute_sigma=False)
+    assert np.allclose(coef, p, rtol=1e-6)
+    assert np.allclose(cov, pcov, rtol=1e-5)
+    assert np.allclose(cov, cov.T)
+
+
+def test_fit_needs_enough_points():
+    with pytest.raises(ValueError, match="at least"):
+        O.fit_polynomial([0.1, 0.2, 0.3], [1, 2, 3], [1, 1, 1], degree=2)
+
+
+# ---------------------------------------------------------------------------
+# calibrate and the file
+# ---------------------------------------------------------------------------
+
+def test_calibrate_example(example):
+    cal, noise, plates = example
+    assert cal["kind"] == O.CALIBRATION_KIND
+    assert cal["degree"] == 2 and len(cal["coefficients"]) == 3
+    # example data: cfu/mL = 8e8 OD + 2e8 OD^2
+    od = np.array([0.2, 0.4, 0.6])
+    cfu, _, _ = O.od600_to_cfu_per_mL(od, cal)
+    assert np.allclose(cfu, 8e8 * od + 2e8 * od ** 2, rtol=0.1)
+    assert 0.01 < cal["reading_rel_sd"] < 0.03
+    assert 0.08 < cal["detection_threshold"] < 0.1
+    assert {"cfu_per_mL_fit", "z"} <= set(plates.columns)
+    assert cal["num_plate_counts"] == len(plates)
+
+
+def test_threshold_override():
+    rep = pd.read_csv(os.path.join(EXAMPLE, "replicates.csv"))
+    plate = pd.read_csv(os.path.join(EXAMPLE, "plate_counts.csv"))
+    cal, _, _ = O.calibrate(rep, plate, threshold=0.11)
+    assert cal["detection_threshold"] == 0.11
+
+
+def test_write_read_round_trip(example, tmp_path):
+    cal = example[0]
+    path = tmp_path / "cal.yaml"
+    O.write_calibration(cal, path, source={"x": "y"})
+    back = O.read_calibration(str(path))
+    assert np.allclose(back["coefficients"], cal["coefficients"])
+    assert np.allclose(back["covariance"], cal["covariance"])
+    assert back["source"] == {"x": "y"}
+    assert open(path).read().startswith("# OD600-to-CFU calibration")
+
+
+def test_example_file_is_current(example):
+    """examples/od600/od600_calibration.yaml is what the CLI makes from the
+    example data."""
+    shipped = O.read_calibration(os.path.join(EXAMPLE, "od600_calibration.yaml"))
+    assert np.allclose(shipped["coefficients"], example[0]["coefficients"])
+
+
+def test_legacy_format_refused():
+    legacy = {"A_CFU": 0.0, "B_CFU": 8e7, "C_CFU": 2e7, "OD600_PCT_STD": 0.02,
+              "OD600_MEAS_THRESHOLD": 0.08}
+    with pytest.raises(ValueError, match="tfs-calibrate-od600"):
+        O.read_calibration(legacy)
+
+
+def test_decreasing_curve_refused(example):
+    bad = dict(example[0], coefficients=[0.0, 1e8, -1e9])
+    with pytest.raises(ValueError, match="not increasing"):
+        O.check_calibration(bad)
+
+
+def test_shape_mismatch_refused(example):
+    bad = dict(example[0], covariance=[[1.0]])
+    with pytest.raises(ValueError, match="covariance"):
+        O.check_calibration(bad)
+
+
+# ---------------------------------------------------------------------------
+# applying it
+# ---------------------------------------------------------------------------
+
+def test_error_components(example):
+    cal = O.read_calibration(example[0])
+    od = np.array([0.15, 0.4])
+    curve, reading = O.cfu_per_mL_error_components(od, cal)
+    for i, x in enumerate(od):
+        J = np.array([1.0, x, x ** 2])
+        assert curve[i] == pytest.approx(np.sqrt(J @ cal["covariance"] @ J))
+        c = cal["coefficients"]
+        slope = c[1] + 2 * c[2] * x
+        assert reading[i] == pytest.approx(slope * cal["reading_rel_sd"] * x)
+    cfu, sd, _ = O.od600_to_cfu_per_mL(od, cal)
+    assert np.allclose(sd, np.hypot(curve, reading))
+
+
+def test_below_threshold_is_an_upper_bound(example):
+    cal = example[0]
+    cfu, _, detectable = O.od600_to_cfu_per_mL([0.01], cal)
+    at, _, _ = O.od600_to_cfu_per_mL([cal["detection_threshold"]], cal)
+    assert not detectable[0]
+    assert cfu[0] == pytest.approx(at[0])
+
+
+def test_inverse_round_trip(example):
+    cal = example[0]
+    od = np.array([0.1, 0.3, 0.59])
+    cfu, _, _ = O.od600_to_cfu_per_mL(od, cal)
+    assert np.allclose(O.cfu_per_mL_to_od600(cfu, cal), od, rtol=1e-9)
+    # totals below the curve at OD 0 map to 0
+    assert O.cfu_per_mL_to_od600([cal["coefficients"][0] - 1e6], cal)[0] == 0.0
+
+
+def test_in_calibrated_range(example):
+    lo, hi = example[0]["calibrated_od600_range"]
+    got = O.in_calibrated_range([lo - 0.01, (lo + hi) / 2, hi + 0.01], example[0])
+    assert list(got) == [False, True, False]
+
+
+def test_accepts_path_or_dict(example, tmp_path):
+    path = tmp_path / "c.yaml"
+    O.write_calibration(example[0], path)
+    a = O.od600_to_cfu_per_mL([0.3], str(path))[0]
+    b = O.od600_to_cfu_per_mL([0.3], example[0])[0]
+    assert a == pytest.approx(b)
+
+
+# ---------------------------------------------------------------------------
+# input checks
+# ---------------------------------------------------------------------------
+
+def test_reading_noise_needs_two_dilutions():
+    rep = pd.DataFrame({"dilution": [1.0, 1.0], "od600": [0.5, 0.51]})
+    with pytest.raises(ValueError, match="at least two dilutions"):
+        O.reading_noise(rep)
+
+
+def test_check_calibration_missing_keys(example):
+    bad = dict(example[0])
+    del bad["covariance"]
+    with pytest.raises(ValueError, match=r"missing \['covariance'\]"):
+        O.check_calibration(bad)
+
+
+def test_check_calibration_wrong_kind(example):
+    bad = dict(example[0], kind="something_else")
+    with pytest.raises(ValueError, match="Not an OD600 calibration"):
+        O.check_calibration(bad)
+
+
+@pytest.mark.parametrize("volume", [None, 0, -1.0])
+def test_tube_totals_refuses_bad_volume(example, volume):
+    od = pd.DataFrame({"sample": ["a"], "od600": [0.3]})
+    with pytest.raises(ValueError, match="tube_volume_mL"):
+        O.tube_totals_from_od600(od, example[0], volume)
+
+
+def test_tube_totals_refuses_non_numeric_reading(example):
+    od = pd.DataFrame({"sample": ["a", "b"], "od600": ["0.3", "oops"]})
+    with pytest.raises(ValueError, match=r"no numeric reading for: \['b'\]"):
+        O.tube_totals_from_od600(od, example[0], 5.0)
diff --git a/tests/tfscreen/process_raw/test_process_binding_cli.py b/tests/tfscreen/process_raw/test_process_binding_cli.py
new file mode 100644
index 00000000..b564df20
--- /dev/null
+++ b/tests/tfscreen/process_raw/test_process_binding_cli.py
@@ -0,0 +1,58 @@
+import os
+
+import pandas as pd
+import pytest
+
+from tfscreen.process_raw.binding import BINDING_COLUMNS
+from tfscreen.process_raw.scripts.process_binding_cli import process_binding
+
+
+def _wells():
+    rows = []
+    for rep, offset in ((1, 0.0), (2, 0.1)):
+        for c, t in ((0.0, 0.9), (1e-6, 0.5)):
+            rows.append(dict(genotype="wt", biorep=rep, date=f"d{rep}",
+                             protein_dimer_conc_molar=2e-6,
+                             iptg_conc_molar=c, theta=t + offset))
+    rows.append(dict(genotype="M42I", biorep=1, date="d3",
+                     protein_dimer_conc_molar=2e-6, iptg_conc_molar=0.0,
+                     theta=0.3))
+    return pd.DataFrame(rows)
+
+
+def test_process_binding_writes_tables(tmpdir):
+    src = os.path.join(tmpdir, "wells.csv")
+    _wells().to_csv(src, index=False)
+    out = os.path.join(tmpdir, "b")
+    process_binding(src, out_prefix=out)
+    df = pd.read_csv(f"{out}.csv")
+    assert list(df.columns) == BINDING_COLUMNS
+    assert set(df["genotype"]) == {"wt", "M42I"}
+    summary = pd.read_csv(f"{out}_summary.csv")
+    assert "n_replicates" in summary.columns
+
+
+def test_process_binding_empty_replicate_cols(tmpdir):
+    src = os.path.join(tmpdir, "wells.csv")
+    _wells().drop(columns=["biorep", "date"]).to_csv(src, index=False)
+    out = os.path.join(tmpdir, "b")
+    process_binding(src, replicate_cols="", theta_std=0.1, out_prefix=out)
+    assert os.path.exists(f"{out}.csv")
+
+
+def test_process_binding_refuses_overwrite(tmpdir):
+    src = os.path.join(tmpdir, "b.csv")
+    _wells().to_csv(src, index=False)
+    with pytest.raises(ValueError, match="overwrite"):
+        process_binding(src, out_prefix=os.path.join(tmpdir, "b"))
+
+
+@pytest.mark.filterwarnings("ignore:.*found in sys.modules:RuntimeWarning")
+def test_cli_module_runs_as_script(monkeypatch):
+    import runpy
+    import sys
+    monkeypatch.setattr(sys, "argv", ["tfs-process-binding", "--help"])
+    with pytest.raises(SystemExit) as exc:
+        runpy.run_module("tfscreen.process_raw.scripts.process_binding_cli",
+                         run_name="__main__")
+    assert exc.value.code == 0
diff --git a/tests/tfscreen/process_raw/test_process_counts_cli.py b/tests/tfscreen/process_raw/test_process_counts_cli.py
index b7810643..e8499d7e 100644
--- a/tests/tfscreen/process_raw/test_process_counts_cli.py
+++ b/tests/tfscreen/process_raw/test_process_counts_cli.py
@@ -1,82 +1,268 @@
-import pytest
+import os
+
+import numpy as np
 import pandas as pd
-from unittest.mock import patch
-
-from tfscreen.process_raw.scripts.process_counts_cli import process_counts
-
-
-# ------------------------
-# Fixture
-# ------------------------
-
-@pytest.fixture
-def fx_sample_df() -> pd.DataFrame:
-    return pd.DataFrame({
-        "sample": ["s1", "s2"],
-        "sample_cfu": [1e8, 1.6e8],
-        "sample_cfu_std": [1e7, 1.6e7],
-    })
-
-
-# ------------------------
-# Test process_counts
-# ------------------------
-
-@patch('pandas.DataFrame.to_csv')
-@patch('tfscreen.process_raw.scripts.process_counts_cli.counts_to_lncfu')
-@patch('tfscreen.process_raw.scripts.process_counts_cli._aggregate_counts')
-@patch('tfscreen.process_raw.scripts.process_counts_cli._prep_sample_df')
-def test_process_counts_orchestration(
-    mock_prep, mock_agg, mock_lncfu, mock_to_csv, fx_sample_df
-):
-    """Verify the overall workflow and data handoffs."""
-    df_prepped = fx_sample_df.set_index("sample")
-    df_aggregated = pd.DataFrame({"s": ["s1"], "aggregated": [True]})
-    df_final = pd.DataFrame({"s": ["s1"], "final_lncfu": [True]})
-
-    mock_prep.return_value = df_prepped
-    mock_agg.return_value = df_aggregated
-    mock_lncfu.return_value = df_final
-
-    process_counts(
-        sample_df=fx_sample_df,
-        counts_csv_path="/counts/path",
-        output_file="/out/final.csv",
-        counts_glob_prefix="test_prefix",
-        min_genotype_obs=20,
-        pseudocount=2,
-        verbose=False
-    )
-
-    mock_prep.assert_called_once_with(fx_sample_df, "/counts/path", "test_prefix", False)
-    # sample_ln_cfu/_std are inferred before counts are aggregated
-    mock_agg.assert_called_once()
-    handed_off = mock_agg.call_args.args[0]
-    assert {"sample_ln_cfu", "sample_ln_cfu_std"} <= set(handed_off.columns)
-    pd.testing.assert_frame_equal(handed_off[df_prepped.columns], df_prepped)
-
-    mock_lncfu.assert_called_once()
-    args, kwargs = mock_lncfu.call_args
-    assert args[0] is handed_off
-    assert args[1] is df_aggregated
-    assert kwargs == {"min_genotype_obs": 20, "pseudocount": 2}
-    mock_to_csv.assert_called_once_with("/out/final.csv", index=False)
-
-
-def test_process_counts_missing_cfu_columns(tmp_path):
-    """ValueError raised when sample_cfu_std is absent."""
-    bad_df = pd.DataFrame({
-        "sample": ["s1", "s2"],
-        "sample_cfu": [1e8, 1.6e8],
-        # sample_cfu_std intentionally omitted
-    })
-    prepped = bad_df.set_index("sample")
-    prepped["obs_file"] = "dummy.csv"
-
-    with patch('tfscreen.process_raw.scripts.process_counts_cli._prep_sample_df', return_value=prepped):
-        with pytest.raises(ValueError, match="sample_ln_cfu_std"):
-            process_counts(
-                sample_df=bad_df,
-                counts_csv_path=str(tmp_path),
-                output_file="/out/final.csv",
-            )
+import pytest
+
+from tfscreen.process_raw import od600 as O
+from tfscreen.process_raw.scripts.process_counts_cli import (
+    add_tube_totals,
+    process_counts,
+)
+
+CALIBRATION = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                           "examples", "od600", "od600_calibration.yaml")
+
+
+def _tubes(libraries=("kanR",), presplit_cols=True):
+    rows = []
+    for lib in libraries:
+        for t in (0, 60):
+            rows.append({"sample": f"{lib}-t{t}", "library": lib,
+                         "replicate": 1, "condition_pre": f"{lib}-pre",
+                         "condition_sel": f"{lib}+sel", "t_pre": 30,
+                         "t_sel": t, "titrant_name": "iptg",
+                         "titrant_conc": 0.0})
+    df = pd.DataFrame(rows)
+    if not presplit_cols:
+        df = df.drop(columns=["replicate", "condition_pre"])
+    return df
+
+
+def _write_counts(d, tubes):
+    os.makedirs(d, exist_ok=True)
+    rng = np.random.default_rng(0)
+    for s, lib in zip(tubes["sample"], tubes["library"]):
+        genotypes = ["wt", "A1V", "C2D", "__unknown__"]
+        if lib == "pheS":
+            genotypes = ["wt", "E3F", "__unknown__"]
+        pd.DataFrame({"genotype": genotypes,
+                      "counts": rng.integers(50, 500, len(genotypes))}
+                     ).to_csv(os.path.join(d, f"counts_{s}_.csv"), index=False)
+    return d
+
+
+# ---------------------------------------------------------------------------
+# tube totals
+# ---------------------------------------------------------------------------
+
+def test_add_tube_totals_from_od600_column():
+    tubes = _tubes()
+    tubes["od600"] = [0.2, 0.4]
+    out = add_tube_totals(tubes, od600_calibration_file=CALIBRATION, tube_volume_mL=5.0)
+    cfu, sd, _ = O.od600_to_cfu_per_mL(np.array([0.2, 0.4]), CALIBRATION)
+    assert np.allclose(out["sample_cfu"], cfu * 5.0)
+    assert np.allclose(out["sample_cfu_std"], sd * 5.0)
+    # the shared curve error and the per-reading error are kept apart
+    assert np.allclose(out["sample_cfu_std"] ** 2,
+                       out["sample_cfu_curve_std"] ** 2
+                       + out["sample_cfu_reading_std"] ** 2)
+    assert out["od600_in_calibrated_range"].all()
+
+
+def test_add_tube_totals_from_od600_file(tmp_path):
+    tubes = _tubes()
+    od = pd.DataFrame({"sample": tubes["sample"], "od600": [0.2, 0.4]})
+    od.to_csv(tmp_path / "od.csv", index=False)
+    out = add_tube_totals(tubes, od600_file=str(tmp_path / "od.csv"),
+                          od600_calibration_file=CALIBRATION, tube_volume_mL=5.0)
+    assert list(out["od600"]) == [0.2, 0.4]
+
+
+def test_add_tube_totals_passthrough_supplied_totals():
+    tubes = _tubes()
+    tubes["sample_cfu"] = [1e8, 2e8]
+    tubes["sample_cfu_std"] = [1e7, 2e7]
+    pd.testing.assert_frame_equal(add_tube_totals(tubes), tubes)
+
+
+@pytest.mark.parametrize("kwargs,match", [
+    ({"tube_volume_mL": 5.0}, "calibration"),
+    ({"od600_calibration_file": CALIBRATION}, "tube_volume_mL"),
+])
+def test_add_tube_totals_od600_needs_calibration_and_volume(kwargs, match):
+    tubes = _tubes()
+    tubes["od600"] = [0.2, 0.4]
+    with pytest.raises(ValueError, match=match):
+        add_tube_totals(tubes, **kwargs)
+
+
+def test_add_tube_totals_refuses_totals_and_od600():
+    tubes = _tubes()
+    tubes["od600"] = [0.2, 0.4]
+    tubes["sample_cfu"] = [1e8, 2e8]
+    with pytest.raises(ValueError, match="Use one or the other"):
+        add_tube_totals(tubes, od600_calibration_file=CALIBRATION, tube_volume_mL=5.0)
+
+
+def test_add_tube_totals_calibration_without_od600():
+    with pytest.raises(ValueError, match="no OD600"):
+        add_tube_totals(_tubes(), od600_calibration_file=CALIBRATION)
+
+
+def test_add_tube_totals_missing_reading(tmp_path):
+    tubes = _tubes()
+    pd.DataFrame({"sample": tubes["sample"][:1], "od600": [0.2]}).to_csv(
+        tmp_path / "od.csv", index=False)
+    with pytest.raises(ValueError, match="No OD600 reading"):
+        add_tube_totals(tubes, od600_file=str(tmp_path / "od.csv"),
+                        od600_calibration_file=CALIBRATION, tube_volume_mL=5.0)
+
+
+def test_tube_totals_refuses_undetectable_reading():
+    od = pd.DataFrame({"sample": ["a", "b"], "od600": [0.01, 0.4]})
+    with pytest.raises(ValueError, match="detection threshold"):
+        O.tube_totals_from_od600(od, CALIBRATION, 5.0)
+
+
+def test_tube_totals_refuses_duplicate_reading():
+    od = pd.DataFrame({"sample": ["a", "a"], "od600": [0.3, 0.4]})
+    with pytest.raises(ValueError, match="more than one reading"):
+        O.tube_totals_from_od600(od, CALIBRATION, 5.0)
+
+
+# ---------------------------------------------------------------------------
+# process_counts end to end
+# ---------------------------------------------------------------------------
+
+def test_process_counts_growth_from_od600(tmp_path):
+    tubes = _tubes()
+    tubes["od600"] = [0.2, 0.4]
+    tubes.to_csv(tmp_path / "tubes.csv", index=False)
+    counts = _write_counts(str(tmp_path / "counts"), tubes)
+
+    out = process_counts(str(tmp_path / "tubes.csv"), counts,
+                         out_prefix=str(tmp_path / "growth"),
+                         od600_calibration_file=CALIBRATION, tube_volume_mL=5.0,
+                         verbose=False)
+    df = pd.read_csv(out)
+    assert out == str(tmp_path / "growth.csv")
+    for col in ("genotype", "counts", "sample_reads", "frequency", "ln_cfu",
+                "ln_cfu_var", "sample_cfu", "sample_cfu_curve_std",
+                "condition_sel", "t_sel"):
+        assert col in df.columns
+    assert "obs_file" not in df.columns
+    assert "__unknown__" not in set(df["genotype"])
+    # ln_cfu = ln(frequency) + ln(tube total)
+    assert np.allclose(df["ln_cfu"],
+                       np.log(df["frequency"]) + np.log(df["sample_cfu"]))
+
+
+def test_process_counts_several_libraries_match_separate_runs(tmp_path):
+    tubes = _tubes(libraries=("kanR", "pheS"))
+    tubes["sample_cfu"] = [1e8, 3e8, 2e8, 5e8]
+    tubes["sample_cfu_std"] = tubes["sample_cfu"] * 0.1
+    counts = _write_counts(str(tmp_path / "counts"), tubes)
+
+    tubes.to_csv(tmp_path / "all.csv", index=False)
+    joint = pd.read_csv(process_counts(str(tmp_path / "all.csv"), counts,
+                                       out_prefix=str(tmp_path / "joint"),
+                                       verbose=False))
+
+    parts = []
+    for lib, sub in tubes.groupby("library"):
+        sub.to_csv(tmp_path / f"{lib}.csv", index=False)
+        parts.append(pd.read_csv(process_counts(
+            str(tmp_path / f"{lib}.csv"), counts,
+            out_prefix=str(tmp_path / f"sep_{lib}"), verbose=False)))
+    separate = pd.concat(parts)
+
+    key = ["library", "sample", "genotype"]
+    joint = joint.sort_values(key, ignore_index=True)
+    separate = separate.sort_values(key, ignore_index=True)[joint.columns]
+    pd.testing.assert_frame_equal(joint, separate)
+    # genotypes stay within their own library
+    assert "E3F" not in set(joint.loc[joint["library"] == "kanR", "genotype"])
+
+
+def test_process_counts_presplit(tmp_path):
+    tubes = _tubes()
+    tubes["sample_cfu"] = [1e8, 3e8]
+    tubes["sample_cfu_std"] = [1e7, 3e7]
+    tubes.to_csv(tmp_path / "tubes.csv", index=False)
+    counts = _write_counts(str(tmp_path / "counts"), tubes)
+
+    cwd = os.getcwd()
+    os.chdir(tmp_path)
+    try:
+        out = process_counts("tubes.csv", counts, presplit=True, verbose=False)
+    finally:
+        os.chdir(cwd)
+    assert out == "tfs_presplit.csv"
+    df = pd.read_csv(tmp_path / out)
+    # tfs-configure-model's presplit reader needs library
+    assert list(df.columns) == ["library", "replicate", "condition_pre",
+                                "genotype", "ln_cfu", "ln_cfu_std"]
+    # canonical genotype order within each replicate x condition_pre: wt first
+    assert df["genotype"].iloc[0] == "wt"
+
+
+def test_process_counts_presplit_needs_design_columns(tmp_path):
+    tubes = _tubes(presplit_cols=False)
+    tubes.to_csv(tmp_path / "tubes.csv", index=False)
+    with pytest.raises(ValueError, match="replicate"):
+        process_counts(str(tmp_path / "tubes.csv"), str(tmp_path), presplit=True)
+
+
+def test_process_counts_missing_totals(tmp_path):
+    tubes = _tubes()
+    tubes.to_csv(tmp_path / "tubes.csv", index=False)
+    counts = _write_counts(str(tmp_path / "counts"), tubes)
+    with pytest.raises(ValueError, match="sample_cfu"):
+        process_counts(str(tmp_path / "tubes.csv"), counts,
+                       out_prefix=str(tmp_path / "x"), verbose=False)
+
+
+def test_process_counts_default_library(tmp_path):
+    tubes = _tubes().drop(columns="library")
+    tubes["sample_cfu"] = [1e8, 3e8]
+    tubes["sample_cfu_std"] = [1e7, 3e7]
+    tubes.to_csv(tmp_path / "tubes.csv", index=False)
+    tubes["library"] = "kanR"
+    counts = _write_counts(str(tmp_path / "counts"), tubes)
+    df = pd.read_csv(process_counts(str(tmp_path / "tubes.csv"), counts,
+                                    out_prefix=str(tmp_path / "g"),
+                                    verbose=False))
+    assert set(df["library"]) == {"default"}
+
+
+# ---------------------------------------------------------------------------
+# sample-indexed tables and the script entry point
+# ---------------------------------------------------------------------------
+
+def test_read_tube_table_resets_sample_index():
+    from tfscreen.process_raw.scripts.process_counts_cli import _read_tube_table
+    tubes = _tubes().set_index("sample")
+    out = _read_tube_table(tubes)
+    assert "sample" in out.columns
+    assert list(out["sample"]) == ["kanR-t0", "kanR-t60"]
+
+
+def test_add_tube_totals_od600_frame_indexed_by_sample():
+    tubes = _tubes()
+    od = pd.DataFrame({"sample": tubes["sample"], "od600": [0.2, 0.4]}
+                      ).set_index("sample")
+    out = add_tube_totals(tubes, od600_file=od,
+                          od600_calibration_file=CALIBRATION, tube_volume_mL=5.0)
+    cfu, _, _ = O.od600_to_cfu_per_mL(np.array([0.2, 0.4]), CALIBRATION)
+    assert np.allclose(out["sample_cfu"], cfu * 5.0)
+
+
+def test_add_tube_totals_refuses_od600_twice(tmp_path):
+    tubes = _tubes()
+    tubes["od600"] = [0.2, 0.4]
+    od = pd.DataFrame({"sample": tubes["sample"], "od600": [0.2, 0.4]})
+    with pytest.raises(ValueError, match="OD600 is given twice"):
+        add_tube_totals(tubes, od600_file=od,
+                        od600_calibration_file=CALIBRATION, tube_volume_mL=5.0)
+
+
+@pytest.mark.filterwarnings("ignore:.*found in sys.modules:RuntimeWarning")
+def test_cli_module_runs_as_script(monkeypatch):
+    import runpy
+    import sys
+    monkeypatch.setattr(sys, "argv", ["tfs-cli", "--help"])
+    with pytest.raises(SystemExit) as exc:
+        runpy.run_module("tfscreen.process_raw.scripts.process_counts_cli",
+                         run_name="__main__")
+    assert exc.value.code == 0
diff --git a/tests/tfscreen/process_raw/test_process_fastq_cli.py b/tests/tfscreen/process_raw/test_process_fastq_cli.py
index 43c6a76f..c0860e50 100644
--- a/tests/tfscreen/process_raw/test_process_fastq_cli.py
+++ b/tests/tfscreen/process_raw/test_process_fastq_cli.py
@@ -412,8 +412,8 @@ def test_process_fastq_happy_path_with_instance(mock_ftc_cls, mock_process_paire
 
     # Run the function with the mock instance
     process_fastq(
-        f1_fastq="r1.fq", f2_fastq="r2.fq", out_dir=str(out_dir),
-        run_config=mock_lm_instance, phred_cutoff=20, num_workers=4
+        library_config=mock_lm_instance, f1_fastq="r1.fq", f2_fastq="r2.fq",
+        out_dir=str(out_dir), phred_cutoff=20, num_workers=4
     )
     
     assert os.path.isdir(out_dir)
@@ -440,4 +440,38 @@ def test_process_fastq_raises_error_for_file_as_dir(tmp_path):
     out_file.touch()
 
     with pytest.raises(FileExistsError):
-        process_fastq("r1.fq", "r2.fq", out_dir=str(out_file), run_config={})
\ No newline at end of file
+        process_fastq({}, "r1.fq", "r2.fq", out_dir=str(out_file))
+
+@patch('tfscreen.process_raw.scripts.process_fastq_cli._create_counts_df')
+@patch('tfscreen.process_raw.scripts.process_fastq_cli._create_stats_df')
+@patch('tfscreen.process_raw.scripts.process_fastq_cli._process_paired_fastq')
+@patch('tfscreen.process_raw.scripts.process_fastq_cli.FastqToCounts')
+def test_process_fastq_builds_library_manager_and_defaults_workers(
+        mock_ftc_cls, mock_process_paired, mock_create_stats,
+        mock_create_counts, tmp_path):
+    """A library YAML path builds a LibraryManager; num_workers=-1 -> None."""
+    config = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                          "examples", "process_raw", "library_config.yaml")
+    mock_process_paired.return_value = (Counter(), Counter())
+    mock_create_stats.return_value = pd.DataFrame({'stats': [1]})
+    mock_create_counts.return_value = pd.DataFrame({'counts': [1]})
+    mock_ftc_cls.return_value.all_expected_genotypes = ["wt"]
+
+    process_fastq(config, "r1.fq", "r2.fq", out_dir=str(tmp_path / "out"))
+
+    lm = mock_ftc_cls.call_args.args[0]
+    assert isinstance(lm, LibraryManager)
+    # num_workers (the last positional) is None, i.e. os.cpu_count() - 1
+    assert mock_process_paired.call_args.args[-1] is None
+    assert os.path.exists(tmp_path / "out" / "counts_r1.fq.csv")
+
+
+@pytest.mark.filterwarnings("ignore:.*found in sys.modules:RuntimeWarning")
+def test_cli_module_runs_as_script(monkeypatch):
+    import runpy
+    import sys
+    monkeypatch.setattr(sys, "argv", ["tfs-process-fastq", "--help"])
+    with pytest.raises(SystemExit) as exc:
+        runpy.run_module("tfscreen.process_raw.scripts.process_fastq_cli",
+                         run_name="__main__")
+    assert exc.value.code == 0
diff --git a/tests/tfscreen/process_raw/test_process_presplit_cli.py b/tests/tfscreen/process_raw/test_process_presplit_cli.py
deleted file mode 100644
index 6746e362..00000000
--- a/tests/tfscreen/process_raw/test_process_presplit_cli.py
+++ /dev/null
@@ -1,268 +0,0 @@
-import pytest
-import numpy as np
-import pandas as pd
-import pandas.testing as pd_testing
-from unittest.mock import patch, MagicMock, ANY
-
-from tfscreen.process_raw.scripts.process_presplit_cli import process_presplit
-
-
-# ------------------------
-# Fixtures
-# ------------------------
-
-@pytest.fixture
-def fx_sample_df() -> pd.DataFrame:
-    """Minimal presplit sample metadata."""
-    return pd.DataFrame({
-        "sample": ["ps_rep1_kanR", "ps_rep2_kanR"],
-        "replicate": [1, 2],
-        "condition_pre": ["kanR-cond", "kanR-cond"],
-        "sample_cfu": [1e8, 1.2e8],
-        "sample_cfu_std": [1e7, 1.2e7],
-    })
-
-
-@pytest.fixture
-def fx_lncfu_df() -> pd.DataFrame:
-    """Simulated output from counts_to_lncfu, including ln_cfu_var."""
-    return pd.DataFrame({
-        "sample": ["ps_rep1_kanR", "ps_rep1_kanR", "ps_rep2_kanR", "ps_rep2_kanR"],
-        "replicate": [1, 1, 2, 2],
-        "condition_pre": ["kanR-cond", "kanR-cond", "kanR-cond", "kanR-cond"],
-        "library": ["default"] * 4,
-        "genotype": ["wt", "A1V", "wt", "A1V"],
-        "ln_cfu": [10.0, 9.5, 10.1, 9.4],
-        "ln_cfu_var": [0.01, 0.02, 0.01, 0.02],
-    })
-
-
-# ------------------------
-# Tests for process_presplit
-# ------------------------
-
-@patch('pandas.DataFrame.to_csv')
-@patch('tfscreen.process_raw.scripts.process_presplit_cli.get_scaled_cfu')
-@patch('tfscreen.process_raw.scripts.process_presplit_cli.counts_to_lncfu')
-@patch('tfscreen.process_raw.scripts.process_presplit_cli._aggregate_counts')
-@patch('tfscreen.process_raw.scripts.process_presplit_cli._prep_sample_df')
-def test_process_presplit_orchestration(
-    mock_prep, mock_agg, mock_lncfu, mock_scaled, mock_to_csv,
-    fx_sample_df, fx_lncfu_df
-):
-    """Verify the overall workflow, data handoffs, and column selection."""
-    df_prepped = fx_sample_df.set_index("sample")
-    df_counts = pd.DataFrame({"sample": ["ps_rep1_kanR"], "genotype": ["wt"], "counts": [100]})
-    # get_scaled_cfu adds ln_cfu_std to the df
-    df_scaled = fx_lncfu_df.copy()
-    df_scaled["ln_cfu_std"] = np.sqrt(df_scaled["ln_cfu_var"])
-
-    mock_prep.return_value = df_prepped
-    mock_agg.return_value = df_counts
-    mock_lncfu.return_value = fx_lncfu_df
-    mock_scaled.return_value = df_scaled
-
-    process_presplit(
-        sample_df=fx_sample_df,
-        counts_csv_path="/counts/path",
-        output_file="/out/presplit.csv",
-        counts_glob_prefix="counts",
-        min_genotype_obs=5,
-        pseudocount=1,
-        verbose=False
-    )
-
-    mock_prep.assert_called_once_with(fx_sample_df, "/counts/path", "counts", False)
-    # _aggregate_counts receives the sample_df with 'library' injected (ANY because
-    # the exact df object varies when library is added)
-    mock_agg.assert_called_once_with(ANY)
-    mock_lncfu.assert_called_once_with(ANY, df_counts,
-                                        min_genotype_obs=5, pseudocount=1)
-    mock_scaled.assert_called_once()
-
-    # Verify to_csv is called once on the result
-    mock_to_csv.assert_called_once_with("/out/presplit.csv", index=False)
-
-
-
-def test_process_presplit_output_columns_integration(fx_sample_df, fx_lncfu_df, tmp_path):
-    """Integration-style: verify the CSV is written with the right columns."""
-    df_prepped = fx_sample_df.set_index("sample")
-    df_scaled = fx_lncfu_df.copy()
-    df_scaled["ln_cfu_std"] = np.sqrt(df_scaled["ln_cfu_var"])
-    output_file = str(tmp_path / "presplit.csv")
-
-    with patch('tfscreen.process_raw.scripts.process_presplit_cli._prep_sample_df',
-               return_value=df_prepped), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli._aggregate_counts',
-               return_value=pd.DataFrame()), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli.counts_to_lncfu',
-               return_value=fx_lncfu_df), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli.get_scaled_cfu',
-               return_value=df_scaled):
-
-        process_presplit(
-            sample_df=fx_sample_df,
-            counts_csv_path="/counts/path",
-            output_file=output_file,
-            verbose=False
-        )
-
-    result = pd.read_csv(output_file)
-    assert set(result.columns) == {"replicate", "condition_pre", "genotype",
-                                    "ln_cfu", "ln_cfu_std"}
-    # Extra columns from counts_to_lncfu (library, sample, ln_cfu_var) must be absent
-    assert "library" not in result.columns
-    assert "ln_cfu_var" not in result.columns
-    assert "sample" not in result.columns
-
-
-def test_process_presplit_output_sorted(fx_sample_df, fx_lncfu_df, tmp_path):
-    """Output rows are sorted by (replicate, condition_pre, genotype)."""
-    df_prepped = fx_sample_df.set_index("sample")
-    # Deliberately shuffle the order in the lncfu df
-    df_shuffled = fx_lncfu_df.iloc[::-1].reset_index(drop=True)
-    df_scaled = df_shuffled.copy()
-    df_scaled["ln_cfu_std"] = np.sqrt(df_scaled["ln_cfu_var"])
-    output_file = str(tmp_path / "presplit_sorted.csv")
-
-    with patch('tfscreen.process_raw.scripts.process_presplit_cli._prep_sample_df',
-               return_value=df_prepped), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli._aggregate_counts',
-               return_value=pd.DataFrame()), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli.counts_to_lncfu',
-               return_value=df_shuffled), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli.get_scaled_cfu',
-               return_value=df_scaled):
-
-        process_presplit(
-            sample_df=fx_sample_df,
-            counts_csv_path="/counts/path",
-            output_file=output_file,
-            verbose=False
-        )
-
-    result = pd.read_csv(output_file)
-    sorted_result = result.sort_values(
-        by=["replicate", "condition_pre", "genotype"]
-    ).reset_index(drop=True)
-    pd_testing.assert_frame_equal(result, sorted_result)
-
-
-def test_process_presplit_missing_replicate_column(tmp_path, fx_sample_df):
-    """ValueError raised when 'replicate' is absent from sample_df."""
-    bad_df = fx_sample_df.drop(columns=["replicate"])
-    prepped = bad_df.set_index("sample")
-    prepped["obs_file"] = "dummy.csv"
-
-    with patch('tfscreen.process_raw.scripts.process_presplit_cli._prep_sample_df',
-               return_value=prepped):
-        with pytest.raises(ValueError, match="Not all required columns seen"):
-            process_presplit(
-                sample_df=bad_df,
-                counts_csv_path=str(tmp_path),
-                output_file="/out/presplit.csv",
-            )
-
-
-def test_process_presplit_missing_condition_pre_column(tmp_path, fx_sample_df):
-    """ValueError raised when 'condition_pre' is absent from sample_df."""
-    bad_df = fx_sample_df.drop(columns=["condition_pre"])
-    prepped = bad_df.set_index("sample")
-    prepped["obs_file"] = "dummy.csv"
-
-    with patch('tfscreen.process_raw.scripts.process_presplit_cli._prep_sample_df',
-               return_value=prepped):
-        with pytest.raises(ValueError, match="Not all required columns seen"):
-            process_presplit(
-                sample_df=bad_df,
-                counts_csv_path=str(tmp_path),
-                output_file="/out/presplit.csv",
-            )
-
-
-def test_process_presplit_missing_cfu_column(tmp_path, fx_sample_df):
-    """ValueError raised when 'sample_cfu_std' is absent from sample_df."""
-    bad_df = fx_sample_df.drop(columns=["sample_cfu_std"])
-    prepped = bad_df.set_index("sample")
-    prepped["obs_file"] = "dummy.csv"
-
-    with patch('tfscreen.process_raw.scripts.process_presplit_cli._prep_sample_df',
-               return_value=prepped):
-        with pytest.raises(ValueError, match="sample_ln_cfu_std"):
-            process_presplit(
-                sample_df=bad_df,
-                counts_csv_path=str(tmp_path),
-                output_file="/out/presplit.csv",
-            )
-
-
-def test_process_presplit_default_library_added(fx_sample_df, fx_lncfu_df, tmp_path):
-    """When sample_df has no 'library' column, 'default' is injected before counts_to_lncfu."""
-    df_prepped = fx_sample_df.set_index("sample")
-    assert "library" not in df_prepped.columns
-
-    df_scaled = fx_lncfu_df.copy()
-    df_scaled["ln_cfu_std"] = np.sqrt(df_scaled["ln_cfu_var"])
-
-    lncfu_calls = []
-
-    def capture_lncfu(sdf, cdf, **kwargs):
-        lncfu_calls.append(sdf.copy())
-        return fx_lncfu_df
-
-    with patch('tfscreen.process_raw.scripts.process_presplit_cli._prep_sample_df',
-               return_value=df_prepped), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli._aggregate_counts',
-               return_value=pd.DataFrame()), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli.counts_to_lncfu',
-               side_effect=capture_lncfu), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli.get_scaled_cfu',
-               return_value=df_scaled), \
-         patch('pandas.DataFrame.to_csv'):
-
-        process_presplit(
-            sample_df=fx_sample_df,
-            counts_csv_path="/counts/path",
-            output_file=str(tmp_path / "out.csv"),
-            verbose=False
-        )
-
-    assert len(lncfu_calls) == 1
-    assert "library" in lncfu_calls[0].columns
-    assert (lncfu_calls[0]["library"] == "default").all()
-
-
-def test_process_presplit_existing_library_preserved(fx_sample_df, fx_lncfu_df, tmp_path):
-    """When sample_df already has a 'library' column, it is not overwritten."""
-    df_with_lib = fx_sample_df.copy()
-    df_with_lib["library"] = ["lib_A", "lib_B"]
-    df_prepped = df_with_lib.set_index("sample")
-
-    df_scaled = fx_lncfu_df.copy()
-    df_scaled["ln_cfu_std"] = np.sqrt(df_scaled["ln_cfu_var"])
-
-    lncfu_calls = []
-
-    def capture_lncfu(sdf, cdf, **kwargs):
-        lncfu_calls.append(sdf.copy())
-        return fx_lncfu_df
-
-    with patch('tfscreen.process_raw.scripts.process_presplit_cli._prep_sample_df',
-               return_value=df_prepped), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli._aggregate_counts',
-               return_value=pd.DataFrame()), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli.counts_to_lncfu',
-               side_effect=capture_lncfu), \
-         patch('tfscreen.process_raw.scripts.process_presplit_cli.get_scaled_cfu',
-               return_value=df_scaled), \
-         patch('pandas.DataFrame.to_csv'):
-
-        process_presplit(
-            sample_df=df_with_lib,
-            counts_csv_path="/counts/path",
-            output_file=str(tmp_path / "out.csv"),
-            verbose=False
-        )
-
-    assert list(lncfu_calls[0]["library"]) == ["lib_A", "lib_B"]
diff --git a/tests/tfscreen/process_raw/test_tube_corrections.py b/tests/tfscreen/process_raw/test_tube_corrections.py
new file mode 100644
index 00000000..53b82f10
--- /dev/null
+++ b/tests/tfscreen/process_raw/test_tube_corrections.py
@@ -0,0 +1,397 @@
+import hashlib
+import json
+import os
+import textwrap
+
+import pandas as pd
+import pytest
+
+from tfscreen.process_raw.scripts.correct_tubes_cli import correct_tubes
+from tfscreen.process_raw.scripts.process_counts_cli import process_counts
+from tfscreen.process_raw.tube_corrections import (
+    DESIGN_COLUMNS,
+    apply_corrections,
+    correct_tube_table,
+    read_corrections,
+)
+
+EXAMPLES = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                        "examples")
+RAW = os.path.join(EXAMPLES, "process_raw")
+TUBES = os.path.join(RAW, "tube_table.csv")
+CORRECTIONS = os.path.join(RAW, "corrections.yaml")
+CALIBRATION = os.path.join(EXAMPLES, "od600", "od600_calibration.yaml")
+
+A = "pheS_pheSp4CP_0mM_t60"
+B = "pheS_pheSp4CP_1mM_t60"
+KAN = "kanR_kanRpkan_0mM_t120"
+
+
+def _sha(path):
+    with open(path, "rb") as fh:
+        return hashlib.sha256(fh.read()).hexdigest()
+
+
+def _entry(**kw):
+    e = {"id": "c1", "action": "swap", "tubes": [A, B], "reason": "QC",
+         "by": "A. Researcher", "date": "2026-10-15"}
+    e.update(kw)
+    return e
+
+
+def _write(tmp_path, entries, name="corr.yaml"):
+    import yaml
+    path = tmp_path / name
+    path.write_text(yaml.safe_dump({"corrections": entries}))
+    return str(path)
+
+
+def _apply(tmp_path, entries):
+    corr = read_corrections(_write(tmp_path, entries))["corrections"]
+    return correct_tube_table(pd.read_csv(TUBES), corr)
+
+
+# ---------------------------------------------------------------------------
+# actions
+# ---------------------------------------------------------------------------
+
+def test_swap_moves_design_columns_only(tmp_path):
+    tubes = pd.read_csv(TUBES)
+    tubes["od600"] = range(len(tubes))
+    corr = read_corrections(_write(tmp_path, [_entry()]))["corrections"]
+    out, changes = correct_tube_table(tubes, corr)
+
+    a0 = tubes.set_index("sample").loc[A]
+    b0 = tubes.set_index("sample").loc[B]
+    a1 = out.set_index("sample").loc[A]
+    b1 = out.set_index("sample").loc[B]
+    for c in DESIGN_COLUMNS:
+        assert a1[c] == b0[c]
+        assert b1[c] == a0[c]
+    # Measurements stay with the physical sample.
+    assert a1["od600"] == a0["od600"]
+    assert b1["od600"] == b0["od600"]
+    assert a1["library"] == a0["library"]
+    assert a1["correction_id"] == "c1" and b1["correction_id"] == "c1"
+    # Untouched tubes keep empty correction ids; order is kept.
+    assert list(out["sample"]) == list(tubes["sample"])
+    assert (out.loc[~out["sample"].isin([A, B]), "correction_id"] == "").all()
+    # Only the column that differed is recorded.
+    assert set(changes["column"]) == {"titrant_conc"}
+    assert len(changes) == 2
+
+
+def test_relabel(tmp_path):
+    out, changes = _apply(tmp_path, [_entry(action="relabel", tubes=[KAN],
+                                            set={"t_sel": 125})])
+    row = out.set_index("sample").loc[KAN]
+    assert row["t_sel"] == 125
+    assert pd.api.types.is_integer_dtype(out["t_sel"])
+    assert changes.iloc[0][["sample", "column", "original", "corrected"]
+                           ].tolist() == [KAN, "t_sel", 120, 125]
+
+
+def test_exclude(tmp_path):
+    out, changes = _apply(tmp_path, [_entry(action="exclude", tubes=[A, KAN])])
+    assert len(out) == len(pd.read_csv(TUBES)) - 2
+    assert not out["sample"].isin([A, KAN]).any()
+    assert list(changes["action"]) == ["exclude", "exclude"]
+
+
+def test_rotation_by_three_relabels(tmp_path):
+    # kanR selection, t 60: rotate 0 mM -> 1 mM labels through a third tube's
+    # time, which would collide one relabel at a time but not all together.
+    t0 = "kanR_kanRpkan_0mM_t60"
+    t1 = "kanR_kanRpkan_1mM_t60"
+    t2 = "kanR_kanRpkan_0mM_t120"
+    entries = [
+        _entry(id="r1", action="relabel", tubes=[t0], set={"titrant_conc": 1.0}),
+        _entry(id="r2", action="relabel", tubes=[t1],
+               set={"titrant_conc": 0.0, "t_sel": 120}),
+        _entry(id="r3", action="relabel", tubes=[t2], set={"t_sel": 60}),
+    ]
+    out, _ = _apply(tmp_path, entries)
+    out = out.set_index("sample")
+    assert out.loc[t0, "titrant_conc"] == 1.0
+    assert (out.loc[t1, "titrant_conc"], out.loc[t1, "t_sel"]) == (0.0, 120)
+    assert out.loc[t2, "t_sel"] == 60
+
+
+def test_example_corrections_file(tmp_path):
+    out = apply_corrections(TUBES, CORRECTIONS,
+                            out_prefix=str(tmp_path / "fixed"))
+    df = pd.read_csv(out)
+    assert len(df) == len(pd.read_csv(TUBES)) - 1
+    changes = pd.read_csv(tmp_path / "fixed_changes.csv")
+    assert set(changes["action"]) == {"swap", "relabel", "exclude"}
+
+
+# ---------------------------------------------------------------------------
+# outputs, provenance and inputs
+# ---------------------------------------------------------------------------
+
+def test_inputs_untouched_and_provenance(tmp_path):
+    evidence = tmp_path / "qc.csv"
+    evidence.write_text("tube,apparent\nA,0.9\n")
+    corr = _write(tmp_path, [_entry(evidence=["qc.csv", "a note"])])
+    before = (_sha(TUBES), _sha(corr), os.path.getmtime(TUBES))
+
+    prefix = str(tmp_path / "out" / "fixed")
+    correct_tubes(TUBES, corr, out_prefix=prefix)
+
+    assert (_sha(TUBES), _sha(corr), os.path.getmtime(TUBES)) == before
+    prov = json.load(open(f"{prefix}_provenance.json"))
+    assert prov["inputs"]["tube_table"]["sha256"] == before[0]
+    assert prov["inputs"]["corrections_file"]["sha256"] == before[1]
+    assert prov["outputs"]["tube_table"]["sha256"] == _sha(f"{prefix}.csv")
+    assert prov["design_columns"] == list(DESIGN_COLUMNS)
+    c = prov["corrections"][0]
+    assert c["id"] == "c1" and c["reason"] == "QC"
+    assert c["evidence"][0]["sha256"] == _sha(evidence)
+    assert "sha256" not in c["evidence"][1]
+    assert "tfscreen_version" in prov
+
+
+def test_refuses_to_overwrite_input(tmp_path):
+    tubes = tmp_path / "t.csv"
+    pd.read_csv(TUBES).to_csv(tubes, index=False)
+    corr = _write(tmp_path, [_entry()])
+    with pytest.raises(ValueError, match="overwrite"):
+        apply_corrections(str(tubes), corr, out_prefix=str(tmp_path / "t"))
+
+
+def test_corrected_table_through_process_counts(tmp_path):
+    od = os.path.join(RAW, "od600.csv")
+    counts = os.path.join(RAW, "counts")
+    kw = dict(od600_file=od, od600_calibration_file=CALIBRATION,
+              tube_volume_mL=5.0, verbose=False)
+
+    fixed = apply_corrections(TUBES, _write(tmp_path, [_entry()]),
+                              out_prefix=str(tmp_path / "fixed"))
+    g0 = pd.read_csv(process_counts(TUBES, counts,
+                                    out_prefix=str(tmp_path / "g0"), **kw))
+    g1 = pd.read_csv(process_counts(fixed, counts,
+                                    out_prefix=str(tmp_path / "g1"), **kw))
+
+    key = ["sample", "genotype"]
+    m = g0.merge(g1, on=key, suffixes=("_0", "_1"))
+    assert len(m) == len(g0) == len(g1)
+    # Each sample keeps its own reads and tube total.
+    for c in ("counts", "sample_reads", "sample_cfu", "sample_cfu_std"):
+        assert (m[f"{c}_0"] == m[f"{c}_1"]).all()
+    # The swapped samples carry each other's labels.
+    lab = m.groupby("sample")[["titrant_conc_0", "titrant_conc_1"]].first()
+    assert lab.loc[A, "titrant_conc_1"] == lab.loc[B, "titrant_conc_0"] == 1.0
+    assert lab.loc[B, "titrant_conc_1"] == lab.loc[A, "titrant_conc_0"] == 0.0
+    others = lab.drop(index=[A, B])
+    assert (others["titrant_conc_0"] == others["titrant_conc_1"]).all()
+    assert set(g1.loc[g1["sample"].isin([A, B]), "correction_id"]) == {"c1"}
+
+
+# ---------------------------------------------------------------------------
+# refusals
+# ---------------------------------------------------------------------------
+
+def test_unknown_tube(tmp_path):
+    with pytest.raises(ValueError, match="no tube named"):
+        _apply(tmp_path, [_entry(tubes=[A, "nope"])])
+
+
+def test_tube_in_two_corrections(tmp_path):
+    with pytest.raises(ValueError, match="also named by correction 'c1'"):
+        _apply(tmp_path, [_entry(),
+                          _entry(id="c2", action="exclude", tubes=[B])])
+
+
+def test_swap_across_libraries(tmp_path):
+    with pytest.raises(ValueError, match="different libraries"):
+        _apply(tmp_path, [_entry(tubes=[A, KAN])])
+
+
+def test_swap_that_changes_nothing(tmp_path):
+    tubes = pd.read_csv(TUBES)
+    twin = tubes[tubes["sample"] == A].assign(sample="twin")
+    tubes = pd.concat([tubes, twin], ignore_index=True)
+    corr = read_corrections(_write(tmp_path, [_entry(tubes=[A, "twin"])]))
+    with pytest.raises(ValueError, match="changes nothing"):
+        correct_tube_table(tubes, corr["corrections"])
+
+
+@pytest.mark.parametrize("key", ["reason", "by", "date", "id", "action"])
+@pytest.mark.parametrize("value", ["", "   ", None])
+def test_empty_required_field(tmp_path, key, value):
+    with pytest.raises(ValueError, match=f"'{key}' is required"):
+        read_corrections(_write(tmp_path, [_entry(**{key: value})]))
+
+
+def test_missing_reason(tmp_path):
+    e = _entry()
+    del e["reason"]
+    with pytest.raises(ValueError, match="'reason' is required"):
+        read_corrections(_write(tmp_path, [e]))
+
+
+@pytest.mark.parametrize("entry, match", [
+    (dict(action="flip"), "unknown action"),
+    (dict(tubes=[A]), "swap takes exactly 2"),
+    (dict(action="relabel", tubes=[A, B], set={"t_sel": 1}),
+     "relabel takes exactly 1"),
+    (dict(action="relabel", tubes=[A]), "needs a non-empty 'set'"),
+    (dict(action="relabel", tubes=[A], set={"library": "kanR"}),
+     "only design columns"),
+    (dict(action="relabel", tubes=[A], set={"od600": 0.3}),
+     "only design columns"),
+    (dict(set={"t_sel": 1}), "'set' is only for relabel"),
+    (dict(tubes=[A, A]), "listed twice"),
+    (dict(tubes=[]), "at least one tube"),
+    (dict(colour="red"), "unknown key"),
+])
+def test_bad_entries(tmp_path, entry, match):
+    with pytest.raises(ValueError, match=match):
+        read_corrections(_write(tmp_path, [_entry(**entry)]))
+
+
+def test_duplicate_id(tmp_path):
+    with pytest.raises(ValueError, match="duplicate id"):
+        read_corrections(_write(tmp_path, [
+            _entry(), _entry(action="exclude", tubes=[KAN])]))
+
+
+def test_bad_file_structure(tmp_path):
+    p = tmp_path / "c.yaml"
+    p.write_text("corrections: []\n")
+    with pytest.raises(ValueError, match="non-empty list"):
+        read_corrections(str(p))
+    p.write_text("fixes: []\n")
+    with pytest.raises(ValueError, match="'corrections' list"):
+        read_corrections(str(p))
+    p.write_text(textwrap.dedent("""\
+        corrections: [{id: a, action: exclude, tubes: [x], reason: r,
+                       by: b, date: 2026-10-15}]
+        notes: hi
+        """))
+    with pytest.raises(ValueError, match="unknown top-level"):
+        read_corrections(str(p))
+
+
+def test_yaml_date_becomes_iso_string(tmp_path):
+    p = tmp_path / "c.yaml"
+    p.write_text("corrections: [{id: a, action: exclude, tubes: [x], "
+                 "reason: r, by: b, date: 2026-10-15}]\n")
+    assert read_corrections(str(p))["corrections"][0]["date"] == "2026-10-15"
+
+
+def test_relabel_that_changes_nothing(tmp_path):
+    with pytest.raises(ValueError, match="changes nothing"):
+        _apply(tmp_path, [_entry(action="relabel", tubes=[KAN],
+                                 set={"t_sel": 120})])
+
+
+def test_relabel_type_checked(tmp_path):
+    with pytest.raises(ValueError, match="numeric"):
+        _apply(tmp_path, [_entry(action="relabel", tubes=[KAN],
+                                 set={"t_sel": "late"})])
+    with pytest.raises(ValueError, match="integers"):
+        _apply(tmp_path, [_entry(action="relabel", tubes=[KAN],
+                                 set={"t_sel": 12.5})])
+
+
+def test_relabel_column_absent_from_table(tmp_path):
+    tubes = pd.read_csv(TUBES).drop(columns="t_pre")
+    corr = read_corrections(_write(tmp_path, [
+        _entry(action="relabel", tubes=[KAN], set={"t_pre": 20})]))
+    with pytest.raises(ValueError, match="no column"):
+        correct_tube_table(tubes, corr["corrections"])
+
+
+def test_relabel_onto_another_tube(tmp_path):
+    with pytest.raises(ValueError, match="share a library and design"):
+        _apply(tmp_path, [_entry(action="relabel", tubes=[KAN],
+                                 set={"t_sel": 60})])
+
+
+def test_refuses_already_corrected_table(tmp_path):
+    tubes = pd.read_csv(TUBES).assign(correction_id="")
+    corr = read_corrections(_write(tmp_path, [_entry()]))
+    with pytest.raises(ValueError, match="corrected before"):
+        correct_tube_table(tubes, corr["corrections"])
+
+
+def test_refuses_duplicate_or_missing_sample(tmp_path):
+    corr = read_corrections(_write(tmp_path, [_entry()]))["corrections"]
+    tubes = pd.read_csv(TUBES)
+    with pytest.raises(ValueError, match="Duplicate tube name"):
+        correct_tube_table(pd.concat([tubes, tubes.iloc[:1]]), corr)
+    with pytest.raises(ValueError, match="no 'sample' column"):
+        correct_tube_table(tubes.drop(columns="sample"), corr)
+
+
+@pytest.mark.filterwarnings("ignore:.*found in sys.modules:RuntimeWarning")
+def test_cli_module_runs_as_script(monkeypatch):
+    import runpy
+    import sys
+    monkeypatch.setattr(sys, "argv", ["tfs-correct-tubes", "--help"])
+    with pytest.raises(SystemExit) as exc:
+        runpy.run_module("tfscreen.process_raw.scripts.correct_tubes_cli",
+                         run_name="__main__")
+    assert exc.value.code == 0
+
+
+# ---------------------------------------------------------------------------
+# edge cases of reading and applying
+# ---------------------------------------------------------------------------
+
+def test_correction_that_is_not_a_mapping(tmp_path):
+    with pytest.raises(ValueError, match="correction 1: each correction "
+                                         "must be a mapping"):
+        read_corrections(_write(tmp_path, ["swap A and B"]))
+
+
+def test_single_tube_may_be_a_bare_string(tmp_path):
+    corr = read_corrections(_write(tmp_path, [
+        _entry(action="exclude", tubes=KAN)]))["corrections"]
+    assert corr[0]["tubes"] == [KAN]
+    df, changes = correct_tube_table(pd.read_csv(TUBES), corr)
+    assert KAN not in set(df["sample"])
+    assert changes["action"].tolist() == ["exclude"]
+
+
+def test_empty_tube_name(tmp_path):
+    with pytest.raises(ValueError, match="empty tube name"):
+        read_corrections(_write(tmp_path, [
+            _entry(action="exclude", tubes=[KAN, "  "])]))
+
+
+def test_swap_with_a_column_missing_in_both_tubes(tmp_path):
+    tubes = pd.read_csv(TUBES)
+    tubes.loc[tubes["sample"].isin([A, B]), "titrant_name"] = float("nan")
+    corr = read_corrections(_write(tmp_path, [_entry()]))["corrections"]
+    df, changes = correct_tube_table(tubes, corr)
+    # Both NaN counts as the same value: only titrant_conc changes.
+    assert set(changes["column"]) == {"titrant_conc"}
+    df = df.set_index("sample")
+    orig = tubes.set_index("sample")
+    assert df.at[A, "titrant_conc"] == orig.at[B, "titrant_conc"]
+    assert df.loc[[A, B], "titrant_name"].isna().all()
+
+
+def test_relabel_text_column(tmp_path):
+    df, changes = _apply(tmp_path, [_entry(action="relabel", tubes=[KAN],
+                                           set={"condition_sel": "kanR+new"})])
+    assert df.set_index("sample").at[KAN, "condition_sel"] == "kanR+new"
+    assert changes["corrected"].tolist() == ["kanR+new"]
+
+
+def test_tube_table_indexed_by_sample(tmp_path, monkeypatch):
+    import tfscreen.process_raw.tube_corrections as TC
+    corr = _write(tmp_path, [_entry()])
+    plain = str(tmp_path / "plain")
+    apply_corrections(TUBES, corr, out_prefix=plain)
+
+    real = TC.read_dataframe
+    monkeypatch.setattr(TC, "read_dataframe",
+                        lambda src: real(src).set_index("sample"))
+    indexed = str(tmp_path / "indexed")
+    apply_corrections(TUBES, corr, out_prefix=indexed)
+    pd.testing.assert_frame_equal(pd.read_csv(f"{indexed}.csv"),
+                                  pd.read_csv(f"{plain}.csv"))
diff --git a/tests/tfscreen/process_raw/test_tube_qc.py b/tests/tfscreen/process_raw/test_tube_qc.py
new file mode 100644
index 00000000..6547b7e9
--- /dev/null
+++ b/tests/tfscreen/process_raw/test_tube_qc.py
@@ -0,0 +1,312 @@
+"""Tests for the tube composition QC (process_raw/tube_qc.py, tfs-check-tubes)."""
+import sys
+from unittest.mock import patch
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from tfscreen.process_raw import tube_qc as Q
+from tfscreen.process_raw.scripts.check_tubes_cli import check_tubes, main
+
+CONCS = [0.0, 1e-4, 1e-3, 1e-2, 3e-2, 1e-1, 1.0]
+
+
+def _simulate(num_genotypes=300, replicates=(1, 2), times=(100.0, 150.0, 200.0),
+              concs=CONCS, condition_sel="pheS+4CP", titrant_name="iptg",
+              seed=0, depth=2e6, tube_noise=0.1):
+    """
+    Long-form counts: each genotype's log abundance grows at
+    k_g + m_g * c / (c + K) for t_sel, from a per-replicate starting pool,
+    then each tube gets log-normal jitter per genotype (PCR, sampling) and
+    reads are drawn as Poisson.
+    """
+    rng = np.random.default_rng(seed)
+    k = rng.normal(0, 0.003, num_genotypes)
+    m = rng.normal(0, 0.01, num_genotypes)
+    rows = []
+    for rep in replicates:
+        base = rng.normal(0, 1, num_genotypes)
+        for t in times:
+            for c in concs:
+                ln = (base + t * (k + m * c / (c + 0.01))
+                      + rng.normal(0, tube_noise, num_genotypes))
+                p = np.exp(ln - ln.max())
+                lam = depth * p / p.sum()
+                counts = rng.poisson(lam)
+                sample = f"{condition_sel}_{titrant_name}_r{rep}_t{t:g}_c{c:g}"
+                for g in range(num_genotypes):
+                    rows.append((f"A{g + 1}G", counts[g], rep, condition_sel,
+                                 titrant_name, t, c, sample))
+    return pd.DataFrame(rows, columns=["genotype", "counts", "replicate",
+                                       "condition_sel", "titrant_name",
+                                       "t_sel", "titrant_conc", "sample"])
+
+
+def _swap(df, rep, t, c1, c2):
+    """Swap the titrant labels (and sample names) of two tubes."""
+    df = df.copy()
+    a = (df["replicate"] == rep) & (df["t_sel"] == t) & (df["titrant_conc"] == c1)
+    b = (df["replicate"] == rep) & (df["t_sel"] == t) & (df["titrant_conc"] == c2)
+    sa, sb = df.loc[a, "sample"].iloc[0], df.loc[b, "sample"].iloc[0]
+    df.loc[a, ["titrant_conc", "sample"]] = [c2, sb]
+    df.loc[b, ["titrant_conc", "sample"]] = [c1, sa]
+    return df
+
+
+@pytest.fixture(scope="module")
+def clean():
+    return _simulate()
+
+
+@pytest.fixture(scope="module")
+def clean_scores(clean):
+    return Q.score_tubes(clean)
+
+
+# --- helpers ---
+
+def test_isotonic_pools_violators():
+    np.testing.assert_allclose(Q._isotonic([1, 3, 2, 4]), [1, 2.5, 2.5, 4])
+    np.testing.assert_allclose(Q._isotonic([0, 1, 2]), [0, 1, 2])
+
+
+def test_order_violations_monotone_is_zero():
+    np.testing.assert_array_equal(Q.order_violations([0, 0.1, 0.5, 1.0]), 0)
+    np.testing.assert_array_equal(Q.order_violations([0.3]), 0)
+
+
+def test_order_violations_signs():
+    v = Q.order_violations([0.0, 0.9, 0.3, 0.6, 1.0])
+    assert v[1] > 0          # above the fit at the next concentration
+    assert v[0] == 0 and v[-1] == 0
+    v = Q.order_violations([0.0, 0.3, 0.6, 0.1])
+    assert v[-1] < 0         # the top concentration below its neighbor
+
+
+def test_deviations_leave_the_tube_out():
+    expected = np.array([0.0, 0.5, 0.8, 1.0])
+    score = 1.2 * expected
+    dv, sd = Q.deviations(score, expected)
+    np.testing.assert_allclose(dv, 0, atol=1e-12)
+    # The low tube's expectation hardly depends on the estimated scale.
+    assert sd[0] == 1.0 and 1.0 < sd[1] < sd[-1]
+    score = score.copy()
+    score[-1] = 0.0          # the top tube cannot set its own scale
+    dv, _ = Q.deviations(score, expected)
+    assert dv[-1] == pytest.approx(-1.2)
+
+
+def test_row_scale_ignores_low_concentrations():
+    # The low tube's ratio (-4) would dominate a plain ratio median.
+    assert Q._row_scale(np.array([-0.1, 0.9]), np.array([0.025, 1.0])) == 0.9
+    assert Q._row_scale(np.array([0.1, 0.2]), np.array([0.05, 0.2])) == 1.0
+
+
+# --- scoring ---
+
+def test_clean_data_not_flagged(clean_scores):
+    s = clean_scores
+    assert not s["flagged"].any()
+    assert len(s) == 2 * 3 * len(CONCS)
+    # Endpoints of the expected profile are 0 and 1 by construction.
+    exp = s.groupby("titrant_conc")["expected"].first()
+    assert exp.iloc[0] == pytest.approx(0, abs=1e-12)
+    assert exp.iloc[-1] == pytest.approx(1)
+    assert (np.diff(exp.to_numpy()) > -0.02).all()
+    # Every row rises with concentration.
+    for _, g in s.groupby(["replicate", "t_sel"]):
+        assert g.sort_values("titrant_conc")["score"].is_monotonic_increasing
+
+
+def test_rows_of_any_scale_start_at_zero(clean_scores):
+    # The effect grows with time; an additive row effect alone shifted the
+    # early and late rows by about +/-0.17 here.
+    s = clean_scores
+    low = s[s["titrant_conc"] == 0.0]
+    assert low["score"].abs().max() < 0.03
+    assert s["deviation_z"].abs().max() < 4
+
+
+def test_row_scale_grows_with_time(clean_scores):
+    b = clean_scores.groupby("t_sel")["row_scale"].mean()
+    assert b.is_monotonic_increasing
+    assert b.iloc[0] < 0.8 < 1.2 < b.iloc[-1]
+
+
+@pytest.mark.parametrize("c1,c2", [(1e-4, 1.0), (0.0, 3e-2), (1e-3, 1e-1)])
+def test_swapped_pair_is_flagged(clean, c1, c2):
+    s = Q.score_tubes(_swap(clean, 2, 150.0, c1, c2))
+    flagged = s[s["flagged"]]
+    assert len(flagged) == 2
+    assert set(flagged["replicate"]) == {2}
+    assert set(flagged["t_sel"]) == {150.0}
+    assert set(flagged["titrant_conc"]) == {c1, c2}
+    hi = flagged[flagged["titrant_conc"] == min(c1, c2)]
+    assert hi["score"].iloc[0] > hi["expected"].iloc[0]
+
+
+def test_missing_concentrations_not_shifted(clean):
+    # Replicate 1 at t 200 keeps only the three lowest concentrations; row
+    # centering would push them toward each other's mean (the study's bias).
+    keep = ~((clean["replicate"] == 1) & (clean["t_sel"] == 200.0)
+             & (clean["titrant_conc"] > 1e-3))
+    s = Q.score_tubes(clean[keep])
+    assert not s["flagged"].any()
+    row = s[(s["replicate"] == 1) & (s["t_sel"] == 200.0)]
+    assert len(row) == 3
+    full = Q.score_tubes(clean)
+    ref = full[(full["replicate"] == 1) & (full["t_sel"] == 200.0)
+               & (full["titrant_conc"] <= 1e-3)]
+    np.testing.assert_allclose(row["score"].to_numpy(),
+                               ref["score"].to_numpy(), atol=0.03)
+
+
+def test_swap_in_incomplete_row(clean):
+    keep = ~((clean["replicate"] == 1) & (clean["t_sel"] == 100.0)
+             & clean["titrant_conc"].isin([1e-3, 1e-2]))
+    s = Q.score_tubes(_swap(clean[keep], 1, 100.0, 1e-4, 1e-1))
+    flagged = s[s["flagged"]]
+    assert set(flagged["titrant_conc"]) == {1e-4, 1e-1}
+    assert set(flagged["t_sel"]) == {100.0}
+
+
+def test_tube_totals_cancel(clean, clean_scores):
+    scaled = clean.copy()
+    one = scaled["sample"] == scaled["sample"].iloc[0]
+    scaled.loc[one, "counts"] *= 10
+    s = Q.score_tubes(scaled)
+    np.testing.assert_allclose(s["score"], clean_scores["score"], atol=1e-12)
+
+
+def test_panels_are_separate_and_any_titrant(clean):
+    other = _simulate(condition_sel="kanR+kan", titrant_name="atc", seed=1)
+    other = _swap(other, 1, 100.0, 1e-4, 1.0)
+    s = Q.score_tubes(pd.concat([clean, other], ignore_index=True))
+    assert set(s["titrant_name"]) == {"iptg", "atc"}
+    flagged = s[s["flagged"]]
+    assert set(flagged["condition_sel"]) == {"kanR+kan"}
+    assert len(flagged) == 2
+
+
+def test_unknown_genotype_ignored(clean, clean_scores):
+    extra = clean.drop_duplicates("sample").assign(genotype="__unknown__",
+                                                   counts=10 ** 6)
+    s = Q.score_tubes(pd.concat([clean, extra], ignore_index=True))
+    np.testing.assert_allclose(s["score"], clean_scores["score"], atol=1e-12)
+
+
+def test_without_sample_column(clean, clean_scores):
+    s = Q.score_tubes(clean.drop(columns="sample"))
+    np.testing.assert_allclose(s["score"], clean_scores["score"], atol=1e-12)
+
+
+# --- errors ---
+
+def test_duplicate_tube_raises(clean):
+    dup = clean.copy()
+    m = (dup["replicate"] == 1) & (dup["t_sel"] == 100.0) & (dup["titrant_conc"] == 0.0)
+    dup.loc[m, "titrant_conc"] = 1e-4
+    with pytest.raises(ValueError, match="one tube per concentration"):
+        Q.score_tubes(dup)
+
+
+def test_single_concentration_raises(clean):
+    with pytest.raises(ValueError, match="only one"):
+        Q.score_tubes(clean[clean["titrant_conc"] == 0.0])
+
+
+def test_too_few_genotypes_raises(clean):
+    with pytest.raises(ValueError, match="genotypes have at least"):
+        Q.score_tubes(clean, min_counts=10 ** 9)
+
+
+def test_missing_column_raises(clean):
+    with pytest.raises(Exception):
+        Q.score_tubes(clean.drop(columns="counts"))
+
+
+# --- CLI ---
+
+def test_check_tubes_writes_table(tmp_path, clean, capsys):
+    f = tmp_path / "growth.csv"
+    _swap(clean, 1, 200.0, 1e-4, 1.0).to_csv(f, index=False)
+    out = str(tmp_path / "qc")
+    check_tubes(str(f), out_prefix=out)
+    s = pd.read_csv(out + ".csv")
+    assert s["flagged"].sum() == 2
+    assert "2 flagged tube(s)" in capsys.readouterr().out
+
+
+def test_main_parses_flags(tmp_path, clean):
+    f = tmp_path / "growth.csv"
+    clean.to_csv(f, index=False)
+    out = str(tmp_path / "qc")
+    with patch.object(sys, "argv", ["tfs-check-tubes", str(f), "--out_prefix",
+                                    out, "--flag_z", "100", "--min_counts", "2"]):
+        main()
+    assert not pd.read_csv(out + ".csv")["flagged"].any()
+
+
+# --- internals ---
+
+def test_deviations_of_a_single_tube():
+    dv, sd = Q.deviations([0.7], [1.0])
+    np.testing.assert_array_equal(dv, [0.0])
+    np.testing.assert_array_equal(sd, [1.0])
+
+
+def _additive_grid(seed=0, G=6, R=2, C=4):
+    rng = np.random.default_rng(seed)
+    a = rng.normal(0, 1, (G, R))
+    d = rng.normal(0, 1, (G, C))
+    return a, d, a[:, :, None] + d[:, None, :]
+
+
+def test_project_defaults_to_unit_row_scale():
+    a, d, L = _additive_grid()
+    s0, e0 = Q._project(L, a, d)
+    s1, e1 = Q._project(L, a, d, np.ones(L.shape[1]))
+    np.testing.assert_allclose(s0, s1)
+    np.testing.assert_allclose(e0, e1)
+    # An exact additive fit scores every row at its expectation.
+    np.testing.assert_allclose(s0, np.broadcast_to(e0, s0.shape), atol=1e-12)
+    assert e0[0] == pytest.approx(0.0) and e0[-1] == pytest.approx(1.0)
+
+
+def test_project_without_an_axis_raises():
+    a, d, L = _additive_grid()
+    d[:, -1] = d[:, 0]
+    with pytest.raises(ValueError, match="no axis to score"):
+        Q._project(L, a, d)
+
+
+def test_fit_profiles_stops_once_scales_settle():
+    a, d, L = _additive_grid()
+    with patch.object(Q, "_project", wraps=Q._project) as proj:
+        fa, fd, fb = Q._fit_profiles(L, n_iter=20)
+    # Converged on the first pass instead of running all 20.
+    assert proj.call_count < 20
+    np.testing.assert_allclose(fb, 1.0)
+    np.testing.assert_allclose(fa[:, :, None] + fd[:, None, :], L, atol=1e-8)
+
+
+def test_panel_without_titrant_effect_raises(clean):
+    flat = clean.copy()
+    # Every tube of a replicate x time row has the same composition.
+    first = flat[flat["titrant_conc"] == 0.0].set_index(
+        ["genotype", "replicate", "t_sel"])["counts"]
+    key = pd.MultiIndex.from_frame(flat[["genotype", "replicate", "t_sel"]])
+    flat["counts"] = first.reindex(key).to_numpy()
+    with pytest.raises(ValueError, match=r"Panel .*no axis to score"):
+        Q.score_tubes(flat)
+
+
+@pytest.mark.filterwarnings("ignore:.*found in sys.modules:RuntimeWarning")
+def test_cli_module_runs_as_script(monkeypatch):
+    import runpy
+    monkeypatch.setattr(sys, "argv", ["tfs-check-tubes", "--help"])
+    with pytest.raises(SystemExit) as exc:
+        runpy.run_module("tfscreen.process_raw.scripts.check_tubes_cli",
+                         run_name="__main__")
+    assert exc.value.code == 0
diff --git a/tests/tfscreen/simulate/empirical/test_build_empirical_cli.py b/tests/tfscreen/simulate/empirical/test_build_empirical_cli.py
index a04e0dcd..f509ce53 100644
--- a/tests/tfscreen/simulate/empirical/test_build_empirical_cli.py
+++ b/tests/tfscreen/simulate/empirical/test_build_empirical_cli.py
@@ -92,7 +92,7 @@ def test_cli_writes_model_with_wt_ref(tmp_path):
 
     out_prefix = str(tmp_path / "emp")
     build_empirical(str(growth_csv), seed=0,
-                             calibration_file=str(calib_csv),
+                             growth_calibration_file=str(calib_csv),
                              out_prefix=out_prefix)
 
     # Both artifacts written (model is a single self-contained JSON).
@@ -114,7 +114,7 @@ def test_cli_writes_model_with_wt_ref(tmp_path):
 
 
 def test_cli_orchestrates_configure_and_prefit(mocker, tmp_path):
-    """Without --calibration_file, the CLI runs configure_model + prefit."""
+    """Without --growth_calibration_file, the CLI runs configure_model + prefit."""
     growth_csv = tmp_path / "growth.csv"
     binding_csv = tmp_path / "binding.csv"
     _build_growth_df().to_csv(growth_csv, index=False)
@@ -152,7 +152,7 @@ def fake_configure(**kwargs):
         }, f)
 
     build_empirical(
-        str(growth_csv), binding_file=str(binding_csv), out_prefix=out_prefix,
+        str(growth_csv), binding_df=str(binding_csv), out_prefix=out_prefix,
         library_config=str(library_config), seed=5)
 
     # configure_model wired with the experimental inputs and fixed prefix.
@@ -181,3 +181,27 @@ def test_cli_requires_binding_or_calibration(tmp_path):
     with pytest.raises(ValueError, match="binding data is required"):
         build_empirical(str(growth_csv), seed=0,
                                  out_prefix=str(tmp_path / "emp"))
+
+
+def test_congression_lambda_is_retired(tmp_path):
+    with pytest.raises(TypeError, match="congression_lambda"):
+        build_empirical("growth.csv", seed=0, congression_lambda=1.0,
+                        out_prefix=str(tmp_path / "emp"))
+
+
+def test_cli_requires_seed_for_prefit(tmp_path):
+    growth_csv = tmp_path / "growth.csv"
+    _build_growth_df().to_csv(growth_csv, index=False)
+    with pytest.raises(ValueError, match="seed is required"):
+        build_empirical(str(growth_csv), out_prefix=str(tmp_path / "emp"))
+
+
+@pytest.mark.filterwarnings("ignore:.*found in sys.modules:RuntimeWarning")
+def test_cli_module_runs_as_script(monkeypatch):
+    import runpy
+    import sys
+    monkeypatch.setattr(sys, "argv", ["tfs-build-empirical", "--help"])
+    with pytest.raises(SystemExit) as exc:
+        runpy.run_module("tfscreen.simulate.scripts.build_empirical_cli",
+                         run_name="__main__")
+    assert exc.value.code == 0
diff --git a/tests/tfscreen/simulate/empirical/test_congression.py b/tests/tfscreen/simulate/empirical/test_congression.py
deleted file mode 100644
index 49d90e83..00000000
--- a/tests/tfscreen/simulate/empirical/test_congression.py
+++ /dev/null
@@ -1,130 +0,0 @@
-"""Tests for Stage-1.5 congression de-attenuation."""
-
-import numpy as np
-import jax.numpy as jnp
-import pandas as pd
-import pytest
-from scipy.special import expit, logit
-
-from tfscreen.tfmodel.generative.components.transformation._congression import (
-    update_thetas,
-)
-from tfscreen.simulate.empirical.congression import (
-    correct_theta_matrix,
-    deattenuate_congression,
-)
-from tfscreen.simulate.empirical.fit_phenotypes import (
-    GenotypeFit, PHENO_PARAMS_TRANSFORMED,
-)
-
-
-def _forward(theta_true, lam):
-    """Apply the congression forward operator E[max(x, M)]."""
-    return np.asarray(update_thetas(
-        jnp.asarray(theta_true), (float(lam),), theta_dist="empirical",
-        population_theta=jnp.asarray(theta_true)))
-
-
-# ---------------------------------------------------------------------------
-# correct_theta_matrix — the fixed-point core
-# ---------------------------------------------------------------------------
-
-def test_roundtrip_recovers_true_population():
-    rng = np.random.default_rng(0)
-    # 300 genotypes at 2 independent conditions.
-    theta_true = expit(rng.normal(0.0, 1.5, size=(2, 300)))
-    theta_obs = _forward(theta_true, lam=1.0)
-
-    # Congression can only inflate (max), so observed >= true.
-    assert np.all(theta_obs >= theta_true - 1e-9)
-    # ...and it compresses the population (masks the low end up).
-    assert np.std(theta_obs) < np.std(theta_true)
-
-    theta_rec, n_iter = correct_theta_matrix(
-        theta_obs, lam=1.0, tol=1e-6, max_iter=200)
-
-    assert np.median(np.abs(theta_rec - theta_true)) < 0.03
-    assert np.std(theta_rec) == pytest.approx(np.std(theta_true), abs=0.03)
-
-
-def test_lambda_zero_is_noop():
-    rng = np.random.default_rng(1)
-    theta_obs = expit(rng.normal(0, 1, size=(1, 50)))
-    theta_rec, n_iter = correct_theta_matrix(theta_obs, lam=0.0)
-    assert np.allclose(theta_rec, theta_obs)
-    assert n_iter == 0
-
-
-def test_correction_pulls_observed_down():
-    rng = np.random.default_rng(2)
-    theta_true = expit(rng.normal(0, 1.5, size=(1, 300)))
-    theta_obs = _forward(theta_true, lam=1.5)
-    theta_rec, _ = correct_theta_matrix(theta_obs, lam=1.5, max_iter=200)
-    # E[max] >= true, so the inverse pulls the observed population down.
-    assert np.mean(theta_rec) < np.mean(theta_obs)
-
-
-# ---------------------------------------------------------------------------
-# deattenuate_congression — the fits-level wrapper
-# ---------------------------------------------------------------------------
-
-def _make_fit(genotype, theta_low, theta_high, log_K, n, dk=0.0):
-    # est_t: [ln_cfu0, dk_geno, logit_low, logit_high, log_K, log_n]
-    est_t = np.array([5.0, dk, logit(theta_low), logit(theta_high),
-                      log_K, np.log(n)])
-    cov_t = np.eye(6) * 0.01
-    return GenotypeFit(
-        genotype=genotype, titrant_name="iptg", n_obs=100, converged=True,
-        param_names_t=["ln_cfu0[0]"] + list(PHENO_PARAMS_TRANSFORMED),
-        est_t=est_t, cov_t=cov_t, pheno_slice=slice(1, 6))
-
-
-def _mean_theta_high(fits, orig_keys):
-    # theta_high = expit(est_t[3]) (induced-end occupancy).
-    return np.mean([expit(fits[k].est_t[3]) for k in orig_keys])
-
-
-def _build_fits(rng, n=25):
-    fits = {}
-    for i in range(n):
-        fits[(f"A{i}V", "iptg")] = _make_fit(
-            f"A{i}V",
-            theta_low=rng.uniform(0.7, 0.98),
-            theta_high=rng.uniform(0.02, 0.5),
-            log_K=rng.uniform(np.log(1e-3), np.log(0.3)),   # spread -> dynamic range
-            n=rng.uniform(0.8, 2.0))
-    fits[("wt", "iptg")] = _make_fit("wt", 0.99, 0.01, np.log(0.017), 2.0)
-    return fits
-
-
-_CONCS = [0.0, 1e-3, 1e-2, 1e-1, 1.0]
-_GROWTH_DF = pd.DataFrame({"titrant_name": ["iptg"] * len(_CONCS),
-                          "titrant_conc": _CONCS})
-
-
-def test_deattenuate_leaves_spiked_untouched_and_corrects_bulk():
-    rng = np.random.default_rng(3)
-    fits = _build_fits(rng)
-    bulk_keys = [k for k in fits if k[0] != "wt"]
-
-    corrected = deattenuate_congression(fits, _GROWTH_DF, lam=1.0, spiked={"wt"})
-
-    # Spiked genotype is congression-free -> identical.
-    assert np.allclose(corrected[("wt", "iptg")].est_t,
-                       fits[("wt", "iptg")].est_t)
-    # Bulk genotypes were corrected.
-    assert any(not np.allclose(corrected[k].est_t, fits[k].est_t)
-               for k in bulk_keys)
-    # De-attenuation pulls the masked induced-end occupancy back down.
-    assert _mean_theta_high(corrected, bulk_keys) < _mean_theta_high(fits, bulk_keys)
-    # dk_geno (index 1) is untouched by the theta correction.
-    for k in bulk_keys:
-        assert corrected[k].est_t[1] == fits[k].est_t[1]
-
-
-def test_deattenuate_lambda_none_is_noop():
-    rng = np.random.default_rng(4)
-    fits = _build_fits(rng)
-    noop = deattenuate_congression(fits, _GROWTH_DF, lam=None, spiked={"wt"})
-    for k in fits:
-        assert np.allclose(noop[k].est_t, fits[k].est_t)
diff --git a/tests/tfscreen/simulate/test_binding_data.py b/tests/tfscreen/simulate/test_binding_data.py
index 6896dc5b..45511f51 100644
--- a/tests/tfscreen/simulate/test_binding_data.py
+++ b/tests/tfscreen/simulate/test_binding_data.py
@@ -47,3 +47,24 @@ def test_generate_binding_data_missing_genotype_raises():
 
     with pytest.raises(ValueError, match="No pre-computed theta"):
         generate_binding_df(binding_cfg, rng, binding_theta_df)
+
+
+def test_generate_binding_data_noise_unclipped_by_default():
+    """
+    Noisy theta_obs is not clipped to [0, 1] unless clip_theta_obs is set.
+
+    The fit's binding likelihood is an unclipped Normal; clipped anchors
+    near 0 and 1 are biased (SVI-overconfidence fix grid, 2026-09-29).
+    """
+    binding_theta_df = pd.DataFrame(
+        [{"genotype": "wt", "titrant_conc": c, "theta_true": t}
+         for c, t in [(0.0, 0.999), (0.1, 0.5), (1.0, 0.001)]])
+    cfg = {"titrant_name": "iptg", "titrant_conc": [0.0, 0.1, 1.0],
+           "noise": 0.3}
+    obs = []
+    for clip in (False, True):
+        res = generate_binding_df({**cfg, "clip_theta_obs": clip},
+                                  np.random.default_rng(1), binding_theta_df)
+        obs.append(res["theta_obs"].to_numpy())
+    assert ((obs[0] < 0) | (obs[0] > 1)).any()
+    np.testing.assert_allclose(obs[1], np.clip(obs[0], 0, 1))
diff --git a/tests/tfscreen/simulate/test_binding_params.py b/tests/tfscreen/simulate/test_binding_params.py
index abe1fda5..167e68cf 100644
--- a/tests/tfscreen/simulate/test_binding_params.py
+++ b/tests/tfscreen/simulate/test_binding_params.py
@@ -91,6 +91,7 @@ def mock_sim_priors(wt_params):
     sp.epi_tau_scale  = 0.0   # no epistasis by default in tests
     sp.epi_slab_scale = 2.0
     sp.epi_slab_df    = 4.0
+    sp.max_hill_n     = None  # no Hill coefficient limit by default
     return sp
 
 
@@ -401,6 +402,41 @@ def test_double_differs_from_wt(self, simple_csv, small_genotypes,
         expected_wt = _hill_theta(0.99, 0.01, -4.1, 2.0, log_conc)
         assert not np.allclose(result["A47V/K84L"], expected_wt)
 
+    def test_max_hill_n_caps_assembled_hill_n(self, wt_params, mock_sim_priors):
+        """max_hill_n limits every assembled genotype's Hill coefficient."""
+        genotypes = ["wt"] + [f"M{i}A" for i in range(2, 42)]
+        sim_data = _make_sim_data_for_genotypes(genotypes)
+        log_conc = np.array(sim_data.log_titrant_conc)
+        mock_sim_priors.sigma_d_log_n = 3.0
+
+        _, free = build_theta_gc_override_hill_mut(
+            {"wt": wt_params}, genotypes, sim_data, mock_sim_priors,
+            log_conc, np.random.default_rng(3))
+        mock_sim_priors.max_hill_n = 4.0
+        result, capped = build_theta_gc_override_hill_mut(
+            {"wt": wt_params}, genotypes, sim_data, mock_sim_priors,
+            log_conc, np.random.default_rng(3))
+
+        n_free = np.array([free[g]["hill_n"] for g in genotypes])
+        n_capped = np.array([capped[g]["hill_n"] for g in genotypes])
+        assert n_free.max() > 4.0
+        np.testing.assert_allclose(n_capped, np.minimum(n_free, 4.0), rtol=1e-6)
+        # theta is built from the capped value
+        g = genotypes[int(np.argmax(n_free))]
+        p = capped[g]
+        np.testing.assert_allclose(
+            result[g], _hill_theta(p["theta_low"], p["theta_high"],
+                                   p["log_hill_K"], 4.0, log_conc), rtol=1e-5)
+
+    def test_max_hill_n_nonpositive_raises(self, wt_params, mock_sim_priors, rng):
+        genotypes = ["wt", "M2A"]
+        sim_data = _make_sim_data_for_genotypes(genotypes)
+        mock_sim_priors.max_hill_n = 0.0
+        with pytest.raises(ValueError, match="max_hill_n"):
+            build_theta_gc_override_hill_mut(
+                {"wt": wt_params}, genotypes, sim_data, mock_sim_priors,
+                np.array(sim_data.log_titrant_conc), rng)
+
     def test_unmeasured_mut_uses_prior(self, wt_params, mock_sim_priors, rng):
         """A mutation not in the CSV gets a random delta from the prior."""
         genotypes = ["wt", "M99A"]  # M99A not in CSV
diff --git a/tests/tfscreen/simulate/test_cell_rules.py b/tests/tfscreen/simulate/test_cell_rules.py
new file mode 100644
index 00000000..3a10676f
--- /dev/null
+++ b/tests/tfscreen/simulate/test_cell_rules.py
@@ -0,0 +1,184 @@
+"""Tests for the co-transformed cell rules (simulate/cell_rules.py)."""
+
+import numpy as np
+import pytest
+
+from tfscreen.simulate.cell_rules import (
+    DK_RULES,
+    THETA_EPS,
+    THETA_RULES,
+    dk_dilution,
+    dk_min,
+    dk_softmin,
+    theta_heterodimer,
+    theta_homodimer,
+    theta_max,
+)
+
+
+def _logit(p):
+    return np.log(p / (1 - p))
+
+
+def _expit(x):
+    return 1.0 / (1.0 + np.exp(-x))
+
+
+def _cells():
+    """Two cells over two conditions: a 2-plasmid cell and a 3-plasmid cell
+    stored with one masked slot in the first."""
+    theta = np.array([[[0.9, 0.2], [0.3, 0.6], [0.5, 0.5]],
+                      [[0.1, 0.8], [0.7, 0.4], [0.95, 0.05]]])
+    shares = np.array([[0.5, 0.5, 0.0],
+                       [1 / 3, 1 / 3, 1 / 3]])
+    activity = np.ones_like(theta)
+    return theta, activity, shares
+
+
+def test_registry():
+    assert set(THETA_RULES) == {"max", "homodimer", "heterodimer"}
+    assert set(DK_RULES) == {"dilution", "softmin", "min"}
+
+
+def test_max_takes_highest_theta_and_its_activity():
+    theta, _, shares = _cells()
+    activity = np.arange(theta.size, dtype=float).reshape(theta.shape) + 1
+    cell, act = theta_max(theta, activity, shares)
+    np.testing.assert_allclose(cell, [[0.9, 0.6], [0.95, 0.8]])
+    np.testing.assert_allclose(act, [[1, 4], [11, 8]])
+
+
+@pytest.mark.parametrize("rule,power", [(theta_homodimer, 1.0),
+                                        (theta_heterodimer, 0.5)])
+def test_partition_rules_match_hand_calculation(rule, power):
+    theta, activity, shares = _cells()
+    cell, act = rule(theta, activity, shares)
+    for c in range(2):
+        n = int(np.sum(shares[c] > 0))
+        for j in range(2):
+            logits = _logit(theta[c, :n, j])
+            expected = _expit(np.log(np.mean(np.exp(power * logits))) / power)
+            assert cell[c, j] == pytest.approx(expected, rel=1e-10)
+    np.testing.assert_array_equal(act, np.ones_like(cell))
+
+
+def test_partition_rules_ordering():
+    """heterodimer <= homodimer <= max; all at least the mean logit."""
+    theta, activity, shares = _cells()
+    het, _ = theta_heterodimer(theta, activity, shares)
+    hom, _ = theta_homodimer(theta, activity, shares)
+    mx, _ = theta_max(theta, activity, shares)
+    assert np.all(het <= hom + 1e-12)
+    assert np.all(hom <= mx + 1e-12)
+    for c in range(2):
+        n = int(np.sum(shares[c] > 0))
+        mean_logit = np.mean(_logit(theta[c, :n]), axis=0)
+        assert np.all(_logit(het[c]) >= mean_logit - 1e-10)
+
+
+def test_strong_binder_loses_log2_and_log4():
+    """A strong binder with a dead partner in a 1:1 cell loses log 2
+    (homodimer) or log 4 (heterodimer) logit units against max."""
+    strong, dead = 0.999, THETA_EPS
+    theta = np.array([[[strong], [dead]]])
+    shares = np.array([[0.5, 0.5]])
+    activity = np.ones_like(theta)
+    hom, _ = theta_homodimer(theta, activity, shares)
+    het, _ = theta_heterodimer(theta, activity, shares)
+    assert _logit(strong) - _logit(hom[0, 0]) == pytest.approx(np.log(2), abs=1e-3)
+    assert _logit(strong) - _logit(het[0, 0]) == pytest.approx(np.log(4), abs=1e-2)
+
+
+@pytest.mark.parametrize("rule", [theta_homodimer, theta_heterodimer, theta_max])
+def test_single_plasmid_keeps_its_theta(rule):
+    theta = np.array([[[0.3, 0.8], [0.9, 0.9]]])
+    shares = np.array([[1.0, 0.0]])
+    cell, _ = rule(theta, np.ones_like(theta), shares)
+    np.testing.assert_allclose(cell, [[0.3, 0.8]], rtol=1e-9)
+
+
+@pytest.mark.parametrize("rule", [theta_homodimer, theta_heterodimer])
+def test_partition_rules_require_activity_one(rule):
+    theta, activity, shares = _cells()
+    activity[0, 1, 0] = 0.5
+    with pytest.raises(ValueError, match="activity 1"):
+        rule(theta, activity, shares)
+
+
+@pytest.mark.parametrize("rule", [theta_homodimer, theta_heterodimer])
+def test_partition_rules_ignore_masked_slot_activity(rule):
+    """A masked slot's activity is not checked."""
+    theta, activity, shares = _cells()
+    activity[0, 2, :] = 0.5          # slot 2 of cell 0 is masked
+    rule(theta, activity, shares)
+
+
+@pytest.mark.parametrize("rule", [theta_homodimer, theta_heterodimer, theta_max])
+def test_nan_in_valid_slot_propagates(rule):
+    theta, activity, shares = _cells()
+    theta[1, 0, 1] = np.nan
+    cell, _ = rule(theta, activity, shares)
+    assert np.isnan(cell[1, 1])
+    assert np.all(np.isfinite(cell[0]))
+
+
+def test_dk_dilution_is_share_weighted_mean():
+    dk = np.array([[0.0, -0.02, 5.0], [-0.03, 0.0, 0.03]])
+    shares = np.array([[0.5, 0.5, 0.0], [1 / 3, 1 / 3, 1 / 3]])
+    np.testing.assert_allclose(dk_dilution(dk, shares), [-0.01, 0.0])
+
+
+def _dk_cells():
+    dk = np.array([[0.0, -0.03, 5.0], [-0.03, 0.0, 0.03]])
+    shares = np.array([[0.5, 0.5, 0.0], [1 / 3, 1 / 3, 1 / 3]])
+    return dk, shares
+
+
+def test_dk_min_takes_worst_valid_slot():
+    dk, shares = _dk_cells()
+    np.testing.assert_allclose(dk_min(dk, shares), [-0.03, -0.03])
+
+
+def test_dk_softmin_matches_hand_calculation():
+    dk, shares = _dk_cells()
+    alpha = 100.0
+    expected = [-np.log(0.5 * np.exp(0.0) + 0.5 * np.exp(3.0)) / alpha,
+                -np.log(np.mean(np.exp(-alpha * dk[1]))) / alpha]
+    np.testing.assert_allclose(dk_softmin(dk, shares, alpha=alpha), expected)
+    # The worked example in the plan: 1:1 cell, dk 0 and -0.03 -> -0.024
+    assert dk_softmin(dk, shares, alpha=alpha)[0] == pytest.approx(-0.0236, abs=1e-4)
+
+
+def test_dk_softmin_limits_and_order():
+    dk, shares = _dk_cells()
+    dil, mn = dk_dilution(dk, shares), dk_min(dk, shares)
+    np.testing.assert_allclose(dk_softmin(dk, shares, alpha=1e-6), dil, atol=1e-9)
+    np.testing.assert_allclose(dk_softmin(dk, shares, alpha=1e6), mn, atol=1e-5)
+    soft = dk_softmin(dk, shares, alpha=50.0)
+    assert np.all(mn <= soft) and np.all(soft <= dil)
+
+
+@pytest.mark.parametrize("rule", [dk_dilution, dk_min])
+def test_dk_rules_without_alpha_refuse_one(rule):
+    dk, shares = _dk_cells()
+    with pytest.raises(ValueError, match="takes no alpha"):
+        rule(dk, shares, alpha=10.0)
+
+
+@pytest.mark.parametrize("alpha", [None, 0.0, -1.0, np.inf])
+def test_dk_softmin_needs_positive_finite_alpha(alpha):
+    dk, shares = _dk_cells()
+    with pytest.raises(ValueError, match="alpha > 0"):
+        dk_softmin(dk, shares, alpha=alpha)
+
+
+@pytest.mark.parametrize("rule,kwargs", [(dk_dilution, {}), (dk_min, {}),
+                                         (dk_softmin, {"alpha": 100.0})])
+def test_dk_rules_single_plasmid_and_nan(rule, kwargs):
+    single = np.array([[-0.02, 7.0]])
+    np.testing.assert_allclose(rule(single, np.array([[1.0, 0.0]]), **kwargs),
+                               [-0.02], atol=1e-12)
+    dk, shares = _dk_cells()
+    dk[1, 0] = np.nan
+    out = rule(dk, shares, **kwargs)
+    assert np.isnan(out[1]) and np.isfinite(out[0])
diff --git a/tests/tfscreen/simulate/test_ddG_truth_sd.py b/tests/tfscreen/simulate/test_ddG_truth_sd.py
new file mode 100644
index 00000000..4fa4f0f2
--- /dev/null
+++ b/tests/tfscreen/simulate/test_ddG_truth_sd.py
@@ -0,0 +1,438 @@
+"""
+The ``ddG_truth_sd`` simulate setting: SD of the simulated per-structure ΔΔG.
+
+PddG/PnnC sample ``theta_ddG_sigma`` (sigma_s, one per structure) and put
+their prior on the constants, ``theta_dlnK ~ MVN(P · prior_mean, P ·
+diag(sigma_s²) · Pᵀ)``.  The simulator draws the per-structure truth ΔΔG =
+prior_mean + sigma_s · z and conditions ``theta_dlnK`` on P · ΔΔG, which has
+the model's distribution.  It conditions sigma_s on ``ddG_truth_sd``
+(default 1 kT), in the unit of ``thermo_options['ddG_units']``, and writes
+it as the ``ddG_sigma`` truth.
+"""
+import os
+import warnings
+from unittest.mock import MagicMock
+
+import jax
+import numpy as np
+import pandas as pd
+import pytest
+import yaml
+from numpyro import handlers
+
+from tfscreen.simulate.sample_theta import ddG_truth_conditions, sample_theta_prior
+from tfscreen.simulate.sim_data_class import build_sim_data
+from tfscreen.simulate.thermo_to_growth import thermo_to_growth
+from tfscreen.tfmodel.generative.registry import model_registry
+
+C12 = "thermo.O2_C12_K5_U0_a.PddG"
+C4 = "thermo.O2_C4_K3_U0_a.PddG"
+C12_STRUCTS = ["H", "HO", "L", "LO", "HE2", "LE2"]
+C4_STRUCTS = ["H", "HD", "L", "LE2"]
+RT = 1.987204259e-3 * 298.15          # kcal/mol per kT at 298.15 K
+
+SAMPLE_DF = pd.DataFrame({
+    "titrant_name": "iptg",
+    "titrant_conc": [0.0, 0.01, 1.0],
+    "condition_pre": "pre",
+    "condition_sel": "sel",
+})
+GROWTH = {"pre": {"m": 0.0, "b": 0.01}, "sel": {"m": 0.01, "b": 0.01}}
+_AA = "ACDEFGHIKLMNPQRSTVWY"
+
+
+def _library(num_sites):
+    """wt plus every substitution at ``num_sites`` alanine positions."""
+    muts = [f"A{site}{aa}" for site in range(1, num_sites + 1)
+            for aa in _AA if aa != "A"]
+    return ["wt"] + muts, muts
+
+
+def _table(path, muts, structs, scale=1.0, seed=3):
+    rng = np.random.default_rng(seed)
+    df = pd.DataFrame(rng.normal(0, 1, (len(muts), len(structs))) * scale,
+                      columns=structs)
+    df.insert(0, "mut", muts)
+    df.to_csv(path, index=False)
+    return str(path), df.set_index("mut")
+
+
+def _draw(component, genotypes, thermo_data, thermo_options=None,
+          ddG_truth_sd=None, seed=0):
+    lib = pd.DataFrame({"genotype": genotypes})
+    sim_data = build_sim_data(lib, SAMPLE_DF, thermo_data=thermo_data,
+                              theta_component=component,
+                              thermo_options=thermo_options)
+    conditions = ddG_truth_conditions(ddG_truth_sd, component, thermo_options)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        return thermo_to_growth(lib["genotype"], sim_data, SAMPLE_DF,
+                                component, jax.random.PRNGKey(seed), GROWTH,
+                                rng=np.random.default_rng(seed),
+                                return_mutation_truth=True,
+                                theta_conditions=conditions)
+
+
+def _truth_minus_table(truth, table, structs, factor=1.0):
+    mut = truth["mutation"].set_index("mutation")
+    return pd.DataFrame({s: mut[f"ddG_{s}"] - table.loc[mut.index, s] * factor
+                         for s in structs})
+
+
+# ── semantics ────────────────────────────────────────────────────────────────
+
+def test_zero_sd_gives_the_table_exactly_in_kT(tmp_path):
+    genotypes, muts = _library(2)
+    path, table = _table(tmp_path / "ddG.csv", muts, C12_STRUCTS)
+    truth = _draw(C12, genotypes, path, {"ddG_units": "kcal/mol"},
+                  ddG_truth_sd=0)[3]
+    diff = _truth_minus_table(truth, table, C12_STRUCTS, factor=1 / RT)
+    np.testing.assert_allclose(diff.to_numpy(), 0.0, atol=1e-5)
+    assert np.isfinite(truth["mutation"].drop(columns="mutation")
+                       .to_numpy()).all()
+
+
+def test_positive_sd_sets_the_spread_independently_per_structure(tmp_path):
+    genotypes, muts = _library(30)                  # 570 mutations
+    path, table = _table(tmp_path / "ddG.csv", muts, C12_STRUCTS)
+    truth = _draw(C12, genotypes, path, ddG_truth_sd=0.5)[3]
+    diff = _truth_minus_table(truth, table, C12_STRUCTS)
+    n = len(diff)
+    # SE of an SD estimate is about sd / sqrt(2n): 0.015 here.
+    np.testing.assert_allclose(diff.std(ddof=1), 0.5, atol=0.06)
+    np.testing.assert_allclose(diff.mean(), 0.0, atol=4 * 0.5 / np.sqrt(n))
+    corr = np.corrcoef(diff.to_numpy().T)
+    off_diag = corr[~np.eye(len(C12_STRUCTS), dtype=bool)]
+    assert np.abs(off_diag).max() < 4 / np.sqrt(n)
+
+
+def test_per_structure_mapping(tmp_path):
+    genotypes, muts = _library(30)
+    path, table = _table(tmp_path / "ddG.csv", muts, C12_STRUCTS)
+    sds = {"LE2": 2.0, "H": 0.0, "HO": 0.25, "L": 1.0, "LO": 0.5, "HE2": 0.0}
+    truth = _draw(C12, genotypes, path, ddG_truth_sd=sds)[3]
+    diff = _truth_minus_table(truth, table, C12_STRUCTS)
+    for s, sd in sds.items():
+        if sd == 0:
+            np.testing.assert_allclose(diff[s], 0.0, atol=1e-5)
+        else:
+            assert diff[s].std(ddof=1) == pytest.approx(sd, rel=0.12)
+
+
+def test_sd_in_kcal_is_converted_like_the_table(tmp_path):
+    """A kcal/mol table with a kcal/mol SD gives the same truth (in kT) as
+    the kT table with the SD in kT."""
+    genotypes, muts = _library(2)
+    path_kT, table = _table(tmp_path / "kT.csv", muts, C4_STRUCTS)
+    kcal = table * RT
+    kcal.reset_index().to_csv(tmp_path / "kcal.csv", index=False)
+    ref = _draw(C4, genotypes, path_kT, ddG_truth_sd=0.5 / RT)[3]["mutation"]
+    got = _draw(C4, genotypes, str(tmp_path / "kcal.csv"),
+                {"ddG_units": "kcal/mol"}, ddG_truth_sd=0.5)[3]["mutation"]
+    pd.testing.assert_frame_equal(ref, got, atol=1e-5, rtol=0)
+    sig = ddG_truth_conditions(0.5, C4, {"ddG_units": "kcal/mol"})
+    np.testing.assert_allclose(sig["theta_ddG_sigma"], 0.5 / RT, rtol=1e-6)
+    sig = ddG_truth_conditions(0.5, C4, {"ddG_units": "kJ/mol"})
+    np.testing.assert_allclose(sig["theta_ddG_sigma"],
+                               0.5 / (8.314462618e-3 * 298.15), rtol=1e-6)
+
+
+def test_prior_mean_stays_the_table(tmp_path):
+    """The recorded prior mean (what a fit's ddG_prior_mean_* reports) is
+    the unperturbed table whatever the truth SD."""
+    genotypes, muts = _library(2)
+    path, table = _table(tmp_path / "ddG.csv", muts, C12_STRUCTS)
+    opts = {"ddG_units": "kcal/mol"}
+    sim_data = build_sim_data(pd.DataFrame({"genotype": genotypes}), SAMPLE_DF,
+                              thermo_data=path, theta_component=C12,
+                              thermo_options=opts)
+    _, _, det = sample_theta_prior(
+        C12, sim_data, jax.random.PRNGKey(0), return_deterministic=True,
+        conditions=ddG_truth_conditions(0.7, C12, opts))
+    np.testing.assert_allclose(det["theta_ddG_prior_mean"],
+                               table.loc[muts, C12_STRUCTS].to_numpy() / RT,
+                               rtol=1e-6)
+
+
+# ── the per-structure draw and the model's distribution on Δln K ───────────
+
+_C4_D = ["d_ln_K_op", "d_ln_K_HL", "d_ln_K_E"]
+_C12_D = ["d_ln_K_h_l", "d_ln_K_h_o", "d_ln_K_h_e", "d_ln_K_l_o", "d_ln_K_l_e"]
+
+
+@pytest.mark.parametrize("component, structs, d_cols", [
+    (C4, C4_STRUCTS, _C4_D),
+    (C12, C12_STRUCTS, _C12_D),
+    ("thermo.O2_C4_K3_U1_a.PddG", C4_STRUCTS, _C4_D + ["d_ln_K_U"]),
+])
+def test_truth_schema_and_exact_projection(component, structs, d_cols,
+                                           tmp_path):
+    """The truth keeps ddG_{structure} and d_ln_K_* columns, and the
+    constants are the projection of the per-structure truth (in U0 too,
+    where the fit's post-hoc ΔΔG could not recover it)."""
+    genotypes, muts = _library(3)
+    path, _ = _table(tmp_path / "ddG.csv", muts, structs)
+    mut = _draw(component, genotypes, path, ddG_truth_sd=0.7)[3]["mutation"]
+    assert list(mut.columns) == (["mutation"] + [f"ddG_{s}" for s in structs]
+                                 + d_cols)
+    proj = model_registry["theta"][component].DLNK_PROJ
+    ddG = mut[[f"ddG_{s}" for s in structs]].to_numpy()
+    np.testing.assert_allclose(mut[d_cols].to_numpy(), ddG @ proj.T,
+                               atol=1e-5)
+
+
+def test_constant_covariance_matches_the_model(tmp_path):
+    """The simulated Δln K has the model's induced covariance P diag(σ²) Pᵀ,
+    as does a draw from the model's own MultivariateNormal."""
+    genotypes, muts = _library(60)                  # 1140 mutations
+    path, table = _table(tmp_path / "ddG.csv", muts, C4_STRUCTS)
+    sds = {"H": 0.3, "HD": 1.0, "L": 0.6, "LE2": 1.5}
+    sigma = np.array([sds[s] for s in C4_STRUCTS])
+    proj = model_registry["theta"][C4].DLNK_PROJ
+    expect = proj @ np.diag(sigma ** 2) @ proj.T
+
+    mut = _draw(C4, genotypes, path, ddG_truth_sd=sds)[3]["mutation"]
+    mut = mut.set_index("mutation")
+    resid = (mut[_C4_D].to_numpy()
+             - table.loc[mut.index, C4_STRUCTS].to_numpy() @ proj.T)
+    np.testing.assert_allclose(np.cov(resid.T), expect, atol=0.15)
+
+    # The model's own draw of theta_dlnK (no per-structure redraw).
+    module = model_registry["theta"][C4]
+    sim_data = build_sim_data(pd.DataFrame({"genotype": genotypes}),
+                              SAMPLE_DF, thermo_data=path,
+                              theta_component=C4)
+    priors = module.ModelPriors(**module.get_hyperparameters())
+    with handlers.trace() as tr, handlers.seed(rng_seed=3), \
+            handlers.condition(data={"theta_ddG_sigma": sigma}):
+        module.define_model("theta", sim_data, priors)
+    loc = np.asarray(tr["theta_ddG_prior_mean"]["value"]) @ proj.T
+    model_resid = np.asarray(tr["theta_dlnK"]["value"]) - loc
+    np.testing.assert_allclose(np.cov(model_resid.T), expect, atol=0.15)
+
+
+def test_draw_conditions_dlnK_on_the_per_structure_truth(tmp_path):
+    """sample_theta_prior returns the per-structure draw as theta_ddG, and
+    the model's Δln K is its projection."""
+    genotypes, muts = _library(2)
+    path, _ = _table(tmp_path / "ddG.csv", muts, C4_STRUCTS)
+    sim_data = build_sim_data(pd.DataFrame({"genotype": genotypes}), SAMPLE_DF,
+                              thermo_data=path, theta_component=C4)
+    _, _, det = sample_theta_prior(
+        C4, sim_data, jax.random.PRNGKey(0), return_deterministic=True,
+        conditions=ddG_truth_conditions(0.8, C4))
+    proj = model_registry["theta"][C4].DLNK_PROJ
+    d = np.column_stack([det[f"theta_{c}"] for c in _C4_D])
+    np.testing.assert_allclose(d, det["theta_ddG"] @ proj.T, atol=1e-5)
+    assert "theta_dlnK" not in det
+    np.testing.assert_allclose(det["theta_ddG_sigma"], 0.8)
+
+
+def test_unconditioned_draw_keeps_one_sigma(tmp_path):
+    """Without conditions σ_s is drawn once from its prior and the
+    per-structure truth uses that draw."""
+    genotypes, muts = _library(1)
+    path, table = _table(tmp_path / "ddG.csv", muts, C4_STRUCTS)
+    sim_data = build_sim_data(pd.DataFrame({"genotype": genotypes}), SAMPLE_DF,
+                              thermo_data=path, theta_component=C4)
+    _, _, det = sample_theta_prior(C4, sim_data, jax.random.PRNGKey(4),
+                                   return_deterministic=True)
+    sigma = det["theta_ddG_sigma"]
+    assert (sigma > 0).all()
+    z = (det["theta_ddG"] - table.loc[muts, C4_STRUCTS].to_numpy()) / sigma
+    assert np.isfinite(z).all() and np.abs(z).max() < 6
+
+
+# ── default, the σ_s truth table, and the fit side ─────────────────────────
+
+@pytest.mark.parametrize("sd, expected", [
+    (None, [1.0] * 6),
+    (0.0, [0.0] * 6),
+    ({"LE2": 2.0, "H": 0.0, "HO": 0.25, "L": 1.0, "LO": 0.5, "HE2": 0.0},
+     [0.0, 0.25, 1.0, 0.5, 0.0, 2.0]),
+])
+def test_truth_records_sigma_per_structure(sd, expected, tmp_path):
+    genotypes, muts = _library(1)
+    path, _ = _table(tmp_path / "ddG.csv", muts, C12_STRUCTS)
+    sigma = _draw(C12, genotypes, path, ddG_truth_sd=sd)[3]["ddG_sigma"]
+    assert list(sigma.columns) == ["structure", "ddG_sigma"]
+    assert list(sigma["structure"]) == C12_STRUCTS
+    np.testing.assert_allclose(sigma["ddG_sigma"], expected)
+
+
+def test_sigma_truth_is_in_kT(tmp_path):
+    genotypes, muts = _library(1)
+    path, _ = _table(tmp_path / "ddG.csv", muts, C4_STRUCTS)
+    sigma = _draw(C4, genotypes, path, {"ddG_units": "kcal/mol"},
+                  ddG_truth_sd=0.5)[3]["ddG_sigma"]
+    np.testing.assert_allclose(sigma["ddG_sigma"], 0.5 / RT, rtol=1e-6)
+
+
+@pytest.mark.parametrize("sd", [1.0, {s: 1 for s in C4_STRUCTS}])
+def test_default_is_one_kT(sd, tmp_path):
+    """No ddG_truth_sd draws the same truth as an explicit 1 kT."""
+    genotypes = ["wt", "H29A", "H29C", "L62A", "H29A/L62A", "H29C/L62A"]
+    muts = ["H29A", "H29C", "L62A"]
+    path, _ = _table(tmp_path / "ddG.csv", muts, C4_STRUCTS)
+    ref = _draw(C4, genotypes, path)[3]["mutation"]
+    got = _draw(C4, genotypes, path, ddG_truth_sd=sd)[3]["mutation"]
+    pd.testing.assert_frame_equal(ref, got)
+
+
+def test_conditioning_leaves_the_fit_side_alone(tmp_path):
+    """sigma_s is a free sample site of the model (the fit), also after a
+    simulator draw that conditioned it."""
+    genotypes, muts = _library(1)
+    path, _ = _table(tmp_path / "ddG.csv", muts, C12_STRUCTS)
+    sim_data = build_sim_data(pd.DataFrame({"genotype": genotypes}), SAMPLE_DF,
+                              thermo_data=path, theta_component=C12)
+    sample_theta_prior(C12, sim_data, jax.random.PRNGKey(0),
+                       conditions=ddG_truth_conditions(0.0, C12))
+    module = model_registry["theta"][C12]
+    priors = module.ModelPriors(**module.get_hyperparameters())
+    with handlers.trace() as tr, handlers.seed(rng_seed=0):
+        module.define_model("theta", sim_data, priors)
+    site = tr["theta_ddG_sigma"]
+    assert site["type"] == "sample"
+    assert not site["is_observed"]
+    assert (np.asarray(site["value"]) > 0).all()
+
+
+# ── validation ───────────────────────────────────────────────────────────────
+
+def test_none_is_one_kT_for_structural_models_only():
+    np.testing.assert_allclose(
+        ddG_truth_conditions(None, C12)["theta_ddG_sigma"], [1.0] * 6)
+    assert ddG_truth_conditions(None, "hill_geno") is None
+    assert ddG_truth_conditions(None, "thermo.O2_C12_K5_U0_a.PK") is None
+
+
+@pytest.mark.parametrize("value, match", [
+    (-0.1, ">= 0"),
+    (float("nan"), ">= 0"),
+    (True, "number"),
+    ("0.5", "number"),
+    ({"H": 1.0}, "missing"),
+    ({**{s: 1.0 for s in C12_STRUCTS}, "HD": 1.0}, "unknown"),
+    ({**{s: 1.0 for s in C12_STRUCTS}, "H": -1}, r"\[H\]"),
+])
+def test_bad_values_raise(value, match):
+    with pytest.raises(ValueError, match=match):
+        ddG_truth_conditions(value, C12)
+
+
+@pytest.mark.parametrize("component", ["hill_geno", "thermo.O2_C12_K5_U0_a.PK"])
+def test_non_structural_component_raises(component):
+    with pytest.raises(ValueError, match="PddG"):
+        ddG_truth_conditions(0.5, component)
+
+
+def test_pnnc_accepts_it():
+    sig = ddG_truth_conditions(0.5, "thermo.O2_C4_K3_U0_a.PnnC")
+    np.testing.assert_allclose(sig["theta_ddG_sigma"], [0.5] * 4)
+
+
+def test_unknown_condition_name_raises(tmp_path):
+    genotypes, muts = _library(1)
+    path, _ = _table(tmp_path / "ddG.csv", muts, C12_STRUCTS)
+    sim_data = build_sim_data(pd.DataFrame({"genotype": genotypes}), SAMPLE_DF,
+                              thermo_data=path, theta_component=C12)
+    with pytest.raises(ValueError, match="not sample sites"):
+        sample_theta_prior(C12, sim_data, jax.random.PRNGKey(0),
+                           conditions={"theta_nope": 1.0})
+
+
+# ── config plumbing: tfs-simulate and tfs-setup-sim-grid ─────────────────────
+
+def test_known_simulate_key():
+    from tfscreen.simulate.selection_experiment import SIMULATE_KNOWN_KEYS
+    assert "ddG_truth_sd" in SIMULATE_KNOWN_KEYS
+
+
+def _mock_library_prediction(mocker, cf):
+    mocker.patch("tfscreen.util.read_yaml", return_value=cf)
+    lm = mocker.patch(
+        "tfscreen.simulate.library_prediction.library_manager.LibraryManager")
+    lm.return_value.build_library_df.return_value = pd.DataFrame(
+        {"genotype": ["wt"]})
+    mocker.patch("tfscreen.simulate.library_prediction.build_sample_dataframes",
+                 return_value=pd.DataFrame({"titrant_conc": [0.0, 1.0]}))
+    mocker.patch("tfscreen.simulate.library_prediction.build_sim_data",
+                 return_value=MagicMock())
+    return mocker.patch(
+        "tfscreen.simulate.library_prediction.thermo_to_growth",
+        return_value=(pd.DataFrame(), pd.DataFrame(),
+                      pd.DataFrame({"genotype": ["wt"]})))
+
+
+_CF = {"condition_blocks": [{}], "theta_component": C12, "growth": {},
+       "dk_geno_zero": True, "thermo_data": "x.csv"}
+
+
+@pytest.mark.parametrize("cf_extra, expected", [
+    ({}, [1.0] * 6),
+    ({"ddG_truth_sd": 0.5}, [0.5] * 6),
+    ({"ddG_truth_sd": 0.5, "thermo_options": {"ddG_units": "kcal/mol"}},
+     [0.5 / RT] * 6),
+])
+def test_library_prediction_passes_the_conditions(mocker, cf_extra, expected):
+    from tfscreen.simulate.library_prediction import library_prediction
+    thermo = _mock_library_prediction(mocker, {**_CF, **cf_extra})
+    library_prediction(cf="config.yaml")
+    got = thermo.call_args.kwargs["theta_conditions"]
+    np.testing.assert_allclose(got["theta_ddG_sigma"], expected, rtol=1e-6)
+
+
+def test_library_prediction_no_conditions_for_other_models(mocker):
+    from tfscreen.simulate.library_prediction import library_prediction
+    thermo = _mock_library_prediction(
+        mocker, {**_CF, "theta_component": "hill_geno"})
+    library_prediction(cf="config.yaml")
+    assert thermo.call_args.kwargs["theta_conditions"] is None
+
+
+@pytest.mark.parametrize("cf_extra", [
+    {"theta_component": "hill_geno"},
+    {"phenotype_source": "empirical"},
+])
+def test_library_prediction_rejects_it(mocker, cf_extra):
+    from tfscreen.simulate.library_prediction import library_prediction
+    thermo = _mock_library_prediction(
+        mocker, {**_CF, **cf_extra, "ddG_truth_sd": 0.5})
+    with pytest.raises(ValueError, match="ddG_truth_sd"):
+        library_prediction(cf="config.yaml")
+    thermo.assert_not_called()
+
+
+def test_setup_sim_grid_varies_it(tmp_path):
+    from tfscreen.simulate.scripts.setup_sim_grid_cli import setup_sim_grid
+    base = tmp_path / "simulate_config.yaml"
+    base.write_text(yaml.dump({"theta_component": C12, "ddG_truth_sd": 1.0,
+                               "thermo_options": {"ddG_units": "kcal/mol"}}))
+    mapping = {s: 0.25 for s in C12_STRUCTS}
+    grid = tmp_path / "grid.yaml"
+    grid.write_text(yaml.dump({
+        "base_config": "simulate_config.yaml",
+        "run_name": "sd{{ ddG_truth_sd }}",
+        "simulate": [{"name": "dial", "variants": [
+            {"ddG_truth_sd": 0}, {"ddG_truth_sd": 0.5}]}],
+    }))
+    out = str(tmp_path / "grid_out")
+    runs = setup_sim_grid(str(grid), out_dir=out)
+    got = []
+    for run in runs:
+        with open(os.path.join(out, run["run"], "tfs_sim_config.yaml")) as fh:
+            cfg = yaml.safe_load(fh)
+        got.append(cfg["ddG_truth_sd"])
+        assert cfg["thermo_options"] == {"ddG_units": "kcal/mol"}
+    assert got == [0, 0.5]
+
+    # A per-structure mapping is one variant value (the whole key).
+    grid.write_text(yaml.dump({
+        "base_config": "simulate_config.yaml",
+        "simulate": [{"name": "dial", "variants": [{"ddG_truth_sd": mapping}]}],
+    }))
+    out2 = str(tmp_path / "grid_out2")
+    runs = setup_sim_grid(str(grid), out_dir=out2)
+    with open(os.path.join(out2, runs[0]["run"], "tfs_sim_config.yaml")) as fh:
+        assert yaml.safe_load(fh)["ddG_truth_sd"] == mapping
diff --git a/tests/tfscreen/simulate/test_design.py b/tests/tfscreen/simulate/test_design.py
new file mode 100644
index 00000000..c35ad38e
--- /dev/null
+++ b/tests/tfscreen/simulate/test_design.py
@@ -0,0 +1,159 @@
+"""Simulation designs: a tube table in place of condition_blocks."""
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from tfscreen.simulate.build_sample_dataframes import (
+    CONDITION_COLUMNS,
+    build_sample_dataframes,
+    design_conditions,
+    read_design,
+)
+
+EXAMPLE = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                       "examples", "simulate-and-analyze")
+
+
+def _design():
+    rows = []
+    for rep, shift in ((1, 0), (2, 7)):
+        for conc in (0.0, 1.0):
+            for t in (100, 120):
+                if rep == 2 and conc == 1.0 and t == 120:
+                    continue          # a missing tube
+                rows.append({"sample": f"k-r{rep}-{conc}-{t + shift}",
+                             "library": "kanR", "replicate": rep,
+                             "condition_pre": "kanR-kan", "t_pre": 30,
+                             "condition_sel": "kanR+kan", "t_sel": t + shift,
+                             "titrant_name": "iptg", "titrant_conc": conc,
+                             "od600": 0.3})
+    return pd.DataFrame(rows)
+
+
+def test_read_design_keeps_tube_table_columns(tmp_path):
+    path = tmp_path / "d.csv"
+    _design().to_csv(path, index=False)
+    d = read_design(str(path))
+    assert list(d.columns) == ["sample", "library", "replicate",
+                               "condition_pre", "t_pre", "condition_sel",
+                               "t_sel", "titrant_name", "titrant_conc"]
+    assert len(d) == 7
+
+
+@pytest.mark.parametrize("change,match", [
+    (lambda d: d.drop(columns="t_sel"), "missing column"),
+    (lambda d: d.assign(sample="same"), "repeats sample"),
+    (lambda d: pd.concat([d, d.iloc[[0]].assign(sample="x")]), "same replicate"),
+])
+def test_read_design_refuses(change, match):
+    with pytest.raises(ValueError, match=match):
+        read_design(change(_design()))
+
+
+def test_design_conditions_is_the_union_over_replicates():
+    conds = design_conditions(read_design(_design()))
+    # replicate 1: 4 tubes; replicate 2: 3 tubes at other times -> 7 conditions
+    assert len(conds) == 7
+    assert set(conds["replicate"]) == {1}
+    blocks = build_sample_dataframes([{
+        "library": "kanR", "titrant_name": "iptg", "titrant_conc": [0.0],
+        "condition_pre": "kanR-kan", "t_pre": 30, "condition_sel": "kanR+kan",
+        "t_sel": [100]}])
+    assert list(conds.columns) == list(blocks.columns)
+
+
+def test_simulate_follows_a_design(tmp_path, monkeypatch):
+    """Each replicate gets exactly its design's tubes, under the design's
+    names, and the raw tube table is the design."""
+    from tfscreen.simulate.scripts.simulate_cli import run_simulation_from_config
+
+    with open(os.path.join(EXAMPLE, "simulate_config.yaml")) as fh:
+        cf = yaml.safe_load(fh)
+    cf.pop("condition_blocks")
+    cf.pop("binding_data", None)
+    cf.pop("presplit_data", None)
+    design = _design()
+    design.to_csv(tmp_path / "design.csv", index=False)
+    cf["design"] = str(tmp_path / "design.csv")
+    with open(tmp_path / "cfg.yaml", "w") as fh:
+        yaml.safe_dump(cf, fh)
+
+    monkeypatch.chdir(EXAMPLE)
+    run_simulation_from_config(str(tmp_path / "cfg.yaml"),
+                               out_prefix=str(tmp_path / "sim"), seed=3)
+
+    tubes = pd.read_csv(tmp_path / "sim_tubes.csv")
+    cols = ["sample", "replicate"] + CONDITION_COLUMNS
+    got = tubes[cols].sort_values("sample").reset_index(drop=True)
+    want = design[cols].sort_values("sample").reset_index(drop=True)
+    pd.testing.assert_frame_equal(got, want, check_dtype=False)
+    files = sorted(os.listdir(tmp_path / "sim_counts"))
+    assert files == sorted(f"counts_{s}.csv" for s in design["sample"])
+    growth = pd.read_csv(tmp_path / "sim_growth.csv")
+    assert set(zip(growth["replicate"], growth["t_sel"])) == \
+        set(zip(design["replicate"], design["t_sel"]))
+
+
+def test_design_and_blocks_refused(tmp_path):
+    from tfscreen.simulate import library_prediction
+
+    with open(os.path.join(EXAMPLE, "simulate_config.yaml")) as fh:
+        cf = yaml.safe_load(fh)
+    _design().to_csv(tmp_path / "design.csv", index=False)
+    cf["design"] = str(tmp_path / "design.csv")
+    with pytest.raises(ValueError, match="not both"):
+        library_prediction(cf)
+
+
+def test_cfu0_per_library_sets_each_librarys_totals(tmp_path, monkeypatch):
+    """cfu0 can be a {library: cells} map; tube totals scale with it."""
+    from tfscreen.simulate.scripts.simulate_cli import run_simulation_from_config
+
+    with open(os.path.join(EXAMPLE, "simulate_config.yaml")) as fh:
+        cf = yaml.safe_load(fh)
+    for k in ("binding_data", "presplit_data"):
+        cf.pop(k, None)
+    monkeypatch.chdir(EXAMPLE)
+
+    base = float(str(cf["cfu0"]).replace("_", ""))
+    totals = {}
+    for name, cfu0 in (("scalar", base),
+                       ("map", {"kanR": base * 4.0, "pheS": base})):
+        c = dict(cf, cfu0=cfu0)
+        with open(tmp_path / f"{name}.yaml", "w") as fh:
+            yaml.safe_dump(c, fh)
+        run_simulation_from_config(str(tmp_path / f"{name}.yaml"),
+                                   out_prefix=str(tmp_path / name), seed=5,
+                                   num_replicates=1, write_raw=False)
+        g = pd.read_csv(tmp_path / f"{name}_growth.csv")
+        totals[name] = (g.drop_duplicates(["library", "condition_sel", "t_sel",
+                                           "titrant_conc"])
+                        .groupby("library")["sample_cfu"].sum())
+    ratio = totals["map"] / totals["scalar"]
+    assert ratio["kanR"] == pytest.approx(4.0, rel=1e-6)
+    assert ratio["pheS"] == pytest.approx(1.0, rel=1e-6)
+
+
+def test_cfu0_map_must_name_every_library(tmp_path, monkeypatch):
+    from tfscreen.simulate.scripts.simulate_cli import run_simulation_from_config
+
+    with open(os.path.join(EXAMPLE, "simulate_config.yaml")) as fh:
+        cf = yaml.safe_load(fh)
+    for k in ("binding_data", "presplit_data"):
+        cf.pop(k, None)
+    cf["cfu0"] = {"kanR": 1e7}
+    with open(tmp_path / "c.yaml", "w") as fh:
+        yaml.safe_dump(cf, fh)
+    monkeypatch.chdir(EXAMPLE)
+    with pytest.raises(ValueError, match="per library"):
+        run_simulation_from_config(str(tmp_path / "c.yaml"),
+                                   out_prefix=str(tmp_path / "x"),
+                                   num_replicates=1, write_raw=False)
+
+
+def test_read_design_accepts_sample_index():
+    d = read_design(_design().set_index("sample"))
+    assert d.columns[0] == "sample"
+    assert list(d["sample"]) == list(_design()["sample"])
diff --git a/tests/tfscreen/simulate/test_joint_fit_sim.py b/tests/tfscreen/simulate/test_joint_fit_sim.py
new file mode 100644
index 00000000..46bef895
--- /dev/null
+++ b/tests/tfscreen/simulate/test_joint_fit_sim.py
@@ -0,0 +1,143 @@
+"""
+Simulator pieces for the joint fit (planning/joint-fit-return.md step 5):
+linear growth's occupancy warp, the growth titrant scale s, and per-genotype
+binding offsets.
+"""
+
+import importlib.util
+import os
+import warnings
+
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+import pytest
+
+from tfscreen.simulate.binding_data import apply_genotype_offsets
+from tfscreen.simulate.growth.growth_linkage import LinearGrowth, warp_theta
+from tfscreen.simulate.growth_parameters_output import generate_growth_parameters_df
+from tfscreen.simulate.library_prediction import (growth_titrant_scale_from,
+                                                  library_prediction)
+from tfscreen.tfmodel.generative.components.growth import linear as fit_linear
+
+_spec = importlib.util.spec_from_file_location(
+    "_thermo_binding_cfg",
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), "test_thermo_binding.py"))
+_tb = importlib.util.module_from_spec(_spec)
+_spec.loader.exec_module(_tb)
+
+
+# --- warp -------------------------------------------------------------------
+
+@pytest.mark.parametrize("a,x0", [(8.0, 0.5), (1.0, 0.3), (20.0, 0.7)])
+def test_warp_endpoints_monotone_and_matches_the_fit(a, x0):
+    t = np.linspace(0.0, 1.0, 101)
+    f = warp_theta(t, a, x0)
+    assert f[0] == pytest.approx(0.0, abs=1e-12)
+    assert f[-1] == pytest.approx(1.0)
+    assert np.all(np.diff(f) > 0)
+    np.testing.assert_allclose(
+        f, np.asarray(fit_linear.warp_theta(jnp.asarray(t), jnp.asarray(a),
+                                            jnp.asarray(x0))), atol=1e-5)
+
+
+def test_linear_growth_warp():
+    g = LinearGrowth()
+    t = np.array([0.0, 0.25, 0.5, 1.0])
+    np.testing.assert_allclose(g.predict(t, b=0.01, m=-0.02),
+                               0.01 - 0.02 * t)
+    np.testing.assert_allclose(g.predict(t, b=0.01, m=-0.02, warp_a=8.0, warp_x0=0.5),
+                               0.01 - 0.02 * warp_theta(t, 8.0, 0.5))
+    with pytest.raises(ValueError, match="warp_a"):
+        g.predict(t, b=0.0, m=1.0, warp_a=0.0)
+
+
+def test_growth_truth_writes_the_warp_in_fit_names():
+    df = generate_growth_parameters_df({
+        "kanR+kan": {"model": "linear", "b": 0.0, "m": -0.02,
+                     "warp_a": 8.0, "warp_x0": 0.4},
+        "kanR-kan": {"b": 0.02, "m": 0.0}}).set_index("condition_rep")
+    assert df.loc["kanR+kan", "warp_log_a"] == pytest.approx(np.log(8.0))
+    assert df.loc["kanR+kan", "warp_x0"] == pytest.approx(0.4)
+    assert np.isnan(df.loc["kanR-kan", "warp_log_a"])
+
+
+# --- binding offsets ----------------------------------------------------------
+
+def test_offsets_one_per_genotype():
+    df = pd.DataFrame({"genotype": ["wt", "wt", "M42I", "M42I", "M42I"],
+                       "theta_obs": [0.9, 0.1, 0.5, 0.4, 0.3]})
+    out, truth = apply_genotype_offsets(df, 0.2, np.random.default_rng(0))
+    off = truth.set_index("genotype")["offset"]
+    np.testing.assert_allclose(out["theta_obs"] - df["theta_obs"],
+                               off.loc[df["genotype"]].to_numpy())
+    same, none = apply_genotype_offsets(df, 0.0, np.random.default_rng(0))
+    assert none is None and same is df
+    with pytest.raises(ValueError):
+        apply_genotype_offsets(df, -1.0, np.random.default_rng(0))
+
+
+# --- growth titrant scale s ---------------------------------------------------
+
+def test_scale_validation():
+    assert growth_titrant_scale_from({}) == 1.0
+    assert growth_titrant_scale_from({"growth_titrant_scale": 3}) == 3.0
+    for bad in (0, -1, float("nan")):
+        with pytest.raises(ValueError):
+            growth_titrant_scale_from({"growth_titrant_scale": bad})
+
+
+def _hill(params, genotypes, concs):
+    p = params.assign(genotype=params["genotype"].astype(str)).set_index("genotype")
+    c = np.where(np.asarray(concs) == 0, 1e-20, concs)
+    out = []
+    for g in genotypes:
+        r = p.loc[g]
+        occ = 1 / (1 + np.exp(-r["hill_n"] * (np.log(c) - r["log_hill_K"])))
+        out.append(r["theta_low"] + (r["theta_high"] - r["theta_low"]) * occ)
+    return np.array(out)
+
+
+def _run(scale, spiked=True):
+    cf = _tb._config("hill_geno", "stratified")
+    if not spiked:
+        del cf["binding_data"]["spiked_binding"]
+    if scale is not None:
+        cf["growth_titrant_scale"] = scale
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        return library_prediction(cf, return_binding_theta=True)
+
+
+def _theta_at(gt, conc):
+    d = gt[np.isclose(gt["titrant_conc"], conc)]
+    return d.assign(genotype=d["genotype"].astype(str)).set_index("genotype")["theta"]
+
+
+def test_growth_sees_s_times_nominal_and_binding_nominal():
+    # s = 10: growth at nominal 0.001 mM is the curve at 0.01 mM, a binding
+    # concentration; growth at 0 stays 0.
+    library_df, _, gt, params, _, lbt = _run(10.0, spiked=False)
+    genos = list(pd.unique(library_df["genotype"].astype(str)))
+    g001 = _theta_at(gt, 0.001).loc[genos].to_numpy()
+    np.testing.assert_allclose(g001, _hill(params, genos, [0.01])[:, 0], rtol=1e-4,
+                               atol=1e-6)
+    b001 = _theta_at(lbt, 0.01).loc[genos].to_numpy()
+    np.testing.assert_allclose(b001, _hill(params, genos, [0.01])[:, 0], rtol=1e-4,
+                               atol=1e-6)
+    np.testing.assert_allclose(g001, b001, rtol=1e-4, atol=1e-6)
+    # at s = 1 the growth curve at 0.001 is not the one at 0.01
+    _, _, gt1, params1, _, _ = _run(None, spiked=False)
+    np.testing.assert_allclose(_theta_at(gt1, 0.001).loc[genos].to_numpy(),
+                               _hill(params1, genos, [0.001])[:, 0], rtol=1e-4,
+                               atol=1e-6)
+
+
+def test_spiked_stratified_growth_curve_follows_s():
+    library_df, _, gt, _, binding_theta_df, _ = _run(10.0, spiked=True)
+    b = binding_theta_df[np.isclose(binding_theta_df["titrant_conc"], 0.01)]
+    b = b.set_index("genotype")["theta_true"]
+    g = _theta_at(gt, 0.001)
+    assert len(b) == 2
+    for geno in b.index:
+        assert g.loc[geno] == pytest.approx(b.loc[geno], rel=1e-4, abs=1e-6)
diff --git a/tests/tfscreen/simulate/test_library_binding_data.py b/tests/tfscreen/simulate/test_library_binding_data.py
index a635a9f7..9b934028 100644
--- a/tests/tfscreen/simulate/test_library_binding_data.py
+++ b/tests/tfscreen/simulate/test_library_binding_data.py
@@ -114,7 +114,22 @@ def test_noise_sets_theta_std_and_perturbs(params_df):
         rng=np.random.default_rng(4),
     )
     assert (bdf["theta_std"] == 0.02).all()
-    assert (bdf["theta_obs"] >= 0.0).all() and (bdf["theta_obs"] <= 1.0).all()
+
+
+def test_noise_is_not_clipped_by_default(params_df):
+    """Noisy theta_obs may leave [0, 1]; clip_theta_obs restores the old clip."""
+    kw = dict(titrant_name="iptg", titrant_conc=CONCS, noise=0.2,
+              parameters_df=params_df, growth_df=_all_survive(params_df),
+              spiked_genotypes=["wt"])
+    bdf, _ = generate_library_binding_df({"choose_by": "random", "num": 2},
+                                         rng=np.random.default_rng(4), **kw)
+    assert ((bdf["theta_obs"] < 0) | (bdf["theta_obs"] > 1)).any()
+    clipped, _ = generate_library_binding_df(
+        {"choose_by": "random", "num": 2}, rng=np.random.default_rng(4),
+        clip_theta_obs=True, **kw)
+    assert (clipped["theta_obs"] >= 0).all() and (clipped["theta_obs"] <= 1).all()
+    np.testing.assert_allclose(clipped["theta_obs"],
+                               np.clip(bdf["theta_obs"], 0, 1))
 
 
 # ---------------------------------------------------------------------------
@@ -141,3 +156,16 @@ def test_file_path_uses_named_genotypes_and_warns_on_nonsurvivor(params_df, tmp_
         )
     assert set(man["genotype"]) == {"H74A", "K84L"}
     assert "D88A" not in set(bdf["genotype"])
+
+
+def test_theta_df_missing_a_binding_concentration_raises(params_df):
+    theta_df = pd.DataFrame({"genotype": ["H74A", "H74A"],
+                             "titrant_conc": [0.0, 1.0],
+                             "theta": [0.9, 0.2]})
+    with pytest.raises(ValueError, match=r"no theta at binding "
+                                         r"concentration\(s\) \[0.001, 0.03\]"):
+        generate_library_binding_df(
+            {"choose_by": "random", "num": 1}, "iptg", CONCS, 0.0,
+            params_df, _all_survive(params_df), spiked_genotypes=["wt"],
+            rng=np.random.default_rng(0), theta_df=theta_df,
+        )
diff --git a/tests/tfscreen/simulate/test_library_prediction.py b/tests/tfscreen/simulate/test_library_prediction.py
index 0bf8188c..d02fd469 100644
--- a/tests/tfscreen/simulate/test_library_prediction.py
+++ b/tests/tfscreen/simulate/test_library_prediction.py
@@ -63,6 +63,8 @@ def test_library_prediction_success(mocker, mock_config):
         library_df=mock_library_df,
         sample_df=mock_sample_df,
         thermo_data=None,
+        thermo_options=None,
+        theta_component="mock_theta",
     )
     mock_jax_key.assert_called_once_with(7)   # seed value
 
@@ -465,3 +467,39 @@ def test_unsupported_theta_component_raises(mocker, tmp_path):
 
     with pytest.raises(ValueError, match="hill"):
         library_prediction(cf="config.yaml")
+
+
+# ---------------------------------------------------------------------------
+# _select_own_theta_spiked
+# ---------------------------------------------------------------------------
+
+def _own_theta_table():
+    rows = []
+    for g, vals in {"wt": [0.9, 0.1], "A1B": [0.8, 0.5], "C2D": [0.2, 0.1]}.items():
+        for c, v in zip([0.0, 1.0], vals):
+            rows.append({"genotype": g, "titrant_conc": c, "theta": v})
+    return pd.DataFrame(rows)
+
+
+def test_select_own_theta_spiked_num_none_measures_every_spike():
+    import numpy as np
+    from tfscreen.simulate.library_prediction import _select_own_theta_spiked
+
+    rows = _select_own_theta_spiked(["A1B", "C2D", "E3F"], None,
+                                    "stratified", _own_theta_table(),
+                                    [0.0, 1.0], "iptg",
+                                    np.random.default_rng(0))
+    assert [(r["genotype"], r["titrant_conc"]) for r in rows] == [
+        ("A1B", 0.0), ("A1B", 1.0), ("C2D", 0.0), ("C2D", 1.0)]
+    assert rows[1]["theta_true"] == 0.5
+    assert all(r["titrant_name"] == "iptg" for r in rows)
+
+
+def test_select_own_theta_spiked_unknown_mode_raises():
+    import numpy as np
+    from tfscreen.simulate.library_prediction import _select_own_theta_spiked
+
+    with pytest.raises(ValueError, match="Unknown spiked_binding choose_by: 'bogus'"):
+        _select_own_theta_spiked(["A1B", "C2D"], 1, "bogus",
+                                 _own_theta_table(), [0.0, 1.0], "iptg",
+                                 np.random.default_rng(0))
diff --git a/tests/tfscreen/simulate/test_library_subsample_sim.py b/tests/tfscreen/simulate/test_library_subsample_sim.py
new file mode 100644
index 00000000..905f1139
--- /dev/null
+++ b/tests/tfscreen/simulate/test_library_subsample_sim.py
@@ -0,0 +1,153 @@
+"""tfs-simulate with a subsampled library (library_genotypes,
+library_subsample; genetics/library_subsample.py)."""
+import glob
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from tfscreen.genetics import LibraryManager, library_composition_table
+from tfscreen.genetics.library_subsample import build_library, subsample_design
+from tfscreen.simulate.scripts.simulate_cli import run_simulation_from_config
+from tfscreen.tfmodel.scripts.configure_model_cli import (
+    check_genotypes_in_library,
+)
+
+EXAMPLE = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                       "examples", "simulate-and-analyze")
+
+
+def _design():
+    rows = []
+    for rep in (1, 2):
+        for conc in (0.0, 1.0):
+            for t in (100, 140):
+                rows.append({"sample": f"k{rep}-{conc}-{t}", "library": "kanR",
+                             "replicate": rep, "condition_pre": "kanR-kan",
+                             "t_pre": 30, "condition_sel": "kanR+kan",
+                             "t_sel": t, "titrant_name": "iptg",
+                             "titrant_conc": conc})
+    return pd.DataFrame(rows)
+
+
+@pytest.fixture
+def cfg_dir(tmp_path):
+    """A small simulate config (design, no binding/presplit) in its own
+    directory, with the design file next to it."""
+    with open(os.path.join(EXAMPLE, "simulate_config.yaml")) as fh:
+        cf = yaml.safe_load(fh)
+    cf.pop("condition_blocks")
+    for k in ("binding_data", "presplit_data"):
+        cf.pop(k, None)
+    d = tmp_path / "cfg"
+    d.mkdir()
+    _design().to_csv(d / "design.csv", index=False)
+    cf["design"] = str(d / "design.csv")
+    cf["library_subsample"] = {"double-1-2": 40}
+    cf["library_subsample_seed"] = 7
+    return d, cf
+
+
+def _run(d, cf, name, **kwargs):
+    path = d / f"{name}.yaml"
+    path.write_text(yaml.safe_dump(cf))
+    out = d.parent / name
+    kwargs.setdefault("seed", 3)
+    run_simulation_from_config(str(path), out_prefix=str(out / "sim"),
+                               **kwargs)
+    return path, out
+
+
+def test_subsampled_library_reaches_every_output(cfg_dir, monkeypatch, capsys):
+    d, cf = cfg_dir
+    monkeypatch.chdir(EXAMPLE)
+    path, out = _run(d, cf, "a")
+    log = capsys.readouterr().out
+    assert "Library subsample (library_subsample_depth: full_library" in log
+
+    kept, summary = build_library(str(path))
+    kept_genos = set(kept["genotype"].astype(str))
+    full = LibraryManager(cf).build_library_df()
+    assert len(kept_genos) < full["genotype"].nunique()
+
+    lib = pd.read_csv(out / "sim_library.csv")
+    assert set(lib["genotype"]) == kept_genos
+    dbl = set(lib.loc[lib["library_origin"] == "double-1-2", "genotype"])
+    assert len(dbl - {"wt"}) <= 40 + 9          # 40 drawn, plus wt/spikes
+
+    growth = pd.read_csv(out / "sim_growth.csv")
+    assert set(growth["genotype"]) <= kept_genos | {"__unknown__"}
+    # Every raw counts file lists exactly the kept library
+    files = sorted(glob.glob(str(out / "sim_counts" / "counts_*.csv")))
+    assert len(files) == 8
+    total = 0
+    for f in files:
+        c = pd.read_csv(f)
+        assert set(c["genotype"]) == kept_genos | {"__unknown__"}
+        total += c["counts"].sum()
+    # full_library depth: reads scaled by the kept share of the pool
+    design = subsample_design(cf, summary)
+    assert total == pytest.approx(design["total_num_reads"], abs=8)
+    assert design["total_num_reads"] < cf["total_num_reads"]
+
+    # tfs-configure-model's library snapshot from the same config is the
+    # simulated library, and it accepts the simulated growth table
+    comp = library_composition_table(str(path))
+    assert set(comp["genotype"]) == kept_genos
+    check_genotypes_in_library(set(comp["genotype"]), growth, "growth_df")
+
+
+def test_subsample_is_reproducible_from_seed(cfg_dir, monkeypatch):
+    d, cf = cfg_dir
+    monkeypatch.chdir(EXAMPLE)
+    _, a = _run(d, cf, "a")
+    _, b = _run(d, cf, "b")
+    pd.testing.assert_frame_equal(pd.read_csv(a / "sim_library.csv"),
+                                  pd.read_csv(b / "sim_library.csv"))
+    pd.testing.assert_frame_equal(pd.read_csv(a / "sim_growth.csv"),
+                                  pd.read_csv(b / "sim_growth.csv"))
+    cf["library_subsample_seed"] = 8
+    _, c = _run(d, cf, "c")
+    assert (set(pd.read_csv(a / "sim_library.csv")["genotype"])
+            != set(pd.read_csv(c / "sim_library.csv")["genotype"]))
+
+
+def test_genotype_list_relative_to_config(cfg_dir, monkeypatch):
+    """library_genotypes is read relative to the config file, wherever the
+    simulation runs; wt and spikes are added to the list."""
+    d, cf = cfg_dir
+    full = LibraryManager(cf).build_library_df()
+    full["genotype"] = full["genotype"].astype(str)
+    listed = sorted(set(full.loc[full["library_origin"] != "double-1-2",
+                                 "genotype"]))
+    dbl = sorted(g for g in set(full.loc[full["library_origin"]
+                                         == "double-1-2", "genotype"])
+                 if "/" in g)[:12]
+    (d / "keep.txt").write_text("\n".join(listed + dbl) + "\n")
+    cf.pop("library_subsample")
+    cf["library_genotypes"] = "keep.txt"
+    monkeypatch.chdir(EXAMPLE)         # not the config's directory
+    _, out = _run(d, cf, "a", write_raw=False)
+    lib = pd.read_csv(out / "sim_library.csv")
+    assert set(lib["genotype"]) <= set(listed + dbl) | {"wt"}
+    assert set(dbl) <= set(lib["genotype"])
+
+
+def test_genotype_outside_library_refused(cfg_dir, monkeypatch):
+    d, cf = cfg_dir
+    (d / "keep.txt").write_text("Q999W\n")
+    cf["library_genotypes"] = "keep.txt"
+    monkeypatch.chdir(EXAMPLE)
+    with pytest.raises(ValueError, match="not in the described library"):
+        _run(d, cf, "a")
+
+
+def test_as_written_keeps_configured_reads(cfg_dir, monkeypatch):
+    d, cf = cfg_dir
+    cf["library_subsample_depth"] = "as_written"
+    monkeypatch.chdir(EXAMPLE)
+    _, out = _run(d, cf, "a", num_replicates=2)
+    total = sum(pd.read_csv(f)["counts"].sum()
+                for f in glob.glob(str(out / "sim_counts" / "counts_*.csv")))
+    assert total == pytest.approx(cf["total_num_reads"], abs=8)
diff --git a/tests/tfscreen/simulate/test_od600.py b/tests/tfscreen/simulate/test_od600.py
new file mode 100644
index 00000000..7e3688aa
--- /dev/null
+++ b/tests/tfscreen/simulate/test_od600.py
@@ -0,0 +1,55 @@
+"""Tests for tfscreen.simulate.od600 (simulated OD600 readings)."""
+import numpy as np
+import pytest
+
+from tfscreen.simulate.od600 import (
+    cfu_per_mL_to_od600,
+    od600_to_cfu_per_mL,
+    read_od600_calibration,
+    simulate_od600,
+)
+
+CAL = {"kind": "od600_to_cfu_per_mL", "degree": 2,
+       "coefficients": [1e6, 8e8, 2e8],
+       "covariance": [[1e10, 0, 0], [0, 1e12, 0], [0, 0, 1e12]],
+       "reading_rel_sd": 0.02, "detection_threshold": 0.08,
+       "calibrated_od600_range": [0.1, 0.8]}
+
+
+@pytest.fixture
+def cal():
+    return read_od600_calibration(dict(CAL))
+
+
+def test_round_trip(cal):
+    od = np.array([0.1, 0.3, 0.7])
+    cfu, _, detectable = od600_to_cfu_per_mL(od, cal)
+    assert detectable.all()
+    assert np.allclose(cfu_per_mL_to_od600(cfu, cal), od)
+
+
+def test_simulate_od600(cal):
+    rng = np.random.default_rng(0)
+    volume = 5.0
+    true_od = 0.4
+    cfu, _, _ = od600_to_cfu_per_mL(np.array([true_od]), cal)
+    total = np.full(20_000, cfu[0] * volume)
+    r = simulate_od600(total, volume, cal, rng)
+    assert r["od600"].mean() == pytest.approx(true_od, rel=0.005)
+    assert r["od600"].std() / true_od == pytest.approx(0.02, rel=0.05)
+    assert r["od600_detectable"].all()
+    assert r["od600_in_range"].all()
+
+
+def test_simulate_flags_blind_and_saturated_tubes(cal):
+    rng = np.random.default_rng(1)
+    low, _, _ = od600_to_cfu_per_mL(np.array([0.02]), dict(cal, detection_threshold=0.0))
+    high, _, _ = od600_to_cfu_per_mL(np.array([1.2]), cal)
+    r = simulate_od600(np.array([low[0], high[0]]) * 5.0, 5.0, cal, rng)
+    assert not r["od600_detectable"][0]
+    assert not r["od600_in_range"][1]
+
+
+def test_accepts_raw_dict():
+    r = simulate_od600(np.array([1e9]), 5.0, dict(CAL), np.random.default_rng(0))
+    assert r["od600"].shape == (1,)
diff --git a/tests/tfscreen/simulate/test_raw_output.py b/tests/tfscreen/simulate/test_raw_output.py
new file mode 100644
index 00000000..5b783964
--- /dev/null
+++ b/tests/tfscreen/simulate/test_raw_output.py
@@ -0,0 +1,178 @@
+"""Tests for tfscreen.simulate.raw_output (the simulator's raw formats)."""
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from tfscreen.process_raw import counts_to_lncfu
+from tfscreen.process_raw.od600 import od600_to_cfu_per_mL
+from tfscreen.process_raw.scripts.process_counts_cli import process_counts
+from tfscreen.simulate.raw_output import sample_names, write_raw_experiment
+
+CALIBRATION = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                           "examples", "od600", "od600_calibration.yaml")
+GENOTYPES = ["wt", "A1V", "C2D", "E3F"]
+
+
+def _experiment(n_tubes=12, od600=None, seed=0):
+    """Simulator-shaped frames: integer sample ids, design columns, totals,
+    counts only for genotypes seen (no __unknown__)."""
+    rng = np.random.default_rng(seed)
+    rows = []
+    for i in range(n_tubes):
+        rows.append({"sample": i, "titrant_name": "iptg",
+                     "titrant_conc": [0.0, 1.0][i % 2],
+                     "condition_pre": "kanR-kan", "t_pre": 30,
+                     "condition_sel": "kanR+kan", "t_sel": 60 * (i // 2),
+                     "replicate": 1, "library": "kanR", "activity": 1.0,
+                     "sample_cfu": 1e8 * (1 + i), "sample_cfu_std": 0.0})
+    tubes = pd.DataFrame(rows).set_index(pd.Index(range(n_tubes)))
+    if od600 is not None:
+        tubes["od600"] = od600
+        tubes["od600_detectable"] = np.asarray(od600) > 0.0929
+        cfu, sd, _ = od600_to_cfu_per_mL(np.asarray(od600), CALIBRATION)
+        tubes["sample_cfu"] = cfu * 5.0
+        tubes["sample_cfu_std"] = sd * 5.0
+    counts = []
+    for i in range(n_tubes):
+        # E3F never seen: the raw files must still list it, with 0
+        for g in GENOTYPES[:3]:
+            counts.append({"sample": i, "genotype": g,
+                           "counts": int(rng.integers(20, 2000)),
+                           "dk_geno": 0.0, "theta": 0.5})
+    return tubes, pd.DataFrame(counts)
+
+
+def _as_fastq_counts(counts, samples):
+    """The counts as tfs-process-fastq would report them: every library
+    genotype in every tube, zeros included, plus __unknown__."""
+    full = pd.MultiIndex.from_product([samples, GENOTYPES + ["__unknown__"]],
+                                      names=["sample", "genotype"])
+    c = counts.set_index(["sample", "genotype"])["counts"].reindex(full,
+                                                                   fill_value=0)
+    return c.reset_index()
+
+
+def test_sample_names_are_not_substrings_of_each_other():
+    names = sample_names(1500)
+    assert len(set(names)) == 1500
+    assert all(len(n) == len(names[0]) for n in names)
+    assert not any(a != b and a in b for a in names[:20] for b in names)
+
+
+def test_raw_files_are_in_fastq_format(tmp_path):
+    tubes, counts = _experiment()
+    out = write_raw_experiment(tubes, counts, GENOTYPES, str(tmp_path / "sim"))
+    files = sorted(os.listdir(out["counts_dir"]))
+    assert files[0] == "counts_tube0001.csv" and len(files) == 12
+    c = pd.read_csv(os.path.join(out["counts_dir"], files[0]))
+    assert list(c.columns) == ["genotype", "counts"]
+    assert list(c["genotype"]) == GENOTYPES + ["__unknown__"]
+    assert c.set_index("genotype").loc[["E3F", "__unknown__"], "counts"].tolist() == [0, 0]
+    t = pd.read_csv(out["tubes"])
+    # design columns and totals only; no simulator truth
+    assert "activity" not in t.columns and "dk_geno" not in t.columns
+    assert {"sample_cfu", "sample_cfu_std"} <= set(t.columns)
+    assert out["command"].startswith("tfs-process-counts ")
+
+
+def test_raw_round_trip_reproduces_direct_growth_table(tmp_path):
+    """tfs-process-counts on the raw files gives the table tfs-simulate
+    builds in memory from the same counts in fastq form. (From the
+    simulator's sparse counts the in-memory table differs only by the
+    pseudocounts of genotypes never seen, which a real counts file lists.)"""
+    tubes, counts = _experiment()
+    direct = counts_to_lncfu(tubes, _as_fastq_counts(counts, tubes.index))
+    out = write_raw_experiment(tubes, counts, GENOTYPES, str(tmp_path / "sim"))
+    processed = pd.read_csv(process_counts(out["tubes"], out["counts_dir"],
+                                           out_prefix=str(tmp_path / "p"),
+                                           verbose=False))
+    key = ["t_sel", "titrant_conc", "genotype"]
+    m = direct.astype({"genotype": str}).merge(processed, on=key,
+                                               suffixes=("_d", "_p"))
+    assert len(m) == len(direct) == len(processed)
+    for col in ("counts", "sample_reads", "ln_cfu", "ln_cfu_var"):
+        np.testing.assert_allclose(m[f"{col}_d"], m[f"{col}_p"], rtol=1e-12)
+
+
+def test_raw_od600_route(tmp_path):
+    od = [0.05] + [0.15 + 0.03 * i for i in range(11)]   # first unreadable
+    tubes, counts = _experiment(od600=od)
+    out = write_raw_experiment(tubes, counts, GENOTYPES, str(tmp_path / "sim"),
+                               od600_config={"calibration": CALIBRATION,
+                                             "tube_volume_mL": 5.0})
+    assert out["dropped"] == ["tube0001"]
+    t = pd.read_csv(out["tubes"])
+    assert "sample_cfu" not in t.columns and "tube0001" not in set(t["sample"])
+    assert not os.path.exists(os.path.join(out["counts_dir"], "counts_tube0001.csv"))
+    assert "--od600_file" in out["command"]
+    assert os.path.exists(out["calibration"])
+
+    processed = pd.read_csv(process_counts(
+        out["tubes"], out["counts_dir"], out_prefix=str(tmp_path / "p"),
+        od600_file=out["od600"], od600_calibration_file=out["calibration"],
+        tube_volume_mL=5.0, verbose=False))
+    kept = tubes.iloc[1:]
+    direct = counts_to_lncfu(kept, _as_fastq_counts(counts, kept.index))
+    m = direct.astype({"genotype": str}).merge(
+        processed, on=["t_sel", "titrant_conc", "genotype"], suffixes=("_d", "_p"))
+    assert len(m) == len(direct)
+    np.testing.assert_allclose(m["sample_cfu_d"], m["sample_cfu_p"], rtol=1e-12)
+    np.testing.assert_allclose(m["ln_cfu_d"], m["ln_cfu_p"], rtol=1e-12)
+
+
+def test_counts_outside_library_refused(tmp_path):
+    tubes, counts = _experiment(n_tubes=2)
+    with pytest.raises(ValueError, match="outside the library"):
+        write_raw_experiment(tubes, counts, ["wt"], str(tmp_path / "sim"))
+
+
+def test_unassigned_reads_round_trip(tmp_path):
+    """Simulated __unknown__ reads (unassigned_read_fraction) land in the raw
+    files' __unknown__ row and count toward the tube's reads, in both the
+    in-memory table and tfs-process-counts."""
+    tubes, counts = _experiment()
+    unknown = pd.DataFrame({"sample": tubes.index, "genotype": "__unknown__",
+                            "counts": 5000})
+    counts = pd.concat([counts, unknown], ignore_index=True)
+    direct = counts_to_lncfu(tubes, _as_fastq_counts(counts, tubes.index))
+    out = write_raw_experiment(tubes, counts, GENOTYPES, str(tmp_path / "sim"))
+    raw = pd.read_csv(f"{out['counts_dir']}/counts_tube0001.csv")
+    assert raw.set_index("genotype").loc["__unknown__", "counts"] == 5000
+    processed = pd.read_csv(process_counts(out["tubes"], out["counts_dir"],
+                                           out_prefix=str(tmp_path / "p"),
+                                           verbose=False))
+    assert "__unknown__" not in set(processed["genotype"])
+    assigned = counts[counts.genotype != "__unknown__"].groupby("sample")["counts"].sum()
+    reads = processed.groupby("t_sel")["sample_reads"].first()
+    assert (reads > assigned.max()).all()
+    key = ["t_sel", "titrant_conc", "genotype"]
+    m = direct.astype({"genotype": str}).merge(processed, on=key,
+                                               suffixes=("_d", "_p"))
+    for col in ("sample_reads", "ln_cfu"):
+        np.testing.assert_allclose(m[f"{col}_d"], m[f"{col}_p"], rtol=1e-12)
+
+
+def test_tube_names_use_design_names_when_unique():
+    from tfscreen.simulate.raw_output import _tube_names
+    tubes = pd.DataFrame({"design_sample": ["a1", "b2", "c3"]})
+    assert _tube_names(tubes) == ["a1", "b2", "c3"]
+
+
+@pytest.mark.parametrize("names,printed", [
+    (["k1", "k10", "k2"], True),      # k1 is a substring of k10
+    (["a", "a", "b"], True),          # repeated name
+    (["a", None, "b"], False),        # a tube without a design name
+])
+def test_tube_names_fall_back_to_fixed_width(names, printed, capsys):
+    from tfscreen.simulate.raw_output import _tube_names
+    tubes = pd.DataFrame({"design_sample": names})
+    assert _tube_names(tubes) == sample_names(3)
+    out = capsys.readouterr().out
+    assert ("cannot name count files uniquely" in out) == printed
+
+
+def test_tube_names_without_design_column():
+    from tfscreen.simulate.raw_output import _tube_names
+    assert _tube_names(pd.DataFrame({"x": [1, 2]})) == sample_names(2)
diff --git a/tests/tfscreen/simulate/test_report_cfu0_cli.py b/tests/tfscreen/simulate/test_report_cfu0_cli.py
deleted file mode 100644
index d1d4edb9..00000000
--- a/tests/tfscreen/simulate/test_report_cfu0_cli.py
+++ /dev/null
@@ -1,191 +0,0 @@
-"""Tests for tfs-report-cfu0 CLI (report_cfu0)."""
-
-import numpy as np
-import pandas as pd
-import pytest
-from unittest.mock import patch
-
-from tfscreen.simulate.scripts.report_cfu0_cli import (
-    report_cfu0,
-    _classify_genotypes,
-)
-from tfscreen.util.cli.generalized_main import generalized_main
-
-
-# ---------------------------------------------------------------------------
-# _classify_genotypes
-# ---------------------------------------------------------------------------
-
-def test_classify_genotypes_basic_classes():
-    library_df = pd.DataFrame({
-        "genotype": ["wt", "wt", "A1V", "A1V/B2C", "C3D", "wt"],
-        "library_origin": ["single-1", "double-1-2", "single-1", "double-1-2",
-                           "spiked", "spiked"],
-    })
-    result = _classify_genotypes(library_df)
-    assert result["wt"] == "wt"
-    assert result["A1V"] == "single"
-    assert result["A1V/B2C"] == "double"
-    assert result["C3D"] == "spiked"
-
-
-def test_classify_genotypes_wt_beats_spiked():
-    """A literal 'wt' genotype is always classified 'wt', even if it also
-    appears under a 'spiked' library_origin."""
-    library_df = pd.DataFrame({
-        "genotype": ["wt", "wt"],
-        "library_origin": ["single-1", "spiked"],
-    })
-    result = _classify_genotypes(library_df)
-    assert result["wt"] == "wt"
-
-
-def test_classify_genotypes_triple_mutant_is_other():
-    library_df = pd.DataFrame({
-        "genotype": ["A1V/B2C/D3E"],
-        "library_origin": ["double-1-2"],
-    })
-    result = _classify_genotypes(library_df)
-    assert result["A1V/B2C/D3E"] == "other"
-
-
-# ---------------------------------------------------------------------------
-# report_cfu0
-# ---------------------------------------------------------------------------
-
-def _make_library_and_phenotype():
-    library_df = pd.DataFrame({
-        "genotype": ["wt", "A1V", "A1V/B2C", "C3D"],
-        "library_origin": ["single-1", "single-1", "double-1-2", "spiked"],
-    })
-    phenotype_df = pd.DataFrame({"genotype": ["wt", "A1V", "A1V/B2C", "C3D"]})
-    theta_df = pd.DataFrame({"genotype": ["wt", "A1V", "A1V/B2C", "C3D"]})
-    params_df = pd.DataFrame({
-        "genotype": ["wt", "A1V", "A1V/B2C", "C3D"],
-        "dk_geno": [0.0, 0.0, 0.0, 0.0],
-        "activity": [1.0, 1.0, 1.0, 1.0],
-    })
-    return library_df, phenotype_df, theta_df, params_df
-
-
-def _make_selection_experiment_result(rep):
-    """Fake one replicate's (sample_df, counts_df), with a distinct ln_cfu_0
-    per genotype so we can check pooling/averaging."""
-    sample_df = pd.DataFrame(
-        [{"sample": 0, "replicate": rep, "library": "kanR"}],
-        index=[0],
-    )
-    counts_df = pd.DataFrame([
-        {"sample": 0, "genotype": "wt", "ln_cfu_0": 14.0, "counts": 100},
-        {"sample": 0, "genotype": "A1V", "ln_cfu_0": 8.0, "counts": 100},
-        {"sample": 0, "genotype": "A1V/B2C", "ln_cfu_0": 1.4, "counts": 100},
-        {"sample": 0, "genotype": "C3D", "ln_cfu_0": -np.inf, "counts": 0},
-    ])
-    return sample_df, counts_df
-
-
-def test_report_cfu0_prints_expected_classes(capsys):
-    library_df, phenotype_df, theta_df, params_df = _make_library_and_phenotype()
-
-    call_count = {"n": 0}
-
-    def fake_selection_experiment(cf, lib_df, pheno_df):
-        call_count["n"] += 1
-        rep = pheno_df["replicate"].iloc[0]
-        return _make_selection_experiment_result(rep)
-
-    with patch("tfscreen.util.read_yaml", return_value={"seed": 0, "growth": {}}), \
-         patch("tfscreen.simulate.scripts.report_cfu0_cli.library_prediction",
-               return_value=(library_df, phenotype_df, theta_df, params_df, None)), \
-         patch("tfscreen.simulate.scripts.report_cfu0_cli.selection_experiment",
-               side_effect=fake_selection_experiment):
-        report_cfu0("config.yaml", num_replicates=2)
-
-    assert call_count["n"] == 2
-
-    out = capsys.readouterr().out
-    assert "wt" in out
-    assert "single" in out
-    assert "double" in out
-    assert "spiked" in out
-
-
-def test_report_cfu0_excludes_infinite_ln_cfu0_from_mean(capsys):
-    """C3D never survives transformation (-inf ln_cfu_0 in every replicate);
-    its class ('spiked') should report mean_n_observed == 0 and no crash from
-    averaging -inf."""
-    library_df, phenotype_df, theta_df, params_df = _make_library_and_phenotype()
-
-    def fake_selection_experiment(cf, lib_df, pheno_df):
-        rep = pheno_df["replicate"].iloc[0]
-        return _make_selection_experiment_result(rep)
-
-    with patch("tfscreen.util.read_yaml", return_value={"seed": 0, "growth": {}}), \
-         patch("tfscreen.simulate.scripts.report_cfu0_cli.library_prediction",
-               return_value=(library_df, phenotype_df, theta_df, params_df, None)), \
-         patch("tfscreen.simulate.scripts.report_cfu0_cli.selection_experiment",
-               side_effect=fake_selection_experiment):
-        report_cfu0("config.yaml", num_replicates=3)
-
-    out = capsys.readouterr().out
-    spiked_line = [l for l in out.splitlines() if l.startswith("spiked")][0]
-    fields = spiked_line.split()
-    # class, n_library, mean_n_observed, mean_ln_cfu0, std_ln_cfu0
-    assert fields[0] == "spiked"
-    assert fields[1] == "1"
-    assert fields[2] == "0.0"
-    assert fields[3] == "nan"
-
-
-def test_report_cfu0_seed_overrides_config():
-    library_df, phenotype_df, theta_df, params_df = _make_library_and_phenotype()
-
-    captured_seeds = []
-
-    def fake_selection_experiment(cf, lib_df, pheno_df):
-        captured_seeds.append(cf["seed"])
-        rep = pheno_df["replicate"].iloc[0]
-        return _make_selection_experiment_result(rep)
-
-    with patch("tfscreen.util.read_yaml", return_value={"seed": 99, "growth": {}}), \
-         patch("tfscreen.simulate.scripts.report_cfu0_cli.library_prediction",
-               return_value=(library_df, phenotype_df, theta_df, params_df, None)), \
-         patch("tfscreen.simulate.scripts.report_cfu0_cli.selection_experiment",
-               side_effect=fake_selection_experiment):
-        report_cfu0("config.yaml", num_replicates=2, seed=7)
-
-    # base_seed becomes 7 (override), then rep seed = base_seed*num_replicates+rep
-    assert captured_seeds == [7 * 2 + 1, 7 * 2 + 2]
-
-
-# ---------------------------------------------------------------------------
-# --seed CLI argument
-# ---------------------------------------------------------------------------
-
-def test_seed_cli_parsed_as_int():
-    captured = {}
-
-    def fake_run(config_file, num_replicates=5, seed=None):
-        captured["seed"] = seed
-
-    generalized_main(
-        fake_run,
-        argv=["config.yaml", "--seed", "42"],
-        manual_arg_types={"seed": int},
-    )
-    assert captured["seed"] == 42
-    assert isinstance(captured["seed"], int)
-
-
-def test_seed_cli_defaults_to_none():
-    captured = {}
-
-    def fake_run(config_file, num_replicates=5, seed=None):
-        captured["seed"] = seed
-
-    generalized_main(
-        fake_run,
-        argv=["config.yaml"],
-        manual_arg_types={"seed": int},
-    )
-    assert captured["seed"] is None
diff --git a/tests/tfscreen/simulate/test_run_simulation.py b/tests/tfscreen/simulate/test_run_simulation.py
index 4ebf74c8..e062ae84 100644
--- a/tests/tfscreen/simulate/test_run_simulation.py
+++ b/tests/tfscreen/simulate/test_run_simulation.py
@@ -117,3 +117,34 @@ def test_run_simulation_no_output(mocker, mock_config, mock_result_dfs):
     
     mock_setup.assert_called_once_with(None, "tfscreen_")
     assert pd.DataFrame.to_csv.call_count == 0
+
+
+def test_run_simulation_subsample_simulates_the_subsampled_design(
+        mocker, mock_result_dfs, tmp_path):
+    lib_df, pheno_df, theta_df, params_df, sample_df, counts_df = mock_result_dfs
+    cfg = {"library_subsample": {"single": 1}, "total_num_reads": 100}
+    cfg_path = tmp_path / "config.yaml"
+
+    mocker.patch("tfscreen.util.read_yaml", return_value=cfg)
+    mocker.patch("tfscreen.simulate.run_simulation.has_subsample",
+                 return_value=True)
+    sub_lib = pd.DataFrame({"genotype": ["wt"]})
+    mock_build = mocker.patch("tfscreen.simulate.run_simulation.build_library",
+                              return_value=(sub_lib, "summary"))
+    mocker.patch("tfscreen.simulate.run_simulation.subsample_design",
+                 return_value={"total_num_reads": 50, "kept_pool_share": 0.5})
+    mock_lib_pred = mocker.patch(
+        "tfscreen.simulate.run_simulation.library_prediction",
+        return_value=(lib_df, pheno_df, theta_df, params_df, None),
+    )
+    mock_sel = mocker.patch("tfscreen.simulate.run_simulation.selection_experiment",
+                            return_value=(sample_df, counts_df))
+
+    run_simulation(str(cfg_path), None)
+
+    mock_build.assert_called_once_with(cfg, base_dir=str(tmp_path))
+    sim_cf = mock_lib_pred.call_args.args[0]
+    assert sim_cf == {"library_subsample": {"single": 1}, "total_num_reads": 50}
+    assert mock_lib_pred.call_args.kwargs["library_df"] is sub_lib
+    assert cfg["total_num_reads"] == 100          # the config is not changed
+    assert mock_sel.call_args.args[0] is sim_cf
diff --git a/tests/tfscreen/simulate/test_sample_theta.py b/tests/tfscreen/simulate/test_sample_theta.py
index a55c7605..d458f9f4 100644
--- a/tests/tfscreen/simulate/test_sample_theta.py
+++ b/tests/tfscreen/simulate/test_sample_theta.py
@@ -259,7 +259,8 @@ def test_stratified_output_shapes():
     binding_sample_df = pd.DataFrame({"titrant_conc": np.linspace(0, 1, n_binding_concs)})
     growth_sample_df  = pd.DataFrame({"titrant_conc": np.linspace(0, 1, n_growth_concs)})
 
-    def build_sim_data_side(library_df, sample_df, thermo_data=None, skip_pairs=False):
+    def build_sim_data_side(library_df, sample_df, thermo_data=None, skip_pairs=False,
+                            theta_component=None, thermo_options=None):
         n_conc = len(sample_df["titrant_conc"].unique())
         return _make_stratified_sim_data(n_conc)
 
@@ -296,7 +297,8 @@ def test_stratified_pool_size_calls():
     binding_sample_df = pd.DataFrame({"titrant_conc": np.linspace(0, 1, n_binding_concs)})
     growth_sample_df  = pd.DataFrame({"titrant_conc": [0.0, 1.0]})
 
-    def build_sim_data_side(library_df, sample_df, thermo_data=None, skip_pairs=False):
+    def build_sim_data_side(library_df, sample_df, thermo_data=None, skip_pairs=False,
+                            theta_component=None, thermo_options=None):
         return _make_stratified_sim_data(len(sample_df["titrant_conc"].unique()))
 
     with patch("tfscreen.simulate.sample_theta.model_registry",
@@ -337,7 +339,8 @@ def fake_simulate(name, data, sim_priors, rng_key):
     binding_sample_df = pd.DataFrame({"titrant_conc": np.linspace(0, 1, n_binding_concs)})
     growth_sample_df  = pd.DataFrame({"titrant_conc": [0.0, 1.0]})
 
-    def build_sim_data_side(library_df, sample_df, thermo_data=None, skip_pairs=False):
+    def build_sim_data_side(library_df, sample_df, thermo_data=None, skip_pairs=False,
+                            theta_component=None, thermo_options=None):
         return _make_stratified_sim_data(len(sample_df["titrant_conc"].unique()))
 
     with patch("tfscreen.simulate.sample_theta.model_registry",
@@ -352,3 +355,26 @@ def build_sim_data_side(library_df, sample_df, thermo_data=None, skip_pairs=Fals
 
     assert not simulate_called, "simulate() must not be called from sample_theta_stratified"
     assert mock_module.run_model.call_count == 2 * pool_size
+
+
+def test_per_state_truth_conditions_accepts_integer_key():
+    """An integer seed gives the same per-state truth as its PRNGKey."""
+    import jax
+    import types
+    from tfscreen.simulate.sample_theta import _per_state_truth_conditions
+
+    proj = np.array([[1.0, -1.0], [0.0, 1.0]])
+    module = types.SimpleNamespace(DLNK_PROJ=proj)
+    trace = {
+        "theta_dlnK": {"type": "sample", "value": np.zeros((3, 2))},
+        "theta_ddG_sigma": {"type": "sample", "value": np.array([0.5, 2.0])},
+        "theta_ddG_prior_mean": {"type": "deterministic",
+                                 "value": np.arange(6.0).reshape(3, 2)},
+    }
+    ddG_int, cond_int = _per_state_truth_conditions(module, trace, {}, 11)
+    ddG_key, cond_key = _per_state_truth_conditions(
+        module, trace, {}, jax.random.PRNGKey(11))
+    np.testing.assert_array_equal(ddG_int, ddG_key)
+    np.testing.assert_allclose(cond_int["theta_dlnK"], ddG_int @ proj.T,
+                               rtol=1e-6)
+    np.testing.assert_array_equal(cond_int["theta_ddG_sigma"], [0.5, 2.0])
diff --git a/tests/tfscreen/simulate/test_selection_experiment.py b/tests/tfscreen/simulate/test_selection_experiment.py
index 1bebdf2b..61ab254f 100644
--- a/tests/tfscreen/simulate/test_selection_experiment.py
+++ b/tests/tfscreen/simulate/test_selection_experiment.py
@@ -1,3 +1,6 @@
+import copy
+import os
+
 import pytest
 import pandas as pd
 import numpy as np
@@ -16,12 +19,15 @@
     _sim_plasmid_probabilities,
     _sim_index_hop,
     _sim_transform,
+    _sim_transform_collapsed,
     _sim_transform_and_mix,
     _sim_growth,
-    MULTI_PLASMID_COMBINE_FCNS,
+    _cell_kt,
+    _check_cell_components,
     SIMULATE_KNOWN_KEYS,
     _sim_sequencing,
     _calc_genotype_cfu0,
+    _plasmid_shares,
     _compute_kt,
     _simulate_library_group,
     selection_experiment
@@ -54,8 +60,9 @@ def base_config() -> dict:
             "libA": 0.6,
             "libB": 0.4,
         },
-        "multi_plasmid_combine_fcn": {
-            "test_selection": "mean"
+        "growth": {
+            "M9": {"model": "linear", "b": 0.09, "m": 0.0},
+            "M9 + Ab": {"model": "linear", "b": 0.5, "m": 0.6},
         },
         "condition_selector": [
             "titrant_name", "titrant_conc",
@@ -76,16 +83,23 @@ def base_library_df() -> pd.DataFrame:
 
 @pytest.fixture
 def base_phenotype_df(base_library_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Phenotypes consistent with base_config["growth"]: k = b + m*theta + dk_geno.
+    """
     genotypes = sorted(list(pd.unique(base_library_df["genotype"])))
     conditions = [
         {"titrant_name": "IPTG", "titrant_conc": 10.0},
         {"titrant_name": "IPTG", "titrant_conc": 100.0},
     ]
-    base_growth_rates = {"A1V": 0.8, "A2V": 1.0, "A3V": 1.2, "A4V": 0.6}
-    
+    theta_by_geno = {"A1V": (0.2, 0.1), "A2V": (0.9, 0.5),
+                     "A3V": (0.6, 0.3), "A4V": (0.4, 0.05)}
+    dk_by_geno = {"A1V": 0.01, "A2V": -0.02, "A3V": 0.0, "A4V": 0.005}
+
     records = []
     for g in genotypes:
-        for c in conditions:
+        for i, c in enumerate(conditions):
+            theta = theta_by_geno[g][i]
+            dk = dk_by_geno[g]
             record = {
                 "genotype": g,
                 "replicate": 1,
@@ -94,10 +108,11 @@ def base_phenotype_df(base_library_df: pd.DataFrame) -> pd.DataFrame:
                 "t_pre": 4.0,
                 "condition_sel": "M9 + Ab",
                 "t_sel": 18.0,
-                "k_pre": 0.1,
-                "k_sel": base_growth_rates[g] * (1 + c["titrant_conc"] / 50.0),
-                "dk_geno": 0.01,
-                "theta": 1.0,
+                "k_pre": 0.09 + dk,
+                "k_sel": 0.5 + 0.6*theta + dk,
+                "dk_geno": dk,
+                "activity": 1.0,
+                "theta": theta,
                 **c
             }
             records.append(record)
@@ -248,26 +263,60 @@ def test_check_lib_spec_success(base_config: dict,
     _check_lib_spec(cf_copy, lib_df_copy, pheno_df_copy)
     assert True # Indicates successful run without errors
 
-def test_check_lib_spec_sets_defaults(base_config: dict, 
-                                      base_library_df: pd.DataFrame, 
-                                      base_phenotype_df: pd.DataFrame):
-    """
-    Tests that the function correctly sets the default for the
-    'multi_plasmid_combine_fcn' if it is not in the config.
-    """
-    cf_copy = base_config.copy()
-    lib_df_copy = base_library_df.copy()
-    pheno_df_copy = base_phenotype_df.copy()
+def test_check_cf_rejects_multi_plasmid_combine_fcn(base_config: dict):
+    """The removed key fails with a message naming its replacements."""
+    cf = dict(base_config)
+    cf["multi_plasmid_combine_fcn"] = {"test_selection": "mean"}
+    with pytest.raises(ValueError, match="congression_theta_rule"):
+        _check_cf(cf)
+
+
+def test_check_cf_congression_rule_defaults(base_config: dict):
+    """Omitted congression rules default to homodimer theta and dilution dk."""
+    cf = _check_cf(dict(base_config))
+    assert cf["congression_theta_rule"] == "homodimer"
+    assert cf["congression_dk_rule"] == "dilution"
 
-    # Remove the key to test default-setting behavior
-    cf_copy.pop("multi_plasmid_combine_fcn")
 
-    result_cf = _check_lib_spec(cf_copy, lib_df_copy, pheno_df_copy)
+def test_check_cf_dk_alpha_default_none(base_config: dict):
+    cf = _check_cf(dict(base_config))
+    assert cf["congression_dk_alpha"] is None
+
+
+def test_check_cf_softmin_takes_alpha(base_config: dict):
+    cf = dict(base_config)
+    cf["congression_dk_rule"] = "softmin"
+    cf["congression_dk_alpha"] = 100
+    cf = _check_cf(cf)
+    assert cf["congression_dk_alpha"] == 100.0
+    assert isinstance(cf["congression_dk_alpha"], float)
+
+
+@pytest.mark.parametrize("alpha", [None, 0, -1.0, float("inf")])
+def test_check_cf_softmin_needs_positive_finite_alpha(base_config: dict, alpha):
+    cf = dict(base_config)
+    cf["congression_dk_rule"] = "softmin"
+    cf["congression_dk_alpha"] = alpha
+    with pytest.raises(ValueError, match="congression_dk_alpha"):
+        _check_cf(cf)
+
+
+@pytest.mark.parametrize("rule", ["dilution", "min"])
+def test_check_cf_other_dk_rules_refuse_alpha(base_config: dict, rule):
+    cf = dict(base_config)
+    cf["congression_dk_rule"] = rule
+    cf["congression_dk_alpha"] = 100.0
+    with pytest.raises(ValueError, match="only used by"):
+        _check_cf(cf)
+
+
+@pytest.mark.parametrize("key", ["congression_theta_rule", "congression_dk_rule"])
+def test_check_cf_bad_congression_rule(base_config: dict, key):
+    cf = dict(base_config)
+    cf[key] = "not_a_rule"
+    with pytest.raises(ValueError, match=key):
+        _check_cf(cf)
 
-    assert "multi_plasmid_combine_fcn" in result_cf
-    # 'test_selection' is the library name in base_phenotype_df
-    expected_default = {"test_selection": "mean"}
-    assert result_cf["multi_plasmid_combine_fcn"] == expected_default
 
 @pytest.mark.parametrize("modification_lambda, match_error", [
     # Genotype in library_df is missing from phenotype_df
@@ -286,9 +335,7 @@ def test_check_lib_spec_sets_defaults(base_config: dict,
     
     # Ratios in library_mixture sum to zero
     (lambda cf, lib, pheno: cf.update({"library_mixture": {"libA": 0, "libB": 0}}), "cannot be zero"),
-    
-    # Invalid multi_plasmid_combine_fcn name
-    (lambda cf, lib, pheno: cf["multi_plasmid_combine_fcn"].update({"test_selection": "bad_fcn"}), "not recognized"),
+
 ])
 def test_check_lib_spec_failures(base_config: dict, 
                                  base_library_df: pd.DataFrame, 
@@ -498,11 +545,13 @@ def test_sim_transform_and_mix(mocker, base_library_df: pd.DataFrame,
     # FIX: Convert to dict, as the code now iterates over .items()
     lib_origin_dict = dict(list(df.groupby(groupby_key)))
     
-    mock_return_libA = (np.ones((2, 2)), np.zeros((2, 2), dtype=bool))
-    mock_return_libB = (np.ones((3, 1)), np.zeros((3, 1), dtype=bool))
+    mock_return_libA = (np.ones((2, 2)), np.zeros((2, 2), dtype=bool),
+                        np.ones(2))
+    mock_return_libB = (np.ones((3, 1)), np.zeros((3, 1), dtype=bool),
+                        np.ones(3))
 
     mock_sim_transform = mocker.patch(
-        "tfscreen.simulate.selection_experiment._sim_transform",
+        "tfscreen.simulate.selection_experiment._sim_transform_collapsed",
         side_effect=[mock_return_libA, mock_return_libB]
     )
     # We don't necessarily need to mock vstack_padded unless strictly testing flow
@@ -543,11 +592,14 @@ def test_sim_transform_and_mix_invariant_to_transform_sizes(rng: Generator):
     where per-cell weights weren't normalized by the number of transformants
     drawn, letting transform_sizes leak into the mixing ratio.
     """
-    df = pd.DataFrame({
-        "library_origin": ["A"] * 5 + ["B"] * 5,
-        "genotype": [f"A{i}" for i in range(5)] + [f"B{i}" for i in range(5)],
-    })
-    df["probs"] = 0.2
+    # every genotype in every origin (zero weight outside its own), as
+    # selection_experiment lays the library out, so genotype indices are
+    # global: A's genotypes are 0-4, B's 5-9
+    genos = [f"A{i}" for i in range(5)] + [f"B{i}" for i in range(5)]
+    df = pd.DataFrame({"library_origin": ["A"] * 10 + ["B"] * 10,
+                       "genotype": genos * 2})
+    df["probs"] = np.r_[np.full(5, 0.2), np.zeros(5),
+                        np.zeros(5), np.full(5, 0.2)]
     lib_origin_dict = dict(list(df.groupby("library_origin")))
 
     library_mixture = {"A": 1.0, "B": 1.0}
@@ -557,101 +609,199 @@ def test_sim_transform_and_mix_invariant_to_transform_sizes(rng: Generator):
         {"A": 1000, "B": 10000},
         {"A": 1000, "B": 100000},
     ):
-        _, _, probs = _sim_transform_and_mix(
+        trans, _, probs = _sim_transform_and_mix(
             lib_origin_dict, transform_sizes, library_mixture, None, rng
         )
-        n_a = transform_sizes["A"]
-        np.testing.assert_allclose(probs[:n_a].sum(), 0.5, atol=1e-9)
-        np.testing.assert_allclose(probs[n_a:].sum(), 0.5, atol=1e-9)
+        from_a = trans[:, 0] < 5
+        np.testing.assert_allclose(probs[from_a].sum(), 0.5, atol=1e-9)
+        np.testing.assert_allclose(probs[~from_a].sum(), 0.5, atol=1e-9)
 
 
 # ----------------------------------------------------------------------------
 # test _sim_growth
 # ----------------------------------------------------------------------------
 
-def test_sim_growth_single_plasmid():
-    """
-    Tests a simple case where all cells have exactly one plasmid.
-    """
-    # 3 cells, each with one plasmid (genotypes 0, 1, 2)
-    transformants = np.array([[0], [1], [2]])
-    trans_mask = np.array([[False], [False], [False]])
+def test_sim_growth():
+    """Final CFU is initial CFU times exp(cell k*t)."""
     trans_freq = np.array([0.2, 0.3, 0.5])
     total_cfu0 = 1.0e6
-    
-    # 3 genotypes, 2 conditions
-    genotype_vs_kt = np.array([
-        [0.1, 0.5], # Genotype 0 k*t values
-        [0.2, 0.6], # Genotype 1 k*t values
-        [0.3, 0.7], # Genotype 2 k*t values
-    ])
+    cell_kt = np.array([[0.1, 0.5],
+                        [0.2, 0.6],
+                        [0.3, 0.7]])
 
-    # Manually calculate expected CFU
-    initial_cfus = total_cfu0 * trans_freq
-    growth_factors = np.exp(genotype_vs_kt)
-    expected_cfu = initial_cfus[:, np.newaxis] * growth_factors
-
-    result_cfu = _sim_growth(
-        transformants, trans_mask, trans_freq, genotype_vs_kt, total_cfu0, "mean"
-    )
+    expected_cfu = (total_cfu0 * trans_freq)[:, np.newaxis] * np.exp(cell_kt)
+    result_cfu = _sim_growth(cell_kt, trans_freq, total_cfu0)
 
     np.testing.assert_allclose(result_cfu, expected_cfu)
 
-@pytest.mark.parametrize("combine_fcn_name", MULTI_PLASMID_COMBINE_FCNS.keys())
-def test_sim_growth_multi_plasmid(combine_fcn_name: str):
+
+# ----------------------------------------------------------------------------
+# test _cell_kt
+# ----------------------------------------------------------------------------
+
+def _cell_kt_inputs(growth_transition=None):
     """
-    Tests that multi-plasmid kt values are combined correctly for all
-    supported combination functions.
+    Three genotypes, two conditions, linear growth k = b + m*A*theta + dk.
+    Pre-growth has m = 0, so only selection depends on theta.
     """
-    # 2 cells: cell 0 has 2 plasmids (geno0, A1V), cell 1 has 1 (A2V)
-    transformants = np.array([[0, 1], [2, 0]]) # Second plasmid for cell 1 is masked
-    trans_mask = np.array([[False, False], [False, True]])
-    trans_freq = np.array([0.4, 0.6])
-    total_cfu0 = 1.0e6
+    cf = {
+        "growth": {"pre": {"model": "linear", "b": 0.1, "m": 0.0},
+                   "sel": {"model": "linear", "b": 0.2, "m": 0.5}},
+        "theta_rescale": "passthrough",
+        "growth_transition": growth_transition,
+        "congression_theta_rule": "max",
+        "congression_dk_rule": "dilution",
+    }
+    theta = np.array([[0.1, 0.9],    # g0
+                      [0.8, 0.2],    # g1
+                      [0.5, 0.5]])   # g2
+    activity = np.array([1.0, 0.5, 1.0])
+    dk = np.array([0.0, -0.04, 0.02])
+    condition_info = pd.DataFrame({"condition_pre": ["pre", "pre"],
+                                   "condition_sel": ["sel", "sel"],
+                                   "t_pre": [1.0, 1.0],
+                                   "t_sel": [10.0, 10.0]})
+
+    def kt(theta_, activity_, dk_):
+        k_pre = 0.1 + dk_
+        k_sel = 0.2 + 0.5*activity_*theta_ + dk_
+        return k_pre*1.0 + k_sel*10.0
+
+    genotype_vs_kt = np.array([[kt(theta[g, c], activity[g], dk[g])
+                                for c in range(2)] for g in range(3)])
+    return cf, theta, activity, dk, condition_info, genotype_vs_kt, kt
+
+
+def test_cell_kt_single_plasmid_cells_match_genotypes():
+    cf, theta, activity, dk, info, gkt, _ = _cell_kt_inputs()
+    transformants = np.array([[0], [1], [2]])
+    trans_mask = np.zeros((3, 1), dtype=bool)
+    result = _cell_kt(transformants, trans_mask, gkt, theta, activity, dk,
+                      info, np.zeros(2), cf)
+    np.testing.assert_allclose(result, gkt)
 
-    genotype_vs_kt = np.array([
-        [0.1, 1.0], # Genotype 0
-        [0.5, 0.8], # Genotype 1
-        [0.3, 0.6], # Genotype 2
-    ])
 
-    # Calculate the expected effective kt for each cell
-    # Cell 0 combines kt from genotypes 0 and 1
-    kt_cell0 = genotype_vs_kt[[0, 1], :]
-    # Cell 1 only has kt from genotype 2
-    kt_cell1 = genotype_vs_kt[[2], :]
-    
-    # Use the actual numpy functions to get the expected combined kt values
-    if combine_fcn_name == "gmean":
-        from scipy.stats import gmean
-        expected_combined_kt_cell0 = gmean(kt_cell0, axis=0)
+def test_cell_kt_co_transformed_cell_uses_cell_physics():
+    """
+    A {g0, g1} cell: theta and activity from the higher-theta plasmid at each
+    condition, dk diluted by share. Not any combination of the two
+    genotypes' k*t.
+    """
+    cf, theta, activity, dk, info, gkt, kt = _cell_kt_inputs()
+    transformants = np.array([[0, 1], [2, 0]])
+    trans_mask = np.array([[False, False], [False, True]])
+    tube_kt = np.array([0.01, -0.02])
+
+    result = _cell_kt(transformants, trans_mask, gkt + tube_kt, theta,
+                      activity, dk, info, tube_kt, cf)
+
+    dk_cell = 0.5*dk[0] + 0.5*dk[1]
+    # condition 0: g1 has the higher theta (0.8, activity 0.5)
+    # condition 1: g0 has the higher theta (0.9, activity 1.0)
+    expected_cell0 = [kt(0.8, 0.5, dk_cell) + tube_kt[0],
+                      kt(0.9, 1.0, dk_cell) + tube_kt[1]]
+    np.testing.assert_allclose(result[0], expected_cell0)
+    # Single-plasmid cell keeps its genotype's k*t (tube noise included)
+    np.testing.assert_allclose(result[1], gkt[2] + tube_kt)
+
+
+@pytest.mark.parametrize("dk_rule,alpha", [("min", None), ("softmin", 100.0)])
+def test_cell_kt_dk_rule(dk_rule, alpha):
+    """The congression_dk_rule (and its alpha) sets a co-transformed cell's
+    dk_geno."""
+    cf, theta, activity, dk, info, gkt, kt = _cell_kt_inputs()
+    cf["congression_dk_rule"] = dk_rule
+    cf["congression_dk_alpha"] = alpha
+    transformants = np.array([[0, 1]])
+    trans_mask = np.zeros((1, 2), dtype=bool)
+    result = _cell_kt(transformants, trans_mask, gkt, theta, activity, dk,
+                      info, np.zeros(2), cf)
+    if dk_rule == "min":
+        dk_cell = min(dk[0], dk[1])
     else:
-        # Get the numpy/numpy.ma function (e.g., np.mean, np.max)
-        np_fcn = MULTI_PLASMID_COMBINE_FCNS[combine_fcn_name]
-        expected_combined_kt_cell0 = np_fcn(kt_cell0, axis=0)
+        dk_cell = -np.log(0.5*np.exp(-alpha*dk[0])
+                          + 0.5*np.exp(-alpha*dk[1])) / alpha
+    np.testing.assert_allclose(result[0], [kt(0.8, 0.5, dk_cell),
+                                           kt(0.9, 1.0, dk_cell)])
+
+
+def test_cell_kt_duplicate_genotype_matches_genotype():
+    """A cell with two copies of one genotype grows like that genotype."""
+    cf, theta, activity, dk, info, gkt, _ = _cell_kt_inputs()
+    transformants = np.array([[1, 1]])
+    trans_mask = np.zeros((1, 2), dtype=bool)
+    result = _cell_kt(transformants, trans_mask, gkt, theta, activity, dk,
+                      info, np.zeros(2), cf)
+    np.testing.assert_allclose(result[0], gkt[1])
+
+
+def test_cell_kt_applies_growth_transition():
+    """Co-transformed cells go through the growth transition model too."""
+    gt = [{"condition_pre": "pre", "model": "memory",
+           "tau0": 3.0, "k1": 1.0, "k2": 1.0}]
+    cf, theta, activity, dk, info, gkt, _ = _cell_kt_inputs(growth_transition=gt)
+
+    # Genotype k*t with the transition model, via the shared path
+    rows = []
+    for g in range(3):
+        for c in range(2):
+            rows.append({"k_pre": 0.1 + dk[g],
+                         "k_sel": 0.2 + 0.5*activity[g]*theta[g, c] + dk[g],
+                         "t_pre": 1.0, "t_sel": 10.0, "theta": theta[g, c],
+                         "condition_pre": "pre"})
+    gkt = _compute_kt(pd.DataFrame(rows), gt).reshape(3, 2)
+
+    # A {g2, g2} cell must reproduce g2's transition-model k*t exactly.
+    result = _cell_kt(np.array([[2, 2]]), np.zeros((1, 2), dtype=bool), gkt,
+                      theta, activity, dk, info, np.zeros(2), cf)
+    np.testing.assert_allclose(result[0], gkt[2])
+
+
+def test_cell_kt_nan_theta_gives_zero_growth():
+    cf, theta, activity, dk, info, gkt, _ = _cell_kt_inputs()
+    theta = theta.copy()
+    theta[1, 0] = np.nan
+    result = _cell_kt(np.array([[0, 1]]), np.zeros((1, 2), dtype=bool), gkt,
+                      theta, activity, dk, info, np.zeros(2), cf)
+    assert result[0, 0] == 0.0
+    assert np.isfinite(result[0, 1]) and result[0, 1] != 0.0
+
+
+def test_cell_kt_no_cells():
+    cf, theta, activity, dk, info, gkt, _ = _cell_kt_inputs()
+    result = _cell_kt(np.empty((0, 0), dtype=int), np.empty((0, 0), dtype=bool),
+                      gkt, theta, activity, dk, info, np.zeros(2), cf)
+    assert result.shape == (0, 2)
 
-    effective_kt = np.vstack([expected_combined_kt_cell0, kt_cell1])
-    
-    # Calculate final expected CFU
-    initial_cfus = total_cfu0 * trans_freq
-    expected_cfu = initial_cfus[:, np.newaxis] * np.exp(effective_kt)
-    
-    result_cfu = _sim_growth(
-        transformants, trans_mask, trans_freq, genotype_vs_kt, total_cfu0, combine_fcn_name
-    )
 
-    np.testing.assert_allclose(result_cfu, expected_cfu)
+# ----------------------------------------------------------------------------
+# test _check_cell_components
+# ----------------------------------------------------------------------------
+
+def test_check_cell_components_passes(base_config, base_phenotype_df):
+    _check_cell_components(_check_cf(dict(base_config)), base_phenotype_df)
+
+
+def test_check_cell_components_needs_growth(base_config, base_phenotype_df):
+    cf = _check_cf(dict(base_config))
+    cf.pop("growth")
+    with pytest.raises(ValueError, match="'growth' block"):
+        _check_cell_components(cf, base_phenotype_df)
+
+
+def test_check_cell_components_needs_columns(base_config, base_phenotype_df):
+    cf = _check_cf(dict(base_config))
+    with pytest.raises(ValueError, match="activity"):
+        _check_cell_components(cf, base_phenotype_df.drop(columns=["activity"]))
+
+
+def test_check_cell_components_mismatch(base_config, base_phenotype_df):
+    cf = _check_cf(dict(base_config))
+    df = base_phenotype_df.copy()
+    df.loc[0, "k_sel"] += 0.1
+    with pytest.raises(ValueError, match="k_sel"):
+        _check_cell_components(cf, df)
 
-def test_sim_growth_invalid_fcn():
-    """
-    Tests that the function raises a ValueError for an invalid combine function.
-    """
-    # We can use dummy arrays as the function will fail before using them
-    dummy_array = np.array([[]])
-    with pytest.raises(ValueError, match="not recognized"):
-        _sim_growth(dummy_array, dummy_array, dummy_array, 
-                    dummy_array, 0, "not_a_real_function")
-        
 
 # ----------------------------------------------------------------------------
 # test _sim_sequencing
@@ -725,6 +875,55 @@ def test_sim_sequencing_single_survivor(rng: Generator):
     expected_counts = np.array([[reads_per_sample], [0]])
     np.testing.assert_array_equal(result, expected_counts)
 
+def test_plasmid_shares():
+    """Each valid slot is 1/n of its cell; masked slots are 0."""
+    trans_mask = np.array([[False, True, True],
+                           [False, False, True],
+                           [False, False, False]])
+    expected = np.array([[1.0, 0.0, 0.0],
+                         [0.5, 0.5, 0.0],
+                         [1/3, 1/3, 1/3]])
+    np.testing.assert_allclose(_plasmid_shares(trans_mask), expected)
+    np.testing.assert_allclose(_plasmid_shares(trans_mask).sum(axis=1), 1.0)
+
+
+def test_plasmid_shares_empty():
+    """An empty transformant array gives an empty share array."""
+    result = _plasmid_shares(np.empty((0, 0), dtype=bool))
+    assert result.shape == (0, 0)
+
+
+def test_sim_sequencing_splits_cell_among_plasmids(mocker):
+    """
+    A co-transformed cell's abundance is split among its plasmids, so it
+    contributes one cell's worth of reads in total.
+    """
+    # Cell 0: {g0}, abundance 100. Cell 1: {g0, g1}, abundance 100.
+    transformants = np.array([[0, 0], [0, 1]])
+    trans_mask = np.array([[False, True], [False, False]])
+    trans_cfu = np.array([[100.0], [100.0]])
+
+    # g0 = 100 + 50, g1 = 50 -> probabilities 0.75 / 0.25.
+    rng = mocker.Mock()
+    rng.choice.return_value = np.zeros(10, dtype=int)
+    _sim_sequencing(transformants, trans_mask, trans_cfu, 2, 10, rng)
+
+    np.testing.assert_allclose(rng.choice.call_args.kwargs["p"], [0.75, 0.25])
+
+
+def test_sim_sequencing_duplicate_genotype_in_cell(mocker):
+    """Two copies of one genotype in a cell give it the whole cell."""
+    transformants = np.array([[0, 0], [1, 0]])
+    trans_mask = np.array([[False, False], [False, True]])
+    trans_cfu = np.array([[100.0], [100.0]])
+
+    rng = mocker.Mock()
+    rng.choice.return_value = np.zeros(10, dtype=int)
+    _sim_sequencing(transformants, trans_mask, trans_cfu, 2, 10, rng)
+
+    np.testing.assert_allclose(rng.choice.call_args.kwargs["p"], [0.5, 0.5])
+
+
 # ----------------------------------------------------------------------------
 # test _calc_genotype_cfu0
 # ----------------------------------------------------------------------------
@@ -763,19 +962,17 @@ def test_calc_genotype_cfu0_multi_plasmid():
     total_cfu0 = 1.0e7
     num_genotypes = 2
 
-    # Expected logic:
+    # Expected logic: a cell splits its frequency among its plasmids.
     # Plasmids:    [g0,   g0,   g1,   g1]
     # From cells:  [c0,   c1,   c1,   c2]
-    # Cell Freqs:  [0.2,  0.5,  0.5,  0.3]
-    #
-    # Total weighted count for g0 = 0.2 + 0.5 = 0.7
-    # Total weighted count for g1 = 0.5 + 0.3 = 0.8
-    # Total weight = 0.7 + 0.8 = 1.5
+    # Share:       [1,    1/2,  1/2,  1]
+    # Weight:      [0.2,  0.25, 0.25, 0.3]
     #
-    # Final frequency for g0 = 0.7 / 1.5
-    # Final frequency for g1 = 0.8 / 1.5
-    expected_cfu0 = np.array([ (0.7/1.5) * total_cfu0,
-                               (0.8/1.5) * total_cfu0 ])
+    # g0 = 0.2 + 0.25 = 0.45
+    # g1 = 0.25 + 0.3 = 0.55
+    # Total = 1.0 (one cell's worth per cell)
+    expected_cfu0 = np.array([0.45 * total_cfu0,
+                              0.55 * total_cfu0])
     
     result = _calc_genotype_cfu0(
         transformants, trans_mask, trans_freq, total_cfu0, num_genotypes
@@ -783,6 +980,42 @@ def test_calc_genotype_cfu0_multi_plasmid():
     
     np.testing.assert_allclose(result, expected_cfu0)
     
+@pytest.mark.parametrize("lam", [None, 0.357, 1.5])
+def test_calc_genotype_cfu0_matches_design_for_any_lambda(lam):
+    """
+    With plasmid copies shared within a cell, simulated genotype abundance
+    matches the library design's pool_fraction whatever lambda is. Bulk
+    origins are transformed with lambda; the spiked origin is monoclonal.
+    """
+    from tfscreen.genetics.library_design import expected_library_composition
+
+    genotypes = ["wt", "A1V", "B2C", "S3T"]
+    weights = {"bulk": [0.4, 0.3, 0.3, 0.0],
+               "spiked": [0.5, 0.0, 0.0, 0.5]}
+    library_df = pd.DataFrame([
+        {"library_origin": origin, "genotype": g, "weight": w, "probs": w}
+        for origin, ws in weights.items() for g, w in zip(genotypes, ws)
+    ])
+    library_mixture = {"bulk": 3.0, "spiked": 1.0}
+    lib_origin_dict = dict(list(library_df.groupby(["library_origin"])))
+
+    rng = np.random.default_rng(0)
+    transformants, trans_mask, trans_freq = _sim_transform_and_mix(
+        lib_origin_dict,
+        {"bulk": 200_000, "spiked": 200_000},
+        library_mixture,
+        lam,
+        rng)
+    cfu0 = _calc_genotype_cfu0(transformants, trans_mask, trans_freq,
+                               1.0, len(genotypes))
+
+    expected = (expected_library_composition(library_df, library_mixture)
+                .set_index("genotype")
+                .loc[genotypes, "pool_fraction"]
+                .to_numpy())
+    np.testing.assert_allclose(cfu0, expected, atol=0.005)
+
+
 def test_calc_genotype_cfu0_no_transformants():
     """
     Tests the edge case where there are no successful transformations.
@@ -839,7 +1072,7 @@ def test_simulate_library_group_integration(base_config: dict,
         lib_origin_dict, # Passed as dict
         ordered_genotypes,
         reads_per_sample,
-        base_config,
+        _check_cf(dict(base_config)),
         rng
     )
     
@@ -927,7 +1160,7 @@ def test_simulate_library_group_handles_sparse_data(
         lib_origin_dict=lib_origin_dict, # Passed as dict
         ordered_genotypes=ordered_genotypes,
         reads_per_sample=reads_per_sample,
-        cf=base_config,
+        cf=_check_cf(dict(base_config)),
         rng=rng
     )
 
@@ -1350,4 +1583,329 @@ def test_check_cf_accepts_base_growth_data(base_config: dict):
     simulate/base_growth_data.py and simulate/scripts/simulate_cli.py)."""
     cf = dict(base_config)
     cf["base_growth_data"] = {"k_ref": 0.025}
-    _check_cf(cf)
\ No newline at end of file
+    _check_cf(cf)
+
+
+# =============================================================================
+# Sampling noise (roadmap step 4, planning/analysis-roadmap.md): founder
+# sampling, demographic growth, a template bottleneck with PCR jackpotting,
+# shared transformation across replicates and OD-only replicates.
+# =============================================================================
+
+# ---------------------------------------------------------------------------
+# config validation
+# ---------------------------------------------------------------------------
+
+def test_defaults_are_off(base_config):
+    cf = _check_cf(copy.deepcopy(base_config))
+    assert cf["founder_sampling"] is False
+    assert cf["demographic_growth"] is False
+    assert cf["shared_transformation"] is False
+    assert cf["pcr_template_molecules"] is None
+    assert cf["pcr_amplification_cv"] is None
+    assert cf["od600"] is None
+
+
+@pytest.mark.parametrize("update,match", [
+    ({"demographic_growth": True}, "founder_sampling"),
+    ({"pcr_amplification_cv": 0.5}, "pcr_template_molecules"),
+    ({"founder_sampling": "yes"}, "true or false"),
+    ({"pcr_template_molecules": 0}, "pcr_template_molecules"),
+    ({"od600": {"tube_volume_mL": 5.0}}, "calibration"),
+    ({"od600": {"calibration": "c.yaml", "bogus": 1}}, "Unknown keys"),
+    ({"od600": "c.yaml"}, "block"),
+])
+def test_invalid_settings(base_config, update, match):
+    cf = copy.deepcopy(base_config)
+    cf.update(update)
+    with pytest.raises(ValueError, match=match):
+        _check_cf(cf)
+
+
+def test_od600_defaults(base_config):
+    cf = copy.deepcopy(base_config)
+    cf["od600"] = {"calibration": "c.yaml"}
+    cf = _check_cf(cf)
+    assert cf["od600"] == {"calibration": "c.yaml", "tube_volume_mL": 5.0,
+                           "num_od_only_replicates": 0,
+                           "sample_cfu_from_od600": False}
+
+
+# ---------------------------------------------------------------------------
+# growth: founders and demographic noise
+# ---------------------------------------------------------------------------
+
+def _moments(kt, n0, draws, **kwargs):
+    rng = np.random.default_rng(1)
+    cell_kt = np.full((1, draws), kt)
+    out = _sim_growth(cell_kt, np.array([1.0]), n0, rng=rng, **kwargs)[0]
+    return out.mean(), out.var()
+
+
+def test_default_growth_is_deterministic():
+    cell_kt = np.array([[0.0, 1.0], [2.0, -1.0]])
+    out = _sim_growth(cell_kt, np.array([0.25, 0.75]), 100.0)
+    assert np.allclose(out, np.array([[0.25], [0.75]]) * 100 * np.exp(cell_kt))
+
+
+def test_founder_sampling_is_poisson():
+    n0, kt = 20.0, 1.5
+    mean, var = _moments(kt, n0, 200_000, founder_sampling=True)
+    fold = np.exp(kt)
+    assert mean == pytest.approx(n0 * fold, rel=0.01)
+    assert var == pytest.approx(n0 * fold ** 2, rel=0.03)
+
+
+def test_demographic_growth_doubles_variance():
+    # Poisson founders then Gamma(n0, e^kt) growth: var = 2 n0 e^(2 kt).
+    n0, kt = 20.0, 1.5
+    mean, var = _moments(kt, n0, 200_000, founder_sampling=True,
+                         demographic_growth=True)
+    fold = np.exp(kt)
+    assert mean == pytest.approx(n0 * fold, rel=0.01)
+    assert var == pytest.approx(2 * n0 * fold ** 2, rel=0.03)
+
+
+def test_demographic_death_is_binomial_thinning():
+    # Poisson(n0) founders each surviving with p: Poisson(n0 p).
+    n0, kt = 50.0, -0.7
+    mean, var = _moments(kt, n0, 200_000, founder_sampling=True,
+                         demographic_growth=True)
+    p = np.exp(kt)
+    assert mean == pytest.approx(n0 * p, rel=0.01)
+    assert var == pytest.approx(n0 * p, rel=0.03)
+
+
+def test_demographic_needs_founders():
+    with pytest.raises(ValueError, match="founder_sampling"):
+        _sim_growth(np.zeros((1, 1)), np.array([1.0]), 10.0,
+                    rng=np.random.default_rng(0), demographic_growth=True)
+
+
+def test_founders_are_independent_per_tube():
+    rng = np.random.default_rng(3)
+    out = _sim_growth(np.zeros((1, 50)), np.array([1.0]), 5.0, rng=rng,
+                      founder_sampling=True)
+    assert len(np.unique(out)) > 1
+
+
+# ---------------------------------------------------------------------------
+# sequencing: template bottleneck and jackpotting
+# ---------------------------------------------------------------------------
+
+def _count_variance_ratio(reads, templates=None, cv=None, num_geno=4000):
+    """Variance of per-genotype counts over the Poisson expectation, for
+    equally abundant single-plasmid genotypes."""
+    transformants = np.arange(num_geno)[:, None]
+    trans_mask = np.zeros_like(transformants, dtype=bool)
+    trans_cfu = np.ones((num_geno, 1))
+    counts = _sim_sequencing(transformants, trans_mask, trans_cfu, num_geno,
+                             reads, np.random.default_rng(5),
+                             pcr_template_molecules=templates,
+                             pcr_amplification_cv=cv)[:, 0]
+    return counts.var() / counts.mean()
+
+
+def test_no_bottleneck_is_poisson():
+    assert _count_variance_ratio(400_000) == pytest.approx(1.0, abs=0.1)
+
+
+@pytest.mark.parametrize("templates,cv", [(200_000, None), (200_000, 1.0),
+                                          (100_000, 0.5)])
+def test_bottleneck_inflates_variance(templates, cv):
+    # Multinomial templates, then reads from the (amplified) templates:
+    # variance / mean = 1 + (reads / templates) (1 + cv^2).
+    reads = 400_000
+    expected = 1 + (reads / templates) * (1 + (cv or 0) ** 2)
+    assert _count_variance_ratio(reads, templates, cv) == pytest.approx(
+        expected, rel=0.1)
+
+
+# ---------------------------------------------------------------------------
+# replicates: shared transformation and OD-only
+# ---------------------------------------------------------------------------
+
+def _run(cf, library_df, phenotype_df, seed, shared_state=None, sequence=True):
+    cf = copy.deepcopy(cf)
+    cf["seed"] = seed
+    return selection_experiment(cf, library_df.copy(), phenotype_df.copy(),
+                                shared_state=shared_state, sequence=sequence)
+
+
+@pytest.mark.parametrize("shared", [True, False])
+def test_shared_transformation(base_config, base_library_df,
+                               base_phenotype_df, shared):
+    cf = copy.deepcopy(base_config)
+    cf["shared_transformation"] = shared
+    state = {}
+    _, counts_1 = _run(cf, base_library_df, base_phenotype_df, 1, state)
+    _, counts_2 = _run(cf, base_library_df, base_phenotype_df, 2, state)
+    cfu0_1 = counts_1.groupby("genotype", observed=True)["ln_cfu_0"].first()
+    cfu0_2 = counts_2.groupby("genotype", observed=True)["ln_cfu_0"].first()
+    assert np.allclose(cfu0_1, cfu0_2) == shared
+
+
+def test_unsequenced_replicate(base_config, base_library_df,
+                               base_phenotype_df):
+    sample_df, counts_df = _run(base_config, base_library_df,
+                                base_phenotype_df, 1, sequence=False)
+    assert len(counts_df) == 0
+    assert len(sample_df) > 0
+    assert (sample_df["sample_cfu"] > 0).all()
+
+
+# ---------------------------------------------------------------------------
+# unassigned_read_fraction: reads that call no library genotype
+# ---------------------------------------------------------------------------
+
+@pytest.mark.parametrize("frac", [0.25, "per_library"])
+def test_unassigned_reads_go_to_unknown(base_config, base_library_df,
+                                        base_phenotype_df, frac):
+    cf = copy.deepcopy(base_config)
+    libs = base_phenotype_df["library"].unique().tolist() \
+        if "library" in base_phenotype_df else []
+    if frac == "per_library":
+        if not libs:
+            pytest.skip("fixture has no library column")
+        cf["unassigned_read_fraction"] = {lib: 0.4 for lib in libs}
+        expect = 0.4
+    else:
+        cf["unassigned_read_fraction"] = frac
+        expect = frac
+    _, base_counts = _run(base_config, base_library_df, base_phenotype_df, 3)
+    _, counts = _run(cf, base_library_df, base_phenotype_df, 3)
+
+    total = counts.groupby("sample")["counts"].sum()
+    base_total = base_counts.groupby("sample")["counts"].sum()
+    # the tube's read budget is unchanged (index hopping moves a few reads
+    # between tubes); a share of it is unassigned
+    assert total.sum() == base_total.sum()
+    np.testing.assert_allclose(total, base_total, rtol=1e-4)
+    unknown = (counts[counts["genotype"] == "__unknown__"]
+               .set_index("sample")["counts"])
+    assert np.allclose(unknown / total.loc[unknown.index], expect, atol=1e-3)
+    assert "__unknown__" not in set(base_counts["genotype"].astype(str))
+
+
+def test_unassigned_fraction_validated(base_config, base_library_df,
+                                       base_phenotype_df):
+    cf = copy.deepcopy(base_config)
+    cf["unassigned_read_fraction"] = 1.0
+    with pytest.raises(ValueError):
+        _run(cf, base_library_df, base_phenotype_df, 1)
+    cf["unassigned_read_fraction"] = {"not_a_library": 0.5}
+    with pytest.raises(ValueError, match="per library"):
+        _run(cf, base_library_df, base_phenotype_df, 1)
+
+
+# ----------------------------------------------------------------------------
+# collapsed transformation: identical cells merged into weighted rows
+# ----------------------------------------------------------------------------
+
+def test_collapsed_transform_single_plasmid_rows_are_clone_counts():
+    rng = np.random.default_rng(0)
+    probs = np.array([0.5, 0.3, 0.2, 0.0])
+    trans, mask, cells = _sim_transform_collapsed(probs, 100_000, None, rng)
+    assert cells.sum() == 100_000
+    assert len(trans) == 3                      # one row per genotype seen
+    assert (~mask).sum(axis=1).tolist() == [1, 1, 1]
+    np.testing.assert_allclose(cells / cells.sum(), probs[trans[:, 0]],
+                               atol=0.01)
+
+
+def test_collapsed_transform_matches_per_cell_composition():
+    """The congressed share and each genotype's cell-equivalents agree with
+    the per-cell transformation (``_sim_transform``)."""
+    rng = np.random.default_rng(1)
+    probs = np.array([0.4, 0.3, 0.2, 0.1])
+    n, lam = 200_000, 0.357
+    t1, m1 = _sim_transform(probs, n, lam, rng)
+    t2, m2, c2 = _sim_transform_collapsed(probs, n, lam, rng)
+    multi1 = ((~m1).sum(axis=1) > 1).mean()
+    multi2 = c2[(~m2).sum(axis=1) > 1].sum() / c2.sum()
+    assert multi2 == pytest.approx(multi1, abs=0.01)
+    s1 = _calc_genotype_cfu0(t1, m1, np.full(n, 1.0 / n), 1.0, 4)
+    s2 = _calc_genotype_cfu0(t2, m2, c2 / c2.sum(), 1.0, 4)
+    np.testing.assert_allclose(s1, s2, atol=0.005)
+
+
+def test_collapsed_transform_cap_on_congressed_cells():
+    rng = np.random.default_rng(2)
+    probs = np.full(10, 0.1)
+    trans, mask, cells = _sim_transform_collapsed(
+        probs, 1_000_000, 0.357, rng, max_congressed_cells=1000)
+    congressed = (~mask).sum(axis=1) > 1
+    assert congressed.sum() == 1000
+    assert cells.sum() == pytest.approx(1_000_000)
+    assert np.allclose(cells[congressed], cells[congressed][0])
+    assert cells[congressed][0] > 100           # each row stands for many
+
+
+def test_merged_rows_founder_sampling_is_exact():
+    """Merging identical clones is exact for founder sampling: one row of k
+    clones gets Poisson(k m) founders, the sum of k Poisson(m)."""
+    rng = np.random.default_rng(3)
+    kt = np.zeros((1, 20_000))
+    merged = _sim_growth(kt, np.array([1.0]), 50.0, rng=rng,
+                         founder_sampling=True)[0]
+    split = _sim_growth(np.zeros((10, 20_000)), np.full(10, 0.1), 50.0,
+                        rng=rng, founder_sampling=True).sum(axis=0)
+    assert merged.mean() == pytest.approx(split.mean(), rel=0.02)
+    assert merged.var() == pytest.approx(split.var(), rel=0.05)
+
+
+def test_per_origin_skew(base_config, base_library_df, base_phenotype_df):
+    origins = base_library_df["library_origin"].unique().tolist()
+    cf = copy.deepcopy(base_config)
+    cf["lib_assembly_skew_sigma"] = {o: 0.5 for o in origins}
+    _run(cf, base_library_df, base_phenotype_df, 1)
+    cf["lib_assembly_skew_sigma"] = {origins[0]: 0.5}
+    if len(origins) > 1:
+        with pytest.raises(ValueError, match="per origin"):
+            _run(cf, base_library_df, base_phenotype_df, 1)
+
+
+def test_collapsed_transform_no_transformants():
+    trans, mask, cells = _sim_transform_collapsed(np.array([0.5, 0.5]), 0,
+                                                  0.357, np.random.default_rng(0))
+    assert trans.shape == (0, 0) and mask.shape == (0, 0)
+    assert mask.dtype == bool and cells.shape == (0,)
+
+
+def test_collapsed_transform_default_rng():
+    trans, mask, cells = _sim_transform_collapsed(np.array([0.5, 0.5]), 1000,
+                                                  None)
+    assert cells.sum() == 1000
+    assert set(trans[:, 0]) <= {0, 1}
+
+
+# ---------------------------------------------------------------------------
+# od600: one simulated reading per tube
+# ---------------------------------------------------------------------------
+
+_OD600_CAL = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                          "examples", "od600", "od600_calibration.yaml")
+
+
+@pytest.mark.parametrize("from_od600", [False, True])
+def test_selection_experiment_od600_readings(base_config, base_library_df,
+                                             base_phenotype_df, from_od600):
+    from tfscreen.process_raw.od600 import od600_to_cfu_per_mL
+    cf = copy.deepcopy(base_config)
+    cf["od600"] = {"calibration": _OD600_CAL, "tube_volume_mL": 5.0,
+                   "sample_cfu_from_od600": from_od600}
+    sample_df, _ = _run(cf, base_library_df, base_phenotype_df, 1,
+                        sequence=False)
+    for col in ("sample_cfu_true", "od600", "od600_detectable",
+                "od600_in_range"):
+        assert col in sample_df.columns
+    assert (sample_df["od600"] >= 0).all()
+    if from_od600:
+        cfu, cfu_std, _ = od600_to_cfu_per_mL(sample_df["od600"].to_numpy(),
+                                              _OD600_CAL)
+        np.testing.assert_allclose(sample_df["sample_cfu"], cfu * 5.0)
+        np.testing.assert_allclose(sample_df["sample_cfu_std"], cfu_std * 5.0)
+    else:
+        np.testing.assert_allclose(sample_df["sample_cfu"],
+                                   sample_df["sample_cfu_true"])
+        assert (sample_df["sample_cfu_std"] == 0).all()
diff --git a/tests/tfscreen/simulate/test_setup_sim_grid_cli.py b/tests/tfscreen/simulate/test_setup_sim_grid_cli.py
index 4c44b570..34b7781c 100644
--- a/tests/tfscreen/simulate/test_setup_sim_grid_cli.py
+++ b/tests/tfscreen/simulate/test_setup_sim_grid_cli.py
@@ -2,6 +2,7 @@
 
 import json
 import os
+import shutil
 
 import pytest
 import yaml
@@ -97,7 +98,7 @@ def test_resolve_paths_unknown_key_unchanged(tmp_path):
 
 def test_setup_sim_grid_creates_subdirs(tmp_path, minimal_grid_yaml):
     out = str(tmp_path / "grid_out")
-    runs = setup_sim_grid(minimal_grid_yaml, out_prefix=out)
+    runs = setup_sim_grid(minimal_grid_yaml, out_dir=out)
     assert len(runs) == 1
     run_dir = os.path.join(out, runs[0]["run"])
     assert os.path.isdir(run_dir)
@@ -105,7 +106,7 @@ def test_setup_sim_grid_creates_subdirs(tmp_path, minimal_grid_yaml):
 
 def test_setup_sim_grid_writes_config(tmp_path, minimal_grid_yaml):
     out = str(tmp_path / "grid_out")
-    runs = setup_sim_grid(minimal_grid_yaml, out_prefix=out)
+    runs = setup_sim_grid(minimal_grid_yaml, out_dir=out)
     cfg_path = os.path.join(out, runs[0]["run"], "tfs_sim_config.yaml")
     assert os.path.isfile(cfg_path)
     with open(cfg_path) as fh:
@@ -115,7 +116,7 @@ def test_setup_sim_grid_writes_config(tmp_path, minimal_grid_yaml):
 
 def test_setup_sim_grid_writes_combo_json(tmp_path, minimal_grid_yaml):
     out = str(tmp_path / "grid_out")
-    runs = setup_sim_grid(minimal_grid_yaml, out_prefix=out)
+    runs = setup_sim_grid(minimal_grid_yaml, out_dir=out)
     combo_path = os.path.join(out, runs[0]["run"], "combo.json")
     assert os.path.isfile(combo_path)
     with open(combo_path) as fh:
@@ -126,7 +127,7 @@ def test_setup_sim_grid_writes_combo_json(tmp_path, minimal_grid_yaml):
 
 def test_setup_sim_grid_writes_summary(tmp_path, minimal_grid_yaml):
     out = str(tmp_path / "grid_out")
-    setup_sim_grid(minimal_grid_yaml, out_prefix=out)
+    setup_sim_grid(minimal_grid_yaml, out_dir=out)
     summary_path = os.path.join(out, "grid_summary.json")
     assert os.path.isfile(summary_path)
     with open(summary_path) as fh:
@@ -158,7 +159,7 @@ def test_setup_sim_grid_cartesian_product(tmp_path, base_config):
     grid_path.write_text(grid)
 
     out = str(tmp_path / "grid_out")
-    runs = setup_sim_grid(str(grid_path), out_prefix=out)
+    runs = setup_sim_grid(str(grid_path), out_dir=out)
     assert len(runs) == 4
 
 
@@ -189,7 +190,7 @@ def test_setup_sim_grid_renders_template(tmp_path, base_config):
     grid_path.write_text(grid)
 
     out = str(tmp_path / "grid_out")
-    runs = setup_sim_grid(str(grid_path), out_prefix=out)
+    runs = setup_sim_grid(str(grid_path), out_dir=out)
     rendered = open(os.path.join(out, runs[0]["run"], "run.sh")).read()
     assert "NUM_REPLICATES=5" in rendered
 
@@ -243,7 +244,7 @@ def test_setup_sim_grid_override_applied(tmp_path, base_config):
     grid_path = tmp_path / "grid.yaml"
     grid_path.write_text(grid)
     out = str(tmp_path / "grid_out")
-    runs = setup_sim_grid(str(grid_path), out_prefix=out)
+    runs = setup_sim_grid(str(grid_path), out_dir=out)
 
     cfg_path = os.path.join(out, runs[0]["run"], "tfs_sim_config.yaml")
     with open(cfg_path) as fh:
@@ -251,3 +252,334 @@ def test_setup_sim_grid_override_applied(tmp_path, base_config):
     assert cfg["observable_calculator"] == "eee"
     # Base config values not overridden are preserved
     assert cfg["reading_frame"] == 0
+
+
+# ---------------------------------------------------------------------------
+# Nested file paths (binding_data.*.choose_by, empirical.phenotype_model)
+# ---------------------------------------------------------------------------
+
+def _read_run_config(out, run):
+    with open(os.path.join(out, run["run"], "tfs_sim_config.yaml")) as fh:
+        return yaml.safe_load(fh)
+
+
+def test_resolve_paths_nested_choose_by(tmp_path):
+    (tmp_path / "hill_params.csv").write_text("x")
+    cfg = {"binding_data": {"spiked_binding": {"choose_by": "hill_params.csv"},
+                            "library_binding": {"choose_by": "stratified",
+                                                "num": 5}}}
+    result = _resolve_paths(cfg, str(tmp_path))
+    assert (result["binding_data"]["spiked_binding"]["choose_by"]
+            == str(tmp_path / "hill_params.csv"))
+    assert result["binding_data"]["library_binding"]["choose_by"] == "stratified"
+    # The input is not modified
+    assert cfg["binding_data"]["spiked_binding"]["choose_by"] == "hill_params.csv"
+
+
+def test_resolve_paths_nested_phenotype_model(tmp_path):
+    result = _resolve_paths({"empirical": {"phenotype_model": "m.json"}},
+                            str(tmp_path))
+    assert result["empirical"]["phenotype_model"] == str(tmp_path / "m.json")
+
+
+def test_resolve_paths_missing_nested_parent(tmp_path):
+    cfg = {"binding_data": None, "empirical": "not-a-dict"}
+    assert _resolve_paths(cfg, str(tmp_path)) == cfg
+
+
+def _write_study(tmp_path, base, grid_body, grid_dir=None):
+    """Write simulate_config.yaml (base) and grid.yaml; return the grid path."""
+    study = tmp_path / "study"
+    study.mkdir(exist_ok=True)
+    (study / "simulate_config.yaml").write_text(yaml.dump(base))
+    grid_dir = grid_dir or study
+    base_ref = os.path.relpath(study / "simulate_config.yaml", grid_dir)
+    grid_path = grid_dir / "grid.yaml"
+    grid_path.write_text(f"base_config: {base_ref}\n" + grid_body)
+    return str(grid_path)
+
+
+_SEED_BLOCK = """\
+simulate:
+  - name: seed
+    variants:
+      - seed: 1
+      - seed: 2
+"""
+
+
+def test_setup_sim_grid_base_config_choose_by_file(tmp_path):
+    """A choose_by params file is copied into inputs/ and referenced from there."""
+    study = tmp_path / "study"
+    study.mkdir()
+    (study / "hill_params.csv").write_text("genotype\nwt\n")
+    base = {"seed": 1,
+            "binding_data": {"spiked_binding": {"choose_by": "hill_params.csv"},
+                             "library_binding": {"choose_by": "random",
+                                                 "num": 3}}}
+    grid_path = _write_study(tmp_path, base, _SEED_BLOCK)
+    out = str(tmp_path / "elsewhere" / "grid_out")
+    runs = setup_sim_grid(grid_path, out_dir=out)
+    assert len(runs) == 2
+    assert os.listdir(os.path.join(out, "inputs")) == ["hill_params.csv"]
+    for run in runs:
+        cfg = _read_run_config(out, run)
+        assert (cfg["binding_data"]["spiked_binding"]["choose_by"]
+                == os.path.join("..", "inputs", "hill_params.csv"))
+        # Keyword left alone
+        assert cfg["binding_data"]["library_binding"]["choose_by"] == "random"
+
+
+def test_setup_sim_grid_is_movable(tmp_path):
+    """The grid works after it is moved and the original inputs are deleted."""
+    study = tmp_path / "study"
+    study.mkdir()
+    (study / "hill_params.csv").write_text("genotype\nwt\n")
+    (study / "struct.h5").write_text("h5")
+    (study / "run.sh").write_text("cat {{ extra }}\n")
+    (study / "extra.txt").write_text("extra")
+    base = {"thermo_data": "struct.h5",
+            "binding_data": {"spiked_binding": {"choose_by": "hill_params.csv"}}}
+    grid_path = _write_study(tmp_path, base, """\
+output_file: run.sh
+simulate:
+  - name: seed
+    variants:
+      - seed: 1
+template:
+  - name: extra
+    variants:
+      - extra: extra.txt
+""")
+    out = tmp_path / "grid_out"
+    runs = setup_sim_grid(grid_path, out_dir=str(out))
+
+    moved = tmp_path / "far" / "away" / "grid_out"
+    moved.parent.mkdir(parents=True)
+    shutil.move(str(out), str(moved))
+    shutil.rmtree(study)
+
+    run_dir = moved / runs[0]["run"]
+    cfg = _read_run_config(str(moved), runs[0])
+    assert (run_dir / cfg["thermo_data"]).read_text() == "h5"
+    assert ((run_dir / cfg["binding_data"]["spiked_binding"]["choose_by"])
+            .read_text() == "genotype\nwt\n")
+    rendered = (run_dir / "run.sh").read_text()
+    assert rendered == f"cat {os.path.join('..', 'inputs', 'extra.txt')}"
+    assert (run_dir / ".." / "inputs" / "extra.txt").read_text() == "extra"
+
+
+def test_setup_sim_grid_same_name_different_files(tmp_path):
+    """Two different files with one name get distinct copies."""
+    study = tmp_path / "study"
+    for sub in ("a", "b"):
+        (study / sub).mkdir(parents=True)
+        (study / sub / "params.csv").write_text(f"genotype\n{sub}\n")
+    grid_path = _write_study(tmp_path, {"seed": 1}, """\
+simulate:
+  - name: binding
+    variants:
+      - binding_data:
+          spiked_binding:
+            choose_by: a/params.csv
+      - binding_data:
+          spiked_binding:
+            choose_by: b/params.csv
+      - binding_data:
+          spiked_binding:
+            choose_by: a/params.csv
+""")
+    out = str(tmp_path / "grid_out")
+    runs = setup_sim_grid(grid_path, out_dir=out)
+    got = [_read_run_config(out, r)["binding_data"]["spiked_binding"]["choose_by"]
+           for r in runs]
+    assert got == [os.path.join("..", "inputs", "params.csv"),
+                   os.path.join("..", "inputs", "params_2.csv"),
+                   os.path.join("..", "inputs", "params.csv")]
+    assert sorted(os.listdir(os.path.join(out, "inputs"))) == [
+        "params.csv", "params_2.csv"]
+    assert open(os.path.join(out, "inputs", "params_2.csv")).read() == "genotype\nb\n"
+
+
+def test_setup_sim_grid_rerun_changed_input_not_overwritten(tmp_path):
+    """Re-running into the same grid never changes a file older runs use."""
+    study = tmp_path / "study"
+    study.mkdir()
+    (study / "hill_params.csv").write_text("v1")
+    base = {"binding_data": {"spiked_binding": {"choose_by": "hill_params.csv"}}}
+    grid_path = _write_study(tmp_path, base, _SEED_BLOCK)
+    out = str(tmp_path / "grid_out")
+    setup_sim_grid(grid_path, out_dir=out)
+    runs = setup_sim_grid(grid_path, out_dir=out)  # unchanged: reused
+    assert os.listdir(os.path.join(out, "inputs")) == ["hill_params.csv"]
+
+    (study / "hill_params.csv").write_text("v2")
+    runs = setup_sim_grid(grid_path, out_dir=out)
+    cfg = _read_run_config(out, runs[0])
+    assert (cfg["binding_data"]["spiked_binding"]["choose_by"]
+            == os.path.join("..", "inputs", "hill_params_2.csv"))
+    assert open(os.path.join(out, "inputs", "hill_params.csv")).read() == "v1"
+
+
+def test_setup_sim_grid_phenotype_model_prefix(tmp_path):
+    """A bare phenotype_model prefix copies the JSON the simulator would load."""
+    study = tmp_path / "study"
+    study.mkdir()
+    (study / "emp_phenotype_model.json").write_text("{}")
+    base = {"empirical": {"phenotype_model": "emp"}}
+    grid_path = _write_study(tmp_path, base, _SEED_BLOCK)
+    out = str(tmp_path / "grid_out")
+    runs = setup_sim_grid(grid_path, out_dir=out)
+    cfg = _read_run_config(out, runs[0])
+    assert (cfg["empirical"]["phenotype_model"]
+            == os.path.join("..", "inputs", "emp_phenotype_model.json"))
+
+
+def test_setup_sim_grid_missing_input_file_raises(tmp_path):
+    base = {"binding_data": {"spiked_binding": {"choose_by": "nope.csv"}}}
+    grid_path = _write_study(tmp_path, base, _SEED_BLOCK)
+    out = tmp_path / "grid_out"
+    with pytest.raises(FileNotFoundError, match="binding_data.spiked_binding.choose_by"):
+        setup_sim_grid(grid_path, out_dir=str(out))
+    assert not out.exists()
+
+
+def test_setup_sim_grid_directory_input_raises(tmp_path):
+    study = tmp_path / "study"
+    (study / "structs").mkdir(parents=True)
+    grid_path = _write_study(tmp_path, {"thermo_data": "structs"}, _SEED_BLOCK)
+    with pytest.raises(ValueError, match="directory"):
+        setup_sim_grid(grid_path, out_dir=str(tmp_path / "grid_out"))
+
+
+def test_setup_sim_grid_unknown_path_key_raises(tmp_path):
+    """A file named by a key setup does not know about is a hard error."""
+    study = tmp_path / "study"
+    study.mkdir()
+    (study / "mystery.csv").write_text("x")
+    base = {"presplit_data": {"source": "mystery.csv"}}
+    grid_path = _write_study(tmp_path, base, _SEED_BLOCK)
+    out = tmp_path / "grid_out"
+    with pytest.raises(ValueError, match="presplit_data.source.*_SIM_PATH_KEYS"):
+        setup_sim_grid(grid_path, out_dir=str(out))
+    assert not out.exists()
+
+
+def test_setup_sim_grid_unknown_absolute_path_raises(tmp_path):
+    grid_path = _write_study(tmp_path, {"some_dir": str(tmp_path)}, _SEED_BLOCK)
+    with pytest.raises(ValueError, match="some_dir"):
+        setup_sim_grid(grid_path, out_dir=str(tmp_path / "grid_out"))
+
+
+def test_setup_sim_grid_template_directory_raises(tmp_path):
+    study = tmp_path / "study"
+    (study / "data").mkdir(parents=True)
+    (study / "run.sh").write_text("{{ d }}")
+    grid_path = _write_study(tmp_path, {"seed": 1}, """\
+output_file: run.sh
+template:
+  - name: d
+    variants:
+      - d: data
+""")
+    with pytest.raises(ValueError, match="Template variable 'd'.*directory"):
+        setup_sim_grid(grid_path, out_dir=str(tmp_path / "grid_out"))
+
+
+def test_setup_sim_grid_keyword_choose_by_unchanged(tmp_path):
+    """A choose_by keyword is kept even when a file of that name exists."""
+    (tmp_path / "stratified").write_text("x")
+    base = {"binding_data": {"spiked_binding": {"choose_by": "stratified"}}}
+    (tmp_path / "simulate_config.yaml").write_text(yaml.dump(base))
+    (tmp_path / "grid.yaml").write_text("""\
+base_config: simulate_config.yaml
+simulate:
+  - name: seed
+    variants:
+      - seed: 1
+""")
+    out = str(tmp_path / "grid_out")
+    runs = setup_sim_grid(str(tmp_path / "grid.yaml"), out_dir=out)
+    cfg = _read_run_config(out, runs[0])
+    assert cfg["binding_data"]["spiked_binding"]["choose_by"] == "stratified"
+
+
+def test_setup_sim_grid_override_choose_by_from_grid_dir(tmp_path):
+    """A binding_data override resolves its choose_by against the grid YAML."""
+    base_dir = tmp_path / "base"
+    base_dir.mkdir()
+    (base_dir / "simulate_config.yaml").write_text(yaml.dump(
+        {"binding_data": {"spiked_binding": {"choose_by": "stratified"}}}))
+    grid_dir = tmp_path / "grid"
+    grid_dir.mkdir()
+    (grid_dir / "measured.csv").write_text("genotype\nwt\n")
+    (grid_dir / "grid.yaml").write_text("""\
+base_config: ../base/simulate_config.yaml
+simulate:
+  - name: binding
+    variants:
+      - binding_data:
+          spiked_binding:
+            choose_by: measured.csv
+""")
+    out = str(tmp_path / "grid_out")
+    runs = setup_sim_grid(str(grid_dir / "grid.yaml"), out_dir=out)
+    cfg = _read_run_config(out, runs[0])
+    assert (cfg["binding_data"]["spiked_binding"]["choose_by"]
+            == os.path.join("..", "inputs", "measured.csv"))
+    assert open(os.path.join(out, "inputs", "measured.csv")).read() == "genotype\nwt\n"
+    run_dir = os.path.join(out, runs[0]["run"])
+    # combo.json keeps the override as written
+    with open(os.path.join(run_dir, "combo.json")) as fh:
+        combo = json.load(fh)
+    assert (combo["simulate"]["binding_data"]["spiked_binding"]["choose_by"]
+            == "measured.csv")
+
+
+def test_phenotype_model_file_resolves_like_the_simulator(tmp_path):
+    from tfscreen.simulate.scripts.setup_sim_grid_cli import _phenotype_model_file
+    prefix = str(tmp_path / "emp")
+    # nothing on disk: the value is returned unchanged (staging then fails)
+    assert _phenotype_model_file(prefix) == prefix
+    (tmp_path / "emp_phenotype_model.json").write_text("{}")
+    assert _phenotype_model_file(prefix) == prefix + "_phenotype_model.json"
+    (tmp_path / "emp.json").write_text("{}")
+    assert _phenotype_model_file(prefix) == prefix + ".json"
+
+
+def test_setup_sim_grid_stages_library_genotypes(tmp_path):
+    """library_genotypes (base config or a grid variable) is copied into
+    inputs/; library_subsample is an ordinary grid variable."""
+    study = tmp_path / "study"
+    study.mkdir()
+    (study / "keep.txt").write_text("A1V\n")
+    (study / "keep2.txt").write_text("A1C\n")
+    base = {"seed": 1, "library_genotypes": "keep.txt"}
+    grid_path = _write_study(tmp_path, base, """\
+simulate:
+  - name: subsample
+    variants:
+      - library_subsample: {double-1-2: 100}
+        library_subsample_seed: 3
+      - library_subsample: {double-1-2: 200}
+        library_subsample_seed: 3
+        library_genotypes: keep2.txt
+""")
+    out = str(tmp_path / "grid_out")
+    runs = setup_sim_grid(grid_path, out_dir=out)
+    assert sorted(os.listdir(os.path.join(out, "inputs"))) == ["keep.txt",
+                                                              "keep2.txt"]
+    cfgs = [_read_run_config(out, r) for r in runs]
+    assert cfgs[0]["library_genotypes"] == os.path.join("..", "inputs",
+                                                        "keep.txt")
+    assert cfgs[1]["library_genotypes"] == os.path.join("..", "inputs",
+                                                        "keep2.txt")
+    assert [c["library_subsample"] for c in cfgs] == [{"double-1-2": 100},
+                                                      {"double-1-2": 200}]
+
+
+def test_setup_sim_grid_missing_library_genotypes_raises(tmp_path):
+    grid_path = _write_study(tmp_path, {"library_genotypes": "nope.txt"},
+                             _SEED_BLOCK)
+    with pytest.raises(FileNotFoundError, match="library_genotypes"):
+        setup_sim_grid(grid_path, out_dir=str(tmp_path / "out"))
diff --git a/tests/tfscreen/simulate/test_simulate_cli.py b/tests/tfscreen/simulate/test_simulate_cli.py
index add4365c..1b5c6f6b 100644
--- a/tests/tfscreen/simulate/test_simulate_cli.py
+++ b/tests/tfscreen/simulate/test_simulate_cli.py
@@ -5,9 +5,26 @@
 from unittest.mock import patch, MagicMock
 import pandas as pd
 
-from tfscreen.simulate.scripts.simulate_cli import run_simulation_from_config
+from tfscreen.simulate.scripts.simulate_cli import (
+    replicate_read_totals,
+    run_simulation_from_config,
+)
 from tfscreen.util.cli.generalized_main import generalized_main
 
+_RAW = "tfscreen.simulate.scripts.simulate_cli.write_raw_experiment"
+
+
+@pytest.fixture(autouse=True)
+def _stub_raw_output(request):
+    """The mocked frames here carry no tube totals; the raw writer has its
+    own tests (test_raw_output.py). Tests that check the call patch it."""
+    if "raw_call" in request.keywords:
+        yield None
+        return
+    with patch(_RAW, return_value={"tubes": "t.csv", "counts_dir": "c",
+                                   "dropped": [], "command": "x"}) as m:
+        yield m
+
 
 # ---------------------------------------------------------------------------
 # Helpers
@@ -32,13 +49,13 @@ def test_seed_cli_parsed_as_int():
     """--seed is registered with type=int so integer strings are accepted."""
     captured = {}
 
-    def fake_run(config_file, output_dir, output_prefix="tfs_sim_",
+    def fake_run(config_file, out_prefix="tfs_sim",
                  num_replicates=2, seed=None):
         captured["seed"] = seed
 
     generalized_main(
         fake_run,
-        argv=["config.yaml", "out_dir", "--seed", "42"],
+        argv=["config.yaml", "--seed", "42"],
         manual_arg_types={"seed": int},
     )
     assert captured["seed"] == 42
@@ -49,13 +66,13 @@ def test_seed_cli_defaults_to_none():
     """Omitting --seed leaves seed as None."""
     captured = {}
 
-    def fake_run(config_file, output_dir, output_prefix="tfs_sim_",
+    def fake_run(config_file, out_prefix="tfs_sim",
                  num_replicates=2, seed=None):
         captured["seed"] = seed
 
     generalized_main(
         fake_run,
-        argv=["config.yaml", "out_dir"],
+        argv=["config.yaml"],
         manual_arg_types={"seed": int},
     )
     assert captured["seed"] is None
@@ -72,7 +89,7 @@ def patched_simulation(tmp_path):
 
     with patch("tfscreen.util.read_yaml", return_value={"seed": 99, "growth": {}}) as mock_yaml, \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
@@ -87,7 +104,7 @@ def test_seed_overrides_config(patched_simulation):
 
     with patch("tfscreen.simulate.scripts.simulate_cli.np.random.default_rng",
                wraps=lambda s: MagicMock()) as mock_rng:
-        run_simulation_from_config("config.yaml", str(tmp_path), seed=7)
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"), seed=7)
 
     mock_rng.assert_called_once_with(7)
 
@@ -98,7 +115,7 @@ def test_seed_none_preserves_config(patched_simulation):
 
     with patch("tfscreen.simulate.scripts.simulate_cli.np.random.default_rng",
                wraps=lambda s: MagicMock()) as mock_rng:
-        run_simulation_from_config("config.yaml", str(tmp_path), seed=None)
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"), seed=None)
 
     mock_rng.assert_called_once_with(99)
 
@@ -118,13 +135,13 @@ def capture_csv(self_df, path, **kwargs):
 
     with patch("tfscreen.util.read_yaml", return_value={"seed": 0, "growth": {}}), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df), \
          patch.object(pd.DataFrame, "to_csv", capture_csv):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     written = "\n".join(written_paths)
     assert "parameters" in written, "parameters.csv must be written"
@@ -142,13 +159,13 @@ def capture_csv(self_df, path, **kwargs):
 
     with patch("tfscreen.util.read_yaml", return_value={"seed": 0, "growth": {}}), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df), \
          patch.object(pd.DataFrame, "to_csv", capture_csv):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     written = "\n".join(written_paths)
     for stem in ("library", "parameters", "genotype_theta", "growth"):
@@ -173,13 +190,13 @@ def capture_csv(self_df, path, **kwargs):
 
     with patch("tfscreen.util.read_yaml", return_value=cfg), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df), \
          patch.object(pd.DataFrame, "to_csv", capture_csv):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     matches = [p for p in written if "growth_parameters" in os.path.basename(p)]
     assert len(matches) == 1
@@ -197,12 +214,12 @@ def test_growth_parameters_csv_written_for_real(tmp_path):
 
     with patch("tfscreen.util.read_yaml", return_value=cfg), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     growth_params_path = tmp_path / "tfs_sim_growth_parameters.csv"
     assert growth_params_path.exists()
@@ -232,13 +249,13 @@ def capture_csv(self_df, path, **kwargs):
 
     with patch("tfscreen.util.read_yaml", return_value=cfg), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df), \
          patch.object(pd.DataFrame, "to_csv", capture_csv):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     matches = [p for p in written if "transformation_lam" in os.path.basename(p)]
     assert len(matches) == 1
@@ -255,12 +272,12 @@ def test_transformation_lam_csv_written_for_real(tmp_path):
 
     with patch("tfscreen.util.read_yaml", return_value=cfg), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     lam_path = tmp_path / "tfs_sim_transformation_lam.csv"
     assert lam_path.exists()
@@ -277,7 +294,7 @@ def test_transformation_lam_participates_in_existence_guard(tmp_path):
     cfg = {"seed": 0, "growth": {}, "transformation_poisson_lambda": 1.5}
     with patch("tfscreen.util.read_yaml", return_value=cfg):
         with pytest.raises(FileExistsError, match="transformation_lam"):
-            run_simulation_from_config("config.yaml", str(tmp_path))
+            run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
 
 # ---------------------------------------------------------------------------
@@ -295,13 +312,13 @@ def capture_csv(self_df, path, **kwargs):
 
     with patch("tfscreen.util.read_yaml", return_value={"seed": 0, "growth": {}}), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df), \
          patch.object(pd.DataFrame, "to_csv", capture_csv):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     basenames = "\n".join(os.path.basename(p) for p in written_paths)
     assert "base_growth" not in basenames
@@ -321,13 +338,13 @@ def capture_csv(self_df, path, **kwargs):
 
     with patch("tfscreen.util.read_yaml", return_value=cfg), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df), \
          patch.object(pd.DataFrame, "to_csv", capture_csv):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     base_growth_paths = [p for p in written if "base_growth" in os.path.basename(p)]
     assert len(base_growth_paths) == 1
@@ -351,13 +368,13 @@ def capture_csv(self_df, path, **kwargs):
 
     with patch("tfscreen.util.read_yaml", return_value={"seed": 0, "growth": {}}), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df), \
          patch.object(pd.DataFrame, "to_csv", capture_csv):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     basenames = "\n".join(os.path.basename(p) for p in written_paths)
     assert "k_ref" not in basenames
@@ -377,13 +394,13 @@ def capture_csv(self_df, path, **kwargs):
 
     with patch("tfscreen.util.read_yaml", return_value=cfg), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df), \
          patch.object(pd.DataFrame, "to_csv", capture_csv):
-        run_simulation_from_config("config.yaml", str(tmp_path))
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     k_ref_paths = [p for p in written if "k_ref" in os.path.basename(p)]
     assert len(k_ref_paths) == 1
@@ -428,12 +445,12 @@ def test_run_simulation_writes_presplit_csv(tmp_path):
 
     with patch("tfscreen.util.read_yaml", return_value=cf), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df):
-        run_simulation_from_config("fake_config.yaml", str(tmp_path))
+        run_simulation_from_config("fake_config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     presplit_path = tmp_path / "tfs_sim_presplit.csv"
     assert presplit_path.exists(), "presplit CSV was not written"
@@ -462,12 +479,12 @@ def test_run_simulation_no_presplit_without_config(tmp_path):
 
     with patch("tfscreen.util.read_yaml", return_value=cf), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df):
-        run_simulation_from_config("fake_config.yaml", str(tmp_path))
+        run_simulation_from_config("fake_config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     presplit_path = tmp_path / "tfs_sim_presplit.csv"
     assert not presplit_path.exists(), "presplit CSV should not be written without config block"
@@ -491,7 +508,6 @@ def test_theta_rng_seed_rejected_as_unknown_key(tmp_path):
         "library_mixture": {},
         "lib_assembly_skew_sigma": 0.0,
         "transformation_poisson_lambda": 1,
-        "multi_plasmid_combine_fcn": "gmean",
         "cfu0": 1e7,
         "tube_noise_sigma": 0.0,
         "total_num_reads": 1000,
@@ -519,14 +535,14 @@ def capture_dump(data, fh, **kwargs):
 
     with patch("tfscreen.util.read_yaml", return_value={"seed": 5, "growth": {}}), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)), \
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)), \
          patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
                return_value=(sample_df, counts_df)), \
          patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
                return_value=growth_df), \
          patch.object(pd.DataFrame, "to_csv"), \
          patch("tfscreen.simulate.scripts.simulate_cli.yaml.dump", capture_dump):
-        run_simulation_from_config("config.yaml", str(tmp_path), output_prefix="test_")
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "test"))
 
     assert "data" in dumped, "yaml.dump was not called"
     assert dumped["data"]["seed"] == 5
@@ -541,8 +557,245 @@ def test_input_config_yaml_existence_check(tmp_path):
 
     with patch("tfscreen.util.read_yaml", return_value={"seed": 0, "growth": {}}), \
          patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
-               return_value=(lib_df, pheno_df, theta_df, params_df, None)) as mock_lib:
+               return_value=(lib_df, pheno_df, theta_df, params_df, None, None, None)) as mock_lib:
         with pytest.raises(FileExistsError, match="input-config.yaml"):
-            run_simulation_from_config("config.yaml", str(tmp_path))
+            run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
 
     mock_lib.assert_not_called()
+
+
+# ---------------------------------------------------------------------------
+# od600 block: per-tube readings and OD-only replicates (roadmap step 4)
+# ---------------------------------------------------------------------------
+
+def _od_run(tmp_path, cf):
+    lib_df = pd.DataFrame({"genotype": ["wt"]})
+    params_df = pd.DataFrame({"genotype": ["wt"], "dk_geno": [0.0],
+                              "activity": [1.0]})
+    calls = []
+
+    def fake_selection(rep_cf, library_df, phenotype_df, shared_state=None,
+                       sequence=True):
+        calls.append({"sequence": sequence, "shared_state": shared_state,
+                      "reads": rep_cf.get("total_num_reads")})
+        rep = int(phenotype_df["replicate"].iloc[0])
+        sample_df = pd.DataFrame([{
+            "sample": 0, "replicate": rep, "library": "lib",
+            "condition_sel": "kanR+kan", "t_sel": 60.0, "sample_cfu": 1e8,
+            "sample_cfu_std": 1e6, "sample_cfu_true": 1e8, "od600": 0.3,
+            "od600_detectable": True, "od600_in_range": True}], index=[0])
+        if not sequence:
+            return sample_df, pd.DataFrame(columns=["sample", "genotype"])
+        counts_df = pd.DataFrame([{"sample": 0, "genotype": "wt",
+                                   "counts": 10, "ln_cfu_0": 1.0}])
+        return sample_df, counts_df
+
+    with patch("tfscreen.util.read_yaml", return_value=cf), \
+         patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
+               return_value=(lib_df, lib_df.copy(), lib_df.copy(), params_df, None, None, None)), \
+         patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
+               side_effect=fake_selection), \
+         patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
+               return_value=pd.DataFrame({"genotype": ["wt"]})):
+        run_simulation_from_config("fake_config.yaml", out_prefix=str(tmp_path / "tfs_sim"),
+                                   num_replicates=2)
+    return calls
+
+
+def test_od600_written_with_od_only_replicates(tmp_path):
+    cf = {"seed": 1, "growth": {}, "cfu0": 1e8, "total_num_reads": 100,
+          "od600": {"calibration": "c.yaml", "num_od_only_replicates": 1}}
+    calls = _od_run(tmp_path, cf)
+
+    assert [c["sequence"] for c in calls] == [True, True, False]
+    # One shared_state dict passed to every replicate.
+    assert all(c["shared_state"] is calls[0]["shared_state"] for c in calls)
+
+    od = pd.read_csv(tmp_path / "tfs_sim_od600.csv")
+    assert list(od["replicate"]) == [1, 2, 3]
+    assert list(od["sequenced"]) == [True, True, False]
+    for col in ("od600", "od600_detectable", "od600_in_range",
+                "sample_cfu_true", "sample_cfu"):
+        assert col in od.columns
+
+
+def test_total_reads_split_across_replicates(tmp_path):
+    """total_num_reads is the total over all sequenced tubes, not per
+    replicate (each replicate used to get the full total)."""
+    cf = {"seed": 1, "growth": {}, "cfu0": 1e8, "total_num_reads": 1000,
+          "od600": {"calibration": "c.yaml", "num_od_only_replicates": 1}}
+    calls = _od_run(tmp_path, cf)
+    assert [c["reads"] for c in calls if c["sequence"]] == [500, 500]
+
+
+def test_replicate_read_totals_by_design_tube_count():
+    design = pd.DataFrame({"replicate": [1] * 60 + [2] * 56 + [3] * 2})
+    reads = replicate_read_totals(118_000, [1, 2, 3], 3, design)
+    assert reads == {1: 60_000, 2: 56_000, 3: 2_000}
+    assert replicate_read_totals(90, [1, 2, 3], 3) == {1: 30, 2: 30, 3: 30}
+    # OD600-only replicates get no reads
+    assert replicate_read_totals(90, [1, 2, 3], 2) == {1: 45, 2: 45}
+
+
+def test_no_od600_file_without_block(tmp_path):
+    cf = {"seed": 1, "growth": {}, "cfu0": 1e8, "total_num_reads": 100}
+    calls = _od_run(tmp_path, cf)
+    assert [c["sequence"] for c in calls] == [True, True]
+    assert not (tmp_path / "tfs_sim_od600.csv").exists()
+
+
+# ---------------------------------------------------------------------------
+# Raw-format output
+# ---------------------------------------------------------------------------
+
+def test_raw_output_written_by_default(patched_simulation, _stub_raw_output):
+    mock_yaml, tmp_path = patched_simulation
+    run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
+    assert _stub_raw_output.call_count == 1
+    args, kwargs = _stub_raw_output.call_args
+    assert args[3] == str(tmp_path / "tfs_sim")
+    assert list(args[2]) == ["wt"]
+    assert kwargs["od600_config"] is None
+
+
+def test_raw_output_can_be_skipped(patched_simulation, _stub_raw_output):
+    mock_yaml, tmp_path = patched_simulation
+    run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"),
+                               write_raw=False)
+    assert _stub_raw_output.call_count == 0
+
+
+def test_growth_table_can_be_skipped(patched_simulation):
+    mock_yaml, tmp_path = patched_simulation
+    with patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu") as c2l:
+        run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"),
+                                   write_growth=False)
+    # nothing needs the in-memory growth table, so it is not built
+    assert c2l.call_count == 0
+
+
+# ---------------------------------------------------------------------------
+# Designs and raw-output messages
+# ---------------------------------------------------------------------------
+
+def _one_replicate_design():
+    return pd.DataFrame({
+        "sample": ["k-1", "k-2"], "library": "kanR", "replicate": 4,
+        "condition_pre": "kanR-kan", "t_pre": 30, "condition_sel": "kanR+kan",
+        "t_sel": [100, 120], "titrant_name": "iptg", "titrant_conc": 0.0})
+
+
+class _Stop(Exception):
+    pass
+
+
+def test_design_sets_num_replicates(tmp_path, capsys):
+    """A design's replicates replace num_replicates, with a message."""
+    design = _one_replicate_design()
+    design.to_csv(tmp_path / "design.csv", index=False)
+    cf = {"seed": 1, "growth": {}, "cfu0": 1e8,
+          "design": str(tmp_path / "design.csv")}
+    lib_df = pd.DataFrame({"genotype": ["wt"]})
+    pheno = design.drop(columns=["sample", "replicate"]).assign(genotype="wt")
+    params_df = pd.DataFrame({"genotype": ["wt"], "dk_geno": [0.0],
+                              "activity": [1.0]})
+    seen = {}
+
+    def fake_selection(rep_cf, library_df, phenotype_df, shared_state=None,
+                       sequence=True):
+        seen["replicate"] = phenotype_df["replicate"].unique().tolist()
+        seen["t_sel"] = sorted(phenotype_df["t_sel"])
+        raise _Stop
+
+    with patch("tfscreen.util.read_yaml", return_value=cf), \
+         patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
+               return_value=(lib_df, pheno, lib_df.copy(), params_df, None, None, None)), \
+         patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
+               side_effect=fake_selection):
+        with pytest.raises(_Stop):
+            run_simulation_from_config("cfg.yaml", out_prefix=str(tmp_path / "s"),
+                                       num_replicates=2)
+    assert "Design has 1 replicate(s) [4]; num_replicates (2) is ignored." \
+        in capsys.readouterr().out
+    assert seen == {"replicate": [4], "t_sel": [100, 120]}
+
+
+def test_design_refuses_od_only_replicates(tmp_path):
+    _one_replicate_design().to_csv(tmp_path / "design.csv", index=False)
+    cf = {"seed": 1, "growth": {}, "cfu0": 1e8,
+          "design": str(tmp_path / "design.csv"),
+          "od600": {"calibration": "c.yaml", "num_od_only_replicates": 1}}
+    with pytest.raises(ValueError, match="num_od_only_replicates cannot be"):
+        _od_run(tmp_path, cf)
+
+
+def test_raw_output_reports_dropped_tubes(patched_simulation, _stub_raw_output,
+                                          capsys):
+    mock_yaml, tmp_path = patched_simulation
+    _stub_raw_output.return_value = {"tubes": "t.csv", "counts_dir": "c",
+                                     "od600": "o.csv", "calibration": "cal.yaml",
+                                     "dropped": ["tube0003"], "command": "x"}
+    run_simulation_from_config("config.yaml", out_prefix=str(tmp_path / "tfs_sim"))
+    out = capsys.readouterr().out
+    assert "Wrote the raw experiment: t.csv, c/, o.csv, cal.yaml" in out
+    assert "OD600 below the detection threshold): ['tube0003']" in out
+
+
+# ---------------------------------------------------------------------------
+# binding offsets and the growth titrant scale truth
+# ---------------------------------------------------------------------------
+
+def test_binding_offsets_and_growth_titrant_scale_written(tmp_path):
+    """genotype_offset_sd writes the offset truth and shifts theta_obs;
+    growth_titrant_scale writes log s."""
+    import numpy as np
+
+    lib_df, pheno_df, theta_df, params_df, sample_df, counts_df, growth_df = _make_mock_dfs()
+    binding_theta_df = pd.DataFrame({"genotype": ["wt", "wt"],
+                                     "titrant_conc": [0.0, 1.0],
+                                     "theta_true": [0.9, 0.1]})
+    cfg = {"seed": 0, "growth": {}, "growth_titrant_scale": 2.0,
+           "binding_data": {"titrant_name": "iptg", "titrant_conc": [0.0, 1.0],
+                            "noise": 0.0, "genotype_offset_sd": 0.1}}
+
+    written = {}
+
+    def capture_csv(self_df, path, **kwargs):
+        written[os.path.basename(str(path))] = self_df.copy()
+
+    with patch("tfscreen.util.read_yaml", return_value=cfg), \
+         patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
+               return_value=(lib_df, pheno_df, theta_df, params_df,
+                             binding_theta_df, None, None)), \
+         patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
+               return_value=(sample_df, counts_df)), \
+         patch("tfscreen.simulate.scripts.simulate_cli.counts_to_lncfu",
+               return_value=growth_df), \
+         patch.object(pd.DataFrame, "to_csv", capture_csv):
+        run_simulation_from_config("config.yaml",
+                                   out_prefix=str(tmp_path / "tfs_sim"),
+                                   num_replicates=1)
+
+    offsets = written["tfs_sim_binding_offsets.csv"]
+    assert offsets["genotype"].tolist() == ["wt"]
+    binding = written["tfs_sim_binding.csv"]
+    np.testing.assert_allclose(binding["theta_obs"].to_numpy(),
+                               np.array([0.9, 0.1]) + offsets["offset"].iloc[0])
+
+    scale = written["tfs_sim_growth_titrant_scale.csv"]
+    assert scale["parameter"].tolist() == ["growth_log_conc_shift"]
+    assert scale["ref"].iloc[0] == pytest.approx(np.log(2.0))
+
+
+def test_binding_offset_and_scale_outputs_join_the_existence_guard(tmp_path):
+    cfg = {"seed": 0, "growth": {}, "growth_titrant_scale": 2.0,
+           "binding_data": {"titrant_name": "iptg", "titrant_conc": [0.0],
+                            "genotype_offset_sd": 0.1}}
+    for name in ("binding_offsets", "growth_titrant_scale"):
+        path = tmp_path / f"tfs_sim_{name}.csv"
+        path.write_text("x\n")
+        with patch("tfscreen.util.read_yaml", return_value=dict(cfg)):
+            with pytest.raises(FileExistsError, match=f"tfs_sim_{name}.csv"):
+                run_simulation_from_config("config.yaml",
+                                           out_prefix=str(tmp_path / "tfs_sim"))
+        path.unlink()
diff --git a/tests/tfscreen/simulate/test_thermo_binding.py b/tests/tfscreen/simulate/test_thermo_binding.py
new file mode 100644
index 00000000..6041e947
--- /dev/null
+++ b/tests/tfscreen/simulate/test_thermo_binding.py
@@ -0,0 +1,274 @@
+"""
+Simulated binding data for the thermodynamic theta components.
+
+Spiked stratified/random binding used to build a wt-only stratified pool,
+which the thermodynamic models could not draw (a numpyro plate of size 0),
+and in-library binding read Hill columns these models do not have.  Spiked
+genotypes now keep their own model-generated theta, and every binding theta
+is evaluated from the drawn parameters at the binding concentrations.  These
+tests simulate a tiny library with each binding mode and check that the
+binding table exists, is finite, equals the theta computed from the written
+K truth at the binding concentrations, and agrees with the growth theta at
+shared concentrations.
+"""
+import inspect
+import warnings
+
+import jax
+import numpy as np
+import pandas as pd
+import pytest
+from numpyro import handlers
+
+from tfscreen.simulate.binding_data import generate_binding_df
+from tfscreen.simulate.library_binding_data import generate_library_binding_df
+from tfscreen.simulate.library_prediction import library_prediction
+from tfscreen.simulate.sample_theta import sample_theta_prior
+from tfscreen.simulate.sim_data_class import build_sim_data
+from tfscreen.tfmodel.generative.registry import model_registry
+
+# Two nnt-degenerate codons (residues 29 and 62 of lac repressor) and two
+# spiked single mutants: a few hundred genotypes, drawn in about a second.
+_WT = ("gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacattcccaa"
+       "ccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccagtctggccctg"
+       "cacgcgccgtcgcaaattgtcgcggcgattaaatctcgcgccgatcaactgggtgccagcgtggtggtgt"
+       "cgatg")
+
+
+def _site_string(char_at):
+    s = ["."] * len(_WT)
+    for pos, c in char_at.items():
+        for k in range(3):
+            s[pos + k] = c
+    return "".join(s)
+
+
+def _spike(codon_pos, codon):
+    return _WT[:codon_pos] + codon + _WT[codon_pos + 3:]
+
+
+_DEGEN = _site_string({6: "n", 105: "n"}).replace("nnn", "nnt")
+_TILES = _site_string({6: "1", 105: "2"})
+
+# Binding concentrations: 0 and 0.01 are growth concentrations, the rest
+# are not, so the theta must be evaluated rather than looked up.
+BINDING_CONCS = [0.0, 0.0003, 0.01, 0.05, 2.0]
+GROWTH_CONCS = [0.0, 0.001, 0.01, 1.0]
+
+_C4_STRUCTS = ["H", "HD", "L", "LE2"]
+
+
+def _config(component, choose_by, thermo_data=None):
+    cf = {
+        "reading_frame": 0,
+        "first_amplicon_residue": 27,
+        "wt_seq": _WT,
+        "degen_sites": _DEGEN,
+        "tiles": _TILES,
+        "tile_combos": ["single-1", "single-2", "double-1-2"],
+        "spiked_seqs": [_spike(6, "gct"), _spike(105, "gct"),
+                        _spike(6, "tgg")],
+        "theta_component": component,
+        "condition_blocks": [{
+            "library": "kanR", "titrant_name": "iptg",
+            "titrant_conc": GROWTH_CONCS,
+            "condition_pre": "pre", "t_pre": 30,
+            "condition_sel": "sel", "t_sel": [100],
+        }],
+        "growth": {"pre": {"m": 0.0, "b": 0.01},
+                   "sel": {"m": -0.01, "b": 0.0}},
+        "dk_geno_zero": True,
+        "seed": 3,
+        "binding_data": {
+            "titrant_name": "iptg",
+            "titrant_conc": BINDING_CONCS,
+            "noise": 0.0,
+            "spiked_binding": {"choose_by": choose_by, "num": 2},
+            "library_binding": {"choose_by": choose_by, "num": 3},
+        },
+    }
+    if thermo_data is not None:
+        cf["thermo_data"] = thermo_data
+    return cf
+
+
+def _ddG_csv(path):
+    rng = np.random.default_rng(3)
+    muts = ["H29A", "H29W", "L62A"]
+    df = pd.DataFrame(rng.normal(0, 1, (len(muts), len(_C4_STRUCTS))),
+                      columns=_C4_STRUCTS)
+    df.insert(0, "mut", muts)
+    df.to_csv(path, index=False)
+    return str(path)
+
+
+def _theta_from_truth(component, params, genotypes, concs):
+    """Theta at ``concs`` from the K columns of ``parameters_df`` (G, C)."""
+    module = model_registry["theta"][component]
+    hyper = module.get_hyperparameters()
+    names = [n for n in inspect.signature(module._compute_theta).parameters
+             if n.startswith("ln_K")]
+    p = params.assign(genotype=params["genotype"].astype(str)).set_index("genotype")
+    ks = []
+    for n in names:
+        v = p.loc[genotypes, n].to_numpy(dtype=float)
+        ks.append(v[None, :] if n.lower().endswith("e") else v)
+    # Both families take M (mM times theta_conc_unit_scale).
+    scale = hyper.get("theta_conc_unit_scale", 1.0)
+    theta = module._compute_theta(*ks, np.asarray(concs, dtype=float) * scale,
+                                  hyper["theta_tf_total_M"])
+    return np.asarray(theta)[0].T
+
+
+def _simulate(component, choose_by, tmp_path):
+    td = (_ddG_csv(tmp_path / "ddG.csv")
+          if component.endswith("PddG") else None)
+    cf = _config(component, choose_by, thermo_data=td)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        out = library_prediction(cf, return_mutation_truth=True,
+                                 return_binding_theta=True)
+    return cf, out
+
+
+CASES = [
+    "thermo.O2_C4_K3_U0_a.PK",
+    "thermo.O2_C12_K5_U0_a.PK",
+    "thermo.O2_C4_K3_U0_a.PddG",
+]
+
+
+@pytest.mark.parametrize("choose_by", ["stratified", "random"])
+@pytest.mark.parametrize("component", CASES)
+def test_spiked_binding_keeps_own_theta(component, choose_by, tmp_path):
+    cf, out = _simulate(component, choose_by, tmp_path)
+    library_df, _, genotype_theta_df, params, binding_theta_df, _, lbt = out
+
+    spiked = set(library_df.loc[library_df["library_origin"] == "spiked",
+                                "genotype"])
+    assert binding_theta_df is not None
+    genos = list(pd.unique(binding_theta_df["genotype"]))
+    assert len(genos) == 2 and set(genos) <= spiked
+    assert np.isfinite(binding_theta_df["theta_true"]).all()
+
+    # Binding theta is the theta of the genotype's own K truth.
+    wide = binding_theta_df.pivot(index="genotype", columns="titrant_conc",
+                                  values="theta_true").loc[genos, BINDING_CONCS]
+    expected = _theta_from_truth(component, params, genos, BINDING_CONCS)
+    np.testing.assert_allclose(wide.to_numpy(), expected, rtol=1e-4, atol=1e-6)
+
+    # At concentrations shared with growth, binding equals growth theta.
+    gt = genotype_theta_df.assign(genotype=genotype_theta_df["genotype"].astype(str))
+    for g in genos:
+        for c in (0.0, 0.01):
+            growth = gt.loc[(gt["genotype"] == g) & (gt["titrant_conc"] == c),
+                            "theta"].item()
+            assert wide.loc[g, c] == pytest.approx(growth, rel=1e-4, abs=1e-6)
+
+    # The measured table (noise 0) reproduces it.
+    measured = generate_binding_df(cf["binding_data"], np.random.default_rng(0),
+                                   binding_theta_df)
+    assert len(measured) == 2 * len(BINDING_CONCS)
+    np.testing.assert_allclose(measured["theta_obs"],
+                               binding_theta_df["theta_true"])
+
+
+@pytest.mark.parametrize("choose_by", ["stratified", "random"])
+@pytest.mark.parametrize("component", CASES)
+def test_library_binding_uses_simulated_theta(component, choose_by, tmp_path):
+    cf, out = _simulate(component, choose_by, tmp_path)
+    library_df, _, _, params, _, _, lbt = out
+    assert lbt is not None
+    assert np.isfinite(lbt["theta"]).all()
+
+    spiked = list(pd.unique(library_df.loc[
+        library_df["library_origin"] == "spiked", "genotype"]))
+    growth_df = pd.DataFrame({"genotype": library_df["genotype"].unique()})
+    bdf, manifest = generate_library_binding_df(
+        cf["binding_data"]["library_binding"], "iptg", BINDING_CONCS, 0.0,
+        params, growth_df, spiked_genotypes=spiked,
+        rng=np.random.default_rng(0), theta_df=lbt)
+
+    genos = list(manifest["genotype"])
+    assert len(genos) == 3
+    assert not set(genos) & (set(spiked) | {"wt"})
+    assert len(bdf) == 3 * len(BINDING_CONCS)
+    assert np.isfinite(bdf["theta_obs"]).all()
+    wide = bdf.pivot(index="genotype", columns="titrant_conc",
+                     values="theta_obs").loc[genos, BINDING_CONCS]
+    expected = _theta_from_truth(component, params, genos, BINDING_CONCS)
+    np.testing.assert_allclose(wide.to_numpy(), expected, rtol=1e-4, atol=1e-6)
+
+
+def test_hill_library_binding_theta_matches_hill_columns(tmp_path):
+    """For hill_geno the evaluated theta equals the Hill-column fallback."""
+    cf = _config("hill_geno", "stratified")
+    del cf["binding_data"]["spiked_binding"]
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        library_df, _, _, params, _, lbt = library_prediction(
+            cf, return_binding_theta=True)
+    growth_df = pd.DataFrame({"genotype": library_df["genotype"].unique()})
+    args = (cf["binding_data"]["library_binding"], "iptg", BINDING_CONCS, 0.0,
+            params, growth_df)
+    from_theta, _ = generate_library_binding_df(
+        *args, spiked_genotypes=None, rng=np.random.default_rng(0),
+        theta_df=lbt)
+    from_hill, _ = generate_library_binding_df(
+        *args, spiked_genotypes=None, rng=np.random.default_rng(0))
+    pd.testing.assert_frame_equal(from_theta, from_hill, rtol=1e-5, atol=1e-6)
+
+
+# ---------------------------------------------------------------------------
+# Zero-mutation (wt-only) libraries
+# ---------------------------------------------------------------------------
+
+ALL_THERMO = sorted(k for k in model_registry["theta"] if k.startswith("thermo."))
+
+
+def _wt_only_sim_data(module):
+    sd = build_sim_data(pd.DataFrame({"genotype": ["wt"]}),
+                        pd.DataFrame({"titrant_conc": [0.0, 0.01, 1.0]}),
+                        skip_pairs=True)
+    names = getattr(module, "STRUCTURE_NAMES", None)
+    if names is None:
+        return sd
+    S = len(names)
+    nn = "PnnC" in module.__name__
+    return sd.replace(
+        num_struct=S, struct_names=tuple(names),
+        struct_features=np.zeros((0, S, 60) if nn else (0, S), np.float32),
+        struct_n_chains=np.ones(S, np.int32) if nn else None)
+
+
+def test_every_thermo_component_listed():
+    assert len(ALL_THERMO) == 12
+
+
+@pytest.mark.parametrize("component", ALL_THERMO)
+def test_zero_mutation_define_model_and_guide(component):
+    module = model_registry["theta"][component]
+    sd = _wt_only_sim_data(module)
+    assert sd.num_mutation == 0
+
+    theta, _ = sample_theta_prior(component, sd, jax.random.PRNGKey(0),
+                                  force_prior_predictive=True)
+    assert theta.shape == (1, 3)
+    assert np.isfinite(theta).all()
+
+    priors = module.ModelPriors(**module.get_hyperparameters())
+    with handlers.seed(rng_seed=0):
+        param = module.guide("theta", sd, priors)
+    assert np.isfinite(np.asarray(module.run_model(param, sd))).all()
+
+
+def test_stratified_pool_draws_thermo_component():
+    """The wt-only stratified pool (the old failure) now draws."""
+    from tfscreen.simulate.sample_theta import sample_theta_stratified
+    b, g = sample_theta_stratified(
+        "thermo.O2_C12_K5_U0_a.PK",
+        binding_sample_df=pd.DataFrame({"titrant_conc": BINDING_CONCS}),
+        growth_sample_df=pd.DataFrame({"titrant_conc": GROWTH_CONCS}),
+        rng_key=jax.random.PRNGKey(0), n_select=2, pool_size=4)
+    assert b.shape == (2, len(BINDING_CONCS)) and np.isfinite(b).all()
+    assert g.shape == (2, len(GROWTH_CONCS)) and np.isfinite(g).all()
diff --git a/tests/tfscreen/simulate/test_thermo_to_growth.py b/tests/tfscreen/simulate/test_thermo_to_growth.py
index d4b3a56b..bea23852 100644
--- a/tests/tfscreen/simulate/test_thermo_to_growth.py
+++ b/tests/tfscreen/simulate/test_thermo_to_growth.py
@@ -7,7 +7,8 @@
 from tfscreen.simulate.thermo_to_growth import (
     _assign_activity,
     _assign_dk_geno,
-    _apply_growth_params,
+    growth_rates,
+    growth_rate_one_condition,
     _sample_horseshoe_activity,
     _sample_hierarchical_activity,
     _theta_param_to_df,
@@ -166,10 +167,18 @@ def test_assign_dk_geno_fixed_value_skips_rng():
 
 
 # ----------------------------------------------------------------------------
-# test _apply_growth_params
+# test growth_rates / growth_rate_one_condition
 # ----------------------------------------------------------------------------
 
-class TestApplyGrowthParams:
+def _k(conds, theta, growth_params, activity=None, dk=None, theta_rescale="passthrough"):
+    n = len(theta)
+    activity = np.ones(n) if activity is None else activity
+    dk = np.zeros(n) if dk is None else dk
+    return growth_rates(np.asarray(conds), np.asarray(theta), activity, dk,
+                        growth_params, theta_rescale)
+
+
+class TestGrowthRates:
 
     @pytest.fixture
     def growth_params(self):
@@ -179,17 +188,15 @@ def growth_params(self):
         }
 
     def test_single_condition_at_theta_zero(self, growth_params):
-        k = _apply_growth_params(np.array(["sel"]), np.array([0.0]), growth_params)
+        k = _k(["sel"], [0.0], growth_params)
         assert np.isclose(k[0], 0.005)
 
     def test_single_condition_at_theta_one(self, growth_params):
-        k = _apply_growth_params(np.array(["sel"]), np.array([1.0]), growth_params)
+        k = _k(["sel"], [1.0], growth_params)
         assert np.isclose(k[0], -0.01 + 0.005)
 
     def test_vector_mixed_conditions(self, growth_params):
-        conds = np.array(["sel", "pre", "sel"])
-        theta = np.array([0.5, 0.3, 0.0])
-        k = _apply_growth_params(conds, theta, growth_params)
+        k = _k(["sel", "pre", "sel"], [0.5, 0.3, 0.0], growth_params)
         expected = np.array([
             -0.01 * 0.5 + 0.005,
              0.002 * 0.3 + 0.020,
@@ -198,35 +205,43 @@ def test_vector_mixed_conditions(self, growth_params):
         np.testing.assert_allclose(k, expected)
 
     def test_returns_numpy_array(self, growth_params):
-        k = _apply_growth_params(np.array(["pre"]), np.array([0.5]), growth_params)
-        assert isinstance(k, np.ndarray)
+        assert isinstance(_k(["pre"], [0.5], growth_params), np.ndarray)
 
     def test_missing_condition_raises(self, growth_params):
         with pytest.raises(KeyError):
-            _apply_growth_params(np.array(["nonexistent"]), np.array([0.5]), growth_params)
+            _k(["nonexistent"], [0.5], growth_params)
 
     def test_activity_scales_theta_contribution(self, growth_params):
-        conds = np.array(["sel", "sel"])
-        theta = np.array([1.0, 1.0])
         activity = np.array([0.5, 2.0])
-        k = _apply_growth_params(conds, theta, growth_params, activity_array=activity)
+        k = _k(["sel", "sel"], [1.0, 1.0], growth_params, activity=activity)
         b = growth_params["sel"]["b"]
         m = growth_params["sel"]["m"]
-        np.testing.assert_allclose(k, b + activity * m * theta)
+        np.testing.assert_allclose(k, b + activity * m * 1.0)
+
+    def test_dk_geno_is_added(self, growth_params):
+        k = _k(["sel", "pre"], [0.5, 0.5], growth_params, dk=np.array([0.01, -0.02]))
+        np.testing.assert_allclose(k, [-0.01*0.5 + 0.005 + 0.01,
+                                       0.002*0.5 + 0.020 - 0.02])
+
+    def test_theta_rescale_applied(self, growth_params):
+        k = _k(["sel"], [0.5], growth_params, theta_rescale="logit")
+        # logit(0.5) = 0
+        assert np.isclose(k[0], 0.005)
+
+    def test_bad_theta_rescale_raises(self, growth_params):
+        with pytest.raises(ValueError, match="theta_rescale"):
+            _k(["sel"], [0.5], growth_params, theta_rescale="nope")
 
-    def test_activity_none_defaults_to_one(self, growth_params):
-        conds = np.array(["sel"])
-        theta = np.array([0.5])
-        k_default = _apply_growth_params(conds, theta, growth_params, activity_array=None)
-        k_explicit = _apply_growth_params(conds, theta, growth_params,
-                                           activity_array=np.array([1.0]))
-        np.testing.assert_allclose(k_default, k_explicit)
+    def test_one_condition_matches_growth_rates(self, growth_params):
+        theta = np.array([0.1, 0.4, 0.9])
+        activity = np.array([1.0, 0.5, 2.0])
+        dk = np.array([0.0, 0.01, -0.01])
+        one = growth_rate_one_condition("sel", theta, activity, dk, growth_params)
+        many = growth_rates(np.array(["sel"]*3), theta, activity, dk, growth_params)
+        np.testing.assert_allclose(one, many)
 
     def test_zero_activity_returns_baseline(self, growth_params):
-        conds = np.array(["sel"])
-        theta = np.array([0.7])
-        k = _apply_growth_params(conds, theta, growth_params,
-                                  activity_array=np.array([0.0]))
+        k = _k(["sel"], [0.7], growth_params, activity=np.array([0.0]))
         assert np.isclose(k[0], growth_params["sel"]["b"])
 
 
@@ -1570,3 +1585,50 @@ def test_one_row_per_genotype(self):
         param = self._make_param_single_titrant(4)
         df = _theta_param_to_df(param, genotypes, sim_idx)
         assert len(df) == 4
+
+
+# ----------------------------------------------------------------------------
+# wt keeps the hill_geno reference curve
+# ----------------------------------------------------------------------------
+
+def _hill_geno_run(seed, sim_overrides=None, theta_gc_override=None):
+    import jax
+    from tfscreen.simulate.sim_data_class import build_sim_data
+    genotypes = ["wt"] + [f"A{i}G" for i in range(2, 40)]
+    sample_df = pd.DataFrame({
+        "condition_pre": ["M9"] * 4, "condition_sel": ["M9+Ab"] * 4,
+        "titrant_name": ["IPTG"] * 4, "titrant_conc": [0.0, 0.01, 0.1, 1.0]})
+    sim_data = build_sim_data(pd.DataFrame({"genotype": genotypes}), sample_df)
+    growth = {"M9": {"m": 0.001, "b": 0.02}, "M9+Ab": {"m": -0.01, "b": 0.005}}
+    # every genotype in a non-normal category: an unpinned wt would never
+    # get the reference curve
+    priors = {"p_stuck_bound": 0.0, "p_never_binds": 0.999, "p_inverted": 0.0}
+    priors.update(sim_overrides or {})
+    return thermo_to_growth(
+        genotypes, sim_data, sample_df, "hill_geno",
+        jax.random.PRNGKey(seed), growth,
+        theta_sim_priors_overrides=priors,
+        theta_gc_override=theta_gc_override,
+        rng=np.random.default_rng(seed))
+
+
+@pytest.mark.parametrize("seed", [0, 1, 2])
+def test_hill_geno_wt_gets_reference_curve(seed):
+    _, theta_df, params = _hill_geno_run(seed, {"wt_log_K": -3.0})
+    wt = params[params["genotype"] == "wt"].iloc[0]
+    assert wt["theta_low"] == pytest.approx(0.99)
+    assert wt["theta_high"] == pytest.approx(0.01)
+    assert wt["log_hill_K"] == pytest.approx(-3.0)
+    assert wt["hill_n"] == pytest.approx(2.0)
+    t = theta_df[theta_df["genotype"] == "wt"].sort_values("titrant_conc")["theta"]
+    assert t.iloc[0] > 0.98 and t.iloc[-1] < 0.05
+    # the other genotypes kept their (never-binds) draws
+    others = params[params["genotype"] != "wt"]
+    assert (others["theta_low"] < 0.5).all()
+
+
+def test_hill_geno_wt_override_wins():
+    override = {"wt": np.array([0.5, 0.5, 0.5, 0.5])}
+    _, theta_df, _ = _hill_geno_run(0, theta_gc_override=override)
+    t = theta_df[theta_df["genotype"] == "wt"]["theta"]
+    np.testing.assert_allclose(t, 0.5)
diff --git a/tests/tfscreen/simulate/test_thermo_truth.py b/tests/tfscreen/simulate/test_thermo_truth.py
new file mode 100644
index 00000000..53ffed37
--- /dev/null
+++ b/tests/tfscreen/simulate/test_thermo_truth.py
@@ -0,0 +1,398 @@
+"""
+Ground truth written for the thermodynamic theta models.
+
+The thermodynamic components (``thermo.O2_C4_K3_*`` and ``thermo.O2_C12_K5_*``)
+carry titrant-independent per-genotype constants as (G,) arrays and their
+per-mutation, per-pair and per-structure values only as deterministic sites.
+These tests draw a tiny library from each family and check that the
+simulator's truth covers every parameter ``tfs-extract-params`` writes for the
+model (names from the component's ``get_extract_specs``).
+"""
+import types
+import warnings
+from unittest.mock import patch
+
+import jax
+import numpy as np
+import pandas as pd
+import pytest
+from flax.struct import dataclass
+
+from tfscreen.genetics.build_mut_geno_matrix import build_mut_geno_matrix
+from tfscreen.simulate.sim_data_class import build_sim_data
+from tfscreen.simulate.thermo_to_growth import (
+    _mutation_truth_tables,
+    _theta_param_to_df,
+    thermo_to_growth,
+)
+from tfscreen.tfmodel.generative.registry import model_registry
+
+GENOTYPES = ["wt", "H29A", "H29C", "L62A", "H29A/L62A", "H29C/L62A"]
+
+SAMPLE_DF = pd.DataFrame({
+    "titrant_name": "iptg",
+    "titrant_conc": [0.0, 0.01, 1.0],
+    "condition_pre": "pre",
+    "condition_sel": "sel",
+})
+
+GROWTH = {"pre": {"m": 0.0, "b": 0.01}, "sel": {"m": 0.01, "b": 0.01}}
+
+# Columns of the ddG prior CSV (struct names of the lac-dimer and MWC models).
+_C4_STRUCTS = ["H", "HD", "L", "LE2"]
+_C12_STRUCTS = ["H", "HO", "L", "LO", "HE2", "LE2"]
+
+
+def _ddG_csv(path, component="O2_C4"):
+    structs = _C12_STRUCTS if "O2_C12" in component else _C4_STRUCTS
+    rng = np.random.default_rng(3)
+    muts = ["H29A", "H29C", "L62A"]
+    df = pd.DataFrame(rng.normal(0, 1, (len(muts), len(structs))),
+                      columns=structs)
+    df.insert(0, "mut", muts)
+    df.to_csv(path, index=False)
+    return str(path)
+
+
+def _draw(component, thermo_data=None, return_mutation_truth=True, seed=0,
+          **sim_kwargs):
+    lib = pd.DataFrame({"genotype": GENOTYPES})
+    sim_data = build_sim_data(lib, SAMPLE_DF, thermo_data=thermo_data,
+                              **sim_kwargs)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        return thermo_to_growth(lib["genotype"], sim_data, SAMPLE_DF, component,
+                                jax.random.PRNGKey(seed), GROWTH,
+                                rng=np.random.default_rng(seed),
+                                return_mutation_truth=return_mutation_truth)
+
+
+def _extract_names(component):
+    """Parameter names tfs-extract-params writes for ``component``."""
+    mut_labels, pair_labels, *_ = build_mut_geno_matrix(GENOTYPES)
+    growth_tm = types.SimpleNamespace(
+        tensor_dim_names=["genotype", "titrant_name"],
+        tensor_dim_labels=[GENOTYPES, ["iptg"]],
+        df=pd.DataFrame({"genotype": GENOTYPES,
+                         "genotype_idx": range(len(GENOTYPES)),
+                         "titrant_name": "iptg", "titrant_name_idx": 0}))
+    ctx = types.SimpleNamespace(growth_tm=growth_tm, mut_labels=mut_labels,
+                                pair_labels=pair_labels)
+    specs = model_registry["theta"][component].get_extract_specs(ctx)
+    names = [p for spec in specs
+             for p in spec.get("out_names", spec["params_to_get"])]
+    # A prior-mean table (ddG_prior_mean_H) is scored against the ddG truth
+    # (ddG_H); it has no truth column of its own.
+    return [n.replace("ddG_prior_mean_", "ddG_") for n in names]
+
+
+# PK covers each family; PddG cases cover the per-structure ddG truth and,
+# for U1, the unfolding shift projected from ddG_H.
+CASES = [
+    ("thermo.O2_C4_K3_U0_a.PK", ["ln_K_op", "ln_K_HL"]),
+    ("thermo.O2_C4_K3_U1_a.PK", ["ln_K_op", "ln_K_HL", "ln_K_U"]),
+    ("thermo.O2_C12_K5_U0_a.PK", ["ln_K_h_l", "ln_K_h_o", "ln_K_l_o"]),
+    ("thermo.O2_C12_K5_U1_a.PK", ["ln_K_h_l", "ln_K_h_o", "ln_K_l_o", "ln_K_u"]),
+    ("thermo.O2_C4_K3_U0_a.PddG", ["ln_K_op", "ln_K_HL"]),
+    ("thermo.O2_C4_K3_U1_a.PddG", ["ln_K_op", "ln_K_HL", "ln_K_U"]),
+    ("thermo.O2_C12_K5_U1_a.PddG", ["ln_K_h_l", "ln_K_h_o", "ln_K_l_o", "ln_K_u"]),
+]
+
+
+@pytest.mark.parametrize("component, scalar_ks", CASES)
+def test_truth_covers_every_extracted_parameter(component, scalar_ks, tmp_path):
+    td = (_ddG_csv(tmp_path / "ddG.csv", component)
+          if component.endswith("PddG") else None)
+    _, _, params, truth = _draw(component, thermo_data=td)
+    mutation_df, pair_df = truth["mutation"], truth["pair"]
+
+    # Titrant-independent (G,) constants are per-genotype columns now.
+    for col in scalar_ks:
+        assert col in params.columns
+        assert params[col].notna().all()
+
+    assert mutation_df is not None and pair_df is not None
+    assert list(mutation_df["mutation"]) == ["H29A", "H29C", "L62A"]
+    assert set(pair_df["pair"]) == {"H29A/L62A", "H29C/L62A"}
+
+    truth_names = set(params.columns) | set(mutation_df.columns) | set(pair_df.columns)
+    if truth["ddG_sigma"] is not None:      # PddG/PnnC: the σ_s truth table
+        truth_names.add("ddG_sigma")
+    missing = [n for n in _extract_names(component) if n not in truth_names]
+    assert missing == []
+
+
+@pytest.mark.parametrize("component, scalar_ks", CASES)
+def test_mutation_truth_matches_genotype_truth(component, scalar_ks, tmp_path):
+    """d_X of a mutation is its single mutant's X minus wt's X; a double's X
+    is wt + both deltas + the pair's epistasis."""
+    td = (_ddG_csv(tmp_path / "ddG.csv", component)
+          if component.endswith("PddG") else None)
+    _, _, params, truth = _draw(component, thermo_data=td)
+    p = params.set_index("genotype")
+    mut = truth["mutation"].set_index("mutation")
+    pair = truth["pair"].set_index("pair")
+    for col in scalar_ks:
+        for m in ["H29A", "H29C", "L62A"]:
+            assert p.loc[m, col] - p.loc["wt", col] == pytest.approx(
+                mut.loc[m, f"d_{col}"], abs=1e-4)
+        if f"epi_{col}" in pair.columns:
+            expected = (p.loc["wt", col] + mut.loc["H29A", f"d_{col}"]
+                        + mut.loc["L62A", f"d_{col}"]
+                        + pair.loc["H29A/L62A", f"epi_{col}"])
+            assert p.loc["H29A/L62A", col] == pytest.approx(expected, abs=1e-4)
+
+
+def test_pddg_writes_per_structure_ddG(tmp_path):
+    td = _ddG_csv(tmp_path / "ddG.csv")
+    _, _, _, truth = _draw("thermo.O2_C4_K3_U0_a.PddG", thermo_data=td)
+    mut = truth["mutation"]
+    for s in _C4_STRUCTS:
+        assert f"ddG_{s}" in mut.columns
+    # The model's projection: d_ln_K_op = ddG_H - ddG_HD.
+    np.testing.assert_allclose(mut["d_ln_K_op"], mut["ddG_H"] - mut["ddG_HD"],
+                               atol=1e-5)
+
+
+@pytest.mark.parametrize("component, structs, u", [
+    ("thermo.O2_C4_K3_U1_a.PddG", _C4_STRUCTS, "ln_K_U"),
+    ("thermo.O2_C12_K5_U1_a.PddG", _C12_STRUCTS, "ln_K_u"),
+])
+def test_u1_pddg_unfolding_truth_is_ddG_H(component, structs, u, tmp_path):
+    """K_U = [U]/[H] and mutations leave U alone, so d_ln_K_U = ddG_H, and the
+    per-genotype ln_K_U truth varies across genotypes."""
+    _, _, params, truth = _draw(component,
+                                thermo_data=_ddG_csv(tmp_path / "ddG.csv",
+                                                     component))
+    mut = truth["mutation"]
+    for s in structs:
+        assert f"ddG_{s}" in mut.columns
+    np.testing.assert_allclose(mut[f"d_{u}"], mut["ddG_H"], atol=1e-5)
+    assert f"epi_{u}" in truth["pair"].columns
+    assert params[u].nunique() > 1
+
+
+def test_mutation_truth_does_not_change_the_draw():
+    pheno_a, theta_a, params_a = _draw("thermo.O2_C4_K3_U0_a.PK",
+                                       return_mutation_truth=False)
+    pheno_b, theta_b, params_b, _ = _draw("thermo.O2_C4_K3_U0_a.PK")
+    pd.testing.assert_frame_equal(theta_a, theta_b)
+    pd.testing.assert_frame_equal(params_a, params_b)
+    pd.testing.assert_frame_equal(pheno_a, pheno_b)
+
+
+def test_hill_components_have_no_mutation_truth():
+    """Components sampled through simulate() have no trace to read."""
+    _, _, params, truth = _draw("hill_geno")
+    assert truth == {"mutation": None, "pair": None, "ddG_sigma": None}
+    assert "log_hill_K" in params.columns
+
+
+# ---------------------------------------------------------------------------
+# helpers
+# ---------------------------------------------------------------------------
+
+@dataclass
+class _OneDParam:
+    ln_K_op: np.ndarray    # (G,)
+    ln_K_E: np.ndarray     # (1, G)
+    tf_total: float        # scalar
+
+
+def test_theta_param_to_df_reads_one_dimensional_fields():
+    param = _OneDParam(ln_K_op=np.array([1.0, 2.0, 3.0]),
+                       ln_K_E=np.array([[4.0, 5.0, 6.0]]),
+                       tf_total=1e-6)
+    df = _theta_param_to_df(param, ["b", "a"], [1, 0], num_genotype_sim=3)
+    assert list(df.columns) == ["genotype", "ln_K_op", "ln_K_E"]
+    assert list(df["ln_K_op"]) == [2.0, 1.0]
+    assert list(df["ln_K_E"]) == [5.0, 4.0]
+
+
+def test_theta_param_to_df_without_num_genotype_skips_one_dimensional():
+    param = _OneDParam(ln_K_op=np.array([1.0, 2.0, 3.0]),
+                       ln_K_E=np.array([[4.0, 5.0, 6.0]]),
+                       tf_total=1e-6)
+    df = _theta_param_to_df(param, ["b", "a"], [1, 0])
+    assert list(df.columns) == ["genotype", "ln_K_E"]
+
+
+def test_mutation_truth_tables_suffixes_multiple_titrants():
+    genotypes = ["wt", "A1B", "C2D", "A1B/C2D"]
+    sim_data = types.SimpleNamespace(num_mutation=2, num_pair=1,
+                                     struct_names=None)
+    det = {"theta_d_ln_K_E": np.array([[1.0, 2.0], [3.0, 4.0]]),
+           "theta_d_ln_K_op": np.array([5.0, 6.0]),
+           "theta_epi_ln_K_op": np.array([7.0]),
+           "theta_ln_K_op": np.zeros(4)}      # per-genotype: not mutation truth
+    mut, pair = _mutation_truth_tables(det, genotypes, sim_data)
+    assert list(mut.columns) == ["mutation", "d_ln_K_E_T0", "d_ln_K_E_T1",
+                                 "d_ln_K_op"]
+    assert list(pair.columns) == ["pair", "epi_ln_K_op"]
+    assert pair["pair"].tolist() == ["A1B/C2D"]
+
+
+def test_mutation_truth_tables_empty_without_mutations():
+    sim_data = types.SimpleNamespace(num_mutation=0, num_pair=0,
+                                     struct_names=None)
+    assert _mutation_truth_tables({"theta_x": 1}, ["wt"], sim_data) == (None, None)
+
+
+# ---------------------------------------------------------------------------
+# tfs-simulate writes the tables
+# ---------------------------------------------------------------------------
+
+def test_simulate_cli_writes_mutation_and_pair_truth(tmp_path):
+    from tfscreen.simulate.scripts.simulate_cli import run_simulation_from_config
+
+    lib_df = pd.DataFrame({"genotype": ["wt", "A1B"]})
+    params_df = pd.DataFrame({"genotype": ["wt", "A1B"], "dk_geno": [0.0, 0.0],
+                              "activity": [1.0, 1.0], "ln_K_op": [1.0, 2.0]})
+    mut_df = pd.DataFrame({"mutation": ["A1B"], "d_ln_K_op": [1.0]})
+    sample_df = pd.DataFrame({"replicate": [1], "library": ["x"]})
+    counts_df = pd.DataFrame({"sample": [0], "genotype": ["wt"], "counts": [1]})
+
+    with patch("tfscreen.util.read_yaml", return_value={"seed": 0, "growth": {}}), \
+         patch("tfscreen.simulate.scripts.simulate_cli.library_prediction",
+               return_value=(lib_df, lib_df, lib_df, params_df, None,
+                             {"mutation": mut_df, "pair": None}, None)), \
+         patch("tfscreen.simulate.scripts.simulate_cli.selection_experiment",
+               return_value=(sample_df, counts_df)):
+        run_simulation_from_config("config.yaml",
+                                   out_prefix=str(tmp_path / "tfs_sim"),
+                                   num_replicates=1, write_raw=False,
+                                   write_growth=False)
+
+    written = pd.read_csv(tmp_path / "tfs_sim_mutation_parameters.csv")
+    pd.testing.assert_frame_equal(written, mut_df)
+    assert not (tmp_path / "tfs_sim_pair_parameters.csv").exists()
+
+
+# ── ΔΔG units, column handling and the default being unchanged ──────────────
+
+def test_default_ddG_units_leave_the_draw_unchanged(tmp_path):
+    comp = "thermo.O2_C4_K3_U0_a.PddG"
+    td = _ddG_csv(tmp_path / "ddG.csv", comp)
+    base = _draw(comp, thermo_data=td)
+    for kwargs in ({"theta_component": comp},
+                   {"theta_component": comp,
+                    "thermo_options": {"ddG_units": "kT"}}):
+        other = _draw(comp, thermo_data=td, **kwargs)
+        for a, b in zip(base[:3], other[:3]):
+            pd.testing.assert_frame_equal(a, b)
+        pd.testing.assert_frame_equal(base[3]["mutation"], other[3]["mutation"])
+        pd.testing.assert_frame_equal(base[3]["pair"], other[3]["pair"])
+
+
+def test_kcal_ddG_matches_kT_draw(tmp_path):
+    """The same energies given in kcal/mol, with ddG_units: kcal/mol, give
+    the kT draw."""
+    comp = "thermo.O2_C4_K3_U0_a.PddG"
+    td = _ddG_csv(tmp_path / "ddG.csv", comp)
+    RT = 1.987204259e-3 * 298.15
+    df = pd.read_csv(td)
+    df[_C4_STRUCTS] = df[_C4_STRUCTS] * RT
+    td_kcal = str(tmp_path / "ddG_kcal.csv")
+    df.to_csv(td_kcal, index=False)
+    ref = _draw(comp, thermo_data=td, theta_component=comp)[3]["mutation"]
+    got = _draw(comp, thermo_data=td_kcal, theta_component=comp,
+                thermo_options={"ddG_units": "kcal/mol"})[3]["mutation"]
+    pd.testing.assert_frame_equal(ref, got, atol=1e-5, rtol=0)
+    # Read as kT, the kcal file would shrink every ΔΔG by RT.
+    raw = _draw(comp, thermo_data=td_kcal, theta_component=comp)[3]["mutation"]
+    assert not np.allclose(raw["ddG_H"], ref["ddG_H"], atol=1e-3)
+
+
+def test_reordered_csv_with_extras_labels_ddG_truth(tmp_path):
+    """Extra columns, a 'Mutation' column and a pandas index are accepted, and
+    the ddG truth columns follow the model's structure order, not the file's."""
+    comp = "thermo.O2_C12_K5_U0_a.PddG"
+    td = _ddG_csv(tmp_path / "ddG.csv", comp)
+    df = pd.read_csv(td).rename(columns={"mut": "Mutation"})
+    df["unfolded"] = 0.0
+    df = df[["Mutation", "unfolded"] + list(reversed(_C12_STRUCTS))]
+    td2 = str(tmp_path / "ddG_messy.csv")
+    df.to_csv(td2)                               # writes 'Unnamed: 0'
+    ref = _draw(comp, thermo_data=td, theta_component=comp)[3]["mutation"]
+    got = _draw(comp, thermo_data=td2, theta_component=comp)[3]["mutation"]
+    pd.testing.assert_frame_equal(ref, got)
+    # Without the component the file order is kept; the truth labels still
+    # follow the model's order.
+    rev = str(tmp_path / "rev.csv")
+    pd.read_csv(td).to_csv(rev, index=False,
+                           columns=["mut"] + list(reversed(_C12_STRUCTS)))
+    got2 = _draw(comp, thermo_data=rev)[3]["mutation"]
+    pd.testing.assert_frame_equal(ref, got2)
+
+
+# ---------------------------------------------------------------------------
+# small branches of the truth helpers
+# ---------------------------------------------------------------------------
+
+@dataclass
+class _WrongLengthParam:
+    ln_K_op: np.ndarray    # (G,)
+    other: np.ndarray      # (2,): not per-genotype when G = 3
+
+
+def test_theta_param_to_df_skips_fields_not_sized_by_genotype():
+    param = _WrongLengthParam(ln_K_op=np.array([1.0, 2.0, 3.0]),
+                              other=np.array([7.0, 8.0]))
+    df = _theta_param_to_df(param, ["a", "b", "c"], [0, 1, 2],
+                            num_genotype_sim=3)
+    assert list(df.columns) == ["genotype", "ln_K_op"]
+
+
+def test_theta_at_concs_hill_override_and_nan_for_curve_less_override():
+    from tfscreen.simulate.thermo_to_growth import _theta_at_concs
+
+    concs = np.array([0.0, 0.1, 1.0])
+    fake_module = types.SimpleNamespace(
+        run_model=lambda param, data: (np.full((len(concs), 3), 0.5),))
+    hill = {"theta_low": 0.1, "theta_high": 0.9, "log_hill_K": 0.0,
+            "hill_n": 2.0}
+    with patch.dict(model_registry["theta"], {"fake": fake_module}), \
+         patch("tfscreen.simulate.sim_data_class.with_titrant_conc",
+               side_effect=lambda sd, c: sd):
+        out = _theta_at_concs("fake", None, None, [1.0, 0.0, 0.1],
+                              ["wt", "A1B", "C2D"], [0, 1, 2],
+                              theta_gc_override={"A1B": np.ones(3),
+                                                 "C2D": np.ones(3)},
+                              theta_params_override={"A1B": hill})
+    wide = out.pivot(index="genotype", columns="titrant_conc", values="theta")
+    np.testing.assert_allclose(wide.loc["wt"], [0.5, 0.5, 0.5])
+    log_c = np.log([1e-20, 0.1, 1.0])
+    expected = 0.1 + 0.8 / (1.0 + np.exp(-2.0 * log_c))
+    np.testing.assert_allclose(wide.loc["A1B"], expected)
+    assert wide.loc["C2D"].isna().all()
+
+
+def test_mutation_truth_tables_label_mismatch_raises():
+    sim_data = types.SimpleNamespace(num_mutation=3, num_pair=0,
+                                     struct_names=None)
+    with pytest.raises(ValueError, match=r"labels \(2/0\) do not match "
+                                         r"sim_data \(3/0\)"):
+        _mutation_truth_tables({"theta_d_x": np.zeros(3)},
+                               ["wt", "A1B", "C2D"], sim_data)
+
+
+def test_mutation_truth_tables_ddG_without_structure_names_numbers_them():
+    sim_data = types.SimpleNamespace(num_mutation=2, num_pair=0,
+                                     struct_names=("H",))   # wrong length
+    det = {"theta_ddG": np.array([[1.0, 2.0], [3.0, 4.0]])}
+    mut, pair = _mutation_truth_tables(det, ["wt", "A1B", "C2D"], sim_data)
+    assert pair is None
+    assert list(mut.columns) == ["mutation", "ddG_0", "ddG_1"]
+    assert mut["ddG_1"].tolist() == [2.0, 4.0]
+
+
+def test_ddG_sigma_truth_table_numbers_structures_without_names():
+    from tfscreen.simulate.thermo_to_growth import _ddG_sigma_truth_table
+
+    sites = {"theta_ddG_sigma": np.array([0.5, 1.5])}
+    df = _ddG_sigma_truth_table(sites, None)
+    assert df["structure"].tolist() == ["0", "1"]
+    assert df["ddG_sigma"].tolist() == [0.5, 1.5]
+    df = _ddG_sigma_truth_table(sites, ["H", "L", "X"])     # wrong length
+    assert df["structure"].tolist() == ["0", "1"]
+    assert _ddG_sigma_truth_table({}, ["H"]) is None
diff --git a/tests/tfscreen/simulate/test_undetectable_tubes.py b/tests/tfscreen/simulate/test_undetectable_tubes.py
new file mode 100644
index 00000000..945cfbad
--- /dev/null
+++ b/tests/tfscreen/simulate/test_undetectable_tubes.py
@@ -0,0 +1,83 @@
+"""Tubes read below the OD600 detection threshold are dropped the same way
+by tfs-simulate's direct growth table and by the raw route through
+tfs-process-counts."""
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+import yaml
+
+from tfscreen.process_raw.scripts.process_counts_cli import process_counts
+from tfscreen.simulate.raw_output import undetectable_tubes
+from tfscreen.simulate.scripts.simulate_cli import run_simulation_from_config
+
+ROOT = os.path.join(os.path.dirname(__file__), "..", "..", "..")
+EXAMPLE = os.path.join(ROOT, "examples", "simulate-and-analyze")
+CALIBRATION = os.path.abspath(os.path.join(ROOT, "examples", "od600",
+                                           "od600_calibration.yaml"))
+TUBE = ["replicate", "titrant_conc", "t_sel"]
+
+
+def test_undetectable_tubes_mask():
+    df = pd.DataFrame({"od600_detectable": [True, False, True]})
+    np.testing.assert_array_equal(undetectable_tubes(df, {"x": 1}),
+                                  [False, True, False])
+    assert not undetectable_tubes(df, None).any()
+    assert not undetectable_tubes(pd.DataFrame({"a": [1]}), {"x": 1}).any()
+
+
+def test_direct_and_raw_routes_keep_the_same_tubes(tmp_path, monkeypatch,
+                                                   capsys):
+    with open(os.path.join(EXAMPLE, "simulate_config.yaml")) as fh:
+        cf = yaml.safe_load(fh)
+    cf.pop("condition_blocks")
+    for k in ("binding_data", "presplit_data"):
+        cf.pop(k, None)
+    rows = []
+    for rep in (1, 2):
+        for conc in (0.0, 1.0):
+            for t in (60, 120, 240):
+                rows.append({"sample": f"k{rep}-{conc}-{t}", "library": "kanR",
+                             "replicate": rep, "condition_pre": "kanR-kan",
+                             "t_pre": 30, "condition_sel": "kanR+kan",
+                             "t_sel": t, "titrant_name": "iptg",
+                             "titrant_conc": conc})
+    pd.DataFrame(rows).to_csv(tmp_path / "design.csv", index=False)
+    cf["design"] = str(tmp_path / "design.csv")
+    # cfu0 low enough that an early, repressed kanR+kan tube reads below the
+    # calibration's detection threshold (0.093)
+    cf["cfu0"] = 5e8
+    cf["od600"] = {"calibration": CALIBRATION, "tube_volume_mL": 5.0,
+                   "sample_cfu_from_od600": True}
+    (tmp_path / "c.yaml").write_text(yaml.safe_dump(cf))
+    monkeypatch.chdir(EXAMPLE)
+    out = str(tmp_path / "sim")
+    run_simulation_from_config(str(tmp_path / "c.yaml"), out_prefix=out,
+                               seed=1)
+    log = capsys.readouterr().out
+
+    od = pd.read_csv(f"{out}_od600.csv")
+    low = od.loc[~od["od600_detectable"].astype(bool), TUBE]
+    assert len(low) >= 1, "the fixture should have an undetectable tube"
+    assert f"Dropped {len(low)} of {len(od)} tube(s)" in log
+
+    direct = pd.read_csv(f"{out}_growth.csv")
+    processed = pd.read_csv(process_counts(
+        f"{out}_tubes.csv", f"{out}_counts", out_prefix=str(tmp_path / "p"),
+        od600_file=f"{out}_tube_od600.csv",
+        od600_calibration_file=f"{out}_od600_calibration.yaml",
+        tube_volume_mL=5.0, verbose=False))
+
+    def tubes(df):
+        return set(map(tuple, df[TUBE].drop_duplicates().to_numpy()))
+
+    assert tubes(direct) == tubes(processed)
+    assert not tubes(direct) & set(map(tuple, low.to_numpy()))
+    assert len(tubes(direct)) == len(od) - len(low)
+    key = TUBE + ["genotype"]
+    m = direct.merge(processed, on=key, suffixes=("_d", "_p"))
+    assert len(m) == len(direct) == len(processed)
+    np.testing.assert_array_equal(m["counts_d"], m["counts_p"])
+    np.testing.assert_allclose(m["sample_cfu_d"], m["sample_cfu_p"],
+                               rtol=1e-9)
diff --git a/tests/tfscreen/tfmodel/analysis/test_ddG_extraction.py b/tests/tfscreen/tfmodel/analysis/test_ddG_extraction.py
new file mode 100644
index 00000000..484db937
--- /dev/null
+++ b/tests/tfscreen/tfmodel/analysis/test_ddG_extraction.py
@@ -0,0 +1,455 @@
+"""
+Per-structure ΔΔG extraction for the structure-based thermodynamic models.
+
+PddG and PnnC sample the per-mutation constants (``theta_dlnK``) under a
+structural prior and record a post-hoc per-structure ΔΔG (``theta_ddG``,
+M × S, in the model's ``STRUCTURE_NAMES`` order; exact in U1, the
+conditional mean under the per-state prior in U0) and its structural prior
+mean (``theta_ddG_prior_mean``: the CSV values or the MLP output).  The
+simulator writes the truth as ``ddG_{structure}`` columns of
+``*_sim_mutation_parameters.csv``.  ``tfs-extract-params`` must write one
+table per structure under those names and ``tfs-summarize-fit`` must score
+them (U1) or flag them (U0), with the structure labels right whatever order
+the input file uses.
+"""
+import glob
+import os
+import types
+import warnings
+
+import h5py
+import numpy as np
+import pandas as pd
+import pytest
+import yaml
+
+from tfscreen.tfmodel.analysis.extraction import _extract_param_est
+from tfscreen.tfmodel.generative.registry import model_registry
+
+_MODELS = [f"thermo.{fam}.{kind}"
+           for fam in ("O2_C4_K3_U0_a", "O2_C4_K3_U1_a",
+                       "O2_C12_K5_U0_a", "O2_C12_K5_U1_a")
+           for kind in ("PddG", "PnnC")]
+
+_MUTS = ["H29A", "H29C", "L62A"]
+_GENOTYPES = ["wt"] + _MUTS + ["H29A/L62A"]
+
+
+def _ctx(pair_labels=()):
+    growth_tm = types.SimpleNamespace(
+        tensor_dim_names=["genotype", "titrant_name"],
+        tensor_dim_labels=[_GENOTYPES, ["iptg"]],
+        df=pd.DataFrame({"genotype": _GENOTYPES,
+                         "genotype_idx": range(len(_GENOTYPES)),
+                         "titrant_name": "iptg", "titrant_name_idx": 0}))
+    return types.SimpleNamespace(growth_tm=growth_tm, mut_labels=list(_MUTS),
+                                 pair_labels=list(pair_labels))
+
+
+def _ddG_specs(component):
+    """The per-mutation ΔΔG specs (``ddG_*``, ``ddG_prior_mean_*``)."""
+    specs = model_registry["theta"][component].get_extract_specs(_ctx())
+    return [s for s in specs
+            if "out_names" in s and s["get_columns"] == ["mutation"]]
+
+
+def _sigma_spec(component):
+    specs = model_registry["theta"][component].get_extract_specs(_ctx())
+    found = [s for s in specs if s.get("out_names") == ["ddG_sigma"]]
+    assert len(found) == 1
+    return found[0]
+
+
+# ── extract specs ────────────────────────────────────────────────────────────
+
+@pytest.mark.parametrize("component", _MODELS)
+def test_specs_name_one_table_per_structure(component):
+    structs = model_registry["theta"][component].STRUCTURE_NAMES
+    specs = _ddG_specs(component)
+    names = [n for s in specs for n in s["out_names"]]
+    assert names == [f"{site}_{s}" for s in structs
+                     for site in ("ddG", "ddG_prior_mean")]
+    for spec in specs:
+        assert spec["in_run_prefix"] == "theta_"
+        assert spec["get_columns"] == ["mutation"]
+        assert list(spec["input_df"]["mutation"]) == _MUTS
+
+
+@pytest.mark.parametrize("component", _MODELS)
+def test_specs_read_each_structure_column(component):
+    """Each table reads its structure's column of the (M, S) site."""
+    structs = model_registry["theta"][component].STRUCTURE_NAMES
+    M, S = len(_MUTS), len(structs)
+    # Sample k, mutation m, structure s holds 100 k + 10 m + s.
+    vals = (100 * np.arange(2)[:, None, None] + 10 * np.arange(M)[None, :, None]
+            + np.arange(S)[None, None, :]).astype(float)
+    post = {"theta_ddG": vals, "theta_ddG_prior_mean": -vals}
+    tables = {}
+    for spec in _ddG_specs(component):
+        tables.update(_extract_param_est(param_posteriors=post,
+                                         q_to_get={"q0": 0.0, "q1": 1.0},
+                                         **spec))
+    for s, struct in enumerate(structs):
+        df = tables[f"ddG_{struct}"]
+        assert list(df.columns) == ["mutation", "q0", "q1"]
+        np.testing.assert_allclose(df["q0"], 10 * np.arange(M) + s)
+        np.testing.assert_allclose(df["q1"], 100 + 10 * np.arange(M) + s)
+        np.testing.assert_allclose(tables[f"ddG_prior_mean_{struct}"]["q1"],
+                                   -(10 * np.arange(M) + s))
+
+
+@pytest.mark.parametrize("component", _MODELS)
+def test_sigma_spec_gives_one_row_per_structure(component):
+    """σ_s (theta_ddG_sigma, shape S) becomes one ddG_sigma table keyed by
+    structure, in STRUCTURE_NAMES order."""
+    structs = model_registry["theta"][component].STRUCTURE_NAMES
+    spec = _sigma_spec(component)
+    assert spec["params_to_get"] == ["ddG_sigma"]
+    assert spec["in_run_prefix"] == "theta_"
+    assert spec["get_columns"] == ["structure"]
+    post = {"theta_ddG_sigma": np.array([np.arange(len(structs)) + 1.0,
+                                         np.arange(len(structs)) + 3.0])}
+    df = _extract_param_est(param_posteriors=post,
+                            q_to_get={"q0": 0.0, "q1": 1.0},
+                            **spec)["ddG_sigma"]
+    assert list(df["structure"]) == list(structs)
+    np.testing.assert_allclose(df["q0"], np.arange(len(structs)) + 1.0)
+    np.testing.assert_allclose(df["q1"], np.arange(len(structs)) + 3.0)
+
+
+def test_out_names_must_match_params():
+    with pytest.raises(ValueError, match="out_names"):
+        _extract_param_est(input_df=pd.DataFrame({"m": [0]}),
+                           params_to_get=["a", "b"], out_names=["x"],
+                           map_column="m", get_columns=[], in_run_prefix="",
+                           param_posteriors={"a": np.zeros((1, 1))},
+                           q_to_get={"q0.5": 0.5})
+
+
+# ── tfs-summarize-fit scoring ────────────────────────────────────────────────
+
+def _q_table(values, spread):
+    df = pd.DataFrame({"mutation": _MUTS})
+    for q, z in (("q0.025", -2.0), ("q0.5", 0.0), ("q0.975", 2.0)):
+        df[q] = np.asarray(values) + z * spread
+    return df
+
+
+def _summarize_run(tmp_path, with_ddG, config_yaml=None):
+    from tfscreen.tfmodel.scripts.summarize_fit_cli import _summarize_params
+    run = tmp_path / "run"
+    run.mkdir()
+    pd.DataFrame({"genotype": _GENOTYPES, "ln_K_op": 0.0}).to_csv(
+        run / "tfs_sim_parameters.csv", index=False)
+    truth = pd.DataFrame({"mutation": _MUTS, "d_ln_K_op": [1.0, 2.0, 3.0],
+                          "ddG_H": [0.5, -1.0, 2.0], "ddG_HD": [3.0, 0.0, 1.0]})
+    truth.to_csv(run / "tfs_sim_mutation_parameters.csv", index=False)
+    _q_table([1.1, 2.1, 2.9], 0.2).to_csv(run / "tfs_params_d_ln_K_op.csv",
+                                          index=False)
+    if with_ddG:
+        for s in ("H", "HD"):
+            _q_table(truth[f"ddG_{s}"] + 0.1, 0.2).to_csv(
+                run / f"tfs_params_ddG_{s}.csv", index=False)
+            _q_table(truth[f"ddG_{s}"] - 0.3, 0.0).to_csv(
+                run / f"tfs_params_ddG_prior_mean_{s}.csv", index=False)
+    out = tmp_path / "summary"
+    out.mkdir()
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+        _summarize_params(str(run), str(out / "tfs_summarize"), config_yaml)
+    return truth, out, [str(w.message) for w in caught]
+
+
+def test_summarize_scores_ddG_tables_against_truth(tmp_path):
+    truth, out, msgs = _summarize_run(tmp_path, with_ddG=True)
+    assert not [m for m in msgs if "_params_ddG" in m]
+    for s in ("H", "HD"):
+        got = pd.read_csv(out / f"tfs_summarize_params_ddG_{s}.csv")
+        np.testing.assert_allclose(got["ref"], truth[f"ddG_{s}"])
+        prior = pd.read_csv(out / f"tfs_summarize_params_ddG_prior_mean_{s}.csv")
+        np.testing.assert_allclose(prior["ref"], truth[f"ddG_{s}"])
+        # Posterior tables get a calibration; the prior mean (a point) not.
+        assert (out / f"tfs_summarize_params_ddG_{s}_pit.csv").exists()
+        assert not glob.glob(str(out / f"*ddG_prior_mean_{s}_*"))
+
+
+def test_summarize_scores_ddG_tables_for_a_u1_fit(tmp_path):
+    cfg = {"components": {"theta": "thermo.O2_C4_K3_U1_a.PddG"}}
+    truth, out, _ = _summarize_run(tmp_path, with_ddG=True, config_yaml=cfg)
+    got = pd.read_csv(out / "tfs_summarize_params_ddG_H.csv")
+    np.testing.assert_allclose(got["ref"], truth["ddG_H"])
+    assert (out / "tfs_summarize_params_ddG_H_pit.csv").exists()
+
+
+@pytest.mark.parametrize("theta", ["thermo.O2_C4_K3_U0_a.PddG",
+                                   "thermo.O2_C12_K5_U0_a.PnnC"])
+def test_summarize_flags_u0_per_state_ddG(tmp_path, capsys, theta):
+    """U0: per-state ΔΔG's common mode comes from the prior, so the table is
+    written with the truth as ref_unidentified and is not scored; the
+    prior-mean tables and d_ln_K are scored as usual."""
+    cfg = {"components": {"theta": theta}}
+    truth, out, msgs = _summarize_run(tmp_path, with_ddG=True, config_yaml=cfg)
+    assert "no unfolded state (U0)" in capsys.readouterr().out
+    for s in ("H", "HD"):
+        got = pd.read_csv(out / f"tfs_summarize_params_ddG_{s}.csv")
+        assert "ref" not in got.columns
+        np.testing.assert_allclose(got["ref_unidentified"], truth[f"ddG_{s}"])
+        assert not got["per_state_identified"].any()
+        assert not glob.glob(str(out / f"tfs_summarize_params_ddG_{s}_*"))
+        assert not (out / f"tfs_summarize_params_ddG_{s}.pdf").exists()
+        prior = pd.read_csv(out / f"tfs_summarize_params_ddG_prior_mean_{s}.csv")
+        np.testing.assert_allclose(prior["ref"], truth[f"ddG_{s}"])
+    got = pd.read_csv(out / "tfs_summarize_params_d_ln_K_op.csv")
+    np.testing.assert_allclose(got["ref"], truth["d_ln_K_op"])
+
+    # tfs-summarize-calibration reads only tables with a ref column.
+    from tfscreen.analysis.calibration_grid import read_quantities
+    names = [q for q, _ in read_quantities(str(out))]
+    assert "ddG_H" not in names and "d_ln_K_op" in names
+
+
+def test_ddG_per_state_identified_by_family():
+    from tfscreen.tfmodel.scripts.summarize_fit_cli import (
+        _ddG_per_state_identified,
+    )
+    for fam, expected in (("O2_C4_K3_U0_a", False), ("O2_C4_K3_U1_a", True),
+                          ("O2_C12_K5_U0_a", False), ("O2_C12_K5_U1_a", True)):
+        for kind in ("PddG", "PnnC"):
+            cfg = {"components": {"theta": f"thermo.{fam}.{kind}"}}
+            assert _ddG_per_state_identified(cfg) is expected
+    assert _ddG_per_state_identified(None) is True
+    assert _ddG_per_state_identified(
+        {"components": {"theta": "thermo.O2_C4_K3_U0_a.PK"}}) is True
+
+
+def test_summarize_scores_ddG_sigma_against_truth(tmp_path):
+    from tfscreen.tfmodel.scripts.summarize_fit_cli import _summarize_ddG_sigma
+    run = tmp_path / "run"
+    run.mkdir()
+    pd.DataFrame({"structure": ["H", "HD"], "ddG_sigma": [0.5, 1.5]}).to_csv(
+        run / "tfs_sim_ddG_sigma.csv", index=False)
+    pd.DataFrame({"structure": ["HD", "H"], "q0.025": [1.0, 0.3],
+                  "q0.5": [1.4, 0.6], "q0.975": [1.9, 0.9]}).to_csv(
+        run / "tfs_params_ddG_sigma.csv", index=False)
+    out = tmp_path / "summary"
+    out.mkdir()
+    _summarize_ddG_sigma(str(run), str(out / "tfs_summarize"))
+    got = pd.read_csv(out / "tfs_summarize_params_ddG_sigma.csv")
+    np.testing.assert_allclose(got["ref"], [1.5, 0.5])
+
+
+def test_summarize_ddG_sigma_silent_for_a_PK_fit(tmp_path):
+    from tfscreen.tfmodel.scripts.summarize_fit_cli import _summarize_ddG_sigma
+    run = tmp_path / "run"
+    run.mkdir()
+    pd.DataFrame({"structure": ["H"], "ddG_sigma": [1.0]}).to_csv(
+        run / "tfs_sim_ddG_sigma.csv", index=False)
+    out = tmp_path / "summary"
+    out.mkdir()
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+        _summarize_ddG_sigma(str(run), str(out / "tfs_summarize"))
+    assert not [w for w in caught if "ddG_sigma" in str(w.message)]
+    assert not list(out.iterdir())
+
+
+def test_summarize_notes_missing_ddG_for_a_PK_fit(tmp_path, capsys):
+    """A PK fit of a PddG simulation writes no ddG: one note, no warnings,
+    and its d_ln_K tables are still scored."""
+    truth, out, msgs = _summarize_run(tmp_path, with_ddG=False)
+    assert not [m for m in msgs if "_params_ddG" in m]
+    assert "wrote no *_params_ddG_*.csv" in capsys.readouterr().out
+    got = pd.read_csv(out / "tfs_summarize_params_d_ln_K_op.csv")
+    np.testing.assert_allclose(got["ref"], truth["d_ln_K_op"])
+
+
+# ── end to end: simulate → MAP → point posterior → extract → summarize ──────
+
+_SIM_CONFIG = dict(
+    reading_frame=0,
+    first_amplicon_residue=27,
+    wt_seq=("gccagccacgtttctgcgaaaacgcgggaaaaagtggaagcggcgatggcggagctgaattacatt"
+            "cccaaccgcgtggcacaacaactggcgggcaaacagtcgttgctgattggcgttgccacctccag"
+            "tctggccctgcacgcgccgtcgcaaattgtcgcggcgattaaatctcgcgccgatcaactgggtg"
+            "ccagcgtggtggtgtcgatg"),
+    degen_sites=("......nnt" + "." * 96 + "nnt" + "." * 108),
+    tiles=("......111" + "." * 96 + "222" + "." * 108),
+    expected_5p="cgcgtggtgaaccag",
+    expected_3p="gtagaacgaagcggc",
+    tile_combos=["single-1", "single-2", "double-1-2"],
+    condition_blocks=[dict(library="kanR", titrant_name="iptg",
+                           titrant_conc=[0, 0.001, 0.1, 1.0],
+                           condition_pre="kanR-kan", t_pre=30,
+                           condition_sel="kanR+kan", t_sel=[160, 200])],
+    growth={"kanR+kan": {"m": -0.009933, "b": 0.0},
+            "kanR-kan": {"m": 0.000808, "b": 0.00467}},
+    dk_geno_hyper_loc=-3.5, dk_geno_hyper_scale=1.0, dk_geno_hyper_shift=0.02,
+    activity_wt=1.0, activity_mut_scale=0.0,
+    transform_sizes={"single-1": 2000, "single-2": 2000, "double-1-2": 2000},
+    library_mixture={"single-1": 100, "single-2": 100, "double-1-2": 10},
+    lib_assembly_skew_sigma=0.5,
+    transformation_poisson_lambda=0,
+    congression_theta_rule="homodimer",
+    congression_dk_rule="dilution",
+    cfu0=65000000.0,
+    tube_noise_sigma=0.002,
+    total_num_reads=300000,
+    prob_index_hop=0.0,
+    seed=1,
+    presplit_data={"noise": 0.0},
+)
+
+# Residues the library above mutates (H29, L62).
+_RESNUMS = [29, 62]
+
+
+def _reordered_ddG_csv(path, structs):
+    """A ΔΔG CSV with the structures reversed, an extra column and the
+    'Mutation' header, so labels can only be right if read by name."""
+    rng = np.random.default_rng(0)
+    aas = "ACDEFGHIKLMNPQRSTVWY"
+    muts = [f"{wt}{pos}{aa}" for wt, pos in (("H", 29), ("L", 62))
+            for aa in aas if aa != wt]
+    df = pd.DataFrame(rng.normal(0, 1.5, (len(muts), len(structs))),
+                      columns=list(structs))
+    df.insert(0, "Mutation", muts)
+    df["unused"] = 99.0
+    df = df[["Mutation", "unused"] + list(reversed(structs))]
+    df.to_csv(path, index=False)
+    return str(path)
+
+
+def _reordered_ensemble(path, structs):
+    """A structure ensemble whose structure_names are in reversed order."""
+    with h5py.File(path, "w") as hf:
+        hf.create_dataset("structure_names",
+                          data=np.array(list(reversed(structs)),
+                                        dtype=h5py.string_dtype()))
+        for i, name in enumerate(structs):
+            rng = np.random.default_rng(i)
+            grp = hf.create_group(name)
+            dist_mat = np.abs(rng.normal(5, 2, (2, 2))).astype(np.float32)
+            np.fill_diagonal(dist_mat, 0.0)
+            grp.create_dataset("logP", data=rng.normal(size=(2, 20)).astype(np.float32))
+            grp.create_dataset("residue_nums", data=np.asarray(_RESNUMS, dtype=np.int32))
+            grp.create_dataset("dist_matrix", data=dist_mat)
+            grp.create_dataset("n_chains_bearing_mut", data=np.int32(2))
+    return str(path)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("component", ["thermo.O2_C4_K3_U0_a.PddG",
+                                       "thermo.O2_C12_K5_U0_a.PnnC"])
+def test_ddG_tables_end_to_end(tmp_path, component):
+    from tfscreen.simulate.scripts.simulate_cli import run_simulation_from_config
+    from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+    from tfscreen.tfmodel.scripts.extract_params_cli import extract_params
+    from tfscreen.tfmodel.scripts.fit_model_cli import fit_model
+    from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+    from tfscreen.tfmodel.scripts.summarize_fit_cli import summarize_fit
+
+    module = model_registry["theta"][component]
+    structs = module.STRUCTURE_NAMES
+    if component.endswith("PddG"):
+        thermo_data = _reordered_ddG_csv(tmp_path / "ddG.csv", structs)
+    else:
+        thermo_data = _reordered_ensemble(tmp_path / "ens.h5", structs)
+
+    cfg = dict(_SIM_CONFIG, theta_component=component, thermo_data=thermo_data)
+    with open(tmp_path / "sim.yaml", "w") as fh:
+        yaml.safe_dump(cfg, fh)
+
+    run = tmp_path / "run"
+    run.mkdir()
+    cwd = os.getcwd()
+    os.chdir(run)
+    try:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            run_simulation_from_config(str(tmp_path / "sim.yaml"),
+                                       out_prefix="tfs_sim", num_replicates=1,
+                                       write_raw=False, write_growth=True)
+            configure_model(growth_df="tfs_sim_growth.csv",
+                            presplit_df="tfs_sim_presplit.csv",
+                            theta_model=component,
+                            library_config="tfs_sim_input-config.yaml",
+                            growth_shares_replicates=True,
+                            thermo_data=thermo_data)
+            fit_model("tfs_configure_config.yaml", seed=1,
+                      analysis_method="map", max_num_epochs=20,
+                      convergence_window_steps=10)
+            sample_posterior("tfs_configure_config.yaml",
+                             "tfs_fit_model_checkpoint.pkl",
+                             laplace="point", num_posterior_samples=5,
+                             skip_growth_observations=True)
+            extract_params("tfs_configure_config.yaml",
+                           "tfs_posterior.h5")
+            summarize_fit(".")
+    finally:
+        os.chdir(cwd)
+
+    truth = pd.read_csv(run / "tfs_sim_mutation_parameters.csv").set_index("mutation")
+    with h5py.File(run / "tfs_posterior.h5", "r") as hf:
+        ddG = np.median(hf["theta_ddG"][:], axis=0)            # (M, S)
+        prior = np.median(hf["theta_ddG_prior_mean"][:], axis=0)
+
+    # The truth's projection to Δln K holds in the model's order, so its ddG
+    # columns are labeled in STRUCTURE_NAMES order.  (The post-hoc U0 ΔΔG
+    # reproduces the fitted constants exactly too.)
+    proj = np.asarray(module._PROJ)
+    truth_d = truth[[f"ddG_{s}" for s in structs]].values @ proj.T
+    for k, col in enumerate(_d_ln_K_order(module)[:proj.shape[0]]):
+        np.testing.assert_allclose(truth_d[:, k], truth[col], atol=1e-4)
+
+    # Same for the fit: the extracted ddG tables, stacked by structure name,
+    # project onto the extracted d_ln_K tables.
+    fit_ddG = np.column_stack([
+        pd.read_csv(run / f"tfs_params_ddG_{s}.csv")["q0.5"] for s in structs])
+    fit_d = fit_ddG @ proj.T
+    for k, col in enumerate(_d_ln_K_order(module)[:proj.shape[0]]):
+        np.testing.assert_allclose(
+            fit_d[:, k], pd.read_csv(run / f"tfs_params_{col}.csv")["q0.5"],
+            atol=1e-4)
+
+    if component.endswith("PddG"):
+        csv = pd.read_csv(thermo_data).set_index("Mutation")
+
+    for s, struct in enumerate(structs):
+        table = pd.read_csv(run / f"tfs_params_ddG_{struct}.csv")
+        # The fit's mutations (those seen in the growth data), in its order.
+        assert len(table) == ddG.shape[0]
+        assert set(table["mutation"]) <= set(truth.index)
+        np.testing.assert_allclose(table["q0.5"], ddG[:, s], atol=1e-5)
+        prior_table = pd.read_csv(run / f"tfs_params_ddG_prior_mean_{struct}.csv")
+        np.testing.assert_allclose(prior_table["q0.5"], prior[:, s], atol=1e-5)
+        if component.endswith("PddG"):
+            # The prior mean is the CSV column of the same name.
+            np.testing.assert_allclose(
+                prior_table["q0.5"],
+                csv.loc[prior_table["mutation"], struct].values, atol=1e-5)
+
+        # Both end-to-end models are U0: the per-state table is flagged,
+        # with the truth as ref_unidentified.
+        scored = pd.read_csv(run / "summary" / f"tfs_summarize_params_ddG_{struct}.csv")
+        np.testing.assert_allclose(scored["ref_unidentified"],
+                                   truth.loc[scored["mutation"], f"ddG_{struct}"])
+        assert (run / "summary" / f"tfs_summarize_params_ddG_prior_mean_{struct}.csv").exists()
+
+    # σ_s: the simulator's truth (1 kT, the default) per structure, the
+    # fit's extracted table, and the summary joining them on structure.
+    sim_sigma = pd.read_csv(run / "tfs_sim_ddG_sigma.csv")
+    assert list(sim_sigma["structure"]) == list(structs)
+    np.testing.assert_allclose(sim_sigma["ddG_sigma"], 1.0)
+    fit_sigma = pd.read_csv(run / "tfs_params_ddG_sigma.csv")
+    assert list(fit_sigma["structure"]) == list(structs)
+    assert (fit_sigma["q0.5"] > 0).all()
+    scored = pd.read_csv(run / "summary" / "tfs_summarize_params_ddG_sigma.csv")
+    np.testing.assert_allclose(scored["ref"], 1.0)
+
+
+def _d_ln_K_order(module):
+    """The model's d_ln_K sites in the order of its ``_PROJ`` rows."""
+    import inspect
+    import re
+    return re.findall(r'deterministic\(f"\{name\}_(d_ln_K_\w+)"',
+                      inspect.getsource(module.define_model))
diff --git a/tests/tfscreen/tfmodel/analysis/test_extract_parameters_congression.py b/tests/tfscreen/tfmodel/analysis/test_extract_parameters_congression.py
index 4e6522a5..15d2e60c 100644
--- a/tests/tfscreen/tfmodel/analysis/test_extract_parameters_congression.py
+++ b/tests/tfscreen/tfmodel/analysis/test_extract_parameters_congression.py
@@ -9,7 +9,7 @@
 def mock_model_congression():
     """Create a ModelOrchestrator instance with minimal mocked internals for congression."""
     model = MagicMock(spec=ModelOrchestrator)
-    model._transformation = "logit_norm"
+    model._transformation = "mixture"
     model._theta = "none"
     model._condition_growth = "none"
     model._dk_geno = "none"
@@ -39,38 +39,22 @@ def mock_posteriors_congression():
     """Create mock posterior samples for congression parameters."""
     num_samples = 5
     # lam is (num_samples, 1)
-    # mu, sigma are (num_samples, num_titrant_name, num_titrant_conc, 1)
-    # flattened: (num_samples, num_titrant_name * num_titrant_conc)
     return {
         "transformation_lam": np.ones((num_samples, 1)) * 1.2,
-        "transformation_mu": np.ones((num_samples, 1, 2, 1)) * 0.5,
-        "transformation_sigma": np.ones((num_samples, 1, 2, 1)) * 0.1
     }
 
 def test_extract_parameters_congression(mock_model_congression, mock_posteriors_congression):
-    """Test extracting lam, mu, and sigma."""
+    """The mixture transformation's only parameter is lambda."""
     params = extract_parameters(mock_model_congression, mock_posteriors_congression)
-    
+
     assert "lam" in params
-    assert "mu" in params
-    assert "sigma" in params
-    
-    # Check lam
+    assert "mu" not in params
+    assert "sigma" not in params
+
     lam_df = params["lam"]
     assert len(lam_df) == 1
     assert lam_df.iloc[0]["parameter"] == "lam"
     assert lam_df.iloc[0]["q0.5"] == 1.2
-    
-    # Check mu
-    mu_df = params["mu"]
-    assert len(mu_df) == 2
-    assert set(mu_df["titrant_conc"]) == {0.0, 1.0}
-    assert np.allclose(mu_df["q0.5"], 0.5)
-    
-    # Check sigma
-    sigma_df = params["sigma"]
-    assert len(sigma_df) == 2
-    assert np.allclose(sigma_df["q0.5"], 0.1)
 
 def test_extract_parameters_no_congression(mock_model_congression):
     """Test that congression parameters are NOT extracted when transformation is none."""
diff --git a/tests/tfscreen/tfmodel/analysis/test_extract_samples.py b/tests/tfscreen/tfmodel/analysis/test_extract_samples.py
index 661cf802..67748885 100644
--- a/tests/tfscreen/tfmodel/analysis/test_extract_samples.py
+++ b/tests/tfscreen/tfmodel/analysis/test_extract_samples.py
@@ -92,8 +92,7 @@ def _lac_dimer_mut_model():
     })
     model.growth_tm = mock_tm
     model.training_tm = mock_tm
-    model.priors.theta.theta_tf_total_M = 6.5e-7
-    model.priors.theta.theta_op_total_M = 2.5e-8
+    model.priors.theta.theta_tf_total_M = 1e-5
     return model
 
 
diff --git a/tests/tfscreen/tfmodel/analysis/test_extract_theta_epistasis.py b/tests/tfscreen/tfmodel/analysis/test_extract_theta_epistasis.py
index d384184a..2e14731b 100644
--- a/tests/tfscreen/tfmodel/analysis/test_extract_theta_epistasis.py
+++ b/tests/tfscreen/tfmodel/analysis/test_extract_theta_epistasis.py
@@ -262,3 +262,25 @@ def test_mult_scale_constant_rejected():
         extract_theta_epistasis(model, _flat_posteriors(
             np.tile([0.5, 0.6, 0.7, 0.9], (5, 1))),
             scale="mult", scale_constant=2.0)
+
+
+def test_relative_X_epistasis_is_additive_without_in_regime():
+    """hill_relative predicts X (not an occupancy): additive epistasis on X,
+    which may sit outside (0, 1), and no in_regime column."""
+    S = 20
+    X = np.tile([1.0, 0.4, 1.3, -0.2], (S, 1))
+    model = _make_model()
+    model._theta = "hill_relative"
+    post = {"theta_hill_n": np.ones((S, 4)),
+            "theta_log_hill_K": np.zeros((S, 4)),
+            "theta_X_high": X.copy(),
+            "theta_X_low": X.copy()}
+
+    result = extract_theta_epistasis(model, post, scale="add")
+
+    assert "in_regime" not in result.columns
+    assert len(result) == 1
+    row = result.iloc[0]
+    assert row["genotype"] == "A10G/C20D"
+    # 00=wt(1.0), 01=A10G(0.4), 10=C20D(1.3), 11=double(-0.2)
+    assert np.isclose(row["q0.5"], (-0.2 - 1.3) - (0.4 - 1.0), atol=1e-6)
diff --git a/tests/tfscreen/tfmodel/analysis/test_extraction_robustness.py b/tests/tfscreen/tfmodel/analysis/test_extraction_robustness.py
index f32ce2e5..2b3297a7 100644
--- a/tests/tfscreen/tfmodel/analysis/test_extraction_robustness.py
+++ b/tests/tfscreen/tfmodel/analysis/test_extraction_robustness.py
@@ -310,17 +310,13 @@ def test_extract_parameters_h5_file(mock_model, tmp_path):
     h5_path = os.path.join(tmp_path, "params.h5")
     
     # We need a model config that actually extracts something
-    mock_model._transformation = "logit_norm"
+    mock_model._transformation = "mixture"
     
     with h5py.File(h5_path, "w") as f:
         f.create_dataset("transformation_lam", data=np.random.rand(10, 1))
-        # shape for mu/sigma is (num_samples, num_titrant_name, num_titrant_conc, 1)
-        f.create_dataset("transformation_mu", data=np.random.rand(10, 1, 1, 1))
-        f.create_dataset("transformation_sigma", data=np.random.rand(10, 1, 1, 1))
-    
+
     params = extract_parameters(mock_model, h5_path)
     assert "lam" in params
-    assert "mu" in params
 
 def test_extract_theta_curves_hdf5_path(mock_model, tmp_path):
     """Test extract_theta_curves loading from an HDF5 file path."""
diff --git a/tests/tfscreen/tfmodel/analysis/test_prediction.py b/tests/tfscreen/tfmodel/analysis/test_prediction.py
index 40f2a725..52833dd9 100644
--- a/tests/tfscreen/tfmodel/analysis/test_prediction.py
+++ b/tests/tfscreen/tfmodel/analysis/test_prediction.py
@@ -11,7 +11,7 @@
     copy_orchestrator,
     _convert_map_params,
     _align_site_to_tm_dims,
-    _build_population_theta_reference,
+    _build_population_references,
     predict,
 )
 
@@ -51,12 +51,12 @@ def dummy_orchestrator():
         "theta_std": [0.01, 0.01]
     })
 
-    # transformation='empirical' (rather than the 'single' default) is load-
-    # bearing for TestGenotypeSubsetPopulationReference below, which relies
-    # on NEEDS_FULL_POPULATION_THETA=True.
+    # transformation='mixture' (rather than the 'single' default) is load-
+    # bearing for TestPredictPopulationReferenceThreading below, which relies
+    # on NEEDS_POPULATION=True.
     return ModelOrchestrator(
         growth_df, binding_df,
-        transformation="empirical", transformation_lambda=(0.36, 0.05),
+        transformation="mixture", transformation_lambda=(0.36, 0.05),
     )
 
 def test_copy_orchestrator_defaults(dummy_orchestrator):
@@ -504,6 +504,11 @@ def __call__(self, rng_key, **kwargs):
             for name, site in model_tr.items()
             if site["type"] == "sample" and not site.get("is_observed", False)
         }
+        # The mixture transformation also reads library-wide deterministic
+        # sites from the posterior.
+        for site in ("theta_growth_pred", "dk_geno", "activity"):
+            fake_posteriors[site] = np.asarray(
+                model_tr[site]["value"])[np.newaxis, ...]
 
         predict(
             dummy_orchestrator,
@@ -523,56 +528,119 @@ def __call__(self, rng_key, **kwargs):
 
 
 # ---------------------------------------------------------------------------
-# _build_population_theta_reference
+# _build_population_references
 # ---------------------------------------------------------------------------
 
-class TestBuildPopulationThetaReference:
-
-    def test_returns_none_and_warns_when_theta_growth_pred_missing(self):
-        """MAP checkpoints store guide params, not deterministic sites, so
-        theta_growth_pred won't be present -- must warn and return None
-        rather than raising, so predict() can still run (without the fix)."""
-        with pytest.warns(UserWarning, match="theta_growth_pred"):
-            result = _build_population_theta_reference({"some_other_param": np.zeros((5,))})
-        assert result is None
-
-    def test_reduces_across_sample_axis_via_median(self):
-        """With more samples than max_samples, the result must be the median
-        over only the first max_samples draws, with the sample axis removed."""
-        # Shape (10, 2, 3): 10 samples, 2x3 spatial.
+def _fake_posteriors(orchestrator, num_draws=1):
+    """
+    Fake posterior with ``num_draws`` identical draws: every latent sample
+    site and the theta_growth_pred/dk_geno/activity deterministic sites, all
+    from one full-batch prior trace (library order), so they are mutually
+    consistent.
+    """
+    from numpyro.handlers import seed, trace as nptrace
+
+    data = orchestrator.get_batch(
+        orchestrator.data, jnp.arange(orchestrator.data.num_genotype))
+    model_tr = nptrace(seed(orchestrator.jax_model, rng_seed=0)).get_trace(
+        data=data, priors=orchestrator.priors)
+    posteriors = {
+        name: np.repeat(np.asarray(site["value"])[np.newaxis, ...],
+                        num_draws, axis=0)
+        for name, site in model_tr.items()
+        if site["type"] == "sample" and not site.get("is_observed", False)
+    }
+    for site in ("theta_growth_pred", "dk_geno", "activity"):
+        posteriors[site] = np.repeat(
+            np.asarray(model_tr[site]["value"])[np.newaxis, ...],
+            num_draws, axis=0)
+    return posteriors
+
+
+def _pred_growth(orchestrator, **kwargs):
+    new = copy_orchestrator(orchestrator, **kwargs)
+    return new.get_batch(new.data,
+                         jnp.arange(new.data.num_genotype)).growth
+
+
+class TestBuildPopulationReferences:
+
+    def test_shapes_cover_the_whole_library(self, dummy_orchestrator):
+        refs = _build_population_references(
+            dummy_orchestrator, _fake_posteriors(dummy_orchestrator),
+            _pred_growth(dummy_orchestrator, genotypes=["wt"]))
+        num_genotype = dummy_orchestrator.data.growth.num_genotype
+        for field in ("external_theta_population", "external_dk_population",
+                      "external_activity_population"):
+            assert refs[field].shape[-1] == num_genotype
+
+    def test_theta_at_fit_grid_matches_stored_theta(self, dummy_orchestrator):
+        """On the fit's own grid, theta rebuilt from the parameters equals
+        the stored theta_growth_pred."""
+        posteriors = _fake_posteriors(dummy_orchestrator)
+        refs = _build_population_references(
+            dummy_orchestrator, posteriors, _pred_growth(dummy_orchestrator))
+        np.testing.assert_allclose(
+            np.asarray(refs["external_theta_population"]),
+            posteriors["theta_growth_pred"][0], rtol=1e-5)
+
+    def test_theta_follows_the_prediction_grid(self, dummy_orchestrator):
+        """Predicting at new concentrations needs co-resident theta at those
+        concentrations, not the fit's."""
+        pred_growth = _pred_growth(dummy_orchestrator,
+                                   titrant_conc=[0.0, 0.25, 0.5, 2.0])
+        refs = _build_population_references(
+            dummy_orchestrator, _fake_posteriors(dummy_orchestrator),
+            pred_growth)
+        assert refs["external_theta_population"].shape[-2] == 4
+
+    def test_median_over_first_max_samples(self, dummy_orchestrator):
+        posteriors = _fake_posteriors(dummy_orchestrator, num_draws=10)
         rng = np.random.default_rng(0)
-        theta_growth_pred = rng.uniform(size=(10, 2, 3))
-        fake_posteriors = {"theta_growth_pred": theta_growth_pred}
-
-        result = _build_population_theta_reference(fake_posteriors, max_samples=4)
-
-        expected = np.median(theta_growth_pred[:4], axis=0)
-        assert result.shape == (2, 3)
-        assert jnp.allclose(result, expected)
-
-    def test_uses_all_samples_when_fewer_than_max(self):
-        theta_growth_pred = np.arange(2 * 4).reshape(2, 4).astype(float)  # (2 samples, 4 genotypes)
-        fake_posteriors = {"theta_growth_pred": theta_growth_pred}
-
-        result = _build_population_theta_reference(fake_posteriors, max_samples=20)
-
-        expected = np.median(theta_growth_pred, axis=0)
-        assert jnp.allclose(result, expected)
+        posteriors["dk_geno"] = rng.normal(size=posteriors["dk_geno"].shape)
+        refs = _build_population_references(
+            dummy_orchestrator, posteriors, _pred_growth(dummy_orchestrator),
+            max_samples=4)
+        np.testing.assert_allclose(
+            np.asarray(refs["external_dk_population"]),
+            np.median(posteriors["dk_geno"][:4], axis=0), rtol=1e-6)
+
+    @pytest.mark.parametrize("site", ["dk_geno", "activity"])
+    def test_missing_site_raises(self, dummy_orchestrator, site):
+        """A raw MAP checkpoint has no deterministic sites: refuse, pointing
+        to tfs-sample-posterior, rather than predict without a background."""
+        posteriors = _fake_posteriors(dummy_orchestrator)
+        posteriors.pop(site)
+        with pytest.raises(ValueError, match="tfs-sample-posterior"):
+            _build_population_references(dummy_orchestrator, posteriors,
+                                         _pred_growth(dummy_orchestrator))
+
+    def test_predict_refuses_raw_map_checkpoint(self, dummy_orchestrator):
+        posteriors = _fake_posteriors(dummy_orchestrator)
+        posteriors.pop("dk_geno")
+        with pytest.raises(ValueError, match="tfs-sample-posterior"):
+            predict(dummy_orchestrator, posteriors,
+                    predict_sites=["growth_pred"], num_samples=None)
+
+    def test_wrong_genotype_count_raises(self, dummy_orchestrator):
+        posteriors = _fake_posteriors(dummy_orchestrator)
+        posteriors["dk_geno"] = posteriors["dk_geno"][..., :1]
+        with pytest.raises(ValueError, match="whole library"):
+            _build_population_references(dummy_orchestrator, posteriors,
+                                         _pred_growth(dummy_orchestrator))
 
 
 # ---------------------------------------------------------------------------
-# predict() threading of the population reference through genotype subsets
+# predict() threading of the population references through genotype subsets
 # ---------------------------------------------------------------------------
 
 class TestPredictPopulationReferenceThreading:
     """
-    Verify predict() builds a full-population theta reference and threads it
-    into pred_data whenever the configured transformation needs one (see
-    generative/model.py / transformation/_congression.py), and that a
-    genotype-subset request still gets a reference sized to the *true* full
-    population, not the requested subset -- this is the core mechanism that
-    fixes the wt/congression bug (predict() previously only ever saw whatever
-    genotype subset was requested).
+    predict() must thread library-wide theta/dk_geno/activity references and
+    the fitted model's co-resident sets into pred_data whenever the
+    transformation needs them (the mixture), and a genotype-subset request
+    must still get references sized to the whole library and co-resident
+    indices into that library, not the requested subset.
     """
 
     def _capture_pred_data(self, mocker):
@@ -597,64 +665,55 @@ def __call__(self, rng_key, **kwargs):
         )
         return captured
 
-    def _fake_posteriors_with_theta_growth_pred(self, orchestrator):
-        """Build a 1-sample fake posterior dict covering every latent sample
-        site (as in TestPredictPriorsPropagate) plus the theta_growth_pred
-        deterministic site, so _build_population_theta_reference has
-        something to read."""
-        from numpyro.handlers import seed, trace as nptrace
+    def _fake_posteriors(self, orchestrator):
+        return _fake_posteriors(orchestrator)
 
-        seeded = seed(orchestrator.jax_model, rng_seed=0)
-        model_tr = nptrace(seeded).get_trace(
-            data=orchestrator.data,
-            priors=orchestrator.priors,
-        )
-        fake_posteriors = {
-            name: _feasible_draw(site)
-            for name, site in model_tr.items()
-            if site["type"] == "sample" and not site.get("is_observed", False)
-        }
-        fake_posteriors["theta_growth_pred"] = np.asarray(
-            model_tr["theta_growth_pred"]["value"]
-        )[np.newaxis, ...]
-        return fake_posteriors
-
-    def test_genotype_subset_still_gets_full_population_sized_reference(
+    def test_genotype_subset_gets_library_references_and_sets(
             self, dummy_orchestrator, mocker):
-        """dummy_orchestrator uses theta='hill_geno',
-        transformation='empirical' (NEEDS_FULL_POPULATION_THETA=True).
-        Requesting a single genotype must still populate
-        pred_data.growth.external_theta_population with the FULL genotype
-        count (2 in this fixture), not 1."""
-        fake_posteriors = self._fake_posteriors_with_theta_growth_pred(dummy_orchestrator)
+        fake_posteriors = self._fake_posteriors(dummy_orchestrator)
         captured = self._capture_pred_data(mocker)
 
-        predict(
-            dummy_orchestrator,
-            fake_posteriors,
-            predict_sites=["growth_pred"],
-            num_samples=None,
-            genotypes=["wt"],
-        )
-
-        pred_data = captured["data"]
-        assert pred_data is not None
-        population = pred_data.growth.external_theta_population
-        assert population is not None
-
-        true_num_genotype = dummy_orchestrator.data.growth.num_genotype
-        assert population.shape[-1] == true_num_genotype
-        assert true_num_genotype > 1, (
-            "fixture must have more than one genotype for this test to be meaningful"
-        )
+        predict(dummy_orchestrator, fake_posteriors,
+                predict_sites=["growth_pred"], num_samples=None,
+                genotypes=["wt"])
+
+        growth = captured["data"].growth
+        num_genotype = dummy_orchestrator.data.growth.num_genotype
+        assert num_genotype > 1
+        for field in ("external_theta_population", "external_dk_population",
+                      "external_activity_population"):
+            assert getattr(growth, field).shape[-1] == num_genotype
+
+        labels = list(dummy_orchestrator.growth_tm.tensor_dim_labels[-1])
+        wt_row = labels.index("wt")
+        np.testing.assert_array_equal(
+            np.asarray(growth.coresident_idx),
+            np.asarray(dummy_orchestrator.data.growth.coresident_idx)[[wt_row]])
+
+    def test_subset_prediction_matches_full_prediction(self, dummy_orchestrator):
+        """A genotype's prediction must not depend on which other genotypes
+        were requested."""
+        fake_posteriors = self._fake_posteriors(dummy_orchestrator)
+        full = predict(dummy_orchestrator, fake_posteriors,
+                       predict_sites=["growth_pred"], num_samples=None)
+        subset = predict(dummy_orchestrator, fake_posteriors,
+                         predict_sites=["growth_pred"], num_samples=None,
+                         genotypes=["wt"])
+
+        keys = ["genotype", "titrant_conc", "t_sel"]
+        full_wt = (full[full["genotype"] == "wt"]
+                   .sort_values(keys).reset_index(drop=True))
+        subset = subset.sort_values(keys).reset_index(drop=True)
+        q_cols = [c for c in subset.columns if c.startswith("q")]
+        np.testing.assert_allclose(subset[q_cols].to_numpy(dtype=float),
+                                   full_wt[q_cols].to_numpy(dtype=float),
+                                   rtol=1e-5)
 
     def test_no_population_reference_when_transformation_does_not_need_one(
             self, mocker):
-        """With transformation='single' (NEEDS_FULL_POPULATION_THETA=False),
-        predict() must not build or thread a population reference at all --
-        pred_data.growth.external_theta_population must stay None, and no
-        'theta_growth_pred not found' warning should fire even when it truly
-        isn't in the posterior dict."""
+        """With transformation='single' (NEEDS_POPULATION=False), predict()
+        must not build or thread population references at all -- they stay
+        None, and nothing is required of the posterior dict."""
         growth_df = pd.DataFrame({
             "library": ["lib"] * 4,
             "genotype": ["wt", "wt", "M42V", "M42V"],
@@ -817,3 +876,118 @@ def test_condition_growth_k_and_m_together(
         assert np.isclose(k_df["sel+2"], 0.333)
         assert np.isclose(m_df["sel+1"], 0.02)
         assert np.isclose(m_df["sel+2"], 0.03)
+
+
+# ---------------------------------------------------------------------------
+# Binding-genotype plates (noise/genotype_offset) and the shape check
+# ---------------------------------------------------------------------------
+
+class TestPredictBindingPlateSites:
+    """theta_binding_noise_offset_z is plated on the binding genotypes; a
+    genotype subset must pick each kept binding genotype's offset by label."""
+
+    @pytest.fixture
+    def offset_orchestrator(self):
+        return self._build("genotype_offset")
+
+    @staticmethod
+    def _build(noise):
+        growth_df = pd.DataFrame({
+            "library": ["lib"] * 6,
+            "genotype": ["wt", "wt", "M42V", "M42V", "K84N", "K84N"],
+            "titrant_name": ["tit1"] * 6,
+            "titrant_conc": [0.0, 1.0] * 3,
+            "condition_pre": ["pre-1"] * 6,
+            "condition_sel": ["sel+1"] * 6,
+            "t_pre": [10.0] * 6,
+            "t_sel": [0.0, 20.0] * 3,
+            "ln_cfu": [0.0, 5.0, 0.0, 3.0, 0.0, 2.0],
+            "ln_cfu_std": [0.1] * 6,
+            "replicate": [1] * 6,
+        })
+        binding_df = pd.DataFrame({
+            "genotype": ["wt", "M42V", "K84N"],
+            "titrant_name": ["tit1"] * 3,
+            "titrant_conc": [0.5] * 3,
+            "theta_obs": [0.5, 0.2, 0.3],
+            "theta_std": [0.01] * 3,
+        })
+        return ModelOrchestrator(growth_df, binding_df, transformation="single",
+                                 theta_binding_noise=noise)
+
+    @staticmethod
+    def _posteriors(orchestrator):
+        from numpyro.handlers import seed, trace as nptrace
+        tr = nptrace(seed(orchestrator.jax_model, rng_seed=0)).get_trace(
+            data=orchestrator.data, priors=orchestrator.priors)
+        return {name: _feasible_draw(site) for name, site in tr.items()
+                if site["type"] == "sample" and not site.get("is_observed", False)}
+
+    def _capture(self, orchestrator, posteriors, **kwargs):
+        """Run predict and return the posterior samples it handed Predictive."""
+        import tfscreen.tfmodel.analysis.prediction as pred_mod
+        captured = {}
+        real = pred_mod.Predictive
+
+        def spy(*args, **kw):
+            captured.update(kw["posterior_samples"])
+            return real(*args, **kw)
+
+        with patch.object(pred_mod, "Predictive", side_effect=spy):
+            out = predict(orchestrator, posteriors, predict_sites=["growth_pred"],
+                          num_samples=None, **kwargs)
+        return out, captured
+
+    def test_binding_labels(self, offset_orchestrator):
+        from tfscreen.tfmodel.analysis.prediction import _binding_labels
+        assert _binding_labels(offset_orchestrator) == ["wt", "M42V", "K84N"]
+        assert _binding_labels(MagicMock(binding_tm=None)) == []
+
+    def test_subset_takes_offsets_by_binding_label(self, offset_orchestrator):
+        post = self._posteriors(offset_orchestrator)
+        post["theta_binding_noise_offset_z"] = np.array([[10.0, 20.0, 30.0]])
+        _, captured = self._capture(offset_orchestrator, post,
+                                    genotypes=["K84N", "wt"])
+        # The subset's binding genotypes are wt, K84N (binding-tensor order).
+        np.testing.assert_allclose(
+            np.asarray(captured["theta_binding_noise_offset_z"]), [[10.0, 30.0]])
+
+    def test_binding_genotype_without_a_value_raises(self, offset_orchestrator,
+                                                     mocker):
+        post = self._posteriors(offset_orchestrator)
+        mocker.patch("tfscreen.tfmodel.analysis.prediction._binding_labels",
+                     side_effect=[["wt", "M42V"], ["wt", "Q1X"]])
+        with pytest.raises(ValueError,
+                           match=r"plated on the binding genotypes and has no "
+                                 r"value for \['Q1X'\]"):
+            predict(offset_orchestrator, post, predict_sites=["growth_pred"],
+                    num_samples=None, genotypes=["wt"])
+
+    def test_site_absent_from_prediction_model_is_skipped(self,
+                                                          offset_orchestrator,
+                                                          mocker):
+        """A posterior site the prediction model does not have is not
+        shape-checked; it is simply unused."""
+        post = self._posteriors(offset_orchestrator)
+        zero_noise = self._build("zero")
+        mocker.patch("tfscreen.tfmodel.analysis.prediction.copy_orchestrator",
+                     return_value=zero_noise)
+        out = predict(offset_orchestrator, post, predict_sites=["growth_pred"],
+                      num_samples=None)
+        post_zero = {k: v for k, v in post.items()
+                     if not k.startswith("theta_binding_noise")}
+        expected = predict(zero_noise, post_zero, predict_sites=["growth_pred"],
+                           num_samples=None)
+        q_cols = [c for c in out.columns if c.startswith("q")]
+        np.testing.assert_allclose(out[q_cols].to_numpy(dtype=float),
+                                   expected[q_cols].to_numpy(dtype=float))
+
+    def test_wrong_shape_site_raises(self, offset_orchestrator):
+        post = self._posteriors(offset_orchestrator)
+        post["theta_binding_noise_offset_scale"] = np.array([[0.1, 0.2]])
+        with pytest.raises(ValueError,
+                           match=r"Posterior site 'theta_binding_noise_offset_"
+                                 r"scale' has shape \(2,\) but the prediction "
+                                 r"model expects \(\)"):
+            predict(offset_orchestrator, post, predict_sites=["growth_pred"],
+                    num_samples=None)
diff --git a/tests/tfscreen/tfmodel/analysis/test_prediction_mutation_subsets.py b/tests/tfscreen/tfmodel/analysis/test_prediction_mutation_subsets.py
new file mode 100644
index 00000000..0b32dbde
--- /dev/null
+++ b/tests/tfscreen/tfmodel/analysis/test_prediction_mutation_subsets.py
@@ -0,0 +1,167 @@
+"""
+Predictions on a genotype subset must equal the full prediction.
+
+predict() rebuilds the model on the requested genotypes (copy_orchestrator).
+For components with per-mutation and per-pair parameters (hill_mut, the
+*_mut activity components, the thermodynamic theta models) the rebuilt model
+used to derive its mutation and pair labels from the subset alone, so the
+fitted per-mutation and per-pair posterior arrays were applied to the wrong
+mutations and pairs. tfs-predict-growth always chunks a full-size library,
+so every chunk was silently wrong.
+"""
+
+import itertools
+
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+import pytest
+from numpyro.handlers import seed, trace
+
+from tfscreen.genetics import build_mut_geno_matrix
+from tfscreen.tfmodel.analysis.prediction import copy_orchestrator, predict
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+
+_MUTS = ["A1C", "D2E", "F3G", "H4I", "K5L", "M6N"]
+
+# Doubles first, in reverse, so any subset sees its mutations and pairs in a
+# different first-seen order than the full library does.
+_GENOTYPES = (["wt"]
+              + ["/".join(p) for p in itertools.combinations(_MUTS, 2)][::-1]
+              + _MUTS)
+
+_KEY = ["genotype", "titrant_conc", "t_sel"]
+
+_MODELS = {
+    "hill_mut": dict(theta="hill_mut", epistasis=True),
+    "activity_hierarchical_mut": dict(theta="hill_geno",
+                                      activity="hierarchical_mut",
+                                      epistasis=True),
+    "thermo_C4_U0_PK": dict(theta="thermo.O2_C4_K3_U0_a.PK",
+                            epistasis=True),
+}
+
+
+def _growth_df():
+    rng = np.random.default_rng(0)
+    rows = []
+    for g in _GENOTYPES:
+        for c in [0.0, 0.01, 0.1, 1.0, 10.0]:
+            for t in [0.0, 10.0]:
+                rows.append(dict(library="lib", genotype=g,
+                                 titrant_name="iptg", titrant_conc=c,
+                                 condition_pre="pre", condition_sel="sel+",
+                                 t_pre=10.0, t_sel=t,
+                                 ln_cfu=10 + rng.normal(), ln_cfu_std=0.1,
+                                 replicate=1))
+    return pd.DataFrame(rows)
+
+
+def _prior_posteriors(orch, num_draws=3):
+    """Several prior draws of every latent, as a posterior dict."""
+    data = orch.get_batch(orch.data, jnp.arange(orch.data.num_genotype))
+    draws = {}
+    for s in range(num_draws):
+        tr = trace(seed(orch.jax_model, rng_seed=s)).get_trace(
+            data=data, priors=orch.priors)
+        for name, site in tr.items():
+            if site["type"] == "sample" and not site.get("is_observed", False):
+                draws.setdefault(name, []).append(np.asarray(site["value"]))
+    return {k: np.stack(v) for k, v in draws.items()}
+
+
+def _predict(orch, posteriors, genotypes=None):
+    df = predict(orch, posteriors, q_to_get=[0.5], num_samples=None,
+                 genotypes=genotypes)
+    return df.set_index(_KEY)["q0.5"]
+
+
+def _max_chunk_error(orch, posteriors, batch_sizes):
+    full = _predict(orch, posteriors)
+    genotypes = list(orch.growth_tm.tensor_dim_labels[-1])
+    worst = 0.0
+    for bs in batch_sizes:
+        for i in range(0, len(genotypes), bs):
+            sub = _predict(orch, posteriors, genotypes[i:i + bs])
+            worst = max(worst,
+                        float(np.max(np.abs(sub - full.loc[sub.index]))))
+    return worst
+
+
+@pytest.fixture(scope="module")
+def growth_df():
+    return _growth_df()
+
+
+@pytest.mark.parametrize("model", list(_MODELS))
+def test_chunked_prediction_matches_full(growth_df, model):
+    """Every chunking (pairs split across chunks included) equals full."""
+    orch = ModelOrchestrator(growth_df, None, **_MODELS[model])
+    assert len(orch.pair_labels) > 0
+    posteriors = _prior_posteriors(orch)
+    assert _max_chunk_error(orch, posteriors, [2, 3, 5, 7]) < 1e-5
+
+
+@pytest.mark.parametrize("model", list(_MODELS))
+def test_genotype_subset_prediction_matches_full(growth_df, model):
+    """A --genotypes_file style subset (a scattered selection) equals full."""
+    orch = ModelOrchestrator(growth_df, None, **_MODELS[model])
+    posteriors = _prior_posteriors(orch)
+    full = _predict(orch, posteriors)
+    subset = ["M6N", "D2E/K5L", "A1C", "H4I/M6N", "wt", "F3G/H4I"]
+    sub = _predict(orch, posteriors, subset)
+    assert set(sub.index.get_level_values(0)) == set(subset)
+    np.testing.assert_allclose(sub.values, full.loc[sub.index].values,
+                               atol=1e-5)
+
+
+def test_subset_orchestrator_keeps_fitted_mutation_labels(growth_df):
+    """The rebuilt model indexes mutations and pairs as the fit did."""
+    orch = ModelOrchestrator(growth_df, None, **_MODELS["hill_mut"])
+    new = copy_orchestrator(orch, genotypes=["M6N", "D2E/K5L"])
+    assert new.mut_labels == orch.mut_labels
+    assert new.pair_labels == orch.pair_labels
+    assert new.data.growth.num_mutation == len(orch.mut_labels)
+    assert new.data.growth.num_pair == len(orch.pair_labels)
+    # Settings stay label-free, so a written configuration does not carry them.
+    assert "mut_labels" not in new.settings
+
+
+def test_map_fit_chunked_prediction_matches_full(growth_df, tmp_path):
+    """A short MAP fit's posterior file predicts the same in chunks."""
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    orch = ModelOrchestrator(growth_df, None, **_MODELS["hill_mut"])
+    ri = RunInference(model=orch, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="delta")
+    svi_state, _, _ = ri.run_optimization(
+        svi=svi, max_num_epochs=50, out_prefix=str(tmp_path / "fit"),
+        checkpoint_interval=1000)
+    map_params = svi.optim.get_params(svi_state.optim_state)
+    ri.get_map_posteriors(map_params, out_prefix=str(tmp_path / "map"))
+    posterior_file = str(tmp_path / "map_posterior.h5")
+    assert _max_chunk_error(orch, posterior_file, [3, 7]) < 1e-5
+
+
+def test_fixed_labels_index_space():
+    """Fixed labels set the row order; absent mutations get zero rows."""
+    muts, pairs, mat, p_idx, g_idx = build_mut_geno_matrix(
+        ["K5L/M6N", "M6N"], mut_labels=_MUTS,
+        pair_labels=["A1C/D2E", "K5L/M6N"])
+    assert muts == _MUTS
+    assert pairs == ["A1C/D2E", "K5L/M6N"]
+    np.testing.assert_array_equal(mat[_MUTS.index("M6N")], [1, 1])
+    np.testing.assert_array_equal(mat[_MUTS.index("K5L")], [1, 0])
+    assert mat[:4].sum() == 0
+    np.testing.assert_array_equal(p_idx, [1])
+    np.testing.assert_array_equal(g_idx, [0])
+
+
+def test_fixed_labels_reject_unknown_mutation_or_pair():
+    with pytest.raises(ValueError, match="mutation\\(s\\) not in"):
+        build_mut_geno_matrix(["A1C/Q9R"], mut_labels=["A1C"],
+                              pair_labels=[])
+    with pytest.raises(ValueError, match="pair\\(s\\) not in"):
+        build_mut_geno_matrix(["A1C/D2E"], mut_labels=["A1C", "D2E"],
+                              pair_labels=[])
diff --git a/tests/tfscreen/tfmodel/analysis/test_shared_shift.py b/tests/tfscreen/tfmodel/analysis/test_shared_shift.py
new file mode 100644
index 00000000..6806c441
--- /dev/null
+++ b/tests/tfscreen/tfmodel/analysis/test_shared_shift.py
@@ -0,0 +1,189 @@
+import numpy as np
+import pandas as pd
+import pytest
+from unittest.mock import MagicMock, patch
+
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+from tfscreen.tfmodel.analysis.shared_shift import (
+    apply_shared_shift,
+    read_shift_table,
+    row_shifts,
+)
+from tfscreen.tfmodel.analysis.extraction import (
+    extract_theta_curves,
+    extract_theta_epistasis,
+)
+
+_GENOTYPES = ["wt", "A10G", "C20D", "A10G/C20D"]
+
+
+def _rows(genotypes=("wt", "mut"), concs=(0.0, 1.0)):
+    return pd.DataFrame([{"genotype": g, "titrant_name": "iptg",
+                          "titrant_conc": c}
+                         for g in genotypes for c in concs])
+
+
+def _shift(rows, value):
+    out = rows.copy()
+    out["shift"] = value
+    return out
+
+
+# --- the table ---
+
+def test_read_shift_table_checks_columns_and_duplicates(tmp_path):
+    rows = _shift(_rows(), 0.03)
+    path = tmp_path / "shift.csv"
+    rows.to_csv(path, index=False)
+    assert len(read_shift_table(str(path))) == len(rows)
+    with pytest.raises(ValueError, match="missing column"):
+        read_shift_table(rows.drop(columns="shift"))
+    with pytest.raises(ValueError, match="more than once"):
+        read_shift_table(pd.concat([rows, rows.iloc[:1]]))
+
+
+def test_row_shifts_align_and_zero_wt():
+    rows = _rows()
+    table = _shift(rows, 0.03).iloc[::-1]          # order must not matter
+    table.loc[table["genotype"] == "wt", "shift"] = 99.0   # wt ignored
+    out = row_shifts(rows, table)
+    assert np.allclose(out[rows["genotype"].to_numpy() == "wt"], 0.0)
+    assert np.allclose(out[rows["genotype"].to_numpy() == "mut"], 0.03)
+
+
+def test_row_shifts_needs_every_non_wt_row():
+    rows = _rows()
+    table = _shift(rows[rows["genotype"] == "wt"], 0.03)   # wt only
+    with pytest.raises(ValueError, match="no shift"):
+        row_shifts(rows, table)
+    # wt rows need no entry at all
+    table = _shift(rows[rows["genotype"] != "wt"], 0.03)
+    assert np.allclose(row_shifts(rows, table)[:2], 0.0)
+
+
+# --- applying it ---
+
+def test_one_shared_multiple_per_draw():
+    rows = _rows(("wt", "a", "b"))
+    table = _shift(rows, 0.0)
+    table.loc[table["genotype"] == "a", "shift"] = 0.02
+    table.loc[table["genotype"] == "b", "shift"] = -0.05
+    samples = np.zeros((500, len(rows)))
+    out = apply_shared_shift(samples, rows, table, seed=3)
+    is_a = rows["genotype"].to_numpy() == "a"
+    is_b = rows["genotype"].to_numpy() == "b"
+    z_a = out[:, is_a] / 0.02
+    z_b = out[:, is_b] / -0.05
+    # every row of a draw moves by the same multiple of its own shift
+    assert np.allclose(z_a, z_a[:, :1])
+    assert np.allclose(z_a[:, 0], z_b[:, 0])
+    assert np.allclose(out[:, rows["genotype"].to_numpy() == "wt"], 0.0)
+    assert abs(np.std(z_a[:, 0]) - 1.0) < 0.1
+
+
+def test_shift_widens_intervals_and_leaves_differences():
+    rows = _rows(("wt", "a", "b"), concs=(1.0,))
+    table = _shift(rows, 0.04)
+    rng = np.random.default_rng(0)
+    base = rng.normal(0.0, 0.01, size=(20000, len(rows)))
+    out = apply_shared_shift(base, rows, table, seed=1)
+    a = rows.index[rows["genotype"] == "a"][0]
+    b = rows.index[rows["genotype"] == "b"][0]
+    # each genotype's spread grows in quadrature
+    assert np.std(out[:, a]) == pytest.approx(np.hypot(0.01, 0.04), rel=0.03)
+    # a difference between two shifted genotypes keeps the original spread
+    assert np.std(out[:, a] - out[:, b]) == pytest.approx(
+        np.std(base[:, a] - base[:, b]), rel=1e-6)
+
+
+def test_apply_refuses_a_single_draw_and_bad_shapes():
+    rows = _rows()
+    table = _shift(rows, 0.03)
+    with pytest.raises(ValueError, match="MAP point"):
+        apply_shared_shift(np.zeros((1, len(rows))), rows, table)
+    with pytest.raises(ValueError, match="shape"):
+        apply_shared_shift(np.zeros((5, len(rows) + 1)), rows, table)
+
+
+def test_seed_makes_it_reproducible():
+    rows = _rows()
+    table = _shift(rows, 0.03)
+    s = np.zeros((50, len(rows)))
+    assert np.array_equal(apply_shared_shift(s, rows, table, seed=7),
+                          apply_shared_shift(s, rows, table, seed=7))
+    assert not np.array_equal(apply_shared_shift(s, rows, table, seed=7),
+                              apply_shared_shift(s, rows, table, seed=8))
+
+
+# --- through the extraction functions (the real hill_geno component) ---
+
+def _model(genotypes, concs=(1.0,)):
+    rows = [{"genotype": g, "titrant_name": "iptg", "titrant_conc": float(c),
+             "map_theta_group": i}
+            for i, g in enumerate(genotypes) for c in concs]
+    model = MagicMock(spec=ModelOrchestrator)
+    model._theta = "hill_geno"
+    tm = MagicMock()
+    tm.df = pd.DataFrame(rows)
+    model.training_tm = tm
+    model.growth_tm = tm
+    return model
+
+
+def _flat(theta, S):
+    theta = np.tile(np.asarray(theta, dtype=float), (S, 1))
+    k = theta.shape[1]
+    return {"theta_hill_n": np.ones((S, k)),
+            "theta_log_hill_K": np.zeros((S, k)),
+            "theta_theta_high": theta.copy(),
+            "theta_theta_low": theta.copy()}
+
+
+def test_extract_theta_curves_applies_the_shift():
+    model = _model(["wt", "mut"])
+    post = _flat([0.6, 0.4], S=4000)
+    rows = _rows(("wt", "mut"), concs=(1.0,))
+    plain = extract_theta_curves(model, post, num_samples=None)
+    shifted = extract_theta_curves(model, post, num_samples=None,
+                                   shared_shift=_shift(rows, 0.05),
+                                   shift_seed=2)
+    wt = shifted["genotype"] == "wt"
+    assert np.allclose(shifted.loc[wt, "q0.975"], plain.loc[wt, "q0.975"])
+    width = (shifted.loc[~wt, "q0.975"] - shifted.loc[~wt, "q0.025"]).iloc[0]
+    assert width == pytest.approx(2 * 1.96 * 0.05, rel=0.05)
+    assert shifted.loc[~wt, "q0.5"].iloc[0] == pytest.approx(0.4, abs=0.005)
+
+
+def test_epistasis_keeps_one_copy_of_a_uniform_shift():
+    model = _model(_GENOTYPES)
+    post = _flat([0.5, 0.4, 0.3, 0.25], S=4000)
+    rows = _rows(_GENOTYPES, concs=(1.0,))
+    plain = extract_theta_epistasis(model, post, scale="add")
+    shifted = extract_theta_epistasis(model, post, scale="add",
+                                      shared_shift=_shift(rows, 0.05),
+                                      shift_seed=4)
+    # ep = X_ab - X_a - X_b + X_wt: the shift enters s - s - s + 0 = -s
+    assert plain["q0.975"].iloc[0] == pytest.approx(plain["q0.025"].iloc[0])
+    width = shifted["q0.975"].iloc[0] - shifted["q0.025"].iloc[0]
+    assert width == pytest.approx(2 * 1.96 * 0.05, rel=0.05)
+    assert shifted["q0.5"].iloc[0] == pytest.approx(plain["q0.5"].iloc[0],
+                                                    abs=0.005)
+
+
+# --- the CLI guard ---
+
+def test_predict_theta_refuses_shift_for_unmeasured_genotypes(tmp_path):
+    from tfscreen.tfmodel.scripts.predict_theta_cli import predict_theta
+    orch = MagicMock()
+    orch._theta = "hill_geno"
+    orch.training_tm.df = _rows(("wt", "A1B"))
+    gfile = tmp_path / "g.txt"
+    gfile.write_text("C2D\n")
+    with patch("tfscreen.tfmodel.scripts.predict_theta_cli.read_configuration",
+               return_value=(orch, {})), \
+         patch("tfscreen.tfmodel.scripts.predict_theta_cli.resolve_param_file",
+               side_effect=lambda pf, o, op: pf):
+        with pytest.raises(ValueError, match="training genotypes only"):
+            predict_theta("cfg.yaml", "post.h5", genotypes_file=str(gfile),
+                          shared_shift=str(tmp_path / "shift.csv"),
+                          out_prefix=str(tmp_path / "out"))
diff --git a/tests/tfscreen/tfmodel/analysis/test_tube_offsets.py b/tests/tfscreen/tfmodel/analysis/test_tube_offsets.py
new file mode 100644
index 00000000..4c6d3950
--- /dev/null
+++ b/tests/tfscreen/tfmodel/analysis/test_tube_offsets.py
@@ -0,0 +1,93 @@
+"""The per-tube sample-offset diagnostic (offset mode)."""
+import numpy as np
+import pandas as pd
+import pytest
+
+from tfscreen.tfmodel.analysis.tube_offsets import (
+    format_tube_offset_summary,
+    tube_offset_diagnostic,
+)
+
+CONCS = [0.0, 0.01, 0.1, 1.0]
+TIMES = [0.0, 60.0, 120.0]
+
+
+def tube_table(offset_fn, seed=0, noise=0.17):
+    """3 replicates x 3 times x 4 IPTG in two selection conditions."""
+    rng = np.random.default_rng(seed)
+    rows = []
+    for cond in ("kanR+kan", "pheS+4CP"):
+        for rep in (1, 2, 3):
+            for t in TIMES:
+                for i, c in enumerate(CONCS):
+                    rows.append(dict(replicate=rep, library="lib",
+                                     condition_pre="pre", condition_sel=cond,
+                                     titrant_name="iptg", titrant_conc=c,
+                                     t_pre=30.0, t_sel=t,
+                                     q0_5=offset_fn(cond, i, t)
+                                     + rng.normal(0, noise)))
+    return pd.DataFrame(rows).rename(columns={"q0_5": "q0.5"})
+
+
+def test_noise_offsets_are_not_structured():
+    _, trends, summary = tube_offset_diagnostic(
+        tube_table(lambda c, i, t: 0.0), sigma=0.17)
+    assert not summary["structured"]
+    assert summary["n_tubes"] == 72
+    assert summary["n_conditions"] == 2
+    assert summary["sd_over_sigma"] == pytest.approx(1.0, abs=0.3)
+    assert set(trends["condition_sel"]) == {"kanR+kan", "pheS+4CP"}
+
+
+def test_iptg_pattern_is_flagged():
+    """The dev-data offset mode: offsets follow IPTG, opposite by marker."""
+    def mode(cond, i, t):
+        return (2.0 - 2.0 * i) if cond == "kanR+kan" else (-1.0 + i)
+    tubes, trends, summary = tube_offset_diagnostic(tube_table(mode),
+                                                    sigma=0.17)
+    assert summary["structured"]
+    assert summary["n_structured"] == 2
+    t = trends.set_index("condition_sel")
+    assert t.loc["kanR+kan", "rho_titrant"] < -0.8
+    assert t.loc["pheS+4CP", "rho_titrant"] > 0.8
+    assert t.loc["kanR+kan", "r2_titrant"] > 0.9
+    assert summary["abs_z_max"] > 10
+    np.testing.assert_allclose(tubes["z"], tubes["offset"] / 0.17)
+    text = format_tube_offset_summary(summary, trends)
+    assert "offset mode" in text
+
+
+def test_time_trend_is_flagged():
+    _, trends, summary = tube_offset_diagnostic(
+        tube_table(lambda c, i, t: 0.01 * t if c == "kanR+kan" else 0.0),
+        sigma=0.17)
+    t = trends.set_index("condition_sel")
+    assert t.loc["kanR+kan", "structured"]
+    assert t.loc["kanR+kan", "q_time"] < 0.05
+    assert not t.loc["pheS+4CP", "structured"]
+
+
+def test_without_sigma_reports_ln_units():
+    tubes, trends, summary = tube_offset_diagnostic(
+        tube_table(lambda c, i, t: 0.0), sigma=None)
+    assert "z" not in tubes.columns
+    assert summary["sigma"] is None and summary["sd_over_sigma"] is None
+    assert trends["mean_z"].isna().all()
+    assert "prior SD" not in format_tube_offset_summary(summary, trends)
+
+
+def test_constant_titrant_gives_nan_trend():
+    df = tube_table(lambda c, i, t: 0.0)
+    df["titrant_conc"] = 0.0
+    _, trends, summary = tube_offset_diagnostic(df, sigma=0.17)
+    assert trends["rho_titrant"].isna().all()
+    assert trends["r2_titrant"].isna().all()
+
+
+def test_missing_columns_raise():
+    with pytest.raises(ValueError, match="t_sel"):
+        tube_offset_diagnostic(tube_table(lambda c, i, t: 0.0)
+                               .drop(columns="t_sel"))
+    with pytest.raises(ValueError, match="q0.5"):
+        tube_offset_diagnostic(tube_table(lambda c, i, t: 0.0)
+                               .drop(columns="q0.5"))
diff --git a/tests/tfscreen/tfmodel/generative/components/growth/test_linear.py b/tests/tfscreen/tfmodel/generative/components/growth/test_linear.py
index 84de4525..db75d83e 100644
--- a/tests/tfscreen/tfmodel/generative/components/growth/test_linear.py
+++ b/tests/tfscreen/tfmodel/generative/components/growth/test_linear.py
@@ -670,6 +670,36 @@ def test_array_prior_initializes_guide_params(self, mock_data):
         k_locs = np.asarray(gtr[f"{name}_k_locs"]["value"])
         assert np.allclose(k_locs, np.array([0.011, 0.021, 0.029]))
 
+    def test_array_m_scales_are_per_condition(self):
+        """Per-condition m_scale_plus/minus arrays: each condition takes the
+        entry its selection flag names (two '+' conditions, one tight from
+        monoculture data and one loose)."""
+        labels = ["kanR+kan", "kanR-kan", "pheS+4CP", "pheS-4CP"]
+        plus = jnp.array([0.0015, 99.0, 0.01, 99.0])     # 99: unused entries
+        minus = jnp.array([99.0, 0.004, 99.0, 0.01])
+        priors = get_priors(condition_labels=labels).replace(
+            m_scale_plus=plus, m_scale_minus=minus)
+        data = TestDefineModelSelectionAware()._make_data(num_condition_rep=4)
+        with seed(rng_seed=0):
+            tr = trace(define_model).get_trace(name="ms", data=data, priors=priors)
+        fn = tr["ms_m"]["fn"]
+        while not hasattr(fn, "scale"):
+            fn = fn.base_dist
+        assert np.allclose(np.asarray(fn.scale), [0.0015, 0.004, 0.01, 0.01])
+
+    def test_scalar_m_scales_unchanged(self):
+        """Scalar m_scale_plus/minus still give the per-class scales."""
+        labels = ["kanR+kan", "kanR-kan"]
+        priors = get_priors(condition_labels=labels)
+        data = TestDefineModelSelectionAware()._make_data(num_condition_rep=2)
+        with seed(rng_seed=0):
+            tr = trace(define_model).get_trace(name="mc", data=data, priors=priors)
+        fn = tr["mc_m"]["fn"]
+        while not hasattr(fn, "scale"):
+            fn = fn.base_dist
+        assert np.allclose(np.asarray(fn.scale),
+                           [priors.m_scale_plus, priors.m_scale_minus])
+
     def test_array_and_scalar_give_same_sites(self, mock_data):
         """Model/guide site sets are identical whether priors are scalar or array."""
         name = "ss"
@@ -701,3 +731,257 @@ def test_baseline_floor_tight(self):
         assert bounds["k"]["floor"] <= 0.002
         assert bounds["k"]["scale_field"] == "k_scale"
         assert bounds["m"]["scale_field"] == "m_scale_plus"
+
+
+# --- k_pinned (hard clamp, for conditional fits) ---
+
+def test_k_and_m_pinned_clamp_both(mock_data):
+    """With k_pinned and m_pinned, both are deterministic at their locs and
+    the guide has no variational site for either."""
+    name = "pin"
+    k_loc = jnp.array([0.01, 0.02, 0.03])
+    m_loc = jnp.array([0.1, 0.2, 0.3])
+    priors = get_priors().replace(k_loc=k_loc, m_loc=m_loc,
+                                  k_pinned=True, m_pinned=True)
+
+    with seed(rng_seed=0):
+        model_trace = trace(define_model).get_trace(
+            name=name, data=mock_data, priors=priors)
+    with seed(rng_seed=0):
+        guide_trace = trace(guide).get_trace(
+            name=name, data=mock_data, priors=priors)
+
+    assert model_trace[f"{name}_k"]["type"] == "deterministic"
+    np.testing.assert_allclose(np.asarray(model_trace[f"{name}_k"]["value"]),
+                               np.asarray(k_loc))
+    assert f"{name}_k_locs" not in guide_trace
+    assert f"{name}_k" not in guide_trace
+
+    with seed(rng_seed=0):
+        params = guide(name=name, data=mock_data, priors=priors)
+    np.testing.assert_allclose(np.asarray(params.k_sel),
+                               np.asarray(k_loc[mock_data.map_condition_sel]))
+
+
+def test_k_pinned_alone_keeps_m_sampled(mock_data):
+    name = "pin"
+    priors = get_priors().replace(k_loc=jnp.array([0.01, 0.02, 0.03]),
+                                  k_pinned=True)
+    with seed(rng_seed=0):
+        model_trace = trace(define_model).get_trace(
+            name=name, data=mock_data, priors=priors)
+    assert model_trace[f"{name}_k"]["type"] == "deterministic"
+    assert model_trace[f"{name}_m"]["type"] == "sample"
+
+
+# --- per-condition pins (a tuple of flags) ---
+
+def _mixed_priors():
+    """k pinned in condition 0 only, m pinned in conditions 0 and 2."""
+    return get_priors().replace(k_loc=jnp.array([0.01, 0.02, 0.03]),
+                                m_loc=jnp.array([0.1, 0.2, 0.3]),
+                                k_pinned=(True, False, False),
+                                m_pinned=(True, False, True))
+
+
+def test_per_condition_pins_sample_only_free_conditions(mock_data):
+    """The free conditions are a '{name}_{param}_free' sample site sized to
+    them; '{name}_{param}' is deterministic, pinned entries at their loc and
+    free entries the sampled values."""
+    name = "pin"
+    priors = _mixed_priors()
+    with seed(rng_seed=0):
+        model_trace = trace(define_model).get_trace(
+            name=name, data=mock_data, priors=priors)
+
+    k_free = model_trace[f"{name}_k_free"]
+    m_free = model_trace[f"{name}_m_free"]
+    assert k_free["type"] == "sample" and k_free["value"].shape == (2,)
+    assert m_free["type"] == "sample" and m_free["value"].shape == (1,)
+    np.testing.assert_allclose(_site_loc(k_free), [0.02, 0.03])
+    np.testing.assert_allclose(_site_loc(m_free), [0.2])
+
+    k = np.asarray(model_trace[f"{name}_k"]["value"])
+    m = np.asarray(model_trace[f"{name}_m"]["value"])
+    assert model_trace[f"{name}_k"]["type"] == "deterministic"
+    assert model_trace[f"{name}_m"]["type"] == "deterministic"
+    np.testing.assert_allclose(k, [0.01, *np.asarray(k_free["value"])])
+    np.testing.assert_allclose(m, [0.1, float(m_free["value"][0]), 0.3])
+
+
+def test_per_condition_pins_guide_matches_model(mock_data):
+    """Model and guide agree on the free sample sites; the guide's free
+    params follow the '{site}_locs' convention and start at the free locs;
+    the guide's full k and m hold the pinned locs."""
+    name = "pin"
+    priors = _mixed_priors()
+    with seed(rng_seed=0):
+        model_trace = trace(define_model).get_trace(
+            name=name, data=mock_data, priors=priors)
+    with seed(rng_seed=0):
+        guide_trace = trace(guide).get_trace(
+            name=name, data=mock_data, priors=priors)
+
+    model_samples = {n for n, s in model_trace.items()
+                     if s["type"] == "sample" and not s.get("is_observed", False)}
+    guide_samples = {n for n, s in guide_trace.items() if s["type"] == "sample"}
+    assert model_samples == guide_samples == {f"{name}_k_free", f"{name}_m_free"}
+    np.testing.assert_allclose(
+        np.asarray(guide_trace[f"{name}_k_free_locs"]["value"]), [0.02, 0.03])
+    np.testing.assert_allclose(
+        np.asarray(guide_trace[f"{name}_m_free_locs"]["value"]), [0.2])
+    assert f"{name}_k_locs" not in guide_trace
+
+    with seed(rng_seed=0):
+        params = guide(name=name, data=mock_data, priors=priors)
+    k_full = np.asarray(params.k_sel)
+    sel = np.asarray(mock_data.map_condition_sel)
+    np.testing.assert_allclose(k_full[sel == 0], 0.01)
+    m_full = np.asarray(params.m_sel)
+    np.testing.assert_allclose(m_full[sel == 0], 0.1)
+    np.testing.assert_allclose(m_full[sel == 2], 0.3)
+
+
+def test_uniform_pin_tuples_match_the_bool(mock_data):
+    """A tuple with every flag the same behaves as the bool."""
+    name = "pin"
+    base = get_priors().replace(m_loc=jnp.array([0.1, 0.2, 0.3]))
+    for flags, expected in (((True,) * 3, "deterministic"),
+                            ((False,) * 3, "sample")):
+        with seed(rng_seed=0):
+            tr = trace(define_model).get_trace(
+                name=name, data=mock_data, priors=base.replace(m_pinned=flags))
+        assert tr[f"{name}_m"]["type"] == expected
+        assert f"{name}_m_free" not in tr
+
+
+def test_pin_tuple_of_wrong_length_raises(mock_data):
+    priors = get_priors().replace(m_pinned=(True, False))
+    with pytest.raises(ValueError, match="do not match"):
+        with seed(rng_seed=0):
+            define_model(name="pin", data=mock_data, priors=priors)
+
+
+# --- titrant-dependent k (selection conditions) ---
+
+from types import SimpleNamespace  # noqa: E402
+
+_CONC = np.array([0.0, 1e-3, 1.0])
+
+
+def _titrant_data():
+    # condition 0: control (pre), 1: kan selection, 2: control, 3: 4CP selection
+    num_cr, C, G = 4, 3, 2
+    sel_map = np.broadcast_to(np.array([1, 2, 3, 3])[:, None, None], (4, C, G))
+    return SimpleNamespace(num_condition_rep=num_cr, num_titrant_conc=C,
+                           titrant_conc=jnp.asarray(_CONC),
+                           map_condition_pre=jnp.zeros((4, C, G), dtype=int),
+                           map_condition_sel=jnp.asarray(sel_map))
+
+
+def _titrant_priors(**kw):
+    return get_priors(is_selection=[False, True, False, True]).replace(**kw)
+
+
+def test_k_titrant_loglinear_is_a_line_in_log_conc():
+    data = _titrant_data()
+    priors = _titrant_priors(k_titrant_loglinear_scale=0.005)
+    tr = trace(seed(define_model, 2)).get_trace("g", data, priors)
+    slope = np.asarray(tr["g_k_titrant_slope"]["value"])
+    assert slope.shape == (2,)
+    params = seed(define_model, 2)("g", data, priors)
+    base = seed(define_model, 2)("g", data, priors.replace(k_titrant_loglinear_scale=0.0))
+    shift = np.asarray(params.k_sel) - np.asarray(base.k_sel)
+    L = np.log(np.maximum(_CONC, 1e-4) / 1e-4)
+    assert np.allclose(shift[0, :, 0], slope[0] * L, atol=1e-7)
+    assert np.allclose(shift[2, :, 1], slope[1] * L, atol=1e-7)
+    assert np.allclose(shift[1], 0.0)
+
+
+def test_k_titrant_refusals():
+    data = _titrant_data()
+    with pytest.raises(ValueError, match=">= 0"):
+        seed(define_model, 0)("g", data, _titrant_priors(k_titrant_loglinear_scale=-0.01))
+    with pytest.raises(ValueError, match="selection flags"):
+        seed(define_model, 0)("g", data, get_priors().replace(k_titrant_loglinear_scale=0.01))
+    flat = SimpleNamespace(**{**vars(data), "map_condition_sel": jnp.array([1, 2, 3, 3])})
+    with pytest.raises(ValueError, match="second-to-last"):
+        seed(define_model, 0)("g", flat, _titrant_priors(k_titrant_loglinear_scale=0.01))
+    # off by default: no new site
+    tr = trace(seed(define_model, 0)).get_trace("g", data, _titrant_priors())
+    assert "g_k_titrant_slope" not in tr
+    assert not hasattr(_titrant_priors(), "k_titrant_free_scale")
+
+
+@pytest.mark.parametrize("fn", [define_model, guide])
+def test_k_titrant_shifts_selection_only(fn):
+    data = _titrant_data()
+    priors = _titrant_priors(k_titrant_loglinear_scale=0.005)
+    params = seed(fn, 1)("g", data, priors)
+    base = seed(fn, 1)("g", data, priors.replace(k_titrant_loglinear_scale=0.0))
+    shift = np.asarray(params.k_sel) - np.asarray(base.k_sel)
+    assert np.allclose(shift[:, 0, :], 0.0)       # no titrant
+    assert np.allclose(shift[1], 0.0)             # control condition
+    assert np.allclose(np.asarray(params.k_pre), np.asarray(base.k_pre))
+
+
+@pytest.mark.parametrize("field", ["k_titrant_loglinear_scale"])
+def test_k_titrant_traces_under_jit(field):
+    # titrant_conc is a traced argument inside the jitted fit
+    import jax
+    data = _titrant_data()
+    priors = _titrant_priors(**{field: 0.01})
+
+    def run(conc):
+        d = SimpleNamespace(**{**vars(data), "titrant_conc": conc})
+        return seed(define_model, 0)("g", d, priors).k_sel
+
+    out = jax.jit(run)(jnp.asarray(_CONC))
+    assert np.allclose(np.asarray(out), np.asarray(run(jnp.asarray(_CONC))))
+
+
+# --- per-condition warp of theta ---
+
+def test_warp_theta_fixes_ends_and_is_monotone():
+    x = jnp.linspace(-0.5, 1.5, 41)
+    for a, x0 in [(0.5, 0.5), (8.0, 0.3), (20.0, 0.8)]:
+        f = np.asarray(_lin_warp(x, jnp.full_like(x, a), jnp.full_like(x, x0)))
+        assert np.all(np.diff(f) > 0)
+        f01 = np.asarray(_lin_warp(jnp.array([0.0, 1.0]), jnp.full(2, a), jnp.full(2, x0)))
+        assert np.allclose(f01, [0.0, 1.0], atol=1e-6)
+    # a -> 0 is (nearly) linear; a == 0 is exactly theta
+    f = np.asarray(_lin_warp(x, jnp.full_like(x, 1e-3), jnp.full_like(x, 0.4)))
+    assert np.allclose(f, np.asarray(x), atol=1e-3)
+    assert np.allclose(np.asarray(_lin_warp(x, jnp.zeros_like(x), jnp.full_like(x, 0.4))),
+                       np.asarray(x))
+    assert _lin_warp(x, None, None) is x
+
+
+def _lin_warp(theta, a, x0):
+    from tfscreen.tfmodel.generative.components.growth.linear import warp_theta
+    return warp_theta(theta, a, x0)
+
+
+@pytest.mark.parametrize("fn", [define_model, guide])
+def test_m_warp_only_warped_conditions(fn):
+    from tfscreen.tfmodel.generative.components.growth.linear import calculate_growth
+    data = _titrant_data()
+    priors = _titrant_priors(m_warp=(False, True, False, False))
+    tr = trace(seed(fn, 3)).get_trace("g", data, priors)
+    assert tr["g_warp_log_a"]["value"].shape == (1,)
+    assert tr["g_warp_x0"]["value"].shape == (1,)
+    params = seed(fn, 3)("g", data, priors)
+    a = np.asarray(params.warp_a_sel)
+    # map_condition_sel rows: [1, 2, 3, 3]; only condition 1 is warped
+    assert (a[0] > 0).all() and (a[1:] == 0).all()
+    theta = jnp.full(np.asarray(data.map_condition_sel).shape, 0.3)
+    g_pre, g_sel = calculate_growth(params, jnp.zeros_like(theta), 1.0, theta)
+    base = params.replace(warp_a_pre=None, warp_x0_pre=None, warp_a_sel=None,
+                          warp_x0_sel=None)
+    _, g_lin = calculate_growth(base, jnp.zeros_like(theta), 1.0, theta)
+    diff = np.asarray(g_sel) - np.asarray(g_lin)
+    assert not np.allclose(diff[0], 0.0) and np.allclose(diff[1:], 0.0)
+    # off by default
+    tr = trace(seed(fn, 3)).get_trace("g", data, _titrant_priors())
+    assert "g_warp_log_a" not in tr
+    assert seed(fn, 3)("g", data, _titrant_priors()).warp_a_sel is None
diff --git a/tests/tfscreen/tfmodel/generative/components/ln_cfu0/test_hierarchical.py b/tests/tfscreen/tfmodel/generative/components/ln_cfu0/test_hierarchical.py
index 98300e15..d2d42c7f 100644
--- a/tests/tfscreen/tfmodel/generative/components/ln_cfu0/test_hierarchical.py
+++ b/tests/tfscreen/tfmodel/generative/components/ln_cfu0/test_hierarchical.py
@@ -1497,3 +1497,82 @@ def test_model_and_guide_two_classes_compatible_sample_sites(mock_data_two_class
         f"  model only: {model_samples - guide_samples}\n"
         f"  guide only: {guide_samples - model_samples}"
     )
+
+
+def test_extract_spec_keys_rows_by_array_position_with_gaps():
+    """
+    ln_cfu0 rows are labeled by their position in the (replicate,
+    condition_pre, genotype) array, even when a genotype is missing from a
+    replicate. map_ln_cfu0 numbers only the combinations present, so
+    indexing the array with it shifted every later label (2026-09-29).
+    """
+    from types import SimpleNamespace
+    import pandas as pd
+    from tfscreen.tfmodel.analysis.extraction import _extract_param_est
+    from tfscreen.tfmodel.generative.components.ln_cfu0.hierarchical import (
+        ln_cfu0_extract_spec,
+    )
+
+    reps, cps, genos = [1, 2], ["kanR/kanR-kan", "pheS/pheS-4CP"], ["wt", "A1B", "C2D"]
+    rows = []
+    for r in reps:
+        for c in cps:
+            lib, cond = c.split("/")
+            for g in genos:
+                if r == 1 and c == cps[0] and g == "A1B":
+                    continue  # the gap
+                rows.append(dict(replicate=r, library=lib, condition_pre=cond,
+                                 genotype=g))
+    df = pd.DataFrame(rows)
+    # map_ln_cfu0 as the tensor manager builds it: consecutive over present rows
+    df["map_ln_cfu0"] = df.groupby(["replicate", "library", "condition_pre",
+                                    "genotype"], sort=False).ngroup()
+    tm = SimpleNamespace(df=df,
+                         tensor_dim_names=["replicate", "condition_pre", "genotype"],
+                         tensor_dim_labels=[reps, cps, genos])
+    spec = ln_cfu0_extract_spec(SimpleNamespace(growth_tm=tm))[0]
+
+    # the model's array: value encodes (replicate, condition_pre, genotype)
+    arr = np.array([[[100 * i + 10 * j + k for k in range(3)] for j in range(2)]
+                    for i in range(2)], dtype=float)
+    post = {"ln_cfu0": np.stack([arr, arr])}
+    out = _extract_param_est(input_df=spec["input_df"],
+                             params_to_get=spec["params_to_get"],
+                             map_column=spec["map_column"],
+                             get_columns=spec["get_columns"],
+                             in_run_prefix=spec["in_run_prefix"],
+                             param_posteriors=post,
+                             q_to_get={"median": 0.5})["ln_cfu0"]
+    for _, r in out.iterrows():
+        i = reps.index(r["replicate"])
+        j = [c.split("/")[1] for c in cps].index(r["condition_pre"])
+        k = genos.index(r["genotype"])
+        assert r["median"] == 100 * i + 10 * j + k
+    assert len(out) == len(df)
+
+
+def _extract_tm(genotypes=("wt", "A1B")):
+    from types import SimpleNamespace
+    import pandas as pd
+    df = pd.DataFrame(dict(replicate=[1] * len(genotypes),
+                           library=["kanR"] * len(genotypes),
+                           condition_pre=["kanR-kan"] * len(genotypes),
+                           genotype=list(genotypes)))
+    df["map_ln_cfu0"] = np.arange(len(df))
+    return SimpleNamespace(df=df,
+                           tensor_dim_names=["replicate", "condition_pre",
+                                             "genotype"],
+                           tensor_dim_labels=[[1], ["kanR/kanR-kan"],
+                                              ["wt", "A1B"]])
+
+
+def test_extract_spec_refuses_rows_off_the_tensor_labels():
+    from types import SimpleNamespace
+    from tfscreen.tfmodel.generative.components.ln_cfu0.hierarchical import (
+        ln_cfu0_extract_spec,
+    )
+    ok = ln_cfu0_extract_spec(SimpleNamespace(growth_tm=_extract_tm()))
+    assert list(ok[0]["input_df"]["_ln_cfu0_flat"]) == [0, 1]
+    tm = _extract_tm(genotypes=("wt", "Z9Y"))
+    with pytest.raises(ValueError, match="not a label of the growth tensor"):
+        ln_cfu0_extract_spec(SimpleNamespace(growth_tm=tm))
diff --git a/tests/tfscreen/tfmodel/generative/components/ln_cfu0/test_hierarchical_factored.py b/tests/tfscreen/tfmodel/generative/components/ln_cfu0/test_hierarchical_factored.py
index 4069724c..a3871558 100644
--- a/tests/tfscreen/tfmodel/generative/components/ln_cfu0/test_hierarchical_factored.py
+++ b/tests/tfscreen/tfmodel/generative/components/ln_cfu0/test_hierarchical_factored.py
@@ -166,8 +166,9 @@ def _default_subs(name, data, priors, *, offset_geno=None, tube_offset=None):
         f"{name}_tube_scale": jnp.array(0.5),
         f"{name}_offset_geno": offset_geno if offset_geno is not None
                                else jnp.zeros((R, B), dtype=float),
-        f"{name}_tube_offset": tube_offset if tube_offset is not None
-                               else jnp.zeros((R, C), dtype=float),
+        # non-centred: tube_offset = tube_scale (0.5) * tube_offset_z
+        f"{name}_tube_offset_z": tube_offset / 0.5 if tube_offset is not None
+                                 else jnp.zeros((R, C), dtype=float),
     }
     for i in range(num_classes):
         subs[f"{name}_hyper_loc_{i}"]   = jnp.array(float(guesses[f"{name}_hyper_loc_{i}"]))
@@ -262,8 +263,8 @@ def test_get_guesses_offset_geno_shape(mock_data):
 def test_get_guesses_tube_offset_shape(mock_data):
     R, C = mock_data.num_replicate, mock_data.num_condition_pre
     guesses = get_guesses("x", mock_data)
-    assert f"x_tube_offset" in guesses
-    assert guesses["x_tube_offset"].shape == (R, C)
+    assert f"x_tube_offset_z" in guesses
+    assert guesses["x_tube_offset_z"].shape == (R, C)
 
 
 def test_get_guesses_tube_scale_present(mock_data):
@@ -276,7 +277,7 @@ def test_get_guesses_uniform_data_offsets_zero(mock_data):
     """All genotypes at same ln_cfu → zero offsets and zero tube offsets."""
     guesses = get_guesses("x", mock_data)
     assert jnp.allclose(guesses["x_offset_geno"], 0.0)
-    assert jnp.allclose(guesses["x_tube_offset"], 0.0)
+    assert jnp.allclose(guesses["x_tube_offset_z"], 0.0)
 
 
 def test_get_guesses_tube_offset_from_condition_shift(mock_data_varied):
@@ -286,7 +287,9 @@ def test_get_guesses_tube_offset_from_condition_shift(mock_data_varied):
     tube_offset[:, 0] ≈ −0.25 (median-centred within each replicate).
     """
     guesses = get_guesses("x", mock_data_varied)
-    tube_offset = np.array(guesses["x_tube_offset"])  # (R, C)
+    # non-centred: offset = tube_scale * z
+    tube_offset = (np.array(guesses["x_tube_offset_z"])
+                   * float(guesses["x_tube_scale"]))  # (R, C)
 
     # The two conditions differ by exactly 0.5; after centering on the
     # per-genotype median, their residuals are ±0.25.
@@ -393,7 +396,7 @@ def test_define_model_offset_geno_zero_gives_hyper_loc_plus_tube(mock_data):
         f"{name}_wt_loc":        jnp.array(12.0),
         f"{name}_tube_scale":    jnp.array(0.5),
         f"{name}_offset_geno":   jnp.zeros((R, B)),
-        f"{name}_tube_offset":   known_tube,
+        f"{name}_tube_offset_z": known_tube / 0.5,
     }
 
     model = substitute(define_model, data=subs)
@@ -513,7 +516,7 @@ def test_define_model_two_classes_separate_locs(mock_data_two_classes):
         f"{name}_wt_loc":        jnp.array(13.0),
         f"{name}_tube_scale":    jnp.array(0.5),
         f"{name}_offset_geno":   jnp.zeros((R, B)),
-        f"{name}_tube_offset":   jnp.zeros((R, C)),
+        f"{name}_tube_offset_z": jnp.zeros((R, C)),
     }
 
     tr   = trace(substitute(define_model, data=subs)).get_trace(
@@ -569,9 +572,9 @@ def test_guide_tube_offset_param_shape(mock_data):
     with seed(rng_seed=0):
         tr = trace(guide).get_trace(name=name, data=mock_data, priors=priors)
 
-    assert f"{name}_tube_offset_locs" in tr
-    assert tr[f"{name}_tube_offset_locs"]["value"].shape == (R, C)
-    assert tr[f"{name}_tube_offset_scales"]["value"].shape == (R, C)
+    assert f"{name}_tube_offset_z_locs" in tr
+    assert tr[f"{name}_tube_offset_z_locs"]["value"].shape == (R, C)
+    assert tr[f"{name}_tube_offset_z_scales"]["value"].shape == (R, C)
 
 
 def test_guide_has_tube_scale_sample_site(mock_data):
@@ -607,7 +610,7 @@ def test_guide_tube_offset_sample_shape(mock_data):
     with seed(rng_seed=0):
         tr = trace(guide).get_trace(name=name, data=mock_data, priors=priors)
 
-    assert tr[f"{name}_tube_offset"]["value"].shape == (R, C)
+    assert tr[f"{name}_tube_offset_z"]["value"].shape == (R, C)
 
 
 def test_guide_two_classes_has_per_class_params(mock_data_two_classes):
@@ -722,7 +725,7 @@ def test_guide_pinned_drops_variational_params(mock_data):
     # Other sites intact
     assert f"{name}_tube_scale" in tr
     assert f"{name}_offset_geno" in tr
-    assert f"{name}_tube_offset" in tr
+    assert f"{name}_tube_offset_z" in tr
 
 
 def test_model_and_guide_compatible_under_pinning(mock_data):
@@ -749,3 +752,53 @@ def test_model_and_guide_compatible_under_pinning(mock_data):
         f"model only: {model_samples - guide_samples}\n"
         f"guide only: {guide_samples - model_samples}"
     )
+
+
+def test_tube_offset_density_bounded_as_scale_collapses(mock_data):
+    """
+    With the tube offsets at 0 the joint density stays bounded as tube_scale
+    goes to 0 (non-centred). The centred form Normal(0, tube_scale) grew
+    like tube_scale^-(R*C) there, so no MAP existed.
+    """
+    from numpyro.infer.util import log_density
+
+    name = "x"
+    priors = get_priors()
+    R, C = mock_data.num_replicate, mock_data.num_condition_pre
+
+    def log_p(scale):
+        subs = _default_subs(name, mock_data, priors)
+        subs[f"{name}_tube_scale"] = jnp.array(scale)
+        subs[f"{name}_tube_offset_z"] = jnp.zeros((R, C))
+        lp, _ = log_density(define_model, (),
+                            dict(name=name, data=mock_data, priors=priors), subs)
+        return float(lp)
+
+    assert log_p(1e-8) - log_p(1e-2) < 1.0
+
+
+def test_get_extract_specs_uses_the_hierarchical_layout():
+    """The factored ln_cfu0 has the same (replicate, condition_pre, genotype)
+    array as hierarchical, so it extracts through the same spec."""
+    from types import SimpleNamespace
+    import pandas as pd
+    from tfscreen.tfmodel.generative.components.ln_cfu0 import (
+        hierarchical_factored,
+    )
+    df = pd.DataFrame(dict(replicate=[1, 1, 2], library=["kanR"] * 3,
+                           condition_pre=["kanR-kan"] * 3,
+                           genotype=["wt", "A1B", "A1B"]))
+    df["map_ln_cfu0"] = np.arange(3)
+    tm = SimpleNamespace(df=df,
+                         tensor_dim_names=["replicate", "condition_pre",
+                                           "genotype"],
+                         tensor_dim_labels=[[1, 2], ["kanR/kanR-kan"],
+                                            ["wt", "A1B"]])
+    specs = hierarchical_factored.get_extract_specs(
+        SimpleNamespace(growth_tm=tm))
+    assert len(specs) == 1
+    spec = specs[0]
+    assert spec["params_to_get"] == ["ln_cfu0"]
+    assert spec["map_column"] == "_ln_cfu0_flat"
+    # (rep, cp, geno) -> (rep * 1 + cp) * 2 + geno
+    assert list(spec["input_df"]["_ln_cfu0_flat"]) == [0, 1, 3]
diff --git a/tests/tfscreen/tfmodel/generative/components/noise/test_genotype_offset.py b/tests/tfscreen/tfmodel/generative/components/noise/test_genotype_offset.py
new file mode 100644
index 00000000..03e0ee7e
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/noise/test_genotype_offset.py
@@ -0,0 +1,161 @@
+"""
+noise/genotype_offset: one offset per binding genotype on the binding side
+(joint-fit plan step 4b).
+"""
+
+import os
+
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from numpyro.handlers import seed, substitute, trace
+
+from tfscreen.tfmodel.analysis.extraction import ExtractionContext
+from tfscreen.tfmodel.generative.components.noise import genotype_offset as go
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+_SMOKE = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                      "..", "..", "..", "..", "..", "smoke-tests", "test_data")
+_GROWTH = os.path.join(_SMOKE, "growth-smoke.csv")
+_BINDING = os.path.join(_SMOKE, "binding-smoke.csv")
+NAME = "theta_binding_noise"
+
+
+def _run(fn, *args, values=None, **kwargs):
+    if values:
+        fn = substitute(fn, data=values)
+    return trace(seed(fn, rng_seed=0)).get_trace(*args, **kwargs)
+
+
+class _Data:
+    def __init__(self, num_genotype, ordinal):
+        self.num_genotype = num_genotype
+        self.binding_ordinal = jnp.asarray(ordinal)
+
+
+def test_adds_one_offset_per_binding_genotype():
+    fx = jnp.full((1, 3, 4), 0.5)
+    data = _Data(4, [0, 1, 2, 3])
+    z = jnp.array([1.0, -1.0, 0.5, 0.0])
+    tr = _run(go.define_model, NAME, fx, go.get_priors(), data,
+              values={f"{NAME}_offset_scale": jnp.asarray(0.2),
+                      f"{NAME}_offset_z": z})
+    out = tr[f"{NAME}_offset"]["value"]
+    np.testing.assert_allclose(out, 0.2 * z)
+    assert tr[f"{NAME}_offset_z"]["value"].shape == (4,)
+    res = substitute(seed(go.define_model, 0),
+                     data={f"{NAME}_offset_scale": jnp.asarray(0.2),
+                           f"{NAME}_offset_z": z})(NAME, fx, go.get_priors(), data)
+    # The same offset at every concentration.
+    np.testing.assert_allclose(res, 0.5 + 0.2 * np.broadcast_to(z, (1, 3, 4)))
+
+
+def test_ordinal_picks_the_batch_genotypes():
+    """Binding-only mini-batch: offsets are library(binding)-sized and the
+    batch's positions read them through binding_ordinal."""
+    fx = jnp.zeros((1, 2, 2))
+    data = _Data(5, [3, 1])
+    z = jnp.arange(5.0)
+    res = substitute(seed(go.define_model, 0),
+                     data={f"{NAME}_offset_scale": jnp.asarray(1.0),
+                           f"{NAME}_offset_z": z})(NAME, fx, go.get_priors(), data)
+    np.testing.assert_allclose(res[0, 0], [3.0, 1.0])
+
+
+def test_fixed_scale_is_deterministic_and_guide_has_no_tau():
+    priors = go.ModelPriors(offset_scale_scale=0.2, offset_scale_fixed=0.1)
+    fx = jnp.zeros((1, 1, 3))
+    data = _Data(3, [0, 1, 2])
+    tr = _run(go.define_model, NAME, fx, priors, data)
+    assert tr[f"{NAME}_offset_scale"]["type"] == "deterministic"
+    assert float(tr[f"{NAME}_offset_scale"]["value"]) == pytest.approx(0.1)
+    g = _run(go.guide, NAME, fx, priors, data)
+    assert f"{NAME}_offset_scale" not in g
+    assert f"{NAME}_offset_scale_loc" not in g
+    assert g[f"{NAME}_offset_z"]["value"].shape == (3,)
+
+
+def _orchestrator(**kwargs):
+    return ModelOrchestrator(growth_df=_GROWTH, binding_df=_BINDING,
+                             theta="hill_geno",
+                             theta_binding_noise="genotype_offset", **kwargs)
+
+
+def _model_trace(orch, values=None):
+    fn = orch.jax_model if values is None else substitute(orch.jax_model, data=values)
+    batch = orch.get_batch(orch.data, np.asarray(orch.data.batch_idx))
+    return trace(seed(fn, rng_seed=0)).get_trace(data=batch, priors=orch.priors)
+
+
+def test_joint_model_offsets_binding_not_growth():
+    orch = _orchestrator()
+    nb = orch.data.num_binding
+    base = _model_trace(orch)
+    assert base[f"{NAME}_offset_z"]["value"].shape == (nb,)
+    fixed = {k: v["value"] for k, v in base.items()
+             if v["type"] == "sample" and not v.get("is_observed", False)}
+    z = jnp.linspace(-1.0, 1.0, nb)
+    t0 = _model_trace(orch, {**fixed, f"{NAME}_offset_z": jnp.zeros(nb)})
+    t1 = _model_trace(orch, {**fixed, f"{NAME}_offset_z": z})
+    tau = float(t1[f"{NAME}_offset_scale"]["value"])
+    np.testing.assert_allclose(
+        np.asarray(t1["binding_pred"]["value"]),
+        np.asarray(t0["binding_pred"]["value"]) + tau * np.asarray(z),
+        rtol=1e-5, atol=1e-7)
+    np.testing.assert_allclose(t1["theta_growth_pred"]["value"],
+                               t0["theta_growth_pred"]["value"])
+    np.testing.assert_allclose(t1["growth_pred"]["value"],
+                               t0["growth_pred"]["value"])
+
+
+def test_extract_specs_label_binding_genotypes():
+    ctx = ExtractionContext(growth_tm=None, mut_labels=[], pair_labels=[],
+                            growth_shares_replicates=False,
+                            binding_genotypes=["wt", "M42I"])
+    specs = go.get_extract_specs(ctx)
+    assert [s["params_to_get"] for s in specs] == [["offset_scale"], ["offset"]]
+    assert list(specs[1]["input_df"]["genotype"]) == ["wt", "M42I"]
+    ctx = ExtractionContext(growth_tm=None, mut_labels=[], pair_labels=[],
+                            growth_shares_replicates=False)
+    assert len(go.get_extract_specs(ctx)) == 1
+
+
+def test_binding_only_minibatch_reads_its_genotypes():
+    orch = ModelOrchestrator(None, _BINDING, binding_only=True,
+                             theta="hill_geno",
+                             theta_binding_noise="genotype_offset")
+    n = orch.data.binding.num_genotype
+    idx = jnp.array([n - 1, 0])
+    batch = orch.get_batch(orch.data, idx)
+    np.testing.assert_array_equal(np.asarray(batch.binding.binding_ordinal), [n - 1, 0])
+    z = jnp.arange(float(n))
+    tr = trace(seed(substitute(orch.jax_model,
+                               data={f"{NAME}_offset_z": z,
+                                     f"{NAME}_offset_scale": jnp.asarray(1.0)}),
+                    rng_seed=0)).get_trace(data=batch, priors=orch.priors)
+    diff = (np.asarray(tr["binding_pred"]["value"])
+            - np.asarray(tr["theta_binding_pred"]["value"]))
+    np.testing.assert_allclose(diff[0, 0], [n - 1, 0.0], atol=1e-6)
+
+
+def test_no_data_gives_one_offset_per_position():
+    """Without BindingData the plate is fx_calc's last axis, read in order."""
+    fx = jnp.zeros((1, 2, 3))
+    z = jnp.array([1.0, 2.0, 3.0])
+    values = {f"{NAME}_offset_scale": jnp.asarray(0.5), f"{NAME}_offset_z": z}
+    tr = _run(go.define_model, NAME, fx, go.get_priors(), None, values=values)
+    assert tr[f"{NAME}_offset_z"]["value"].shape == (3,)
+    res = substitute(seed(go.define_model, 0), data=values)(
+        NAME, fx, go.get_priors(), None)
+    np.testing.assert_allclose(res, np.broadcast_to(0.5 * z, (1, 2, 3)))
+
+
+def test_data_without_ordinal_reads_offsets_in_order():
+    fx = jnp.zeros((1, 1, 2))
+    data = _Data(2, [0, 1])
+    data.binding_ordinal = None
+    res = substitute(seed(go.define_model, 0),
+                     data={f"{NAME}_offset_scale": jnp.asarray(1.0),
+                           f"{NAME}_offset_z": jnp.array([4.0, 5.0])})(
+        NAME, fx, go.get_priors(), data)
+    np.testing.assert_allclose(res[0, 0], [4.0, 5.0])
diff --git a/tests/tfscreen/tfmodel/generative/components/noise/test_logit_normal.py b/tests/tfscreen/tfmodel/generative/components/noise/test_logit_normal.py
index 37fa233a..2eee3d75 100644
--- a/tests/tfscreen/tfmodel/generative/components/noise/test_logit_normal.py
+++ b/tests/tfscreen/tfmodel/generative/components/noise/test_logit_normal.py
@@ -1,3 +1,5 @@
+from collections import namedtuple
+
 import pytest
 import jax
 import jax.numpy as jnp
@@ -216,3 +218,49 @@ def test_guide_sigma_scale_constraint(name, priors, fx_calc):
         )
     scale_val = guide_trace[f"{name}_sigma_logit_scale"]["value"]
     assert float(scale_val) > 0.0
+
+
+# ---------------------------------------------------------------------------
+# Library-size sampling under genotype mini-batching
+# ---------------------------------------------------------------------------
+
+_BatchData = namedtuple("_BatchData", ["num_genotype", "batch_idx"])
+
+
+@pytest.fixture
+def batch_data():
+    # 5-genotype library; fx_calc's 3 columns are genotypes 4, 0 and 2.
+    return _BatchData(num_genotype=5, batch_idx=jnp.array([4, 0, 2]))
+
+
+@pytest.mark.parametrize("fn", [define_model, guide])
+def test_epsilon_sampled_at_library_size(fn, name, priors, fx_calc, batch_data):
+    with seed(rng_seed=0):
+        tr = trace(fn).get_trace(name=name, fx_calc=fx_calc, priors=priors,
+                                 data=batch_data)
+    assert tr[f"{name}_epsilon"]["value"].shape == (2, 5)
+
+
+@pytest.mark.parametrize("fn", [define_model, guide])
+def test_epsilon_sliced_to_batch_order(fn, name, priors, fx_calc, batch_data):
+    epsilon = jnp.array([[0.0, 1.0, 2.0, 3.0, 4.0],
+                         [-1.0, -2.0, -3.0, -4.0, -5.0]])
+    substituted = substitute(fn, data={f"{name}_sigma_logit": jnp.array(1.0),
+                                       f"{name}_epsilon": epsilon})
+    out = seed(substituted, rng_seed=0)(name=name, fx_calc=fx_calc,
+                                        priors=priors, data=batch_data)
+    logit = jnp.log(fx_calc / (1.0 - fx_calc))
+    expected = jax.nn.sigmoid(logit + epsilon[:, batch_data.batch_idx])
+    assert jnp.allclose(out, expected, atol=1e-6)
+
+
+def test_presliced_epsilon_used_directly(name, priors, fx_calc, batch_data):
+    """A batch-sized substitution (posterior forward pass) is not re-sliced."""
+    epsilon = jnp.full(fx_calc.shape, 0.5)
+    substituted = substitute(define_model,
+                             data={f"{name}_sigma_logit": jnp.array(1.0),
+                                   f"{name}_epsilon": epsilon})
+    out = seed(substituted, rng_seed=0)(name=name, fx_calc=fx_calc,
+                                        priors=priors, data=batch_data)
+    logit = jnp.log(fx_calc / (1.0 - fx_calc))
+    assert jnp.allclose(out, jax.nn.sigmoid(logit + 0.5), atol=1e-6)
diff --git a/tests/tfscreen/tfmodel/generative/components/sample_offset/test_level.py b/tests/tfscreen/tfmodel/generative/components/sample_offset/test_level.py
new file mode 100644
index 00000000..3441c21a
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/sample_offset/test_level.py
@@ -0,0 +1,94 @@
+"""Tests for the per-tube level offset (sample_offset: level)."""
+from unittest.mock import MagicMock
+
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from numpyro import handlers
+
+from tfscreen.tfmodel.generative.components.sample_offset.level import (
+    ModelPriors,
+    define_model,
+    get_guesses,
+    get_priors,
+    guide,
+)
+
+SHAPE = (2, 3, 1, 2, 1, 4)
+
+
+def _data():
+    data = MagicMock()
+    (data.num_replicate, data.num_time, data.num_condition_pre,
+     data.num_condition_sel, data.num_titrant_name,
+     data.num_titrant_conc) = SHAPE
+    return data
+
+
+def _trace(fn, **subs):
+    f = handlers.substitute(fn, data=subs) if subs else fn
+    return handlers.trace(handlers.seed(f, 0)).get_trace(
+        "sample_offset", _data(), get_priors())
+
+
+def test_priors():
+    assert isinstance(get_priors(), ModelPriors)
+    assert get_priors().sigma_prior_scale == 0.2
+
+
+@pytest.mark.parametrize("fn", [define_model, guide])
+def test_shape_broadcasts_over_genotypes(fn):
+    with handlers.seed(rng_seed=0):
+        out = fn("sample_offset", _data(), get_priors())
+    assert out.shape == (*SHAPE, 1)
+
+
+def test_one_offset_per_tube_constant_in_time():
+    num_tubes = int(np.prod(SHAPE))
+    offsets = jnp.arange(num_tubes, dtype=float) / 100.0
+    tr = _trace(define_model, sample_offset_sigma=0.1,
+                sample_offset_offset=offsets)
+    assert tr["sample_offset_offset"]["value"].shape == (num_tubes,)
+    with handlers.substitute(data={"sample_offset_sigma": 0.1,
+                                   "sample_offset_offset": offsets}):
+        out = define_model("sample_offset", _data(), get_priors())
+    # The offset is the level itself: no scaling by elapsed time.
+    assert np.allclose(np.asarray(out).ravel(), np.asarray(offsets))
+
+
+def test_guide_follows_loc_scale_convention():
+    tr = _trace(guide)
+    params = {k for k, v in tr.items() if v["type"] == "param"}
+    assert params == {"sample_offset_sigma_loc", "sample_offset_sigma_scale",
+                      "sample_offset_offset_loc", "sample_offset_offset_scale"}
+
+
+def test_guesses():
+    assert set(get_guesses("sample_offset", _data())) == {"sample_offset_sigma"}
+
+
+def test_sigma_fixed_holds_sigma():
+    """
+    sigma_fixed > 0 makes sigma a deterministic site at that value and drops
+    its guide parameters; 0 (the default) keeps it learned.
+    """
+    import dataclasses
+    assert get_priors().sigma_fixed == 0.0
+    priors = dataclasses.replace(get_priors(), sigma_fixed=0.17)
+
+    tr = handlers.trace(handlers.seed(define_model, 0)).get_trace(
+        "sample_offset", _data(), priors)
+    assert tr["sample_offset_sigma"]["type"] == "deterministic"
+    assert float(tr["sample_offset_sigma"]["value"]) == pytest.approx(0.17)
+    assert float(tr["sample_offset_offset"]["fn"].base_dist.scale) == \
+        pytest.approx(0.17)
+
+    tr = handlers.trace(handlers.seed(guide, 0)).get_trace(
+        "sample_offset", _data(), priors)
+    params = {k for k, v in tr.items() if v["type"] == "param"}
+    assert params == {"sample_offset_offset_loc", "sample_offset_offset_scale"}
+    assert "sample_offset_sigma" not in tr
+
+    tr = handlers.trace(handlers.seed(define_model, 0)).get_trace(
+        "sample_offset", _data(), get_priors())
+    assert tr["sample_offset_sigma"]["type"] == "sample"
diff --git a/tests/tfscreen/tfmodel/generative/components/sample_offset/test_normal.py b/tests/tfscreen/tfmodel/generative/components/sample_offset/test_normal.py
index 7fc07719..c22ecf2c 100644
--- a/tests/tfscreen/tfmodel/generative/components/sample_offset/test_normal.py
+++ b/tests/tfscreen/tfmodel/generative/components/sample_offset/test_normal.py
@@ -16,7 +16,6 @@
     get_hyperparameters,
     get_priors,
     get_guesses,
-    get_extract_specs,
 )
 
 
@@ -52,9 +51,6 @@ def test_get_guesses_returns_small_positive(self):
         assert "sample_offset_sigma_env" in guesses
         assert float(guesses["sample_offset_sigma_env"]) > 0
 
-    def test_get_extract_specs_returns_list(self):
-        assert isinstance(get_extract_specs(None), list)
-
     def test_define_model_output_shape(self):
         """define_model must return shape (R, T, CP, CS, TN, TC, 1)."""
         R, T, CP, CS, TN, TC = 2, 3, 1, 2, 1, 4
diff --git a/tests/tfscreen/tfmodel/generative/components/sample_offset/test_tubes.py b/tests/tfscreen/tfmodel/generative/components/sample_offset/test_tubes.py
new file mode 100644
index 00000000..004bfce4
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/sample_offset/test_tubes.py
@@ -0,0 +1,90 @@
+"""Tube labels for the per-tube sample offsets (extraction)."""
+import os
+
+import numpy as np
+import pytest
+from numpyro import handlers
+
+from tfscreen.tfmodel.analysis.extraction import extract_parameters
+from tfscreen.tfmodel.generative.components.sample_offset import level, normal
+from tfscreen.tfmodel.generative.components.sample_offset._tubes import (
+    TUBE_DIMS,
+    tube_index,
+)
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "..", "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+
+KEY = ["replicate", "condition_pre", "condition_sel", "titrant_name",
+       "titrant_conc", "t_sel"]
+
+
+@pytest.fixture(scope="module", params=["level", "normal"])
+def model(request):
+    o = ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=_BINDING_CSV,
+                          batch_size=6, sample_offset=request.param)
+    return request.param, o
+
+
+def _site(kind):
+    return ("sample_offset_offset", "sample_offset_sigma") if kind == "level" \
+        else ("sample_offset_delta_k", "sample_offset_sigma_env")
+
+
+def test_extracted_offsets_match_the_model_tubes(model):
+    """
+    Each extracted row carries the value the model adds to that tube's
+    growth rows, so the labels follow the component's own reshape.
+    """
+    kind, o = model
+    site, sigma_site = _site(kind)
+    g = o.data.growth
+    shape = (g.num_replicate, g.num_time, g.num_condition_pre,
+             g.num_condition_sel, g.num_titrant_name, g.num_titrant_conc)
+    values = np.random.default_rng(0).normal(size=int(np.prod(shape)))
+
+    module = level if kind == "level" else normal
+    with handlers.seed(rng_seed=0):
+        sub = handlers.substitute(module.define_model,
+                                  data={site: values, sigma_site: 0.3})
+        out = np.asarray(sub("sample_offset", g, module.get_priors()))
+    if kind == "normal":
+        out = values.reshape(*shape, 1)
+
+    posteriors = {site: values[None, :], sigma_site: np.array([[0.3]])}
+    params = extract_parameters(o, posteriors, q_to_get=[0.5])
+    ext = params[site]
+    assert not ext.duplicated(KEY).any()
+    assert params[sigma_site]["q0.5"].iloc[0] == pytest.approx(0.3)
+
+    df = o.training_tm.df
+    idx = tuple(df[f"{d}_idx"].to_numpy(dtype=int) for d in TUBE_DIMS)
+    expected = df[KEY].assign(expected=out[idx + (0,)])
+    merged = ext.merge(expected.drop_duplicates(KEY), on=KEY, how="left")
+    assert merged["expected"].notna().all()
+    np.testing.assert_allclose(merged["q0.5"], merged["expected"])
+    # one row per tube that holds growth data
+    assert len(ext) == len(np.unique(tube_index(o.training_tm)))
+
+
+def test_zero_offset_extracts_nothing():
+    from tfscreen.tfmodel.generative.components.sample_offset import zero
+    assert zero.get_extract_specs(None) == []
+
+
+def test_tube_index_refuses_rows_without_tube_columns(model):
+    _, o = model
+    tm = o.training_tm
+    from types import SimpleNamespace
+    stub = SimpleNamespace(df=tm.df.drop(columns=["time_idx", "titrant_conc_idx"]),
+                           tensor_dim_names=tm.tensor_dim_names,
+                           tensor_dim_labels=tm.tensor_dim_labels)
+    with pytest.raises(ValueError, match="lack the tube index columns") as e:
+        tube_index(stub)
+    assert "time_idx" in str(e.value) and "titrant_conc_idx" in str(e.value)
+    assert "replicate_idx" not in str(e.value)
diff --git a/tests/tfscreen/tfmodel/generative/components/test_component_population.py b/tests/tfscreen/tfmodel/generative/components/test_component_population.py
new file mode 100644
index 00000000..840e62d2
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/test_component_population.py
@@ -0,0 +1,184 @@
+"""
+Library-ordered per-genotype values from the dk_geno and activity components
+(``return_population=True``), for the congression mixture's co-resident
+lookup. See ``generative/components/_population.py``.
+"""
+
+import os
+from types import SimpleNamespace
+
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from numpyro.handlers import seed, substitute, trace
+
+from tfscreen.tfmodel.generative.components._population import per_genotype
+from tfscreen.tfmodel.generative.components.dk_geno import pinned
+from tfscreen.tfmodel.generative.registry import model_registry
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+
+
+# ---------------------------------------------------------------------------
+# per_genotype helper
+# ---------------------------------------------------------------------------
+
+def _double_plus_index(x, genotype_idx):
+    return 2.0 * x + genotype_idx
+
+
+def test_per_genotype_library_sized():
+    data = SimpleNamespace(num_genotype=4, batch_idx=jnp.array([2, 0]))
+    x = jnp.array([1.0, 2.0, 3.0, 4.0])
+    batch, population = per_genotype(_double_plus_index, [x], data,
+                                     return_population=True)
+    np.testing.assert_allclose(population, [2.0, 5.0, 8.0, 11.0])
+    np.testing.assert_allclose(batch, population[jnp.array([2, 0])])
+
+
+def test_per_genotype_default_is_batch_only():
+    data = SimpleNamespace(num_genotype=4, batch_idx=jnp.array([2, 0]))
+    x = jnp.array([1.0, 2.0, 3.0, 4.0])
+    batch, population = per_genotype(_double_plus_index, [x], data)
+    assert population is None
+    np.testing.assert_allclose(batch, [8.0, 2.0])
+
+
+def test_per_genotype_substituted_latent_has_no_population():
+    """A batch-sized substitution says nothing about the rest of the library."""
+    data = SimpleNamespace(num_genotype=4, batch_idx=jnp.array([2, 0]))
+    x = jnp.array([3.0, 1.0])               # already sliced to the batch
+    batch, population = per_genotype(_double_plus_index, [x], data,
+                                     return_population=True)
+    assert population is None
+    np.testing.assert_allclose(batch, [8.0, 2.0])
+
+
+def test_per_genotype_mixed_latents_slice_library_sized_one():
+    data = SimpleNamespace(num_genotype=4, batch_idx=jnp.array([2, 0]))
+    library = jnp.array([1.0, 2.0, 3.0, 4.0])
+    sliced = jnp.array([10.0, 20.0])
+    batch, population = per_genotype(lambda a, b, idx: a + b, [library, sliced],
+                                     data, return_population=True)
+    assert population is None
+    np.testing.assert_allclose(batch, [13.0, 21.0])
+
+
+# ---------------------------------------------------------------------------
+# Every dk_geno and activity component
+# ---------------------------------------------------------------------------
+
+# Every registered dk_geno and activity variant, so a new component is
+# covered automatically. dk_geno "pinned" needs a pins file to build an
+# orchestrator and is tested on its own below.
+_VARIANTS = [(axis, variant)
+             for axis in ("dk_geno", "activity")
+             for variant in sorted(model_registry[axis])
+             if (axis, variant) != ("dk_geno", "pinned")]
+
+# wt's pinned value.
+_WT_VALUE = {"dk_geno": 0.0, "activity": 1.0}
+
+
+@pytest.fixture(scope="module")
+def orchestrators():
+    return {v: ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                 binding_df=_BINDING_CSV,
+                                 **{v[0]: v[1]})
+            for v in _VARIANTS}
+
+
+def _shuffled_full_batch(orchestrator, seed_value=0):
+    data = orchestrator.data
+    pinned_idx = np.asarray(data.batch_idx)[:data.num_binding]
+    rest = np.random.default_rng(seed_value).permutation(
+        np.asarray(data.not_binding_idx))
+    return np.concatenate([pinned_idx, rest])
+
+
+def _run(fn, name, data, priors, **kwargs):
+    """Run a component under a fixed seed; return (trace, output)."""
+    box = {}
+
+    def call():
+        box["out"] = fn(name, data, priors, **kwargs)
+
+    tr = trace(seed(call, rng_seed=0)).get_trace()
+    return tr, box["out"]
+
+
+@pytest.mark.parametrize("axis,variant", _VARIANTS)
+@pytest.mark.parametrize("which", ["define_model", "guide"])
+@pytest.mark.parametrize("batch", ["full", "mini"])
+def test_population_matches_batch(orchestrators, axis, variant, which, batch):
+    orchestrator = orchestrators[(axis, variant)]
+    idx = _shuffled_full_batch(orchestrator)
+    if batch == "mini":
+        idx = idx[:orchestrator.data.num_binding + 3]
+    data = orchestrator.get_batch(orchestrator.data, jnp.asarray(idx)).growth
+    priors = getattr(orchestrator.priors.growth, axis)
+    fn = getattr(model_registry[axis][variant], which)
+
+    _, (tensor, population) = _run(fn, axis, data, priors,
+                                   return_population=True)
+    _, default_tensor = _run(fn, axis, data, priors)
+
+    num_genotype = orchestrator.data.num_genotype
+    assert population.shape == (num_genotype,)
+    batch_values = np.asarray(tensor).reshape(-1)
+    np.testing.assert_allclose(batch_values,
+                               np.asarray(population)[idx], rtol=1e-6)
+    # The default call is unchanged: same batch values, no population.
+    np.testing.assert_allclose(np.asarray(default_tensor), np.asarray(tensor),
+                               rtol=1e-6)
+    wt = np.asarray(data.wt_indexes)
+    np.testing.assert_allclose(np.asarray(population)[wt], _WT_VALUE[axis])
+
+
+# Components whose per-genotype latents are sampled at library size and so
+# can arrive as a batch-sized substitution in the posterior forward pass.
+_SUBSTITUTABLE = {
+    ("dk_geno", "hierarchical_geno"): ["dk_geno_offset"],
+    ("activity", "hierarchical_geno"): ["activity_offset"],
+    ("activity", "horseshoe_geno"): ["activity_local_scale", "activity_offset"],
+}
+
+
+@pytest.mark.parametrize("axis,variant", list(_SUBSTITUTABLE))
+def test_substituted_latents_give_no_population(orchestrators, axis, variant):
+    orchestrator = orchestrators[(axis, variant)]
+    idx = _shuffled_full_batch(orchestrator)[:orchestrator.data.num_binding + 3]
+    data = orchestrator.get_batch(orchestrator.data, jnp.asarray(idx)).growth
+    priors = getattr(orchestrator.priors.growth, axis)
+    fn = model_registry[axis][variant].define_model
+
+    library_trace, _ = _run(fn, axis, data, priors)
+    values = {site: library_trace[site]["value"][..., idx]
+              for site in _SUBSTITUTABLE[(axis, variant)]}
+
+    def call():
+        return fn(axis, data, priors, return_population=True)
+
+    tensor, population = substitute(seed(call, rng_seed=0), data=values)()
+    assert population is None
+    assert np.asarray(tensor).shape[-1] == len(idx)
+
+
+@pytest.mark.parametrize("which", ["define_model", "guide"])
+def test_pinned_dk_geno_population(which):
+    values = jnp.array([0.5, -0.01, -0.02, 0.03])
+    data = SimpleNamespace(num_genotype=4, batch_idx=jnp.array([3, 0, 2]),
+                           wt_indexes=jnp.array([0]))
+    priors = pinned.get_priors(dk_geno_values=values)
+    tensor, population = getattr(pinned, which)("dk_geno", data, priors,
+                                                return_population=True)
+    np.testing.assert_allclose(population, [0.0, -0.01, -0.02, 0.03])
+    np.testing.assert_allclose(np.asarray(tensor).reshape(-1),
+                               np.asarray(population)[[3, 0, 2]])
diff --git a/tests/tfscreen/tfmodel/generative/components/test_horseshoe.py b/tests/tfscreen/tfmodel/generative/components/test_horseshoe.py
new file mode 100644
index 00000000..44ca0579
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/test_horseshoe.py
@@ -0,0 +1,61 @@
+"""Tests for the overflow-safe horseshoe helpers (components/_horseshoe.py)."""
+import jax
+import jax.numpy as jnp
+import numpy as np
+import numpyro.distributions as dist
+import pytest
+
+from tfscreen.tfmodel.generative.components._horseshoe import (
+    HalfCauchy,
+    regularized_scale,
+)
+
+
+def _naive(lam, tau, c2):
+    return np.sqrt(c2 * lam ** 2 / (c2 + tau ** 2 * lam ** 2))
+
+
+@pytest.mark.parametrize("lam", [1e-6, 0.01, 0.5, 1.0, 7.0, 300.0])
+@pytest.mark.parametrize("tau", [0.0, 0.01, 1.0, 3.0])
+def test_regularized_scale_matches_formula(lam, tau):
+    got = float(regularized_scale(jnp.float32(lam), jnp.float32(tau),
+                                  jnp.float32(4.0)))
+    assert got == pytest.approx(_naive(lam, tau, 4.0), rel=1e-5)
+
+
+def test_regularized_scale_saturates_without_overflow():
+    # lam^2 overflows float32 here; the old form gave inf / inf = NaN.
+    lam = jnp.float32(1e25)
+    val = regularized_scale(lam, jnp.float32(2.0), jnp.float32(9.0))
+    assert float(val) == pytest.approx(1.5, rel=1e-5)    # sqrt(c2) / tau
+    g = jax.grad(lambda l: regularized_scale(l, 2.0, 9.0))(lam)
+    assert np.isfinite(float(g))
+
+
+def test_regularized_scale_gradient_at_tau_zero():
+    g = jax.grad(lambda t: regularized_scale(3.0, t, 4.0))(0.0)
+    assert np.isfinite(float(g))
+
+
+@pytest.mark.parametrize("x", [1e-4, 0.3, 1.0, 12.0, 1e3])
+@pytest.mark.parametrize("scale", [0.1, 1.0, 5.0])
+def test_half_cauchy_matches_numpyro(x, scale):
+    got = float(HalfCauchy(scale).log_prob(jnp.float32(x)))
+    ref = float(dist.HalfCauchy(scale).log_prob(jnp.float32(x)))
+    assert got == pytest.approx(ref, abs=1e-5)
+
+
+def test_half_cauchy_finite_far_in_the_tail():
+    x = jnp.float32(1e25)
+    assert float(dist.HalfCauchy(1.0).log_prob(x)) == -np.inf   # numpyro's
+    lp = float(HalfCauchy(1.0).log_prob(x))
+    assert np.isfinite(lp)
+    assert lp == pytest.approx(np.log(2 / np.pi) - 2 * np.log(1e25), rel=1e-5)
+    assert np.isfinite(float(jax.grad(lambda v: HalfCauchy(1.0).log_prob(v))(x)))
+
+
+def test_half_cauchy_is_a_half_cauchy():
+    d = HalfCauchy(2.0)
+    assert isinstance(d, dist.HalfCauchy)
+    x = d.sample(jax.random.PRNGKey(0), (20_000,))
+    assert float(jnp.median(x)) == pytest.approx(2.0, rel=0.05)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/test__simple.py b/tests/tfscreen/tfmodel/generative/components/theta/test__simple.py
index cda478a6..eff049fc 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/test__simple.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/test__simple.py
@@ -29,7 +29,8 @@
     "scatter_theta",
     "geno_theta_idx",
     "titrant_conc",
-])
+    "batch_idx",
+], defaults=(None,))
 
 
 @pytest.fixture
@@ -37,8 +38,8 @@ def mock_data():
     """
     Two titrants, three concentrations, four genotypes.
 
-    The simple theta component does not use batch_idx/scale_vector, so
-    those fields are intentionally omitted.
+    batch_idx is the identity, so geno_theta_idx selects library genotypes
+    directly. scale_vector is unused and omitted.
     """
     return MockData(
         num_titrant_name=2,
@@ -47,6 +48,7 @@ def mock_data():
         scatter_theta=1,
         geno_theta_idx=jnp.array([1, 3], dtype=jnp.int32),
         titrant_conc=jnp.array([0.0, 1.0, 10.0]),
+        batch_idx=jnp.arange(4, dtype=jnp.int32),
     )
 
 
@@ -354,6 +356,7 @@ def test_extreme_theta_values_finite_mu(mock_data):
         scatter_theta=0,
         geno_theta_idx=jnp.array([0, 1], dtype=jnp.int32),
         titrant_conc=jnp.array([0.0, 1.0, 10.0]),
+        batch_idx=jnp.arange(2, dtype=jnp.int32),
     )
 
     theta_param = define_model(name="x", data=data, priors=priors)
@@ -446,6 +449,21 @@ def test_run_model_3d_slices_correct_genotypes(self, mock_data, mock_priors_3d):
             assert jnp.allclose(out[..., col],
                                  mock_priors_3d.theta_values[..., geno_idx])
 
+    def test_run_model_3d_follows_batch_idx(self, mock_data, mock_priors_3d):
+        """
+        With a non-identity batch_idx (the reshuffled full-batch training
+        index), batch position j must read genotype batch_idx[j], not
+        library genotype j.
+        """
+        data = mock_data._replace(scatter_theta=0,
+                                  batch_idx=jnp.array([2, 0, 3, 1]),
+                                  geno_theta_idx=jnp.arange(4))
+        theta_param = define_model(name="pg", data=data, priors=mock_priors_3d)
+        out = run_model(theta_param, data)
+        for pos, geno in enumerate([2, 0, 3, 1]):
+            assert jnp.allclose(out[..., pos],
+                                mock_priors_3d.theta_values[..., geno])
+
     def test_run_model_3d_concentration_mapping(self, mock_data, mock_priors_3d):
         """Concentration remapping must work correctly with per-genotype theta."""
         theta_param = define_model(name="pg", data=mock_data, priors=mock_priors_3d)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py b/tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py
index a308fb2d..5fd0c7f6 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/test_categorical_geno.py
@@ -49,8 +49,9 @@ def mock_data():
     scale_vector = jnp.ones(batch_size, dtype=float)
     map_theta = jnp.array([0, 5, 10, 23, 1], dtype=jnp.int32)
     
-    # New required fields
-    geno_theta_idx = jnp.array([1, 3], dtype=jnp.int32)
+    # geno_theta_idx is batch-relative (as get_batch builds it); batch_idx
+    # maps batch positions to library genotypes.
+    geno_theta_idx = jnp.arange(batch_size, dtype=jnp.int32)
     titrant_conc = jnp.array([0.0, 1.0, 10.0])
     
     return MockData(
@@ -66,20 +67,24 @@ def mock_data():
         titrant_conc=titrant_conc
     )
 
+def _distinct_offsets(mock_data):
+    """Library-sized offsets that differ by genotype, so indexing is visible."""
+    shape = (mock_data.num_titrant_name, mock_data.num_titrant_conc,
+             mock_data.num_genotype)
+    return jnp.arange(np.prod(shape), dtype=float).reshape(shape) / 10.0
+
+
 @pytest.fixture
 def model_setup(mock_data):
     """
-    Provides a deterministic ThetaParam object (BATCHED) for testing run_model.
+    Provides a deterministic, library-sized ThetaParam for testing run_model.
     """
     name = "test_theta_cat"
     priors = get_priors()
-    base_guesses = get_guesses(name, mock_data)
-    
-    batch_guesses = base_guesses.copy()
-    full_offsets = base_guesses[f"{name}_logit_theta_offset"]
-    batch_guesses[f"{name}_logit_theta_offset"] = full_offsets[..., mock_data.batch_idx]
+    guesses = get_guesses(name, mock_data)
+    guesses[f"{name}_logit_theta_offset"] = _distinct_offsets(mock_data)
 
-    substituted_model = substitute(define_model, data=batch_guesses)
+    substituted_model = substitute(define_model, data=guesses)
     theta_param = substituted_model(name=name,
                                     data=mock_data,
                                     priors=priors)
@@ -123,22 +128,20 @@ def test_define_model_shapes_and_values(mock_data):
     name = "test_theta_cat"
     priors = get_priors()
     
-    base_guesses = get_guesses(name, mock_data)
-    batch_guesses = base_guesses.copy()
-    full_offsets = base_guesses[f"{name}_logit_theta_offset"]
-    batch_guesses[f"{name}_logit_theta_offset"] = full_offsets[..., mock_data.batch_idx]
-    
-    substituted_model = substitute(define_model, data=batch_guesses)
+    guesses = get_guesses(name, mock_data)
+
+    substituted_model = substitute(define_model, data=guesses)
     theta_param = substituted_model(name=name,
                                     data=mock_data,
                                     priors=priors)
 
-    expected_batch_shape = (
+    # theta stays library-sized (library order); run_model selects the batch.
+    expected_shape = (
         mock_data.num_titrant_name,
         mock_data.num_titrant_conc,
-        mock_data.batch_size
+        mock_data.num_genotype
     )
-    assert theta_param.theta.shape == expected_batch_shape
+    assert theta_param.theta.shape == expected_shape
     assert theta_param.mu.shape == (mock_data.num_titrant_name, mock_data.num_titrant_conc, 1)
     assert jnp.allclose(theta_param.theta, 0.5)
     assert jnp.allclose(theta_param.concentrations, mock_data.titrant_conc)
@@ -151,18 +154,14 @@ def test_run_model_no_scatter(model_setup, mock_data):
     data = mock_data._replace(scatter_theta=0)
     theta_calc = run_model(theta_param, data)
     
-    # We no longer assert 'is' because run_model slices genotypes
-    # it should be equal if indices match full theta_param.theta
-    # In model_setup, theta_param.theta already has batch_size genotypes.
-    # mock_data.geno_theta_idx is [1, 3] which matches mock_data.batch_idx.
-    
+    # Batch position j must hold library genotype batch_idx[j].
     expected_shape = (
         mock_data.num_titrant_name,
         mock_data.num_titrant_conc,
         mock_data.batch_size
     )
     assert theta_calc.shape == expected_shape
-    assert jnp.allclose(theta_calc, theta_param.theta)
+    assert jnp.allclose(theta_calc, theta_param.theta[..., mock_data.batch_idx])
 
 def test_run_model_with_scatter(model_setup, mock_data):
     """
@@ -194,7 +193,7 @@ def test_run_model_concentration_mapping(model_setup, mock_data):
     # Check values mapping
     # Orig concentrations: [0.0, 1.0, 10.0] -> indices [0, 1, 2]
     # new_conc [1.0, 0.0] should map to indices [1, 0]
-    expected = theta_param.theta[:, [1, 0], :]
+    expected = theta_param.theta[:, [1, 0], :][..., mock_data.batch_idx]
     assert jnp.allclose(theta_calc, expected)
 
 def test_guide_logic_and_shapes(mock_data):
@@ -212,7 +211,7 @@ def test_guide_logic_and_shapes(mock_data):
     expected_sample_shape = (
         mock_data.num_titrant_name,
         mock_data.num_titrant_conc,
-        mock_data.batch_size
+        mock_data.num_genotype
     )
     assert theta_param.theta.shape == expected_sample_shape
     assert theta_param.mu.shape == (mock_data.num_titrant_name, mock_data.num_titrant_conc, 1)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/test_full_population_contract.py b/tests/tfscreen/tfmodel/generative/components/theta/test_full_population_contract.py
index 9bb64aa7..62cf426f 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/test_full_population_contract.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/test_full_population_contract.py
@@ -1,17 +1,14 @@
 """
 Cross-component API-contract test for theta components used with
-transformation="empirical".
-
-The congression correction (transformation/_congression.py) needs a theta
-reference covering the *full* genotype population, not just whatever
-genotypes are active in the current forward pass.  generative/model.py
-gets this "for free" for hill_mut, hill_geno, and the thermo.* components by
-re-calling run_model with batch_idx = geno_theta_idx = arange(num_genotype)
-against the already-full-size ThetaParam these components assemble in
-define_model/guide (see model_orchestrator._THETA_MODELS_INCOMPATIBLE_WITH_EMPIRICAL
-and model_orchestrator._check_theta_transformation_compatibility for the one
-component, categorical_geno, that does not yet satisfy this contract and is
-rejected outright rather than silently mishandled).
+transformation="mixture".
+
+The congression mixture (transformation/mixture.py) looks co-resident
+genotypes up in a theta population covering the *full* library, not just the
+genotypes active in the current forward pass. generative/model.py builds it
+by re-calling run_model with batch_idx = geno_theta_idx = arange(num_genotype)
+against the full-size ThetaParam each component assembles in
+define_model/guide. Every theta component now satisfies this (categorical_geno
+since step 3.3a); test_population_theta.py checks it registry-wide.
 
 This test locks in the contract for the components that DO support it:
 run_model(theta_param, subset_data) must equal
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/test_growth_conc_shift.py b/tests/tfscreen/tfmodel/generative/components/theta/test_growth_conc_shift.py
new file mode 100644
index 00000000..85ee1236
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/theta/test_growth_conc_shift.py
@@ -0,0 +1,115 @@
+"""
+hill_geno's growth titrant shift log s (joint-fit plan step 4a): growth
+sees each curve at s times the nominal titrant, binding at the nominal one.
+"""
+
+import os
+
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from numpyro.handlers import seed, substitute, trace
+
+from tfscreen.tfmodel.analysis.extraction import ExtractionContext
+from tfscreen.tfmodel.generative.components.theta import hill_geno
+from tfscreen.tfmodel.generative.model import growth_titrant
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+_SMOKE = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                      "..", "..", "..", "..", "..", "smoke-tests", "test_data")
+SITE = "theta_growth_log_conc_shift"
+
+
+def _orchestrator(scale):
+    orch = ModelOrchestrator(growth_df=os.path.join(_SMOKE, "growth-smoke.csv"),
+                             binding_df=os.path.join(_SMOKE, "binding-smoke.csv"),
+                             theta="hill_geno")
+    if scale:
+        p = orch.priors
+        orch._priors = p.replace(theta=p.theta.replace(
+            theta_growth_log_conc_shift_scale=scale))
+    return orch
+
+
+def _trace(orch, which="model", values=None):
+    fn = orch.jax_model if which == "model" else orch.jax_model_guide
+    if values:
+        fn = substitute(fn, data=values)
+    batch = orch.get_batch(orch.data, np.asarray(orch.data.batch_idx))
+    return trace(seed(fn, rng_seed=0)).get_trace(data=batch, priors=orch.priors)
+
+
+def test_off_by_default():
+    orch = _orchestrator(0.0)
+    assert orch.priors.theta.theta_growth_log_conc_shift_scale == 0.0
+    for which in ("model", "guide"):
+        assert SITE not in _trace(orch, which)
+
+
+def test_on_samples_one_global_site():
+    orch = _orchestrator(1.0)
+    for which in ("model", "guide"):
+        tr = _trace(orch, which)
+        assert tr[SITE]["type"] == "sample"
+        assert np.shape(tr[SITE]["value"]) == ()
+
+
+def test_shift_moves_growth_theta_and_not_binding():
+    orch = _orchestrator(1.0)
+    base = _trace(orch)
+    fixed = {k: v["value"] for k, v in base.items()
+             if v["type"] == "sample" and not v.get("is_observed", False)}
+    t0 = _trace(orch, values={**fixed, SITE: jnp.asarray(0.0)})
+    t2 = _trace(orch, values={**fixed, SITE: jnp.asarray(np.log(2.0))})
+
+    np.testing.assert_allclose(t2["theta_binding_pred"]["value"],
+                               t0["theta_binding_pred"]["value"])
+    g0 = np.asarray(t0["theta_growth_pred"]["value"])
+    g2 = np.asarray(t2["theta_growth_pred"]["value"])
+    assert not np.allclose(g0, g2)
+
+    # The shifted growth theta is the curve evaluated at 2x the nominal
+    # concentration.
+    batch = orch.get_batch(orch.data, np.asarray(orch.data.batch_idx))
+    sample_theta, calc_theta, _ = orch.main_control_kwargs["theta"]
+    theta = substitute(seed(sample_theta, rng_seed=0),
+                       data={**fixed, SITE: jnp.asarray(np.log(2.0))})(
+        "theta", batch.growth, orch.priors.theta)
+    doubled = batch.growth.replace(
+        titrant_conc=batch.growth.titrant_conc * 2.0,
+        log_titrant_conc=batch.growth.log_titrant_conc + np.log(2.0))
+    np.testing.assert_allclose(
+        g2, np.asarray(calc_theta(theta.replace(growth_log_conc_shift=None),
+                                  doubled)), rtol=1e-5)
+
+
+def test_growth_titrant_keeps_zero_and_passes_through_without_shift():
+    orch = _orchestrator(0.0)
+    g = orch.data.growth
+
+    class Off:
+        growth_log_conc_shift = None
+
+    assert growth_titrant(Off(), g) is g
+
+    class On:
+        growth_log_conc_shift = jnp.asarray(np.log(3.0))
+
+    out = growth_titrant(On(), g)
+    conc = np.asarray(g.titrant_conc)
+    np.testing.assert_allclose(np.asarray(out.titrant_conc), conc * 3.0)
+    assert np.all(np.asarray(out.titrant_conc)[conc == 0] == 0)
+
+
+@pytest.mark.parametrize("scale, n_specs", [(0.0, 1), (1.0, 2)])
+def test_extract_spec_only_when_on(scale, n_specs):
+    priors = hill_geno.get_priors().replace(
+        theta_growth_log_conc_shift_scale=scale)
+    ctx = ExtractionContext(growth_tm=type("TM", (), {"df": None})(),
+                            mut_labels=[], pair_labels=[],
+                            growth_shares_replicates=False,
+                            theta_priors=priors)
+    specs = hill_geno.get_extract_specs(ctx)
+    assert len(specs) == n_specs
+    if n_specs == 2:
+        assert specs[1]["params_to_get"] == ["growth_log_conc_shift"]
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py b/tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py
index 0fa74c2c..a0fb50b1 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/test_hill_geno.py
@@ -633,3 +633,68 @@ def test_simulate_theta_param_compatible_with_run_model(mock_data):
     C = data.num_titrant_conc
     # simulate() always produces T=1 (no per-titrant structure); run_model reflects that
     assert result.shape == (1, C, G)
+
+
+@pytest.mark.parametrize("fixed", [0.0, 0.5])
+def test_log_hill_n_hyper_scale_fixed(mock_data, fixed):
+    """theta_log_hill_n_hyper_scale_fixed > 0 holds the log(n) population SD:
+    a deterministic model site at that value and no guide parameters for it;
+    0 keeps it learned. Model and guide sample sites match either way."""
+    name = "nfix"
+    priors = get_priors().replace(theta_log_hill_n_hyper_scale_fixed=fixed)
+    assert get_hyperparameters()["theta_log_hill_n_hyper_scale_fixed"] == 0.0
+    with seed(rng_seed=0):
+        mtr = trace(define_model).get_trace(name=name, data=mock_data, priors=priors)
+    with seed(rng_seed=0):
+        gtr = trace(guide).get_trace(name=name, data=mock_data, priors=priors)
+    site = mtr[f"{name}_log_hill_n_hyper_scale"]
+    if fixed:
+        assert site["type"] == "deterministic"
+        assert np.allclose(site["value"], fixed)
+        assert f"{name}_log_hill_n_hyper_scale_loc" not in gtr
+    else:
+        assert site["type"] == "sample"
+        assert f"{name}_log_hill_n_hyper_scale_loc" in gtr
+    model_samples = {n for n, s in mtr.items() if s["type"] == "sample"}
+    guide_samples = {n for n, s in gtr.items() if s["type"] == "sample"}
+    assert model_samples == guide_samples
+
+
+@pytest.mark.parametrize("h", ["logit_low", "logit_delta", "log_hill_K"])
+def test_other_hyper_scales_fixed(mock_data, h):
+    """Every population SD can be held, one at a time: a deterministic model
+    site at the held value, no guide parameters for it, the others learned,
+    and the curve built with the held value."""
+    name = "hfix"
+    field = f"theta_{h}_hyper_scale_fixed"
+    assert get_hyperparameters()[field] == 0.0
+    priors = get_priors().replace(**{field: 0.7})
+    with seed(rng_seed=0):
+        mtr = trace(define_model).get_trace(name=name, data=mock_data, priors=priors)
+    with seed(rng_seed=0):
+        gtr = trace(guide).get_trace(name=name, data=mock_data, priors=priors)
+    assert mtr[f"{name}_{h}_hyper_scale"]["type"] == "deterministic"
+    assert np.allclose(mtr[f"{name}_{h}_hyper_scale"]["value"], 0.7)
+    assert f"{name}_{h}_hyper_scale_loc" not in gtr
+    for o in {"logit_low", "logit_delta", "log_hill_K", "log_hill_n"} - {h}:
+        assert mtr[f"{name}_{o}_hyper_scale"]["type"] == "sample"
+        assert f"{name}_{o}_hyper_scale_loc" in gtr
+    assert ({n for n, s in mtr.items() if s["type"] == "sample"}
+            == {n for n, s in gtr.items() if s["type"] == "sample"})
+
+    # The held SD scales the offsets: with every offset 1 and the location
+    # at 0, the parameter is the held value.
+    G, T = mock_data.num_genotype, mock_data.num_titrant_name
+    loc = {"logit_low": "logit_low_hyper_loc", "logit_delta": "logit_delta_hyper_loc",
+           "log_hill_K": "log_hill_K_hyper_loc"}[h]
+    off = {"logit_low": "logit_low_offset", "logit_delta": "logit_delta_offset",
+           "log_hill_K": "log_hill_K_offset"}[h]
+    values = {f"{name}_{loc}": jnp.zeros(T), f"{name}_{off}": jnp.ones((T, G))}
+    if h == "logit_delta":
+        values.update({f"{name}_logit_low_hyper_loc": jnp.zeros(T),
+                       f"{name}_logit_low_offset": jnp.zeros((T, G))})
+    tp = substitute(seed(define_model, 0), data=values)(name, mock_data, priors)
+    got = {"logit_low": jnp.log(tp.theta_low / (1 - tp.theta_low)),
+           "logit_delta": jnp.log(tp.theta_high / (1 - tp.theta_high)),
+           "log_hill_K": tp.log_hill_K}[h]
+    np.testing.assert_allclose(np.asarray(got), 0.7, rtol=1e-5)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py b/tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py
index d17fbc18..80b34c57 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/test_hill_mut.py
@@ -680,6 +680,51 @@ def test_deterministic_same_key(self, mock_data_epi):
 # The fix guards the plate block with `if num_mut > 0` and returns zero-shape
 # offset arrays when M=0.  These tests lock in that behaviour.
 
+class TestSimulateMaxHillN:
+    """SimPriors.max_hill_n caps the simulated Hill coefficient."""
+
+    def _params(self, **overrides):
+        params = get_sim_hyperparameters()
+        params.update(sigma_d_log_n=3.0, epi_tau_scale=1.0, epi_slab_scale=5.0)
+        params.update(overrides)
+        return params
+
+    def test_default_is_no_limit(self):
+        assert get_sim_hyperparameters()["max_hill_n"] is None
+        assert SimPriors(**{k: v for k, v in get_sim_hyperparameters().items()
+                            if k != "max_hill_n"}).max_hill_n is None
+
+    def test_limit_caps_hill_n(self, mock_data_epi):
+        free = SimPriors(**self._params())
+        capped = SimPriors(**self._params(max_hill_n=4.0))
+        # First seed whose uncapped draw exceeds the limit (the fixture has
+        # only a few genotypes).
+        for seed in range(50):
+            _, p_free = simulate("theta", mock_data_epi, free, jax.random.PRNGKey(seed))
+            if np.asarray(p_free.hill_n).max() > 4.0:
+                break
+        else:
+            pytest.fail("no draw exceeded the limit")
+        _, p_cap = simulate("theta", mock_data_epi, capped, jax.random.PRNGKey(seed))
+        n_free = np.asarray(p_free.hill_n)
+        n_cap = np.asarray(p_cap.hill_n)
+        assert n_cap.max() <= 4.0 + 1e-5
+        np.testing.assert_allclose(n_cap, np.minimum(n_free, 4.0), rtol=1e-6)
+
+    def test_limit_applies_to_theta(self, mock_data_epi):
+        """theta_gc is computed from the capped Hill coefficient."""
+        data = mock_data_epi._replace(scatter_theta=0)
+        capped = SimPriors(**self._params(max_hill_n=4.0))
+        theta_gc, theta_param = simulate("theta", data, capped, jax.random.PRNGKey(5))
+        np.testing.assert_allclose(np.asarray(run_model(theta_param, data))[0],
+                                   theta_gc.T, rtol=1e-5, atol=1e-6)
+
+    def test_nonpositive_limit_raises(self, mock_data_epi):
+        with pytest.raises(ValueError, match="max_hill_n"):
+            simulate("theta", mock_data_epi, SimPriors(**self._params(max_hill_n=0.0)),
+                     jax.random.PRNGKey(0))
+
+
 @pytest.fixture
 def mock_data_no_mutation():
     """1 genotype (wt only), 0 mutations, 0 pairs — the stratified pool case."""
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/test_hill_relative.py b/tests/tfscreen/tfmodel/generative/components/theta/test_hill_relative.py
new file mode 100644
index 00000000..1199cc2d
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/theta/test_hill_relative.py
@@ -0,0 +1,287 @@
+"""Tests for the relative Hill theta component (X scale; roadmap step 5)."""
+from types import SimpleNamespace
+
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+import pytest
+from numpyro import handlers
+from scipy.special import expit
+
+from tfscreen.tfmodel.generative.components.theta import hill_relative as hr
+
+LOG_CONC = np.log(np.array([1e-20, 1e-3, 1e-2, 1e-1, 1.0]))
+GAUGE = jnp.array([LOG_CONC[0], LOG_CONC[-1]])
+
+
+def _data(num_genotype=4, wt=1, T=1):
+    idx = jnp.arange(num_genotype)
+    return SimpleNamespace(num_titrant_name=T, num_genotype=num_genotype,
+                           wt_indexes=jnp.array([wt]),
+                           theta_gauge_log_conc=GAUGE,
+                           log_titrant_conc=jnp.asarray(LOG_CONC),
+                           batch_idx=idx, geno_theta_idx=idx,
+                           scatter_theta=0)
+
+
+def _X(theta_param, data):
+    return np.asarray(hr.run_model(theta_param, data))      # (T, C, G)
+
+
+@pytest.mark.parametrize("log_K,n", [(-4.1, 2.0), (-2.0, 0.7), (-6.0, 4.0)])
+def test_gauge_baselines_pin_wt(log_K, n):
+    low, high = hr.gauge_baselines(jnp.array(log_K), jnp.array(n), GAUGE)
+    occ = expit(n * (np.asarray(GAUGE) - log_K))
+    X = float(low) + (float(high) - float(low)) * occ
+    assert X == pytest.approx([1.0, 0.0], abs=1e-5)
+
+
+def test_gauge_span_is_floored():
+    # a wt curve that does not move across the gauge range stays finite
+    low, high = hr.gauge_baselines(jnp.array(10.0), jnp.array(1.0), GAUGE)
+    assert np.isfinite(float(low)) and np.isfinite(float(high))
+
+
+@pytest.mark.parametrize("fn", [hr.define_model, hr.guide])
+def test_wt_is_gauged_others_free(fn):
+    data = _data()
+    with handlers.seed(rng_seed=3):
+        tp = fn("theta", data, hr.get_priors())
+    X = _X(tp, data)
+    assert X.shape == (1, len(LOG_CONC), 4)
+    assert X[0, 0, 1] == pytest.approx(1.0, abs=1e-5)
+    assert X[0, -1, 1] == pytest.approx(0.0, abs=1e-5)
+    # other genotypes are not pinned
+    assert not np.allclose(X[0, 0, [0, 2, 3]], 1.0)
+
+
+def test_sites_and_deterministics():
+    data = _data()
+    tr = handlers.trace(handlers.seed(hr.define_model, 0)).get_trace(
+        "theta", data, hr.get_priors())
+    for site in ("theta_X_low", "theta_X_high", "theta_log_hill_K",
+                 "theta_hill_n"):
+        assert tr[site]["type"] == "deterministic"
+        assert tr[site]["value"].shape == (1, 4)
+    # per-genotype offsets are library-sized
+    assert tr["theta_X_low_offset"]["value"].shape == (1, 4)
+
+
+def test_run_model_follows_batch_idx():
+    data = _data()
+    with handlers.seed(rng_seed=0):
+        tp = hr.define_model("theta", data, hr.get_priors())
+    full = _X(tp, data)
+    order = jnp.array([2, 0, 3, 1])
+    sub = _X(tp, SimpleNamespace(**{**vars(data), "batch_idx": order}))
+    assert np.allclose(sub, full[..., np.asarray(order)])
+
+
+def test_scatter_shape():
+    data = _data()
+    with handlers.seed(rng_seed=0):
+        tp = hr.define_model("theta", data, hr.get_priors())
+    data.scatter_theta = 1
+    assert hr.run_model(tp, data).shape == (1, 1, 1, 1, 1, len(LOG_CONC), 4)
+
+
+def test_population_moments_are_none():
+    assert hr.get_population_moments(None, None) == (None, None)
+
+
+def test_priors_and_guesses():
+    p = hr.get_priors()
+    assert p.theta_X_low_hyper_loc_loc == 1.0
+    assert p.theta_X_delta_hyper_loc_loc == -1.0
+    g = hr.get_guesses("theta", _data())
+    assert g["theta_X_low_offset"].shape == (1, 4)
+    assert set(k for k in g if k.endswith("_hyper_loc")) == {
+        "theta_X_low_hyper_loc", "theta_X_delta_hyper_loc",
+        "theta_log_hill_K_hyper_loc", "theta_log_hill_n_hyper_loc"}
+
+
+def test_compute_theta_samples_matches_run_model():
+    data = _data()
+    with handlers.seed(rng_seed=1):
+        tp = hr.define_model("theta", data, hr.get_priors())
+    post = {"theta_X_low": np.asarray(tp.X_low)[None],
+            "theta_X_high": np.asarray(tp.X_high)[None],
+            "theta_log_hill_K": np.asarray(tp.log_hill_K)[None],
+            "theta_hill_n": np.asarray(tp.hill_n)[None]}
+    conc = np.exp(LOG_CONC)
+    conc[0] = 0.0
+    calc = pd.DataFrame({"titrant_conc": np.tile(conc, 4),
+                         "map_theta_group": np.repeat(np.arange(4), len(conc))})
+    got = hr.compute_theta_samples(calc, post)[0].reshape(4, len(conc))
+    assert np.allclose(got, _X(tp, data)[0].T, atol=1e-5)
+
+
+def test_predict_unmeasured_uses_population_means():
+    post = {"theta_X_low_hyper_loc": np.array([[1.0]]),
+            "theta_X_delta_hyper_loc": np.array([[-1.0]]),
+            "theta_log_hill_K_hyper_loc": np.array([[np.log(0.01)]]),
+            "theta_log_hill_n_hyper_loc": np.array([[np.log(2.0)]])}
+    grid = pd.DataFrame({"titrant_name": ["iptg", "iptg"],
+                         "titrant_conc": [0.0, 0.01]})
+    out = hr.predict_unmeasured(["A1V", "B2C"], ["iptg"], grid, None, None,
+                                post, {"q0.5": 0.5})
+    at0 = out[out["titrant_conc"] == 0.0]["q0.5"]
+    atK = out[out["titrant_conc"] == 0.01]["q0.5"]
+    assert np.allclose(at0, 1.0) and np.allclose(atK, 0.5)
+
+
+def test_x_scale_truth_gauges_wt():
+    wt = np.array([0.95, 0.6, 0.05])            # wt theta at c_lo, mid, c_hi
+    X = hr.x_scale_truth(wt, wt[0], wt[-1])
+    assert X == pytest.approx([1.0, (0.6 - 0.05) / 0.9, 0.0])
+    # activity multiplies occupancy
+    assert hr.x_scale_truth(0.5, 1.0, 0.0, activity=0.5) == pytest.approx(0.25)
+
+
+def test_x_scale_growth_truth_preserves_growth():
+    rng = np.random.default_rng(0)
+    k, m = 0.02, -0.015
+    lo, hi = 0.9, 0.1
+    theta = rng.uniform(0, 1, 10)
+    k_X, m_X = hr.x_scale_growth_truth(k, m, lo, hi)
+    X = hr.x_scale_truth(theta, lo, hi)
+    assert np.allclose(k + m * theta, k_X + m_X * X)
+
+
+@pytest.mark.parametrize("fixed", [0.0, 0.5])
+def test_log_hill_n_hyper_scale_fixed(fixed):
+    """As hill_geno: a held log(n) population SD is a deterministic site with
+    no guide parameters, and the gauge still pins wt."""
+    data = _data()
+    priors = hr.get_priors().replace(theta_log_hill_n_hyper_scale_fixed=fixed)
+    assert hr.get_hyperparameters()["theta_log_hill_n_hyper_scale_fixed"] == 0.0
+    mtr = handlers.trace(handlers.seed(hr.define_model, 0)).get_trace(
+        "theta", data, priors)
+    gtr = handlers.trace(handlers.seed(hr.guide, 0)).get_trace(
+        "theta", data, priors)
+    site = mtr["theta_log_hill_n_hyper_scale"]
+    if fixed:
+        assert site["type"] == "deterministic"
+        assert np.allclose(site["value"], fixed)
+        assert "theta_log_hill_n_hyper_scale_loc" not in gtr
+    else:
+        assert site["type"] == "sample"
+    assert ({n for n, s in mtr.items() if s["type"] == "sample"}
+            == {n for n, s in gtr.items() if s["type"] == "sample"})
+    with handlers.seed(rng_seed=3):
+        tp = hr.guide("theta", data, priors)
+    X = _X(tp, data)
+    assert X[0, 0, 1] == pytest.approx(1.0, abs=1e-5)
+    assert X[0, -1, 1] == pytest.approx(0.0, abs=1e-5)
+
+
+@pytest.mark.parametrize("h", ["X_low", "X_delta", "log_hill_K"])
+def test_other_hyper_scales_fixed(h):
+    """Every hyperscale can be held, one at a time, without touching the
+    others or the gauge."""
+    data = _data()
+    field = f"theta_{h}_hyper_scale_fixed"
+    assert hr.get_hyperparameters()[field] == 0.0
+    priors = hr.get_priors().replace(**{field: 0.7})
+    mtr = handlers.trace(handlers.seed(hr.define_model, 0)).get_trace(
+        "theta", data, priors)
+    gtr = handlers.trace(handlers.seed(hr.guide, 0)).get_trace(
+        "theta", data, priors)
+    assert mtr[f"theta_{h}_hyper_scale"]["type"] == "deterministic"
+    assert np.allclose(mtr[f"theta_{h}_hyper_scale"]["value"], 0.7)
+    assert f"theta_{h}_hyper_scale_loc" not in gtr
+    others = {"X_low", "X_delta", "log_hill_K", "log_hill_n"} - {h}
+    for o in others:
+        assert mtr[f"theta_{o}_hyper_scale"]["type"] == "sample"
+    assert ({n for n, s in mtr.items() if s["type"] == "sample"}
+            == {n for n, s in gtr.items() if s["type"] == "sample"})
+    with handlers.seed(rng_seed=3):
+        tp = hr.guide("theta", data, priors)
+    X = _X(tp, data)
+    assert X[0, 0, 1] == pytest.approx(1.0, abs=1e-5)
+    assert X[0, -1, 1] == pytest.approx(0.0, abs=1e-5)
+
+
+def _as_h5(post):
+    """The posteriors as an in-memory HDF5 file: datasets have a shape but
+    no reshape, as in a tfs-sample-posterior .h5."""
+    import h5py
+    f = h5py.File("posterior.h5", "w", driver="core", backing_store=False)
+    for k, v in post.items():
+        f.create_dataset(k, data=np.asarray(v))
+    return f
+
+
+def test_compute_theta_samples_reads_h5_datasets():
+    data = _data()
+    with handlers.seed(rng_seed=1):
+        tp = hr.define_model("theta", data, hr.get_priors())
+    post = {"theta_X_low": np.asarray(tp.X_low)[None],
+            "theta_X_high": np.asarray(tp.X_high)[None],
+            "theta_log_hill_K": np.asarray(tp.log_hill_K)[None],
+            "theta_hill_n": np.asarray(tp.hill_n)[None]}
+    conc = np.exp(LOG_CONC)
+    calc = pd.DataFrame({"titrant_conc": np.tile(conc, 4),
+                         "map_theta_group": np.repeat(np.arange(4), len(conc))})
+    expected = hr.compute_theta_samples(calc, post)
+    with _as_h5(post) as f:
+        got = hr.compute_theta_samples(calc, f)
+    assert np.allclose(got, expected)
+
+
+def test_predict_unmeasured_reads_h5_datasets():
+    post = {"theta_X_low_hyper_loc": np.array([[1.0]]),
+            "theta_X_delta_hyper_loc": np.array([[-1.0]]),
+            "theta_log_hill_K_hyper_loc": np.array([[np.log(0.01)]]),
+            "theta_log_hill_n_hyper_loc": np.array([[np.log(2.0)]])}
+    grid = pd.DataFrame({"titrant_name": ["iptg", "iptg"],
+                         "titrant_conc": [0.0, 0.01]})
+    with _as_h5(post) as f:
+        out = hr.predict_unmeasured(["A1V"], ["iptg"], grid, None, None,
+                                    f, {"q0.5": 0.5})
+    assert np.allclose(out[out["titrant_conc"] == 0.0]["q0.5"], 1.0)
+    assert np.allclose(out[out["titrant_conc"] == 0.01]["q0.5"], 0.5)
+
+
+def test_get_extract_specs_reads_the_growth_rows():
+    df = pd.DataFrame({"genotype": ["wt"], "titrant_name": ["iptg"],
+                       "map_theta_group": [0]})
+    specs = hr.get_extract_specs(SimpleNamespace(
+        growth_tm=SimpleNamespace(df=df)))
+    assert len(specs) == 1
+    spec = specs[0]
+    assert spec["input_df"] is df
+    assert spec["params_to_get"] == ["hill_n", "log_hill_K", "X_high", "X_low"]
+    assert spec["map_column"] == "map_theta_group"
+    assert spec["get_columns"] == ["genotype", "titrant_name"]
+    assert spec["in_run_prefix"] == "theta_"
+
+
+@pytest.mark.parametrize("fn", [hr.define_model, hr.guide])
+def test_wt_hill_K_and_n_held(fn):
+    data = _data()
+    priors = hr.get_priors().replace(theta_wt_hill_K_fixed=0.03,
+                                     theta_wt_hill_n_fixed=1.2)
+    with handlers.seed(rng_seed=5):
+        tp = fn("theta", data, priors)
+    K, n = np.asarray(tp.log_hill_K), np.asarray(tp.hill_n)
+    assert K[0, 1] == pytest.approx(np.log(0.03))
+    assert n[0, 1] == pytest.approx(1.2)
+    # other genotypes keep their own K and n; wt is still gauged
+    assert not np.allclose(K[0, [0, 2, 3]], np.log(0.03))
+    X = _X(tp, data)
+    assert X[0, 0, 1] == pytest.approx(1.0, abs=1e-5)
+    assert X[0, -1, 1] == pytest.approx(0.0, abs=1e-5)
+    occ = expit(1.2 * (LOG_CONC - np.log(0.03)))
+    low, high = hr.gauge_baselines(jnp.array(np.log(0.03)), jnp.array(1.2), GAUGE)
+    assert X[0, :, 1] == pytest.approx(float(low) + (float(high) - float(low)) * occ,
+                                       abs=1e-5)
+
+
+def test_wt_hill_held_off_by_default_and_refuses_negative():
+    p = hr.get_priors()
+    assert p.theta_wt_hill_K_fixed == 0.0 and p.theta_wt_hill_n_fixed == 0.0
+    data = _data()
+    with pytest.raises(ValueError, match="theta_wt_hill"):
+        with handlers.seed(rng_seed=0):
+            hr.define_model("theta", data, p.replace(theta_wt_hill_n_fixed=-1.0))
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py
index 1befcdd3..c5549970 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PK.py
@@ -226,7 +226,7 @@ def test_get_hyperparameters_has_required_keys():
         "theta_ln_K_l_o_wt_loc", "theta_ln_K_l_o_wt_scale",
         "theta_ln_K_h_e_wt_loc", "theta_ln_K_h_e_wt_scale",
         "theta_ln_K_l_e_wt_loc", "theta_ln_K_l_e_wt_scale",
-        "theta_tf_total_M", "theta_op_total_M",
+        "theta_tf_total_M",
         "theta_sigma_d_ln_K_h_l_scale", "theta_sigma_d_ln_K_h_o_scale",
         "theta_sigma_d_ln_K_l_o_scale",
         "theta_sigma_d_ln_K_h_e_scale", "theta_sigma_d_ln_K_l_e_scale",
@@ -242,8 +242,7 @@ def test_get_priors_type():
 
 def test_get_priors_physical_constants():
     p = get_priors()
-    assert p.theta_tf_total_M == pytest.approx(6.5e-7)
-    assert p.theta_op_total_M == pytest.approx(2.5e-8)
+    assert p.theta_tf_total_M == pytest.approx(1e-5)
 
 
 def test_get_priors_matches_hyperparameters():
@@ -528,12 +527,11 @@ def test_no_epi_no_epistasis_sample_sites_in_guide(self, mock_data_no_epi):
         sample_names = {k for k, v in tr.items() if v["type"] == "sample"}
         assert not any("epi" in k for k in sample_names)
 
-    def test_tf_op_totals_preserved(self, mock_data_no_epi):
+    def test_tf_total_preserved(self, mock_data_no_epi):
         priors = get_priors()
         with seed(rng_seed=0):
             tp = guide("theta", mock_data_no_epi, priors)
-        assert tp.tf_total == pytest.approx(6.5e-7)
-        assert tp.op_total == pytest.approx(2.5e-8)
+        assert tp.tf_total == pytest.approx(1e-5)
 
     def test_variational_params_registered_no_epi(self, mock_data_no_epi):
         priors = get_priors()
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py
index 1021c7bc..8fff0aad 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PddG.py
@@ -11,7 +11,7 @@
 import jax.numpy as jnp
 from collections import namedtuple
 from dataclasses import dataclass
-from numpyro.handlers import trace, seed
+from numpyro.handlers import condition, trace, seed
 import unittest.mock as mock
 
 from tfscreen.genetics.build_mut_geno_matrix import build_mut_sparse_indices
@@ -36,6 +36,11 @@
 # ---------------------------------------------------------------------------
 
 _S = 6    # number of MWC structures
+
+
+def _sigma_zero(fn):
+    """``fn`` with every structure's σ_s conditioned to 0: ΔΔG = prior means."""
+    return condition(fn, data={"theta_ddG_sigma": jnp.zeros(_S)})
 _M = 3    # mutations
 _G = 4    # genotypes: wt, M1, M2, M1+M2 (last mutation unused in matrix)
 
@@ -213,7 +218,7 @@ def test_hyperparameters_has_required_keys(self):
             "theta_ln_K_l_o_wt_loc", "theta_ln_K_l_o_wt_scale",
             "theta_ln_K_h_e_wt_loc", "theta_ln_K_h_e_wt_scale",
             "theta_ln_K_l_e_wt_loc", "theta_ln_K_l_e_wt_scale",
-            "theta_tf_total_M", "theta_op_total_M", "theta_conc_unit_scale",
+            "theta_tf_total_M", "theta_conc_unit_scale",
         ]
         for k in required:
             assert k in hp, f"Missing key: {k}"
@@ -231,13 +236,12 @@ def test_model_priors_has_no_nn_fields(self):
 
     def test_physical_constants(self):
         p = get_priors()
-        assert p.theta_tf_total_M == pytest.approx(6.5e-7)
-        assert p.theta_op_total_M == pytest.approx(2.5e-8)
+        assert p.theta_tf_total_M == pytest.approx(1e-5)
 
     def test_get_guesses_required_keys(self):
         g = get_guesses("theta", _make_mock())
         for k in ("theta_ln_K_h_l_wt", "theta_ln_K_h_o_wt", "theta_ln_K_l_o_wt",
-                  "theta_ln_K_h_e_wt", "theta_ln_K_l_e_wt", "theta_ddG_offset"):
+                  "theta_ln_K_h_e_wt", "theta_ln_K_l_e_wt", "theta_dlnK"):
             assert k in g, f"Missing guess key: {k}"
 
     def test_get_guesses_no_epi_keys(self):
@@ -246,8 +250,8 @@ def test_get_guesses_no_epi_keys(self):
         assert "theta_epi_lambda" not in g
         assert "theta_epi_offset" not in g
 
-    def test_get_guesses_ddG_offset_shape(self):
-        assert get_guesses("theta", _make_mock())["theta_ddG_offset"].shape == (_S, _M)
+    def test_get_guesses_ddG_shape(self):
+        assert get_guesses("theta", _make_mock())["theta_dlnK"].shape == (_M, 5)
 
     def test_get_guesses_wt_K_shapes(self):
         T = 2
@@ -311,7 +315,7 @@ def test_population_moment_shapes(self):
     def test_sample_sites_present(self):
         _, tr = _run_model(_make_mock())
         for site in ("theta_ln_K_h_l_wt", "theta_ln_K_h_o_wt", "theta_ln_K_l_o_wt",
-                     "theta_ln_K_h_e_wt", "theta_ln_K_l_e_wt", "theta_ddG_offset"):
+                     "theta_ln_K_h_e_wt", "theta_ln_K_l_e_wt", "theta_dlnK"):
             assert site in tr, f"Missing sample site: {site}"
 
     def test_deterministic_sites_present(self):
@@ -340,7 +344,7 @@ def test_permuted_struct_names_accepted(self):
         assert isinstance(out, ThetaParam)
 
     def test_ddG_prior_means_shift_delta_lnK(self):
-        """With sigma_s=0, ddG equals the prior means exactly, so mutations with
+        """With σ_s conditioned to 0, ddG equals the prior means exactly, so mutations with
         nonzero prior produce nonzero delta_lnK in the trace."""
         # All prior means zero → all delta_lnK zero
         zero_prior = np.zeros((_M, _S), dtype=np.float32)
@@ -348,13 +352,11 @@ def test_ddG_prior_means_shift_delta_lnK(self):
         priors = get_priors()
 
         def _zero_sigma_s_param(name, init, **kwargs):
-            if name.endswith("_ddG_sigma_s"):
-                return jnp.zeros_like(init)
             return jnp.zeros_like(init)
 
         with mock.patch("numpyro.param", side_effect=_zero_sigma_s_param):
             with seed(rng_seed=0):
-                out_zero = define_model("theta", data_zero, priors)
+                out_zero = _sigma_zero(define_model)("theta", data_zero, priors)
 
         # All mutations zero → all genotype K values identical (WT shift only)
         np.testing.assert_allclose(
@@ -370,7 +372,7 @@ def _zero_sigma_s_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=_zero_sigma_s_param):
             with seed(rng_seed=0):
-                out_nonzero = define_model("theta", data_nonzero, priors)
+                out_nonzero = _sigma_zero(define_model)("theta", data_nonzero, priors)
 
         # Genotype containing mutation 0 (genotype index 1) should differ from WT
         assert not np.allclose(
@@ -401,10 +403,9 @@ def test_permuted_features_give_same_result_as_canonical(self):
             np.asarray(out_s.ln_K_h_l), atol=1e-5,
         )
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_model(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
 
 # ---------------------------------------------------------------------------
@@ -436,8 +437,8 @@ def test_variational_params_registered(self):
         param_names = {k for k, v in tr.items() if v["type"] == "param"}
         for p in ("theta_ln_K_h_l_wt_loc", "theta_ln_K_h_l_wt_scale",
                   "theta_ln_K_h_o_wt_loc", "theta_ln_K_l_o_wt_loc",
-                  "theta_ddG_offset_locs", "theta_ddG_offset_scales",
-                  "theta_ddG_sigma_s"):
+                  "theta_dlnK_locs", "theta_dlnK_scales",
+                  "theta_ddG_sigma_locs", "theta_ddG_sigma_scales"):
             assert p in param_names, f"Missing variational param: {p}"
 
     def test_no_nn_params_registered(self):
@@ -453,20 +454,19 @@ def test_no_epi_params_registered(self):
         assert not any("epi_lambda" in k for k in param_names)
         assert not any("epi_offset" in k for k in param_names)
 
-    def test_ddG_sigma_s_shape(self):
+    def test_ddG_sigma_shape(self):
         _, tr = _run_guide(_make_mock())
-        sigma_val = tr["theta_ddG_sigma_s"]["value"]
+        sigma_val = tr["theta_ddG_sigma"]["value"]
         assert sigma_val.shape == (_S,)
 
-    def test_ddG_offset_locs_shape(self):
+    def test_dlnK_locs_shape(self):
         _, tr = _run_guide(_make_mock())
-        locs_val = tr["theta_ddG_offset_locs"]["value"]
-        assert locs_val.shape == (_S, _M)
+        locs_val = tr["theta_dlnK_locs"]["value"]
+        assert locs_val.shape == (_M, 5)
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_guide(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
     def test_permuted_features_give_same_output_as_canonical(self):
         canonical_prior = _make_ddG_prior()
@@ -535,6 +535,11 @@ def _make_ctx(genotypes=None, titrant_names=None, mut_labels=None, pair_labels=N
         pair_labels=pair_labels,
     )
 
+def _base_specs(specs):
+    """Specs other than the per-structure ddG ones (tested in
+    test_ddG_extraction.py)."""
+    return [s for s in specs if "out_names" not in s]
+
 
 class TestGetExtractSpecs:
 
@@ -543,11 +548,11 @@ def test_returns_list(self):
 
     def test_three_specs(self):
         # scalar K values, T-dim K values, per-mutation d_ln_K values
-        assert len(get_extract_specs(_make_ctx())) == 3
+        assert len(_base_specs(get_extract_specs(_make_ctx()))) == 3
 
     def test_four_specs_when_pair_labels_present(self):
         ctx = _make_ctx(pair_labels=["M1+M2"])
-        assert len(get_extract_specs(ctx)) == 4
+        assert len(_base_specs(get_extract_specs(ctx))) == 4
 
     def test_first_spec_has_scalar_K_values(self):
         params = get_extract_specs(_make_ctx())[0]["params_to_get"]
@@ -596,7 +601,6 @@ def test_third_spec_has_one_row_per_mutation(self):
 _PAIR_LABELS_P = ["K84L/M42I"]
 _TITRANT_NAMES_P = ["IPTG"]
 _TF_TOTAL = 6.5e-7
-_OP_TOTAL  = 2.5e-8
 _CONC_SCALE = 1e-3
 
 _WT_LN_K_H_L =  1.84
@@ -651,7 +655,6 @@ def _common_kwargs_p(post, pair_labels=None):
         param_posteriors=post,
         q_to_get={"median": 0.5, "lower": 0.025, "upper": 0.975},
         tf_total=_TF_TOTAL,
-        op_total=_OP_TOTAL,
         conc_unit_scale=_CONC_SCALE,
     )
 
@@ -727,7 +730,6 @@ def test_multiple_titrants_broadcast_correctly(self):
             param_posteriors=post,
             q_to_get={"median": 0.5},
             tf_total=_TF_TOTAL,
-            op_total=_OP_TOTAL,
             conc_unit_scale=_CONC_SCALE,
         )
         result = ddG_predict(**kw)
@@ -770,7 +772,6 @@ def test_scalar_d_he_le_matches_lnK_mut_when_replicated(self):
             pair_labels=[],
             q_to_get={"median": 0.5},
             tf_total=_TF_TOTAL,
-            op_total=_OP_TOTAL,
             conc_unit_scale=_CONC_SCALE,
         )
         res_ddG = ddG_predict(param_posteriors=post_ddG, **kw)
@@ -779,3 +780,33 @@ def test_scalar_d_he_le_matches_lnK_mut_when_replicated(self):
         np.testing.assert_allclose(
             res_ddG["median"].values, res_mut["median"].values, atol=1e-5
         )
+
+
+# ---------------------------------------------------------------------------
+# guide with pair epistasis
+# ---------------------------------------------------------------------------
+
+def test_guide_epistasis_uses_regularized_horseshoe_scale():
+    # one pair (M1, M2) on the double mutant (genotype 3)
+    data = _make_mock()._replace(
+        num_pair=1,
+        pair_nnz_pair_idx=np.array([0], dtype=np.int32),
+        pair_nnz_geno_idx=np.array([3], dtype=np.int32))
+    out, tr = _run_guide(data)
+    for site in ("theta_epi_tau", "theta_epi_c2", "theta_epi_lambda",
+                 "theta_epi_offset"):
+        assert tr[site]["type"] == "sample"
+
+    tau = np.asarray(tr["theta_epi_tau"]["value"], dtype=float)
+    c2 = np.asarray(tr["theta_epi_c2"]["value"], dtype=float)
+    lam = np.asarray(tr["theta_epi_lambda"]["value"], dtype=float)
+    off = np.asarray(tr["theta_epi_offset"]["value"], dtype=float)
+    lam_tilde = np.sqrt(c2 * lam ** 2 / (c2 + tau ** 2 * lam ** 2))
+    epi_ddG = (off * tau * lam_tilde).T                      # (P, S)
+    expected = float(np.asarray(_project_ddG(jnp.asarray(epi_ddG)))[0, 0])
+
+    K = np.asarray(out.ln_K_h_l, dtype=float)
+    # additive part cancels in the double-mutant cycle, leaving the pair term
+    cycle = K[3] - K[1] - K[2] + K[0]
+    assert np.isfinite(cycle)
+    assert cycle == pytest.approx(expected, rel=1e-4, abs=1e-5)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py
index 34b660fe..f3ad7981 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_PnnC.py
@@ -11,7 +11,7 @@
 import jax.numpy as jnp
 from dataclasses import dataclass
 from collections import namedtuple
-from numpyro.handlers import trace, seed
+from numpyro.handlers import condition, trace, seed
 
 from tfscreen.genetics.build_mut_geno_matrix import build_mut_sparse_indices
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U0_a.PnnC import (
@@ -35,6 +35,11 @@
 # ---------------------------------------------------------------------------
 
 _S = 6    # number of MWC structures
+
+
+def _sigma_zero(fn):
+    """``fn`` with every structure's σ_s conditioned to 0: ΔΔG = prior means."""
+    return condition(fn, data={"theta_ddG_sigma": jnp.zeros(_S)})
 _M = 2    # mutations
 _G = 4    # genotypes: wt, M1, M2, M1+M2
 
@@ -272,7 +277,7 @@ def test_hyperparameters_has_required_keys(self):
             "theta_ln_K_l_o_wt_loc", "theta_ln_K_l_o_wt_scale",
             "theta_ln_K_h_e_wt_loc", "theta_ln_K_h_e_wt_scale",
             "theta_ln_K_l_e_wt_loc", "theta_ln_K_l_e_wt_scale",
-            "theta_tf_total_M", "theta_op_total_M",
+            "theta_tf_total_M",
             "theta_nn_hidden_size",
             "theta_epi_tau_scale", "theta_epi_slab_scale",
             "theta_epi_slab_df",   "theta_epi_d0",
@@ -285,8 +290,7 @@ def test_get_priors_type(self):
 
     def test_physical_constants(self):
         p = get_priors()
-        assert p.theta_tf_total_M == pytest.approx(6.5e-7)
-        assert p.theta_op_total_M == pytest.approx(2.5e-8)
+        assert p.theta_tf_total_M == pytest.approx(1e-5)
 
     def test_get_guesses_no_epi_keys(self):
         data = _make_mock()
@@ -296,7 +300,7 @@ def test_get_guesses_no_epi_keys(self):
         assert "theta_ln_K_l_o_wt" in g
         assert "theta_ln_K_h_e_wt" in g
         assert "theta_ln_K_l_e_wt" in g
-        assert "theta_ddG_offset"  in g
+        assert "theta_dlnK"  in g
         assert "theta_epi_tau" not in g
 
     def test_get_guesses_with_epi_keys(self):
@@ -307,10 +311,10 @@ def test_get_guesses_with_epi_keys(self):
         assert "theta_epi_lambda" in g
         assert "theta_epi_offset" in g
 
-    def test_get_guesses_ddG_offset_shape(self):
+    def test_get_guesses_ddG_shape(self):
         data = _make_mock()
         g = get_guesses("theta", data)
-        assert g["theta_ddG_offset"].shape == (_S, _M)
+        assert g["theta_dlnK"].shape == (_M, 5)
 
     def test_get_guesses_epi_shapes_with_epi(self):
         data = _make_mock(num_pair=1)
@@ -383,7 +387,7 @@ def test_population_moment_shapes(self):
     def test_sample_sites_present(self):
         _, tr = _run_model(_make_mock())
         for site in ("theta_ln_K_h_l_wt", "theta_ln_K_h_o_wt", "theta_ln_K_l_o_wt",
-                     "theta_ln_K_h_e_wt", "theta_ln_K_l_e_wt", "theta_ddG_offset"):
+                     "theta_ln_K_h_e_wt", "theta_ln_K_l_e_wt", "theta_dlnK"):
             assert site in tr, f"Missing sample site: {site}"
 
     def test_deterministic_sites_present(self):
@@ -433,7 +437,7 @@ def test_permuted_features_give_same_params_as_canonical(self):
         )
 
     def test_zero_nn_and_offsets_all_genotypes_equal(self):
-        """With all NN weights=0 and ddG_offsets=0, all mutations have zero effect."""
+        """With all NN weights=0 and σ_s=0, all mutations have zero effect."""
         data = _make_mock()
         priors = get_priors()
         import unittest.mock as mock
@@ -443,7 +447,7 @@ def _zero_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=_zero_param):
             with seed(rng_seed=0):
-                out = define_model("theta", data, priors)
+                out = _sigma_zero(define_model)("theta", data, priors)
 
         np.testing.assert_allclose(
             np.asarray(out.ln_K_h_l[0]), np.asarray(out.ln_K_h_l[1]), atol=1e-5
@@ -509,8 +513,8 @@ def test_variational_params_registered(self):
         param_names = {k for k, v in tr.items() if v["type"] == "param"}
         for p in ("theta_ln_K_h_l_wt_loc", "theta_ln_K_h_l_wt_scale",
                   "theta_ln_K_h_o_wt_loc", "theta_ln_K_l_o_wt_loc",
-                  "theta_ddG_offset_locs",  "theta_ddG_offset_scales",
-                  "theta_ddG_sigma_s"):
+                  "theta_dlnK_locs", "theta_dlnK_scales",
+                  "theta_ddG_sigma_locs", "theta_ddG_sigma_scales"):
             assert p in param_names, f"Missing variational param: {p}"
 
     def test_nn_params_registered_per_structure(self):
@@ -545,10 +549,9 @@ def test_epi_params_registered_when_num_pair_nonzero(self):
         assert "theta_epi_offset_locs"   in param_names
         assert "theta_epi_offset_scales" in param_names
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_guide(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
 
 # ---------------------------------------------------------------------------
@@ -597,6 +600,11 @@ def _make_ctx(genotypes=None, titrant_names=None, mut_labels=None, pair_labels=N
         pair_labels=pair_labels,
     )
 
+def _base_specs(specs):
+    """Specs other than the per-structure ddG ones (tested in
+    test_ddG_extraction.py)."""
+    return [s for s in specs if "out_names" not in s]
+
 
 class TestGetExtractSpecs:
 
@@ -605,11 +613,11 @@ def test_returns_list(self):
 
     def test_three_specs_without_epi(self):
         # scalar K, T-dim K, per-mutation d_ln_K
-        assert len(get_extract_specs(_make_ctx())) == 3
+        assert len(_base_specs(get_extract_specs(_make_ctx()))) == 3
 
     def test_four_specs_with_epi(self):
         ctx = _make_ctx(pair_labels=["M1M2"])
-        assert len(get_extract_specs(ctx)) == 4
+        assert len(_base_specs(get_extract_specs(ctx))) == 4
 
     def test_first_spec_has_scalar_K_values(self):
         specs = get_extract_specs(_make_ctx())
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_predict_unmeasured.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_predict_unmeasured.py
index e1cde8a9..9cc6b2b8 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_predict_unmeasured.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_predict_unmeasured.py
@@ -24,8 +24,7 @@
 TITRANT_NAMES = ["IPTG"]
 
 # Physical constants (monomer units for tf_total, consistent with MWC convention)
-TF_TOTAL = 6.5e-7
-OP_TOTAL = 2.5e-8
+TF_TOTAL = 1e-5
 CONC_SCALE = 1e-3   # concentrations in mM → M
 
 # Biophysically plausible WT values (from lnK_mut get_hyperparameters defaults)
@@ -102,7 +101,6 @@ def _common_kwargs(post, pair_labels=None):
         param_posteriors=post,
         q_to_get={"median": 0.5, "lower": 0.025, "upper": 0.975},
         tf_total=TF_TOTAL,
-        op_total=OP_TOTAL,
         conc_unit_scale=CONC_SCALE,
     )
 
@@ -211,7 +209,6 @@ def test_multiple_titrants_broadcast_correctly(self):
             param_posteriors=post,
             q_to_get={"median": 0.5},
             tf_total=TF_TOTAL,
-            op_total=OP_TOTAL,
             conc_unit_scale=CONC_SCALE,
         )
         result = lnK_nn_predict(**kw)
@@ -252,7 +249,6 @@ def test_scalar_d_he_le_vs_titrant(self):
             pair_labels=[],
             q_to_get={"median": 0.5},
             tf_total=TF_TOTAL,
-            op_total=OP_TOTAL,
             conc_unit_scale=CONC_SCALE,
         )
         res_nn  = lnK_nn_predict(param_posteriors=post_nn,  **kw)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_thermo.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_thermo.py
index a216b3a4..982e09c7 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_thermo.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U0_a/test_thermo.py
@@ -90,7 +90,6 @@ def _wt_theta_param(T=2, G=4, C=4):
         ln_K_l_o=jnp.full(G, -2.3),
         ln_K_l_e=jnp.full((T, G), 13.5),
         tf_total=6.5e-7,
-        op_total=2.5e-8,
         mu=jnp.zeros((T, C, 1)),
         sigma=jnp.ones((T, C, 1)),
         conc_unit_scale=1.0,   # test concentrations are already in M
@@ -109,7 +108,7 @@ def test_output_shape(self):
         theta = _compute_theta(
             jnp.full(G, -7.4), jnp.full(G, 23.0), jnp.full((T, G), 11.5),
             jnp.full(G, 13.8), jnp.full((T, G), 15.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert theta.shape == (T, len(_CONC), G)
 
@@ -119,7 +118,7 @@ def test_values_in_0_1(self):
         theta = _compute_theta(
             jnp.full(G, -7.4), jnp.full(G, 23.0), jnp.full((T, G), 11.5),
             jnp.full(G, 13.8), jnp.full((T, G), 15.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert jnp.all(theta >= 0)
         assert jnp.all(theta <= 1)
@@ -131,7 +130,7 @@ def test_all_finite(self):
             jnp.linspace(-10.0, -5.0, G), jnp.linspace(18.0, 25.0, G),
             jnp.full((T, G), 11.5),
             jnp.linspace(10.0, 16.0, G), jnp.full((T, G), 15.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert jnp.all(jnp.isfinite(theta))
 
@@ -146,7 +145,7 @@ def test_monotone_decreasing_in_effector(self):
         theta = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         for c in range(len(conc) - 1):
             assert jnp.all(theta[:, c, :] >= theta[:, c + 1, :])
@@ -165,7 +164,7 @@ def test_apo_matches_analytic_formula(self):
             jnp.full((1, G), -100.0),   # K_h_e negligible
             jnp.array(ln_K_l_o_vals), jnp.full((1, G), -100.0),
             jnp.array([0.0, 1e-8]),     # [0] is apo; [1] ≈ apo (tiny conc)
-            tf_total, 0.0,
+            tf_total,
         )                                # (1, 2, G)
 
         for g in range(G):
@@ -182,7 +181,7 @@ def test_higher_K_h_o_gives_higher_theta_at_zero_effector(self):
             jnp.array([17.0, 21.0]),   # G=0 weaker, G=1 stronger
             jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert theta[0, 0, 1] > theta[0, 0, 0]
 
@@ -198,7 +197,7 @@ def test_higher_K_h_l_gives_lower_theta_at_zero_effector(self):
             jnp.array([-2.0, 2.0]),  # G=0 H-favored, G=1 L-favored
             jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert theta[0, 0, 0] > theta[0, 0, 1]
 
@@ -222,7 +221,7 @@ def test_titrant_dim_broadcasts_independently(self):
         theta = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), ln_K_h_e,
             jnp.full(G, -2.3), ln_K_l_e,
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         # T=0: large drop from apo→saturated IPTG
         drop_t0 = theta[0, 0, 0] - theta[0, 1, 0]
@@ -243,7 +242,7 @@ def test_wt_params_give_high_apo_theta(self):
         theta = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5),
-            jnp.array([0.0]), 6.5e-7, 2.5e-8,
+            jnp.array([0.0]), 6.5e-7,
         )
         assert float(theta[0, 0, 0]) >= 0.85
 
@@ -255,7 +254,7 @@ def test_e_free_bounded_by_e_total(self):
         theta = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert jnp.all(theta >= 0)
         assert jnp.all(theta <= 1)
@@ -343,7 +342,7 @@ def test_geno_theta_idx_selects_genotypes(self):
             ln_K_h_e=jnp.full((T, G), 11.5),
             ln_K_l_o=jnp.full(G, 13.8),
             ln_K_l_e=jnp.full((T, G), 15.0),
-            tf_total=6.5e-7, op_total=2.5e-8,
+            tf_total=6.5e-7,
             mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
             conc_unit_scale=1.0,
         )
@@ -357,7 +356,7 @@ def test_geno_theta_idx_selects_genotypes(self):
         theta_all = _compute_theta(
             tp.ln_K_h_l, tp.ln_K_h_o, tp.ln_K_h_e,
             tp.ln_K_l_o, tp.ln_K_l_e,
-            data.titrant_conc, tp.tf_total, tp.op_total,
+            data.titrant_conc, tp.tf_total,
         )                                          # (T, C, G)
         assert jnp.allclose(result[:, :, 0], theta_all[:, :, 3], atol=1e-6)
         assert jnp.allclose(result[:, :, 1], theta_all[:, :, 0], atol=1e-6)
@@ -380,7 +379,7 @@ def test_nontrivial_batch_idx_selects_correct_genotypes(self):
             ln_K_h_e=jnp.full((T, G), 11.5),
             ln_K_l_o=jnp.full(G, 13.8),
             ln_K_l_e=jnp.full((T, G), 15.0),
-            tf_total=6.5e-7, op_total=2.5e-8,
+            tf_total=6.5e-7,
             mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
             conc_unit_scale=1.0,
         )
@@ -394,7 +393,7 @@ def test_nontrivial_batch_idx_selects_correct_genotypes(self):
         theta_all = _compute_theta(
             tp.ln_K_h_l, tp.ln_K_h_o, tp.ln_K_h_e,
             tp.ln_K_l_o, tp.ln_K_l_e,
-            data.titrant_conc, tp.tf_total, tp.op_total,
+            data.titrant_conc, tp.tf_total,
         )   # (T, C, G)
 
         for local_i, global_g in enumerate(batch_idx):
@@ -413,7 +412,7 @@ def test_apo_matches_analytic_formula(self):
             ln_K_h_e=jnp.full((1, G), -100.0),
             ln_K_l_o=jnp.full(G, ln_K_l_o_val),
             ln_K_l_e=jnp.full((1, G), -100.0),
-            tf_total=tf_total, op_total=0.0,
+            tf_total=tf_total,
             mu=jnp.zeros((1, 2, 1)), sigma=jnp.ones((1, 2, 1)),
             conc_unit_scale=1.0,
         )
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py
index 5be3b501..74cf2144 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PK.py
@@ -236,7 +236,7 @@ def test_get_hyperparameters_has_required_keys():
         "theta_ln_K_l_e_wt_loc", "theta_ln_K_l_e_wt_scale",
         # New: unfolded state
         "theta_ln_K_u_wt_loc", "theta_ln_K_u_wt_scale",
-        "theta_tf_total_M", "theta_op_total_M",
+        "theta_tf_total_M",
         "theta_sigma_d_ln_K_h_l_scale", "theta_sigma_d_ln_K_h_o_scale",
         "theta_sigma_d_ln_K_l_o_scale",
         "theta_sigma_d_ln_K_h_e_scale", "theta_sigma_d_ln_K_l_e_scale",
@@ -262,8 +262,7 @@ def test_get_priors_type():
 
 def test_get_priors_physical_constants():
     p = get_priors()
-    assert p.theta_tf_total_M == pytest.approx(6.5e-7)
-    assert p.theta_op_total_M == pytest.approx(2.5e-8)
+    assert p.theta_tf_total_M == pytest.approx(1e-5)
 
 
 def test_get_priors_K_u_fields():
@@ -651,12 +650,11 @@ def test_no_epi_no_epistasis_sample_sites_in_guide(self, mock_data_no_epi):
         sample_names = {k for k, v in tr.items() if v["type"] == "sample"}
         assert not any("epi" in k for k in sample_names)
 
-    def test_tf_op_totals_preserved(self, mock_data_no_epi):
+    def test_tf_total_preserved(self, mock_data_no_epi):
         priors = get_priors()
         with seed(rng_seed=0):
             tp = guide("theta", mock_data_no_epi, priors)
-        assert tp.tf_total == pytest.approx(6.5e-7)
-        assert tp.op_total == pytest.approx(2.5e-8)
+        assert tp.tf_total == pytest.approx(1e-5)
 
     def test_variational_params_registered_no_epi(self, mock_data_no_epi):
         priors = get_priors()
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py
index 3fe2f0a9..a1a939e8 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PddG.py
@@ -7,8 +7,9 @@
 Key differences from mwc_dimer variant:
 - ThetaParam gains ln_K_u (G,) and conc_unit_scale fields
 - ModelPriors gains theta_ln_K_u_wt_loc and theta_ln_K_u_wt_scale
-- define_model samples theta_ln_K_u_wt and broadcasts it homogeneously
-- ln_K_u[g] == ln_K_u_wt for all g (no per-mutation unfolding effect)
+- define_model samples theta_ln_K_u_wt, and each mutation shifts ln_K_u by
+  its ΔΔG_H (K_u = [U]/[H] with ΔΔG_U = 0), exposed as d_ln_K_u
+  (test_unfolding_projection.py covers the projection in depth)
 - guide samples theta_ln_K_u_wt with variational params theta_ln_K_u_wt_loc/scale
 - struct_features is (M, S) float32 ddG prior means (no NN, no epistasis)
 - get_extract_specs always returns 3 specs (no epistasis spec)
@@ -21,7 +22,7 @@
 import jax.numpy as jnp
 from collections import namedtuple
 from dataclasses import dataclass
-from numpyro.handlers import trace, seed
+from numpyro.handlers import condition, trace, seed
 import unittest.mock as mock
 
 from tfscreen.genetics.build_mut_geno_matrix import build_mut_sparse_indices
@@ -46,6 +47,11 @@
 # ---------------------------------------------------------------------------
 
 _S = 6    # number of MWC structures (H, HO, L, LO, HE2, LE2)
+
+
+def _sigma_zero(fn):
+    """``fn`` with every structure's σ_s conditioned to 0: ΔΔG = prior means."""
+    return condition(fn, data={"theta_ddG_sigma": jnp.zeros(_S)})
 _M = 3    # mutations
 _G = 4    # genotypes: wt, M1, M2, M1+M2
 
@@ -224,7 +230,7 @@ def test_hyperparameters_has_required_keys(self):
             "theta_ln_K_l_e_wt_loc", "theta_ln_K_l_e_wt_scale",
             # unfolding constant — new in mwc_dimer_unfolded
             "theta_ln_K_u_wt_loc", "theta_ln_K_u_wt_scale",
-            "theta_tf_total_M", "theta_op_total_M", "theta_conc_unit_scale",
+            "theta_tf_total_M", "theta_conc_unit_scale",
         ]
         for k in required:
             assert k in hp, f"Missing key: {k}"
@@ -252,15 +258,14 @@ def test_model_priors_has_no_nn_fields(self):
 
     def test_physical_constants(self):
         p = get_priors()
-        assert p.theta_tf_total_M == pytest.approx(6.5e-7)
-        assert p.theta_op_total_M == pytest.approx(2.5e-8)
+        assert p.theta_tf_total_M == pytest.approx(1e-5)
 
     def test_get_guesses_required_keys(self):
         g = get_guesses("theta", _make_mock())
         for k in ("theta_ln_K_h_l_wt", "theta_ln_K_h_o_wt", "theta_ln_K_l_o_wt",
                   "theta_ln_K_h_e_wt", "theta_ln_K_l_e_wt",
                   "theta_ln_K_u_wt",   # new in mwc_dimer_unfolded
-                  "theta_ddG_offset"):
+                  "theta_dlnK"):
             assert k in g, f"Missing guess key: {k}"
 
     def test_get_guesses_ln_K_u_wt_value(self):
@@ -273,8 +278,8 @@ def test_get_guesses_no_epi_keys(self):
         assert "theta_epi_lambda" not in g
         assert "theta_epi_offset" not in g
 
-    def test_get_guesses_ddG_offset_shape(self):
-        assert get_guesses("theta", _make_mock())["theta_ddG_offset"].shape == (_S, _M)
+    def test_get_guesses_ddG_shape(self):
+        assert get_guesses("theta", _make_mock())["theta_dlnK"].shape == (_M, 6)
 
     def test_get_guesses_wt_K_shapes(self):
         T = 2
@@ -334,12 +339,15 @@ def test_ln_K_u_shape(self):
         out, _ = _run_model(_make_mock())
         assert out.ln_K_u.shape == (_G,)
 
-    def test_ln_K_u_is_homogeneous(self):
-        """ln_K_u[g] must be identical for all genotypes (no per-mutation unfolding)."""
-        out, _ = _run_model(_make_mock())
+    def test_ln_K_u_shifts_by_ddG_H(self):
+        """ln_K_u[g] − ln_K_u[wt] is the summed ΔΔG_H (K_u = [U]/[H], ΔΔG_U = 0)."""
+        out, tr = _run_model(_make_mock())
+        ddG_H = np.asarray(tr["theta_ddG"]["value"])[:, 0]
         vals = np.asarray(out.ln_K_u)
-        np.testing.assert_allclose(vals, vals[0], atol=1e-6,
-                                   err_msg="ln_K_u is not homogeneous across genotypes")
+        np.testing.assert_allclose(vals - vals[0], _MUT_GENO.T @ ddG_H,
+                                   atol=1e-5)
+        np.testing.assert_allclose(tr["theta_d_ln_K_u"]["value"], ddG_H,
+                                   atol=1e-6)
 
     def test_conc_unit_scale_preserved(self):
         out, _ = _run_model(_make_mock())
@@ -356,7 +364,7 @@ def test_sample_sites_present(self):
         for site in ("theta_ln_K_h_l_wt", "theta_ln_K_h_o_wt", "theta_ln_K_l_o_wt",
                      "theta_ln_K_h_e_wt", "theta_ln_K_l_e_wt",
                      "theta_ln_K_u_wt",   # new in mwc_dimer_unfolded
-                     "theta_ddG_offset"):
+                     "theta_dlnK"):
             assert site in tr, f"Missing sample site: {site}"
 
     def test_deterministic_sites_present(self):
@@ -368,11 +376,11 @@ def test_deterministic_sites_present(self):
                      "theta_ln_K_u"):   # new deterministic site
             assert site in tr, f"Missing deterministic site: {site}"
 
-    def test_theta_ln_K_u_det_site_is_homogeneous(self):
-        """The theta_ln_K_u deterministic site must be constant across all genotypes."""
-        _, tr = _run_model(_make_mock())
-        ln_K_u_vals = np.asarray(tr["theta_ln_K_u"]["value"])
-        np.testing.assert_allclose(ln_K_u_vals, ln_K_u_vals[0], atol=1e-6)
+    def test_theta_ln_K_u_det_site_matches_param(self):
+        """The theta_ln_K_u deterministic site is the assembled (G,) ln_K_u."""
+        out, tr = _run_model(_make_mock())
+        np.testing.assert_allclose(np.asarray(tr["theta_ln_K_u"]["value"]),
+                                   np.asarray(out.ln_K_u), atol=1e-6)
 
     def test_no_epi_sites(self):
         """lnK_ddG_prior has no epistasis — ever."""
@@ -393,7 +401,7 @@ def test_permuted_struct_names_accepted(self):
         assert isinstance(out, ThetaParam)
 
     def test_ddG_prior_means_shift_delta_lnK(self):
-        """With sigma_s=0, ddG equals the prior means exactly; nonzero prior produces
+        """With σ_s conditioned to 0, ddG equals the prior means exactly; nonzero prior produces
         nonzero delta_lnK and makes genotypes with that mutation differ from WT."""
         zero_prior = np.zeros((_M, _S), dtype=np.float32)
         data_zero  = _make_mock(ddG_prior=zero_prior)
@@ -404,7 +412,7 @@ def _zero_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=_zero_param):
             with seed(rng_seed=0):
-                out_zero = define_model("theta", data_zero, priors)
+                out_zero = _sigma_zero(define_model)("theta", data_zero, priors)
 
         # All mutations zero → genotype 0 (WT) and genotype 1 (M1) identical K_h_l
         np.testing.assert_allclose(
@@ -419,7 +427,7 @@ def _zero_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=_zero_param):
             with seed(rng_seed=0):
-                out_nonzero = define_model("theta", data_nonzero, priors)
+                out_nonzero = _sigma_zero(define_model)("theta", data_nonzero, priors)
 
         assert not np.allclose(
             np.asarray(out_nonzero.ln_K_h_l[0]),  # WT
@@ -448,10 +456,9 @@ def test_permuted_features_give_same_result_as_canonical(self):
             np.asarray(out_s.ln_K_h_l), atol=1e-5,
         )
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_model(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
 
 # ---------------------------------------------------------------------------
@@ -474,10 +481,11 @@ def test_ln_K_u_shape(self):
         out, _ = _run_guide(_make_mock())
         assert out.ln_K_u.shape == (_G,)
 
-    def test_ln_K_u_is_homogeneous(self):
+    def test_ln_K_u_varies_in_guide(self):
+        """The guide assembles ln_K_u from ΔΔG_H too, so genotypes differ."""
         out, _ = _run_guide(_make_mock())
         vals = np.asarray(out.ln_K_u)
-        np.testing.assert_allclose(vals, vals[0], atol=1e-6)
+        assert not np.allclose(vals, vals[0], atol=1e-6)
 
     def test_sample_sites_match_model(self):
         data = _make_mock()
@@ -492,8 +500,8 @@ def test_variational_params_registered(self):
         param_names = {k for k, v in tr.items() if v["type"] == "param"}
         for p in ("theta_ln_K_h_l_wt_loc", "theta_ln_K_h_l_wt_scale",
                   "theta_ln_K_h_o_wt_loc", "theta_ln_K_l_o_wt_loc",
-                  "theta_ddG_offset_locs", "theta_ddG_offset_scales",
-                  "theta_ddG_sigma_s",
+                  "theta_dlnK_locs", "theta_dlnK_scales",
+                  "theta_ddG_sigma_locs", "theta_ddG_sigma_scales",
                   # unfolding constant variational params — new in mwc_dimer_unfolded
                   "theta_ln_K_u_wt_loc",  "theta_ln_K_u_wt_scale"):
             assert p in param_names, f"Missing variational param: {p}"
@@ -511,20 +519,19 @@ def test_no_epi_params_registered(self):
         assert not any("epi_lambda" in k for k in param_names)
         assert not any("epi_offset" in k for k in param_names)
 
-    def test_ddG_sigma_s_shape(self):
+    def test_ddG_sigma_shape(self):
         _, tr = _run_guide(_make_mock())
-        sigma_val = tr["theta_ddG_sigma_s"]["value"]
+        sigma_val = tr["theta_ddG_sigma"]["value"]
         assert sigma_val.shape == (_S,)
 
-    def test_ddG_offset_locs_shape(self):
+    def test_dlnK_locs_shape(self):
         _, tr = _run_guide(_make_mock())
-        locs_val = tr["theta_ddG_offset_locs"]["value"]
-        assert locs_val.shape == (_S, _M)
+        locs_val = tr["theta_dlnK_locs"]["value"]
+        assert locs_val.shape == (_M, 6)
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_guide(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
     def test_permuted_features_give_same_output_as_canonical(self):
         canonical_prior = _make_ddG_prior()
@@ -593,6 +600,11 @@ def _make_ctx(genotypes=None, titrant_names=None, mut_labels=None, pair_labels=N
         pair_labels=pair_labels,
     )
 
+def _base_specs(specs):
+    """Specs other than the per-structure ddG ones (tested in
+    test_ddG_extraction.py)."""
+    return [s for s in specs if "out_names" not in s]
+
 
 class TestGetExtractSpecs:
 
@@ -601,11 +613,11 @@ def test_returns_list(self):
 
     def test_three_specs(self):
         # scalar K (incl. ln_K_u), T-dim K, per-mutation d_ln_K
-        assert len(get_extract_specs(_make_ctx())) == 3
+        assert len(_base_specs(get_extract_specs(_make_ctx()))) == 3
 
     def test_four_specs_when_pair_labels_present(self):
         ctx = _make_ctx(pair_labels=["M1+M2"])
-        assert len(get_extract_specs(ctx)) == 4
+        assert len(_base_specs(get_extract_specs(ctx))) == 4
 
     def test_first_spec_has_scalar_K_values_including_ln_K_u(self):
         """ln_K_u is broadcast from WT and should appear in the scalar K spec."""
@@ -656,7 +668,6 @@ def test_third_spec_has_one_row_per_mutation(self):
 _PAIR_LABELS_P = ["K84L/M42I"]
 _TITRANT_NAMES_P = ["IPTG"]
 _TF_TOTAL  = 6.5e-7
-_OP_TOTAL  = 2.5e-8
 _CONC_SCALE = 1e-3
 
 _WT_LN_K_H_L =  1.84
@@ -682,7 +693,7 @@ def _make_ddG_posteriors(T, M, P=0, S=5, seed=0):
     KEY DIFFERENCES from lnK_mut:
     - d_ln_K_h_e and d_ln_K_l_e are (S, M) with no T dimension.
     - epi_h_e and epi_l_e are (S, P) with no T dimension.
-    - ln_K_u_wt is a scalar (S,) — no per-mutation delta.
+    - ln_K_u carries a per-mutation d_ln_K_u (S, M), = ΔΔG_H in the model.
     """
     rng = np.random.default_rng(seed)
     post = {}
@@ -697,8 +708,10 @@ def _make_ddG_posteriors(T, M, P=0, S=5, seed=0):
     post["theta_d_ln_K_l_o"]  = rng.standard_normal((S, M)).astype(np.float32) * 0.3
     post["theta_d_ln_K_h_e"]  = rng.standard_normal((S, M)).astype(np.float32) * 0.3  # no T
     post["theta_d_ln_K_l_e"]  = rng.standard_normal((S, M)).astype(np.float32) * 0.3  # no T
+    post["theta_d_ln_K_u"]    = rng.standard_normal((S, M)).astype(np.float32) * 0.3
     if P > 0:
-        for key in ["theta_epi_ln_K_h_l", "theta_epi_ln_K_h_o", "theta_epi_ln_K_l_o"]:
+        for key in ["theta_epi_ln_K_h_l", "theta_epi_ln_K_h_o", "theta_epi_ln_K_l_o",
+                    "theta_epi_ln_K_u"]:
             post[key] = np.zeros((S, P), dtype=np.float32)
         for key in ["theta_epi_ln_K_h_e", "theta_epi_ln_K_l_e"]:
             post[key] = np.zeros((S, P), dtype=np.float32)   # no T!
@@ -715,7 +728,6 @@ def _common_kwargs_p(post, pair_labels=None):
         param_posteriors=post,
         q_to_get={"median": 0.5, "lower": 0.025, "upper": 0.975},
         tf_total=_TF_TOTAL,
-        op_total=_OP_TOTAL,
         conc_unit_scale=_CONC_SCALE,
     )
 
@@ -759,28 +771,33 @@ def test_upper_ge_median_ge_lower(self):
         assert (result["upper"] >= result["median"]).all()
         assert (result["median"] >= result["lower"]).all()
 
-    def test_ln_K_u_is_homogeneous(self):
-        """All target genotypes receive the same ln_K_u (WT only, no per-mutation delta)."""
+    def test_d_ln_K_u_alone_changes_prediction(self):
+        """With every folded-state delta zero, d_ln_K_u alone moves theta."""
         S = 10
         post = _make_ddG_posteriors(T=1, M=2, S=S)
-        # Zero out all mutation deltas so only WT K values differ
-        post["theta_d_ln_K_h_l"][:] = 0
-        post["theta_d_ln_K_h_o"][:] = 0
-        post["theta_d_ln_K_l_o"][:] = 0
-        post["theta_d_ln_K_h_e"][:] = 0
-        post["theta_d_ln_K_l_e"][:] = 0
+        for k in ["theta_d_ln_K_h_l", "theta_d_ln_K_h_o", "theta_d_ln_K_l_o",
+                  "theta_d_ln_K_h_e", "theta_d_ln_K_l_e"]:
+            post[k][:] = 0
+        post["theta_d_ln_K_u"][:] = 0
 
         kw = _common_kwargs_p(post)
-        kw["target_genotypes"] = ["wt", "M42I", "K84L", "M42I/K84L"]
         kw["q_to_get"] = {"median": 0.5}
-        result = ddG_predict(**kw)
-
-        # With zero mutation deltas, all genotypes get identical predictions
+        flat = ddG_predict(**kw)
         for conc in [0.0, 0.1, 1.0, 10.0]:
-            rows = result[result["titrant_conc"] == conc]["median"].values
+            rows = flat[flat["titrant_conc"] == conc]["median"].values
             np.testing.assert_allclose(rows, rows[0], atol=1e-5,
                                        err_msg=f"Predictions differ at conc={conc}")
 
+        post["theta_ln_K_u_wt"][:] = 0.0          # K_u comparable to Z
+        post["theta_d_ln_K_u"][:, 0] = 4.0        # destabilise M42I
+        kw = _common_kwargs_p(post)
+        kw["q_to_get"] = {"median": 0.5}
+        shifted = ddG_predict(**kw).set_index(["genotype", "titrant_conc"])
+        wt = shifted.loc["wt", "median"].values
+        assert np.all(shifted.loc["M42I", "median"].values < wt)
+        np.testing.assert_allclose(shifted.loc["K84L", "median"].values, wt,
+                                   atol=1e-5)
+
     def test_epistasis_shifts_double_mutant(self):
         T, M, P, S = 1, 2, 1, 30
         post = _make_ddG_posteriors(T, M, P=P, S=S, seed=4)
@@ -817,7 +834,7 @@ def test_scalar_d_he_le_matches_lnK_mut_when_replicated(self):
             d_he_scalar[:, None, :], (S, T, M)).astype(np.float32)
         post_mut["theta_d_ln_K_l_e"] = np.broadcast_to(
             d_le_scalar[:, None, :], (S, T, M)).astype(np.float32)
-        post_mut["theta_d_ln_K_u"]   = np.zeros((S, M), dtype=np.float32)
+        post_mut["theta_d_ln_K_u"]   = post_ddG["theta_d_ln_K_u"].copy()
 
         kw = dict(
             target_genotypes=["wt", "M42I", "K84L"],
@@ -830,7 +847,6 @@ def test_scalar_d_he_le_matches_lnK_mut_when_replicated(self):
             pair_labels=[],
             q_to_get={"median": 0.5},
             tf_total=_TF_TOTAL,
-            op_total=_OP_TOTAL,
             conc_unit_scale=_CONC_SCALE,
         )
         res_ddG = ddG_predict(param_posteriors=post_ddG, **kw)
@@ -839,3 +855,33 @@ def test_scalar_d_he_le_matches_lnK_mut_when_replicated(self):
         np.testing.assert_allclose(
             res_ddG["median"].values, res_mut["median"].values, atol=1e-5
         )
+
+
+# ---------------------------------------------------------------------------
+# guide with pair epistasis
+# ---------------------------------------------------------------------------
+
+def test_guide_epistasis_uses_regularized_horseshoe_scale():
+    # one pair (M1, M2) on the double mutant (genotype 3)
+    data = _make_mock()._replace(
+        num_pair=1,
+        pair_nnz_pair_idx=np.array([0], dtype=np.int32),
+        pair_nnz_geno_idx=np.array([3], dtype=np.int32))
+    out, tr = _run_guide(data)
+    for site in ("theta_epi_tau", "theta_epi_c2", "theta_epi_lambda",
+                 "theta_epi_offset"):
+        assert tr[site]["type"] == "sample"
+
+    tau = np.asarray(tr["theta_epi_tau"]["value"], dtype=float)
+    c2 = np.asarray(tr["theta_epi_c2"]["value"], dtype=float)
+    lam = np.asarray(tr["theta_epi_lambda"]["value"], dtype=float)
+    off = np.asarray(tr["theta_epi_offset"]["value"], dtype=float)
+    lam_tilde = np.sqrt(c2 * lam ** 2 / (c2 + tau ** 2 * lam ** 2))
+    epi_ddG = (off * tau * lam_tilde).T                      # (P, S)
+    expected = float(np.asarray(_project_ddG(jnp.asarray(epi_ddG)))[0, 0])
+
+    K = np.asarray(out.ln_K_h_l, dtype=float)
+    # additive part cancels in the double-mutant cycle, leaving the pair term
+    cycle = K[3] - K[1] - K[2] + K[0]
+    assert np.isfinite(cycle)
+    assert cycle == pytest.approx(expected, rel=1e-4, abs=1e-5)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py
index e443d598..4552a02d 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_PnnC.py
@@ -7,8 +7,9 @@
 Key differences from mwc_dimer variant:
 - ThetaParam gains ln_K_u (G,) and conc_unit_scale fields
 - ModelPriors gains theta_ln_K_u_wt_loc and theta_ln_K_u_wt_scale
-- define_model samples theta_ln_K_u_wt and broadcasts it homogeneously
-- ln_K_u[g] == ln_K_u_wt for all g (no per-mutation unfolding effect)
+- define_model samples theta_ln_K_u_wt, and each mutation shifts ln_K_u by
+  its ΔΔG_H (K_u = [U]/[H] with ΔΔG_U = 0), exposed as d_ln_K_u
+  (test_unfolding_projection.py covers the projection in depth)
 - guide samples theta_ln_K_u_wt with variational params theta_ln_K_u_wt_loc/scale
 """
 
@@ -18,7 +19,7 @@
 import jax.numpy as jnp
 from dataclasses import dataclass
 from collections import namedtuple
-from numpyro.handlers import trace, seed
+from numpyro.handlers import condition, trace, seed
 
 from tfscreen.genetics.build_mut_geno_matrix import build_mut_sparse_indices
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C12_K5_U1_a.PnnC import (
@@ -42,6 +43,11 @@
 # ---------------------------------------------------------------------------
 
 _S = 6    # number of MWC structures (H, HO, L, LO, HE2, LE2)
+
+
+def _sigma_zero(fn):
+    """``fn`` with every structure's σ_s conditioned to 0: ΔΔG = prior means."""
+    return condition(fn, data={"theta_ddG_sigma": jnp.zeros(_S)})
 _M = 3    # mutations
 _G = 4    # genotypes: wt, M1, M2, M1+M2
 
@@ -250,7 +256,7 @@ def test_hyperparameters_has_required_keys(self):
             "theta_ln_K_l_e_wt_loc", "theta_ln_K_l_e_wt_scale",
             # unfolding constant keys — new in mwc_dimer_unfolded
             "theta_ln_K_u_wt_loc", "theta_ln_K_u_wt_scale",
-            "theta_tf_total_M", "theta_op_total_M", "theta_conc_unit_scale",
+            "theta_tf_total_M", "theta_conc_unit_scale",
             "theta_nn_hidden_size",
             "theta_epi_tau_scale", "theta_epi_slab_scale",
             "theta_epi_slab_df",   "theta_epi_d0",
@@ -273,8 +279,7 @@ def test_model_priors_has_ln_K_u_fields(self):
 
     def test_physical_constants(self):
         p = get_priors()
-        assert p.theta_tf_total_M == pytest.approx(6.5e-7)
-        assert p.theta_op_total_M == pytest.approx(2.5e-8)
+        assert p.theta_tf_total_M == pytest.approx(1e-5)
 
     def test_get_guesses_no_epi_keys(self):
         data = _make_mock()
@@ -285,7 +290,7 @@ def test_get_guesses_no_epi_keys(self):
         assert "theta_ln_K_h_e_wt" in g
         assert "theta_ln_K_l_e_wt" in g
         assert "theta_ln_K_u_wt"   in g
-        assert "theta_ddG_offset"   in g
+        assert "theta_dlnK"   in g
         assert "theta_epi_tau" not in g
 
     def test_get_guesses_ln_K_u_wt_value(self):
@@ -300,8 +305,8 @@ def test_get_guesses_with_epi_keys(self):
         assert "theta_epi_lambda" in g
         assert "theta_epi_offset" in g
 
-    def test_get_guesses_ddG_offset_shape(self):
-        assert get_guesses("theta", _make_mock())["theta_ddG_offset"].shape == (_S, _M)
+    def test_get_guesses_ddG_shape(self):
+        assert get_guesses("theta", _make_mock())["theta_dlnK"].shape == (_M, 6)
 
     def test_get_guesses_epi_shapes_with_epi(self):
         data = _make_mock(num_pair=1)
@@ -369,12 +374,15 @@ def test_ln_K_u_shape(self):
         out, _ = _run_model(_make_mock())
         assert out.ln_K_u.shape == (_G,)
 
-    def test_ln_K_u_is_homogeneous(self):
-        """ln_K_u[g] must be identical for all genotypes (no per-mutation unfolding)."""
-        out, _ = _run_model(_make_mock())
+    def test_ln_K_u_shifts_by_ddG_H(self):
+        """ln_K_u[g] − ln_K_u[wt] is the summed ΔΔG_H (K_u = [U]/[H], ΔΔG_U = 0)."""
+        out, tr = _run_model(_make_mock())
+        ddG_H = np.asarray(tr["theta_ddG"]["value"])[:, 0]
         vals = np.asarray(out.ln_K_u)
-        np.testing.assert_allclose(vals, vals[0], atol=1e-6,
-                                   err_msg="ln_K_u is not homogeneous across genotypes")
+        np.testing.assert_allclose(vals - vals[0], _MUT_GENO.T @ ddG_H,
+                                   atol=1e-5)
+        np.testing.assert_allclose(tr["theta_d_ln_K_u"]["value"], ddG_H,
+                                   atol=1e-6)
 
     def test_conc_unit_scale_preserved(self):
         out, _ = _run_model(_make_mock())
@@ -391,7 +399,7 @@ def test_sample_sites_present(self):
         for site in ("theta_ln_K_h_l_wt", "theta_ln_K_h_o_wt", "theta_ln_K_l_o_wt",
                      "theta_ln_K_h_e_wt", "theta_ln_K_l_e_wt",
                      "theta_ln_K_u_wt",   # new in mwc_dimer_unfolded
-                     "theta_ddG_offset"):
+                     "theta_dlnK"):
             assert site in tr, f"Missing sample site: {site}"
 
     def test_deterministic_sites_present(self):
@@ -403,11 +411,11 @@ def test_deterministic_sites_present(self):
                      "theta_ln_K_u"):   # new deterministic site
             assert site in tr, f"Missing deterministic site: {site}"
 
-    def test_theta_ln_K_u_det_site_is_homogeneous(self):
-        """The theta_ln_K_u deterministic site must be a constant-valued (G,) array."""
-        _, tr = _run_model(_make_mock())
-        ln_K_u_vals = np.asarray(tr["theta_ln_K_u"]["value"])
-        np.testing.assert_allclose(ln_K_u_vals, ln_K_u_vals[0], atol=1e-6)
+    def test_theta_ln_K_u_det_site_matches_param(self):
+        """The theta_ln_K_u deterministic site is the assembled (G,) ln_K_u."""
+        out, tr = _run_model(_make_mock())
+        np.testing.assert_allclose(np.asarray(tr["theta_ln_K_u"]["value"]),
+                                   np.asarray(out.ln_K_u), atol=1e-6)
 
     def test_wrong_struct_names_raises(self):
         data = _make_mock(struct_names=('H', 'HO', 'L', 'LO', 'HE2', 'WRONG'))
@@ -460,10 +468,9 @@ def test_epi_absent_when_contact_distances_none(self):
         _, tr = _run_model(data)
         assert "theta_epi_tau" not in tr
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_model(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
 
 # ---------------------------------------------------------------------------
@@ -486,10 +493,11 @@ def test_ln_K_u_shape(self):
         out, _ = _run_guide(_make_mock())
         assert out.ln_K_u.shape == (_G,)
 
-    def test_ln_K_u_is_homogeneous(self):
+    def test_ln_K_u_varies_in_guide(self):
+        """The guide assembles ln_K_u from ΔΔG_H too, so genotypes differ."""
         out, _ = _run_guide(_make_mock())
         vals = np.asarray(out.ln_K_u)
-        np.testing.assert_allclose(vals, vals[0], atol=1e-6)
+        assert not np.allclose(vals, vals[0], atol=1e-6)
 
     def test_sample_sites_match_model_no_epi(self):
         data = _make_mock()
@@ -512,8 +520,8 @@ def test_variational_params_registered(self):
         param_names = {k for k, v in tr.items() if v["type"] == "param"}
         for p in ("theta_ln_K_h_l_wt_loc", "theta_ln_K_h_l_wt_scale",
                   "theta_ln_K_h_o_wt_loc", "theta_ln_K_l_o_wt_loc",
-                  "theta_ddG_offset_locs",  "theta_ddG_offset_scales",
-                  "theta_ddG_sigma_s",
+                  "theta_dlnK_locs", "theta_dlnK_scales",
+                  "theta_ddG_sigma_locs", "theta_ddG_sigma_scales",
                   # unfolding constant variational params — new in mwc_dimer_unfolded
                   "theta_ln_K_u_wt_loc",   "theta_ln_K_u_wt_scale"):
             assert p in param_names, f"Missing variational param: {p}"
@@ -546,10 +554,9 @@ def test_epi_params_registered_when_num_pair_nonzero(self):
         assert "theta_epi_offset_locs"   in param_names
         assert "theta_epi_offset_scales" in param_names
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_guide(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
 
 # ---------------------------------------------------------------------------
@@ -598,6 +605,11 @@ def _make_ctx(genotypes=None, titrant_names=None, mut_labels=None, pair_labels=N
         pair_labels=pair_labels,
     )
 
+def _base_specs(specs):
+    """Specs other than the per-structure ddG ones (tested in
+    test_ddG_extraction.py)."""
+    return [s for s in specs if "out_names" not in s]
+
 
 class TestGetExtractSpecs:
 
@@ -606,11 +618,11 @@ def test_returns_list(self):
 
     def test_three_specs_without_epi(self):
         # scalar K (incl. ln_K_u), T-dim K, per-mutation d_ln_K
-        assert len(get_extract_specs(_make_ctx())) == 3
+        assert len(_base_specs(get_extract_specs(_make_ctx()))) == 3
 
     def test_four_specs_with_epi(self):
         ctx = _make_ctx(pair_labels=["M1M2"])
-        assert len(get_extract_specs(ctx)) == 4
+        assert len(_base_specs(get_extract_specs(ctx))) == 4
 
     def test_first_spec_has_scalar_K_values_including_ln_K_u(self):
         """ln_K_u is broadcast from WT and should appear in the scalar K spec."""
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py
index f792a0d3..4590cd7f 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C12_K5_U1_a/test_thermo.py
@@ -95,7 +95,6 @@ def _wt_theta_param(T=2, G=4, C=4):
         ln_K_l_e=jnp.full((T, G), 13.5),
         ln_K_u=jnp.full(G, -12.0),
         tf_total=6.5e-7,
-        op_total=2.5e-8,
         mu=jnp.zeros((T, C, 1)),
         sigma=jnp.ones((T, C, 1)),
         conc_unit_scale=1.0,   # test concentrations are already in M
@@ -114,7 +113,7 @@ def test_output_shape(self):
         theta = _compute_theta(
             jnp.full(G, -7.4), jnp.full(G, 23.0), jnp.full((T, G), 11.5),
             jnp.full(G, 13.8), jnp.full((T, G), 15.0), jnp.full(G, -12.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert theta.shape == (T, len(_CONC), G)
 
@@ -124,7 +123,7 @@ def test_values_in_0_1(self):
         theta = _compute_theta(
             jnp.full(G, -7.4), jnp.full(G, 23.0), jnp.full((T, G), 11.5),
             jnp.full(G, 13.8), jnp.full((T, G), 15.0), jnp.full(G, -12.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert jnp.all(theta >= 0)
         assert jnp.all(theta <= 1)
@@ -137,7 +136,7 @@ def test_all_finite(self):
             jnp.full((T, G), 11.5),
             jnp.linspace(10.0, 16.0, G), jnp.full((T, G), 15.0),
             jnp.full(G, -12.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert jnp.all(jnp.isfinite(theta))
 
@@ -151,7 +150,7 @@ def test_monotone_decreasing_in_effector(self):
         theta = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5), jnp.full(G, -12.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         for c in range(len(conc) - 1):
             assert jnp.all(theta[:, c, :] >= theta[:, c + 1, :])
@@ -171,7 +170,7 @@ def test_apo_matches_analytic_formula(self):
             jnp.array(ln_K_l_o_vals), jnp.full((1, G), -100.0),
             jnp.full(G, ln_K_u_val),
             jnp.array([0.0, 1e-8]),     # [0] is apo; [1] ≈ apo (tiny conc)
-            tf_total, 0.0,
+            tf_total,
         )                               # (1, 2, G)
 
         for g in range(G):
@@ -190,12 +189,12 @@ def test_large_K_u_collapses_theta_to_zero(self):
         theta_wt = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5), jnp.full(G, -12.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         theta_unfolded = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5), jnp.full(G, 20.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         # WT theta should be much larger than fully-unfolded theta
         assert jnp.all(theta_wt > theta_unfolded)
@@ -218,7 +217,7 @@ def test_small_K_u_matches_mwc_dimer_behavior(self):
             jnp.full(G, ln_K_l_o_val), jnp.full((1, G), -100.0),
             jnp.full(G, -50.0),         # K_u essentially 0
             jnp.array([0.0]),
-            tf_total, 0.0,
+            tf_total,
         )
         # Expected from mwc_dimer apo formula (K_u ≈ 0)
         K_h_l = np.exp(ln_K_h_l_val)
@@ -241,7 +240,7 @@ def test_K_u_reduces_theta_at_all_concentrations(self):
                 jnp.full(G, 1.84), jnp.full(G, 19.9), jnp.full((1, G), 10.9),
                 jnp.full(G, -2.3), jnp.full((1, G), 13.5),
                 jnp.full(G, ln_K_u_val),
-                conc, 6.5e-7, 2.5e-8,
+                conc, 6.5e-7,
             )
             if prev_theta is not None:
                 assert jnp.all(theta <= prev_theta), (
@@ -258,7 +257,7 @@ def test_higher_K_h_o_gives_higher_theta_at_zero_effector(self):
             jnp.array([17.0, 21.0]),   # G=0 weaker, G=1 stronger
             jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5), jnp.full(G, -12.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert theta[0, 0, 1] > theta[0, 0, 0]
 
@@ -270,7 +269,7 @@ def test_higher_K_h_l_gives_lower_theta_at_zero_effector(self):
             jnp.array([-2.0, 2.0]),  # G=0 H-favored, G=1 L-favored
             jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5), jnp.full(G, -12.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert theta[0, 0, 0] > theta[0, 0, 1]
 
@@ -284,7 +283,7 @@ def test_wt_params_give_high_apo_theta(self):
         theta = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5), jnp.full(G, -12.0),
-            jnp.array([0.0]), 6.5e-7, 2.5e-8,
+            jnp.array([0.0]), 6.5e-7,
         )
         assert float(theta[0, 0, 0]) >= 0.85
 
@@ -296,7 +295,7 @@ def test_e_free_bounded_by_e_total(self):
         theta = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), jnp.full((1, G), 10.9),
             jnp.full(G, -2.3), jnp.full((1, G), 13.5), jnp.full(G, -12.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         assert jnp.all(theta >= 0)
         assert jnp.all(theta <= 1)
@@ -315,7 +314,7 @@ def test_titrant_dim_broadcasts_independently(self):
         theta = _compute_theta(
             jnp.full(G, 1.84), jnp.full(G, 19.9), ln_K_h_e,
             jnp.full(G, -2.3), ln_K_l_e, jnp.full(G, -12.0),
-            conc, 6.5e-7, 2.5e-8,
+            conc, 6.5e-7,
         )
         drop_t0 = theta[0, 0, 0] - theta[0, 1, 0]
         drop_t1 = theta[1, 0, 0] - theta[1, 1, 0]
@@ -405,7 +404,7 @@ def test_geno_theta_idx_selects_genotypes(self):
             ln_K_l_o=jnp.full(G, 13.8),
             ln_K_l_e=jnp.full((T, G), 15.0),
             ln_K_u=jnp.full(G, -12.0),
-            tf_total=6.5e-7, op_total=2.5e-8,
+            tf_total=6.5e-7,
             mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
             conc_unit_scale=1.0,
         )
@@ -419,7 +418,7 @@ def test_geno_theta_idx_selects_genotypes(self):
         theta_all = _compute_theta(
             tp.ln_K_h_l, tp.ln_K_h_o, tp.ln_K_h_e,
             tp.ln_K_l_o, tp.ln_K_l_e, tp.ln_K_u,
-            data.titrant_conc, tp.tf_total, tp.op_total,
+            data.titrant_conc, tp.tf_total,
         )                                          # (T, C, G)
         assert jnp.allclose(result[:, :, 0], theta_all[:, :, 3], atol=1e-6)
         assert jnp.allclose(result[:, :, 1], theta_all[:, :, 0], atol=1e-6)
@@ -443,7 +442,7 @@ def test_nontrivial_batch_idx_selects_correct_genotypes(self):
             ln_K_l_o=jnp.full(G, 13.8),
             ln_K_l_e=jnp.full((T, G), 15.0),
             ln_K_u=jnp.full(G, -12.0),
-            tf_total=6.5e-7, op_total=2.5e-8,
+            tf_total=6.5e-7,
             mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
             conc_unit_scale=1.0,
         )
@@ -457,7 +456,7 @@ def test_nontrivial_batch_idx_selects_correct_genotypes(self):
         theta_all = _compute_theta(
             tp.ln_K_h_l, tp.ln_K_h_o, tp.ln_K_h_e,
             tp.ln_K_l_o, tp.ln_K_l_e, tp.ln_K_u,
-            data.titrant_conc, tp.tf_total, tp.op_total,
+            data.titrant_conc, tp.tf_total,
         )   # (T, C, G)
 
         for local_i, global_g in enumerate(batch_idx):
@@ -477,7 +476,7 @@ def test_apo_matches_analytic_formula(self):
             ln_K_l_o=jnp.full(G, ln_K_l_o_val),
             ln_K_l_e=jnp.full((1, G), -100.0),
             ln_K_u=jnp.full(G, ln_K_u_val),
-            tf_total=tf_total, op_total=0.0,
+            tf_total=tf_total,
             mu=jnp.zeros((1, 2, 1)), sigma=jnp.ones((1, 2, 1)),
             conc_unit_scale=1.0,
         )
@@ -502,7 +501,7 @@ def test_large_K_u_collapses_theta_via_run_model(self):
             ln_K_l_o=jnp.full(G, -2.3),
             ln_K_l_e=jnp.full((T, G), 13.5),
             ln_K_u=jnp.full(G, 20.0),   # K_u >> 1 → TF all unfolded
-            tf_total=6.5e-7, op_total=2.5e-8,
+            tf_total=6.5e-7,
             mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
             conc_unit_scale=1.0,
         )
@@ -540,7 +539,7 @@ def test_conc_unit_scale_applied(self):
             ln_K_l_o=jnp.full(G, -2.3),
             ln_K_l_e=jnp.full((T, G), 13.5),
             ln_K_u=jnp.full(G, -12.0),
-            tf_total=6.5e-7, op_total=2.5e-8,
+            tf_total=6.5e-7,
             mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
             conc_unit_scale=1e-3,
         )
@@ -552,7 +551,7 @@ def test_conc_unit_scale_applied(self):
             ln_K_l_o=jnp.full(G, -2.3),
             ln_K_l_e=jnp.full((T, G), 13.5),
             ln_K_u=jnp.full(G, -12.0),
-            tf_total=6.5e-7, op_total=2.5e-8,
+            tf_total=6.5e-7,
             mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
             conc_unit_scale=1.0,
         )
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py
index 1caa3d62..992ba50b 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PK.py
@@ -204,7 +204,7 @@ def test_get_hyperparameters_keys():
     hp = get_hyperparameters()
     assert isinstance(hp, dict)
     for key in ["theta_ln_K_op_wt_loc", "theta_ln_K_HL_wt_loc", "theta_ln_K_E_wt_loc",
-                "theta_tf_total_M", "theta_op_total_M",
+                "theta_tf_total_M",
                 "theta_sigma_d_ln_K_op_scale",
                 "theta_epi_tau_scale", "theta_epi_slab_scale", "theta_epi_slab_df"]:
         assert key in hp
@@ -213,8 +213,7 @@ def test_get_hyperparameters_keys():
 def test_get_priors_values():
     priors = get_priors()
     assert isinstance(priors, ModelPriors)
-    assert priors.theta_tf_total_M == pytest.approx(6.5e-7)
-    assert priors.theta_op_total_M == pytest.approx(2.5e-8)
+    assert priors.theta_tf_total_M == pytest.approx(1e-5)
     assert priors.theta_ln_K_op_wt_loc == get_hyperparameters()["theta_ln_K_op_wt_loc"]
 
 
@@ -416,12 +415,11 @@ def test_no_epi_no_epistasis_sample_sites(self, mock_data_no_epi):
         sample_names = {k for k, v in tr.items() if v["type"] == "sample"}
         assert not any("epi" in k for k in sample_names)
 
-    def test_tf_op_totals_preserved(self, mock_data_no_epi):
+    def test_tf_total_preserved(self, mock_data_no_epi):
         priors = get_priors()
         with seed(rng_seed=0):
             tp = guide(name="theta", data=mock_data_no_epi, priors=priors)
-        assert tp.tf_total == pytest.approx(6.5e-7)
-        assert tp.op_total == pytest.approx(2.5e-8)
+        assert tp.tf_total == pytest.approx(1e-5)
 
 
 # ---------------------------------------------------------------------------
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py
index 1b18e948..fe91c4c2 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PddG.py
@@ -12,7 +12,7 @@
 import jax.numpy as jnp
 from collections import namedtuple
 from dataclasses import dataclass
-from numpyro.handlers import trace, seed
+from numpyro.handlers import condition, trace, seed
 import unittest.mock as mock
 
 from tfscreen.genetics.build_mut_geno_matrix import build_mut_sparse_indices
@@ -37,6 +37,11 @@
 # ---------------------------------------------------------------------------
 
 _S = 4    # number of structures: H, HD, L, LE2
+
+
+def _sigma_zero(fn):
+    """``fn`` with every structure's σ_s conditioned to 0: ΔΔG = prior means."""
+    return condition(fn, data={"theta_ddG_sigma": jnp.zeros(_S)})
 _M = 2    # number of mutations
 _G = 4    # number of genotypes: wt, M0, M1, M0+M1
 
@@ -202,11 +207,11 @@ def test_K_HL_is_H_minus_L(self):
         out = _project_ddG(ddG)
         assert float(out[0, 1]) == pytest.approx(4.0 - 2.0)
 
-    def test_K_E_is_L_minus_LE2(self):
-        # H=0, HD=0, L=5, LE2=2  → K_E = 5-2 = 3
+    def test_K_E_is_half_L_minus_LE2(self):
+        # H=0, HD=0, L=5, LE2=2  → per-site K_E = (5-2)/2
         ddG = jnp.array([[0.0, 0.0, 5.0, 2.0]])
         out = _project_ddG(ddG)
-        assert float(out[0, 2]) == pytest.approx(5.0 - 2.0)
+        assert float(out[0, 2]) == pytest.approx((5.0 - 2.0) / 2)
 
     def test_batch_dim(self):
         # Leading batch dim beyond (M,)
@@ -227,7 +232,7 @@ def test_hyperparameters_has_required_keys(self):
             "theta_ln_K_op_wt_loc", "theta_ln_K_op_wt_scale",
             "theta_ln_K_HL_wt_loc", "theta_ln_K_HL_wt_scale",
             "theta_ln_K_E_wt_loc",  "theta_ln_K_E_wt_scale",
-            "theta_tf_total_M", "theta_op_total_M",
+            "theta_tf_total_M",
         ]
         for k in required:
             assert k in hp, f"Missing hyperparameter key: {k}"
@@ -235,7 +240,8 @@ def test_hyperparameters_has_required_keys(self):
     def test_hyperparameters_has_no_nn_keys(self):
         hp = get_hyperparameters()
         assert "theta_nn_hidden_size" not in hp
-        assert "theta_conc_unit_scale" not in hp
+        # Shared with the O2_C12 models: data in mM, converted to M.
+        assert hp["theta_conc_unit_scale"] == pytest.approx(1e-3)
 
     def test_get_priors_returns_model_priors_instance(self):
         assert isinstance(get_priors(), ModelPriors)
@@ -243,22 +249,21 @@ def test_get_priors_returns_model_priors_instance(self):
     def test_model_priors_has_no_nn_fields(self):
         priors = get_priors()
         assert not hasattr(priors, "theta_nn_hidden_size")
-        assert not hasattr(priors, "theta_conc_unit_scale")
+        assert priors.theta_conc_unit_scale == pytest.approx(1e-3)
 
     def test_physical_constants(self):
         priors = get_priors()
-        assert priors.theta_tf_total_M == pytest.approx(6.5e-7)
-        assert priors.theta_op_total_M == pytest.approx(2.5e-8)
+        assert priors.theta_tf_total_M == pytest.approx(1e-5)
 
     def test_get_guesses_required_keys(self):
         g = get_guesses("theta", _make_mock())
         for k in ("theta_ln_K_op_wt", "theta_ln_K_HL_wt",
-                  "theta_ln_K_E_wt", "theta_ddG_offset"):
+                  "theta_ln_K_E_wt", "theta_dlnK"):
             assert k in g, f"Missing guess key: {k}"
 
-    def test_get_guesses_ddG_offset_shape(self):
+    def test_get_guesses_ddG_shape(self):
         g = get_guesses("theta", _make_mock())
-        assert g["theta_ddG_offset"].shape == (_S, _M)
+        assert g["theta_dlnK"].shape == (_M, 3)
 
     def test_get_guesses_no_epi_keys(self):
         g = get_guesses("theta", _make_mock())
@@ -294,7 +299,7 @@ def test_K_shapes(self):
     def test_sample_sites_present(self):
         _, tr = _run_model(_make_mock())
         for site in ("theta_ln_K_op_wt", "theta_ln_K_HL_wt",
-                     "theta_ln_K_E_wt",  "theta_ddG_offset"):
+                     "theta_ln_K_E_wt",  "theta_dlnK"):
             assert site in tr, f"Missing sample site: {site}"
 
     def test_deterministic_sites_present(self):
@@ -323,7 +328,7 @@ def test_permuted_struct_names_accepted(self):
         assert isinstance(out, ThetaParam)
 
     def test_ddG_prior_shifts_delta_lnK(self):
-        """With sigma_s=0, ddG equals the prior means exactly; mutations with
+        """With σ_s conditioned to 0, ddG equals the prior means exactly; mutations with
         nonzero prior produce nonzero delta_lnK shifts relative to WT."""
         zero_prior = np.zeros((_M, _S), dtype=np.float32)
         data_zero = _make_mock(ddG_prior=zero_prior)
@@ -334,7 +339,7 @@ def _zero_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=_zero_param):
             with seed(rng_seed=0):
-                out_zero = define_model("theta", data_zero, priors)
+                out_zero = _sigma_zero(define_model)("theta", data_zero, priors)
 
         # With zero prior all genotypes should have the same K_op (only WT shift)
         np.testing.assert_allclose(
@@ -350,7 +355,7 @@ def _zero_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=_zero_param):
             with seed(rng_seed=0):
-                out_nz = define_model("theta", data_nonzero, priors)
+                out_nz = _sigma_zero(define_model)("theta", data_nonzero, priors)
 
         # Genotype 1 carries mutation 0; should differ from WT (genotype 0) in K_op
         assert not np.allclose(
@@ -359,10 +364,9 @@ def _zero_param(name, init, **kwargs):
             atol=1e-5,
         )
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_model(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
 
 # ---------------------------------------------------------------------------
@@ -395,8 +399,8 @@ def test_variational_params_registered(self):
         param_names = {k for k, v in tr.items() if v["type"] == "param"}
         for p in ("theta_ln_K_op_wt_loc", "theta_ln_K_op_wt_scale",
                   "theta_ln_K_HL_wt_loc", "theta_ln_K_HL_wt_scale",
-                  "theta_ddG_offset_locs", "theta_ddG_offset_scales",
-                  "theta_ddG_sigma_s"):
+                  "theta_dlnK_locs", "theta_dlnK_scales",
+                  "theta_ddG_sigma_locs", "theta_ddG_sigma_scales"):
             assert p in param_names, f"Missing variational param: {p}"
 
     def test_no_nn_params_registered(self):
@@ -411,20 +415,19 @@ def test_no_epi_params_registered(self):
         assert not any("epi_lambda" in k for k in param_names)
         assert not any("epi_offset" in k for k in param_names)
 
-    def test_ddG_sigma_s_shape(self):
+    def test_ddG_sigma_shape(self):
         _, tr = _run_guide(_make_mock())
-        sigma_val = tr["theta_ddG_sigma_s"]["value"]
+        sigma_val = tr["theta_ddG_sigma"]["value"]
         assert sigma_val.shape == (_S,)
 
-    def test_ddG_offset_locs_shape(self):
+    def test_dlnK_locs_shape(self):
         _, tr = _run_guide(_make_mock())
-        locs_val = tr["theta_ddG_offset_locs"]["value"]
-        assert locs_val.shape == (_S, _M)
+        locs_val = tr["theta_dlnK_locs"]["value"]
+        assert locs_val.shape == (_M, 3)
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_guide(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
 
 # ---------------------------------------------------------------------------
@@ -474,6 +477,11 @@ def _make_ctx(genotypes=None, titrant_names=None, mut_labels=None, pair_labels=N
         pair_labels=pair_labels,
     )
 
+def _base_specs(specs):
+    """Specs other than the per-structure ddG ones (tested in
+    test_ddG_extraction.py)."""
+    return [s for s in specs if "out_names" not in s]
+
 
 class TestGetExtractSpecs:
 
@@ -482,11 +490,11 @@ def test_returns_list(self):
 
     def test_three_specs(self):
         # scalar Ks, titrant-dim Ks, per-mutation delta Ks — no epi spec
-        assert len(get_extract_specs(_make_ctx())) == 3
+        assert len(_base_specs(get_extract_specs(_make_ctx()))) == 3
 
     def test_four_specs_when_pair_labels_present(self):
         ctx = _make_ctx(pair_labels=["M1+M2"])
-        assert len(get_extract_specs(ctx)) == 4
+        assert len(_base_specs(get_extract_specs(ctx))) == 4
 
     def test_first_spec_has_ln_K_op_and_ln_K_HL(self):
         params = get_extract_specs(_make_ctx())[0]["params_to_get"]
@@ -516,3 +524,33 @@ def test_third_spec_has_one_row_per_mutation(self):
         ctx = _make_ctx(mut_labels=["M1", "M2", "M3"])
         df = get_extract_specs(ctx)[2]["input_df"]
         assert len(df) == 3
+
+
+# ---------------------------------------------------------------------------
+# guide with pair epistasis
+# ---------------------------------------------------------------------------
+
+def test_guide_epistasis_uses_regularized_horseshoe_scale():
+    # one pair (M1, M2) on the double mutant (genotype 3)
+    data = _make_mock()._replace(
+        num_pair=1,
+        pair_nnz_pair_idx=np.array([0], dtype=np.int32),
+        pair_nnz_geno_idx=np.array([3], dtype=np.int32))
+    out, tr = _run_guide(data)
+    for site in ("theta_epi_tau", "theta_epi_c2", "theta_epi_lambda",
+                 "theta_epi_offset"):
+        assert tr[site]["type"] == "sample"
+
+    tau = np.asarray(tr["theta_epi_tau"]["value"], dtype=float)
+    c2 = np.asarray(tr["theta_epi_c2"]["value"], dtype=float)
+    lam = np.asarray(tr["theta_epi_lambda"]["value"], dtype=float)
+    off = np.asarray(tr["theta_epi_offset"]["value"], dtype=float)
+    lam_tilde = np.sqrt(c2 * lam ** 2 / (c2 + tau ** 2 * lam ** 2))
+    epi_ddG = (off * tau * lam_tilde).T                      # (P, S)
+    expected = float(np.asarray(_project_ddG(jnp.asarray(epi_ddG)))[0, 0])
+
+    K = np.asarray(out.ln_K_op, dtype=float)
+    # additive part cancels in the double-mutant cycle, leaving the pair term
+    cycle = K[3] - K[1] - K[2] + K[0]
+    assert np.isfinite(cycle)
+    assert cycle == pytest.approx(expected, rel=1e-4, abs=1e-5)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py
index 27a0b87b..0c053e10 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_PnnC.py
@@ -7,7 +7,7 @@
 from collections import namedtuple
 from dataclasses import dataclass
 from functools import partial
-from numpyro.handlers import trace, seed
+from numpyro.handlers import condition, trace, seed
 
 from tfscreen.genetics.build_mut_geno_matrix import build_mut_sparse_indices
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.PnnC import (
@@ -32,6 +32,11 @@
 # ──────────────────────────────────────────────────────────────────────────────
 
 _S = 4   # number of structures
+
+
+def _sigma_zero(fn):
+    """``fn`` with every structure's σ_s conditioned to 0: ΔΔG = prior means."""
+    return condition(fn, data={"theta_ddG_sigma": jnp.zeros(_S)})
 _M = 2   # number of mutations
 _G = 4   # number of genotypes: wt, mut0, mut1, double
 
@@ -167,10 +172,10 @@ def test_K_HL_equals_H_minus_L(self):
         out = _project_ddG(ddG)
         assert out[0, 1] == pytest.approx(3.0 - 1.0)   # K_HL = H - L
 
-    def test_K_E_equals_L_minus_LE2(self):
+    def test_K_E_is_half_L_minus_LE2(self):
         ddG = jnp.array([[0.0, 0.0, 5.0, 2.0]])
         out = _project_ddG(ddG)
-        assert out[0, 2] == pytest.approx(5.0 - 2.0)   # K_E = L - LE2
+        assert out[0, 2] == pytest.approx((5.0 - 2.0) / 2)   # per-site K_E = (L - LE2)/2
 
     def test_leading_batch_dim(self):
         ddG = jnp.ones((2, 7, 4))
@@ -189,7 +194,7 @@ def test_hyperparameters_keys(self):
             "theta_ln_K_op_wt_loc", "theta_ln_K_op_wt_scale",
             "theta_ln_K_HL_wt_loc", "theta_ln_K_HL_wt_scale",
             "theta_ln_K_E_wt_loc",  "theta_ln_K_E_wt_scale",
-            "theta_tf_total_M", "theta_op_total_M",
+            "theta_tf_total_M",
             "theta_nn_hidden_size",
             "theta_epi_tau_scale", "theta_epi_slab_scale", "theta_epi_slab_df",
             "theta_epi_d0",
@@ -202,8 +207,7 @@ def test_get_priors_type(self):
 
     def test_physical_constants(self):
         priors = get_priors()
-        assert priors.theta_tf_total_M == pytest.approx(6.5e-7)
-        assert priors.theta_op_total_M == pytest.approx(2.5e-8)
+        assert priors.theta_tf_total_M == pytest.approx(1e-5)
 
     def test_get_guesses_keys_no_epi(self):
         data = _make_mock()
@@ -211,7 +215,7 @@ def test_get_guesses_keys_no_epi(self):
         assert "theta_ln_K_op_wt"  in g
         assert "theta_ln_K_HL_wt"  in g
         assert "theta_ln_K_E_wt"   in g
-        assert "theta_ddG_offset"  in g
+        assert "theta_dlnK"  in g
         assert "theta_epi_tau" not in g
 
     def test_get_guesses_keys_with_epi(self):
@@ -222,10 +226,10 @@ def test_get_guesses_keys_with_epi(self):
         assert "theta_epi_lambda" in g
         assert "theta_epi_offset" in g
 
-    def test_get_guesses_ddG_offset_shape(self):
+    def test_get_guesses_ddG_shape(self):
         data = _make_mock()
         g = get_guesses("theta", data)
-        assert g["theta_ddG_offset"].shape == (_S, _M)
+        assert g["theta_dlnK"].shape == (_M, 3)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -270,7 +274,7 @@ def test_sample_sites_present(self):
         assert "theta_ln_K_op_wt"  in tr
         assert "theta_ln_K_HL_wt"  in tr
         assert "theta_ln_K_E_wt"   in tr
-        assert "theta_ddG_offset"  in tr
+        assert "theta_ddG"  in tr
 
     def test_deterministic_sites_present(self):
         data = _make_mock()
@@ -289,7 +293,7 @@ def test_wrong_struct_names_raises(self):
             _run_model(data)
 
     def test_zero_nn_weights_zero_offsets_all_genotypes_at_wt(self):
-        """With NN weights=0 and ddG_offset=0, all mutations have zero effect."""
+        """With NN weights=0 and σ_s=0, all mutations have zero effect."""
         data = _make_mock()
         priors = get_priors()
 
@@ -302,7 +306,7 @@ def mock_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=mock_param):
             with seed(rng_seed=0):
-                out = define_model("theta", data, priors)
+                out = _sigma_zero(define_model)("theta", data, priors)
 
         # All four genotypes should have the same K values when all effects are zero
         np.testing.assert_allclose(
@@ -380,14 +384,16 @@ def test_variational_params_registered(self):
         param_names = {k for k, v in tr.items() if v["type"] == "param"}
         assert "theta_ln_K_op_wt_loc"   in param_names
         assert "theta_ln_K_op_wt_scale" in param_names
-        assert "theta_ddG_offset_locs"  in param_names
-        assert "theta_ddG_offset_scales" in param_names
+        assert "theta_dlnK_locs"   in param_names
+        assert "theta_dlnK_scales" in param_names
 
-    def test_sigma_s_param_registered_in_guide(self):
+    def test_sigma_guide_params_registered(self):
         data = _make_mock()
         _, tr = _run_guide(data)
         param_names = {k for k, v in tr.items() if v["type"] == "param"}
-        assert "theta_ddG_sigma_s" in param_names
+        assert "theta_ddG_sigma_locs" in param_names
+        assert "theta_ddG_sigma_scales" in param_names
+        assert tr["theta_ddG_sigma"]["type"] == "sample"
 
     def test_nn_params_registered_in_guide(self):
         data = _make_mock()
@@ -450,6 +456,11 @@ def _make_ctx(genotypes=None, titrant_names=None, mut_labels=None, pair_labels=N
         pair_labels=pair_labels,
     )
 
+def _base_specs(specs):
+    """Specs other than the per-structure ddG ones (tested in
+    test_ddG_extraction.py)."""
+    return [s for s in specs if "out_names" not in s]
+
 
 class TestGetExtractSpecs:
     def test_returns_list(self):
@@ -460,7 +471,7 @@ def test_returns_list(self):
     def test_has_three_specs_without_epi(self):
         ctx = _make_ctx()
         specs = get_extract_specs(ctx)
-        assert len(specs) == 3
+        assert len(_base_specs(specs)) == 3
 
     def test_has_four_specs_with_epi(self):
         ctx = _make_ctx(
@@ -469,7 +480,7 @@ def test_has_four_specs_with_epi(self):
             pair_labels=["K84L/M42I"],
         )
         specs = get_extract_specs(ctx)
-        assert len(specs) == 4
+        assert len(_base_specs(specs)) == 4
 
     def test_first_spec_has_ln_K_op_HL(self):
         ctx = _make_ctx()
@@ -522,3 +533,100 @@ def test_in_run_prefix_is_theta(self):
         specs = get_extract_specs(ctx)
         for spec in specs:
             assert spec["in_run_prefix"] == "theta_"
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# The NN prior learns under MAP (regression: all-zero init froze W1/b1/W2)
+# ──────────────────────────────────────────────────────────────────────────────
+
+class TestNnLearnsUnderMap:
+    """
+    A short MAP on a synthetic problem where each structure's ΔΔG is a known
+    linear function of its features.  The observations are the projected
+    single-mutant Δln_K values, so the test runs the whole PnnC path
+    (NN → ΔΔG prior → projection → per-genotype K).
+    """
+
+    M = 60
+
+    def _problem(self):
+        import numpyro
+        import numpyro.distributions as dist
+
+        M, S, F = self.M, _S, 60
+        rng = np.random.default_rng(0)
+        features = rng.standard_normal((M, S, F)).astype(np.float32)
+        coef = rng.standard_normal((S, F)).astype(np.float32) / np.sqrt(F)
+        true_ddG = np.einsum("msf,sf->ms", features, coef)
+        true_dlnK = np.asarray(_project_ddG(jnp.asarray(true_ddG)))   # (M, 3)
+
+        G = M + 1
+        mut_geno = np.zeros((M, G), dtype=np.float32)
+        mut_geno[np.arange(M), np.arange(1, G)] = 1.0      # genotype 0 is wt
+        mut_idx, geno_idx = build_mut_sparse_indices(mut_geno)
+        conc = jnp.array(_CONC)
+        data = MockData(
+            num_titrant_name=2, num_titrant_conc=len(_CONC), num_genotype=G,
+            titrant_conc=conc,
+            log_titrant_conc=jnp.log(jnp.where(conc == 0, 1e-20, conc)),
+            geno_theta_idx=jnp.arange(G, dtype=jnp.int32), scatter_theta=1,
+            num_mutation=M, num_pair=0, mut_geno_matrix=mut_geno,
+            mut_nnz_mut_idx=mut_idx, mut_nnz_geno_idx=geno_idx,
+            pair_nnz_pair_idx=np.zeros(0, dtype=np.int32),
+            pair_nnz_geno_idx=np.zeros(0, dtype=np.int32),
+            num_struct=S, struct_names=STRUCTURE_NAMES,
+            struct_features=features,
+            struct_n_chains=np.array([2, 2, 2, 2], dtype=np.int32),
+            struct_contact_pair_idx=None, struct_contact_distances=None,
+        )
+        priors = get_priors()
+
+        def model():
+            out = define_model("theta", data, priors)
+            pred = jnp.stack([out.ln_K_op[1:] - out.ln_K_op[0],
+                              out.ln_K_HL[1:] - out.ln_K_HL[0],
+                              out.ln_K_E[0, 1:] - out.ln_K_E[0, 0]], axis=-1)
+            numpyro.sample("obs", dist.Normal(pred, 0.05).to_event(2),
+                           obs=jnp.asarray(true_dlnK))
+
+        return model, data, priors, true_dlnK
+
+    def test_map_moves_weights_and_nn_tracks_truth(self):
+        import jax
+        import numpyro
+        from numpyro import handlers
+        from numpyro.infer import SVI, Trace_ELBO
+        from numpyro.infer.autoguide import AutoDelta
+        from tfscreen.tfmodel.generative.components.theta.thermo.nn import (
+            compute_nn_predictions,
+        )
+
+        model, data, priors, true_dlnK = self._problem()
+        svi = SVI(model, AutoDelta(model), numpyro.optim.Adam(0.01), Trace_ELBO())
+        state0 = svi.init(jax.random.PRNGKey(0))
+        init = svi.get_params(state0)
+        res = svi.run(jax.random.PRNGKey(0), 1000, progress_bar=False,
+                      init_state=state0)
+        fit = res.params
+
+        for s in STRUCTURE_NAMES:
+            dW1 = np.linalg.norm(np.asarray(fit[f"theta_nn_{s}_W1"]
+                                            - init[f"theta_nn_{s}_W1"]))
+            W2 = np.linalg.norm(np.asarray(fit[f"theta_nn_{s}_W2"]))
+            assert dW1 > 0.1, f"W1 for {s} did not move (|dW1|={dW1})"
+            assert W2 > 0.05, f"W2 for {s} stayed at zero (|W2|={W2})"
+
+        # NN prior mean, projected to the identifiable Δln_K
+        with handlers.substitute(data=fit):
+            nn_means = compute_nn_predictions(
+                "theta", jnp.asarray(data.struct_features),
+                list(data.struct_names), data.struct_n_chains,
+                hidden_size=priors.theta_nn_hidden_size)
+        nn_dlnK = np.asarray(_project_ddG(nn_means))
+        for k in range(3):
+            r = np.corrcoef(nn_dlnK[:, k], true_dlnK[:, k])[0, 1]
+            mse = np.mean((nn_dlnK[:, k] - true_dlnK[:, k]) ** 2)
+            const_mse = np.var(true_dlnK[:, k])   # best constant predictor
+            assert r > 0.8, f"Δln_K[{k}]: NN prior mean r={r:.3f} with truth"
+            assert mse < 0.25 * const_mse, \
+                f"Δln_K[{k}]: NN MSE {mse:.3f} vs constant {const_mse:.3f}"
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py
index 9614975c..3c220709 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_predict_unmeasured.py
@@ -6,6 +6,8 @@
 import numpy as np
 import pandas as pd
 
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
+
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.PK import (
     predict_unmeasured as lnK_mut_predict,
 )
@@ -23,14 +25,14 @@
 
 TITRANT_NAMES = ["IPTG"]
 
-# Physical constants matching the lnK_mut defaults
-TF_TOTAL = 6.5e-7
-OP_TOTAL = 2.5e-8
+# Physical constants matching the PK defaults (tf_total in M, monomer units;
+# concentrations below are used as M, conc_unit_scale left at 1.0)
+TF_TOTAL = mwc.DEFAULT_TF_TOTAL_M
 
-# Biophysically plausible WT values (matching lnK_mut get_hyperparameters defaults)
-WT_LN_K_OP = 23.0
-WT_LN_K_HL = -9.0
-WT_LN_K_E  = 33.4
+# WT values matching the PK get_hyperparameters defaults
+WT_LN_K_OP = mwc.DEFAULT_LN_K_H_O
+WT_LN_K_HL = mwc.DEFAULT_LN_K_H_L
+WT_LN_K_E  = mwc.DEFAULT_LN_K_E_C4
 
 
 def _make_titrant_df(concs=None):
@@ -89,7 +91,6 @@ def _common_kwargs(post, pair_labels=None):
         param_posteriors=post,
         q_to_get={"median": 0.5, "lower": 0.025, "upper": 0.975},
         tf_total=TF_TOTAL,
-        op_total=OP_TOTAL,
     )
 
 
@@ -203,7 +204,6 @@ def test_multiple_titrants_broadcast_correctly(self):
             param_posteriors=post,
             q_to_get={"median": 0.5},
             tf_total=TF_TOTAL,
-            op_total=OP_TOTAL,
         )
         result = lnK_nn_predict(**kw)
         assert len(result) == 4   # 2 genotypes × 2 titrants
@@ -239,7 +239,6 @@ def test_scalar_d_KE_vs_titrant_d_KE(self):
             pair_labels=[],
             q_to_get={"median": 0.5},
             tf_total=TF_TOTAL,
-            op_total=OP_TOTAL,
         )
         res_nn  = lnK_nn_predict(param_posteriors=post_nn,  **kw)
         res_mut = lnK_mut_predict(param_posteriors=post_mut, **kw)
@@ -247,3 +246,41 @@ def test_scalar_d_KE_vs_titrant_d_KE(self):
         np.testing.assert_allclose(
             res_nn["median"].values, res_mut["median"].values, atol=1e-5
         )
+
+
+# ---------------------------------------------------------------------------
+# PddG — the same (S, M) d_ln_K_E assembly as PnnC
+# ---------------------------------------------------------------------------
+
+from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.PddG import (
+    predict_unmeasured as pddg_predict,
+)
+
+
+class TestPddGPredict:
+
+    def test_matches_pnnc_assembly(self):
+        post = _make_lnK_nn_posteriors(T=1, M=2, S=8, seed=4)
+        res_ddg = pddg_predict(**_common_kwargs(post))
+        res_nn = lnK_nn_predict(**_common_kwargs(post))
+        assert len(res_ddg) == 4 * 4
+        np.testing.assert_allclose(res_ddg["median"].values,
+                                   res_nn["median"].values, atol=1e-6)
+
+    def test_conc_unit_scale_converts_mM_to_M(self):
+        post = _make_lnK_nn_posteriors(T=1, M=2, S=8, seed=5)
+        kw_M = _common_kwargs(post)
+        res_M = pddg_predict(**kw_M)
+
+        kw_mM = _common_kwargs(post)
+        kw_mM["manual_titrant_df"] = _make_titrant_df(
+            concs=[0.0, 1e-3, 1e-2, 1e-1])          # same concentrations in mM
+        res_mM = pddg_predict(conc_unit_scale=1e-3, **kw_mM)
+
+        np.testing.assert_allclose(res_mM["median"].values,
+                                   res_M["median"].values, atol=1e-6)
+        assert np.isfinite(res_M.loc[res_M["genotype"] == "wt", "median"]).all()
+        # Without the scale the mM numbers are read as M: a different curve.
+        res_unscaled = pddg_predict(**kw_mM)
+        assert not np.allclose(res_unscaled["median"].values,
+                               res_M["median"].values, atol=1e-3)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py
index e28ffad5..32d57d11 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U0_a/test_thermo.py
@@ -5,13 +5,18 @@
 
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U0_a.thermo import (
     ThetaParam,
-    _solve_free_effector,
     _compute_theta,
     _population_moments,
     run_model,
     get_population_moments,
 )
 
+
+def _apo(K_op, K_HL, K_U=0.0, tf_total=650.0):
+    """Apo θ of M2: h = (tf/2)/(1 + K_HL + K_U), W = h·K_op, θ = W/(1+W)."""
+    W = (tf_total / 2.0) * K_op / (1.0 + K_HL + K_U)
+    return W / (1.0 + W)
+
 # ---------------------------------------------------------------------------
 # Minimal mock data (only fields used by thermo functions)
 # ---------------------------------------------------------------------------
@@ -47,63 +52,16 @@ def theta_param(mock_data):
     T, G = mock_data.num_titrant_name, mock_data.num_genotype
     C = mock_data.num_titrant_conc
     return ThetaParam(
-        ln_K_op=jnp.full(G, 2.3),
+        ln_K_op=jnp.full(G, -4.0),
         ln_K_HL=jnp.full(G, -9.0),
         ln_K_E=jnp.full((T, G), -4.0),
         tf_total=650.0,
-        op_total=25.0,
+        conc_unit_scale=1.0,
         mu=jnp.zeros((T, C, 1)),
         sigma=jnp.ones((T, C, 1)),
     )
 
 
-# ---------------------------------------------------------------------------
-# _solve_free_effector
-# ---------------------------------------------------------------------------
-
-class TestSolveFreeEffector:
-
-    def test_zero_effector_returns_zero(self):
-        e_total = jnp.zeros((1, 3, 1))
-        a  = jnp.ones((2, 1, 4)) * 1e-4
-        Z0 = jnp.ones((1, 1, 4)) * 252.0
-        e_free = _solve_free_effector(e_total, tf_total=650.0, a=a, Z0=Z0)
-        assert jnp.allclose(e_free, 0.0, atol=1e-6)
-
-    def test_e_free_leq_e_total(self):
-        e_total = jnp.array([0.0, 100.0, 1000.0])[None, :, None]
-        a  = jnp.ones((1, 1, 1)) * 1e-4
-        Z0 = jnp.ones((1, 1, 1)) * 252.0
-        e_free = _solve_free_effector(e_total, tf_total=650.0, a=a, Z0=Z0)
-        assert jnp.all(e_free >= 0)
-        assert jnp.all(e_free <= e_total + 1e-9)
-
-    def test_output_shape(self):
-        T, C, G = 2, 3, 4
-        e_total = jnp.ones((1, C, 1)) * 500.0
-        a  = jnp.ones((T, 1, G)) * 1e-5
-        Z0 = jnp.ones((1, 1, G)) * 252.0
-        e_free = _solve_free_effector(e_total, tf_total=650.0, a=a, Z0=Z0)
-        assert e_free.shape == (T, C, G)
-
-    def test_finite_values(self):
-        e_total = jnp.array([0.0, 100.0, 1000.0])[None, :, None]
-        a  = jnp.ones((1, 1, 1)) * 1e-4
-        Z0 = jnp.ones((1, 1, 1)) * 252.0
-        e_free = _solve_free_effector(e_total, tf_total=650.0, a=a, Z0=Z0)
-        assert jnp.all(jnp.isfinite(e_free))
-
-    def test_high_affinity_depletes_effector(self):
-        """Very high K_HL·K_E should deplete free effector significantly."""
-        e_total = jnp.array([1000.0])[None, :, None]
-        a_high = jnp.ones((1, 1, 1)) * 10.0    # high affinity
-        a_low  = jnp.ones((1, 1, 1)) * 1e-10   # negligible affinity
-        Z0 = jnp.ones((1, 1, 1)) * 252.0
-        e_free_high = _solve_free_effector(e_total, 650.0, a_high, Z0)
-        e_free_low  = _solve_free_effector(e_total, 650.0, a_low,  Z0)
-        assert jnp.all(e_free_high < e_free_low)
-
-
 # ---------------------------------------------------------------------------
 # _compute_theta
 # ---------------------------------------------------------------------------
@@ -114,14 +72,14 @@ def test_output_shape(self):
         T, G = 2, 4
         conc = jnp.array([0.0, 100.0, 1000.0])
         theta = _compute_theta(jnp.zeros(G), jnp.full(G, -9.0),
-                               jnp.full((T, G), -4.0), conc, 650.0, 25.0)
+                               jnp.full((T, G), -4.0), conc, 650.0)
         assert theta.shape == (T, 3, G)
 
     def test_values_in_0_1(self):
         G = 4
         conc = jnp.array([0.0, 50.0, 500.0, 5000.0])
         theta = _compute_theta(jnp.zeros(G), jnp.full(G, -9.0),
-                               jnp.full((1, G), -4.0), conc, 650.0, 25.0)
+                               jnp.full((1, G), -4.0), conc, 650.0)
         assert jnp.all(theta >= 0)
         assert jnp.all(theta <= 1)
 
@@ -130,29 +88,29 @@ def test_all_finite(self):
         conc = jnp.linspace(0, 1000, 6)
         theta = _compute_theta(jnp.linspace(0.0, 4.0, G),
                                jnp.linspace(-12.0, -6.0, G),
-                               jnp.full((2, G), -5.0), conc, 650.0, 25.0)
+                               jnp.full((2, G), -5.0), conc, 650.0)
         assert jnp.all(jnp.isfinite(theta))
 
     def test_theta_monotone_decreasing_in_effector(self):
         """Higher effector → lower theta (effector competes with operator binding)."""
         G = 2
         conc = jnp.array([0.0, 100.0, 1000.0])
-        theta = _compute_theta(jnp.full(G, 2.3), jnp.full(G, -9.0),
-                               jnp.full((1, G), -4.0), conc, 650.0, 25.0)
+        theta = _compute_theta(jnp.full(G, -4.0), jnp.full(G, -9.0),
+                               jnp.full((1, G), -4.0), conc, 650.0)
         assert jnp.all(theta[:, 0, :] >= theta[:, 1, :])
         assert jnp.all(theta[:, 1, :] >= theta[:, 2, :])
 
     def test_zero_effector_matches_apo_formula(self):
-        """At e_total=0, theta = K_op*[op] / (1 + K_op*[op] + K_HL)."""
+        """At e_total=0, theta = W/(1+W) with W = (tf/2)·K_op / (1 + K_HL)."""
         G = 2
-        ln_K_op = jnp.array([2.3, 2.0])
+        ln_K_op = jnp.array([-4.0, -5.0])
         ln_K_HL = jnp.array([-9.0, -8.0])
         conc = jnp.array([0.0, 1.0])
         theta = _compute_theta(ln_K_op, ln_K_HL, jnp.full((1, G), -4.0),
-                               conc, 650.0, 25.0)
-        K_op = np.exp(np.array([2.3, 2.0]))
+                               conc, 650.0)
+        K_op = np.exp(np.array([-4.0, -5.0]))
         K_HL = np.exp(np.array([-9.0, -8.0]))
-        expected = K_op * 25.0 / (1.0 + K_op * 25.0 + K_HL)
+        expected = _apo(K_op, K_HL)
         assert jnp.allclose(theta[0, 0, :], jnp.array(expected), atol=1e-5)
 
     def test_higher_K_op_gives_higher_theta(self):
@@ -162,7 +120,7 @@ def test_higher_K_op_gives_higher_theta(self):
         ln_K_op = jnp.array([1.0, 3.0])   # G=1 weaker, G=2 stronger
         ln_K_HL = jnp.full(G, -9.0)
         theta = _compute_theta(ln_K_op, ln_K_HL, jnp.full((1, G), -4.0),
-                               conc, 650.0, 25.0)
+                               conc, 650.0)
         assert theta[0, 0, 1] > theta[0, 0, 0]
 
 
@@ -236,13 +194,13 @@ def test_all_finite(self, theta_param, mock_data):
     def test_zero_effector_matches_apo_formula(self, mock_data):
         G = mock_data.num_genotype
         T = mock_data.num_titrant_name
-        ln_K_op_val, ln_K_HL_val = 2.3, -9.0
+        ln_K_op_val, ln_K_HL_val = -4.0, -9.0
         tp = ThetaParam(
             ln_K_op=jnp.full(G, ln_K_op_val),
             ln_K_HL=jnp.full(G, ln_K_HL_val),
             ln_K_E=jnp.full((T, G), -4.0),
             tf_total=650.0,
-            op_total=25.0,
+            conc_unit_scale=1.0,
             mu=jnp.zeros((T, 3, 1)),
             sigma=jnp.ones((T, 3, 1)),
         )
@@ -250,7 +208,7 @@ def test_zero_effector_matches_apo_formula(self, mock_data):
         result = run_model(tp, data)
         K_op = np.exp(ln_K_op_val)
         K_HL = np.exp(ln_K_HL_val)
-        expected = K_op * 25.0 / (1.0 + K_op * 25.0 + K_HL)
+        expected = _apo(K_op, K_HL)
         assert jnp.allclose(result[:, 0, :], expected, atol=1e-5)
 
     def test_uses_data_titrant_conc(self, theta_param):
@@ -284,7 +242,7 @@ def test_nontrivial_batch_idx_selects_correct_genotypes(self, mock_data):
         ln_K_E  = jnp.full((T, G_full), -4.0)
         tp_full = ThetaParam(
             ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E,
-            tf_total=650.0, op_total=25.0,
+            tf_total=650.0, conc_unit_scale=1.0,
             mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
         )
 
@@ -301,7 +259,7 @@ def test_nontrivial_batch_idx_selects_correct_genotypes(self, mock_data):
 
         theta_all = _compute_theta(
             tp_full.ln_K_op, tp_full.ln_K_HL, tp_full.ln_K_E,
-            batch_data.titrant_conc, tp_full.tf_total, tp_full.op_total,
+            batch_data.titrant_conc, tp_full.tf_total,
         )   # (T, C, G_full)
 
         for local_i, global_g in enumerate(batch_idx):
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py
index 4b70cf88..09feffc4 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PK.py
@@ -205,7 +205,7 @@ def test_get_hyperparameters_keys():
     assert isinstance(hp, dict)
     for key in ["theta_ln_K_op_wt_loc", "theta_ln_K_HL_wt_loc", "theta_ln_K_E_wt_loc",
                 "theta_ln_K_U_wt_loc", "theta_ln_K_U_wt_scale",
-                "theta_tf_total_M", "theta_op_total_M",
+                "theta_tf_total_M",
                 "theta_sigma_d_ln_K_op_scale", "theta_sigma_d_ln_K_U_scale",
                 "theta_epi_tau_scale", "theta_epi_slab_scale", "theta_epi_slab_df"]:
         assert key in hp
@@ -222,8 +222,7 @@ def test_get_hyperparameters_K_U_defaults():
 def test_get_priors_values():
     priors = get_priors()
     assert isinstance(priors, ModelPriors)
-    assert priors.theta_tf_total_M == pytest.approx(6.5e-7)
-    assert priors.theta_op_total_M == pytest.approx(2.5e-8)
+    assert priors.theta_tf_total_M == pytest.approx(1e-5)
     assert priors.theta_ln_K_op_wt_loc == get_hyperparameters()["theta_ln_K_op_wt_loc"]
     assert priors.theta_ln_K_U_wt_loc == pytest.approx(-12.0)
     assert priors.theta_ln_K_U_wt_scale == pytest.approx(3.0)
@@ -492,12 +491,11 @@ def test_no_epi_no_epistasis_sample_sites(self, mock_data_no_epi):
         sample_names = {k for k, v in tr.items() if v["type"] == "sample"}
         assert not any("epi" in k for k in sample_names)
 
-    def test_tf_op_totals_preserved(self, mock_data_no_epi):
+    def test_tf_total_preserved(self, mock_data_no_epi):
         priors = get_priors()
         with seed(rng_seed=0):
             tp = guide(name="theta", data=mock_data_no_epi, priors=priors)
-        assert tp.tf_total == pytest.approx(6.5e-7)
-        assert tp.op_total == pytest.approx(2.5e-8)
+        assert tp.tf_total == pytest.approx(1e-5)
 
     def test_guide_has_K_U_variational_params(self, mock_data_no_epi):
         """Guide must register variational parameters for the ln_K_U hierarchy."""
@@ -570,3 +568,75 @@ def test_large_K_U_mutation_reduces_theta(mock_data_no_epi):
     # At apo (C=0), mutant genotypes (indices 1, 2, 3) have larger K_U → lower theta.
     # Only compare at C=0: at saturating effector both thetas collapse to ~0 (float32 floor).
     assert jnp.all(theta_unstable[:, 0, 1:] < theta_stable[:, 0, 1:])
+
+
+# ---------------------------------------------------------------------------
+# predict_unmeasured
+# ---------------------------------------------------------------------------
+
+import pandas as pd
+
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
+from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.PK import (
+    predict_unmeasured,
+)
+
+
+def _unmeasured_posteriors(S=6, T=1, M=2, d_U=None, seed_val=0):
+    rng = np.random.default_rng(seed_val)
+    post = {
+        "theta_ln_K_op_wt": np.full(S, mwc.DEFAULT_LN_K_H_O, dtype=np.float32),
+        "theta_ln_K_HL_wt": np.full(S, mwc.DEFAULT_LN_K_H_L, dtype=np.float32),
+        "theta_ln_K_E_wt": np.full((S, T), mwc.DEFAULT_LN_K_E_C4, dtype=np.float32),
+        "theta_ln_K_U_wt": np.full(S, -12.0, dtype=np.float32),
+        "theta_d_ln_K_op": (rng.standard_normal((S, M)) * 0.3).astype(np.float32),
+        "theta_d_ln_K_HL": (rng.standard_normal((S, M)) * 0.3).astype(np.float32),
+        "theta_d_ln_K_E": (rng.standard_normal((S, T, M)) * 0.3).astype(np.float32),
+        "theta_d_ln_K_U": (np.zeros((S, M)) if d_U is None else d_U).astype(np.float32),
+    }
+    return post
+
+
+def _unmeasured_kwargs(post, concs, genotypes=("wt", "M42I", "K84L", "Z99Q")):
+    return dict(
+        target_genotypes=list(genotypes),
+        titrant_names=["IPTG"],
+        manual_titrant_df=pd.DataFrame({"titrant_name": ["IPTG"] * len(concs),
+                                        "titrant_conc": concs}),
+        mut_labels=["M42I", "K84L"],
+        pair_labels=[],
+        param_posteriors=post,
+        q_to_get={"median": 0.5},
+        tf_total=mwc.DEFAULT_TF_TOTAL_M,
+    )
+
+
+class TestPredictUnmeasured:
+
+    def test_conc_unit_scale_converts_mM_to_M(self):
+        post = _unmeasured_posteriors()
+        res_M = predict_unmeasured(**_unmeasured_kwargs(post, [0.0, 1e-5, 1e-4]))
+        res_mM = predict_unmeasured(conc_unit_scale=1e-3,
+                                    **_unmeasured_kwargs(post, [0.0, 1e-2, 1e-1]))
+        np.testing.assert_allclose(res_mM["median"].values,
+                                   res_M["median"].values, atol=1e-6)
+
+    def test_unknown_mutation_nan_and_wt_in_unit_interval(self):
+        post = _unmeasured_posteriors()
+        res = predict_unmeasured(**_unmeasured_kwargs(post, [0.0, 1e-4]))
+        assert res.loc[res["genotype"] == "Z99Q", "median"].isna().all()
+        wt = res.loc[res["genotype"] == "wt", "median"]
+        assert ((wt >= 0) & (wt <= 1)).all()
+
+    def test_unfolding_mutation_lowers_repression(self):
+        S, M = 6, 2
+        d_U = np.zeros((S, M))
+        d_U[:, 0] = 15.0                     # M42I mostly unfolded
+        post = _unmeasured_posteriors(S=S, M=M, d_U=d_U)
+        for k in ("theta_d_ln_K_op", "theta_d_ln_K_HL", "theta_d_ln_K_E"):
+            post[k] = np.zeros_like(post[k])
+        res = predict_unmeasured(**_unmeasured_kwargs(post, [0.0],
+                                                      genotypes=("wt", "M42I")))
+        wt = float(res.loc[res["genotype"] == "wt", "median"].iloc[0])
+        mut = float(res.loc[res["genotype"] == "M42I", "median"].iloc[0])
+        assert mut < wt
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py
index ac05badc..95ec10fb 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PddG.py
@@ -5,8 +5,9 @@
 get_priors, get_guesses, define_model, guide, get_extract_specs.
 
 Key difference from base lac_dimer/lnK_ddG_prior: the unfolded variant adds
-a scalar ln_K_U_wt parameter that is sampled for all genotypes but yields a
-homogeneous ln_K_U (no per-mutation effects on K_U).
+a scalar ln_K_U_wt parameter, and each mutation shifts ln_K_U by its ΔΔG_H
+(K_U = [U]/[H] with ΔΔG_U = 0).  test_unfolding_projection.py covers the
+projection in depth.
 """
 
 import pytest
@@ -15,7 +16,7 @@
 import jax.numpy as jnp
 from collections import namedtuple
 from dataclasses import dataclass
-from numpyro.handlers import trace, seed
+from numpyro.handlers import condition, trace, seed
 import unittest.mock as mock
 
 from tfscreen.genetics.build_mut_geno_matrix import build_mut_sparse_indices
@@ -40,6 +41,11 @@
 # ---------------------------------------------------------------------------
 
 _S = 4    # number of lac_dimer_unfolded structures: H, HD, L, LE2
+
+
+def _sigma_zero(fn):
+    """``fn`` with every structure's σ_s conditioned to 0: ΔΔG = prior means."""
+    return condition(fn, data={"theta_ddG_sigma": jnp.zeros(_S)})
 _M = 2    # mutations
 _G = 4    # genotypes: wt, M1, M2, M1+M2
 
@@ -176,9 +182,9 @@ def test_K_HL_is_H_minus_L(self):
         ddG = jnp.array([[3.0, 0.0, 1.0, 0.0]])
         assert _project_ddG(ddG)[0, 1] == pytest.approx(3.0 - 1.0)
 
-    def test_K_E_is_L_minus_LE2(self):
+    def test_K_E_is_half_L_minus_LE2(self):
         ddG = jnp.array([[0.0, 0.0, 5.0, 2.0]])
-        assert _project_ddG(ddG)[0, 2] == pytest.approx(5.0 - 2.0)
+        assert _project_ddG(ddG)[0, 2] == pytest.approx((5.0 - 2.0) / 2)
 
     def test_H_perturbation_does_not_affect_K_E(self):
         """Changing H alone should not change d_ln_K_E (which depends only on L and LE2)."""
@@ -205,7 +211,7 @@ def test_hyperparameters_has_required_keys(self):
             "theta_ln_K_HL_wt_loc", "theta_ln_K_HL_wt_scale",
             "theta_ln_K_E_wt_loc",  "theta_ln_K_E_wt_scale",
             "theta_ln_K_U_wt_loc",  "theta_ln_K_U_wt_scale",
-            "theta_tf_total_M", "theta_op_total_M",
+            "theta_tf_total_M",
         ]
         for k in required:
             assert k in hp, f"Missing key: {k}"
@@ -235,13 +241,12 @@ def test_model_priors_has_no_nn_fields(self):
 
     def test_physical_constants(self):
         p = get_priors()
-        assert p.theta_tf_total_M == pytest.approx(6.5e-7)
-        assert p.theta_op_total_M == pytest.approx(2.5e-8)
+        assert p.theta_tf_total_M == pytest.approx(1e-5)
 
     def test_get_guesses_required_keys(self):
         g = get_guesses("theta", _make_mock())
         for k in ("theta_ln_K_op_wt", "theta_ln_K_HL_wt", "theta_ln_K_E_wt",
-                  "theta_ln_K_U_wt", "theta_ddG_offset"):
+                  "theta_ln_K_U_wt", "theta_dlnK"):
             assert k in g, f"Missing guess key: {k}"
 
     def test_get_guesses_ln_K_U_wt_value(self):
@@ -254,8 +259,8 @@ def test_get_guesses_no_epi_keys(self):
         assert "theta_epi_lambda" not in g
         assert "theta_epi_offset" not in g
 
-    def test_get_guesses_ddG_offset_shape(self):
-        assert get_guesses("theta", _make_mock())["theta_ddG_offset"].shape == (_S, _M)
+    def test_get_guesses_ddG_shape(self):
+        assert get_guesses("theta", _make_mock())["theta_dlnK"].shape == (_M, 4)
 
     def test_get_guesses_wt_K_shapes(self):
         T = 2
@@ -310,12 +315,15 @@ def test_K_shapes(self):
         assert out.ln_K_E.shape  == (T, _G)
         assert out.ln_K_U.shape  == (_G,)
 
-    def test_ln_K_U_homogeneous(self):
-        """All elements of ln_K_U must be equal — no per-mutation K_U effects."""
-        out, _ = _run_model(_make_mock())
+    def test_ln_K_U_shifts_by_ddG_H(self):
+        """ln_K_U[g] − ln_K_U[wt] is the summed ΔΔG_H of g's mutations."""
+        out, tr = _run_model(_make_mock())
+        ddG_H = np.asarray(tr["theta_ddG"]["value"])[:, 0]
         vals = np.asarray(out.ln_K_U)
-        np.testing.assert_allclose(vals, vals[0], atol=1e-6,
-                                   err_msg="ln_K_U should be homogeneous across genotypes")
+        np.testing.assert_allclose(vals - vals[0], _MUT_GENO.T @ ddG_H,
+                                   atol=1e-5)
+        np.testing.assert_allclose(tr["theta_d_ln_K_U"]["value"], ddG_H,
+                                   atol=1e-6)
 
     def test_population_moment_shapes(self):
         out, _ = _run_model(_make_mock())
@@ -326,7 +334,7 @@ def test_population_moment_shapes(self):
     def test_sample_sites_present(self):
         _, tr = _run_model(_make_mock())
         for site in ("theta_ln_K_op_wt", "theta_ln_K_HL_wt", "theta_ln_K_E_wt",
-                     "theta_ln_K_U_wt", "theta_ddG_offset"):
+                     "theta_ln_K_U_wt", "theta_dlnK"):
             assert site in tr, f"Missing sample site: {site}"
 
     def test_deterministic_sites_present(self):
@@ -376,7 +384,7 @@ def test_permuted_features_give_same_result_as_canonical(self):
         )
 
     def test_ddG_prior_means_shift_delta_lnK(self):
-        """With sigma_s=0, ddG equals the prior means exactly; nonzero prior produces
+        """With σ_s conditioned to 0, ddG equals the prior means exactly; nonzero prior produces
         nonzero delta_lnK for the affected mutation."""
         zero_prior = np.zeros((_M, _S), dtype=np.float32)
         data_zero = _make_mock(ddG_prior=zero_prior)
@@ -387,7 +395,7 @@ def _zero_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=_zero_param):
             with seed(rng_seed=0):
-                out_zero = define_model("theta", data_zero, priors)
+                out_zero = _sigma_zero(define_model)("theta", data_zero, priors)
 
         np.testing.assert_allclose(
             np.asarray(out_zero.ln_K_op[0]),
@@ -400,7 +408,7 @@ def _zero_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=_zero_param):
             with seed(rng_seed=0):
-                out_nonzero = define_model("theta", data_nonzero, priors)
+                out_nonzero = _sigma_zero(define_model)("theta", data_nonzero, priors)
 
         assert not np.allclose(
             np.asarray(out_nonzero.ln_K_op[0]),   # WT
@@ -408,10 +416,9 @@ def _zero_param(name, init, **kwargs):
             atol=1e-5,
         )
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_model(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
 
 # ---------------------------------------------------------------------------
@@ -432,10 +439,15 @@ def test_K_shapes(self):
         assert out.ln_K_E.shape  == (T, _G)
         assert out.ln_K_U.shape  == (_G,)
 
-    def test_ln_K_U_homogeneous_in_guide(self):
-        out, _ = _run_guide(_make_mock())
+    def test_ln_K_U_shifts_by_ddG_H_in_guide(self):
+        data = _make_mock()
+        out, tr = _run_guide(data)
+        # The guide samples Δln K, shape (M, 4); its last column is
+        # Δln K_U = ΔΔG_H.
+        ddG_H = np.asarray(tr["theta_dlnK"]["value"])[:, 3]
         vals = np.asarray(out.ln_K_U)
-        np.testing.assert_allclose(vals, vals[0], atol=1e-6)
+        np.testing.assert_allclose(vals - vals[0], _MUT_GENO.T @ ddG_H,
+                                   atol=1e-5)
 
     def test_sample_sites_match_model(self):
         data = _make_mock()
@@ -450,8 +462,8 @@ def test_variational_params_registered(self):
         param_names = {k for k, v in tr.items() if v["type"] == "param"}
         for p in ("theta_ln_K_op_wt_loc", "theta_ln_K_op_wt_scale",
                   "theta_ln_K_HL_wt_loc", "theta_ln_K_HL_wt_scale",
-                  "theta_ddG_offset_locs", "theta_ddG_offset_scales",
-                  "theta_ddG_sigma_s"):
+                  "theta_dlnK_locs", "theta_dlnK_scales",
+                  "theta_ddG_sigma_locs", "theta_ddG_sigma_scales"):
             assert p in param_names, f"Missing variational param: {p}"
 
     def test_ln_K_U_wt_variational_params_registered(self):
@@ -474,20 +486,19 @@ def test_no_epi_params_registered(self):
         assert not any("epi_lambda" in k for k in param_names)
         assert not any("epi_offset" in k for k in param_names)
 
-    def test_ddG_sigma_s_shape(self):
+    def test_ddG_sigma_shape(self):
         _, tr = _run_guide(_make_mock())
-        sigma_val = tr["theta_ddG_sigma_s"]["value"]
+        sigma_val = tr["theta_ddG_sigma"]["value"]
         assert sigma_val.shape == (_S,)
 
-    def test_ddG_offset_locs_shape(self):
+    def test_dlnK_locs_shape(self):
         _, tr = _run_guide(_make_mock())
-        locs_val = tr["theta_ddG_offset_locs"]["value"]
-        assert locs_val.shape == (_S, _M)
+        locs_val = tr["theta_dlnK_locs"]["value"]
+        assert locs_val.shape == (_M, 4)
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         out, _ = _run_guide(_make_mock())
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
     def test_permuted_features_give_same_output_as_canonical(self):
         canonical_prior = _make_ddG_prior()
@@ -556,6 +567,11 @@ def _make_ctx(genotypes=None, titrant_names=None, mut_labels=None, pair_labels=N
         pair_labels=pair_labels,
     )
 
+def _base_specs(specs):
+    """Specs other than the per-structure ddG ones (tested in
+    test_ddG_extraction.py)."""
+    return [s for s in specs if "out_names" not in s]
+
 
 class TestGetExtractSpecs:
 
@@ -565,11 +581,11 @@ def test_returns_list(self):
     def test_three_specs(self):
         # scalar K values (ln_K_op, ln_K_HL), titrant K values (ln_K_E),
         # per-mutation d_ln_K values — ln_K_U is WT-only, not extracted per-genotype
-        assert len(get_extract_specs(_make_ctx())) == 3
+        assert len(_base_specs(get_extract_specs(_make_ctx()))) == 3
 
     def test_four_specs_when_pair_labels_present(self):
         ctx = _make_ctx(pair_labels=["M1+M2"])
-        assert len(get_extract_specs(ctx)) == 4
+        assert len(_base_specs(get_extract_specs(ctx))) == 4
 
     def test_first_spec_has_scalar_K_values(self):
         params = get_extract_specs(_make_ctx())[0]["params_to_get"]
@@ -604,3 +620,33 @@ def test_map_geno_covers_all_genotypes(self):
         ctx = _make_ctx(genotypes=genos)
         df = get_extract_specs(ctx)[0]["input_df"]
         assert set(df["genotype"]) == set(genos)
+
+
+# ---------------------------------------------------------------------------
+# guide with pair epistasis
+# ---------------------------------------------------------------------------
+
+def test_guide_epistasis_uses_regularized_horseshoe_scale():
+    # one pair (M1, M2) on the double mutant (genotype 3)
+    data = _make_mock()._replace(
+        num_pair=1,
+        pair_nnz_pair_idx=np.array([0], dtype=np.int32),
+        pair_nnz_geno_idx=np.array([3], dtype=np.int32))
+    out, tr = _run_guide(data)
+    for site in ("theta_epi_tau", "theta_epi_c2", "theta_epi_lambda",
+                 "theta_epi_offset"):
+        assert tr[site]["type"] == "sample"
+
+    tau = np.asarray(tr["theta_epi_tau"]["value"], dtype=float)
+    c2 = np.asarray(tr["theta_epi_c2"]["value"], dtype=float)
+    lam = np.asarray(tr["theta_epi_lambda"]["value"], dtype=float)
+    off = np.asarray(tr["theta_epi_offset"]["value"], dtype=float)
+    lam_tilde = np.sqrt(c2 * lam ** 2 / (c2 + tau ** 2 * lam ** 2))
+    epi_ddG = (off * tau * lam_tilde).T                      # (P, S)
+    expected = float(np.asarray(_project_ddG(jnp.asarray(epi_ddG)))[0, 0])
+
+    K = np.asarray(out.ln_K_op, dtype=float)
+    # additive part cancels in the double-mutant cycle, leaving the pair term
+    cycle = K[3] - K[1] - K[2] + K[0]
+    assert np.isfinite(cycle)
+    assert cycle == pytest.approx(expected, rel=1e-4, abs=1e-5)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py
index 57e30952..465851f5 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_PnnC.py
@@ -7,7 +7,7 @@
 from collections import namedtuple
 from dataclasses import dataclass
 from functools import partial
-from numpyro.handlers import trace, seed
+from numpyro.handlers import condition, trace, seed
 
 from tfscreen.genetics.build_mut_geno_matrix import build_mut_sparse_indices
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.PnnC import (
@@ -32,6 +32,11 @@
 # ──────────────────────────────────────────────────────────────────────────────
 
 _S = 4   # number of structures: H, HD, L, LE2
+
+
+def _sigma_zero(fn):
+    """``fn`` with every structure's σ_s conditioned to 0: ΔΔG = prior means."""
+    return condition(fn, data={"theta_ddG_sigma": jnp.zeros(_S)})
 _M = 2   # number of mutations
 _G = 4   # genotypes: wt, mut0, mut1, double
 
@@ -172,10 +177,10 @@ def test_K_HL_equals_H_minus_L(self):
         out = _project_ddG(ddG)
         assert out[0, 1] == pytest.approx(3.0 - 1.0)  # K_HL = H - L
 
-    def test_K_E_equals_L_minus_LE2(self):
+    def test_K_E_is_half_L_minus_LE2(self):
         ddG = jnp.array([[0.0, 0.0, 5.0, 2.0]])
         out = _project_ddG(ddG)
-        assert out[0, 2] == pytest.approx(5.0 - 2.0)  # K_E = L - LE2
+        assert out[0, 2] == pytest.approx((5.0 - 2.0) / 2)  # per-site K_E = (L - LE2)/2
 
     def test_leading_batch_dim(self):
         ddG = jnp.ones((2, 7, 4))
@@ -195,7 +200,7 @@ def test_hyperparameters_keys(self):
             "theta_ln_K_HL_wt_loc", "theta_ln_K_HL_wt_scale",
             "theta_ln_K_E_wt_loc",  "theta_ln_K_E_wt_scale",
             "theta_ln_K_U_wt_loc",  "theta_ln_K_U_wt_scale",
-            "theta_tf_total_M", "theta_op_total_M",
+            "theta_tf_total_M",
             "theta_nn_hidden_size",
             "theta_epi_tau_scale", "theta_epi_slab_scale", "theta_epi_slab_df",
             "theta_epi_d0",
@@ -220,8 +225,7 @@ def test_priors_has_ln_K_U_wt_fields(self):
 
     def test_physical_constants(self):
         priors = get_priors()
-        assert priors.theta_tf_total_M == pytest.approx(6.5e-7)
-        assert priors.theta_op_total_M == pytest.approx(2.5e-8)
+        assert priors.theta_tf_total_M == pytest.approx(1e-5)
 
     def test_get_guesses_keys_no_epi(self):
         data = _make_mock()
@@ -230,7 +234,7 @@ def test_get_guesses_keys_no_epi(self):
         assert "theta_ln_K_HL_wt"  in g
         assert "theta_ln_K_E_wt"   in g
         assert "theta_ln_K_U_wt"   in g
-        assert "theta_ddG_offset"  in g
+        assert "theta_dlnK"  in g
         assert "theta_epi_tau" not in g
 
     def test_get_guesses_ln_K_U_wt_value(self):
@@ -246,10 +250,10 @@ def test_get_guesses_keys_with_epi(self):
         assert "theta_epi_lambda" in g
         assert "theta_epi_offset" in g
 
-    def test_get_guesses_ddG_offset_shape(self):
+    def test_get_guesses_ddG_shape(self):
         data = _make_mock()
         g = get_guesses("theta", data)
-        assert g["theta_ddG_offset"].shape == (_S, _M)
+        assert g["theta_dlnK"].shape == (_M, 4)
 
 
 # ──────────────────────────────────────────────────────────────────────────────
@@ -308,13 +312,14 @@ def test_ln_K_U_shape(self):
         out, _ = _run_model(data)
         assert out.ln_K_U.shape == (_G,)
 
-    def test_ln_K_U_homogeneous(self):
-        """All elements of ln_K_U must be equal — K_U is WT-only with no per-mut effects."""
+    def test_ln_K_U_shifts_by_ddG_H(self):
+        """ln_K_U[g] − ln_K_U[wt] is the summed ΔΔG_H (K_U = [U]/[H], ΔΔG_U = 0)."""
         data = _make_mock()
-        out, _ = _run_model(data)
+        out, tr = _run_model(data)
+        ddG_H = np.asarray(tr["theta_ddG"]["value"])[:, 0]
         vals = np.asarray(out.ln_K_U)
-        np.testing.assert_allclose(vals, vals[0], atol=1e-6,
-                                   err_msg="ln_K_U should be homogeneous across genotypes")
+        np.testing.assert_allclose(vals - vals[0], _MUT_GENO.T @ ddG_H,
+                                   atol=1e-5)
 
     def test_sample_sites_present(self):
         data = _make_mock()
@@ -322,7 +327,7 @@ def test_sample_sites_present(self):
         assert "theta_ln_K_op_wt" in tr
         assert "theta_ln_K_HL_wt" in tr
         assert "theta_ln_K_E_wt"  in tr
-        assert "theta_ddG_offset" in tr
+        assert "theta_ddG" in tr
         assert "theta_ln_K_U_wt"  in tr
 
     def test_deterministic_sites_present(self):
@@ -359,7 +364,7 @@ def test_epi_sites_absent_when_no_contact_distances(self):
         assert "theta_epi_tau" not in tr
 
     def test_zero_nn_weights_zero_offsets_all_genotypes_at_wt(self):
-        """With NN weights=0 and ddG_offset=0, all mutations have zero effect."""
+        """With NN weights=0 and σ_s=0, all mutations have zero effect."""
         data = _make_mock()
         priors = get_priors()
 
@@ -370,7 +375,7 @@ def mock_param(name, init, **kwargs):
 
         with mock.patch("numpyro.param", side_effect=mock_param):
             with seed(rng_seed=0):
-                out = define_model("theta", data, priors)
+                out = _sigma_zero(define_model)("theta", data, priors)
 
         # All four genotypes should have the same K_op and K_HL when all effects are zero
         np.testing.assert_allclose(
@@ -380,18 +385,23 @@ def mock_param(name, init, **kwargs):
             np.asarray(out.ln_K_op[1]), np.asarray(out.ln_K_op[2]), atol=1e-5
         )
 
-    def test_ln_K_U_homogeneous_with_epi(self):
-        """K_U homogeneity holds even when epistasis is active."""
+    def test_ln_K_U_takes_pair_epistasis_on_H(self):
+        """The double mutant's ln_K_U adds the pair's epistatic ΔΔG_H."""
         data = _make_mock(num_pair=1)
-        out, _ = _run_model(data)
+        out, tr = _run_model(data)
+        ddG_H = np.asarray(tr["theta_ddG"]["value"])[:, 0]
+        epi_H = np.asarray(tr["theta_epi_ddG"]["value"])[:, 0]
+        np.testing.assert_allclose(tr["theta_epi_ln_K_U"]["value"], epi_H,
+                                   atol=1e-6)
+        expected = _MUT_GENO.T @ ddG_H
+        expected[3] += epi_H[0]
         vals = np.asarray(out.ln_K_U)
-        np.testing.assert_allclose(vals, vals[0], atol=1e-6)
+        np.testing.assert_allclose(vals - vals[0], expected, atol=1e-5)
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         data = _make_mock()
         out, _ = _run_model(data)
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
     def test_population_moment_shapes(self):
         data = _make_mock()
@@ -416,11 +426,12 @@ def test_ln_K_U_shape(self):
         out, _ = _run_guide(data)
         assert out.ln_K_U.shape == (_G,)
 
-    def test_ln_K_U_homogeneous_in_guide(self):
+    def test_ln_K_U_varies_in_guide(self):
+        """The guide assembles ln_K_U from ΔΔG_H too, so genotypes differ."""
         data = _make_mock()
         out, _ = _run_guide(data)
         vals = np.asarray(out.ln_K_U)
-        np.testing.assert_allclose(vals, vals[0], atol=1e-6)
+        assert not np.allclose(vals, vals[0], atol=1e-6)
 
     def test_sample_sites_match_model(self):
         """Guide must contain exactly the same pyro.sample site names as the model."""
@@ -449,8 +460,8 @@ def test_variational_params_registered(self):
         assert "theta_ln_K_op_wt_scale"  in param_names
         assert "theta_ln_K_HL_wt_loc"    in param_names
         assert "theta_ln_K_HL_wt_scale"  in param_names
-        assert "theta_ddG_offset_locs"   in param_names
-        assert "theta_ddG_offset_scales" in param_names
+        assert "theta_dlnK_locs"   in param_names
+        assert "theta_dlnK_scales" in param_names
 
     def test_ln_K_U_wt_variational_params_registered(self):
         """Guide must register variational parameters for ln_K_U_wt."""
@@ -460,11 +471,13 @@ def test_ln_K_U_wt_variational_params_registered(self):
         assert "theta_ln_K_U_wt_loc"   in param_names
         assert "theta_ln_K_U_wt_scale" in param_names
 
-    def test_sigma_s_param_registered_in_guide(self):
+    def test_sigma_guide_params_registered(self):
         data = _make_mock()
         _, tr = _run_guide(data)
         param_names = {k for k, v in tr.items() if v["type"] == "param"}
-        assert "theta_ddG_sigma_s" in param_names
+        assert "theta_ddG_sigma_locs" in param_names
+        assert "theta_ddG_sigma_scales" in param_names
+        assert tr["theta_ddG_sigma"]["type"] == "sample"
 
     def test_nn_params_registered_in_guide(self):
         data = _make_mock()
@@ -474,11 +487,10 @@ def test_nn_params_registered_in_guide(self):
         for sname in STRUCTURE_NAMES:
             assert f"theta_nn_{sname}_W1" in param_names, f"Missing theta_nn_{sname}_W1"
 
-    def test_tf_op_totals_preserved(self):
+    def test_tf_total_preserved(self):
         data = _make_mock()
         out, _ = _run_guide(data)
-        assert out.tf_total == pytest.approx(6.5e-7)
-        assert out.op_total == pytest.approx(2.5e-8)
+        assert out.tf_total == pytest.approx(1e-5)
 
     def test_K_shapes(self):
         data = _make_mock()
@@ -541,6 +553,11 @@ def _make_ctx(genotypes=None, titrant_names=None, mut_labels=None, pair_labels=N
         pair_labels=pair_labels,
     )
 
+def _base_specs(specs):
+    """Specs other than the per-structure ddG ones (tested in
+    test_ddG_extraction.py)."""
+    return [s for s in specs if "out_names" not in s]
+
 
 class TestGetExtractSpecs:
     def test_returns_list(self):
@@ -551,7 +568,7 @@ def test_returns_list(self):
     def test_has_three_specs_without_epi(self):
         ctx = _make_ctx()
         specs = get_extract_specs(ctx)
-        assert len(specs) == 3
+        assert len(_base_specs(specs)) == 3
 
     def test_has_four_specs_with_epi(self):
         ctx = _make_ctx(
@@ -560,7 +577,7 @@ def test_has_four_specs_with_epi(self):
             pair_labels=["K84L/M42I"],
         )
         specs = get_extract_specs(ctx)
-        assert len(specs) == 4
+        assert len(_base_specs(specs)) == 4
 
     def test_first_spec_has_ln_K_op_and_ln_K_HL(self):
         ctx = _make_ctx()
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py
index 4131fbbe..e127df77 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/O2_C4_K3_U1_a/test_thermo.py
@@ -5,13 +5,18 @@
 
 from tfscreen.tfmodel.generative.components.theta.thermo.O2_C4_K3_U1_a.thermo import (
     ThetaParam,
-    _solve_free_effector,
     _compute_theta,
     _population_moments,
     run_model,
     get_population_moments,
 )
 
+
+def _apo(K_op, K_HL, K_U=0.0, tf_total=650.0):
+    """Apo θ of M2: h = (tf/2)/(1 + K_HL + K_U), W = h·K_op, θ = W/(1+W)."""
+    W = (tf_total / 2.0) * K_op / (1.0 + K_HL + K_U)
+    return W / (1.0 + W)
+
 # ---------------------------------------------------------------------------
 # Minimal mock data (only fields used by thermo functions)
 # ---------------------------------------------------------------------------
@@ -47,76 +52,17 @@ def theta_param(mock_data):
     T, G = mock_data.num_titrant_name, mock_data.num_genotype
     C = mock_data.num_titrant_conc
     return ThetaParam(
-        ln_K_op=jnp.full(G, 2.3),
+        ln_K_op=jnp.full(G, -4.0),
         ln_K_HL=jnp.full(G, -9.0),
         ln_K_E=jnp.full((T, G), -4.0),
         ln_K_U=jnp.full(G, -12.0),
         tf_total=650.0,
-        op_total=25.0,
+        conc_unit_scale=1.0,
         mu=jnp.zeros((T, C, 1)),
         sigma=jnp.ones((T, C, 1)),
     )
 
 
-# ---------------------------------------------------------------------------
-# _solve_free_effector
-# ---------------------------------------------------------------------------
-
-class TestSolveFreeEffector:
-
-    def test_zero_effector_returns_zero(self):
-        e_total = jnp.zeros((1, 3, 1))
-        a  = jnp.ones((2, 1, 4)) * 1e-4
-        # Z0 includes K_U term: 1 + K_op*op + K_HL + K_U
-        Z0 = jnp.ones((1, 1, 4)) * 252.0
-        e_free = _solve_free_effector(e_total, tf_total=650.0, a=a, Z0=Z0)
-        assert jnp.allclose(e_free, 0.0, atol=1e-6)
-
-    def test_e_free_leq_e_total(self):
-        e_total = jnp.array([0.0, 100.0, 1000.0])[None, :, None]
-        a  = jnp.ones((1, 1, 1)) * 1e-4
-        Z0 = jnp.ones((1, 1, 1)) * 252.0
-        e_free = _solve_free_effector(e_total, tf_total=650.0, a=a, Z0=Z0)
-        assert jnp.all(e_free >= 0)
-        assert jnp.all(e_free <= e_total + 1e-9)
-
-    def test_output_shape(self):
-        T, C, G = 2, 3, 4
-        e_total = jnp.ones((1, C, 1)) * 500.0
-        a  = jnp.ones((T, 1, G)) * 1e-5
-        Z0 = jnp.ones((1, 1, G)) * 252.0
-        e_free = _solve_free_effector(e_total, tf_total=650.0, a=a, Z0=Z0)
-        assert e_free.shape == (T, C, G)
-
-    def test_finite_values(self):
-        e_total = jnp.array([0.0, 100.0, 1000.0])[None, :, None]
-        a  = jnp.ones((1, 1, 1)) * 1e-4
-        Z0 = jnp.ones((1, 1, 1)) * 252.0
-        e_free = _solve_free_effector(e_total, tf_total=650.0, a=a, Z0=Z0)
-        assert jnp.all(jnp.isfinite(e_free))
-
-    def test_high_affinity_depletes_effector(self):
-        """Very high K_HL·K_E should deplete free effector significantly."""
-        e_total = jnp.array([1000.0])[None, :, None]
-        a_high = jnp.ones((1, 1, 1)) * 10.0    # high affinity
-        a_low  = jnp.ones((1, 1, 1)) * 1e-10   # negligible affinity
-        Z0 = jnp.ones((1, 1, 1)) * 252.0
-        e_free_high = _solve_free_effector(e_total, 650.0, a_high, Z0)
-        e_free_low  = _solve_free_effector(e_total, 650.0, a_low,  Z0)
-        assert jnp.all(e_free_high < e_free_low)
-
-    def test_larger_Z0_reduces_depletion(self):
-        """Larger Z0 (e.g. large K_U) means less TF sequestering effector → less depletion."""
-        e_total = jnp.array([500.0])[None, :, None]
-        a  = jnp.ones((1, 1, 1)) * 1e-3
-        Z0_small = jnp.ones((1, 1, 1)) * 10.0
-        Z0_large = jnp.ones((1, 1, 1)) * 1e6   # dominated by K_U
-        e_free_small = _solve_free_effector(e_total, 650.0, a, Z0_small)
-        e_free_large = _solve_free_effector(e_total, 650.0, a, Z0_large)
-        # More protein in unfolded state → less L state available → less effector bound
-        assert jnp.all(e_free_large >= e_free_small)
-
-
 # ---------------------------------------------------------------------------
 # _compute_theta
 # ---------------------------------------------------------------------------
@@ -128,7 +74,7 @@ def test_output_shape(self):
         conc = jnp.array([0.0, 100.0, 1000.0])
         theta = _compute_theta(jnp.zeros(G), jnp.full(G, -9.0),
                                jnp.full((T, G), -4.0), jnp.full(G, -12.0),
-                               conc, 650.0, 25.0)
+                               conc, 650.0)
         assert theta.shape == (T, 3, G)
 
     def test_values_in_0_1(self):
@@ -136,7 +82,7 @@ def test_values_in_0_1(self):
         conc = jnp.array([0.0, 50.0, 500.0, 5000.0])
         theta = _compute_theta(jnp.zeros(G), jnp.full(G, -9.0),
                                jnp.full((1, G), -4.0), jnp.full(G, -12.0),
-                               conc, 650.0, 25.0)
+                               conc, 650.0)
         assert jnp.all(theta >= 0)
         assert jnp.all(theta <= 1)
 
@@ -146,32 +92,32 @@ def test_all_finite(self):
         theta = _compute_theta(jnp.linspace(0.0, 4.0, G),
                                jnp.linspace(-12.0, -6.0, G),
                                jnp.full((2, G), -5.0), jnp.full(G, -12.0),
-                               conc, 650.0, 25.0)
+                               conc, 650.0)
         assert jnp.all(jnp.isfinite(theta))
 
     def test_theta_monotone_decreasing_in_effector(self):
         """Higher effector → lower theta (effector competes with operator binding)."""
         G = 2
         conc = jnp.array([0.0, 100.0, 1000.0])
-        theta = _compute_theta(jnp.full(G, 2.3), jnp.full(G, -9.0),
+        theta = _compute_theta(jnp.full(G, -4.0), jnp.full(G, -9.0),
                                jnp.full((1, G), -4.0), jnp.full(G, -12.0),
-                               conc, 650.0, 25.0)
+                               conc, 650.0)
         assert jnp.all(theta[:, 0, :] >= theta[:, 1, :])
         assert jnp.all(theta[:, 1, :] >= theta[:, 2, :])
 
     def test_zero_effector_matches_apo_formula(self):
         """At e_total=0, theta = K_op*[op] / (1 + K_op*[op] + K_HL + K_U)."""
         G = 2
-        ln_K_op = jnp.array([2.3, 2.0])
+        ln_K_op = jnp.array([-4.0, -5.0])
         ln_K_HL = jnp.array([-9.0, -8.0])
         ln_K_U  = jnp.array([-12.0, -10.0])
         conc = jnp.array([0.0, 1.0])
         theta = _compute_theta(ln_K_op, ln_K_HL, jnp.full((1, G), -4.0), ln_K_U,
-                               conc, 650.0, 25.0)
-        K_op = np.exp(np.array([2.3, 2.0]))
+                               conc, 650.0)
+        K_op = np.exp(np.array([-4.0, -5.0]))
         K_HL = np.exp(np.array([-9.0, -8.0]))
         K_U  = np.exp(np.array([-12.0, -10.0]))
-        expected = K_op * 25.0 / (1.0 + K_op * 25.0 + K_HL + K_U)
+        expected = _apo(K_op, K_HL, K_U)
         assert jnp.allclose(theta[0, 0, :], jnp.array(expected), atol=1e-5)
 
     def test_higher_K_op_gives_higher_theta(self):
@@ -181,7 +127,7 @@ def test_higher_K_op_gives_higher_theta(self):
         ln_K_op = jnp.array([1.0, 3.0])   # G=0 weaker, G=1 stronger
         ln_K_HL = jnp.full(G, -9.0)
         theta = _compute_theta(ln_K_op, ln_K_HL, jnp.full((1, G), -4.0), jnp.full(G, -12.0),
-                               conc, 650.0, 25.0)
+                               conc, 650.0)
         assert theta[0, 0, 1] > theta[0, 0, 0]
 
     def test_large_K_U_collapses_theta_to_zero(self):
@@ -189,37 +135,37 @@ def test_large_K_U_collapses_theta_to_zero(self):
         G = 2
         conc = jnp.array([0.0, 100.0])
         ln_K_U_large = jnp.full(G, 20.0)   # K_U ≈ 5e8, completely dominates Z
-        theta = _compute_theta(jnp.full(G, 2.3), jnp.full(G, -9.0),
+        theta = _compute_theta(jnp.full(G, -4.0), jnp.full(G, -9.0),
                                jnp.full((1, G), -4.0), ln_K_U_large,
-                               conc, 650.0, 25.0)
+                               conc, 650.0)
         assert jnp.all(theta < 0.01)
 
     def test_small_K_U_matches_lac_dimer_formula(self):
         """When K_U → 0, the five-state model reduces to four-state lac_dimer."""
         G = 2
         conc = jnp.array([0.0])
-        ln_K_op = jnp.array([2.3, 2.0])
+        ln_K_op = jnp.array([-4.0, -5.0])
         ln_K_HL = jnp.array([-9.0, -8.0])
         ln_K_U_tiny = jnp.full(G, -50.0)   # K_U ≈ 2e-22, negligible
         theta = _compute_theta(ln_K_op, ln_K_HL, jnp.full((1, G), -4.0), ln_K_U_tiny,
-                               conc, 650.0, 25.0)
-        K_op = np.exp(np.array([2.3, 2.0]))
+                               conc, 650.0)
+        K_op = np.exp(np.array([-4.0, -5.0]))
         K_HL = np.exp(np.array([-9.0, -8.0]))
-        # Four-state apo formula: theta = K_op*[op] / (1 + K_op*[op] + K_HL)
-        expected_4state = K_op * 25.0 / (1.0 + K_op * 25.0 + K_HL)
+        # Apo formula without U: W = (tf/2)·K_op / (1 + K_HL)
+        expected_4state = _apo(K_op, K_HL)
         assert jnp.allclose(theta[0, 0, :], jnp.array(expected_4state), atol=1e-4)
 
     def test_K_U_lowers_theta_vs_no_K_U(self):
         """Adding K_U > 0 to the partition function reduces theta relative to K_U=0."""
         G = 2
         conc = jnp.array([0.0])
-        ln_K_op = jnp.full(G, 2.3)
+        ln_K_op = jnp.full(G, -4.0)
         ln_K_HL = jnp.full(G, -9.0)
         ln_K_E  = jnp.full((1, G), -4.0)
         theta_no_U = _compute_theta(ln_K_op, ln_K_HL, ln_K_E, jnp.full(G, -50.0),
-                                    conc, 650.0, 25.0)
+                                    conc, 650.0)
         theta_with_U = _compute_theta(ln_K_op, ln_K_HL, ln_K_E, jnp.full(G, 0.0),
-                                      conc, 650.0, 25.0)
+                                      conc, 650.0)
         assert jnp.all(theta_with_U < theta_no_U)
 
 
@@ -294,14 +240,14 @@ def test_zero_effector_matches_apo_formula(self, mock_data):
         """At zero effector: theta = K_op*[op] / (1 + K_op*[op] + K_HL + K_U)."""
         G = mock_data.num_genotype
         T = mock_data.num_titrant_name
-        ln_K_op_val, ln_K_HL_val, ln_K_U_val = 2.3, -9.0, -12.0
+        ln_K_op_val, ln_K_HL_val, ln_K_U_val = -4.0, -9.0, -12.0
         tp = ThetaParam(
             ln_K_op=jnp.full(G, ln_K_op_val),
             ln_K_HL=jnp.full(G, ln_K_HL_val),
             ln_K_E=jnp.full((T, G), -4.0),
             ln_K_U=jnp.full(G, ln_K_U_val),
             tf_total=650.0,
-            op_total=25.0,
+            conc_unit_scale=1.0,
             mu=jnp.zeros((T, 3, 1)),
             sigma=jnp.ones((T, 3, 1)),
         )
@@ -310,7 +256,7 @@ def test_zero_effector_matches_apo_formula(self, mock_data):
         K_op = np.exp(ln_K_op_val)
         K_HL = np.exp(ln_K_HL_val)
         K_U  = np.exp(ln_K_U_val)
-        expected = K_op * 25.0 / (1.0 + K_op * 25.0 + K_HL + K_U)
+        expected = _apo(K_op, K_HL, K_U)
         assert jnp.allclose(result[:, 0, :], expected, atol=1e-5)
 
     def test_uses_data_titrant_conc(self, theta_param):
@@ -334,11 +280,11 @@ def test_large_K_U_reduces_theta(self, mock_data):
         T = mock_data.num_titrant_name
         C = mock_data.num_titrant_conc
         common = dict(
-            ln_K_op=jnp.full(G, 2.3),
+            ln_K_op=jnp.full(G, -4.0),
             ln_K_HL=jnp.full(G, -9.0),
             ln_K_E=jnp.full((T, G), -4.0),
             tf_total=650.0,
-            op_total=25.0,
+            conc_unit_scale=1.0,
             mu=jnp.zeros((T, C, 1)),
             sigma=jnp.ones((T, C, 1)),
         )
@@ -365,7 +311,7 @@ def test_nontrivial_batch_idx_selects_correct_genotypes(self, mock_data):
         ln_K_U  = jnp.linspace(-15.0, -9.0, G_full)
         tp_full = ThetaParam(
             ln_K_op=ln_K_op, ln_K_HL=ln_K_HL, ln_K_E=ln_K_E, ln_K_U=ln_K_U,
-            tf_total=650.0, op_total=25.0,
+            tf_total=650.0, conc_unit_scale=1.0,
             mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
         )
 
@@ -379,7 +325,7 @@ def test_nontrivial_batch_idx_selects_correct_genotypes(self, mock_data):
 
         theta_all = _compute_theta(
             tp_full.ln_K_op, tp_full.ln_K_HL, tp_full.ln_K_E, tp_full.ln_K_U,
-            batch_data.titrant_conc, tp_full.tf_total, tp_full.op_total,
+            batch_data.titrant_conc, tp_full.tf_total,
         )   # (T, C, G_full)
 
         for local_i, global_g in enumerate(batch_idx):
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_conc_units.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_conc_units.py
new file mode 100644
index 00000000..321923b0
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_conc_units.py
@@ -0,0 +1,114 @@
+"""
+Concentration units shared by the thermodynamic theta models.
+
+Every ``O2_C4`` and ``O2_C12`` component reads ``titrant_conc`` in mM (the
+units of tfscreen growth and binding tables and simulator configs) and
+multiplies it by ``theta_conc_unit_scale`` (1e-3) so that the equations run
+in M, with every effector and operator constant in M⁻¹ and tf_total in M
+(monomer units). The exact nesting of the two families is tested in
+``test_mwc.py``.
+"""
+import dataclasses
+import importlib
+from collections import namedtuple
+
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+import pytest
+
+
+_PKG = "tfscreen.tfmodel.generative.components.theta.thermo"
+_FAMILIES = ["O2_C4_K3_U0_a", "O2_C4_K3_U1_a",
+             "O2_C12_K5_U0_a", "O2_C12_K5_U1_a"]
+_KINDS = ["PK", "PddG", "PnnC"]
+
+# mM grid spanning the simulator configs (0 to 1 mM) and beyond.
+_CONC_MM = np.array([0.0, 1e-5, 1e-4, 1e-3, 3e-3, 0.01, 0.03, 0.1, 0.3, 1.0, 10.0])
+
+Data = namedtuple("Data", ["titrant_conc", "batch_idx", "geno_theta_idx",
+                           "scatter_theta"])
+
+
+def _module(family, kind):
+    return importlib.import_module(f"{_PKG}.{family}.{kind}")
+
+
+def _thermo(family):
+    return importlib.import_module(f"{_PKG}.{family}.thermo")
+
+
+# ---------------------------------------------------------------------------
+# One convention for both families
+# ---------------------------------------------------------------------------
+
+@pytest.mark.parametrize("kind", _KINDS)
+@pytest.mark.parametrize("family", _FAMILIES)
+def test_every_thermo_component_converts_mM_to_M(family, kind):
+    module = _module(family, kind)
+    hp = module.get_hyperparameters()
+    assert hp["theta_conc_unit_scale"] == pytest.approx(1e-3)
+    assert "theta_conc_unit_scale" in {f.name for f in
+                                       dataclasses.fields(module.ModelPriors)}
+    assert hp["theta_tf_total_M"] == pytest.approx(1e-5)
+    # Operator total does not enter either family (TF is in large excess).
+    assert "theta_op_total_M" not in hp
+
+
+def _theta_param(family, G=3, T=1, C=len(_CONC_MM), scale=1e-3):
+    """A ThetaParam with distinct per-genotype constants."""
+    th = _thermo(family)
+    g = jnp.linspace(0.0, 1.0, G)
+    common = dict(tf_total=1e-5,
+                  mu=jnp.zeros((T, C, 1)), sigma=jnp.ones((T, C, 1)),
+                  conc_unit_scale=scale)
+    if family.startswith("O2_C4"):
+        ks = dict(ln_K_op=19.9 + g, ln_K_HL=1.84 + g,
+                  ln_K_E=jnp.tile(12.4 - g, (T, 1)))
+        if family.endswith("U1_a"):
+            ks["ln_K_U"] = -12.0 + g
+    else:
+        ks = dict(ln_K_h_l=1.84 + g, ln_K_h_o=19.9 + g,
+                  ln_K_h_e=jnp.tile(10.9 - g, (T, 1)), ln_K_l_o=-2.3 + g,
+                  ln_K_l_e=jnp.tile(13.5 - g, (T, 1)))
+        if family.endswith("U1_a"):
+            ks["ln_K_u"] = -12.0 + g
+    return th.ThetaParam(**ks, **common)
+
+
+@pytest.mark.parametrize("family", _FAMILIES)
+def test_run_model_reads_data_concentrations_as_mM(family):
+    """mM data with scale 1e-3 gives the theta of the same data in M."""
+    th = _thermo(family)
+    G = 3
+    data_mM = Data(titrant_conc=jnp.array(_CONC_MM), batch_idx=jnp.arange(G),
+                   geno_theta_idx=jnp.arange(G), scatter_theta=0)
+    data_M = data_mM._replace(titrant_conc=jnp.array(_CONC_MM * 1e-3))
+    got = th.run_model(_theta_param(family, scale=1e-3), data_mM)
+    want = th.run_model(_theta_param(family, scale=1.0), data_M)
+    np.testing.assert_allclose(np.asarray(got), np.asarray(want), rtol=1e-6)
+
+
+def _posteriors(param):
+    """One-sample posterior dict from a ThetaParam's K fields."""
+    return {f"theta_{f.name}": np.asarray(getattr(param, f.name))[None]
+            for f in dataclasses.fields(param) if f.name.startswith("ln_K")}
+
+
+@pytest.mark.parametrize("family", _FAMILIES)
+def test_numpy_path_matches_run_model(family):
+    """compute_theta_samples applies the same mM-to-M conversion."""
+    th = _thermo(family)
+    G = 3
+    param = _theta_param(family)
+    data = Data(titrant_conc=jnp.array(_CONC_MM), batch_idx=jnp.arange(G),
+                geno_theta_idx=jnp.arange(G), scatter_theta=0)
+    want = np.asarray(th.run_model(param, data))[0]          # (C, G)
+    calc_df = pd.DataFrame({
+        "titrant_conc": np.tile(_CONC_MM, G),
+        "genotype_idx": np.repeat(np.arange(G), len(_CONC_MM)),
+        "titrant_name_idx": 0})
+    got = th.compute_theta_samples(calc_df, _posteriors(param),
+                                   tf_total=param.tf_total,
+                                   conc_unit_scale=1e-3)[0]
+    np.testing.assert_allclose(got, want.T.ravel(), rtol=1e-4, atol=1e-7)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_ddG_constant_prior.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_ddG_constant_prior.py
new file mode 100644
index 00000000..1dd21dfb
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_ddG_constant_prior.py
@@ -0,0 +1,124 @@
+"""
+U1 equivalence of the Δln K prior and the per-state ΔΔG prior (thermo/prior.py).
+
+In the U1 models the projection P (folded rows plus the unfolding row
+Δln K_U = ΔΔG_H) is square and invertible, so the prior on the constants,
+Δln K ~ MVN(P · μ, P · diag(σ²) · Pᵀ), is a change of variables from the
+per-state prior ΔΔG ~ Normal(μ, σ) (the form of commit c4f03d42, written out
+here as a reference).  The Jacobian |det P| is constant, so the two log
+densities differ by M · log|det P| everywhere and share their MAP point,
+mapped through P⁻¹.
+"""
+
+import importlib
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import numpyro
+import numpyro.distributions as dist
+import pytest
+from numpyro.infer import SVI, Trace_ELBO
+from numpyro.infer.autoguide import AutoDelta
+from numpyro.infer.initialization import init_to_value
+
+from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    dlnK_guesses,
+    posthoc_ddG,
+    sample_dlnK,
+)
+
+_PKG = "tfscreen.tfmodel.generative.components.theta.thermo"
+_M = 150
+_OBS_SD = 0.1
+_U1 = ["O2_C4_K3_U1_a", "O2_C12_K5_U1_a"]
+
+
+def _per_state(name, structs, num_mut, means):
+    """Reference: the per-state centered prior of commit c4f03d42."""
+    S = len(structs)
+    with numpyro.plate(f"{name}_struct_plate", S, dim=-1):
+        sigma = numpyro.sample(f"{name}_ddG_sigma",
+                               dist.LogNormal(DEFAULT_DDG_SIGMA_LOC,
+                                              DEFAULT_DDG_SIGMA_SCALE))
+        with numpyro.plate(f"{name}_mut_plate", num_mut, dim=-2):
+            return numpyro.sample(f"{name}_ddG", dist.Normal(means, sigma))
+
+
+def _problem(family, sigma_true=0.5, seed=0):
+    module = importlib.import_module(f"{_PKG}.{family}.PddG")
+    P = np.asarray(module.DLNK_PROJ)
+    structs = list(module.STRUCTURE_NAMES)
+    S = len(structs)
+    rng = np.random.default_rng(seed)
+    table = rng.normal(0, 2, (_M, S)).astype(np.float32)
+    truth = table + sigma_true * rng.normal(0, 1, (_M, S)).astype(np.float32)
+    obs = truth @ P.T + _OBS_SD * rng.normal(0, 1, (_M, P.shape[0]))
+    return P, structs, jnp.asarray(table), jnp.asarray(obs, jnp.float32)
+
+
+def _observe(dlnK, obs):
+    with numpyro.plate("mut", _M, dim=-2), \
+         numpyro.plate("constant", obs.shape[1], dim=-1):
+        numpyro.sample("obs", dist.Normal(dlnK, _OBS_SD), obs=obs)
+
+
+@pytest.mark.parametrize("family", _U1)
+def test_log_densities_differ_by_the_constant_jacobian(family):
+    P, structs, table, _ = _problem(family)
+    S = len(structs)
+    log_det = np.log(abs(np.linalg.det(P)))
+
+    def per_state():
+        _per_state("theta", structs, _M, table)
+
+    def constants():
+        sample_dlnK("theta", structs, _M, table, P)
+
+    rng = np.random.default_rng(1)
+    diffs = []
+    for _ in range(4):
+        ddG = jnp.asarray(table + rng.normal(0, 1.5, (_M, S)), jnp.float32)
+        sigma = jnp.asarray(np.exp(rng.normal(0, 0.5, S)), jnp.float32)
+        lp_state = numpyro.infer.util.log_density(
+            per_state, (), {}, {"theta_ddG": ddG, "theta_ddG_sigma": sigma})[0]
+        lp_const = numpyro.infer.util.log_density(
+            constants, (), {}, {"theta_dlnK": ddG @ jnp.asarray(P.T, jnp.float32),
+                                "theta_ddG_sigma": sigma})[0]
+        diffs.append(float(lp_state - lp_const))
+    np.testing.assert_allclose(diffs, _M * log_det, rtol=1e-4, atol=0.05)
+
+
+def _map(model, init, steps=4000):
+    guide = AutoDelta(model, init_loc_fn=init_to_value(values=init))
+    svi = SVI(model, guide, numpyro.optim.Adam(0.01), Trace_ELBO())
+    return svi.run(jax.random.PRNGKey(0), steps, progress_bar=False).params
+
+
+@pytest.mark.parametrize("family", _U1)
+def test_map_point_matches_the_per_state_form(family):
+    P, structs, table, obs = _problem(family)
+    Pj = jnp.asarray(P, jnp.float32)
+
+    def per_state():
+        _observe(_per_state("theta", structs, _M, table) @ Pj.T, obs)
+
+    def constants():
+        _observe(sample_dlnK("theta", structs, _M, table, P), obs)
+
+    ref = _map(per_state, {"theta_ddG": table,
+                           "theta_ddG_sigma": jnp.ones(len(structs))})
+    new = _map(constants, dlnK_guesses("theta", table, P))
+
+    sigma_ref = np.asarray(ref["theta_ddG_sigma_auto_loc"])
+    sigma_new = np.asarray(new["theta_ddG_sigma_auto_loc"])
+    np.testing.assert_allclose(sigma_new, sigma_ref, rtol=2e-3)
+
+    ddG_ref = np.asarray(ref["theta_ddG_auto_loc"])
+    ddG_new = np.asarray(posthoc_ddG(new["theta_dlnK_auto_loc"], table,
+                                     jnp.asarray(sigma_new), P))
+    # Same point up to Adam's residual (typically 1e-6, at most a few 1e-3).
+    np.testing.assert_allclose(ddG_new, ddG_ref, atol=5e-3)
+    assert np.median(np.abs(ddG_new - ddG_ref)) < 1e-4
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_ddG_sigma_fixed.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_ddG_sigma_fixed.py
new file mode 100644
index 00000000..7caecdad
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_ddG_sigma_fixed.py
@@ -0,0 +1,319 @@
+"""
+Held per-state ΔΔG scale σ_s (``theta_ddG_sigma_fixed``; thermo/prior.py).
+
+With ``theta_ddG_sigma_fixed`` > 0 every PddG/PnnC module records
+``theta_ddG_sigma`` as a deterministic site of the held value instead of a
+sample site, the guide has no σ_s parameters or site, the MultivariateNormal
+on Δln K uses the held value, and a fit still extracts a ``ddG_sigma``
+table (the held value in every row).  The default 0 samples σ_s as before.
+"""
+
+import importlib
+import importlib.util
+import os
+import warnings
+from pathlib import Path
+
+import h5py
+import jax.numpy as jnp
+import numpy as np
+import numpyro
+import numpyro.distributions as dist
+import pandas as pd
+import pytest
+import yaml
+from numpyro.handlers import seed, trace
+
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
+from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
+    constant_scale_tril,
+    ddG_sigma_fixed,
+    guide_dlnK,
+    projection,
+    sample_dlnK,
+)
+
+_HERE = Path(__file__).resolve().parent
+_PKG = "tfscreen.tfmodel.generative.components.theta.thermo"
+_FAMILIES = ["O2_C4_K3_U0_a", "O2_C4_K3_U1_a", "O2_C12_K5_U0_a",
+             "O2_C12_K5_U1_a"]
+_MODULES = [(fam, kind) for fam in _FAMILIES for kind in ("PddG", "PnnC")]
+_HELD = 1.69
+_SITE = "theta_ddG_sigma"
+
+
+def _load_test_module(path, name):
+    spec = importlib.util.spec_from_file_location(name, path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _sample_sites(tr):
+    return {k for k, v in tr.items() if v["type"] == "sample"}
+
+
+def _params(tr):
+    return {k for k, v in tr.items() if v["type"] == "param"}
+
+
+# ── prior.py ────────────────────────────────────────────────────────────────
+
+class TestPriorBlock:
+    P = projection(mwc.PROJ_C4)
+    S, M = 4, 5
+
+    def _means(self):
+        return jnp.arange(self.M * self.S, dtype=jnp.float32).reshape(
+            self.M, self.S) / 10.0
+
+    def _model_trace(self, sigma_fixed):
+        def model():
+            return sample_dlnK("theta", ["H", "HD", "L", "LE2"], self.M,
+                               self._means(), self.P, sigma_fixed=sigma_fixed)
+        with seed(rng_seed=0):
+            return trace(model).get_trace()
+
+    def _guide_trace(self, sigma_fixed):
+        def g():
+            return guide_dlnK("theta", self.M, self.S, self.P, self._means(),
+                              sigma_fixed=sigma_fixed)
+        with seed(rng_seed=0):
+            return trace(g).get_trace()
+
+    def test_held_sigma_is_deterministic(self):
+        tr = self._model_trace(_HELD)
+        assert tr[_SITE]["type"] == "deterministic"
+        np.testing.assert_allclose(tr[_SITE]["value"], np.full(self.S, _HELD),
+                                   rtol=1e-6)
+        assert _sample_sites(tr) == {"theta_dlnK"}
+
+    def test_mvn_uses_the_held_sigma(self):
+        tr = self._model_trace(_HELD)
+        fn = tr["theta_dlnK"]["fn"]
+        want = constant_scale_tril(jnp.asarray(self.P, dtype=jnp.float32),
+                                   jnp.full(self.S, _HELD))
+        np.testing.assert_allclose(np.asarray(fn.scale_tril).reshape(-1, 3, 3)[0],
+                                   np.asarray(want),
+                                   rtol=1e-5, atol=1e-6)
+        np.testing.assert_allclose(np.asarray(fn.loc),
+                                   np.asarray(self._means()) @ self.P.T,
+                                   rtol=1e-6)
+
+    def test_guide_skips_held_sigma(self):
+        gtr = self._guide_trace(_HELD)
+        assert _SITE not in gtr
+        assert _params(gtr) == {"theta_dlnK_locs", "theta_dlnK_scales"}
+        assert _sample_sites(gtr) == _sample_sites(self._model_trace(_HELD))
+
+    def test_default_samples_sigma(self):
+        tr = self._model_trace(0.0)
+        assert tr[_SITE]["type"] == "sample"
+        assert isinstance(tr[_SITE]["fn"].base_dist, dist.LogNormal)
+        gtr = self._guide_trace(0.0)
+        assert {"theta_ddG_sigma_locs", "theta_ddG_sigma_scales"} <= _params(gtr)
+        assert _sample_sites(gtr) == _sample_sites(tr)
+
+    def test_wt_only_library_with_held_sigma(self):
+        def model():
+            return sample_dlnK("theta", ["H", "HD", "L", "LE2"], 0,
+                               jnp.zeros((0, 4)), self.P, sigma_fixed=_HELD)
+        with seed(rng_seed=0):
+            tr = trace(model).get_trace()
+        assert _sample_sites(tr) == set()
+        assert tr[_SITE]["type"] == "deterministic"
+
+    def test_ddG_sigma_fixed_reads_priors(self):
+        class _P:
+            theta_ddG_sigma_fixed = 0.5
+        assert ddG_sigma_fixed(_P()) == 0.5
+        assert ddG_sigma_fixed(object()) == 0.0
+
+
+# ── all 8 modules ────────────────────────────────────────────────────────────
+
+def _module_and_mock(fam, kind):
+    module = importlib.import_module(f"{_PKG}.{fam}.{kind}")
+    test_mod = _load_test_module(_HERE / fam / f"test_{kind}.py",
+                                 f"_held_sigma_{fam}_{kind}")
+    return module, test_mod._make_mock()
+
+
+def _traces(module, data, priors):
+    with seed(rng_seed=0):
+        mtr = trace(lambda: module.define_model("theta", data, priors)).get_trace()
+    with seed(rng_seed=0):
+        gtr = trace(lambda: module.guide("theta", data, priors)).get_trace()
+    return mtr, gtr
+
+
+@pytest.mark.parametrize("fam, kind", _MODULES)
+def test_default_is_zero_and_static(fam, kind):
+    module = importlib.import_module(f"{_PKG}.{fam}.{kind}")
+    assert module.get_hyperparameters()["theta_ddG_sigma_fixed"] == 0.0
+    priors = module.get_priors()
+    assert priors.theta_ddG_sigma_fixed == 0.0
+    # Static (pytree_node=False): not a leaf, so jit sees a Python float.
+    held = priors.replace(theta_ddG_sigma_fixed=_HELD)
+    import jax
+    assert len(jax.tree_util.tree_leaves(held)) == \
+        len(jax.tree_util.tree_leaves(priors))
+
+
+@pytest.mark.parametrize("fam, kind", _MODULES)
+def test_module_holds_sigma(fam, kind):
+    module, data = _module_and_mock(fam, kind)
+    S = len(module.STRUCTURE_NAMES)
+    held = module.get_priors().replace(theta_ddG_sigma_fixed=_HELD)
+    mtr, gtr = _traces(module, data, held)
+
+    assert mtr[_SITE]["type"] == "deterministic"
+    np.testing.assert_allclose(mtr[_SITE]["value"], np.full(S, _HELD),
+                               rtol=1e-6)
+    assert _SITE not in gtr
+    assert not {"theta_ddG_sigma_locs", "theta_ddG_sigma_scales"} & _params(gtr)
+    assert _sample_sites(mtr) == _sample_sites(gtr)
+
+    # The MVN on Δln K uses the held σ_s.
+    want = constant_scale_tril(jnp.asarray(module.DLNK_PROJ, dtype=jnp.float32),
+                               jnp.full(S, _HELD))
+    C = want.shape[0]
+    got = np.asarray(mtr["theta_dlnK"]["fn"].scale_tril).reshape(-1, C, C)[0]
+    np.testing.assert_allclose(got, np.asarray(want), rtol=1e-5, atol=1e-6)
+
+
+@pytest.mark.parametrize("fam, kind", _MODULES)
+def test_module_default_samples_sigma(fam, kind):
+    module, data = _module_and_mock(fam, kind)
+    mtr, gtr = _traces(module, data, module.get_priors())
+    assert mtr[_SITE]["type"] == "sample"
+    assert gtr[_SITE]["type"] == "sample"
+    assert {"theta_ddG_sigma_locs", "theta_ddG_sigma_scales"} <= _params(gtr)
+
+
+# ── tfs-configure-model --set_priors ─────────────────────────────────────────
+
+_EXTRACTION_TEST = (_HERE.parents[3] / "analysis" / "test_ddG_extraction.py")
+
+
+def _simulate_and_configure(tmp_path, set_priors=None):
+    """Simulate a tiny C4_U0 PddG screen and configure a fit of it."""
+    from tfscreen.simulate.scripts.simulate_cli import run_simulation_from_config
+    from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+
+    helpers = _load_test_module(_EXTRACTION_TEST, "_held_sigma_extraction")
+    component = "thermo.O2_C4_K3_U0_a.PddG"
+    module = importlib.import_module(f"{_PKG}.O2_C4_K3_U0_a.PddG")
+    thermo_data = helpers._reordered_ddG_csv(tmp_path / "ddG.csv",
+                                             module.STRUCTURE_NAMES)
+    cfg = dict(helpers._SIM_CONFIG, theta_component=component,
+               thermo_data=thermo_data)
+    with open(tmp_path / "sim.yaml", "w") as fh:
+        yaml.safe_dump(cfg, fh)
+
+    run = tmp_path / "run"
+    run.mkdir()
+    cwd = os.getcwd()
+    os.chdir(run)
+    try:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            run_simulation_from_config(str(tmp_path / "sim.yaml"),
+                                       out_prefix="tfs_sim", num_replicates=1,
+                                       write_raw=False, write_growth=True)
+            configure_model(growth_df="tfs_sim_growth.csv",
+                            presplit_df="tfs_sim_presplit.csv",
+                            theta_model=component,
+                            library_config="tfs_sim_input-config.yaml",
+                            growth_shares_replicates=True,
+                            thermo_data=thermo_data,
+                            set_priors=set_priors)
+    finally:
+        os.chdir(cwd)
+    return run, module
+
+
+@pytest.mark.parametrize("name", ["theta.theta_ddG_sigma_fixed",
+                                  "theta_ddG_sigma_fixed"])
+def test_set_priors_names_resolve(tmp_path, name):
+    """Both spellings set the PddG/PnnC row of a priors CSV."""
+    from tfscreen.tfmodel.priors_edit import (
+        apply_prior_overrides,
+        parse_prior_overrides,
+    )
+
+    module = importlib.import_module(f"{_PKG}.O2_C4_K3_U0_a.PddG")
+    rows = {f"theta.{k}": v for k, v in module.get_hyperparameters().items()}
+    rows["growth.sample_offset.sigma_fixed"] = 0.0
+    path = tmp_path / "priors.csv"
+    pd.DataFrame({"parameter": list(rows), "value": list(rows.values())}
+                 ).to_csv(path, index=False)
+    out = apply_prior_overrides(str(path),
+                                parse_prior_overrides([f"{name}={_HELD}"]))
+    assert out == {"theta.theta_ddG_sigma_fixed": _HELD}
+    df = pd.read_csv(path).set_index("parameter")["value"]
+    assert float(df["theta.theta_ddG_sigma_fixed"]) == _HELD
+    assert float(df["growth.sample_offset.sigma_fixed"]) == 0.0
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("name", ["theta.theta_ddG_sigma_fixed",
+                                  "theta_ddG_sigma_fixed"])
+def test_set_priors_round_trip(tmp_path, name):
+    from tfscreen.tfmodel.configuration_io import read_configuration
+
+    run, _ = _simulate_and_configure(tmp_path, [f"{name}={_HELD}"])
+    df = pd.read_csv(run / "tfs_configure_priors.csv")
+    row = df.loc[df["parameter"] == "theta.theta_ddG_sigma_fixed", "value"]
+    assert float(row.item()) == _HELD
+    cwd = os.getcwd()
+    os.chdir(run)
+    try:
+        o, _ = read_configuration("tfs_configure_config.yaml")
+    finally:
+        os.chdir(cwd)
+    assert float(o.priors.theta.theta_ddG_sigma_fixed) == _HELD
+
+
+@pytest.mark.slow
+def test_held_sigma_map_laplace_extract(tmp_path):
+    """MAP, full Laplace and extraction on a tiny PddG fit with σ_s held."""
+    from tfscreen.tfmodel.configuration_io import read_configuration
+    from tfscreen.tfmodel.scripts.extract_params_cli import extract_params
+    from tfscreen.tfmodel.scripts.fit_model_cli import fit_model
+    from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+
+    run, module = _simulate_and_configure(
+        tmp_path, [f"theta.theta_ddG_sigma_fixed={_HELD}"])
+    structs = list(module.STRUCTURE_NAMES)
+
+    cwd = os.getcwd()
+    os.chdir(run)
+    try:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            o, _ = read_configuration("tfs_configure_config.yaml")
+            assert float(o.priors.theta.theta_ddG_sigma_fixed) == _HELD
+            fit_model("tfs_configure_config.yaml", seed=1,
+                      analysis_method="map", max_num_epochs=20,
+                      convergence_window_steps=10)
+            sample_posterior("tfs_configure_config.yaml",
+                             "tfs_fit_model_checkpoint.pkl",
+                             laplace="full", num_posterior_samples=5,
+                             skip_growth_observations=True)
+            extract_params("tfs_configure_config.yaml", "tfs_posterior.h5")
+    finally:
+        os.chdir(cwd)
+
+    # No σ_s in the MAP parameters (no sample site, no guide params).
+    params = np.load(next(run.glob("*_params.npz")))
+    assert not [k for k in params.files if k.startswith(_SITE)]
+
+    # The posterior carries the deterministic site at the held value.
+    with h5py.File(run / "tfs_posterior.h5", "r") as hf:
+        sig = np.asarray(hf[_SITE][:])
+    np.testing.assert_allclose(sig, _HELD, rtol=1e-6)
+
+    fit_sigma = pd.read_csv(run / "tfs_params_ddG_sigma.csv")
+    assert list(fit_sigma["structure"]) == structs
+    np.testing.assert_allclose(fit_sigma["q0.5"], _HELD, rtol=1e-6)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_ddG_sigma_map.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_ddG_sigma_map.py
new file mode 100644
index 00000000..4853396a
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_ddG_sigma_map.py
@@ -0,0 +1,126 @@
+"""
+MAP regression test for the structural prior's σ_s (thermo/prior.py).
+
+History.  Until 2026-10-11 σ_s was a ``pyro.param`` and ΔΔG was written
+``prior_mean + σ_s · offset``; MAP ran σ_s to several kT whatever the truth.
+Commit c4f03d42 made σ_s a sample site and sampled per-state ΔΔG centered on
+the prior means.  That fixed the U1 models, but in the U0 models (no
+unfolded state) a shift equal across all states changes no constant, and
+joint MAP used it to collapse one state's σ_s: on C4_U0 with true σ_s = 0.3
+kT, σ_LE2 went to 0.001.
+
+Now the prior sits on the constants, Δln K ~ MVN(P · table, P · diag(σ²) ·
+Pᵀ).  We simulate per-state ΔΔG = table + Normal(0, σ) for a few hundred
+mutations, observe the model's constants Δln K = P · ΔΔG with small noise,
+fit by MAP with AutoDelta, and check that every σ_s comes back near the
+truth (no collapse, U0 included) and that the fitted Δln K beat both a
+flat-prior (PK-like) fit and the table alone.
+"""
+
+import importlib
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import numpyro
+import numpyro.distributions as dist
+import pytest
+from numpyro.infer import SVI, Trace_ELBO
+from numpyro.infer.autoguide import AutoDelta
+from numpyro.infer.initialization import init_to_value
+
+from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
+    dlnK_guesses,
+    sample_dlnK,
+)
+
+_PKG = "tfscreen.tfmodel.generative.components.theta.thermo"
+_M = 300          # mutations
+_OBS_SD = 0.05    # SD of each observed Δln K
+_STEPS = 3000
+
+
+def _projection(family):
+    """(C, S) projection the PddG model samples Δln K through."""
+    module = importlib.import_module(f"{_PKG}.{family}.PddG")
+    return np.asarray(module.DLNK_PROJ), list(module.STRUCTURE_NAMES)
+
+
+def _simulate(family, sigma_true, seed=0):
+    proj, structs = _projection(family)
+    S = len(structs)
+    rng = np.random.default_rng(seed)
+    table = rng.normal(0, 2, (_M, S)).astype(np.float32)
+    truth = table + sigma_true * rng.normal(0, 1, (_M, S)).astype(np.float32)
+    true_dlnK = truth @ proj.T
+    obs = true_dlnK + _OBS_SD * rng.normal(0, 1, true_dlnK.shape)
+    return (proj, structs, jnp.asarray(table), true_dlnK,
+            jnp.asarray(obs, dtype=jnp.float32))
+
+
+def _map(model, init):
+    guide = AutoDelta(model, init_loc_fn=init_to_value(values=init))
+    svi = SVI(model, guide, numpyro.optim.Adam(0.01), Trace_ELBO())
+    return svi.run(jax.random.PRNGKey(0), _STEPS, progress_bar=False).params
+
+
+def _observe(dlnK, obs):
+    with numpyro.plate("mut", _M, dim=-2), \
+         numpyro.plate("constant", obs.shape[1], dim=-1):
+        numpyro.sample("obs", dist.Normal(dlnK, _OBS_SD), obs=obs)
+
+
+def _fit_structural(proj, structs, table, obs):
+    def model():
+        _observe(sample_dlnK("theta", structs, _M, table, proj), obs)
+
+    p = _map(model, dlnK_guesses("theta", table, proj))
+    return (np.asarray(p["theta_dlnK_auto_loc"]),
+            np.asarray(p["theta_ddG_sigma_auto_loc"]))
+
+
+def _fit_flat(obs):
+    """A PK-like fit: Δln K under a wide prior, no structural means."""
+    def model():
+        with numpyro.plate("constant", obs.shape[1], dim=-1), \
+             numpyro.plate("mut_dlnK", _M, dim=-2):
+            dlnK = numpyro.sample("theta_dlnK", dist.Normal(0.0, 10.0))
+        _observe(dlnK, obs)
+
+    p = _map(model, {"theta_dlnK": jnp.zeros(obs.shape)})
+    return np.asarray(p["theta_dlnK_auto_loc"])
+
+
+def _rmse(a, b):
+    return float(np.sqrt(np.mean((np.asarray(a) - np.asarray(b)) ** 2)))
+
+
+@pytest.mark.parametrize("family, sigma_true", [
+    ("O2_C4_K3_U1_a", 1.0),
+    ("O2_C4_K3_U1_a", 0.3),
+    ("O2_C4_K3_U0_a", 1.0),
+    ("O2_C4_K3_U0_a", 0.3),     # collapsed σ_LE2 to 0.001 in the per-state form
+    ("O2_C12_K5_U0_a", 1.0),
+    ("O2_C12_K5_U0_a", 0.3),
+])
+def test_map_recovers_sigma_and_shrinks_dlnK(family, sigma_true):
+    proj, structs, table, true_dlnK, obs = _simulate(family, sigma_true)
+    dlnK, sigma = _fit_structural(proj, structs, table, obs)
+    flat = _fit_flat(obs)
+
+    # Every σ_s lands near the truth: no state collapses.
+    assert sigma.shape == (len(structs),)
+    np.testing.assert_array_less(sigma, 1.6 * sigma_true)
+    np.testing.assert_array_less(sigma_true / 1.6, sigma)
+
+    # The fitted Δln K beat the table alone and are no worse than the flat
+    # fit.  At σ = 1 kT the constants' prior SD dwarfs the 0.05 data noise,
+    # so shrinkage is ~1e-4 of the error and the two fits tie; at 0.3 kT
+    # the shrinkage is a real (if small) gain.
+    table_dlnK = np.asarray(table) @ proj.T
+    err, flat_err = _rmse(dlnK, true_dlnK), _rmse(flat, true_dlnK)
+    assert err < _rmse(table_dlnK, true_dlnK)
+    if sigma_true < 0.5:
+        assert err < flat_err
+    else:
+        assert err < 1.002 * flat_err
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py
index 8e51d337..8018c41c 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_io.py
@@ -394,3 +394,449 @@ def test_empty_mut_labels_gives_zero_rows(self, tmp_path):
         _write_ddG_csv(p, self._full_csv_rows())
         result = load_ddG_prior_csv(p, [])
         assert result['struct_features'].shape == (0, len(self._STRUCTS))
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Units, column handling, numbering and coverage checks
+# ──────────────────────────────────────────────────────────────────────────────
+
+import warnings
+
+from tfscreen.tfmodel.generative.components.theta.thermo.io import (
+    THERMO_OPTION_DEFAULTS,
+    ddG_to_kT_factor,
+    load_thermo_data,
+    normalize_thermo_options,
+    required_struct_names,
+)
+
+_C4 = ("H", "HD", "L", "LE2")
+_C12 = ("H", "HO", "L", "LO", "HE2", "LE2")
+
+# First 60 residues of LacI as they appear (0-based, V0 = residue 1) in the
+# 12-state ensemble CSV this loader has to handle.
+_LACI_60 = "VKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNRVAQQLAGKQS"
+_ENSEMBLE_COLS = ["unfolded", "L", "HO", "LE2", "HOE2", "LE", "HOE", "H",
+                  "LO", "LOE", "LOE2", "HE", "HE2"]
+
+
+def _ensemble_like_csv(path, n_pos=60):
+    """Mimic notebooks/other-data/ddG_ensemble_clean_with_effector.csv:
+    pandas index column, 'Mutation', 0-based numbering, 12 states + unfolded."""
+    rng = np.random.default_rng(0)
+    labels = [f"{wt}{i}{mt}" for i, wt in enumerate(_LACI_60[:n_pos])
+              for mt in "ACDEFGHIKLMNPQRSTVWY"]
+    df = pd.DataFrame(rng.normal(2, 1, (len(labels), len(_ENSEMBLE_COLS))),
+                      columns=_ENSEMBLE_COLS)
+    df["unfolded"] = 0.0
+    df.insert(0, "Mutation", labels)
+    df.to_csv(path)            # writes the 'Unnamed: 0' index column
+    return str(path)
+
+
+def _lib_label(pos1, mt):
+    """Library (1-based) label for LacI residue pos1."""
+    return f"{_LACI_60[pos1 - 1]}{pos1}{mt}"
+
+
+class TestThermoOptions:
+
+    def test_defaults(self):
+        assert normalize_thermo_options(None) == THERMO_OPTION_DEFAULTS
+        assert normalize_thermo_options({}) == THERMO_OPTION_DEFAULTS
+
+    def test_kT_factor_is_exactly_one(self):
+        assert ddG_to_kT_factor("kT") == 1.0
+
+    def test_kcal_factor(self):
+        assert ddG_to_kT_factor("kcal/mol", 298.15) == pytest.approx(
+            1.0 / (1.987204259e-3 * 298.15), rel=1e-12)
+        assert ddG_to_kT_factor("kcal/mol", 298.15) == pytest.approx(1.6878, abs=1e-4)
+
+    def test_kJ_factor_and_temperature(self):
+        assert ddG_to_kT_factor("kJ/mol", 310.0) == pytest.approx(
+            1.0 / (8.314462618e-3 * 310.0), rel=1e-12)
+
+    def test_unit_aliases(self):
+        assert normalize_thermo_options({"ddG_units": "KCAL"})["ddG_units"] == "kcal/mol"
+        assert normalize_thermo_options({"ddG_units": "kj/mol"})["ddG_units"] == "kJ/mol"
+
+    @pytest.mark.parametrize("opts, match", [
+        ({"ddG_units": "eV"}, "not recognised"),
+        ({"temperature_K": -1}, "temperature_K"),
+        ({"residue_offset": 1.5}, "residue_offset"),
+        ({"max_missing_fraction": 2}, "max_missing_fraction"),
+        ({"units": "kT"}, "Unknown thermo_options"),
+        ({"column_map": "HD"}, "model_name=csv_column"),
+    ])
+    def test_bad_options_raise(self, opts, match):
+        with pytest.raises(ValueError, match=match):
+            normalize_thermo_options(opts)
+
+    def test_column_map_string(self):
+        opts = normalize_thermo_options({"column_map": "HD=HO, LE2=LOE2"})
+        assert opts["column_map"] == {"HD": "HO", "LE2": "LOE2"}
+
+    def test_required_struct_names(self):
+        assert required_struct_names("thermo.O2_C4_K3_U1_a.PddG") == _C4
+        assert required_struct_names("thermo.O2_C12_K5_U0_a.PnnC") == _C12
+        assert required_struct_names("thermo.O2_C4_K3_U0_a.PK") is None
+        assert required_struct_names("hill_geno") is None
+        assert required_struct_names(None) is None
+
+
+class TestDdGCsvUnits:
+
+    _ROWS = [["A10G", 1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+             ["C20R", 0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
+             ["D30E", -1.0, -2.0, -3.0, -4.0, -5.0, -6.0]]
+    _MUTS = ["A10G", "C20R", "D30E"]
+
+    def test_default_uses_values_as_is(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        _write_ddG_csv(p, self._ROWS)
+        raw = np.array([r[1:] for r in self._ROWS], dtype=np.float32)
+        for opts in (None, {}, {"ddG_units": "kT", "temperature_K": 350.0}):
+            out = load_ddG_prior_csv(p, self._MUTS, thermo_options=opts)
+            np.testing.assert_array_equal(out["struct_features"], raw)
+
+    def test_kcal_converted_to_kT(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        _write_ddG_csv(p, self._ROWS)
+        out = load_ddG_prior_csv(p, self._MUTS,
+                                 thermo_options={"ddG_units": "kcal/mol"})
+        RT = 1.987204259e-3 * 298.15
+        expected = np.array([r[1:] for r in self._ROWS]) / RT
+        np.testing.assert_allclose(out["struct_features"], expected, rtol=1e-6)
+
+    def test_kJ_converted_to_kT(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        _write_ddG_csv(p, self._ROWS)
+        out = load_ddG_prior_csv(p, self._MUTS, thermo_options={
+            "ddG_units": "kJ/mol", "temperature_K": 310.0})
+        RT = 8.314462618e-3 * 310.0
+        expected = np.array([r[1:] for r in self._ROWS]) / RT
+        np.testing.assert_allclose(out["struct_features"], expected, rtol=1e-6)
+
+
+class TestDdGCsvColumns:
+
+    def _csv(self, tmp_path, columns, rows, index=False):
+        p = str(tmp_path / "ddG.csv")
+        pd.DataFrame(rows, columns=columns).to_csv(p, index=index)
+        return p
+
+    def test_mutation_alias_and_unnamed_index(self, tmp_path):
+        p = self._csv(tmp_path, ["Mutation", "H", "HD", "L", "LE2"],
+                      [["A10G", 1.0, 2.0, 3.0, 4.0]], index=True)
+        assert "Unnamed: 0" in pd.read_csv(p).columns
+        out = load_ddG_prior_csv(p, ["A10G"])
+        assert out["struct_names"] == _C4
+        np.testing.assert_array_equal(out["struct_features"][0], [1, 2, 3, 4])
+
+    def test_lowercase_mutation_alias(self, tmp_path):
+        p = self._csv(tmp_path, ["mutation", "H"], [["A10G", 1.0]])
+        assert load_ddG_prior_csv(p, ["A10G"])["struct_names"] == ("H",)
+
+    def test_two_mutation_columns_raise(self, tmp_path):
+        p = self._csv(tmp_path, ["mut", "Mutation", "H"], [["A10G", "A10G", 1.0]])
+        with pytest.raises(ValueError, match="more than one mutation column"):
+            load_ddG_prior_csv(p, ["A10G"])
+
+    def test_extra_columns_ignored_and_reordered(self, tmp_path, capsys):
+        p = self._csv(tmp_path, ["mut", "LE2", "extra", "L", "HD", "H", "unfolded"],
+                      [["A10G", 4.0, 9.0, 3.0, 2.0, 1.0, 0.0]])
+        out = load_ddG_prior_csv(p, ["A10G"], struct_names=_C4)
+        assert out["struct_names"] == _C4
+        np.testing.assert_array_equal(out["struct_features"][0], [1, 2, 3, 4])
+        assert "ignoring column(s)" in capsys.readouterr().out
+
+    def test_missing_required_columns_listed(self, tmp_path):
+        p = self._csv(tmp_path, ["mut", "H", "L"], [["A10G", 1.0, 3.0]])
+        with pytest.raises(ValueError, match=r"missing structure column\(s\) \['HD', 'LE2'\]"):
+            load_ddG_prior_csv(p, ["A10G"], struct_names=_C4)
+
+    def test_column_map(self, tmp_path):
+        p = self._csv(tmp_path, ["mut", "H", "HO", "L", "LE2"],
+                      [["A10G", 1.0, 2.0, 3.0, 4.0]])
+        for cmap in ({"HD": "HO"}, "HD=HO"):
+            out = load_ddG_prior_csv(p, ["A10G"], struct_names=_C4,
+                                     thermo_options={"column_map": cmap})
+            np.testing.assert_array_equal(out["struct_features"][0], [1, 2, 3, 4])
+
+    def test_column_map_to_absent_column(self, tmp_path):
+        p = self._csv(tmp_path, ["mut", "H", "L", "LE2"], [["A10G", 1.0, 3.0, 4.0]])
+        with pytest.raises(ValueError, match="mapped to 'HO'"):
+            load_ddG_prior_csv(p, ["A10G"], struct_names=_C4,
+                               thermo_options={"column_map": {"HD": "HO"}})
+
+    def test_column_map_unknown_structure(self, tmp_path):
+        p = self._csv(tmp_path, ["mut", "H", "HD", "L", "LE2"],
+                      [["A10G", 1.0, 2.0, 3.0, 4.0]])
+        with pytest.raises(ValueError, match="not structures of this model"):
+            load_ddG_prior_csv(p, ["A10G"], struct_names=_C4,
+                               thermo_options={"column_map": {"HO": "H"}})
+
+    def test_non_numeric_column_raises(self, tmp_path):
+        p = self._csv(tmp_path, ["mut", "H"], [["A10G", "big"]])
+        with pytest.raises(ValueError, match="not numeric"):
+            load_ddG_prior_csv(p, ["A10G"])
+
+    def test_duplicate_labels_raise(self, tmp_path):
+        p = self._csv(tmp_path, ["mut", "H"], [["A10G", 1.0], ["A10G", 2.0]])
+        with pytest.raises(ValueError, match="more than once"):
+            load_ddG_prior_csv(p, ["A10G"])
+
+
+class TestDdGCsvCoverage:
+
+    def _csv(self, tmp_path, labels):
+        p = str(tmp_path / "ddG.csv")
+        df = pd.DataFrame({"mut": labels, "H": np.arange(len(labels), dtype=float)})
+        df.to_csv(p, index=False)
+        return p
+
+    def test_full_coverage_reported(self, tmp_path, capsys):
+        p = self._csv(tmp_path, ["A10G", "C20R"])
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            load_ddG_prior_csv(p, ["A10G", "C20R"])
+        assert "ΔΔG for 2/2 library mutations (100.0%)" in capsys.readouterr().out
+
+    def test_missing_above_threshold_warns_and_zero_fills(self, tmp_path):
+        p = self._csv(tmp_path, ["A10G"])
+        with pytest.warns(UserWarning, match=r"no ΔΔG for 2 of 3.*C20R, D30E"):
+            out = load_ddG_prior_csv(p, ["A10G", "C20R", "D30E"])
+        np.testing.assert_array_equal(out["struct_features"][1:], 0.0)
+
+    def test_threshold_is_configurable(self, tmp_path):
+        p = self._csv(tmp_path, ["A10G"])
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            load_ddG_prior_csv(p, ["A10G", "C20R"],
+                               thermo_options={"max_missing_fraction": 0.5})
+        with pytest.warns(UserWarning, match="max_missing_fraction 0.4"):
+            load_ddG_prior_csv(p, ["A10G", "C20R"],
+                               thermo_options={"max_missing_fraction": 0.4})
+
+    def test_warning_list_is_truncated(self, tmp_path):
+        p = self._csv(tmp_path, ["A1G"])
+        muts = ["A1G"] + [f"A{i}G" for i in range(2, 30)]
+        with pytest.warns(UserWarning, match=r"\(18 more\)"):
+            load_ddG_prior_csv(p, muts)
+
+    def test_zero_coverage_raises(self, tmp_path):
+        p = self._csv(tmp_path, ["A10G", "C20R"])
+        with pytest.raises(ValueError, match="covers none of the 2 library mutations"):
+            load_ddG_prior_csv(p, ["E40K", "F50L"])
+
+    def test_wt_mismatch_raises_with_offset_hint(self, tmp_path):
+        # File numbered one lower than the library: library H29 is file H28.
+        p = self._csv(tmp_path, ["H28A", "V29A", "S27A"])
+        with pytest.raises(ValueError, match=r"wild-type residues disagree.*"
+                                             r"H29A \(file wt V29\).*"
+                                             r"residue_offset is 1"):
+            load_ddG_prior_csv(p, ["H29A", "S28A"])
+
+    def test_residue_offset_lines_up_numbering(self, tmp_path):
+        p = self._csv(tmp_path, ["H28A", "V29A", "S27A"])
+        out = load_ddG_prior_csv(p, ["H29A", "S28A"],
+                                 thermo_options={"residue_offset": 1})
+        np.testing.assert_array_equal(out["struct_features"][:, 0], [0.0, 2.0])
+
+    def test_inconsistent_file_wt_raises(self, tmp_path):
+        p = self._csv(tmp_path, ["A10G", "C10R"])
+        with pytest.raises(ValueError, match="two wild-type residues"):
+            load_ddG_prior_csv(p, ["A10G"])
+
+    def test_blank_values_count_as_missing(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        pd.DataFrame({"mut": ["A10G", "C20R"], "H": [1.0, np.nan]}).to_csv(p, index=False)
+        with pytest.warns(UserWarning, match="blank values: C20R"):
+            out = load_ddG_prior_csv(p, ["A10G", "C20R"])
+        assert out["struct_features"][1, 0] == 0.0
+
+
+class TestEnsembleLikeCsv:
+    """The 12-state ensemble CSV layout (see _ensemble_like_csv)."""
+
+    _LIB = [_lib_label(29, "A"), _lib_label(30, "G"), _lib_label(52, "D")]  # H29A ...
+
+    def test_c4_names_missing_HD(self, tmp_path):
+        p = _ensemble_like_csv(tmp_path / "ens.csv")
+        with pytest.raises(ValueError, match=r"missing structure column\(s\) \['HD'\]"):
+            load_thermo_data(p, self._LIB, theta_name="thermo.O2_C4_K3_U0_a.PddG")
+
+    def test_c12_numbering_offset_detected(self, tmp_path):
+        p = _ensemble_like_csv(tmp_path / "ens.csv")
+        with pytest.raises(ValueError, match=r"H29A \(file wt V29\).*residue_offset is 1"):
+            load_thermo_data(p, self._LIB, theta_name="thermo.O2_C12_K5_U0_a.PddG")
+
+    def test_c12_loads_with_offset(self, tmp_path):
+        p = _ensemble_like_csv(tmp_path / "ens.csv")
+        out = load_thermo_data(p, self._LIB, theta_name="thermo.O2_C12_K5_U1_a.PddG",
+                               thermo_options={"residue_offset": 1})
+        assert out["struct_names"] == _C12
+        df = pd.read_csv(p).set_index("Mutation")
+        np.testing.assert_allclose(out["struct_features"][0],
+                                   df.loc["H28A", list(_C12)].to_numpy(), rtol=1e-6)
+
+    def test_c4_loads_with_offset_and_map(self, tmp_path):
+        p = _ensemble_like_csv(tmp_path / "ens.csv")
+        out = load_thermo_data(p, self._LIB, theta_name="thermo.O2_C4_K3_U0_a.PddG",
+                               thermo_options={"residue_offset": 1,
+                                               "column_map": {"HD": "HO"}})
+        df = pd.read_csv(p).set_index("Mutation")
+        np.testing.assert_allclose(out["struct_features"][2],
+                                   df.loc["V51D", ["H", "HO", "L", "LE2"]].to_numpy(),
+                                   rtol=1e-6)
+
+    def test_real_file_if_present(self):
+        import os
+        real = os.path.expanduser(
+            "~/work/programming/git-clones/tfscreen/notebooks/other-data/"
+            "ddG_ensemble_clean_with_effector.csv")
+        if not os.path.exists(real):
+            pytest.skip("real ensemble CSV not on this machine")
+        with pytest.raises(ValueError, match=r"\['HD'\]"):
+            load_thermo_data(real, self._LIB, theta_name="thermo.O2_C4_K3_U0_a.PddG")
+        with pytest.raises(ValueError, match="residue_offset is 1"):
+            load_thermo_data(real, self._LIB, theta_name="thermo.O2_C12_K5_U0_a.PddG")
+        out = load_thermo_data(real, self._LIB, theta_name="thermo.O2_C12_K5_U0_a.PddG",
+                               thermo_options={"residue_offset": 1})
+        assert out["struct_features"].shape == (3, 6)
+
+
+class TestStructEnsembleChecks:
+
+    def test_missing_residue_reports_coverage(self, tmp_path):
+        p = str(tmp_path / "ens.h5")
+        _write_h5(p, {"H": {"res_nums": [10, 20]}})
+        with pytest.raises(ValueError, match=r"1 of 2 library mutations are absent "
+                                             r"\(1/2 covered.*D30E"):
+            load_struct_ensemble(p, ["A10G", "D30E"])
+
+    def test_residue_offset_applied(self, tmp_path):
+        p = str(tmp_path / "ens.h5")
+        logP = np.arange(40, dtype=np.float32).reshape(2, 20)
+        _write_h5(p, {"H": {"res_nums": [9, 19], "logP": logP}})
+        out = load_struct_ensemble(p, ["C20R"], thermo_options={"residue_offset": 1})
+        np.testing.assert_array_equal(out["struct_features"][0, 0, :20], logP[1])
+
+    def test_wt_aa_mismatch_raises(self, tmp_path):
+        p = str(tmp_path / "ens.h5")
+        _write_h5(p, {"H": {"res_nums": [9, 10, 11]}})
+        with h5py.File(p, "a") as hf:
+            hf["H"].create_dataset("wt_aa", data=["A", "V", "C"],
+                                   dtype=h5py.string_dtype())
+        with pytest.raises(ValueError, match=r"A10G \(file wt V10\).*residue_offset is 1"):
+            load_struct_ensemble(p, ["A10G", "C12R"])
+
+    def test_wt_aa_match_passes(self, tmp_path):
+        p = str(tmp_path / "ens.h5")
+        _write_h5(p, {"H": {"res_nums": [10, 11]}})
+        with h5py.File(p, "a") as hf:
+            hf["H"].create_dataset("wt_aa", data=["A", "X"], dtype=h5py.string_dtype())
+        out = load_struct_ensemble(p, ["A10G", "C11R"])
+        assert out["struct_features"].shape == (2, 1, 60)
+
+
+class TestPddGStructPerm:
+
+    @pytest.mark.parametrize("module, names", [
+        ("O2_C4_K3_U0_a", _C4), ("O2_C4_K3_U1_a", _C4),
+        ("O2_C12_K5_U0_a", _C12), ("O2_C12_K5_U1_a", _C12),
+    ])
+    def test_extras_ignored_missing_listed(self, module, names):
+        import importlib
+        import types
+        mod = importlib.import_module(
+            f"tfscreen.tfmodel.generative.components.theta.thermo.{module}.PddG")
+        data = types.SimpleNamespace(struct_names=("unfolded",) + tuple(reversed(names)))
+        perm = mod._get_struct_perm(data)
+        assert [data.struct_names[i] for i in perm] == list(names)
+        data = types.SimpleNamespace(struct_names=names[1:])
+        with pytest.raises(ValueError, match=rf"lacks \['{names[0]}'\]"):
+            mod._get_struct_perm(data)
+
+
+class TestIoEdgeCases:
+    """Small branches of the option parsing and coverage checks."""
+
+    def test_struct_ensemble_with_no_mutations(self, tmp_path):
+        p = str(tmp_path / "ens.h5")
+        _write_h5(p, {"H": {"res_nums": [10]}})
+        out = load_struct_ensemble(p, [])
+        assert out["struct_names"] == ("H",)
+        assert out["struct_features"].shape[0] == 0
+
+    def test_column_map_string_skips_empty_items(self):
+        opts = normalize_thermo_options({"column_map": "HD=HO,, ,LE2=LOE2,"})
+        assert opts["column_map"] == {"HD": "HO", "LE2": "LOE2"}
+
+    def test_column_map_of_wrong_type_raises(self):
+        with pytest.raises(ValueError, match="column_map must be a dict.*got int"):
+            normalize_thermo_options({"column_map": 3})
+
+    def test_thermo_options_not_a_dict_raises(self):
+        with pytest.raises(ValueError, match="thermo_options must be a dict; got list"):
+            normalize_thermo_options(["kT"])
+
+    def test_unparseable_library_label_skips_wt_check(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        pd.DataFrame({"mut": ["A10G"], "H": [1.5]}).to_csv(p, index=False)
+        with pytest.warns(UserWarning):
+            out = load_ddG_prior_csv(p, ["A10G", "notamutation"])
+        np.testing.assert_array_equal(out["struct_features"][:, 0], [1.5, 0.0])
+
+    def test_column_map_names_mutation_column(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        pd.DataFrame({"label": ["A10G"], "H": [2.0]}).to_csv(p, index=False)
+        out = load_ddG_prior_csv(p, ["A10G"],
+                                 thermo_options={"column_map": {"mut": "label"}})
+        assert out["struct_names"] == ("H",)
+        np.testing.assert_array_equal(out["struct_features"][0], [2.0])
+
+    def test_column_map_mutation_column_absent_raises(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        pd.DataFrame({"mut": ["A10G"], "H": [2.0]}).to_csv(p, index=False)
+        with pytest.raises(ValueError, match="names mutation column 'label'"):
+            load_ddG_prior_csv(p, ["A10G"],
+                               thermo_options={"column_map": {"mut": "label"}})
+
+    def test_column_map_without_model_identity_entry(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        pd.DataFrame({"mut": ["A10G"], "H": [1.0], "L": [3.0]}).to_csv(
+            p, index=False)
+        # A structure mapped to its own column keeps its place and values.
+        out = load_ddG_prior_csv(p, ["A10G"],
+                                 thermo_options={"column_map": {"H": "H"}})
+        assert out["struct_names"] == ("H", "L")
+        np.testing.assert_array_equal(out["struct_features"][0], [1.0, 3.0])
+
+    def test_column_map_without_model_adds_structure(self, tmp_path):
+        # HO is kept as its own structure and copied to HD: one file column
+        # supplies two structures (failed with an ambiguous-truth-value error
+        # until 2026-10-25)
+        p = str(tmp_path / "ddG.csv")
+        pd.DataFrame({"mut": ["A10G"], "H": [1.0], "HO": [2.0]}).to_csv(
+            p, index=False)
+        out = load_ddG_prior_csv(p, ["A10G"],
+                                 thermo_options={"column_map": {"HD": "HO"}})
+        assert out["struct_names"] == ("H", "HO", "HD")
+        np.testing.assert_array_equal(out["struct_features"][0], [1.0, 2.0, 2.0])
+
+    def test_column_map_two_structures_from_one_column(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        pd.DataFrame({"mut": ["A10G"], "H": [1.0], "L": [3.0],
+                      "LE2": [4.0]}).to_csv(p, index=False)
+        out = load_ddG_prior_csv(p, ["A10G"], struct_names=_C4,
+                                 thermo_options={"column_map": {"HD": "H"}})
+        np.testing.assert_array_equal(out["struct_features"][0], [1, 1, 3, 4])
+
+    def test_column_map_without_model_to_absent_column_raises(self, tmp_path):
+        p = str(tmp_path / "ddG.csv")
+        pd.DataFrame({"mut": ["A10G"], "H": [1.0]}).to_csv(p, index=False)
+        with pytest.raises(ValueError, match="maps 'HD' to column 'HO'"):
+            load_ddG_prior_csv(p, ["A10G"],
+                               thermo_options={"column_map": {"HD": "HO"}})
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_mwc.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_mwc.py
new file mode 100644
index 00000000..2f53ee2f
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_mwc.py
@@ -0,0 +1,713 @@
+"""
+The standard MWC dimer shared by the O2_C4 (M2) and O2_C12 (M3) models.
+
+Covers the two-site effector polynomial (against a brute-force sum over
+explicit species), the free-effector cubic and its Newton solve, the exact
+nesting of M2 in M3 (K_h_e = K_l_o = 0), the 80 µM default wt midpoints and
+the structural projection rows.
+"""
+import importlib
+import itertools
+import math
+from collections import namedtuple
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+import pytest
+from scipy.optimize import brentq
+
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
+
+_PKG = "tfscreen.tfmodel.generative.components.theta.thermo"
+_TF = mwc.DEFAULT_TF_TOTAL_M
+
+Data = namedtuple("Data", ["titrant_conc", "batch_idx", "geno_theta_idx",
+                           "scatter_theta"])
+
+
+def _module(family, kind):
+    return importlib.import_module(f"{_PKG}.{family}.{kind}")
+
+
+# ---------------------------------------------------------------------------
+# Brute force: explicit species, numerical mass balance
+# ---------------------------------------------------------------------------
+
+def _brute_force(K_h_l, K_h_o, K_h_e, K_l_o, K_l_e, K_u, e_total, tf_total):
+    """
+    θ and free effector from an explicit list of dimer species.
+
+    Each conformation X in {H, L} has two distinguishable effector sites, so
+    four microstates (s1, s2) with weight c_X·(K_x_e·e)^(s1+s2); U has one.
+    TF not bound to operator obeys r = h·Σw, effector obeys
+    e_total = e + h·Σ w·(s1+s2). Operator-bound species carry an extra
+    K_x_o and give W = h·Σ w·K_x_o, θ = W/(1+W). The polynomial is never
+    written down, so this checks the statistical factor of 2 independently.
+    """
+    r = tf_total / 2.0
+    confs = [(1.0, K_h_e, K_h_o), (K_h_l, K_l_e, K_l_o)]
+
+    def sums(e):
+        tot = K_u
+        bound = 0.0
+        op = 0.0
+        for c, Ke, Ko in confs:
+            for s1, s2 in itertools.product((0, 1), repeat=2):
+                n = s1 + s2
+                w = c * (Ke * e) ** n
+                tot += w
+                bound += w * n
+                op += w * Ko
+        return tot, bound, op
+
+    def balance(e):
+        tot, bound, _ = sums(e)
+        return e + r * bound / tot - e_total
+
+    e = 0.0 if e_total == 0 else brentq(balance, 0.0, e_total,
+                                        xtol=1e-300, rtol=1e-14)
+    tot, _, op = sums(e)
+    W = r / tot * op
+    return W / (1.0 + W), e
+
+
+_CASES = [
+    # K_h_l, K_h_o, K_h_e, K_l_o, K_l_e, K_u
+    (6.3, 4.2e8, 2.1e4, 0.1, 2.9e5, 0.0),
+    (0.5, 1e7, 3e5, 1e5, 2e4, 0.0),
+    (6.3, 4.2e8, 0.0, 0.0, 2.5e5, 0.0),      # M2
+    (20.0, 1e9, 1e3, 1e2, 1e6, 3.0),         # with U
+]
+_E = [0.0, 1e-7, 3e-6, 1e-5, 8e-5, 1e-3, 0.1]
+
+
+@pytest.mark.parametrize("ks", _CASES)
+@pytest.mark.parametrize("tf_total", [_TF, 1e-9])
+def test_theta_matches_brute_force_species_sum(ks, tf_total):
+    for e_total in _E:
+        want_theta, want_e = _brute_force(*ks, e_total, tf_total)
+        got = mwc.mwc_theta(*ks, np.array(e_total), tf_total, xp=np)
+        assert float(got) == pytest.approx(want_theta, rel=1e-10, abs=1e-14)
+        B0, B1, B2 = mwc._poly_coeffs(ks[0], ks[2], ks[4], ks[5])
+        e = mwc.free_effector(B0, B1, B2, np.array(e_total), tf_total / 2,
+                              xp=np)
+        assert float(e) == pytest.approx(want_e, rel=1e-10, abs=1e-20)
+
+
+def test_two_independent_sites_give_macroscopic_ratio_one_quarter():
+    """P(e) = (1 + K·e)² has K1 = 2K and K2 = K/2, so K2/K1 = 1/4."""
+    K = 3.7e4
+    B0, B1, B2 = mwc._poly_coeffs(0.0, K, 0.0, 0.0)     # H only
+    K1 = B1 / B0
+    K2 = B2 / B1
+    assert K1 == pytest.approx(2 * K)
+    assert K2 / K1 == pytest.approx(0.25)
+
+
+def test_jax_and_numpy_paths_agree():
+    rng = np.random.default_rng(0)
+    ks = [np.exp(rng.normal(m, 1.5, 50)) for m in
+          (1.8, 19.9, 10.0, -2.3, 12.6, -3.0)]
+    e = np.logspace(-8, -1, 50)
+    got_np = mwc.mwc_theta(*ks, e, _TF, xp=np)
+    got_jax = np.asarray(mwc.mwc_theta(*[jnp.asarray(k) for k in ks],
+                                       jnp.asarray(e), _TF))
+    np.testing.assert_allclose(got_jax, got_np, rtol=2e-4, atol=1e-6)
+
+
+# ---------------------------------------------------------------------------
+# Free-effector cubic
+# ---------------------------------------------------------------------------
+
+def test_newton_solve_matches_bracketing_root_finder():
+    """NEWTON_ITERATIONS steps reach float64 precision over broad draws."""
+    rng = np.random.default_rng(1)
+    worst = 0.0
+    for _ in range(2000):
+        K_h_l = math.exp(rng.normal(1.84, 2.0))
+        K_h_e = math.exp(rng.normal(10.0, 2.0))
+        K_l_e = math.exp(rng.normal(12.6, 2.0))
+        tf = 10 ** rng.uniform(-7, -4.5)
+        e_total = 10 ** rng.uniform(-8, -1)
+        B0, B1, B2 = mwc._poly_coeffs(K_h_l, K_h_e, K_l_e, 0.0)
+        r = tf / 2
+
+        def g(x):
+            return (e_total - x) * (B0 + B1 * x + B2 * x * x) \
+                - r * (B1 * x + 2 * B2 * x * x)
+
+        want = brentq(g, 0.0, e_total, xtol=1e-300, rtol=1e-15)
+        got = float(mwc.free_effector(B0, B1, B2, np.array(e_total), r,
+                                      xp=np))
+        worst = max(worst, abs(got - want) / e_total)
+    assert worst < 1e-12
+
+
+def test_free_effector_bounds_and_depletion():
+    e_total = np.array([0.0, 1e-6, 1e-5, 1e-4])
+    B0, B1, B2 = mwc._poly_coeffs(6.3, 2e4, 3e5, 0.0)
+    e = mwc.free_effector(B0, B1, B2, e_total, _TF / 2, xp=np)
+    assert np.all(e >= 0) and np.all(e <= e_total)
+    assert e[0] == 0.0
+    # At tf_total = 1e-5 M a micromolar effector is mostly bound.
+    assert e[1] < 0.5 * e_total[1]
+    e_weak = mwc.free_effector(B0, B1, B2, e_total, 1e-15, xp=np)
+    np.testing.assert_allclose(e_weak, e_total, rtol=1e-8)
+
+
+# ---------------------------------------------------------------------------
+# M2 is M3 with K_h_e = K_l_o = 0
+# ---------------------------------------------------------------------------
+
+def _random_m2(S, rng):
+    return (rng.normal(mwc.DEFAULT_LN_K_H_O, 2.0, S),
+            rng.normal(mwc.DEFAULT_LN_K_H_L, 2.0, S),
+            rng.normal(mwc.DEFAULT_LN_K_E_C4, 2.0, S),
+            rng.normal(-3.0, 2.0, S))
+
+
+@pytest.mark.parametrize("unfold", [False, True])
+def test_c4_is_exactly_c12_without_h_effector_and_l_operator(unfold):
+    """theta_c4 equals theta_c12 with ln K_h_e, ln K_l_o = -inf (numpy)."""
+    rng = np.random.default_rng(2)
+    S = 300
+    ln_op, ln_hl, ln_e, ln_u = _random_m2(S, rng)
+    ln_u = ln_u if unfold else None
+    conc = np.logspace(-8, -1, 40)
+    a = mwc.theta_c4(ln_op[:, None], ln_hl[:, None], ln_e[:, None],
+                     None if ln_u is None else ln_u[:, None],
+                     conc[None, :], _TF, xp=np)
+    b = mwc.theta_c12(ln_hl[:, None], ln_op[:, None], np.full((S, 1), -np.inf),
+                      np.full((S, 1), -np.inf), ln_e[:, None],
+                      None if ln_u is None else ln_u[:, None],
+                      conc[None, :], _TF, xp=np)
+    np.testing.assert_array_equal(a, b)
+
+
+@pytest.mark.parametrize("u", ["U0", "U1"])
+def test_c4_components_equal_c12_components_with_depletion(u):
+    """
+    The families' own compute_theta_samples and run_model agree to float
+    tolerance when C12 has ln K_h_e, ln K_l_o -> -inf, at tf_total = 1e-5 M
+    where effector depletion matters below about 10 µM.
+    """
+    c4 = _module(f"O2_C4_K3_{u}_a", "thermo")
+    c12 = _module(f"O2_C12_K5_{u}_a", "thermo")
+    rng = np.random.default_rng(3)
+    S = 200
+    ln_op, ln_hl, ln_e, ln_u = _random_m2(S, rng)
+    conc_mM = np.concatenate([[0.0], np.logspace(-5, 1, 50)])
+    calc_df = pd.DataFrame({"titrant_conc": conc_mM, "genotype_idx": 0,
+                            "titrant_name_idx": 0})
+    post4 = {"theta_ln_K_op": ln_op[:, None], "theta_ln_K_HL": ln_hl[:, None],
+             "theta_ln_K_E": ln_e[:, None, None]}
+    post12 = {"theta_ln_K_h_l": ln_hl[:, None], "theta_ln_K_h_o": ln_op[:, None],
+              "theta_ln_K_h_e": np.full((S, 1, 1), -np.inf),
+              "theta_ln_K_l_o": np.full((S, 1), -np.inf),
+              "theta_ln_K_l_e": ln_e[:, None, None]}
+    if u == "U1":
+        post4["theta_ln_K_U"] = ln_u[:, None]
+        post12["theta_ln_K_u"] = ln_u[:, None]
+    t4 = c4.compute_theta_samples(calc_df, post4, tf_total=_TF,
+                                  conc_unit_scale=1e-3)
+    t12 = c12.compute_theta_samples(calc_df, post12, tf_total=_TF,
+                                    conc_unit_scale=1e-3)
+    np.testing.assert_allclose(t4, t12, rtol=1e-12, atol=1e-15)
+    # Depletion is not negligible here: a near-zero tf_total changes theta.
+    t4_nodep = c4.compute_theta_samples(calc_df, post4, tf_total=1e-15,
+                                        conc_unit_scale=1e-3)
+    assert np.max(np.abs(t4 - t4_nodep)) > 1e-3
+
+    # JAX path (run_model), float32.
+    G = 5
+    k = dict(ln_op=jnp.asarray(ln_op[:G]), ln_hl=jnp.asarray(ln_hl[:G]),
+             ln_e=jnp.asarray(ln_e[:G])[None, :], ln_u=jnp.asarray(ln_u[:G]))
+    data = Data(titrant_conc=jnp.asarray(conc_mM), batch_idx=jnp.arange(G),
+                geno_theta_idx=jnp.arange(G), scatter_theta=0)
+    mom = dict(mu=jnp.zeros((1, conc_mM.size, 1)),
+               sigma=jnp.ones((1, conc_mM.size, 1)),
+               tf_total=_TF, conc_unit_scale=1e-3)
+    extra4 = {"ln_K_U": k["ln_u"]} if u == "U1" else {}
+    extra12 = {"ln_K_u": k["ln_u"]} if u == "U1" else {}
+    p4 = c4.ThetaParam(ln_K_op=k["ln_op"], ln_K_HL=k["ln_hl"],
+                       ln_K_E=k["ln_e"], **extra4, **mom)
+    p12 = c12.ThetaParam(ln_K_h_l=k["ln_hl"], ln_K_h_o=k["ln_op"],
+                         ln_K_h_e=jnp.full((1, G), -jnp.inf),
+                         ln_K_l_o=jnp.full(G, -jnp.inf),
+                         ln_K_l_e=k["ln_e"], **extra12, **mom)
+    np.testing.assert_allclose(np.asarray(c4.run_model(p4, data)),
+                               np.asarray(c12.run_model(p12, data)),
+                               rtol=1e-6, atol=1e-7)
+
+
+def test_m2_apo_and_saturation_limits():
+    """θ(0) = W/(1+W), W = r·K_op/(1 + K_HL); θ → 0 at saturating effector."""
+    K_op, K_HL, K_E = 4.2e8, 6.3, 2.5e5
+    r = _TF / 2
+    W = r * K_op / (1 + K_HL)
+    got = mwc.theta_c4(math.log(K_op), math.log(K_HL), math.log(K_E), None,
+                       np.array([0.0, 10.0]), _TF, xp=np)
+    assert got[0] == pytest.approx(W / (1 + W), rel=1e-12)
+    assert got[1] < 1e-6
+
+
+# ---------------------------------------------------------------------------
+# Default wild type: midpoint at 80 µM IPTG
+# ---------------------------------------------------------------------------
+
+def _wt_curve(family):
+    """Default wt θ(e) and its saturating limit."""
+    hp = _module(family, "PK").get_hyperparameters()
+    assert hp["theta_tf_total_M"] == pytest.approx(1e-5)
+    if family.startswith("O2_C4"):
+        t_sat = 0.0
+        def f(e):
+            return mwc.theta_c4(hp["theta_ln_K_op_wt_loc"],
+                                hp["theta_ln_K_HL_wt_loc"],
+                                hp["theta_ln_K_E_wt_loc"], None,
+                                np.asarray(e, float),
+                                hp["theta_tf_total_M"], xp=np)
+    else:
+        ks = [math.exp(hp[f"theta_ln_K_{k}_wt_loc"])
+              for k in ("h_l", "h_o", "h_e", "l_o", "l_e")]
+        t_sat = mwc.theta_saturated(*ks, 0.0, hp["theta_tf_total_M"], xp=np)
+
+        def f(e):
+            return mwc.theta_c12(hp["theta_ln_K_h_l_wt_loc"],
+                                 hp["theta_ln_K_h_o_wt_loc"],
+                                 hp["theta_ln_K_h_e_wt_loc"],
+                                 hp["theta_ln_K_l_o_wt_loc"],
+                                 hp["theta_ln_K_l_e_wt_loc"], None,
+                                 np.asarray(e, float),
+                                 hp["theta_tf_total_M"], xp=np)
+    return f, float(t_sat)
+
+
+@pytest.mark.parametrize("family", ["O2_C4_K3_U0_a", "O2_C12_K5_U0_a"])
+def test_default_wt_midpoint_is_80_uM(family):
+    f, t_sat = _wt_curve(family)
+    t0 = float(f(0.0))
+    # Saturating effector at 10 M is the analytic limit to 1e-3.
+    assert float(f(10.0)) == pytest.approx(t_sat, abs=1e-3)
+    half = 0.5 * (t0 + t_sat)
+    mid = math.exp(brentq(lambda le: float(f(math.exp(le))) - half,
+                          math.log(1e-9), math.log(1.0)))
+    assert mid == pytest.approx(80e-6, rel=1e-6)
+    assert t0 > 0.99
+
+
+def _calibrate_c12_scale():
+    """
+    Common factor on (Sochor K_l_e/ρ, Sochor K_l_e) that puts the midpoint at
+    80 µM, with ρ the default selectivity.
+    """
+    rho = mwc.MWC_EFFECTOR_SELECTIVITY
+
+    def mid_err(ln_f):
+        K_l_e = mwc.SOCHOR_K_L_E * math.exp(ln_f)
+        args = (mwc.SOCHOR_K_H_L, mwc.SOCHOR_K_H_O, K_l_e / rho,
+                mwc.SOCHOR_K_L_O, K_l_e, 0.0)
+        t0 = float(mwc.mwc_theta(*args, np.array(0.0), _TF, xp=np))
+        ts = float(mwc.theta_saturated(*args, _TF, xp=np))
+        tm = float(mwc.mwc_theta(*args, np.array(80e-6), _TF, xp=np))
+        return tm - 0.5 * (t0 + ts)
+    return math.exp(brentq(mid_err, -5.0, 3.0, xtol=1e-15))
+
+
+def _calibrate_c12_selectivity():
+    """ρ = K_l_e/K_h_e that gives θ_sat = 0.05, by bracketing root finder."""
+    def err(ln_rho):
+        rho = math.exp(ln_rho)
+        t = mwc.theta_saturated(mwc.SOCHOR_K_H_L, mwc.SOCHOR_K_H_O, 1.0,
+                                mwc.SOCHOR_K_L_O, rho, 0.0, _TF, xp=np)
+        return float(t) - 0.05
+    return math.exp(brentq(err, 0.0, 10.0, xtol=1e-15))
+
+
+def _calibrate_c4_ln_K_E():
+    def mid_err(ln_k):
+        th = [float(mwc.theta_c4(mwc.DEFAULT_LN_K_H_O, mwc.DEFAULT_LN_K_H_L,
+                                 ln_k, None, np.array(e), _TF, xp=np))
+              for e in (0.0, 80e-6)]
+        return th[1] - 0.5 * th[0]        # θ_sat = 0 for M2
+    return brentq(mid_err, 0.0, 25.0, xtol=1e-14)
+
+
+def test_default_constants_are_the_calibrated_ones():
+    assert mwc.MWC_EFFECTOR_SCALE == pytest.approx(_calibrate_c12_scale(),
+                                                   rel=1e-8)
+    assert mwc.DEFAULT_LN_K_E_C4 == pytest.approx(_calibrate_c4_ln_K_E(),
+                                                  abs=1e-8)
+    assert mwc.MWC_EFFECTOR_SELECTIVITY == pytest.approx(
+        _calibrate_c12_selectivity(), rel=1e-10)
+    assert mwc.MWC_EFFECTOR_SELECTIVITY == pytest.approx(79.58, abs=0.01)
+    # C12 keeps Sochor's K_h_l, K_h_o and K_l_o and widens K_l_e/K_h_e from
+    # Sochor's 13.6 to the selectivity that gives θ_sat = 0.05.
+    assert math.exp(mwc.DEFAULT_LN_K_H_L) == pytest.approx(6.3)
+    assert math.exp(mwc.DEFAULT_LN_K_H_O) == pytest.approx(4.2e8)
+    assert math.exp(mwc.DEFAULT_LN_K_L_O) == pytest.approx(0.1)
+    assert math.exp(mwc.DEFAULT_LN_K_L_E - mwc.DEFAULT_LN_K_H_E) == \
+        pytest.approx(mwc.MWC_EFFECTOR_SELECTIVITY, rel=1e-12)
+    assert math.exp(mwc.DEFAULT_LN_K_L_E) == pytest.approx(
+        mwc.SOCHOR_K_L_E * mwc.MWC_EFFECTOR_SCALE, rel=1e-12)
+
+
+def test_c12_default_wt_saturates_at_five_percent():
+    """M3's default wt falls to θ = 0.05 at saturating effector (1e-5 M TF)."""
+    f, t_sat = _wt_curve("O2_C12_K5_U0_a")
+    assert t_sat == pytest.approx(0.05, rel=1e-10)
+    assert float(f(10.0)) == pytest.approx(0.05, abs=1e-4)
+    assert float(f(0.0)) > 0.99
+
+
+@pytest.mark.parametrize("kind", ["PK", "PddG", "PnnC"])
+@pytest.mark.parametrize("u", ["U0", "U1"])
+def test_every_component_uses_the_shared_defaults(kind, u):
+    c4 = _module(f"O2_C4_K3_{u}_a", kind).get_hyperparameters()
+    c12 = _module(f"O2_C12_K5_{u}_a", kind).get_hyperparameters()
+    assert c4["theta_ln_K_op_wt_loc"] == mwc.DEFAULT_LN_K_H_O
+    assert c4["theta_ln_K_HL_wt_loc"] == mwc.DEFAULT_LN_K_H_L
+    assert c4["theta_ln_K_E_wt_loc"] == mwc.DEFAULT_LN_K_E_C4
+    assert c12["theta_ln_K_h_l_wt_loc"] == mwc.DEFAULT_LN_K_H_L
+    assert c12["theta_ln_K_h_o_wt_loc"] == mwc.DEFAULT_LN_K_H_O
+    assert c12["theta_ln_K_l_o_wt_loc"] == mwc.DEFAULT_LN_K_L_O
+    assert c12["theta_ln_K_h_e_wt_loc"] == mwc.DEFAULT_LN_K_H_E
+    assert c12["theta_ln_K_l_e_wt_loc"] == mwc.DEFAULT_LN_K_L_E
+    for hp in (c4, c12):
+        assert hp["theta_tf_total_M"] == mwc.DEFAULT_TF_TOTAL_M == 1e-5
+        assert "theta_op_total_M" not in hp
+
+
+# ---------------------------------------------------------------------------
+# Projection rows
+# ---------------------------------------------------------------------------
+
+def _structure_ddG_from_dlnK(names, d):
+    """
+    Structure free-energy shifts that produce the constant shifts ``d``.
+
+    ΔΔG_X adds to state X's free energy and Δln K_{A→B} = ΔΔG_A − ΔΔG_B.
+    Take ΔΔG_H = a (arbitrary); then ΔΔG_L = a − Δln K_h_l,
+    ΔΔG_HO = a − Δln K_h_o, ΔΔG_LO = ΔΔG_L − Δln K_l_o and, with two
+    per-site constants, ΔΔG_XE2 = ΔΔG_X − 2·Δln K_x_e.
+    """
+    a = 0.7
+    G = {"H": a, "L": a - d["h_l"]}
+    G["HO"] = G["HD"] = a - d["h_o"]
+    G["LO"] = G["L"] - d.get("l_o", 0.0)
+    G["HE2"] = a - 2 * d.get("h_e", 0.0)
+    G["LE2"] = G["L"] - 2 * d["l_e"]
+    return np.array([G[n] for n in names])
+
+
+def test_c12_projection_recovers_per_site_constants():
+    rng = np.random.default_rng(4)
+    for _ in range(20):
+        d = dict(zip(["h_l", "h_o", "h_e", "l_o", "l_e"], rng.normal(0, 2, 5)))
+        ddG = _structure_ddG_from_dlnK(mwc.STRUCTURE_NAMES_C12, d)
+        got = mwc.PROJ_C12 @ ddG
+        np.testing.assert_allclose(
+            got, [d["h_l"], d["h_o"], d["h_e"], d["l_o"], d["l_e"]], atol=1e-12)
+        assert mwc.PROJ_U_C12 @ ddG == pytest.approx(ddG[0])   # Δln K_u = ΔΔG_H
+
+
+def test_c4_projection_recovers_per_site_constants():
+    rng = np.random.default_rng(5)
+    for _ in range(20):
+        d = dict(zip(["h_o", "h_l", "l_e"], rng.normal(0, 2, 3)))
+        ddG = _structure_ddG_from_dlnK(mwc.STRUCTURE_NAMES_C4, d)
+        got = mwc.PROJ_C4 @ ddG        # (K_op, K_HL, K_E)
+        np.testing.assert_allclose(got, [d["h_o"], d["h_l"], d["l_e"]],
+                                   atol=1e-12)
+        assert mwc.PROJ_U_C4 @ ddG == pytest.approx(ddG[0])
+
+
+def test_c4_rows_are_the_c12_rows_of_the_same_constants():
+    """K_op, K_HL, K_E rows equal C12's K_h_o, K_h_l, K_l_e rows (HD ≡ HO)."""
+    c12 = dict(zip(mwc.STRUCTURE_NAMES_C12, mwc.PROJ_C12.T))
+    as_c12 = np.stack([c12[{"HD": "HO"}.get(n, n)] for n in
+                       mwc.STRUCTURE_NAMES_C4], axis=1)   # (5, 4)
+    np.testing.assert_array_equal(mwc.PROJ_C4[0], as_c12[1])   # K_op = K_h_o
+    np.testing.assert_array_equal(mwc.PROJ_C4[1], as_c12[0])   # K_HL = K_h_l
+    np.testing.assert_array_equal(mwc.PROJ_C4[2], as_c12[4])   # K_E = K_l_e
+
+
+def test_projected_shift_reproduces_the_doubly_liganded_weight():
+    """
+    A ΔΔG on LE2 alone, projected, changes θ exactly as raising LE2's free
+    energy in the brute-force species sum would: LE2's weight K_l_e²·e² is
+    multiplied by exp(−ΔΔG_LE2), and LE's by exp(−ΔΔG_LE2/2) per site.
+    """
+    ddG_LE2 = 1.3
+    ddG = np.zeros(4)
+    ddG[3] = ddG_LE2
+    d = mwc.PROJ_C4 @ ddG
+    assert d[2] == pytest.approx(-ddG_LE2 / 2)
+    K_l_e = 2.5e5
+    for e in (1e-6, 8e-5, 1e-3):
+        got = mwc.theta_c4(mwc.DEFAULT_LN_K_H_O, mwc.DEFAULT_LN_K_H_L,
+                           math.log(K_l_e) + d[2], None, np.array(e), _TF,
+                           xp=np)
+        want, _ = _brute_force(6.3, 4.2e8, 0.0, 0.0,
+                               K_l_e * math.exp(-ddG_LE2 / 2), 0.0, e, _TF)
+        assert float(got) == pytest.approx(want, rel=1e-10)
+
+
+@pytest.mark.parametrize("family, proj", [("O2_C4_K3_U0_a", mwc.PROJ_C4),
+                                          ("O2_C4_K3_U1_a", mwc.PROJ_C4),
+                                          ("O2_C12_K5_U0_a", mwc.PROJ_C12),
+                                          ("O2_C12_K5_U1_a", mwc.PROJ_C12)])
+@pytest.mark.parametrize("kind", ["PddG", "PnnC"])
+def test_components_use_the_shared_projection(family, proj, kind):
+    module = _module(family, kind)
+    np.testing.assert_array_equal(np.asarray(module._PROJ), proj)
+    if family.endswith("U1_a"):
+        u = mwc.PROJ_U_C4 if "C4" in family else mwc.PROJ_U_C12
+        np.testing.assert_array_equal(np.asarray(module._PROJ_U), u)
+
+
+# ---------------------------------------------------------------------------
+# Overflow safety: log-space θ at extreme constants
+#
+# The Laplace draws of the PddG pilot fits (2026-10-10) reached ln K of +/-60
+# and beyond. The linear-space θ overflowed exp(ln K) and K² in float32 and
+# wrote NaN theta for whole genotypes; the linear Newton solve also stopped
+# short at large K_E. θ is now computed from log constants throughout.
+# ---------------------------------------------------------------------------
+
+_FAMILIES = ["O2_C4_K3_U0_a", "O2_C4_K3_U1_a", "O2_C12_K5_U0_a",
+             "O2_C12_K5_U1_a"]
+_KINDS = ["PK", "PddG", "PnnC"]
+
+_E_EXTREME = [0.0, 1e-9, 1e-7, 1e-5, 8e-5, 1e-3, 0.1, 10.0]
+
+# Constants far outside the defaults but within float64 for the brute force
+# (linear K: K_h_l, K_h_o, K_h_e, K_l_o, K_l_e, K_u).
+_EXTREME_CASES = [
+    (6.3, math.exp(60), 0.0, 0.0, math.exp(40), 0.0),        # M2, huge K_E
+    (math.exp(30), 4.2e8, 0.0, 0.0, 2.5e5, 0.0),               # all L
+    (math.exp(-30), math.exp(-40), 1e3, 1e2, math.exp(60), math.exp(20)),
+    (6.3, math.exp(100), math.exp(50), math.exp(-50), math.exp(80), 0.0),
+    (math.exp(-60), math.exp(70), math.exp(-40), math.exp(30),
+     math.exp(-20), math.exp(-80)),
+]
+
+
+@pytest.mark.parametrize("ks", _EXTREME_CASES)
+def test_theta_matches_brute_force_at_extreme_constants(ks):
+    for e_total in _E_EXTREME[:-1]:
+        want, want_e = _brute_force(*ks, e_total, _TF)
+        got = float(mwc.mwc_theta(*ks, np.array(e_total), _TF, xp=np))
+        assert got == pytest.approx(want, rel=1e-9, abs=1e-300)
+        if e_total == _TF:
+            # At the equivalence point (e_total = 2r) with a huge effector
+            # constant the free effector is ~1e-20 M, set by a cancellation
+            # the brute force's linear mass balance cannot resolve (it is
+            # 0.5% off a 60-digit solve here; the log-space solve is not).
+            continue
+        B0, B1, B2 = mwc._poly_coeffs(ks[0], ks[2], ks[4], ks[5])
+        e = float(mwc.free_effector(B0, B1, B2, np.array(e_total), _TF / 2,
+                                    xp=np))
+        assert e == pytest.approx(want_e, rel=1e-9, abs=1e-300)
+
+
+def _random_logs(n, sd, rng):
+    """ln K draws about the defaults with SD ``sd`` (kT)."""
+    return (rng.normal(mwc.DEFAULT_LN_K_H_L, sd, n),
+            rng.normal(mwc.DEFAULT_LN_K_H_O, sd, n),
+            rng.normal(mwc.DEFAULT_LN_K_H_E, sd, n),
+            rng.normal(mwc.DEFAULT_LN_K_L_O, sd, n),
+            rng.normal(mwc.DEFAULT_LN_K_L_E, sd, n),
+            rng.normal(-3.0, sd, n))
+
+
+@pytest.mark.parametrize("sd", [30.0, 150.0])
+def test_theta_finite_in_unit_interval_at_extreme_draws(sd):
+    rng = np.random.default_rng(4)
+    lns = [v[:, None] for v in _random_logs(400, sd, rng)]
+    e = np.array(_E_EXTREME)[None, :]
+    want = mwc.theta_c12(*lns, e, _TF, xp=np)
+    assert np.all(np.isfinite(want))
+    assert np.all((want >= 0) & (want <= 1))
+    got = np.asarray(mwc.theta_c12(*[jnp.asarray(v) for v in lns],
+                                   jnp.asarray(e), _TF))
+    assert got.dtype == np.float32
+    assert np.all(np.isfinite(got)) and np.all((got >= 0) & (got <= 1))
+    np.testing.assert_allclose(got, want, atol=2e-5)
+    c4 = np.asarray(mwc.theta_c4(*[jnp.asarray(v) for v in
+                                   (lns[1], lns[0], lns[4], lns[5])],
+                                 jnp.asarray(e), _TF))
+    assert np.all(np.isfinite(c4)) and np.all((c4 >= 0) & (c4 <= 1))
+
+
+@pytest.mark.parametrize("xp", [np, jnp])
+def test_theta_limits_at_extreme_constants(xp):
+    """θ → 1 as K_op → ∞, θ → 0 as K_op → 0 or K_HL → ∞ (L holds no operator)."""
+    e = xp.asarray(np.array(_E_EXTREME))
+    hl, op, ke = mwc.DEFAULT_LN_K_H_L, mwc.DEFAULT_LN_K_H_O, \
+        mwc.DEFAULT_LN_K_E_C4
+    big = 500.0
+    np.testing.assert_allclose(mwc.theta_c4(big, hl, ke, None, e, _TF, xp=xp),
+                               1.0, atol=1e-6)
+    np.testing.assert_allclose(mwc.theta_c4(-big, hl, ke, None, e, _TF,
+                                            xp=xp), 0.0, atol=1e-30)
+    np.testing.assert_allclose(mwc.theta_c4(op, big, ke, None, e, _TF, xp=xp),
+                               0.0, atol=1e-30)
+    np.testing.assert_allclose(mwc.theta_c4(op, hl, ke, big, e, _TF, xp=xp),
+                               0.0, atol=1e-30)
+    # K_HL → 0: all apo H, effector-blind; θ = r·K_op/(1 + r·K_op)
+    W = _TF / 2 * math.exp(op)
+    np.testing.assert_allclose(mwc.theta_c4(op, -big, ke, None, e, _TF, xp=xp),
+                               W / (1 + W), rtol=1e-5)
+    # K_E → 0: no induction; θ(e) = θ(0)
+    t = mwc.theta_c4(op, hl, -big, None, e, _TF, xp=xp)
+    np.testing.assert_allclose(t, t[0], rtol=1e-6)
+    # Huge K_E with effector in excess of TF: fully induced
+    t = mwc.theta_c4(op, hl, big, None, xp.asarray(np.array([1e-3, 0.1])),
+                     _TF, xp=xp)
+    np.testing.assert_allclose(t, 0.0, atol=1e-30)
+
+
+def test_theta_saturated_from_logs_at_extreme_constants():
+    ks = (6.3, math.exp(200), math.exp(150), math.exp(-50), math.exp(250),
+          0.0)
+    t = float(mwc.theta_saturated(*ks, _TF, xp=np))
+    assert 0.0 <= t <= 1.0 and math.isfinite(t)
+    assert float(mwc.theta_saturated(6.3, 4.2e8, 0.0, 0.0, 2.5e5, 0.0, _TF,
+                                     xp=np)) == 0.0
+
+
+def _c4_sum(p, e):
+    return mwc.theta_c4(p[0], p[1], p[2], p[3], e, _TF).sum()
+
+
+def _c12_sum(p, e):
+    return mwc.theta_c12(p[0], p[1], p[2], p[3], p[4], p[5], e, _TF).sum()
+
+
+_GRAD_POINTS_C4 = [
+    (mwc.DEFAULT_LN_K_H_O, mwc.DEFAULT_LN_K_H_L, mwc.DEFAULT_LN_K_E_C4, -3.0),
+    (300.0, 1.8, 12.4, -3.0), (-300.0, 1.8, 12.4, -3.0),
+    (19.9, 300.0, 12.4, -3.0), (19.9, -300.0, 12.4, -3.0),
+    (19.9, 1.8, 300.0, -3.0), (19.9, 1.8, -300.0, -3.0),
+    (19.9, 1.8, 12.4, 300.0), (300.0, 300.0, 300.0, 300.0),
+    (-300.0, -300.0, -300.0, -300.0),
+]
+
+
+@pytest.mark.parametrize("p", _GRAD_POINTS_C4)
+def test_gradient_and_hessian_finite_at_extreme_values(p):
+    e = jnp.asarray(np.array(_E_EXTREME), dtype=jnp.float32)
+    p = jnp.asarray(p, dtype=jnp.float32)
+    g = jax.grad(_c4_sum)(p, e)
+    H = jax.hessian(_c4_sum)(p, e)
+    assert np.all(np.isfinite(np.asarray(g)))
+    assert np.all(np.isfinite(np.asarray(H)))
+    p12 = jnp.concatenate([p[1:2], p[0:1], p[2:3] - 4.0, p[0:1] - 20.0,
+                           p[2:3], p[3:4]])
+    assert np.all(np.isfinite(np.asarray(jax.grad(_c12_sum)(p12, e))))
+    assert np.all(np.isfinite(np.asarray(jax.hessian(_c12_sum)(p12, e))))
+
+
+def _numeric_grad(f, p, h=1e-5):
+    p = np.asarray(p, float)
+    out = np.zeros_like(p)
+    for i in range(p.size):
+        d = np.zeros_like(p)
+        d[i] = h
+        out[i] = (f(p + d) - f(p - d)) / (2 * h)
+    return out
+
+
+@pytest.mark.parametrize("p", [
+    (1.84, 19.9, 8.2, -2.3, 12.6, -3.0),
+    (0.5, 16.0, 12.0, 4.0, 10.0, 1.0),
+    (6.0, 30.0, 6.0, -5.0, 20.0, -10.0),
+])
+def test_implicit_derivatives_match_finite_differences(p):
+    """
+    The free effector's derivatives come from the implicit function theorem
+    (custom JVP), not from the Newton iterations. Check the gradient and the
+    Hessian (the Laplace uses it) against central differences in float64.
+    """
+    e_np = np.array([1e-7, 3e-6, 1e-5, 8e-5, 1e-3])
+
+    def f_np(q):
+        return float(np.sum(mwc.theta_c12(*q, e_np, _TF, xp=np)))
+
+    with jax.enable_x64(True):
+        e = jnp.asarray(e_np)
+        q = jnp.asarray(p, dtype=jnp.float64)
+        g = np.asarray(jax.grad(_c12_sum)(q, e))
+        H = np.asarray(jax.hessian(_c12_sum)(q, e))
+        assert float(_c12_sum(q, e)) == pytest.approx(f_np(p), rel=1e-12)
+
+        def g_np(qq):
+            return np.asarray(jax.grad(_c12_sum)(jnp.asarray(qq), e))
+
+        H_fd = np.stack([_numeric_grad(lambda qq: g_np(qq)[i], p)
+                         for i in range(len(p))])
+    np.testing.assert_allclose(g, _numeric_grad(f_np, p), rtol=1e-6,
+                               atol=1e-9)
+    np.testing.assert_allclose(H, H_fd, rtol=1e-5, atol=1e-8)
+
+
+def _extreme_component_inputs(family, rng, G):
+    """Per-genotype log constants with SD 60 kT about the defaults."""
+    sd = 60.0
+    if "C4" in family:
+        k = {"ln_K_op": rng.normal(mwc.DEFAULT_LN_K_H_O, sd, G),
+             "ln_K_HL": rng.normal(mwc.DEFAULT_LN_K_H_L, sd, G),
+             "ln_K_E": rng.normal(mwc.DEFAULT_LN_K_E_C4, sd, (1, G))}
+        if "U1" in family:
+            k["ln_K_U"] = rng.normal(-3.0, sd, G)
+    else:
+        k = {"ln_K_h_l": rng.normal(mwc.DEFAULT_LN_K_H_L, sd, G),
+             "ln_K_h_o": rng.normal(mwc.DEFAULT_LN_K_H_O, sd, G),
+             "ln_K_h_e": rng.normal(mwc.DEFAULT_LN_K_H_E, sd, (1, G)),
+             "ln_K_l_o": rng.normal(mwc.DEFAULT_LN_K_L_O, sd, G),
+             "ln_K_l_e": rng.normal(mwc.DEFAULT_LN_K_L_E, sd, (1, G))}
+        if "U1" in family:
+            k["ln_K_u"] = rng.normal(-3.0, sd, G)
+    return k
+
+
+@pytest.mark.parametrize("family", _FAMILIES)
+@pytest.mark.parametrize("kind", _KINDS)
+def test_every_component_theta_finite_at_extreme_constants(family, kind):
+    """run_model (JAX, float32) and compute_theta_samples (numpy) of all 12
+    thermodynamic components give finite θ in [0, 1] at extreme draws."""
+    module = _module(family, kind)
+    rng = np.random.default_rng(5)
+    G = 200
+    k = _extreme_component_inputs(family, rng, G)
+    # Not 0.01 mM: that is the equivalence point e_total = 2r, where at
+    # these constants θ jumps between 0 and 1 within the float32 rounding of
+    # e_total (an ill-posed input, not a solver error).
+    conc_mM = np.array([0.0, 1e-4, 3e-2, 0.08, 1.0, 100.0])
+
+    data = Data(titrant_conc=jnp.asarray(conc_mM), batch_idx=jnp.arange(G),
+                geno_theta_idx=jnp.arange(G), scatter_theta=0)
+    param = module.ThetaParam(**{n: jnp.asarray(v) for n, v in k.items()},
+                              mu=jnp.zeros((1, conc_mM.size, 1)),
+                              sigma=jnp.ones((1, conc_mM.size, 1)),
+                              tf_total=_TF, conc_unit_scale=1e-3)
+    t_jax = np.asarray(module.run_model(param, data))
+    assert t_jax.shape == (1, conc_mM.size, G)
+    assert np.all(np.isfinite(t_jax))
+    assert np.all((t_jax >= 0) & (t_jax <= 1))
+
+    calc_df = pd.DataFrame({"titrant_conc": np.tile(conc_mM, G),
+                            "genotype_idx": np.repeat(np.arange(G),
+                                                      conc_mM.size),
+                            "titrant_name_idx": 0})
+    post = {f"theta_{n}": np.asarray(v)[None] for n, v in k.items()}
+    t_np = module.compute_theta_samples(calc_df, post, tf_total=_TF,
+                                        conc_unit_scale=1e-3)
+    assert np.all(np.isfinite(t_np))
+    assert np.all((t_np >= 0) & (t_np <= 1))
+    np.testing.assert_allclose(
+        t_np[0].reshape(G, conc_mM.size)[:, 1:],
+        t_jax[0].T[:, 1:], atol=2e-5)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py
index 772fa057..9b5262d5 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_nn.py
@@ -33,16 +33,14 @@ def test_output_shape(self):
 
         assert out.shape == (M, S)
 
-    def test_zero_weights_give_zero_output(self):
-        """With zero-initialised weights the output must be exactly zero."""
+    def test_initial_output_is_zero(self):
+        """W2 and b2 start at zero, so the initial output is exactly zero."""
         M, S = 4, 2
         features     = _make_features(M, S)
         struct_names = ["H", "L"]
         n_chains     = np.array([2, 1], dtype=np.int32)
 
-        # pyro.param returns the init value when called outside a model
-        # — use numpyro.handlers.substitute with empty dict so param sites
-        # return their initial values (zeros).
+        # pyro.param returns the init value under a bare seed handler.
         with numpyro.handlers.seed(rng_seed=0):
             out = compute_nn_predictions("theta", features, struct_names, n_chains)
 
@@ -100,3 +98,107 @@ def test_output_dtype_float32(self):
             out = compute_nn_predictions("theta", features, struct_names, n_chains)
 
         assert out.dtype == jnp.float32
+
+
+# ---------------------------------------------------------------------------
+# Initialization and gradient flow
+# ---------------------------------------------------------------------------
+
+def _init_params(name, features, struct_names, n_chains):
+    """The param values compute_nn_predictions registers on a fresh store."""
+    with numpyro.handlers.seed(rng_seed=0):
+        tr = numpyro.handlers.trace(compute_nn_predictions).get_trace(
+            name, features, struct_names, n_chains)
+    return {k: v["value"] for k, v in tr.items() if v["type"] == "param"}
+
+
+def _loss_fn(name, features, struct_names, n_chains, target):
+    """Squared error of the NN output against target, as a function of params."""
+    def loss(params):
+        with numpyro.handlers.substitute(data=params):
+            out = compute_nn_predictions(name, features, struct_names, n_chains)
+        return jnp.sum((out - target) ** 2)
+    return loss
+
+
+class TestInitAndGradients:
+    def test_W1_init_nonzero_he_scaled(self):
+        """W1 starts at N(0, 2/feat_dim); b1, W2 and b2 start at zero."""
+        features = _make_features(4, 2)
+        params = _init_params("theta", features, ["H", "L"], np.ones(2))
+        for s in ["H", "L"]:
+            W1 = np.asarray(params[f"theta_nn_{s}_W1"])
+            assert W1.shape == (60, _DEFAULT_HIDDEN_SIZE)
+            assert np.all(np.isfinite(W1))
+            assert np.std(W1) == pytest.approx(np.sqrt(2.0 / 60), rel=0.15)
+            np.testing.assert_array_equal(np.asarray(params[f"theta_nn_{s}_b1"]), 0.0)
+            np.testing.assert_array_equal(np.asarray(params[f"theta_nn_{s}_W2"]), 0.0)
+            np.testing.assert_array_equal(np.asarray(params[f"theta_nn_{s}_b2"]), 0.0)
+
+    def test_W1_init_deterministic_and_distinct_per_structure(self):
+        """Same site name gives the same W1 under any seed; structures differ."""
+        features = _make_features(4, 2)
+        with numpyro.handlers.seed(rng_seed=0):
+            tr0 = numpyro.handlers.trace(compute_nn_predictions).get_trace(
+                "theta", features, ["H", "L"], np.ones(2))
+        with numpyro.handlers.seed(rng_seed=123):
+            tr1 = numpyro.handlers.trace(compute_nn_predictions).get_trace(
+                "theta", features, ["L", "H"], np.ones(2))
+        for s in ["H", "L"]:
+            np.testing.assert_array_equal(
+                np.asarray(tr0[f"theta_nn_{s}_W1"]["value"]),
+                np.asarray(tr1[f"theta_nn_{s}_W1"]["value"]))
+        assert not np.allclose(np.asarray(tr0["theta_nn_H_W1"]["value"]),
+                               np.asarray(tr0["theta_nn_L_W1"]["value"]))
+
+    def test_substituted_values_override_init(self):
+        """A restored value (checkpoint, init_params) wins over the init."""
+        features = _make_features(3, 1)
+        W1 = jnp.full((60, _DEFAULT_HIDDEN_SIZE), 0.5)
+        with numpyro.handlers.seed(rng_seed=0):
+            with numpyro.handlers.substitute(data={"theta_nn_H_W1": W1}):
+                tr = numpyro.handlers.trace(compute_nn_predictions).get_trace(
+                    "theta", features, ["H"], np.ones(1))
+        np.testing.assert_array_equal(np.asarray(tr["theta_nn_H_W1"]["value"]),
+                                      np.asarray(W1))
+
+    def test_gradient_flows_to_all_weights(self):
+        """
+        At init W2 gets a nonzero gradient (hidden units are active).  W1 and
+        b1 get none while W2 == 0, but do after a single gradient step.
+        """
+        M, S = 20, 2
+        names = ["H", "L"]
+        n_chains = np.array([2, 1])
+        features = _make_features(M, S)
+        target = jnp.array(np.random.RandomState(1).randn(M, S).astype(np.float32))
+        loss = _loss_fn("theta", features, names, n_chains, target)
+        params = _init_params("theta", features, names, n_chains)
+
+        g0 = jax.grad(loss)(params)
+        for s in names:
+            assert np.linalg.norm(np.asarray(g0[f"theta_nn_{s}_W2"])) > 1e-3
+            assert np.linalg.norm(np.asarray(g0[f"theta_nn_{s}_b2"])) > 1e-3
+
+        params1 = {k: v - 1e-3 * g0[k] for k, v in params.items()}
+        g1 = jax.grad(loss)(params1)
+        for s in names:
+            for p in ["W1", "b1", "W2", "b2"]:
+                assert np.linalg.norm(np.asarray(g1[f"theta_nn_{s}_{p}"])) > 1e-6, \
+                    f"no gradient on theta_nn_{s}_{p}"
+
+    def test_all_zero_init_blocks_gradient(self):
+        """The old all-zero init: only b2 has a gradient, ever (regression)."""
+        M, S = 20, 2
+        names = ["H", "L"]
+        n_chains = np.ones(S)
+        features = _make_features(M, S)
+        target = jnp.array(np.random.RandomState(1).randn(M, S).astype(np.float32))
+        loss = _loss_fn("theta", features, names, n_chains, target)
+        params = {k: jnp.zeros_like(v) for k, v in
+                  _init_params("theta", features, names, n_chains).items()}
+        g = jax.grad(loss)(params)
+        for s in names:
+            for p in ["W1", "b1", "W2"]:
+                np.testing.assert_array_equal(np.asarray(g[f"theta_nn_{s}_{p}"]), 0.0)
+            assert np.linalg.norm(np.asarray(g[f"theta_nn_{s}_b2"])) > 1e-3
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_prior.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_prior.py
index 186a6c45..c3f40d48 100644
--- a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_prior.py
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_prior.py
@@ -1,95 +1,382 @@
-"""Tests for struct/prior.py — NN-informed ΔΔG sampling."""
+"""Tests for thermo/prior.py: the structural prior on Δln K."""
 
+import jax
 import jax.numpy as jnp
 import numpy as np
 import numpyro
 import numpyro.distributions as dist
 import pytest
 
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
 from tfscreen.tfmodel.generative.components.theta.thermo.prior import (
-    sample_ddG,
+    DEFAULT_DDG_SIGMA_LOC,
+    DEFAULT_DDG_SIGMA_SCALE,
+    constant_scale_tril,
+    ddG_per_state_identified,
+    dlnK_guesses,
+    guide_dlnK,
+    identifiable_sigma,
+    per_state_truth,
+    posthoc_ddG,
+    projection,
+    sample_dlnK,
 )
 
+P_C4_U0 = projection(mwc.PROJ_C4)
+P_C4_U1 = projection(mwc.PROJ_C4, mwc.PROJ_U_C4)
+P_C12_U0 = projection(mwc.PROJ_C12)
+P_C12_U1 = projection(mwc.PROJ_C12, mwc.PROJ_U_C12)
+FAMILIES = {"C4_U0": P_C4_U0, "C4_U1": P_C4_U1,
+            "C12_U0": P_C12_U0, "C12_U1": P_C12_U1}
+STATES = {4: ["H", "HD", "L", "LE2"], 6: list(mwc.STRUCTURE_NAMES_C12)}
+
+
+def _trace(proj, num_mut, means, seed=42, **kwargs):
+    """Run sample_dlnK inside a seeded trace; return (output, trace)."""
+    S = proj.shape[1]
 
-def _trace_sample_ddG(name, struct_names, num_mut, nn_means):
-    """Run sample_ddG inside numpyro.handlers.trace and return (output, trace)."""
     def model():
-        return sample_ddG(name, struct_names, num_mut, nn_means)
+        return sample_dlnK("theta", STATES[S], num_mut, means, proj, **kwargs)
 
-    with numpyro.handlers.seed(rng_seed=42):
+    with numpyro.handlers.seed(rng_seed=seed):
         trace = numpyro.handlers.trace(model).get_trace()
-    with numpyro.handlers.seed(rng_seed=42):
+    with numpyro.handlers.seed(rng_seed=seed):
         out = model()
     return out, trace
 
 
-class TestSampleDdG:
-    def test_output_shape(self):
-        M, S = 5, 3
-        nn_means     = jnp.zeros((M, S))
-        struct_names = [f"S{i}" for i in range(S)]
-        out, _ = _trace_sample_ddG("theta", struct_names, M, nn_means)
-        assert out.shape == (M, S)
-
-    def test_offset_site_in_trace(self):
-        M, S = 4, 2
-        nn_means     = jnp.zeros((M, S))
-        struct_names = ["H", "L"]
-        _, trace = _trace_sample_ddG("theta", struct_names, M, nn_means)
-        assert "theta_ddG_offset" in trace
-
-    def test_deterministic_site_in_trace(self):
-        M, S = 3, 2
-        nn_means     = jnp.zeros((M, S))
-        struct_names = ["H", "L"]
-        _, trace = _trace_sample_ddG("theta", struct_names, M, nn_means)
-        assert "theta_ddG" in trace
-
-    def test_zero_sigma_zero_nn_gives_zero(self):
-        """With sigma_s → 0 the output should approach nn_means."""
-        M, S = 4, 2
-        nn_means     = jnp.ones((M, S)) * 3.0
-        struct_names = ["H", "L"]
-
-        import unittest.mock as mock
-
-        # Patch pyro.param to return near-zero sigma_s and zero offsets
-        sigma_near_zero = jnp.full(S, 1e-9)
-
-        def mock_param(name, init, **kwargs):
-            if "sigma_s" in name:
-                return sigma_near_zero
-            return init
-
-        with mock.patch("numpyro.param", side_effect=mock_param):
-            with numpyro.handlers.seed(rng_seed=0):
-                out = sample_ddG("theta", struct_names, M, nn_means)
-
-        # With negligible sigma_s, output ≈ nn_means
-        np.testing.assert_allclose(np.asarray(out), np.asarray(nn_means), atol=1e-5)
-
-    def test_nn_means_shift_output(self):
-        """Non-zero nn_means should shift the mean of sampled ΔΔG values."""
-        M, S = 100, 1
-        nn_means_zero = jnp.zeros((M, S))
-        nn_means_pos  = jnp.full((M, S), 5.0)
-
-        def run(nn_m):
-            def model():
-                return sample_ddG("theta", ["H"], M, nn_m)
-            with numpyro.handlers.seed(rng_seed=1):
-                return model()
-
-        out_zero = np.asarray(run(nn_means_zero))
-        out_pos  = np.asarray(run(nn_means_pos))
-        # Mean should be shifted by 5.0 (within sampling noise for 100 samples)
-        assert out_pos.mean() > out_zero.mean()
-
-    def test_sigma_s_param_registered(self):
-        """sigma_s must be a 'param' trace entry, not a 'sample' site."""
-        M, S = 3, 2
-        nn_means     = jnp.zeros((M, S))
-        struct_names = ["H", "L"]
-        _, trace = _trace_sample_ddG("theta", struct_names, M, nn_means)
-        assert "theta_ddG_sigma_s" in trace
-        assert trace["theta_ddG_sigma_s"]["type"] == "param"
+class TestProjection:
+    def test_shapes_and_unfolding_row_last(self):
+        assert P_C4_U0.shape == (3, 4)
+        assert P_C4_U1.shape == (4, 4)
+        assert P_C12_U0.shape == (5, 6)
+        assert P_C12_U1.shape == (6, 6)
+        np.testing.assert_array_equal(P_C4_U1[-1], mwc.PROJ_U_C4)
+        np.testing.assert_array_equal(P_C12_U1[:5], mwc.PROJ_C12)
+
+    def test_u0_has_the_common_shift_null_vector(self):
+        for P in (P_C4_U0, P_C12_U0):
+            np.testing.assert_allclose(P @ np.ones(P.shape[1]), 0.0)
+
+    @pytest.mark.parametrize("fam", list(FAMILIES))
+    def test_per_state_identified_only_for_u1(self, fam):
+        assert ddG_per_state_identified(FAMILIES[fam]) == fam.endswith("U1")
+
+    @pytest.mark.parametrize("fam", list(FAMILIES))
+    def test_every_sigma_identifiable_from_the_covariance(self, fam):
+        assert identifiable_sigma(FAMILIES[fam])
+
+    def test_identifiable_sigma_detects_a_tied_pair(self):
+        # Two states that enter every constant identically cannot be told
+        # apart through the covariance.
+        P = np.array([[1.0, 1.0, -1.0]])
+        assert not identifiable_sigma(P)
+
+
+class TestScaleTril:
+    @pytest.mark.parametrize("fam", list(FAMILIES))
+    def test_matches_the_induced_covariance(self, fam):
+        P = FAMILIES[fam]
+        sigma = jnp.linspace(0.2, 3.0, P.shape[1])
+        L = np.asarray(constant_scale_tril(jnp.asarray(P, jnp.float32), sigma))
+        cov = P @ np.diag(np.asarray(sigma) ** 2) @ P.T
+        np.testing.assert_allclose(L @ L.T, cov, atol=1e-5)
+        np.testing.assert_allclose(L, np.tril(L))
+        assert np.all(np.diag(L) > 0)
+
+    def test_one_sigma_at_zero_keeps_u0_covariance_positive_definite(self):
+        for P in (P_C4_U0, P_C12_U0):
+            for s in range(P.shape[1]):
+                sigma = np.ones(P.shape[1])
+                sigma[s] = 0.0
+                L = np.asarray(constant_scale_tril(
+                    jnp.asarray(P, jnp.float32), jnp.asarray(sigma)))
+                assert np.all(np.abs(np.diag(L)) > 1e-3)
+
+    def test_gradient_matches_slogdet(self):
+        P = jnp.asarray(P_C12_U0, jnp.float32)
+        sigma = jnp.linspace(0.3, 2.0, 6)
+        g1 = jax.grad(lambda s: jnp.sum(jnp.log(jnp.diagonal(
+            constant_scale_tril(P, s)))))(sigma)
+        g2 = jax.grad(lambda s: 0.5 * jnp.linalg.slogdet(
+            P @ jnp.diag(s ** 2) @ P.T)[1])(sigma)
+        np.testing.assert_allclose(g1, g2, rtol=1e-4)
+
+
+class TestSampleDlnK:
+    def test_sites_and_shapes(self):
+        M = 5
+        means = jnp.arange(M * 4, dtype=jnp.float32).reshape(M, 4)
+        out, trace = _trace(P_C4_U0, M, means)
+        assert out.shape == (M, 3)
+        assert trace["theta_dlnK"]["type"] == "sample"
+        assert trace["theta_dlnK"]["value"].shape == (M, 3)
+        assert trace["theta_ddG_sigma"]["type"] == "sample"
+        assert trace["theta_ddG_sigma"]["value"].shape == (4,)
+        assert trace["theta_ddG"]["type"] == "deterministic"
+        assert trace["theta_ddG"]["value"].shape == (M, 4)
+        assert trace["theta_ddG_prior_mean"]["type"] == "deterministic"
+        np.testing.assert_allclose(trace["theta_ddG_prior_mean"]["value"], means)
+        np.testing.assert_allclose(out, trace["theta_dlnK"]["value"])
+
+    def test_dlnK_is_mvn_at_projected_means_with_induced_covariance(self):
+        M = 4
+        means = jnp.arange(M * 4, dtype=jnp.float32).reshape(M, 4)
+        _, trace = _trace(P_C4_U1, M, means)
+        fn = trace["theta_dlnK"]["fn"]
+        while not isinstance(fn, dist.MultivariateNormal):
+            fn = fn.base_dist
+        np.testing.assert_allclose(np.broadcast_to(fn.loc, (M, 4)),
+                                   np.asarray(means) @ P_C4_U1.T, rtol=1e-6)
+        sigma = np.asarray(trace["theta_ddG_sigma"]["value"])
+        np.testing.assert_allclose(np.asarray(fn.covariance_matrix).reshape(-1, 4, 4)[0],
+                                   P_C4_U1 @ np.diag(sigma ** 2) @ P_C4_U1.T,
+                                   rtol=1e-4, atol=1e-5)
+        assert trace["theta_dlnK"]["fn"].batch_shape == (M,)
+        assert trace["theta_dlnK"]["fn"].event_shape == (4,)
+
+    def test_sigma_is_a_lognormal_sample_site_per_state(self):
+        _, trace = _trace(P_C4_U0, 3, jnp.zeros((3, 4)),
+                          sigma_loc=0.5, sigma_scale=0.7)
+        fn = trace["theta_ddG_sigma"]["fn"]
+        while not isinstance(fn, dist.LogNormal):
+            fn = fn.base_dist
+        np.testing.assert_allclose(fn.loc, 0.5)
+        np.testing.assert_allclose(fn.scale, 0.7)
+
+    def test_no_param_sites_and_no_per_state_latent(self):
+        _, trace = _trace(P_C4_U0, 3, jnp.zeros((3, 4)))
+        assert not [k for k, v in trace.items() if v["type"] == "param"]
+        samples = {k for k, v in trace.items() if v["type"] == "sample"}
+        assert samples == {"theta_ddG_sigma", "theta_dlnK"}
+
+    def test_conditioned_sigma_sets_the_constant_covariance(self):
+        M = 20000
+        sigma = jnp.array([0.3, 1.0, 2.0, 0.5])
+
+        def model():
+            return sample_dlnK("theta", STATES[4], M, jnp.zeros((M, 4)),
+                               P_C4_U0)
+
+        with numpyro.handlers.seed(rng_seed=0):
+            out = np.asarray(numpyro.handlers.condition(
+                model, data={"theta_ddG_sigma": sigma})())
+        cov = P_C4_U0 @ np.diag(np.asarray(sigma) ** 2) @ P_C4_U0.T
+        np.testing.assert_allclose(np.cov(out.T), cov, atol=0.08)
+
+    def test_wt_only_library_skips_the_dlnK_site(self):
+        out, trace = _trace(P_C4_U1, 0, jnp.zeros((0, 4)))
+        assert out.shape == (0, 4)
+        assert "theta_dlnK" not in trace
+        assert "theta_ddG_sigma" in trace
+        assert trace["theta_ddG"]["value"].shape == (0, 4)
+
+    def test_default_prior_spans_a_tenth_to_ten_kT(self):
+        lo = np.exp(DEFAULT_DDG_SIGMA_LOC - 1.96 * DEFAULT_DDG_SIGMA_SCALE)
+        hi = np.exp(DEFAULT_DDG_SIGMA_LOC + 1.96 * DEFAULT_DDG_SIGMA_SCALE)
+        assert 0.08 < lo < 0.12
+        assert 8.0 < hi < 12.0
+
+    def test_log_density_is_finite_and_bounded_as_one_u0_sigma_shrinks(self):
+        """The old per-state form was unbounded here; the constant form is not."""
+        M = 200
+        rng = np.random.default_rng(0)
+        means = rng.normal(0, 2, (M, 4)).astype(np.float32)
+        truth = means + rng.normal(0, 0.3, (M, 4)).astype(np.float32)
+        dlnK = jnp.asarray(truth @ P_C4_U0.T)
+
+        def model():
+            sample_dlnK("theta", STATES[4], M, jnp.asarray(means), P_C4_U0)
+
+        def logp(s_last):
+            sigma = jnp.array([0.3, 0.3, 0.3, s_last])
+            return float(numpyro.infer.util.log_density(
+                model, (), {}, {"theta_dlnK": dlnK,
+                                "theta_ddG_sigma": sigma})[0])
+
+        vals = [logp(s) for s in (0.3, 1e-2, 1e-4, 1e-6)]
+        assert np.all(np.isfinite(vals))
+        assert vals[0] > max(vals[1:])
+
+
+class TestPosthocDdG:
+    @pytest.mark.parametrize("fam", ["C4_U1", "C12_U1"])
+    def test_u1_exact_inversion(self, fam):
+        P = FAMILIES[fam]
+        rng = np.random.default_rng(1)
+        ddG = rng.normal(0, 1, (7, P.shape[1])).astype(np.float32)
+        out = posthoc_ddG(jnp.asarray(ddG @ P.T),
+                          jnp.asarray(rng.normal(0, 1, ddG.shape)),
+                          jnp.ones(P.shape[1]), P)
+        np.testing.assert_allclose(out, ddG, atol=1e-5)
+
+    @pytest.mark.parametrize("fam", ["C4_U0", "C12_U0"])
+    def test_u0_conditional_mean_formula(self, fam):
+        P = FAMILIES[fam]
+        S = P.shape[1]
+        rng = np.random.default_rng(2)
+        mu = rng.normal(0, 1, (6, S))
+        sigma = np.linspace(0.3, 2.0, S)
+        dlnK = rng.normal(0, 1, (6, P.shape[0]))
+        Sig = np.diag(sigma ** 2)
+        expect = mu + (Sig @ P.T @ np.linalg.solve(P @ Sig @ P.T,
+                                                   (dlnK - mu @ P.T).T)).T
+        out = posthoc_ddG(jnp.asarray(dlnK, jnp.float32),
+                          jnp.asarray(mu, jnp.float32),
+                          jnp.asarray(sigma, jnp.float32), P)
+        np.testing.assert_allclose(out, expect, atol=1e-4)
+        # It reproduces the constants exactly.
+        np.testing.assert_allclose(np.asarray(out) @ P.T, dlnK, atol=1e-4)
+
+    @pytest.mark.parametrize("fam", list(FAMILIES))
+    def test_constants_at_the_projected_table_give_the_table(self, fam):
+        """σ = 0 truth (Δln K = P·μ) gives the table back, in every family."""
+        P = FAMILIES[fam]
+        mu = np.random.default_rng(3).normal(0, 1, (5, P.shape[1]))
+        out = posthoc_ddG(jnp.asarray(mu @ P.T, jnp.float32),
+                          jnp.asarray(mu, jnp.float32),
+                          jnp.linspace(0.2, 2.0, P.shape[1]), P)
+        np.testing.assert_allclose(out, mu, atol=1e-5)
+
+    def test_u0_common_mode_comes_from_the_prior(self):
+        """Shifting every state's prior mean shifts the U0 post-hoc ΔΔG."""
+        P = P_C4_U0
+        rng = np.random.default_rng(4)
+        mu = rng.normal(0, 1, (3, 4))
+        dlnK = rng.normal(0, 1, (3, 3))
+        sigma = jnp.ones(4)
+        a = np.asarray(posthoc_ddG(jnp.asarray(dlnK), jnp.asarray(mu), sigma, P))
+        b = np.asarray(posthoc_ddG(jnp.asarray(dlnK), jnp.asarray(mu + 2.0),
+                                   sigma, P))
+        np.testing.assert_allclose(b - a, 2.0, atol=1e-5)
+
+    def test_site_records_posthoc_value(self):
+        M = 6
+        means = jnp.asarray(np.random.default_rng(5).normal(0, 1, (M, 4)),
+                            jnp.float32)
+        _, trace = _trace(P_C4_U0, M, means)
+        expect = posthoc_ddG(trace["theta_dlnK"]["value"], means,
+                             trace["theta_ddG_sigma"]["value"], P_C4_U0)
+        np.testing.assert_allclose(trace["theta_ddG"]["value"], expect,
+                                   rtol=1e-5, atol=1e-6)
+
+
+class TestSigmaIdentifiability:
+    """σ_s comes back from Δln K alone (no data noise), in every family."""
+
+    @pytest.mark.parametrize("fam", list(FAMILIES))
+    def test_sigma_recovered_from_noise_free_constants(self, fam):
+        P = FAMILIES[fam]
+        S = P.shape[1]
+        M = 4000
+        rng = np.random.default_rng(6)
+        sigma_true = np.linspace(0.3, 1.5, S)[rng.permutation(S)]
+        means = rng.normal(0, 2, (M, S)).astype(np.float32)
+        _, dlnK = per_state_truth(means, sigma_true, P, jax.random.PRNGKey(0))
+
+        def model():
+            sample_dlnK("theta", STATES[S], M, jnp.asarray(means), P)
+
+        def neg_logp(log_sigma):
+            return -numpyro.infer.util.log_density(
+                model, (), {}, {"theta_dlnK": jnp.asarray(dlnK),
+                                "theta_ddG_sigma": jnp.exp(log_sigma)})[0] / M
+
+        x = jnp.zeros(S)
+        step = jax.jit(jax.value_and_grad(neg_logp))
+        for _ in range(4000):
+            _, g = step(x)
+            x = x - 0.5 * g
+        np.testing.assert_allclose(np.exp(np.asarray(x)), sigma_true, rtol=0.1)
+
+    def test_c4_u0_covariance_entries_give_sigma_h_and_l(self):
+        P = P_C4_U0
+        sigma = np.array([0.7, 1.1, 0.4, 1.3])
+        cov = P @ np.diag(sigma ** 2) @ P.T
+        assert cov[0, 1] == pytest.approx(sigma[0] ** 2)        # σ_H²
+        assert -2 * cov[1, 2] == pytest.approx(sigma[2] ** 2)   # σ_L²
+
+
+class TestGuideDlnK:
+    def _traces(self, P=P_C4_U1, M=3):
+        S = P.shape[1]
+        means = jnp.arange(M * S, dtype=jnp.float32).reshape(M, S)
+
+        def model():
+            return sample_dlnK("theta", STATES[S], M, means, P)
+
+        def guide():
+            return guide_dlnK("theta", M, S, P, means)
+
+        with numpyro.handlers.seed(rng_seed=0):
+            mtr = numpyro.handlers.trace(model).get_trace()
+        with numpyro.handlers.seed(rng_seed=0):
+            gtr = numpyro.handlers.trace(guide).get_trace()
+        return means, mtr, gtr
+
+    @pytest.mark.parametrize("fam", list(FAMILIES))
+    def test_sample_sites_match_model(self, fam):
+        _, mtr, gtr = self._traces(FAMILIES[fam])
+
+        def sites(tr):
+            return {k: (v["value"].shape, v["fn"].event_shape)
+                    for k, v in tr.items() if v["type"] == "sample"}
+        assert sites(mtr) == sites(gtr)
+
+    def test_params_follow_loc_scale_convention(self):
+        means, _, gtr = self._traces()
+        params = {k: v["value"] for k, v in gtr.items() if v["type"] == "param"}
+        assert set(params) == {"theta_dlnK_locs", "theta_dlnK_scales",
+                               "theta_ddG_sigma_locs", "theta_ddG_sigma_scales"}
+        np.testing.assert_allclose(params["theta_dlnK_locs"],
+                                   np.asarray(means) @ P_C4_U1.T)
+        np.testing.assert_allclose(params["theta_ddG_sigma_locs"], 0.0)
+
+    def test_wt_only_library(self):
+        def guide():
+            return guide_dlnK("theta", 0, 4, P_C4_U0, jnp.zeros((0, 4)))
+
+        with numpyro.handlers.seed(rng_seed=0):
+            gtr = numpyro.handlers.trace(guide).get_trace()
+        assert "theta_dlnK" not in gtr
+        assert "theta_ddG_sigma" in gtr
+
+    def test_elbo_is_finite(self):
+        M, S = 3, 4
+        means = jnp.zeros((M, S))
+
+        def model():
+            sample_dlnK("theta", STATES[S], M, means, P_C4_U0)
+
+        def guide():
+            guide_dlnK("theta", M, S, P_C4_U0, means)
+
+        svi = numpyro.infer.SVI(model, guide, numpyro.optim.Adam(1e-3),
+                                numpyro.infer.Trace_ELBO())
+        state = svi.init(jax.random.PRNGKey(0))
+        _, loss = svi.update(state)
+        assert np.isfinite(float(loss))
+
+
+def test_guesses_start_at_projected_prior_means_and_one_kT():
+    means = np.arange(8, dtype=np.float32).reshape(2, 4)
+    g = dlnK_guesses("theta", means, P_C4_U0)
+    np.testing.assert_allclose(g["theta_dlnK"], means @ P_C4_U0.T)
+    np.testing.assert_allclose(g["theta_ddG_sigma"], np.ones(4))
+
+
+class TestPerStateTruth:
+    def test_zero_sigma_gives_the_table(self):
+        means = np.random.default_rng(0).normal(0, 1, (5, 4))
+        ddG, dlnK = per_state_truth(means, np.zeros(4), P_C4_U0,
+                                    jax.random.PRNGKey(0))
+        np.testing.assert_allclose(ddG, means, atol=1e-6)
+        np.testing.assert_allclose(dlnK, means @ P_C4_U0.T, atol=1e-5)
+
+    def test_spread_and_projection(self):
+        M = 20000
+        sigma = np.array([0.3, 1.0, 2.0, 0.5])
+        ddG, dlnK = per_state_truth(np.zeros((M, 4)), sigma, P_C4_U0,
+                                    jax.random.PRNGKey(1))
+        np.testing.assert_allclose(ddG.std(axis=0), sigma, rtol=0.03)
+        np.testing.assert_allclose(dlnK, ddG @ P_C4_U0.T, atol=1e-5)
diff --git a/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_unfolding_projection.py b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_unfolding_projection.py
new file mode 100644
index 00000000..8bba40f8
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/theta/thermo/test_unfolding_projection.py
@@ -0,0 +1,271 @@
+"""
+Unfolding projection of the structure-based U1 thermodynamic models.
+
+PddG and PnnC in ``O2_C4_K3_U1_a`` and ``O2_C12_K5_U1_a`` shift the free
+energy of each folded structure X by ΔΔG_X and leave the unfolded state U
+alone (ΔΔG_U = 0).  Since K_U = [U]/[H], Δln K_U = ΔΔG_H − ΔΔG_U = ΔΔG_H.
+Until 2026-10-10 these models held ln_K_U at its wt value for every genotype,
+which moved U with H and so made every mutation stability-neutral.
+"""
+import importlib
+import types
+from collections import namedtuple
+from unittest import mock
+
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+import pytest
+from numpyro.handlers import seed, substitute, trace
+
+from tfscreen.genetics.build_mut_geno_matrix import build_mut_sparse_indices
+from tfscreen.tfmodel.generative.components.theta.thermo import mwc
+from tfscreen.tfmodel.generative.components.theta.thermo.prior import projection
+
+_PKG = "tfscreen.tfmodel.generative.components.theta.thermo"
+
+# genotypes: wt, A1C, D2E, A1C/D2E
+_GENOTYPES = ["wt", "A1C", "D2E", "A1C/D2E"]
+_MUT_LABELS = ["A1C", "D2E"]
+_PAIR_LABELS = ["A1C/D2E"]
+_M, _G, _T = 2, 4, 1
+_MUT_GENO = np.array([[0, 1, 0, 1],
+                      [0, 0, 1, 1]], dtype=np.float32)
+_MUT_NNZ_MUT_IDX, _MUT_NNZ_GENO_IDX = build_mut_sparse_indices(_MUT_GENO)
+
+_C4 = dict(
+    family="O2_C4_K3_U1_a",
+    structs=("H", "HD", "L", "LE2"),
+    folded=("ln_K_op", "ln_K_HL", "ln_K_E"),
+    unfold="ln_K_U",
+    proj=projection(mwc.PROJ_C4, mwc.PROJ_U_C4),
+    wt={"ln_K_op_wt": mwc.DEFAULT_LN_K_H_O, "ln_K_HL_wt": mwc.DEFAULT_LN_K_H_L,
+        "ln_K_U_wt": 0.0, "ln_K_E_wt": np.full(_T, mwc.DEFAULT_LN_K_E_C4)},
+    conc=np.array([0.0, 0.1, 1.0]),
+    predict_kw={"conc_unit_scale": 1e-3},
+)
+_C12 = dict(
+    family="O2_C12_K5_U1_a",
+    structs=("H", "HO", "L", "LO", "HE2", "LE2"),
+    folded=("ln_K_h_l", "ln_K_h_o", "ln_K_h_e", "ln_K_l_o", "ln_K_l_e"),
+    unfold="ln_K_u",
+    proj=projection(mwc.PROJ_C12, mwc.PROJ_U_C12),
+    wt={"ln_K_h_l_wt": mwc.DEFAULT_LN_K_H_L, "ln_K_h_o_wt": mwc.DEFAULT_LN_K_H_O,
+        "ln_K_l_o_wt": mwc.DEFAULT_LN_K_L_O, "ln_K_u_wt": 0.0,
+        "ln_K_h_e_wt": np.full(_T, mwc.DEFAULT_LN_K_H_E),
+        "ln_K_l_e_wt": np.full(_T, mwc.DEFAULT_LN_K_L_E)},
+    conc=np.array([0.0, 0.1, 1.0]),
+    predict_kw={"conc_unit_scale": 1e-3},
+)
+
+CASES = [(_C4, "PddG"), (_C4, "PnnC"), (_C12, "PddG"), (_C12, "PnnC")]
+IDS = [f"{f['family']}.{k}" for f, k in CASES]
+
+Data = namedtuple("Data", [
+    "num_titrant_name", "num_titrant_conc", "num_genotype", "titrant_conc",
+    "geno_theta_idx", "batch_idx", "scatter_theta", "num_mutation", "num_pair",
+    "mut_geno_matrix", "mut_nnz_mut_idx", "mut_nnz_geno_idx",
+    "pair_nnz_pair_idx", "pair_nnz_geno_idx", "num_struct", "struct_names",
+    "struct_features", "struct_n_chains", "struct_contact_pair_idx",
+    "struct_contact_distances",
+])
+
+
+def _modules(fam, kind):
+    return (importlib.import_module(f"{_PKG}.{fam['family']}.{kind}"),
+            importlib.import_module(f"{_PKG}.{fam['family']}.thermo"))
+
+
+def _data(fam, kind, ddG_means, num_pair=0):
+    S = len(fam["structs"])
+    features = (np.asarray(ddG_means, dtype=np.float32) if kind == "PddG"
+                else np.zeros((_M, S, 60), dtype=np.float32))
+    return Data(
+        num_titrant_name=_T, num_titrant_conc=len(fam["conc"]),
+        num_genotype=_G, titrant_conc=jnp.array(fam["conc"]),
+        geno_theta_idx=jnp.arange(_G), batch_idx=jnp.arange(_G),
+        scatter_theta=0, num_mutation=_M, num_pair=num_pair,
+        mut_geno_matrix=_MUT_GENO, mut_nnz_mut_idx=_MUT_NNZ_MUT_IDX,
+        mut_nnz_geno_idx=_MUT_NNZ_GENO_IDX,
+        pair_nnz_pair_idx=np.array([0] if num_pair else [], dtype=np.int32),
+        pair_nnz_geno_idx=np.array([3] if num_pair else [], dtype=np.int32),
+        num_struct=S, struct_names=fam["structs"], struct_features=features,
+        struct_n_chains=np.full(S, 2, dtype=np.int32),
+        struct_contact_pair_idx=None,
+        struct_contact_distances=(np.full((num_pair, S), 5.0, dtype=np.float32)
+                                  if num_pair else None),
+    )
+
+
+def _fixed_sites(fam, ddG_means, num_pair=0, epi_H=0.0):
+    """Values for every sample site so model and guide see the same draw."""
+    S = len(fam["structs"])
+    sites = {f"theta_{k}": jnp.asarray(v) for k, v in fam["wt"].items()}
+    # Δln K = P · ΔΔG with ΔΔG at the prior means.
+    sites["theta_dlnK"] = jnp.asarray(np.asarray(ddG_means) @ fam["proj"].T,
+                                      dtype=jnp.float32)
+    sites["theta_ddG_sigma"] = jnp.ones(S)
+    if num_pair:
+        offset = np.full((S, num_pair), 0.3)
+        offset[0] = epi_H                            # H column of epi ddG
+        sites.update({
+            "theta_epi_tau": jnp.array(0.5),
+            "theta_epi_c2": jnp.array(4.0),
+            "theta_epi_lambda": jnp.ones((S, num_pair)),
+            "theta_epi_offset": jnp.asarray(offset),
+        })
+    return sites
+
+
+def _run(fam, kind, ddG_means, fn="define_model", num_pair=0, epi_H=0.0):
+    module, _ = _modules(fam, kind)
+    data = _data(fam, kind, ddG_means, num_pair=num_pair)
+    priors = module.get_priors()
+    sites = _fixed_sites(fam, ddG_means, num_pair=num_pair, epi_H=epi_H)
+    target = getattr(module, fn)
+
+    def _call():
+        with trace() as tr, substitute(data=sites), seed(rng_seed=0):
+            out = target("theta", data, priors)
+        return out, tr
+
+    if kind == "PnnC":
+        # Pin the NN prior means to the requested ΔΔG.
+        with mock.patch.object(module, "compute_nn_predictions",
+                               return_value=jnp.asarray(ddG_means)):
+            return _call()
+    return _call()
+
+
+def _theta(fam, param, ln_K_unfold=None):
+    """theta (T, C, G) from a ThetaParam, optionally with another ln_K_U."""
+    _, thermo = _modules(fam, "PddG")
+    if ln_K_unfold is not None:
+        param = param.replace(**{fam["unfold"]: ln_K_unfold})
+    return np.asarray(thermo.run_model(param, _data(fam, "PddG",
+                                                    np.zeros((_M, 1)))))
+
+
+def _means(fam, rows):
+    S = len(fam["structs"])
+    out = np.zeros((_M, S), dtype=np.float32)
+    for m, row in rows.items():
+        out[m] = row
+    return out
+
+
+@pytest.mark.parametrize("fam, kind", CASES, ids=IDS)
+def test_H_only_ddG_shifts_unfolding_by_ddG_H(fam, kind):
+    S = len(fam["structs"])
+    ddG_H = 3.0
+    h_only = np.zeros(S)
+    h_only[0] = ddG_H
+    out, tr = _run(fam, kind, _means(fam, {0: h_only}))
+    ln_K_u = np.asarray(getattr(out, fam["unfold"]))
+
+    # Δln K_U = ΔΔG_H for the mutant carrying it, and only for it.
+    np.testing.assert_allclose(ln_K_u - ln_K_u[0], [0, ddG_H, 0, ddG_H],
+                               atol=1e-5)
+    d_site = f"theta_d_{fam['unfold']}"
+    np.testing.assert_allclose(tr[d_site]["value"], [ddG_H, 0.0], atol=1e-6)
+    np.testing.assert_allclose(tr[f"theta_{fam['unfold']}"]["value"], ln_K_u)
+
+    # The shift changes theta: holding K_U at wt gives a different curve.
+    theta = _theta(fam, out)
+    pinned = _theta(fam, out, jnp.full(_G, ln_K_u[0]))
+    assert not np.allclose(theta[..., 1], pinned[..., 1], atol=1e-4)
+    np.testing.assert_allclose(theta[..., 2], pinned[..., 2], atol=1e-6)
+
+
+@pytest.mark.parametrize("fam, kind", CASES, ids=IDS)
+def test_uniform_ddG_is_a_pure_stability_effect(fam, kind):
+    S = len(fam["structs"])
+    shift = 2.5
+    out, _ = _run(fam, kind, _means(fam, {0: np.full(S, shift)}))
+
+    # Every folded-state constant is unchanged ...
+    for k in fam["folded"]:
+        v = np.asarray(getattr(out, k))
+        np.testing.assert_allclose(v[..., 1], v[..., 0], atol=1e-5,
+                                   err_msg=k)
+    # ... while K_U rises by the shift, so the mutant still loses theta.
+    ln_K_u = np.asarray(getattr(out, fam["unfold"]))
+    assert ln_K_u[1] - ln_K_u[0] == pytest.approx(shift, abs=1e-5)
+    theta = _theta(fam, out)
+    assert np.all(theta[..., 1] < theta[..., 0])
+
+
+@pytest.mark.parametrize("num_pair", [0, 1])
+@pytest.mark.parametrize("fam, kind", CASES, ids=IDS)
+def test_model_and_guide_assemble_the_same_constants(fam, kind, num_pair):
+    rng = np.random.default_rng(1)
+    means = rng.normal(0, 1, (_M, len(fam["structs"]))).astype(np.float32)
+    model_out, _ = _run(fam, kind, means, num_pair=num_pair, epi_H=0.7)
+    guide_out, _ = _run(fam, kind, means, fn="guide", num_pair=num_pair,
+                        epi_H=0.7)
+    for k in fam["folded"] + (fam["unfold"],):
+        np.testing.assert_allclose(np.asarray(getattr(guide_out, k)),
+                                   np.asarray(getattr(model_out, k)),
+                                   atol=1e-5, err_msg=k)
+
+
+@pytest.mark.parametrize("fam, kind", CASES, ids=IDS)
+def test_pair_epistasis_projects_onto_unfolding(fam, kind):
+    rng = np.random.default_rng(2)
+    means = rng.normal(0, 1, (_M, len(fam["structs"]))).astype(np.float32)
+    out, tr = _run(fam, kind, means, num_pair=1, epi_H=0.7)
+    u = fam["unfold"]
+    d = np.asarray(tr[f"theta_d_{u}"]["value"])
+    epi = np.asarray(tr[f"theta_epi_{u}"]["value"])
+    np.testing.assert_allclose(epi, np.asarray(tr["theta_epi_ddG"]["value"])[:, 0],
+                               atol=1e-6)
+    assert abs(epi[0]) > 1e-3
+    ln_K_u = np.asarray(getattr(out, u))
+    assert ln_K_u[3] - ln_K_u[0] == pytest.approx(d[0] + d[1] + epi[0],
+                                                  abs=1e-5)
+
+
+@pytest.mark.parametrize("num_pair", [0, 1])
+@pytest.mark.parametrize("fam, kind", CASES, ids=IDS)
+def test_predict_unmeasured_matches_model(fam, kind, num_pair):
+    """predict_unmeasured rebuilds ln_K_U from d_ln_K_U/epi_ln_K_U."""
+    module, _ = _modules(fam, kind)
+    rng = np.random.default_rng(3)
+    means = rng.normal(0, 1.5, (_M, len(fam["structs"]))).astype(np.float32)
+    out, tr = _run(fam, kind, means, num_pair=num_pair, epi_H=0.7)
+
+    post = {f"theta_{k}": np.asarray(v)[None] for k, v in fam["wt"].items()}
+    for site, rec in tr.items():
+        if site.startswith(("theta_d_", "theta_epi_ln")):
+            post[site] = np.asarray(rec["value"])[None]
+
+    titrant_df = pd.DataFrame({"titrant_name": "iptg",
+                               "titrant_conc": fam["conc"]})
+    priors = module.get_priors()
+    result = module.predict_unmeasured(
+        _GENOTYPES, ["iptg"], titrant_df, _MUT_LABELS,
+        _PAIR_LABELS if num_pair else [], post, {"median": 0.5},
+        tf_total=priors.theta_tf_total_M,
+        **fam["predict_kw"])
+
+    theta = _theta(fam, out)[0]           # (C, G)
+    for g_idx, g in enumerate(_GENOTYPES):
+        got = result.loc[result["genotype"] == g, "median"].to_numpy()
+        np.testing.assert_allclose(got, theta[:, g_idx], atol=1e-4,
+                                   err_msg=g)
+
+
+@pytest.mark.parametrize("fam, kind", CASES, ids=IDS)
+def test_extract_specs_include_unfolding_delta(fam, kind):
+    module, _ = _modules(fam, kind)
+    growth_tm = types.SimpleNamespace(
+        tensor_dim_names=["genotype", "titrant_name"],
+        tensor_dim_labels=[_GENOTYPES, ["iptg"]],
+        df=pd.DataFrame({"genotype": _GENOTYPES,
+                         "genotype_idx": range(_G),
+                         "titrant_name": "iptg", "titrant_name_idx": 0}))
+    ctx = types.SimpleNamespace(growth_tm=growth_tm, mut_labels=_MUT_LABELS,
+                                pair_labels=_PAIR_LABELS)
+    names = [p for s in module.get_extract_specs(ctx) for p in s["params_to_get"]]
+    assert f"d_{fam['unfold']}" in names
+    assert f"epi_{fam['unfold']}" in names
diff --git a/tests/tfscreen/tfmodel/generative/components/transformation/test__congression.py b/tests/tfscreen/tfmodel/generative/components/transformation/test__congression.py
deleted file mode 100644
index 7d269d7b..00000000
--- a/tests/tfscreen/tfmodel/generative/components/transformation/test__congression.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import pytest
-import jax.numpy as jnp
-import numpy as np
-import numpyro
-from numpyro import distributions as dist
-from unittest.mock import MagicMock, patch
-
-from tfscreen.tfmodel.generative.components.transformation import (
-    _congression as transformation_congression,
-)
-
-# -------------------------------------------------------------------------
-# Math Utilities
-# -------------------------------------------------------------------------
-
-def test_logit_normal_cdf():
-    """Test CDF calculation values."""
-    mu, sigma = 0.0, 1.0
-    x = jnp.array([0.0, 0.5, 1.0])
-    # logit(0.5) = 0.
-    # Phi((0 - 0)/1) = Phi(0) = 0.5
-    
-    cdf = transformation_congression._logit_normal_cdf(x, mu, sigma)
-    
-    assert jnp.isclose(cdf[0], 0.0, atol=1e-5)
-    assert jnp.isclose(cdf[1], 0.5, atol=1e-5)
-    assert jnp.isclose(cdf[2], 1.0, atol=1e-5)
-
-def test_empirical_cdf():
-    """Test empirical CDF calculation."""
-    theta = jnp.array([0.1, 0.5, 0.9])
-    t_grid = jnp.array([0.0, 0.2, 0.5, 0.8, 1.0])
-    
-    # theta has 3 elements. y = [0.166, 0.5, 0.833] (using 0.5/n)
-    # Sorted: 0.1, 0.5, 0.9
-    cdf = transformation_congression._empirical_cdf(theta, t_grid)
-    
-    assert cdf.shape == (5,)
-    assert cdf[0] == 0.16666667 # Interp 0.0 at 0.1 returns y[0]
-    assert jnp.isclose(cdf[2], 0.5) # 0.5 is exactly in theta
-    assert jnp.isclose(cdf[4], 5/6, atol=1e-5) # Interp 1.0 at 0.9 returns y[2]
-
-
-# -------------------------------------------------------------------------
-# Correction Logic
-# -------------------------------------------------------------------------
-
-def test_calculate_expected_observed_max():
-    """Verify correction logic for congression."""
-    # Case 1: Target > Background
-    x_val = 0.9
-    lam = 1.0
-    mu, sigma = -2.0, 1.0 # Low mean
-    
-    res = transformation_congression.calculate_expected_observed_max(x_val, mu, sigma, lam)
-    
-    assert jnp.isclose(res, x_val, atol=0.05)
-    assert res >= x_val 
-    
-    # Case 2: Target < Background
-    x_val = 0.1
-    mu, sigma = 2.0, 1.0
-    
-    res = transformation_congression.calculate_expected_observed_max(x_val, mu, sigma, lam)
-    
-    assert res > 0.5
-
-def test_calculate_expected_observed_min():
-    """Verify correction logic for min."""
-    x_val = 0.9
-    mu, sigma = -2.0, 1.0 
-    lam = 1.0
-    
-    res = transformation_congression.calculate_expected_observed_min(x_val, mu, sigma, lam)
-    
-    assert res < x_val
-    assert res > 0.0
-
-def test_update_thetas_shapes():
-    """Verify shape processing with plated params."""
-    theta = jnp.ones((2, 5)) * 0.5
-    
-    lam = 1.0
-    mu = jnp.array([[0.0], [0.0]]) # shape (2, 1)
-    sigma = jnp.array([[1.0], [1.0]]) # shape (2, 1)
-    
-    params = (lam, mu, sigma)
-    
-    res = transformation_congression.update_thetas(theta, params=params)
-    assert res.shape == (2, 5)
-
-    # With mask (line 129)
-    mask = jnp.array([True, False, True, False, True]) # Length must match num_genotypes (5)
-    res_mask = transformation_congression.update_thetas(theta, params=params, mask=mask)
-    assert res_mask.shape == (2, 5)
-    # Check that entries where mask is False are unchanged
-    assert jnp.all(res_mask[:, 1] == theta[:, 1])
-    assert jnp.all(res_mask[:, 3] == theta[:, 3])
-
-    # Empirical mode call within shapes test
-    res_emp = transformation_congression.update_thetas(theta, params=(lam,), theta_dist="empirical")
-    assert res_emp.shape == (2, 5)
-    
-def test_update_thetas_empirical_values():
-    """Verify empirical mode update_thetas with realistic values."""
-    theta = jnp.array([[0.1, 0.5, 0.9], [0.2, 0.6, 1.0]])
-    lam = 1.0
-    params = (lam,)
-    
-    res = transformation_congression.update_thetas(theta, params=params, theta_dist="empirical")
-    assert res.shape == (2, 3)
-    # Result should be >= input for max-congression
-    assert jnp.all(res >= theta - 1e-6)
-
-
-# -------------------------------------------------------------------------
-# population_theta: population-wide background CDF for empirical mode
-# -------------------------------------------------------------------------
-
-def test_update_thetas_population_theta_defaults_to_theta():
-    """Omitting population_theta must reproduce the pre-existing behaviour of
-    building the background CDF from theta itself (backward compatibility)."""
-    theta = jnp.array([[0.1, 0.5, 0.9]])
-    lam = 1.0
-    params = (lam,)
-
-    res_default = transformation_congression.update_thetas(
-        theta, params=params, theta_dist="empirical")
-    res_explicit_none = transformation_congression.update_thetas(
-        theta, params=params, theta_dist="empirical", population_theta=None)
-
-    assert jnp.allclose(res_default, res_explicit_none)
-
-
-def test_update_thetas_population_theta_changes_correction():
-    """A population_theta that differs from theta must change the correction:
-    the empirical CDF (and hence the congression-corrected values) should
-    reflect where theta falls in the *population*, not in itself."""
-    theta = jnp.array([[0.5, 0.5, 0.5]])
-    lam = 1.0
-    params = (lam,)
-
-    # theta is smack in the middle of a population that is itself centered
-    # at 0.5 -> minimal correction expected either way, so use an asymmetric
-    # population instead: one where 0.5 sits near the *bottom* of the range.
-    low_population = jnp.array([[0.4, 0.45, 0.5, 0.9, 0.95]])
-    high_population = jnp.array([[0.05, 0.1, 0.5, 0.55, 0.6]])
-
-    res_low_pop = transformation_congression.update_thetas(
-        theta, params=params, theta_dist="empirical", population_theta=low_population)
-    res_high_pop = transformation_congression.update_thetas(
-        theta, params=params, theta_dist="empirical", population_theta=high_population)
-
-    # Same theta, different reference populations -> different corrections.
-    assert not jnp.allclose(res_low_pop, res_high_pop)
-
-
-def test_update_thetas_population_theta_uses_own_shape_for_lambda_broadcast():
-    """population_theta may have a different (larger) trailing genotype
-    dimension than theta; the correction must still broadcast to theta's
-    shape without error, using population_theta's leading dims for lambda."""
-    theta = jnp.array([[0.2, 0.8]])          # (1, 2) — the "batch" being corrected
-    population_theta = jnp.linspace(0.0, 1.0, 50).reshape(1, 50)  # (1, 50) — full population
-    lam = 1.0
-    params = (lam,)
-
-    res = transformation_congression.update_thetas(
-        theta, params=params, theta_dist="empirical", population_theta=population_theta)
-
-    assert res.shape == theta.shape
-    assert jnp.all(jnp.isfinite(res))
-
-
-def test_update_thetas_population_theta_ignored_for_logit_norm():
-    """population_theta must be a no-op for logit_norm mode, which uses the
-    smooth analytic (mu, sigma) CDF rather than raw samples."""
-    theta = jnp.array([[0.1, 0.5, 0.9]])
-    lam = 1.0
-    mu = jnp.array([[0.0]])
-    sigma = jnp.array([[1.0]])
-    params = (lam, mu, sigma)
-
-    res_without = transformation_congression.update_thetas(
-        theta, params=params, theta_dist="logit_norm")
-    res_with = transformation_congression.update_thetas(
-        theta, params=params, theta_dist="logit_norm",
-        population_theta=jnp.array([[0.99, 0.99, 0.99]]))
-
-    assert jnp.allclose(res_without, res_with)
-
-
-# -------------------------------------------------------------------------
-# Model Interface
-# -------------------------------------------------------------------------
-
-def test_getters():
-    """Test get_hyperparameters, get_guesses, get_priors."""
-    params = transformation_congression.get_hyperparameters()
-    assert "lam_loc" in params
-    assert "mu_anchoring_scale" in params
-    assert "sigma_anchoring_scale" in params
-
-    guesses = transformation_congression.get_guesses("test", MagicMock())
-    assert "test_lam" in guesses
-    assert "test_mu" in guesses
-    assert "test_sigma" in guesses
-
-    priors = transformation_congression.get_priors()
-    assert isinstance(priors, transformation_congression.ModelPriors)
-
-
-# -------------------------------------------------------------------------
-# lam_mean/lam_std -- experimentally measured lambda moment-matching
-# -------------------------------------------------------------------------
-
-def test_get_hyperparameters_placeholder_default():
-    """With no lam_mean/lam_std, a weakly-informative placeholder is used --
-    never a specific 'real' experimental value (that would be misleading
-    now that the real value must be supplied explicitly by the caller)."""
-    params = transformation_congression.get_hyperparameters()
-    assert params["lam_loc"] == 0.0
-    assert params["lam_scale"] == 1.0
-
-
-def test_get_hyperparameters_moment_matching():
-    """lam_mean/lam_std (linear space) must moment-match onto the
-    LogNormal's underlying-Normal parameters, i.e. the resulting
-    LogNormal(lam_loc, lam_scale) has the requested arithmetic mean/std."""
-    lam_mean, lam_std = 0.3572, 0.13
-    params = transformation_congression.get_hyperparameters(
-        lam_mean=lam_mean, lam_std=lam_std)
-
-    lam_loc = params["lam_loc"]
-    lam_scale = params["lam_scale"]
-
-    # Analytic mean/variance of a LogNormal(loc, scale).
-    implied_mean = np.exp(lam_loc + lam_scale**2 / 2.0)
-    implied_var = (np.exp(lam_scale**2) - 1.0) * np.exp(2 * lam_loc + lam_scale**2)
-
-    assert np.isclose(implied_mean, lam_mean)
-    assert np.isclose(implied_var, lam_std**2)
-
-
-def test_get_hyperparameters_requires_both_lam_mean_and_lam_std():
-    """A lone lam_mean or lam_std (without its partner) is ambiguous and
-    must raise rather than silently guessing the other."""
-    with pytest.raises(ValueError, match="together"):
-        transformation_congression.get_hyperparameters(lam_mean=0.36)
-    with pytest.raises(ValueError, match="together"):
-        transformation_congression.get_hyperparameters(lam_std=0.05)
-
-
-@pytest.mark.parametrize("lam_mean,lam_std", [(0.0, 0.1), (-0.1, 0.1), (0.36, 0.0), (0.36, -0.1)])
-def test_get_hyperparameters_rejects_nonpositive_lam(lam_mean, lam_std):
-    """lam_mean and lam_std must both be strictly positive (LogNormal support)."""
-    with pytest.raises(ValueError):
-        transformation_congression.get_hyperparameters(lam_mean=lam_mean, lam_std=lam_std)
-
-
-def test_get_guesses_uses_lam_mean_as_guess():
-    """When lam_mean is supplied, it becomes the initial guess directly
-    (rather than the old hardcoded 0.3572)."""
-    guesses = transformation_congression.get_guesses("test", MagicMock(), lam_mean=0.5)
-    assert guesses["test_lam"] == 0.5
-
-
-def test_get_guesses_placeholder_without_lam_mean():
-    guesses = transformation_congression.get_guesses("test", MagicMock())
-    assert guesses["test_lam"] == 1.0
-
-
-def test_get_priors_forwards_lam_mean_std():
-    priors = transformation_congression.get_priors(lam_mean=0.3572, lam_std=0.13)
-    assert isinstance(priors, transformation_congression.ModelPriors)
-    expected = transformation_congression.get_hyperparameters(lam_mean=0.3572, lam_std=0.13)
-    assert priors.lam_loc == expected["lam_loc"]
-    assert priors.lam_scale == expected["lam_scale"]
-
-def test_define_model():
-    """Test Numpyro model definition with plates."""
-    priors = transformation_congression.get_priors()
-    data = MagicMock()
-    data.num_titrant_name = 2
-    data.num_titrant_conc = 3
-    
-    # Run with seeded handler
-    with numpyro.handlers.seed(rng_seed=0):
-        # Without anchors
-        lam, mu, sigma = transformation_congression.define_model("test", data, priors)
-        assert lam.shape == ()
-        assert mu.shape == (2, 3, 1)
-        
-        # With anchors
-        anchors = (jnp.zeros((2, 3, 1)), jnp.ones((2, 3, 1)))
-        lam2, mu2, sigma2 = transformation_congression.define_model("test_anc", data, priors, anchors=anchors)
-        assert mu2.shape == (2, 3, 1)
-
-
-def test_guide():
-    """Test Numpyro guide definition with plates."""
-    priors = transformation_congression.get_priors()
-    data = MagicMock()
-    data.num_titrant_name = 2
-    data.num_titrant_conc = 3
-    
-    # Run with seeded handler and trace to check params
-    with numpyro.handlers.seed(rng_seed=0):
-        with numpyro.handlers.trace() as tr:
-            lam, mu, sigma = transformation_congression.guide("test", data, priors)
-            
-    assert "test_mu_loc" in tr
-    assert tr["test_mu_loc"]['value'].shape == (2, 3, 1)
-    
-    assert mu.shape == (2, 3, 1)
-    assert sigma.shape == (2, 3, 1)
-
-    # Run with empirical mode
-    priors_emp = transformation_congression.ModelPriors(0.0, 0.1, 0.5, 0.2, mode="empirical")
-    with numpyro.handlers.seed(rng_seed=2):
-        res_emp = transformation_congression.guide("test_emp", data, priors_emp)
-        assert len(res_emp) == 1
-        assert res_emp[0].shape == ()
-
-    # Run with anchors to hit line 334
-    with numpyro.handlers.seed(rng_seed=1):
-        anchors = (jnp.zeros((2, 3, 1)), jnp.ones((2, 3, 1)))
-        lam_anc, mu_anc, sigma_anc = transformation_congression.guide("test_anc", data, priors, anchors=anchors)
-        assert mu_anc.shape == (2, 3, 1)
-
diff --git a/tests/tfscreen/tfmodel/generative/components/transformation/test_congression_mask.py b/tests/tfscreen/tfmodel/generative/components/transformation/test_congression_mask.py
deleted file mode 100644
index 4f000186..00000000
--- a/tests/tfscreen/tfmodel/generative/components/transformation/test_congression_mask.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import pytest
-import jax.numpy as jnp
-from tfscreen.tfmodel.generative.components.transformation import _congression as transformation_congression
-
-def test_update_thetas_with_mask():
-    """Verify that the mask correctly selects between corrected and uncorrected thetas."""
-    
-    # Setup data
-    num_geno = 4
-    theta = jnp.ones((2, num_geno)) * 0.5
-    
-    # params: lam=1.0, mu=0.0, sigma=1.0 (some correction will happen)
-    params = (1.0, 0.0, 1.0)
-    
-    # Mask: genotypes 0 and 2 are corrected (True), 1 and 3 are NOT (False)
-    mask = jnp.array([True, False, True, False])
-    
-    # Get results
-    # First, get what it would be without mask (all corrected)
-    res_no_mask = transformation_congression.update_thetas(theta, params, mask=None)
-    
-    # Now, with mask
-    res_with_mask = transformation_congression.update_thetas(theta, params, mask=mask)
-    
-    # Check shape
-    assert res_with_mask.shape == (2, num_geno)
-    
-    # Genotypes 0 and 2 should match res_no_mask
-    assert jnp.allclose(res_with_mask[:, 0], res_no_mask[:, 0])
-    assert jnp.allclose(res_with_mask[:, 2], res_no_mask[:, 2])
-    
-    # Genotypes 1 and 3 should match original theta
-    assert jnp.allclose(res_with_mask[:, 1], theta[:, 1])
-    assert jnp.allclose(res_with_mask[:, 3], theta[:, 3])
-    
-    # Sanity check: verify that correction actually did something
-    # For mu=0, sigma=1, lam=1, F(0.5) = 0.5
-    assert not jnp.allclose(res_no_mask[:, 0], theta[:, 0])
-    # Expected value should be > 0.5 (shifts towards the mean of 0.5)
-    assert jnp.all(res_no_mask > 0.5)
-    assert jnp.all(res_no_mask < 0.8)
-
-def test_update_thetas_no_mask_is_default():
-    """Verify that passing mask=None is the same as all True."""
-    num_geno = 3
-    theta = jnp.ones((1, num_geno)) * 0.4
-    params = (1.0, 0.0, 1.0)
-    
-    res1 = transformation_congression.update_thetas(theta, params, mask=None)
-    res2 = transformation_congression.update_thetas(theta, params, mask=jnp.ones(num_geno, dtype=bool))
-    
-    assert jnp.allclose(res1, res2)
-
-if __name__ == "__main__":
-    test_update_thetas_with_mask()
-    test_update_thetas_no_mask_is_default()
-    print("Mask tests passed!")
diff --git a/tests/tfscreen/tfmodel/generative/components/transformation/test_empirical.py b/tests/tfscreen/tfmodel/generative/components/transformation/test_empirical.py
deleted file mode 100644
index 2faa2de8..00000000
--- a/tests/tfscreen/tfmodel/generative/components/transformation/test_empirical.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import pytest
-import jax.numpy as jnp
-
-from tfscreen.tfmodel.generative.components.transformation import (
-    empirical,
-    _congression as congression,
-)
-
-
-def test_get_hyperparameters_mode():
-    params = empirical.get_hyperparameters()
-    assert params["mode"] == "empirical"
-
-
-def test_needs_full_population_theta_flag():
-    """Empirical mode's background CDF is built from raw genotype samples, so
-    it must request a population-wide theta reference from jax_model."""
-    assert empirical.NEEDS_FULL_POPULATION_THETA is True
-
-
-def test_get_priors_mode():
-    priors = empirical.get_priors()
-    assert isinstance(priors, congression.ModelPriors)
-    assert priors.mode == "empirical"
-
-
-def test_get_hyperparameters_forwards_lam_mean_std():
-    params = empirical.get_hyperparameters(lam_mean=0.3572, lam_std=0.13)
-    expected = congression.get_hyperparameters(lam_mean=0.3572, lam_std=0.13)
-    assert params["lam_loc"] == expected["lam_loc"]
-    assert params["lam_scale"] == expected["lam_scale"]
-    assert params["mode"] == "empirical"
-
-
-def test_get_priors_forwards_lam_mean_std():
-    priors = empirical.get_priors(lam_mean=0.3572, lam_std=0.13)
-    assert priors.mode == "empirical"
-    expected = congression.get_hyperparameters(lam_mean=0.3572, lam_std=0.13)
-    assert priors.lam_loc == expected["lam_loc"]
-    assert priors.lam_scale == expected["lam_scale"]
-
-
-def test_update_thetas_bound_with_empirical():
-    """Pre-bound update_thetas should invoke empirical path taking only (lam,)."""
-    theta = jnp.array([[0.1, 0.5, 0.9]])
-    lam = 1.0
-    result = empirical.update_thetas(theta, params=(lam,))
-    assert result.shape == theta.shape
-    # Congression correction must push occupancies upward (or keep them equal).
-    assert jnp.all(result >= theta - 1e-5)
-
-
-def test_update_thetas_uses_empirical_not_logit_norm():
-    """empirical.update_thetas must use the empirical CDF path.
-
-    With the same lambda but a very skewed logit_norm background (high mu,
-    large sigma), the empirical correction based on the actual theta distribution
-    will differ from the logit_norm correction.  This confirms that the baked-in
-    theta_dist="empirical" is honoured and not silently overridden.
-    """
-    from tfscreen.tfmodel.generative.components.transformation import logit_norm
-
-    theta = jnp.array([[0.1, 0.3, 0.5, 0.7, 0.9]])
-    lam = 2.0
-
-    result_emp = empirical.update_thetas(theta, params=(lam,))
-
-    # logit_norm requires (lam, mu, sigma); use extreme params to ensure divergence.
-    # mu/sigma shape: (batch_dim, 1) matching theta.shape[0].
-    mu = jnp.array([[5.0]])
-    sigma = jnp.array([[0.5]])
-    result_ln = logit_norm.update_thetas(theta, params=(lam, mu, sigma))
-
-    # The two corrections should differ for at least one element.
-    assert not jnp.allclose(result_emp, result_ln, atol=1e-3)
diff --git a/tests/tfscreen/tfmodel/generative/components/transformation/test_logit_norm.py b/tests/tfscreen/tfmodel/generative/components/transformation/test_logit_norm.py
deleted file mode 100644
index 4d5bdb7c..00000000
--- a/tests/tfscreen/tfmodel/generative/components/transformation/test_logit_norm.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import pytest
-import jax.numpy as jnp
-import numpyro
-
-from tfscreen.tfmodel.generative.components.transformation import (
-    logit_norm,
-    _congression as congression,
-)
-
-
-def test_get_hyperparameters_mode():
-    params = logit_norm.get_hyperparameters()
-    assert params["mode"] == "logit_norm"
-
-
-def test_needs_full_population_theta_flag():
-    """logit_norm's background CDF is the smooth analytic (mu, sigma) family,
-    not raw samples, so it must not request a population-wide theta reference."""
-    assert logit_norm.NEEDS_FULL_POPULATION_THETA is False
-
-
-def test_get_priors_mode():
-    priors = logit_norm.get_priors()
-    assert isinstance(priors, congression.ModelPriors)
-    assert priors.mode == "logit_norm"
-
-
-def test_get_hyperparameters_forwards_lam_mean_std():
-    params = logit_norm.get_hyperparameters(lam_mean=0.3572, lam_std=0.13)
-    expected = congression.get_hyperparameters(lam_mean=0.3572, lam_std=0.13)
-    assert params["lam_loc"] == expected["lam_loc"]
-    assert params["lam_scale"] == expected["lam_scale"]
-    assert params["mode"] == "logit_norm"
-
-
-def test_get_priors_forwards_lam_mean_std():
-    priors = logit_norm.get_priors(lam_mean=0.3572, lam_std=0.13)
-    assert priors.mode == "logit_norm"
-    expected = congression.get_hyperparameters(lam_mean=0.3572, lam_std=0.13)
-    assert priors.lam_loc == expected["lam_loc"]
-    assert priors.lam_scale == expected["lam_scale"]
-
-
-def test_update_thetas_bound_with_logit_norm():
-    """Pre-bound update_thetas should invoke logit_norm path without theta_dist arg."""
-    theta = jnp.array([[0.2, 0.5, 0.8]])
-    # mu/sigma shape: (batch_dim, 1) where batch_dim matches theta.shape[0].
-    mu = jnp.array([[0.0]])
-    sigma = jnp.array([[1.0]])
-    lam = 1.0
-    result = logit_norm.update_thetas(theta, params=(lam, mu, sigma))
-    assert result.shape == theta.shape
-    # Congression correction must push occupancies upward (or keep them equal).
-    assert jnp.all(result >= theta - 1e-5)
-
-
-def test_update_thetas_logit_norm_distinguishable_from_empirical():
-    """logit_norm mode requires (lam, mu, sigma); empirical mode only (lam,)."""
-    theta = jnp.array([[0.3, 0.7]])
-    lam = 0.5
-    mu = jnp.array([[0.0]])
-    sigma = jnp.array([[1.0]])
-
-    result_ln = logit_norm.update_thetas(theta, params=(lam, mu, sigma))
-
-    # empirical path would flatten mu/sigma as lam — passing only (lam,) should
-    # produce a different (empirical-based) correction.
-    from tfscreen.tfmodel.generative.components.transformation import empirical
-    result_emp = empirical.update_thetas(theta, params=(lam,))
-
-    # Both should have correct shape.
-    assert result_ln.shape == theta.shape
-    assert result_emp.shape == theta.shape
diff --git a/tests/tfscreen/tfmodel/generative/components/transformation/test_mixture.py b/tests/tfscreen/tfmodel/generative/components/transformation/test_mixture.py
new file mode 100644
index 00000000..9752a3e0
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/components/transformation/test_mixture.py
@@ -0,0 +1,450 @@
+"""
+Tests for the congression mixture transformation (transformation/mixture.py).
+"""
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import numpyro
+import pytest
+from scipy.stats import poisson
+
+from tfscreen.tfmodel.generative.components.transformation import mixture
+
+
+def _ztp_pmf(m, lam):
+    """P(M = m) for M ~ zero-truncated Poisson(lam)."""
+    return poisson.pmf(m, lam) / (1.0 - np.exp(-lam))
+
+
+# ---------------------------------------------------------------------------
+# Priors, guesses, define_model / guide
+# ---------------------------------------------------------------------------
+
+def test_get_hyperparameters_placeholder_default():
+    params = mixture.get_hyperparameters()
+    assert params == {"lam_loc": 0.0, "lam_scale": 1.0}
+
+
+def test_get_hyperparameters_moment_matching():
+    """(mean, std) in linear space moment-match the LogNormal."""
+    lam_mean, lam_std = 0.3572, 0.13
+    params = mixture.get_hyperparameters(lam_mean=lam_mean, lam_std=lam_std)
+    loc, scale = params["lam_loc"], params["lam_scale"]
+    assert np.isclose(np.exp(loc + scale**2 / 2.0), lam_mean)
+    assert np.isclose((np.exp(scale**2) - 1.0) * np.exp(2 * loc + scale**2),
+                      lam_std**2)
+
+
+def test_get_hyperparameters_requires_both():
+    with pytest.raises(ValueError, match="together"):
+        mixture.get_hyperparameters(lam_mean=0.36)
+    with pytest.raises(ValueError, match="together"):
+        mixture.get_hyperparameters(lam_std=0.05)
+
+
+@pytest.mark.parametrize("lam_mean,lam_std",
+                         [(0.0, 0.1), (-0.1, 0.1), (0.36, 0.0), (0.36, -0.1)])
+def test_get_hyperparameters_rejects_nonpositive(lam_mean, lam_std):
+    with pytest.raises(ValueError):
+        mixture.get_hyperparameters(lam_mean=lam_mean, lam_std=lam_std)
+
+
+def test_get_guesses():
+    assert mixture.get_guesses("t", MagicMock(), lam_mean=0.5) == {"t_lam": 0.5}
+    assert mixture.get_guesses("t", MagicMock()) == {"t_lam": 1.0}
+
+
+def test_get_priors_forwards():
+    priors = mixture.get_priors(lam_mean=0.3572, lam_std=0.13)
+    expected = mixture.get_hyperparameters(lam_mean=0.3572, lam_std=0.13)
+    assert priors.lam_loc == expected["lam_loc"]
+    assert priors.lam_scale == expected["lam_scale"]
+
+
+def test_define_model_and_guide_sample_scalar_lambda():
+    priors = mixture.get_priors()
+    with numpyro.handlers.seed(rng_seed=0):
+        with numpyro.handlers.trace() as tr:
+            lam = mixture.define_model("t", MagicMock(), priors)
+    assert jnp.shape(lam) == ()
+    assert "t_lam" in tr and "t_lam_value" in tr
+
+    with numpyro.handlers.seed(rng_seed=0):
+        with numpyro.handlers.trace() as tr:
+            lam = mixture.guide("t", MagicMock(), priors)
+    assert jnp.shape(lam) == ()
+    assert "t_lam_loc" in tr and "t_lam_scale" in tr
+
+
+def test_needs_population_flag():
+    assert mixture.NEEDS_POPULATION is True
+
+
+# ---------------------------------------------------------------------------
+# Class weights
+# ---------------------------------------------------------------------------
+
+def _counts(sets):
+    return jnp.array(np.concatenate([np.full(k, n) for n, k in
+                                     enumerate(sets, start=1)]), dtype=jnp.int32)
+
+
+@pytest.mark.parametrize("sets", [[12, 3, 1], [16], [0, 4], [2, 0, 5]])
+@pytest.mark.parametrize("lam", [0.05, 0.357, 1.5])
+def test_weights_sum_to_one(sets, lam):
+    f = jnp.array([0.0, 0.3, 1.0])
+    log_w = mixture._log_class_weights(jnp.array(lam), f, _counts(sets))
+    np.testing.assert_allclose(np.exp(log_w).sum(axis=0), 1.0, rtol=1e-6)
+
+
+@pytest.mark.parametrize("sets", [[12, 3, 1], [0, 4], [2, 0, 5]])
+def test_weights_match_zero_truncated_poisson_over_strata(sets):
+    """A set with n co-residents is a cell of M = n + 1 plasmids."""
+    lam, f = 0.357, 0.8
+    log_w = np.asarray(mixture._log_class_weights(jnp.array(lam),
+                                                  jnp.array([f]),
+                                                  _counts(sets)))[:, 0]
+    w_cong = f * (1.0 - _ztp_pmf(1, lam))
+    assert np.isclose(np.exp(log_w[0]), 1.0 - w_cong)
+
+    present = [n for n, k in enumerate(sets, start=1) if k > 0]
+    pmf = _ztp_pmf(np.array(present) + 1, lam)
+    pmf = dict(zip(present, pmf / pmf.sum()))
+    n_of_set = np.asarray(_counts(sets))
+    expected = np.array([w_cong * pmf[n] / sets[n - 1] for n in n_of_set])
+    np.testing.assert_allclose(np.exp(log_w[1:]), expected, rtol=1e-5)
+
+
+@pytest.mark.parametrize("lam", [1e-6, 5e-3, 1e-2, 2e-2, 0.357, 3.0])
+def test_congressed_fraction_is_stable_across_the_series_switch(lam):
+    """1 - P(M = 1) switches to a series at small lambda; both sides agree."""
+    got = float(np.exp(mixture._log_p_congressed(jnp.array(lam))))
+    lam64 = np.float64(lam)
+    expected = (lam64 / 2.0 - lam64**2 / 12.0 if lam < 1e-3
+                else 1.0 - lam64 / np.expm1(lam64))
+    np.testing.assert_allclose(got, expected, rtol=1e-4)
+    grad = float(jax.grad(mixture._log_p_congressed)(jnp.array(lam)))
+    assert np.isfinite(grad)
+
+
+def test_weights_match_simulated_cell_shares():
+    """
+    The fit's lambda is the simulator's transformation_poisson_lambda: the
+    clean and congressed weights equal a genotype's abundance share by
+    co-resident count when cells carry zero-truncated Poisson(lambda)
+    plasmids that split the cell's abundance (selection_experiment).
+    """
+    from tfscreen.simulate.selection_experiment import _plasmid_shares
+    from tfscreen.util.numerical import zero_truncated_poisson
+
+    lam, num_cells, num_geno = 0.357, 400_000, 20
+    rng = np.random.default_rng(0)
+    m = zero_truncated_poisson(num_cells, lam, rng)
+    plasmids = rng.choice(num_geno, size=(num_cells, m.max()))
+    mask = np.arange(m.max())[None, :] >= m[:, None]
+    share = np.sum(_plasmid_shares(mask) * ((plasmids == 0) & ~mask), axis=1)
+
+    sim_clean = share[m == 1].sum() / share.sum()
+    sim_one = share[m == 2].sum() / share.sum()
+
+    log_w = np.asarray(mixture._log_class_weights(
+        jnp.array(lam), jnp.array([1.0]), _counts([12, 3, 1])))[:, 0]
+    w = np.exp(log_w)
+    assert np.isclose(w[0], sim_clean, atol=0.005)
+    assert np.isclose(w[1:13].sum(), sim_one, atol=0.005)
+
+
+def test_spike_only_genotype_has_no_congressed_cells_and_finite_gradient():
+    f = jnp.array([0.0, 1.0])
+    n = _counts([12, 3, 1])
+    log_w = mixture._log_class_weights(jnp.array(0.357), f, n)
+    assert np.all(np.isneginf(np.asarray(log_w[1:, 0])))
+    assert np.isclose(float(log_w[0, 0]), 0.0)
+
+    def mix(lam):
+        growth = jnp.arange(log_w.shape[0], dtype=float)[:, None] * 0.1
+        return jnp.sum(jax.scipy.special.logsumexp(
+            mixture._log_class_weights(lam, f, n) + growth, axis=0))
+
+    assert np.isfinite(float(jax.grad(mix)(jnp.array(0.357))))
+
+
+# ---------------------------------------------------------------------------
+# cell_classes against an independent loop implementation
+# ---------------------------------------------------------------------------
+
+def _toy(seed=0, num_genotype=6, num_tn=2, num_tc=3,
+         batch_idx=(4, 0, 2), sets=(3, 2, 1), rule="max",
+         dk_rule="dilution", dk_alpha=None):
+    rng = np.random.default_rng(seed)
+    batch_idx = np.array(batch_idx)
+    counts = np.asarray(_counts(list(sets)))
+    num_sets, n_max = len(counts), len(sets)
+
+    coresident_idx = np.full((num_genotype, num_sets, n_max), -1)
+    for g in range(num_genotype):
+        for k, n in enumerate(counts):
+            coresident_idx[g, k, :n] = rng.integers(0, num_genotype, size=n)
+
+    theta_pop = rng.uniform(0.05, 0.95, (1, 1, 1, 1, num_tn, num_tc,
+                                         num_genotype))
+    activity_pop = rng.uniform(0.5, 2.0, num_genotype)
+    if rule != "max":
+        activity_pop = np.ones(num_genotype)   # partition rules need activity 1
+    dk_pop = rng.normal(0.0, 0.01, num_genotype)
+    bulk_fraction = rng.uniform(0.0, 1.0, num_genotype)
+    bulk_fraction[batch_idx[1]] = 0.0          # a spike-only genotype
+
+    data = SimpleNamespace(batch_idx=jnp.array(batch_idx),
+                           bulk_fraction=jnp.array(bulk_fraction),
+                           coresident_idx=jnp.array(coresident_idx),
+                           coresident_n=jnp.array(counts),
+                           congression_theta_rule=rule,
+                           congression_dk_rule=dk_rule,
+                           congression_dk_alpha=dk_alpha)
+
+    # The batch's own values; theta perturbed (noise acts on the focal theta
+    # only), so the focal plasmid is not just its population value.
+    theta = theta_pop[..., batch_idx] * 0.9
+    activity = activity_pop[batch_idx].reshape(1, 1, 1, 1, 1, 1, -1)
+    dk_geno = dk_pop[batch_idx].reshape(1, 1, 1, 1, 1, 1, -1)
+    return data, (theta, activity, dk_geno), (theta_pop, activity_pop, dk_pop)
+
+
+def _reference_classes(data, focal, population, lam):
+    theta, activity, dk_geno = focal
+    theta_pop, activity_pop, dk_pop = population
+    batch_idx = np.asarray(data.batch_idx)
+    idx = np.asarray(data.coresident_idx)
+    counts = np.asarray(data.coresident_n)
+    num_b, num_k = len(batch_idx), idx.shape[1]
+    num_tn, num_tc = theta.shape[4], theta.shape[5]
+
+    c_theta = np.zeros((1 + num_k, num_tn, num_tc, num_b))
+    c_act = np.zeros_like(c_theta)
+    c_dk = np.zeros((1 + num_k, num_b))
+    c_theta[0] = theta[0, 0, 0, 0]
+    c_act[0] = activity[0, 0, 0, 0, 0, 0][None, None, :]
+    c_dk[0] = dk_geno.reshape(-1)
+
+    for b, g in enumerate(batch_idx):
+        for k in range(num_k):
+            co = [h for h in idx[g, k] if h >= 0]
+            for i in range(num_tn):
+                for j in range(num_tc):
+                    values = [theta[0, 0, 0, 0, i, j, b]] + \
+                             [theta_pop[0, 0, 0, 0, i, j, h] for h in co]
+                    acts = [activity.reshape(-1)[b]] + \
+                           [activity_pop[h] for h in co]
+                    if data.congression_theta_rule == "max":
+                        best = int(np.argmax(values))
+                        c_theta[1 + k, i, j, b] = values[best]
+                        c_act[1 + k, i, j, b] = acts[best]
+                    else:
+                        p = {"homodimer": 1.0, "heterodimer": 0.5}[
+                            data.congression_theta_rule]
+                        v = np.clip(np.array(values), 1e-6, 1 - 1e-6)
+                        logit = np.log(v / (1 - v))
+                        cell = np.log(np.mean(np.exp(p * logit))) / p
+                        c_theta[1 + k, i, j, b] = 1.0 / (1.0 + np.exp(-cell))
+                        c_act[1 + k, i, j, b] = 1.0
+            dks = np.array([dk_geno.reshape(-1)[b]] + [dk_pop[h] for h in co])
+            if data.congression_dk_rule == "dilution":
+                c_dk[1 + k, b] = np.mean(dks)
+            elif data.congression_dk_rule == "min":
+                c_dk[1 + k, b] = np.min(dks)
+            else:
+                a = data.congression_dk_alpha
+                c_dk[1 + k, b] = -np.log(np.mean(np.exp(-a * dks))) / a
+
+    f = np.asarray(data.bulk_fraction)[batch_idx]
+    w_cong = f * (1.0 - _ztp_pmf(1, lam))
+    present = sorted(set(counts))
+    pmf = _ztp_pmf(np.array(present) + 1, lam)
+    pmf = dict(zip(present, pmf / pmf.sum()))
+    per_stratum = {n: int(np.sum(counts == n)) for n in present}
+    w = np.zeros((1 + num_k, num_b))
+    w[0] = 1.0 - w_cong
+    for k, n in enumerate(counts):
+        w[1 + k] = w_cong * pmf[n] / per_stratum[n]
+
+    return c_theta, c_act, c_dk, w
+
+
+@pytest.mark.parametrize("rule,dk_rule,dk_alpha",
+                         [("max", "dilution", None),
+                          ("homodimer", "dilution", None),
+                          ("heterodimer", "dilution", None),
+                          ("homodimer", "softmin", 100.0),
+                          ("homodimer", "min", None),
+                          ("max", "min", None)])
+def test_cell_classes_match_reference(rule, dk_rule, dk_alpha):
+    data, focal, population = _toy(rule=rule, dk_rule=dk_rule,
+                                   dk_alpha=dk_alpha)
+    lam = 0.357
+    classes = mixture.cell_classes(focal, population, jnp.array(lam), data)
+    c_theta, c_act, c_dk, w = _reference_classes(data, focal, population, lam)
+
+    num_classes = c_theta.shape[0]
+    assert classes.theta.shape == (num_classes, 1, 1, 1, 1) + c_theta.shape[1:]
+    np.testing.assert_allclose(np.asarray(classes.theta)[:, 0, 0, 0, 0],
+                               c_theta, rtol=1e-6)
+    np.testing.assert_allclose(np.asarray(classes.activity)[:, 0, 0, 0, 0],
+                               c_act, rtol=1e-6)
+    np.testing.assert_allclose(
+        np.asarray(classes.dk_geno).reshape(num_classes, -1), c_dk,
+        rtol=1e-6, atol=1e-9)
+    np.testing.assert_allclose(
+        np.exp(np.asarray(classes.log_weight)).reshape(num_classes, -1), w,
+        rtol=1e-5, atol=1e-12)
+
+
+def test_cell_classes_mixture_reduces_to_clean_as_lambda_vanishes():
+    """As lambda -> 0 every cell is clean: the mixed growth is the clean
+    class's, whatever the congressed classes' growth."""
+    data, focal, population = _toy()
+    classes = mixture.cell_classes(focal, population, jnp.array(1e-8), data)
+    growth = jnp.arange(classes.log_weight.shape[0], dtype=float).reshape(
+        -1, *([1] * (classes.log_weight.ndim - 1)))
+    mixed = jax.scipy.special.logsumexp(classes.log_weight + growth, axis=0)
+    np.testing.assert_allclose(np.asarray(mixed), 0.0, atol=1e-6)
+
+
+def test_partition_rules_order_and_single_plasmid():
+    """heterodimer <= homodimer <= max per cell, all between the mean and the
+    max logit; a cell with no co-residents keeps its own theta."""
+    out = {}
+    for rule in ("max", "homodimer", "heterodimer"):
+        data, focal, population = _toy(rule=rule)
+        out[rule] = np.asarray(mixture.cell_classes(
+            focal, population, jnp.array(0.357), data).theta)
+    assert np.all(out["heterodimer"] <= out["homodimer"] + 1e-6)
+    assert np.all(out["homodimer"] <= out["max"] + 1e-6)
+
+    data, focal, population = _toy(rule="homodimer", sets=(1,))
+    data.coresident_idx = jnp.full_like(data.coresident_idx, -1)
+    classes = mixture.cell_classes(focal, population, jnp.array(0.357), data)
+    np.testing.assert_allclose(np.asarray(classes.theta)[1],
+                               np.asarray(focal[0]), rtol=1e-5)
+
+
+def test_partition_rule_matches_simulator():
+    """The fit's homodimer/heterodimer cell theta equals simulate/cell_rules."""
+    from tfscreen.simulate.cell_rules import THETA_RULES, THETA_EPS
+    assert THETA_EPS == mixture.THETA_EPS
+    for rule in ("homodimer", "heterodimer"):
+        data, focal, population = _toy(rule=rule)
+        classes = mixture.cell_classes(focal, population, jnp.array(0.357), data)
+        theta, _, _ = focal
+        theta_pop = population[0]
+        idx = np.asarray(data.coresident_idx)
+        for b, g in enumerate(np.asarray(data.batch_idx)):
+            for k in range(idx.shape[1]):
+                co = [h for h in idx[g, k] if h >= 0]
+                slots = np.stack([theta[0, 0, 0, 0, :, :, b]] +
+                                 [theta_pop[0, 0, 0, 0, :, :, h] for h in co])
+                m = len(co) + 1
+                cell, act = THETA_RULES[rule](
+                    slots.reshape(1, m, -1), np.ones((1, m, slots[0].size)),
+                    np.full((1, m), 1.0 / m))
+                np.testing.assert_allclose(
+                    np.asarray(classes.theta)[1 + k, 0, 0, 0, 0, :, :, b].ravel(),
+                    cell[0], rtol=1e-5)
+
+
+
+def _cell_dk(dk_rule, dk_alpha=None, **kwargs):
+    data, focal, population = _toy(rule="homodimer", dk_rule=dk_rule,
+                                   dk_alpha=dk_alpha, **kwargs)
+    classes = mixture.cell_classes(focal, population, jnp.array(0.357), data)
+    return np.asarray(classes.dk_geno)
+
+
+def test_dk_rules_order_and_limits():
+    """min <= softmin <= dilution; softmin tends to dilution as alpha -> 0
+    and to min as alpha -> inf."""
+    dilution, minimum = _cell_dk("dilution"), _cell_dk("min")
+    soft = _cell_dk("softmin", 100.0)
+    assert np.all(minimum <= soft + 1e-12)
+    assert np.all(soft <= dilution + 1e-12)
+    assert np.any(minimum < dilution - 1e-4)
+    # Small alpha: within alpha * var / 2 (< 1e-5 here) of dilution. (In
+    # float32 the soft-min's absolute error is ~1e-7 / alpha, so much smaller
+    # alpha is a job for 'dilution'.)
+    np.testing.assert_allclose(_cell_dk("softmin", 0.1), dilution, atol=1e-5)
+    np.testing.assert_allclose(_cell_dk("softmin", 1e5), minimum, atol=1e-4)
+    # The clean class keeps the focal dk under every rule.
+    np.testing.assert_array_equal(soft[0], dilution[0])
+
+
+@pytest.mark.parametrize("dk_rule,dk_alpha", [("softmin", 100.0),
+                                              ("min", None)])
+def test_dk_rule_single_plasmid_keeps_own_dk(dk_rule, dk_alpha):
+    data, focal, population = _toy(rule="homodimer", sets=(1,),
+                                   dk_rule=dk_rule, dk_alpha=dk_alpha)
+    data.coresident_idx = jnp.full_like(data.coresident_idx, -1)
+    classes = mixture.cell_classes(focal, population, jnp.array(0.357), data)
+    np.testing.assert_allclose(np.asarray(classes.dk_geno)[1].ravel(),
+                               np.asarray(focal[2]).ravel(), rtol=1e-6)
+
+
+@pytest.mark.parametrize("dk_rule,dk_alpha", [("dilution", None),
+                                              ("softmin", 100.0),
+                                              ("min", None)])
+def test_dk_rule_matches_simulator(dk_rule, dk_alpha):
+    """The fit's congressed-cell dk equals simulate/cell_rules's DK_RULES."""
+    from tfscreen.simulate.cell_rules import DK_RULES
+    data, focal, population = _toy(rule="homodimer", dk_rule=dk_rule,
+                                   dk_alpha=dk_alpha)
+    classes = mixture.cell_classes(focal, population, jnp.array(0.357), data)
+    dk_geno, dk_pop = np.asarray(focal[2]).ravel(), population[2]
+    idx = np.asarray(data.coresident_idx)
+    n_max = idx.shape[2] + 1
+    for b, g in enumerate(np.asarray(data.batch_idx)):
+        for k in range(idx.shape[1]):
+            co = [h for h in idx[g, k] if h >= 0]
+            m = len(co) + 1
+            slots = np.zeros((1, n_max))
+            slots[0, :m] = [dk_geno[b]] + [dk_pop[h] for h in co]
+            shares = np.zeros((1, n_max))
+            shares[0, :m] = 1.0 / m
+            expected = DK_RULES[dk_rule](slots, shares, alpha=dk_alpha)
+            np.testing.assert_allclose(
+                np.asarray(classes.dk_geno)[1 + k].ravel()[b], expected[0],
+                rtol=1e-5, atol=1e-9)
+
+
+@pytest.mark.parametrize("dk_rule,dk_alpha", [("softmin", 100.0),
+                                              ("min", None)])
+def test_dk_rule_gradient_is_finite(dk_rule, dk_alpha):
+    data, focal, population = _toy(rule="homodimer", dk_rule=dk_rule,
+                                   dk_alpha=dk_alpha)
+    theta, activity, _ = focal
+
+    def total(dk_geno, dk_pop):
+        classes = mixture.cell_classes(
+            (theta, activity, dk_geno),
+            (population[0], population[1], dk_pop), jnp.array(0.357), data)
+        return jnp.sum(classes.dk_geno)
+
+    grads = jax.grad(total, argnums=(0, 1))(jnp.array(focal[2]),
+                                            jnp.array(population[2]))
+    for grad in grads:
+        assert np.all(np.isfinite(np.asarray(grad)))
+
+
+def test_cell_classes_refuses_unknown_theta_rule():
+    data, focal, population = _toy(rule="homodimer")
+    data.congression_theta_rule = "average"
+    with pytest.raises(ValueError, match="unknown congression_theta_rule 'average'"):
+        mixture.cell_classes(focal, population, jnp.array(0.357), data)
+
+
+def test_cell_classes_refuses_unknown_dk_rule():
+    data, focal, population = _toy(rule="homodimer", dk_rule="max")
+    with pytest.raises(ValueError, match="unknown congression_dk_rule 'max'"):
+        mixture.cell_classes(focal, population, jnp.array(0.357), data)
diff --git a/tests/tfscreen/tfmodel/generative/components/transformation/test_single.py b/tests/tfscreen/tfmodel/generative/components/transformation/test_single.py
index 8d13b492..1c05d9d5 100644
--- a/tests/tfscreen/tfmodel/generative/components/transformation/test_single.py
+++ b/tests/tfscreen/tfmodel/generative/components/transformation/test_single.py
@@ -1,10 +1,9 @@
-import pytest
 import jax.numpy as jnp
+import numpy as np
 from unittest.mock import MagicMock
-from collections import namedtuple
 
 from tfscreen.tfmodel.generative.components.transformation import single as transformation_single
-from tfscreen.tfmodel.data_class import GrowthData
+
 
 def test_get_hyperparameters():
     """Verify get_hyperparameters returns empty dict."""
@@ -12,41 +11,41 @@ def test_get_hyperparameters():
     assert params == {}
     assert isinstance(params, dict)
 
+
 def test_get_guesses():
     """Verify get_guesses returns empty dict."""
     guesses = transformation_single.get_guesses("test", MagicMock())
     assert guesses == {}
     assert isinstance(guesses, dict)
 
+
 def test_get_priors():
     """Verify get_priors returns a ModelPriors instance."""
     priors = transformation_single.get_priors()
     assert isinstance(priors, transformation_single.ModelPriors)
 
-def test_define_model():
-    """Verify define_model returns 1.0, 1.0, 1.0."""
-    res = transformation_single.define_model("test", MagicMock(), MagicMock())
-    assert res == (1.0, 1.0, 1.0)
-
-def test_guide():
-    """Verify guide returns 1.0, 1.0, 1.0."""
-    res = transformation_single.guide("test", MagicMock(), MagicMock())
-    assert res == (1.0, 1.0, 1.0)
-
-def test_update_thetas():
-    """Verify update_thetas acts as a pass-through."""
-    theta = jnp.array([0.1, 0.5, 0.9])
-    
-    # Standard call
-    res = transformation_single.update_thetas(theta, params=(1.0, 1.0, 1.0))
-    assert jnp.array_equal(res, theta)
-    
-    # With different params
-    res2 = transformation_single.update_thetas(theta, params=(100.0, 5.0, 0.1))
-    assert jnp.array_equal(res2, theta)
-
-
-def test_needs_full_population_theta_flag():
-    """Single-plasmid mode applies no congression correction at all, so it
-    must not request a population-wide theta reference."""
-    assert transformation_single.NEEDS_FULL_POPULATION_THETA is False
+
+def test_define_model_and_guide_have_no_parameters():
+    assert transformation_single.define_model("test", MagicMock(), MagicMock()) is None
+    assert transformation_single.guide("test", MagicMock(), MagicMock()) is None
+
+
+def test_cell_classes_is_one_class_of_weight_one():
+    """Every cell is clean: one class holding the genotype's own values."""
+    theta = jnp.linspace(0.1, 0.9, 2 * 3 * 4).reshape(1, 1, 1, 1, 2, 3, 4)
+    activity = jnp.array([1.0, 0.5, 2.0, 1.0]).reshape(1, 1, 1, 1, 1, 1, 4)
+    dk_geno = jnp.array([0.0, -0.01, 0.02, 0.0]).reshape(1, 1, 1, 1, 1, 1, 4)
+
+    classes = transformation_single.cell_classes((theta, activity, dk_geno),
+                                                 None, None, MagicMock())
+
+    np.testing.assert_array_equal(classes.theta, theta[None])
+    np.testing.assert_array_equal(classes.activity, activity[None])
+    np.testing.assert_array_equal(classes.dk_geno, dk_geno[None])
+    assert classes.log_weight.shape[0] == 1
+    np.testing.assert_array_equal(np.exp(classes.log_weight), 1.0)
+
+
+def test_needs_population_flag():
+    """No congression, so no library-wide population is needed."""
+    assert transformation_single.NEEDS_POPULATION is False
diff --git a/tests/tfscreen/tfmodel/generative/observe/test_observe_growth_counts.py b/tests/tfscreen/tfmodel/generative/observe/test_observe_growth_counts.py
new file mode 100644
index 00000000..22c4b537
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/observe/test_observe_growth_counts.py
@@ -0,0 +1,172 @@
+"""Tests for the read-count growth observer (growth_likelihood='counts')."""
+from collections import namedtuple
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from numpyro.handlers import seed, substitute, trace
+from scipy import stats
+
+from tfscreen.tfmodel.generative.observe.growth_counts import (
+    GrowthCountsObsPriors,
+    count_distribution,
+    get_priors,
+    guide,
+    log_mean,
+    observe,
+)
+
+MockData = namedtuple("MockData", [
+    "counts", "ln_sample_reads", "sample_ln_cfu", "num_replicate", "num_time",
+    "num_condition_pre", "num_condition_sel", "num_titrant_name",
+    "num_titrant_conc", "batch_size", "scale_vector", "good_mask"])
+
+SHAPE = (1, 1, 1, 1, 1, 2, 3)
+
+
+@pytest.fixture
+def data():
+    counts = jnp.array([0.0, 4.0, 120.0, 7.0, 0.0, 3000.0]).reshape(SHAPE)
+    good = jnp.ones(SHAPE, dtype=bool).at[..., 0, 1].set(False)
+    return MockData(counts=counts,
+                    ln_sample_reads=jnp.full(SHAPE, jnp.log(1e6)),
+                    sample_ln_cfu=jnp.full(SHAPE, jnp.log(1e8)),
+                    num_replicate=1, num_time=1, num_condition_pre=1,
+                    num_condition_sel=1, num_titrant_name=1,
+                    num_titrant_conc=2, batch_size=3,
+                    scale_vector=jnp.array([2.0, 1.0, 1.0]),
+                    good_mask=good)
+
+
+# ---------------------------------------------------------------------------
+# the distribution
+# ---------------------------------------------------------------------------
+
+@pytest.mark.parametrize("mu,phi,inv_r", [(3.0, 5.0, 0.01), (0.02, 8.0, 0.1),
+                                          (500.0, 0.5, 0.2)])
+def test_moments(mu, phi, inv_r):
+    d = count_distribution(jnp.log(mu), phi, inv_r)
+    assert float(d.mean) == pytest.approx(mu, rel=1e-4)
+    assert float(d.variance) == pytest.approx(mu * (1 + phi) + mu ** 2 * inv_r,
+                                              rel=1e-4)
+
+
+def test_log_prob_matches_scipy_negative_binomial():
+    mu, phi, inv_r = 40.0, 3.0, 0.05
+    c = 1.0 / (phi / mu + inv_r)                 # concentration
+    p = c / (c + mu)                             # scipy's success probability
+    k = np.arange(0, 200)
+    expected = stats.nbinom.logpmf(k, c, p)
+    got = count_distribution(jnp.log(mu), phi, inv_r).log_prob(jnp.asarray(k, float))
+    assert np.allclose(np.asarray(got), expected, atol=1e-4)
+
+
+@pytest.mark.parametrize("mu,phi,inv_r", [(3e4, 5.0, 0.01), (1e6, 5.0, 0.01),
+                                          (1e6, 0.1, 1e-4), (0.01, 8.0, 0.05)])
+def test_log_prob_accurate_at_high_depth(mu, phi, inv_r):
+    # numpyro's own negative binomial loses several nats to float32
+    # cancellation at 1e4-1e6 reads; this one must not.
+    c = 1.0 / (phi / mu + inv_r)
+    p = c / (c + mu)
+    sd = np.sqrt(mu * (1 + phi) + mu ** 2 * inv_r)
+    k = np.unique(np.clip(np.round(np.linspace(mu - 6 * sd, mu + 6 * sd, 41)),
+                          0, None))
+    got = count_distribution(jnp.float32(np.log(mu)), jnp.float32(phi),
+                             jnp.float32(inv_r)).log_prob(jnp.asarray(k, jnp.float32))
+    assert np.allclose(np.asarray(got), stats.nbinom.logpmf(k, c, p), atol=1e-3)
+
+
+def test_sample_moments():
+    d = count_distribution(jnp.log(50.0), 3.0, 0.02)
+    x = np.asarray(d.sample(jax.random.PRNGKey(0), (200_000,)))
+    assert x.mean() == pytest.approx(50.0, rel=0.01)
+    assert x.var() == pytest.approx(50 * 4 + 50 ** 2 * 0.02, rel=0.03)
+
+
+def test_poisson_limit():
+    mu = 12.0
+    k = np.arange(0, 60)
+    got = count_distribution(jnp.log(mu), 1e-8, 1e-10).log_prob(jnp.asarray(k, float))
+    assert np.allclose(np.asarray(got), stats.poisson.logpmf(k, mu), atol=1e-4)
+
+
+def test_tiny_mean_is_finite_with_finite_gradient():
+    def lp(log_mu):
+        return count_distribution(log_mu, 5.0, 0.01).log_prob(jnp.array(2.0))
+    assert np.isfinite(float(lp(-40.0)))
+    assert np.isfinite(float(jax.grad(lp)(-40.0)))
+
+
+def test_log_mean_is_frequency_times_depth(data):
+    ln_cfu_pred = jnp.full(SHAPE, jnp.log(1e5))     # 1e5 of 1e8 cells
+    mu = jnp.exp(log_mean(data, ln_cfu_pred))
+    assert np.allclose(np.asarray(mu), 1e6 * 1e-3)
+
+
+# ---------------------------------------------------------------------------
+# model / guide
+# ---------------------------------------------------------------------------
+
+def _trace(fn, data, **subs):
+    f = substitute(fn, data=subs) if subs else fn
+    return trace(seed(f, 0)).get_trace("growth", data, jnp.full(SHAPE, 12.0),
+                                       priors=get_priors())
+
+
+def test_observe_sites_and_log_likelihood(data):
+    tr = _trace(observe, data, growth_phi=4.0, growth_inv_r=0.02)
+    assert {"growth_phi", "growth_inv_r", "growth_obs"} <= set(tr)
+    site = tr["growth_obs"]
+    assert site["is_observed"]
+    assert np.array_equal(np.asarray(site["value"]), np.asarray(data.counts))
+
+    # Masked cell contributes nothing; scale_vector weights by genotype.
+    log_mu = log_mean(data, jnp.full(SHAPE, 12.0))
+    raw = count_distribution(log_mu, 4.0, 0.02).log_prob(data.counts)
+    expected = jnp.sum(jnp.where(data.good_mask, raw, 0.0) * data.scale_vector)
+    lp = site["fn"].log_prob(site["value"]) * site["scale"]
+    assert float(jnp.sum(lp)) == pytest.approx(float(expected), rel=1e-5)
+
+
+def test_guide_registers_dispersion_only(data):
+    tr = _trace(guide, data)
+    assert set(k for k, v in tr.items() if v["type"] == "sample") == {
+        "growth_phi", "growth_inv_r"}
+    params = {k for k, v in tr.items() if v["type"] == "param"}
+    assert params == {"growth_phi_loc", "growth_phi_scale",
+                      "growth_inv_r_loc", "growth_inv_r_scale"}
+
+
+def test_priors():
+    p = get_priors()
+    assert isinstance(p, GrowthCountsObsPriors)
+    assert np.exp(p.phi_loc) == pytest.approx(5.0)
+    assert np.exp(p.inv_r_loc) == pytest.approx(0.01)
+
+
+@pytest.mark.parametrize("log_mu", [-200.0, -80.0, -40.0, 0.0, 14.0])
+@pytest.mark.parametrize("log_phi", [-15.0, 1.6, 10.0])
+@pytest.mark.parametrize("k", [0.0, 1.0, 30.0, 1e6])
+def test_finite_value_gradient_and_hessian(log_mu, log_phi, k):
+    # A genotype predicted near extinction (log mu << 0; the MAP of an
+    # all-zero genotype goes there) used to underflow the concentration
+    # mu / phi in float32 and turn the gradient and Hessian to NaN
+    # (count-likelihood grid, 2026-09-27).
+    def lp(p):
+        return count_distribution(p[0], jnp.exp(p[1]), jnp.exp(p[2])).log_prob(
+            jnp.float32(k))
+    p = jnp.array([log_mu, log_phi, -4.6], dtype=jnp.float32)
+    assert np.isfinite(float(lp(p)))
+    assert np.all(np.isfinite(np.asarray(jax.grad(lp)(p))))
+    assert np.all(np.isfinite(np.asarray(jax.hessian(lp)(p))))
+
+
+def test_concentration_floor_leaves_ordinary_rows_alone():
+    # the floor (exp(-30)) is far below any concentration a real row has
+    mu, phi, inv_r = 0.01, 8.0, 0.05
+    c = 1.0 / (phi / mu + inv_r)
+    k = np.arange(0, 5)
+    got = count_distribution(jnp.log(mu), phi, inv_r).log_prob(jnp.asarray(k, float))
+    assert np.allclose(np.asarray(got), stats.nbinom.logpmf(k, c, c / (c + mu)),
+                       atol=1e-5)
diff --git a/tests/tfscreen/tfmodel/generative/test_joint_warp.py b/tests/tfscreen/tfmodel/generative/test_joint_warp.py
new file mode 100644
index 00000000..376cedee
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/test_joint_warp.py
@@ -0,0 +1,127 @@
+"""
+The joint fit's model pieces together (joint-fit plan step 4c): hill_geno's
+absolute theta, the congression mixture's partition-function rule, linear
+growth's m_warp on one condition, the growth titrant shift and the binding
+genotype offsets.
+"""
+
+import os
+
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from numpyro.handlers import seed, substitute, trace
+
+from tfscreen.tfmodel.analysis.extraction import ExtractionContext
+from tfscreen.tfmodel.generative.components.growth import linear
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+_SMOKE = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                      "..", "..", "..", "smoke-tests", "test_data")
+LOG_A = "condition_growth_warp_log_a"
+X0 = "condition_growth_warp_x0"
+
+
+def _orchestrator(warp):
+    orch = ModelOrchestrator(
+        growth_df=os.path.join(_SMOKE, "growth-smoke.csv"),
+        binding_df=os.path.join(_SMOKE, "binding-smoke.csv"),
+        theta="hill_geno", transformation="mixture",
+        transformation_lambda=(0.36, 0.05),
+        congression_theta_rule="homodimer",
+        theta_binding_noise="genotype_offset")
+    p = orch.priors
+    n = orch.data.growth.num_condition_rep
+    flags = tuple(i == warp for i in range(n)) if warp is not None else False
+    orch._priors = p.replace(
+        theta=p.theta.replace(theta_growth_log_conc_shift_scale=1.0),
+        growth=p.growth.replace(condition_growth=p.growth.condition_growth.replace(
+            m_warp=flags)))
+    return orch
+
+
+def _trace(orch, values=None):
+    fn = orch.jax_model if values is None else substitute(orch.jax_model, data=values)
+    batch = orch.get_batch(orch.data, np.asarray(orch.data.batch_idx))
+    return trace(seed(fn, rng_seed=0)).get_trace(data=batch, priors=orch.priors)
+
+
+def _latents(tr):
+    return {k: v["value"] for k, v in tr.items()
+            if v["type"] == "sample" and not v.get("is_observed", False)}
+
+
+def _warped_index(orch):
+    """A selection condition (its growth depends on theta)."""
+    df = orch.growth_tm.map_groups["condition_rep"].sort_values("map_condition_rep")
+    labels = df["condition_rep"].astype(str).tolist()
+    return next(i for i, c in enumerate(labels) if "+" in c)
+
+
+def test_all_pieces_trace_together():
+    orch = _orchestrator(None)
+    w = _warped_index(orch)
+    orch = _orchestrator(w)
+    tr = _trace(orch)
+    assert tr[LOG_A]["value"].shape == (1,)
+    assert tr[X0]["value"].shape == (1,)
+    assert "theta_growth_log_conc_shift" in tr
+    assert tr["theta_binding_noise_offset_z"]["value"].shape == (orch.data.num_binding,)
+    assert np.all(np.isfinite(np.asarray(tr["growth_pred"]["value"])))
+
+
+def test_near_linear_warp_matches_no_warp_through_the_mixture():
+    plain = _orchestrator(None)
+    w = _warped_index(plain)
+    warped = _orchestrator(w)
+    values = _latents(_trace(plain))
+    # a = e^-4: close to linear, and far enough from 0 that float32 keeps
+    # the warp's differences of logistics (at e^-12 they cancel to ~4%).
+    near = _trace(warped, {**values, LOG_A: jnp.array([-4.0]),
+                           X0: jnp.array([0.5])})
+    base = _trace(plain, values)
+    np.testing.assert_allclose(np.asarray(near["growth_pred"]["value"]),
+                               np.asarray(base["growth_pred"]["value"]),
+                               rtol=1e-4, atol=1e-4)
+
+    steep = _trace(warped, {**values, LOG_A: jnp.array([2.5]),
+                            X0: jnp.array([0.5])})
+    diff = np.abs(np.asarray(steep["growth_pred"]["value"])
+                  - np.asarray(base["growth_pred"]["value"]))
+    assert diff.max() > 1e-3
+    # Binding never sees the warp.
+    np.testing.assert_allclose(steep["binding_pred"]["value"],
+                               base["binding_pred"]["value"])
+
+
+def test_warp_extract_spec_names_the_warped_condition():
+    orch = _orchestrator(None)
+    w = _warped_index(orch)
+    orch = _orchestrator(w)
+    ctx = ExtractionContext(growth_tm=orch.growth_tm, mut_labels=[],
+                            pair_labels=[],
+                            growth_shares_replicates=orch._growth_shares_replicates,
+                            condition_growth_priors=orch.priors.growth.condition_growth)
+    specs = linear.get_extract_specs(ctx)
+    assert len(specs) == 2
+    warp = specs[1]
+    assert warp["params_to_get"] == ["warp_log_a", "warp_x0"]
+    df = orch.growth_tm.map_groups["condition_rep"].sort_values("map_condition_rep")
+    assert len(warp["input_df"]) == 1
+    assert (warp["input_df"]["map_condition_rep"].iloc[0]
+            == df["map_condition_rep"].iloc[w])
+
+    plain_ctx = ExtractionContext(growth_tm=orch.growth_tm, mut_labels=[],
+                                  pair_labels=[], growth_shares_replicates=False,
+                                  condition_growth_priors=linear.get_priors())
+    assert len(linear.get_extract_specs(plain_ctx)) == 1
+
+
+@pytest.mark.parametrize("rule", ["homodimer", "heterodimer"])
+def test_partition_rules_accept_hill_geno(rule):
+    orch = ModelOrchestrator(
+        growth_df=os.path.join(_SMOKE, "growth-smoke.csv"),
+        binding_df=os.path.join(_SMOKE, "binding-smoke.csv"),
+        theta="hill_geno", transformation="mixture",
+        transformation_lambda=(0.36, 0.05), congression_theta_rule=rule)
+    assert np.all(np.isfinite(np.asarray(_trace(orch)["growth_pred"]["value"])))
diff --git a/tests/tfscreen/tfmodel/generative/test_mixture_model.py b/tests/tfscreen/tfmodel/generative/test_mixture_model.py
new file mode 100644
index 00000000..2a16d081
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/test_mixture_model.py
@@ -0,0 +1,235 @@
+"""
+The congression mixture inside the full model (generative/model.py with
+transformation="mixture"), on the smoke-test data.
+"""
+
+import os
+
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from numpyro.handlers import seed, substitute, trace
+
+from tfscreen.tfmodel.generative.registry import model_registry
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+
+
+def _orchestrator(transformation, **kwargs):
+    if transformation == "mixture":
+        kwargs.setdefault("transformation_lambda", (0.36, 0.05))
+    return ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=_BINDING_CSV,
+                             transformation=transformation, **kwargs)
+
+
+def _full_batch(orchestrator):
+    return orchestrator.get_batch(orchestrator.data,
+                                  jnp.arange(orchestrator.data.num_genotype))
+
+
+def _trace(orchestrator, latents=None):
+    model = seed(orchestrator.jax_model, rng_seed=0)
+    if latents is not None:
+        model = substitute(model, data=latents)
+    return trace(model).get_trace(data=_full_batch(orchestrator),
+                                  priors=orchestrator.priors)
+
+
+def _latents(tr):
+    return {name: site["value"] for name, site in tr.items()
+            if site["type"] == "sample" and not site.get("is_observed", False)}
+
+
+# ---------------------------------------------------------------------------
+# Every growth component broadcasts over the class axis
+# ---------------------------------------------------------------------------
+
+_GROWTH_VARIANTS = (
+    [("growth_transition", v) for v in sorted(model_registry["growth_transition"])]
+    + [("condition_growth", v) for v in sorted(model_registry["condition_growth"])]
+)
+
+
+@pytest.mark.parametrize("axis,variant", _GROWTH_VARIANTS)
+def test_mixture_runs_with_every_growth_component(axis, variant):
+    mixture = _orchestrator("mixture", **{axis: variant})
+    single = _orchestrator("single", **{axis: variant})
+
+    mixture_pred = np.asarray(_trace(mixture)["growth_pred"]["value"])
+    single_pred = np.asarray(_trace(single)["growth_pred"]["value"])
+
+    assert mixture_pred.shape == single_pred.shape
+    assert np.all(np.isfinite(mixture_pred))
+
+
+# ---------------------------------------------------------------------------
+# Reductions to the single model
+# ---------------------------------------------------------------------------
+
+def test_vanishing_lambda_reduces_to_single():
+    """
+    The congressed weight is about lambda, but with prior draws a congressed
+    class can outgrow the clean one by tens of ln units, so lambda must be
+    far below exp(-max growth gap) for the mixture to collapse visibly.
+    """
+    mixture = _orchestrator("mixture")
+    single = _orchestrator("single")
+
+    latents = _latents(_trace(mixture))
+    latents["transformation_lam"] = jnp.array(1e-30)
+    mixture_pred = np.asarray(_trace(mixture, latents)["growth_pred"]["value"])
+
+    latents.pop("transformation_lam")
+    single_pred = np.asarray(_trace(single, latents)["growth_pred"]["value"])
+
+    np.testing.assert_allclose(mixture_pred, single_pred, rtol=1e-5, atol=1e-5)
+
+
+def test_spike_only_genotype_matches_single_and_bulk_genotypes_change():
+    """
+    A genotype with bulk_fraction = 0 has no congressed cells, so it grows
+    as in the single model at any lambda; bulk genotypes do not.
+    """
+    probe = _orchestrator("single")
+    labels = list(probe.growth_tm.tensor_dim_labels[-1])
+    spiked = [g for g in labels if g != "wt"][0]
+
+    mixture = _orchestrator("mixture", spiked_genotypes=[spiked])
+    single = _orchestrator("single", spiked_genotypes=[spiked])
+
+    latents = _latents(_trace(mixture))
+    latents["transformation_lam"] = jnp.array(1.0)
+    mixture_pred = np.asarray(_trace(mixture, latents)["growth_pred"]["value"])
+    latents.pop("transformation_lam")
+    single_pred = np.asarray(_trace(single, latents)["growth_pred"]["value"])
+
+    g = labels.index(spiked)
+    np.testing.assert_allclose(mixture_pred[..., g], single_pred[..., g],
+                               rtol=1e-5, atol=1e-5)
+
+    bulk = [i for i in range(len(labels)) if i != g]
+    assert not np.allclose(mixture_pred[..., bulk], single_pred[..., bulk])
+
+
+def test_theta_growth_pred_is_the_genotype_own_theta():
+    """theta_growth_pred stays uncorrected (tfs-predict-theta reads it)."""
+    mixture = _orchestrator("mixture")
+    single = _orchestrator("single")
+    latents = _latents(_trace(mixture))
+    mixture_theta = _trace(mixture, latents)["theta_growth_pred"]["value"]
+    latents.pop("transformation_lam")
+    single_theta = _trace(single, latents)["theta_growth_pred"]["value"]
+    np.testing.assert_allclose(np.asarray(mixture_theta),
+                               np.asarray(single_theta))
+
+
+# ---------------------------------------------------------------------------
+# Posterior forward passes over genotype chunks
+# ---------------------------------------------------------------------------
+
+@pytest.mark.slow
+def test_mixture_posteriors_independent_of_chunk_size():
+    """
+    get_posteriors runs the model over genotype chunks; the congressed cells
+    still look co-residents up across the whole library, so the saved
+    predictions must not depend on the chunk size.
+    """
+    import tempfile
+
+    import h5py
+
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    orchestrator = _orchestrator("mixture")
+    inference = RunInference(model=orchestrator, seed=1)
+    svi = inference.setup_svi(adam_step_size=1e-3)
+    state, _, _ = inference.run_optimization(
+        svi=svi, max_num_epochs=1,
+        out_prefix=os.path.join(tempfile.mkdtemp(), "fit"))
+
+    out = {}
+    for forward_batch_size in (512, 2):
+        prefix = os.path.join(tempfile.mkdtemp(), "p")
+        RunInference(model=orchestrator, seed=7).get_posteriors(
+            svi=svi, svi_state=state, out_prefix=prefix,
+            num_posterior_samples=3, forward_batch_size=forward_batch_size)
+        with h5py.File(f"{prefix}_posterior.h5") as f:
+            out[forward_batch_size] = f["growth_pred"][()]
+
+    np.testing.assert_allclose(out[512], out[2], rtol=1e-5, atol=1e-5)
+
+
+# ---------------------------------------------------------------------------
+# Congression theta rule and activity defaults
+# ---------------------------------------------------------------------------
+
+def test_defaults_are_homodimer_and_fixed_activity():
+    orchestrator = _orchestrator("mixture")
+    assert orchestrator.settings["activity"] == "fixed"
+    assert orchestrator.settings["congression_theta_rule"] == "homodimer"
+    assert orchestrator.data.growth.congression_theta_rule == "homodimer"
+    assert orchestrator.settings["congression_dk_rule"] == "dilution"
+    assert orchestrator.settings["congression_dk_alpha"] is None
+    assert orchestrator.data.growth.congression_dk_rule == "dilution"
+
+
+def test_max_rule_allows_learned_activity():
+    orchestrator = _orchestrator("mixture", activity="horseshoe_geno",
+                                 congression_theta_rule="max")
+    assert orchestrator.data.growth.congression_theta_rule == "max"
+
+
+@pytest.mark.parametrize("rule", ["homodimer", "heterodimer", "max"])
+def test_every_rule_runs_and_rule_survives_batching(rule):
+    orchestrator = _orchestrator("mixture", congression_theta_rule=rule)
+    batch = _full_batch(orchestrator)
+    assert batch.growth.congression_theta_rule == rule
+    pred = np.asarray(_trace(orchestrator)["growth_pred"]["value"])
+    assert np.all(np.isfinite(pred))
+
+
+def test_rules_give_different_predictions():
+    """Same latents, different rules: the congressed classes differ."""
+    base = _orchestrator("mixture", congression_theta_rule="max")
+    latents = _latents(_trace(base))
+    latents["transformation_lam"] = jnp.array(1.0)
+    preds = {}
+    for rule in ("max", "homodimer", "heterodimer"):
+        o = _orchestrator("mixture", congression_theta_rule=rule)
+        preds[rule] = np.asarray(_trace(o, latents)["growth_pred"]["value"])
+    assert not np.allclose(preds["max"], preds["homodimer"])
+    assert not np.allclose(preds["homodimer"], preds["heterodimer"])
+
+
+
+@pytest.mark.parametrize("dk_rule,dk_alpha", [("dilution", None),
+                                              ("softmin", 100.0),
+                                              ("min", None)])
+def test_every_dk_rule_runs_and_survives_batching(dk_rule, dk_alpha):
+    orchestrator = _orchestrator("mixture", congression_dk_rule=dk_rule,
+                                 congression_dk_alpha=dk_alpha)
+    batch = _full_batch(orchestrator)
+    assert batch.growth.congression_dk_rule == dk_rule
+    assert batch.growth.congression_dk_alpha == dk_alpha
+    pred = np.asarray(_trace(orchestrator)["growth_pred"]["value"])
+    assert np.all(np.isfinite(pred))
+
+
+def test_dk_rules_give_different_predictions():
+    """Same latents, different dk rules: the congressed classes differ."""
+    base = _orchestrator("mixture")
+    latents = _latents(_trace(base))
+    latents["transformation_lam"] = jnp.array(1.0)
+    preds = {}
+    for dk_rule, dk_alpha in (("dilution", None), ("min", None)):
+        o = _orchestrator("mixture", congression_dk_rule=dk_rule,
+                          congression_dk_alpha=dk_alpha)
+        preds[dk_rule] = np.asarray(_trace(o, latents)["growth_pred"]["value"])
+    assert not np.allclose(preds["dilution"], preds["min"])
diff --git a/tests/tfscreen/tfmodel/generative/test_model.py b/tests/tfscreen/tfmodel/generative/test_model.py
index cd91b463..0423a8b0 100644
--- a/tests/tfscreen/tfmodel/generative/test_model.py
+++ b/tests/tfscreen/tfmodel/generative/test_model.py
@@ -11,13 +11,17 @@
     DataClass, PriorsClass, GrowthData, BindingData, 
     GrowthPriors, BindingPriors
 )
+from tfscreen.tfmodel.generative.components.transformation._classes import (
+    single_class,
+)
 
 # --- Mocks ---
 
 # Define minimal mocks for the nested data structures
 class MockGrowthData(namedtuple("MockGrowthData", [
-        "t_pre", "t_sel", "congression_mask", "num_genotype",
-        "external_theta_population", "batch_idx", "geno_theta_idx",
+        "t_pre", "t_sel", "num_genotype",
+        "external_theta_population", "external_dk_population",
+        "external_activity_population", "batch_idx", "geno_theta_idx",
         "scatter_theta"])):
     """Namedtuple with a flax-struct-like ``.replace()`` for exercising the
     full-population congression branch in jax_model without a real GrowthData."""
@@ -45,8 +49,9 @@ def mock_data():
     t_sel = jnp.array(3.0)
     
     growth = MockGrowthData(
-        t_pre=t_pre, t_sel=t_sel, congression_mask="mock_mask",
+        t_pre=t_pre, t_sel=t_sel,
         num_genotype=1, external_theta_population=None,
+        external_dk_population=None, external_activity_population=None,
         batch_idx=jnp.array([0]), geno_theta_idx=jnp.array([0]),
         # Deliberately non-default (1, not 0) so tests can confirm jax_model
         # preserves it rather than silently resetting it when building the
@@ -98,17 +103,25 @@ def mock_control():
     growth_transition_model = MagicMock(return_value=105.0)
     
     ln_cfu0_model = MagicMock(return_value=jnp.array([5.0])) # ln_cfu0 (must be array for softmax)
-    activity_model = MagicMock(return_value=1.0) # activity
-    dk_geno_model = MagicMock(return_value=0.0) # dk_geno
-    
-    transformation_model = MagicMock(return_value=(1.0, 1.0, 1.0)) # (lam, mu, sigma)
-    transformation_update = MagicMock(
-        side_effect=lambda t, params, mask=None, population_theta=None: t
-    )  # pass-through
+
+    # activity / dk_geno: scalars; with return_population=True they also
+    # return a library-ordered population.
+    activity_model = MagicMock(
+        side_effect=lambda *a, return_population=False, **k:
+            (1.0, jnp.array([1.0])) if return_population else 1.0)
+    dk_geno_model = MagicMock(
+        side_effect=lambda *a, return_population=False, **k:
+            (0.0, jnp.array([0.0])) if return_population else 0.0)
+
+    transformation_model = MagicMock(return_value=1.0)  # lambda
+    # One class holding every cell (the "single" behavior).
+    cell_classes = MagicMock(
+        side_effect=lambda focal, population, params, data:
+            single_class(jnp.asarray(focal[0]), focal[1], focal[2]))
     
     # Noise models just pass through or add noise. Let's pass through for simplicity.
-    theta_binding_noise_model = MagicMock(side_effect=lambda n, x, p: x)
-    theta_growth_noise_model = MagicMock(side_effect=lambda n, x, p: x)
+    theta_binding_noise_model = MagicMock(side_effect=lambda n, x, p, data=None: x)
+    theta_growth_noise_model = MagicMock(side_effect=lambda n, x, p, data=None: x)
     growth_noise_model = MagicMock(return_value=0.0)  # sigma_k = 0 → no extra noise
     sample_offset_model = MagicMock(return_value=0.0)  # delta_sample = 0 → no offset
 
@@ -123,7 +136,7 @@ def mock_control():
         "ln_cfu0": ln_cfu0_model,
         "activity": activity_model,
         "dk_geno": dk_geno_model,
-        "transformation": (transformation_model, transformation_update, False),
+        "transformation": (transformation_model, cell_classes, False),
         "theta_binding_noise": theta_binding_noise_model,
         "theta_growth_noise": theta_growth_noise_model,
         "growth_noise": growth_noise_model,
@@ -154,8 +167,8 @@ def test_jax_model_execution_flow(mock_data, mock_priors, mock_control):
     # theta_model called with (name, growth_data, theta_priors)
     mock_control["theta"][0].assert_called_once_with("theta", mock_data.growth, "prior_theta")
 
-    # get_moments called with (theta, growth_data)
-    mock_control["theta"][2].assert_called_once_with(10.0, mock_data.growth)
+    # get_moments is no longer used (its only consumer, logit_norm, is gone)
+    mock_control["theta"][2].assert_not_called()
     
     # Calc Theta
     # Called twice: once for binding, once for growth
@@ -186,15 +199,17 @@ def test_jax_model_execution_flow(mock_data, mock_priors, mock_control):
         "ln_cfu0", mock_data.growth, "prior_cfu0"
     )
     
-    # Transformation
+    # Transformation: lambda, then the cell classes built from the
+    # genotype's (noisy) theta, activity and dk_geno
     mock_control["transformation"][0].assert_called_once_with(
-        "transformation", mock_data.growth, "prior_trans", anchors=(0.0, 1.0)
-    )
-    mock_control["transformation"][1].assert_called_once_with(
-        jnp.array(20.0), # theta_growth
-        params=(1.0, 1.0, 1.0),
-        mask="mock_mask"
+        "transformation", mock_data.growth, "prior_trans"
     )
+    (focal, population, params, data), _ = \
+        mock_control["transformation"][1].call_args
+    assert jnp.isclose(focal[0], 20.0) and focal[1] == 1.0 and focal[2] == 0.0
+    assert population is None
+    assert params == 1.0
+    assert data is mock_data.growth
     
     # Observers
     mock_control["observe_growth"].assert_called_once()
@@ -251,16 +266,21 @@ def test_jax_model_guide_flow(mock_data, mock_priors, mock_control):
 
 
 # ---------------------------------------------------------------------------
-# transformation_needs_population wiring (congression full-population fix)
+# transformation_needs_population wiring (congression mixture)
 # ---------------------------------------------------------------------------
 
+def _needs_population(mock_control):
+    transformation_model, cell_classes, _ = mock_control["transformation"]
+    mock_control["transformation"] = (transformation_model, cell_classes, True)
+    return cell_classes
+
+
 def test_jax_model_population_not_needed_skips_extra_calc_theta(
         mock_data, mock_priors, mock_control):
     """
-    When the transformation component doesn't need a population reference
-    (the "single" / "logit_norm" case, needs_population=False), calc_theta
-    must be called exactly twice (binding, growth) and transformation_update
-    must not receive a population_theta kwarg at all.
+    Without a population need ("single"), calc_theta runs exactly twice
+    (binding, growth), dk_geno/activity are called without
+    return_population, and cell_classes gets no population.
     """
     assert mock_control["transformation"][2] is False
 
@@ -268,65 +288,77 @@ def test_jax_model_population_not_needed_skips_extra_calc_theta(
         jax_model(mock_data, mock_priors, **mock_control)
 
     assert mock_control["theta"][1].call_count == 2
-
-    _, kwargs = mock_control["transformation"][1].call_args
-    assert "population_theta" not in kwargs
+    assert "return_population" not in mock_control["dk_geno"].call_args.kwargs
+    assert "return_population" not in mock_control["activity"].call_args.kwargs
+    (_, population, _, _), _ = mock_control["transformation"][1].call_args
+    assert population is None
 
 
 def test_jax_model_population_needed_computes_locally(
         mock_data, mock_priors, mock_control):
     """
-    When needs_population=True and no external override is supplied
-    (data.growth.external_theta_population is None — the training-time case),
-    jax_model must compute the population reference itself by calling
-    calc_theta a third time against a full-population data view (batch_idx /
-    geno_theta_idx spanning arange(num_genotype)). scatter_theta must be left
-    untouched (not reset to 0) so the result's leading dimensions match
-    theta_growth's exactly, which update_thetas relies on for broadcasting.
+    With a population need and no external references (training), jax_model
+    computes theta over a full-population data view (batch_idx /
+    geno_theta_idx = arange(num_genotype), scatter_theta untouched) and takes
+    dk_geno/activity populations from their components.
     """
-    transformation_model, transformation_update, _ = mock_control["transformation"]
-    mock_control["transformation"] = (transformation_model, transformation_update, True)
+    cell_classes = _needs_population(mock_control)
 
     with numpyro.handlers.seed(rng_seed=0):
         jax_model(mock_data, mock_priors, **mock_control)
 
     # binding, growth, and the extra population pass
     assert mock_control["theta"][1].call_count == 3
-
     third_call_data = mock_control["theta"][1].call_args_list[2].args[1]
     assert jnp.array_equal(third_call_data.batch_idx, jnp.arange(mock_data.growth.num_genotype))
     assert jnp.array_equal(third_call_data.geno_theta_idx, jnp.arange(mock_data.growth.num_genotype))
     assert third_call_data.scatter_theta == mock_data.growth.scatter_theta
 
-    _, kwargs = transformation_update.call_args
-    # calc_theta's mock is `lambda t, d: t * 2.0`; theta == 10.0 either way,
-    # so the population call reduces to the same 20.0 as the growth call.
-    assert jnp.isclose(kwargs["population_theta"], 20.0)
+    assert mock_control["dk_geno"].call_args.kwargs["return_population"] is True
+    assert mock_control["activity"].call_args.kwargs["return_population"] is True
+
+    (_, population, _, _), _ = cell_classes.call_args
+    theta_pop, activity_pop, dk_pop = population
+    assert jnp.isclose(theta_pop, 20.0)
+    assert jnp.array_equal(activity_pop, jnp.array([1.0]))
+    assert jnp.array_equal(dk_pop, jnp.array([0.0]))
 
 
 def test_jax_model_population_needed_uses_external_override(
         mock_data, mock_priors, mock_control):
     """
-    When needs_population=True and data.growth.external_theta_population is
-    already supplied (the prediction-time case, where data.growth may only
-    span a genotype subset), jax_model must use it directly rather than
-    computing a local (and, in that scenario, wrong) full-population value —
-    so calc_theta must be called only twice, not three times.
+    External references (prediction on a genotype subset) are used as-is: no
+    local population theta pass, and they replace the components' values.
     """
-    transformation_model, transformation_update, _ = mock_control["transformation"]
-    mock_control["transformation"] = (transformation_model, transformation_update, True)
+    cell_classes = _needs_population(mock_control)
 
-    external_population = jnp.array(999.0)
-    growth_with_override = mock_data.growth.replace(
-        external_theta_population=external_population
+    growth = mock_data.growth.replace(
+        external_theta_population=jnp.array(999.0),
+        external_dk_population=jnp.array([7.0]),
+        external_activity_population=jnp.array([3.0]),
     )
-    data_with_override = mock_data._replace(growth=growth_with_override)
-
     with numpyro.handlers.seed(rng_seed=0):
-        jax_model(data_with_override, mock_priors, **mock_control)
+        jax_model(mock_data._replace(growth=growth), mock_priors, **mock_control)
 
-    # No extra local population pass — only binding + growth.
     assert mock_control["theta"][1].call_count == 2
+    (_, population, _, _), _ = cell_classes.call_args
+    assert population[0] is growth.external_theta_population
+    assert population[1] is growth.external_activity_population
+    assert population[2] is growth.external_dk_population
 
-    _, kwargs = transformation_update.call_args
-    assert kwargs["population_theta"] is external_population
\ No newline at end of file
+
+def test_jax_model_population_missing_raises(mock_data, mock_priors,
+                                             mock_control):
+    """
+    If a component cannot supply its population (batch-sized latents) and no
+    external reference is given, jax_model refuses rather than building
+    congressed cells from nothing.
+    """
+    _needs_population(mock_control)
+    mock_control["dk_geno"] = MagicMock(
+        side_effect=lambda *a, return_population=False, **k:
+            (0.0, None) if return_population else 0.0)
+
+    with pytest.raises(ValueError, match="dk_geno"):
+        with numpyro.handlers.seed(rng_seed=0):
+            jax_model(mock_data, mock_priors, **mock_control)
diff --git a/tests/tfscreen/tfmodel/generative/test_model_math.py b/tests/tfscreen/tfmodel/generative/test_model_math.py
index 7eaff61c..fa1cf85b 100644
--- a/tests/tfscreen/tfmodel/generative/test_model_math.py
+++ b/tests/tfscreen/tfmodel/generative/test_model_math.py
@@ -34,10 +34,10 @@
 # ---------------------------------------------------------------------------
 
 # Only expose the fields actually consumed by the real simple components and
-# by jax_model itself (t_pre, t_sel, congression_mask).
+# by jax_model itself (t_pre, t_sel).
 _MockGrowthData = namedtuple(
     "_MockGrowthData",
-    ["batch_size", "t_pre", "t_sel", "congression_mask"],
+    ["batch_size", "t_pre", "t_sel"],
 )
 
 _MockBindingData = namedtuple("_MockBindingData", [])
@@ -70,7 +70,6 @@ def mock_data():
         batch_size=BATCH_SIZE,
         t_pre=T_PRE,
         t_sel=T_SEL,
-        congression_mask=None,
     )
     return _MockData(growth=growth, binding=_MockBindingData())
 
@@ -127,8 +126,8 @@ def _build_control(mock_data, real_priors, is_guide=False):
         "dk_geno": dk_geno_fixed.define_model,
         "growth_transition": growth_transition_instant.define_model,
         "transformation": (transformation_single.define_model,
-                          transformation_single.update_thetas,
-                          transformation_single.NEEDS_FULL_POPULATION_THETA),
+                          transformation_single.cell_classes,
+                          transformation_single.NEEDS_POPULATION),
         "theta_growth_noise": noise_zero.define_model,
         "theta_binding_noise": noise_zero.define_model,
         "growth_noise": growth_noise_zero.define_model,
diff --git a/tests/tfscreen/tfmodel/generative/test_population_theta.py b/tests/tfscreen/tfmodel/generative/test_population_theta.py
new file mode 100644
index 00000000..3ad2df3e
--- /dev/null
+++ b/tests/tfscreen/tfmodel/generative/test_population_theta.py
@@ -0,0 +1,94 @@
+"""
+Full-population theta must be library-ordered for every theta component.
+
+``generative/model.py`` evaluates theta for every genotype (the congression
+background) by calling the theta component's ``run_model`` on the growth
+data with ``batch_idx = geno_theta_idx = arange(num_genotype)``. The
+congression mixture looks co-resident genotypes up in that result by library
+index, so entry g must be genotype g, whatever batch (full and reshuffled, or
+a mini-batch) the latents were sampled in.
+"""
+
+import os
+
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from numpyro.handlers import seed
+
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+
+# The thermo PnnC/PddG variants need structure/ddG files; they share the PK
+# variants' run_model.
+_THETA_VARIANTS = [
+    "hill_geno",
+    "hill_mut",
+    "categorical_geno",
+    "_simple",
+    "thermo.O2_C4_K3_U0_a.PK",
+    "thermo.O2_C4_K3_U1_a.PK",
+    "thermo.O2_C12_K5_U0_a.PK",
+    "thermo.O2_C12_K5_U1_a.PK",
+]
+
+
+def _orchestrator(theta):
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     theta=theta)
+    if theta == "_simple":
+        # Per-genotype curves, so genotype order is visible.
+        g = orchestrator.data.growth
+        values = np.random.default_rng(0).uniform(
+            0.05, 0.95,
+            (g.num_titrant_name, g.num_titrant_conc, g.num_genotype))
+        priors = orchestrator.priors
+        orchestrator._priors = priors.replace(
+            theta=priors.theta.replace(theta_values=jnp.asarray(values)))
+    return orchestrator
+
+
+def _shuffled_index(orchestrator, mini):
+    data = orchestrator.data
+    pinned = np.asarray(data.batch_idx)[:data.num_binding]
+    rest = np.random.default_rng(1).permutation(
+        np.asarray(data.not_binding_idx))
+    idx = np.concatenate([pinned, rest])
+    return idx[:data.num_binding + 3] if mini else idx
+
+
+@pytest.mark.parametrize("theta", _THETA_VARIANTS)
+@pytest.mark.parametrize("mini", [False, True])
+@pytest.mark.parametrize("which", ["model", "guide"])
+def test_population_theta_is_library_ordered(theta, mini, which):
+    orchestrator = _orchestrator(theta)
+    if which == "guide":
+        # The guide model is jax_model partially applied to the guide control.
+        control = orchestrator.jax_model_guide.keywords
+    else:
+        control = orchestrator.main_control_kwargs
+    sample_theta, calc_theta, _ = control["theta"]
+
+    idx = _shuffled_index(orchestrator, mini)
+    growth = orchestrator.get_batch(orchestrator.data, jnp.asarray(idx)).growth
+
+    theta_param = seed(sample_theta, rng_seed=0)("theta", growth,
+                                                 orchestrator.priors.theta)
+    batch_theta = np.asarray(calc_theta(theta_param, growth))
+
+    num_genotype = growth.num_genotype
+    full = jnp.arange(num_genotype)
+    population_data = growth.replace(batch_idx=full, geno_theta_idx=full)
+    population_theta = np.asarray(calc_theta(theta_param, population_data))
+
+    assert population_theta.shape[-1] == num_genotype
+    np.testing.assert_allclose(population_theta[..., idx], batch_theta,
+                               rtol=1e-6, atol=1e-7)
diff --git a/tests/tfscreen/tfmodel/generative/test_reference_math.py b/tests/tfscreen/tfmodel/generative/test_reference_math.py
index b401a328..b3e9e754 100644
--- a/tests/tfscreen/tfmodel/generative/test_reference_math.py
+++ b/tests/tfscreen/tfmodel/generative/test_reference_math.py
@@ -263,136 +263,7 @@ def test_known_values(self):
 
 
 # ══════════════════════════════════════════════════════════════════════════════
-# 3. transformation/_congression.py — _logit_normal_cdf() and _empirical_cdf()
-# ══════════════════════════════════════════════════════════════════════════════
-
-class TestCongression:
-    """
-    The congression (co-transformation correction) functions use jax.vmap and
-    jax.scipy — the most JAX-specific code in the codebase.  These golden-value
-    tests catch any divergence introduced by the vmap→loop/torch.vmap port.
-    """
-
-    def _import(self):
-        from tfscreen.tfmodel.generative.components.transformation._congression import (
-            _logit_normal_cdf,
-            _empirical_cdf,
-            update_thetas,
-        )
-        return _logit_normal_cdf, _empirical_cdf, update_thetas
-
-    # ── _logit_normal_cdf ────────────────────────────────────────────────────
-
-    def test_logit_normal_cdf_at_midpoint(self):
-        """CDF(0.5) when mu=0, sigma=1 should equal 0.5 (symmetric)."""
-        _logit_normal_cdf, _, _ = self._import()
-        import jax.numpy as jnp
-        result = float(_logit_normal_cdf(jnp.array(0.5), mu=0.0, sigma=1.0))
-        npt.assert_allclose(result, 0.5, atol=1e-6)
-
-    def test_logit_normal_cdf_monotone(self):
-        """CDF must be strictly increasing over (0, 1)."""
-        _logit_normal_cdf, _, _ = self._import()
-        import jax.numpy as jnp
-        xs = jnp.linspace(0.01, 0.99, 20)
-        cdfs = np.array([float(_logit_normal_cdf(x, mu=0.0, sigma=1.0)) for x in xs])
-        assert np.all(np.diff(cdfs) > 0)
-
-    def test_logit_normal_cdf_known_value(self):
-        """
-        Golden value (NumPyro/JAX reference).
-        x=0.7, mu=0.5, sigma=0.8
-        logit(0.7) = log(0.7/0.3) ≈ 0.8473
-        Phi((0.8473 - 0.5) / 0.8) = Phi(0.4341) ≈ 0.6679
-        """
-        _logit_normal_cdf, _, _ = self._import()
-        import jax.numpy as jnp
-        result = float(_logit_normal_cdf(jnp.array(0.7), mu=0.5, sigma=0.8))
-        npt.assert_allclose(result, 0.6679, atol=1e-3)
-
-    # ── _empirical_cdf ───────────────────────────────────────────────────────
-
-    def test_empirical_cdf_shape(self):
-        """Output grid length should match t_grid."""
-        _, _empirical_cdf, _ = self._import()
-        import jax.numpy as jnp
-        theta = jnp.array([[0.1, 0.3, 0.5, 0.7, 0.9],
-                            [0.2, 0.4, 0.6, 0.8, 0.95]])
-        t_grid = jnp.linspace(0.0, 1.0, 32)
-        result = _empirical_cdf(theta, t_grid)
-        assert result.shape == (2, 32)
-
-    def test_empirical_cdf_monotone(self):
-        """Empirical CDF must be non-decreasing over the grid."""
-        _, _empirical_cdf, _ = self._import()
-        import jax.numpy as jnp
-        rng = np.random.default_rng(0)
-        theta = jnp.array(rng.uniform(0.05, 0.95, (3, 20)))
-        t_grid = jnp.linspace(0.0, 1.0, 64)
-        result = np.array(_empirical_cdf(theta, t_grid))
-        assert np.all(np.diff(result, axis=-1) >= -1e-7)
-
-    def test_empirical_cdf_bounds(self):
-        """CDF values should lie in [0, 1]."""
-        _, _empirical_cdf, _ = self._import()
-        import jax.numpy as jnp
-        rng = np.random.default_rng(1)
-        theta = jnp.array(rng.uniform(0.05, 0.95, (4, 15)))
-        t_grid = jnp.linspace(0.01, 0.99, 50)
-        result = np.array(_empirical_cdf(theta, t_grid))
-        assert result.min() >= 0.0 - 1e-7
-        assert result.max() <= 1.0 + 1e-7
-
-    def test_empirical_cdf_known_values(self):
-        """
-        Golden values from NumPyro/JAX reference run.
-        5-point uniform sample [0.1,0.3,0.5,0.7,0.9], t_grid at 0.0,0.5,1.0
-        Empirical CDF at 0.0 ≈ 0.0, at 0.5 = 0.5 (median), at 1.0 ≈ 1.0.
-        """
-        _, _empirical_cdf, _ = self._import()
-        import jax.numpy as jnp
-        theta  = jnp.array([[0.1, 0.3, 0.5, 0.7, 0.9]])
-        t_grid = jnp.array([0.0, 0.5, 1.0])
-        result = np.array(_empirical_cdf(theta, t_grid))  # (1, 3)
-        # At 0.0: below all sorted values → interpolates to 0.1
-        # At 0.5: exactly the 3rd point (i=2, y=(2+0.5)/5=0.5)
-        # At 1.0: beyond all sorted values → 0.9
-        npt.assert_allclose(result[0, 1], 0.5, atol=1e-5)
-        assert result[0, 0] < result[0, 1] < result[0, 2]
-
-    # ── update_thetas ────────────────────────────────────────────────────────
-
-    def test_update_thetas_logit_norm_shape(self):
-        """update_thetas with logit_norm dist returns same shape as input."""
-        _, _, update_thetas = self._import()
-        import jax.numpy as jnp
-        rng = np.random.default_rng(2)
-        theta = jnp.array(rng.uniform(0.1, 0.9, (2, 10)))
-        lam, mu, sigma = 0.5, 0.0, 1.0
-        result = update_thetas(theta, params=(lam, mu, sigma), theta_dist="logit_norm")
-        assert result.shape == theta.shape
-
-    def test_update_thetas_empirical_shape(self):
-        """update_thetas with empirical dist returns same shape as input."""
-        _, _, update_thetas = self._import()
-        import jax.numpy as jnp
-        rng = np.random.default_rng(3)
-        theta = jnp.array(rng.uniform(0.1, 0.9, (2, 12)))
-        result = update_thetas(theta, params=(0.3,), theta_dist="empirical")
-        assert result.shape == theta.shape
-
-    def test_update_thetas_zero_lambda_is_identity(self):
-        """When lam=0 (no cotransformation), output equals input."""
-        _, _, update_thetas = self._import()
-        import jax.numpy as jnp
-        rng = np.random.default_rng(4)
-        theta = jnp.array(rng.uniform(0.1, 0.9, (3, 8)))
-        result = update_thetas(theta, params=(0.0, 0.0, 1.0), theta_dist="logit_norm")
-        npt.assert_allclose(np.array(result), np.array(theta), rtol=1e-5)
-
-
-# ══════════════════════════════════════════════════════════════════════════════
-# 4. noise/beta.py — alpha/beta concentration computation
+# 3. noise/beta.py — alpha/beta concentration computation
 # ══════════════════════════════════════════════════════════════════════════════
 
 class TestBetaNoiseMath:
diff --git a/tests/tfscreen/tfmodel/generative/test_registry.py b/tests/tfscreen/tfmodel/generative/test_registry.py
index 93d3846d..edb21c43 100644
--- a/tests/tfscreen/tfmodel/generative/test_registry.py
+++ b/tests/tfscreen/tfmodel/generative/test_registry.py
@@ -32,7 +32,8 @@
 }
 
 OBSERVE_KEYS = {"observe_binding", "observe_growth",
-                "observe_presplit", "observe_base_growth"}
+                "observe_presplit", "observe_base_growth",
+                "observe_growth_counts"}
 
 # Attributes that non-rescale component modules should expose
 STANDARD_COMPONENT_ATTRS = {"get_priors", "define_model", "guide", "get_guesses"}
@@ -49,7 +50,7 @@
     "ln_cfu0": 2,
     "dk_geno": 2,
     "activity": 5,
-    "transformation": 3,
+    "transformation": 2,
     "theta_rescale": 2,
     "theta": 4,
     "theta_growth_noise": 3,
@@ -150,13 +151,16 @@ def test_get_priors_returns_object(self, name, mod):
         assert priors is not None, f"Component '{name}'.get_priors() returned None"
 
     @pytest.mark.parametrize("name,mod", model_registry.get("transformation", {}).items())
-    def test_transformation_has_update_thetas(self, name, mod):
-        assert hasattr(mod, "update_thetas"), (
-            f"transformation/{name} missing 'update_thetas'"
+    def test_transformation_has_cell_classes(self, name, mod):
+        assert hasattr(mod, "cell_classes"), (
+            f"transformation/{name} missing 'cell_classes'"
+        )
+        assert callable(mod.cell_classes)
+        assert isinstance(getattr(mod, "NEEDS_POPULATION", None), bool), (
+            f"transformation/{name} must declare NEEDS_POPULATION"
         )
-        assert callable(mod.update_thetas)
 
-    @pytest.mark.parametrize("key", list(OBSERVE_KEYS))
+    @pytest.mark.parametrize("key", sorted(OBSERVE_KEYS))
     def test_observe_modules_have_observe_and_guide(self, key):
         mod = model_registry[key]
         for attr in OBSERVE_ATTRS:
diff --git a/tests/tfscreen/tfmodel/genotype_fit/test_congression.py b/tests/tfscreen/tfmodel/genotype_fit/test_congression.py
deleted file mode 100644
index 4ea08205..00000000
--- a/tests/tfscreen/tfmodel/genotype_fit/test_congression.py
+++ /dev/null
@@ -1,111 +0,0 @@
-"""Tests for the congression de-attenuation history/trajectory outputs."""
-
-import numpy as np
-import jax.numpy as jnp
-import pandas as pd
-from scipy.special import expit, logit
-
-from tfscreen.tfmodel.generative.components.transformation._congression import (
-    update_thetas,
-)
-from tfscreen.tfmodel.genotype_fit.congression import (
-    correct_theta_matrix,
-    deattenuate_congression,
-)
-from tfscreen.tfmodel.genotype_fit.fit import GenotypeFit, PHENO_PARAMS_TRANSFORMED
-
-
-def _forward(theta_true, lam):
-    return np.asarray(update_thetas(
-        jnp.asarray(theta_true), (float(lam),), theta_dist="empirical",
-        population_theta=jnp.asarray(theta_true)))
-
-
-def _make_fit(genotype, theta_low, theta_high, log_K, n, dk=0.0):
-    est_t = np.array([5.0, dk, logit(theta_low), logit(theta_high),
-                      log_K, np.log(n)])
-    return GenotypeFit(
-        genotype=genotype, titrant_name="iptg", n_obs=100, converged=True,
-        param_names_t=["ln_cfu0[0]"] + list(PHENO_PARAMS_TRANSFORMED),
-        est_t=est_t, cov_t=np.eye(6) * 0.01, pheno_slice=slice(1, 6))
-
-
-def _build_fits(rng, n=25):
-    fits = {}
-    for i in range(n):
-        fits[(f"A{i}V", "iptg")] = _make_fit(
-            f"A{i}V", theta_low=rng.uniform(0.7, 0.98),
-            theta_high=rng.uniform(0.02, 0.5),
-            log_K=rng.uniform(np.log(1e-3), np.log(0.3)), n=rng.uniform(0.8, 2.0))
-    fits[("wt", "iptg")] = _make_fit("wt", 0.99, 0.01, np.log(0.017), 2.0)
-    return fits
-
-
-_CONCS = [0.0, 1e-3, 1e-2, 1e-1, 1.0]
-_GROWTH_DF = pd.DataFrame({"titrant_name": ["iptg"] * len(_CONCS),
-                          "titrant_conc": _CONCS})
-
-
-def test_correct_theta_matrix_history_matches_final():
-    rng = np.random.default_rng(0)
-    theta_true = expit(rng.normal(0.0, 1.5, size=(2, 200)))
-    theta_obs = _forward(theta_true, lam=1.0)
-
-    rec, n_iter, history = correct_theta_matrix(
-        theta_obs, lam=1.0, tol=1e-6, max_iter=200, return_history=True)
-
-    # First entry is the clamped observed matrix; last is the returned estimate.
-    assert np.allclose(history[0], np.clip(theta_obs, 1e-6, 1 - 1e-6))
-    assert np.allclose(history[-1], rec)
-    # Two-return form is unchanged (backward compatible).
-    rec2, n_iter2 = correct_theta_matrix(theta_obs, lam=1.0, tol=1e-6,
-                                         max_iter=200)
-    assert np.allclose(rec2, rec)
-    assert n_iter2 == n_iter
-    # The trajectory monotonically closes the gap to the observed population.
-    err0 = np.nanmax(np.abs(_forward(history[0], 1.0) - theta_obs))
-    errN = np.nanmax(np.abs(_forward(history[-1], 1.0) - theta_obs))
-    assert errN <= err0
-
-
-def test_correct_theta_matrix_history_lambda_zero():
-    theta_obs = expit(np.random.default_rng(1).normal(0, 1, size=(1, 20)))
-    rec, n_iter, history = correct_theta_matrix(theta_obs, lam=0.0,
-                                                return_history=True)
-    assert n_iter == 0
-    assert np.allclose(rec, theta_obs)
-    assert len(history) == 1
-
-
-def test_deattenuate_returns_history_df():
-    rng = np.random.default_rng(3)
-    fits = _build_fits(rng)
-    bulk_keys = [k for k in fits if k[0] != "wt"]
-
-    corrected, history = deattenuate_congression(
-        fits, _GROWTH_DF, lam=1.0, spiked={"wt"}, return_theta_history=True)
-
-    assert list(history.columns) == [
-        "genotype", "titrant_name", "titrant_conc", "iter", "theta"]
-    # History covers the bulk genotypes at every concentration, never wt.
-    assert set(history["genotype"]) == {k[0] for k in bulk_keys}
-    assert "wt" not in set(history["genotype"])
-    assert set(history["titrant_conc"]) == set(_CONCS)
-    # iter starts at 0 and the correction actually iterated.
-    assert history["iter"].min() == 0
-    assert history["iter"].max() >= 1
-
-    # The corrected fits match the no-history call.
-    corrected_only = deattenuate_congression(
-        fits, _GROWTH_DF, lam=1.0, spiked={"wt"})
-    for k in fits:
-        assert np.allclose(corrected[k].est_t, corrected_only[k].est_t)
-
-
-def test_deattenuate_history_noop_when_lambda_none():
-    fits = _build_fits(np.random.default_rng(4))
-    corrected, history = deattenuate_congression(
-        fits, _GROWTH_DF, lam=None, spiked={"wt"}, return_theta_history=True)
-    assert history.empty
-    for k in fits:
-        assert np.allclose(corrected[k].est_t, fits[k].est_t)
diff --git a/tests/tfscreen/tfmodel/genotype_fit/test_fit_genotypes_cli.py b/tests/tfscreen/tfmodel/genotype_fit/test_fit_genotypes_cli.py
index 5c4dd064..e7ee3889 100644
--- a/tests/tfscreen/tfmodel/genotype_fit/test_fit_genotypes_cli.py
+++ b/tests/tfscreen/tfmodel/genotype_fit/test_fit_genotypes_cli.py
@@ -12,7 +12,6 @@
     "growth_k":      [0.02,        0.0,   0.0],
     "growth_m":      [0.0,        -0.03, -0.06],
 })
-# wt is spiked; A1V/A2V are bulk (>=2 needed to build a congression background).
 TRUTH = {
     "wt":  dict(theta_low=0.90, theta_high=0.08, log_K=np.log(3e-3), n=1.4, dk_geno=0.0),
     "A1V": dict(theta_low=0.70, theta_high=0.20, log_K=np.log(3e-2), n=1.0, dk_geno=0.006),
@@ -54,29 +53,7 @@ def paths(tmp_path):
     return str(growth), str(calib), tmp_path
 
 
-@pytest.fixture
-def library_config(tmp_path):
-    """Library YAML covering wt/A1V/A2V, with wt as the only spiked genotype."""
-    import yaml
-
-    config = {
-        "reading_frame": 0,
-        "first_amplicon_residue": 1,
-        "wt_seq":      "gccgcaaaaccggaatgc",
-        "degen_sites": "nntnnt............",
-        "tiles":       "111222............",
-        "tile_combos": ["single-1", "single-2", "double-1-2"],
-        "spiked_seqs": [".................."],          # wt only
-        "library_mixture": {"single-1": 10, "single-2": 10,
-                            "double-1-2": 100, "spiked": 1},
-    }
-    path = tmp_path / "library.yaml"
-    with open(path, "w") as f:
-        yaml.dump(config, f)
-    return str(path)
-
-
-def test_raw_only_writes_params_and_theta(paths):
+def test_writes_params_and_theta(paths):
     growth, calib, tmp_path = paths
     prefix = str(tmp_path / "run")
     fit_genotypes(growth, calib, out_prefix=prefix, dk_geno_prior_sd=0.0)
@@ -90,7 +67,7 @@ def test_raw_only_writes_params_and_theta(paths):
     assert list(theta.columns) == [
         "genotype", "titrant_name", "titrant_conc", "theta_raw"]
 
-    # No congression artefacts.
+    # The retired congression de-attenuation writes nothing.
     assert not (tmp_path / "run_params_deattenuated.csv").exists()
     assert not (tmp_path / "run_theta_history.csv").exists()
 
@@ -100,41 +77,8 @@ def test_raw_only_writes_params_and_theta(paths):
         assert res.loc[geno, "theta_low"] == pytest.approx(p["theta_low"], abs=2e-3)
 
 
-def test_congression_writes_deattenuated_and_history(paths, library_config):
+def test_congression_lambda_is_retired(paths):
     growth, calib, tmp_path = paths
-    prefix = str(tmp_path / "run")
-
-    fit_genotypes(growth, calib, out_prefix=prefix, dk_geno_prior_sd=0.0,
-                  congression_lambda=1.0, library_config=library_config,
-                  save_theta_history=True)
-
-    deatt = pd.read_csv(f"{prefix}_params_deattenuated.csv")
-    theta = pd.read_csv(f"{prefix}_theta.csv")
-    history = pd.read_csv(f"{prefix}_theta_history.csv")
-
-    assert set(deatt["genotype"]) == set(TRUTH)
-    assert "theta_deattenuated" in theta.columns
-    assert "theta_raw" in theta.columns
-
-    # wt is spiked -> its de-attenuated theta equals its raw theta.
-    wt = theta[theta["genotype"] == "wt"]
-    assert np.allclose(wt["theta_raw"], wt["theta_deattenuated"])
-
-    # Bulk genotypes were corrected: at least one theta_deattenuated differs.
-    bulk = theta[theta["genotype"] != "wt"]
-    assert not np.allclose(bulk["theta_raw"], bulk["theta_deattenuated"])
-
-    # History covers only the bulk genotypes and iterates.
-    assert set(history["genotype"]) == {"A1V", "A2V"}
-    assert history["iter"].max() >= 1
-
-
-def test_congression_without_history_skips_history_file(paths, library_config):
-    growth, calib, tmp_path = paths
-    prefix = str(tmp_path / "run")
-
-    fit_genotypes(growth, calib, out_prefix=prefix, dk_geno_prior_sd=0.0,
-                  congression_lambda=1.0, library_config=library_config)
-
-    assert (tmp_path / "run_params_deattenuated.csv").exists()
-    assert not (tmp_path / "run_theta_history.csv").exists()
+    with pytest.raises(TypeError, match="congression_lambda"):
+        fit_genotypes(growth, calib, out_prefix=str(tmp_path / "run"),
+                      congression_lambda=1.0)
diff --git a/tests/tfscreen/tfmodel/inference/loss_traces/real_svi_200k_genotypes.npy b/tests/tfscreen/tfmodel/inference/loss_traces/real_svi_200k_genotypes.npy
new file mode 100644
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diff --git a/tests/tfscreen/tfmodel/inference/test_batch_safety.py b/tests/tfscreen/tfmodel/inference/test_batch_safety.py
new file mode 100644
index 00000000..beb66a40
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_batch_safety.py
@@ -0,0 +1,481 @@
+"""
+Tests for tfscreen.tfmodel.inference.batch_safety.
+
+The registry-wide tests guard against per-genotype latents sampled in plates
+sized ``data.batch_size``.  Those alias every non-binding genotype onto batch
+positions under any numpyro autoguide (AutoDelta/MAP included); see the
+module docstring of ``batch_safety`` for the mechanism.
+"""
+import os
+
+import jax.numpy as jnp
+import numpyro
+import numpyro.distributions as dist
+import pytest
+from flax import struct
+
+from tfscreen.tfmodel.inference.batch_safety import (
+    find_batch_dependent_latents,
+    find_batch_order_mismatches,
+    find_orchestrator_batch_dependent_latents,
+    find_orchestrator_batch_order_mismatches,
+    orchestrator_latent_dimension,
+)
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+
+
+# ---------------------------------------------------------------------------
+# Toy models
+# ---------------------------------------------------------------------------
+
+@struct.dataclass
+class _ToyData:
+    num_genotype: int = struct.field(pytree_node=False)
+    batch_size: int = struct.field(pytree_node=False)
+    batch_idx: jnp.ndarray
+
+
+def _toy_get_batch(data, idx):
+    return _ToyData(num_genotype=data.num_genotype,
+                    batch_size=int(idx.shape[0]),
+                    batch_idx=idx)
+
+
+def _toy_full_data(num_genotype=5):
+    return _ToyData(num_genotype=num_genotype,
+                    batch_size=num_genotype,
+                    batch_idx=jnp.arange(num_genotype))
+
+
+def _batch_sized_model(data, priors):
+    numpyro.sample("global_p", dist.Normal(0.0, 1.0))
+    with numpyro.plate("bad_genotype_plate", data.batch_size):
+        numpyro.sample("bad_offset", dist.Normal(0.0, 1.0))
+
+
+def _full_sized_model(data, priors):
+    numpyro.sample("global_p", dist.Normal(0.0, 1.0))
+    with numpyro.plate("good_genotype_plate", data.num_genotype):
+        offset = numpyro.sample("good_offset", dist.Normal(0.0, 1.0))
+    numpyro.deterministic("per_batch", offset[data.batch_idx])
+
+
+def test_flags_batch_sized_latent():
+    full = _toy_full_data()
+    found = find_batch_dependent_latents(_batch_sized_model, None, full,
+                                         _toy_get_batch,
+                                         jnp.arange(5), jnp.arange(3))
+    assert found == {"bad_offset": ((5,), (3,))}
+
+
+def test_full_sized_latent_is_safe():
+    full = _toy_full_data()
+    found = find_batch_dependent_latents(_full_sized_model, None, full,
+                                         _toy_get_batch,
+                                         jnp.arange(5), jnp.arange(3))
+    assert found == {}
+
+
+def test_library_sized_latent_follows_batch_order():
+    """
+    Each batch position must read its own genotype's offset.  The full-batch
+    training index is binding-first and reshuffled every step, so a
+    permutation of the whole library is the case that matters.
+    """
+    from collections import namedtuple
+    from numpyro.handlers import seed, substitute, trace
+    from tfscreen.tfmodel.generative.components.dk_geno import (
+        hierarchical_geno as dk,
+    )
+
+    Data = namedtuple("Data", ["num_genotype", "batch_size", "batch_idx",
+                               "wt_indexes"])
+    batch_idx = jnp.array([3, 0, 4, 1, 2])
+    data = Data(num_genotype=5, batch_size=5, batch_idx=batch_idx,
+                wt_indexes=jnp.array([0]))
+    offsets = jnp.array([0.1, 0.2, 0.3, 0.4, 0.5])
+    values = {"dk_geno_offset": offsets,
+              "dk_geno_hyper_loc": -3.5,
+              "dk_geno_hyper_scale": 0.5,
+              "dk_geno_hyper_shift": 0.02}
+
+    model = substitute(seed(dk.define_model, rng_seed=0), data=values)
+    tr = trace(model).get_trace("dk_geno", data, dk.get_priors())
+
+    expected = 0.02 - jnp.exp(-3.5 + offsets[batch_idx] * 0.5)
+    expected = jnp.where(batch_idx == 0, 0.0, expected)  # wt pinned to 0
+    assert jnp.allclose(tr["dk_geno"]["value"], expected)
+
+
+def test_equal_length_index_sets_rejected():
+    full = _toy_full_data()
+    with pytest.raises(ValueError, match="different lengths"):
+        find_batch_dependent_latents(_full_sized_model, None, full,
+                                     _toy_get_batch,
+                                     jnp.arange(3), jnp.array([4, 0, 1]))
+
+
+# ---------------------------------------------------------------------------
+# Registry-wide: every per-genotype component must be mini-batch safe
+# ---------------------------------------------------------------------------
+
+# One axis varied at a time from ModelOrchestrator's defaults.  dk_geno
+# "pinned" is omitted because it needs a pins file; it samples no
+# per-genotype latents.  The thermo PnnC/PddG theta variants are omitted
+# because they need structure/ddG files; they share the PK variants'
+# per-mutation latents and differ only in the prior on them.
+_SAFE_VARIANTS = [
+    ("condition_growth", "linear"),
+    ("condition_growth", "power"),
+    ("condition_growth", "saturation"),
+    ("growth_transition", "instant"),
+    ("growth_transition", "memory"),
+    ("growth_transition", "baranyi"),
+    ("growth_transition", "baranyi_k"),
+    ("growth_transition", "baranyi_tau"),
+    ("growth_transition", "two_pop"),
+    ("activity", "fixed"),
+    ("activity", "hierarchical_geno"),
+    ("activity", "horseshoe_geno"),
+    ("activity", "hierarchical_mut"),
+    ("activity", "horseshoe_mut"),
+    ("dk_geno", "fixed"),
+    ("dk_geno", "hierarchical_geno"),
+    ("ln_cfu0", "hierarchical"),
+    ("ln_cfu0", "hierarchical_factored"),
+    ("theta", "hill_geno"),
+    ("theta", "hill_relative"),
+    ("theta", "hill_mut"),
+    ("theta", "categorical_geno"),
+    ("theta", "_simple"),
+    ("theta", "thermo.O2_C4_K3_U0_a.PK"),
+    ("theta", "thermo.O2_C4_K3_U1_a.PK"),
+    ("theta", "thermo.O2_C12_K5_U0_a.PK"),
+    ("theta", "thermo.O2_C12_K5_U1_a.PK"),
+    ("theta_rescale", "passthrough"),
+    ("theta_rescale", "logit"),
+    ("transformation", "single"),
+    ("transformation", "mixture"),
+    ("theta_growth_noise", "zero"),
+    ("theta_growth_noise", "logit_normal"),
+    ("theta_binding_noise", "zero"),
+    ("theta_binding_noise", "beta"),
+    ("theta_binding_noise", "genotype_offset"),
+    ("growth_noise", "zero"),
+    ("growth_noise", "normal_kt"),
+    ("sample_offset", "zero"),
+    ("sample_offset", "normal"),
+]
+
+# Extra constructor arguments some variants require. The relative theta
+# component (X scale) takes no binding data and no theta noise.
+_VARIANT_KWARGS = {
+    ("transformation", "mixture"): {"transformation_lambda": (1.0, 0.1)},
+    ("theta", "hill_relative"): {"binding_df": None,
+                                 "theta_growth_noise": "zero"},
+}
+
+
+def _single_library_growth():
+    """
+    The smoke growth data as one library. hierarchical_factored shares a
+    genotype's baseline across condition_pre, which the orchestrator refuses
+    when the pre-conditions come from different libraries (the smoke data's
+    kanR and pheS), so it is exercised on a one-library copy.
+    """
+    import pandas as pd
+    df = pd.read_csv(_GROWTH_CSV)
+    df["library"] = "kanR"
+    return df
+
+
+def _variant_kwargs(axis, variant):
+    """
+    Constructor arguments for one variant, growth data included (binding
+    data unless refused).
+    """
+    growth = (_single_library_growth()
+              if (axis, variant) == ("ln_cfu0", "hierarchical_factored")
+              else _GROWTH_CSV)
+    return {"growth_df": growth, "binding_df": _BINDING_CSV, axis: variant,
+            **_VARIANT_KWARGS.get((axis, variant), {})}
+
+
+def _owned_by(axis, site_name):
+    """True when site_name belongs to the component registered under axis."""
+    if not site_name.startswith(f"{axis}_"):
+        return False
+    # theta's sites share a prefix with the theta noise components.
+    if axis == "theta":
+        return not site_name.startswith(("theta_growth_noise_",
+                                         "theta_binding_noise_"))
+    return True
+
+
+@pytest.mark.parametrize("axis,variant", _SAFE_VARIANTS)
+def test_component_latents_are_batch_safe(axis, variant):
+    orchestrator = ModelOrchestrator(batch_size=6,
+                                     **_variant_kwargs(axis, variant))
+    found = find_orchestrator_batch_dependent_latents(orchestrator)
+    found = {k: v for k, v in found.items() if _owned_by(axis, k)}
+    assert found == {}, (
+        f"{axis}={variant} has latents sampled at batch size "
+        f"(shape with full library, shape with one fewer genotype): {found}"
+    )
+
+
+def test_default_model_is_batch_safe():
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     batch_size=6)
+    assert find_orchestrator_batch_dependent_latents(orchestrator) == {}
+
+
+@pytest.mark.parametrize("theta,activity", [
+    ("hill_mut", "horseshoe_mut"),
+    ("thermo.O2_C4_K3_U0_a.PK", "horseshoe_geno"),
+    ("thermo.O2_C12_K5_U1_a.PK", "horseshoe_geno"),
+])
+def test_epistasis_latents_are_batch_safe(theta, activity):
+    """Pair-indexed epistasis latents must be library-sized too."""
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     batch_size=6,
+                                     theta=theta,
+                                     activity=activity,
+                                     epistasis=True)
+    assert find_orchestrator_batch_dependent_latents(orchestrator) == {}
+
+
+@pytest.mark.parametrize("theta", ["hill_geno", "categorical_geno"])
+def test_binding_only_latents_are_batch_safe(theta):
+    """Binding-only mode mini-batches the binding genotypes themselves."""
+    orchestrator = ModelOrchestrator(None, _BINDING_CSV,
+                                     binding_only=True,
+                                     batch_size=3,
+                                     theta=theta)
+    assert find_orchestrator_batch_dependent_latents(orchestrator) == {}
+
+
+def test_beta_growth_noise_is_batch_dependent():
+    """
+    Documented exception: beta noise's latent is the noisy theta itself,
+    drawn per observation from a Beta centred on the batch's theta, so it
+    cannot be sampled at library size.  The detector must keep flagging it
+    so autoguides can refuse it under mini-batching.
+    """
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     batch_size=6,
+                                     theta_growth_noise="beta")
+    found = find_orchestrator_batch_dependent_latents(orchestrator)
+    found = {k for k in found if _owned_by("theta_growth_noise", k)}
+    assert found == {"theta_growth_noise_dist"}
+
+
+# ---------------------------------------------------------------------------
+# Batch order: predictions must follow batch_idx, not library order
+# ---------------------------------------------------------------------------
+
+def _sliced_model(data, priors):
+    """Library-sized latent, sliced to the batch: correct."""
+    with numpyro.plate("genotype_plate", data.num_genotype):
+        offset = numpyro.sample("offset", dist.Normal(0.0, 1.0))
+    numpyro.deterministic("growth_pred", offset[data.batch_idx])
+
+
+def _unsliced_at_full_batch_model(data, priors):
+    """Slices only when mini-batching: wrong at a reordered full batch."""
+    with numpyro.plate("genotype_plate", data.num_genotype):
+        offset = numpyro.sample("offset", dist.Normal(0.0, 1.0))
+    if data.batch_size < data.num_genotype:
+        offset = offset[data.batch_idx]
+    numpyro.deterministic("growth_pred", offset)
+
+
+def test_order_check_passes_sliced_model():
+    found = find_batch_order_mismatches(_sliced_model, None, _toy_full_data(),
+                                        _toy_get_batch, jnp.arange(5),
+                                        jnp.array([0, 3, 1, 4, 2]))
+    assert found == {}
+
+
+def test_order_check_flags_unsliced_model():
+    found = find_batch_order_mismatches(_unsliced_at_full_batch_model, None,
+                                        _toy_full_data(), _toy_get_batch,
+                                        jnp.arange(5),
+                                        jnp.array([0, 3, 1, 4, 2]))
+    assert set(found) == {"growth_pred"}
+    assert found["growth_pred"] > 0
+
+
+def test_order_check_needs_reorderings():
+    with pytest.raises(ValueError, match="reorderings"):
+        find_batch_order_mismatches(_sliced_model, None, _toy_full_data(),
+                                    _toy_get_batch, jnp.arange(5),
+                                    jnp.array([0, 1, 2, 3, 3]))
+
+
+# The full-batch training index keeps the binding genotypes first and
+# reshuffles the rest every step, so run at full batch (batch_size=None).
+# beta *growth* theta noise is the documented exception (tested below).
+@pytest.mark.parametrize("axis,variant", _SAFE_VARIANTS)
+def test_component_predictions_follow_batch_order(axis, variant):
+    orchestrator = ModelOrchestrator(**_variant_kwargs(axis, variant))
+    found = find_orchestrator_batch_order_mismatches(orchestrator)
+    assert found == {}, (
+        f"{axis}={variant}: predictions do not follow a reordered full "
+        f"batch (largest abs difference per site): {found}"
+    )
+
+
+@pytest.mark.parametrize("theta,activity", [
+    ("hill_mut", "hierarchical_mut"),
+    ("hill_mut", "horseshoe_mut"),
+    ("thermo.O2_C4_K3_U0_a.PK", "horseshoe_geno"),
+])
+def test_epistasis_predictions_follow_batch_order(theta, activity):
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     theta=theta,
+                                     activity=activity,
+                                     epistasis=True)
+    assert find_orchestrator_batch_order_mismatches(orchestrator) == {}
+
+
+@pytest.mark.parametrize("theta,noise", [("hill_geno", "zero"),
+                                         ("categorical_geno", "zero"),
+                                         ("hill_geno", "genotype_offset")])
+def test_binding_only_predictions_follow_batch_order(theta, noise):
+    orchestrator = ModelOrchestrator(None, _BINDING_CSV,
+                                     binding_only=True,
+                                     theta=theta,
+                                     theta_binding_noise=noise)
+    assert find_orchestrator_batch_order_mismatches(orchestrator) == {}
+
+
+def test_simple_theta_per_genotype_follows_batch_order():
+    """
+    The pre-fit's _simple theta holds per-genotype, library-ordered curves.
+    It must read them through batch_idx, not by batch position.
+    """
+    import numpy as np
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     theta="_simple")
+    g = orchestrator.data.growth
+    values = np.random.default_rng(0).uniform(
+        0.05, 0.95, (g.num_titrant_name, g.num_titrant_conc, g.num_genotype))
+    priors = orchestrator.priors
+    orchestrator._priors = priors.replace(
+        theta=priors.theta.replace(theta_values=jnp.asarray(values)))
+    assert find_orchestrator_batch_order_mismatches(orchestrator) == {}
+
+
+def test_beta_growth_noise_is_batch_positional():
+    """
+    Documented exception: beta noise's latent is the noisy theta itself, one
+    draw per batch position, so it does not follow a reordering. The order
+    check must keep flagging it.
+    """
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     theta_growth_noise="beta")
+    assert "growth_pred" in find_orchestrator_batch_order_mismatches(orchestrator)
+
+
+# ---------------------------------------------------------------------------
+# orchestrator_latent_dimension
+# ---------------------------------------------------------------------------
+
+def test_latent_dimension_toy():
+    """global_p (1) + good_offset (one per library genotype)."""
+    from types import SimpleNamespace
+    orch = SimpleNamespace(data=_toy_full_data(5), priors=None,
+                           jax_model=_full_sized_model,
+                           get_batch=_toy_get_batch)
+    assert orchestrator_latent_dimension(orch) == 6
+
+
+def test_latent_dimension_matches_autocontinuous():
+    """Matches the latent_dim an AutoMultivariateNormal builds itself."""
+    from numpyro.infer.autoguide import AutoMultivariateNormal
+    from numpyro.handlers import seed
+
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     batch_size=6)
+    num_genotype = orchestrator.data.num_genotype
+    data = orchestrator.get_batch(orchestrator.data, jnp.arange(num_genotype))
+
+    guide = AutoMultivariateNormal(orchestrator.jax_model)
+    seed(guide, rng_seed=0)(data=data, priors=orchestrator.priors)
+
+    assert orchestrator_latent_dimension(orchestrator) == guide.latent_dim
+
+
+# ---------------------------------------------------------------------------
+# Edge cases of the checks
+# ---------------------------------------------------------------------------
+
+def test_orchestrator_dependence_check_skips_single_genotype():
+    """One genotype leaves no smaller prefix to trace against."""
+    from types import SimpleNamespace
+    orch = SimpleNamespace(data=_toy_full_data(1), priors=None,
+                           jax_model=_batch_sized_model,
+                           get_batch=_toy_get_batch)
+    assert find_orchestrator_batch_dependent_latents(orch) == {}
+
+
+def _order_dependent_shape_model(data, priors):
+    """A prediction whose shape changes with the batch order."""
+    with numpyro.plate("genotype_plate", data.num_genotype):
+        offset = numpyro.sample("offset", dist.Normal(0.0, 1.0))
+    pred = offset[data.batch_idx]
+    if int(data.batch_idx[0]) != 0:
+        pred = jnp.stack([pred, pred])
+    numpyro.deterministic("growth_pred", pred)
+
+
+def test_order_check_flags_shape_change_as_inf():
+    found = find_batch_order_mismatches(_order_dependent_shape_model, None,
+                                        _toy_full_data(), _toy_get_batch,
+                                        jnp.arange(5),
+                                        jnp.array([3, 0, 1, 4, 2]))
+    assert found == {"growth_pred": float("inf")}
+
+
+def _order_orch(num_rest, num_binding=0):
+    from types import SimpleNamespace
+    n = num_binding + num_rest
+    data = SimpleNamespace(num_genotype=n, batch_idx=jnp.arange(n),
+                           num_binding=num_binding,
+                           not_binding_idx=jnp.arange(num_binding, n))
+    return SimpleNamespace(data=data, priors=None,
+                           jax_model=_unsliced_at_full_batch_model,
+                           get_batch=_toy_get_batch)
+
+
+def test_orchestrator_order_check_skips_fewer_than_two_to_reorder():
+    """With at most one non-binding genotype there is nothing to reorder,
+    so even an order-unsafe model passes."""
+    assert find_orchestrator_batch_order_mismatches(_order_orch(1, 2)) == {}
+
+
+def test_orchestrator_order_check_reverses_an_identity_shuffle():
+    """Seed 0 permutes two genotypes to the identity; the check reverses
+    them instead, so the unsliced model is still caught."""
+    import numpy as np
+    assert np.array_equal(np.random.default_rng(0).permutation(np.arange(2)),
+                          np.arange(2))
+    found = find_orchestrator_batch_order_mismatches(_order_orch(2),
+                                                     seed_value=0)
+    assert set(found) == {"growth_pred"}
diff --git a/tests/tfscreen/tfmodel/inference/test_convergence.py b/tests/tfscreen/tfmodel/inference/test_convergence.py
new file mode 100644
index 00000000..a54bd58c
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_convergence.py
@@ -0,0 +1,732 @@
+"""
+Tests for inference/convergence.py: the loss and parameter tests, and the
+window-by-window decisions, on synthetic and recorded loss traces.
+"""
+
+import os
+
+import numpy as np
+import pytest
+import jax.numpy as jnp
+
+from tfscreen.tfmodel.inference import convergence as conv
+from tfscreen.tfmodel.inference.convergence import (
+    ConvergenceMonitor,
+    loss_skew,
+    loss_trend,
+    param_drift,
+    summarize_excess,
+)
+
+TRACES = os.path.join(os.path.dirname(__file__), "loss_traces")
+WINDOW = 2000
+
+
+def _windows(trace, window=WINDOW):
+    """Consecutive full windows of a per-step loss trace."""
+    for start in range(0, len(trace) - window + 1, window):
+        yield start + window, trace[start:start + window]
+
+
+def _feed(monitor, trace, window=WINDOW, param_excess=None):
+    """Run a monitor over a trace (loss test only unless param_excess given)."""
+    decisions = []
+    for step, losses in _windows(trace, window):
+        decisions.append(monitor.end_window(step, loss_trend(losses),
+                                            param_excess))
+        if decisions[-1] in (conv.CONVERGED, conv.DIVERGED):
+            break
+    return decisions
+
+
+# -----------------------------------------------------------------------------
+# loss_trend
+# -----------------------------------------------------------------------------
+
+def test_loss_trend_exact_line():
+    losses = 1000.0 - 0.5 * np.arange(WINDOW)
+    stats = loss_trend(losses)
+    assert stats["drop"] == pytest.approx(0.5 * WINDOW, rel=1e-6)
+    assert stats["t"] > 1e6 or np.isinf(stats["t"])
+    # level: median of the last of 20 blocks
+    assert stats["level"] == pytest.approx(np.median(losses[-100:]))
+
+
+def test_loss_trend_rising_loss_has_negative_drop():
+    stats = loss_trend(np.linspace(0, 100, WINDOW))
+    assert stats["drop"] < 0
+
+
+def test_loss_trend_constant_is_zero():
+    stats = loss_trend(np.full(WINDOW, 5.0))
+    assert stats["drop"] == 0
+    assert stats["t"] == 0
+
+
+def test_loss_trend_needs_three_losses():
+    with pytest.raises(ValueError):
+        loss_trend([1.0, 2.0])
+
+
+def test_loss_trend_ignores_leading_partial_block():
+    losses = np.concatenate([[1e9], np.full(2000, 3.0)])
+    assert loss_trend(losses)["drop"] == 0
+
+
+@pytest.mark.parametrize("noise", ["normal", "lognormal", "student_t"])
+def test_loss_trend_flat_noise_rarely_looks_like_improvement(noise):
+    """A flat loss with white noise -- symmetric, right-skewed or heavy
+    tailed -- is called improving (t > 3) in only a few percent of windows."""
+    rng = np.random.default_rng(0)
+    false_calls = 0
+    for _ in range(300):
+        if noise == "normal":
+            eps = rng.normal(size=WINDOW)
+        elif noise == "lognormal":
+            eps = rng.lognormal(sigma=1.0, size=WINDOW)
+        else:
+            eps = rng.standard_t(df=2, size=WINDOW)
+        stats = loss_trend(1e5 + 1e4 * eps)
+        false_calls += stats["t"] > 3
+    assert false_calls / 300 < 0.03
+
+
+def test_loss_trend_detects_decrease_well_below_step_noise():
+    """A drop of half the per-step noise SD per window is still found (the
+    expected t is ~5): the test is against the noise of the trend, not of
+    a step."""
+    rng = np.random.default_rng(1)
+    losses = (1e5 - 5000 * np.arange(WINDOW) / WINDOW
+              + 1e4 * rng.normal(size=WINDOW))
+    assert loss_trend(losses)["t"] > 3
+
+
+# -----------------------------------------------------------------------------
+# loss_skew
+# -----------------------------------------------------------------------------
+
+@pytest.mark.parametrize("noise", ["normal", "exponential", "lognormal",
+                                   "student_t3"])
+def test_loss_skew_benign_noise_is_small(noise):
+    """Monte Carlo ELBO noise, right-skewed or heavy-tailed but without
+    separate penalties, stays well under MAX_LOSS_SKEW."""
+    rng = np.random.default_rng(0)
+    draw = {"normal": lambda n: rng.normal(size=n),
+            "exponential": lambda n: rng.exponential(size=n),
+            "lognormal": lambda n: rng.lognormal(sigma=1.0, size=n),
+            "student_t3": lambda n: rng.standard_t(3, size=n)}[noise]
+    for _ in range(20):
+        stats = loss_trend(1e5 + 1e3 * draw(WINDOW))
+        assert stats["skew"] < 1.5 < conv.MAX_LOSS_SKEW
+
+
+def test_loss_skew_rare_penalties_are_large():
+    """Rare draws with a huge penalty: the median ignores them, the mean does
+    not.  The skew is the mean shift (rate x penalty) in per-step noise SDs:
+    here 5-30% of steps carrying 240 noise SDs."""
+    rng = np.random.default_rng(1)
+    for rate in (0.05, 0.1, 0.3):
+        losses = (4e4 + 1e3 * rng.normal(size=WINDOW)
+                  + 2.4e5 * (rng.random(WINDOW) < rate))
+        stats = loss_trend(losses)
+        assert stats["skew"] > 3 * conv.MAX_LOSS_SKEW
+        assert stats["mean"] > stats["level"] + 3e3
+
+
+def test_loss_skew_ignores_trend():
+    """A falling loss is not read as skew (residuals are about the line)."""
+    rng = np.random.default_rng(2)
+    losses = 1e5 - 20.0 * np.arange(WINDOW) + 10.0 * rng.normal(size=WINDOW)
+    assert abs(loss_trend(losses)["skew"]) < 0.5
+
+
+def test_loss_skew_constant_and_empty():
+    assert loss_trend(np.full(WINDOW, 5.0))["skew"] == 0.0
+    assert loss_skew(np.zeros(10)) == 0.0
+    assert loss_skew([]) == 0.0
+
+
+def test_loss_skew_deterministic_repeats_are_not_skew():
+    """A deterministic loss whose steps mostly repeat one value: the spread
+    is floored at rtol * |level|, so float-level differences are not skew."""
+    losses = np.full(WINDOW, 1e5)
+    losses[::7] += 1e-3
+    assert loss_skew(losses - 1e5, level=1e5) < conv.MAX_LOSS_SKEW
+
+
+# -----------------------------------------------------------------------------
+# param_drift / summarize_excess
+# -----------------------------------------------------------------------------
+
+def _block_centers(n=8, window=WINDOW):
+    width = window / n
+    return (np.arange(n) + 0.5) * width
+
+
+def test_param_drift_linear_movement_in_sd_units():
+    centers = _block_centers()
+    sd = np.array([0.5, 2.0])
+    # moves by exactly 1 SD per window, no noise
+    means = (centers[:, None] / WINDOW) * sd[None, :]
+    drift, excess = param_drift(means, centers, WINDOW, sd, z=3)
+    np.testing.assert_allclose(drift, [1.0, 1.0], rtol=1e-6)
+    np.testing.assert_allclose(excess, [1.0, 1.0], rtol=1e-6)
+
+
+def test_param_drift_stationary_noise_has_little_excess():
+    rng = np.random.default_rng(2)
+    centers = _block_centers()
+    means = rng.normal(scale=0.3, size=(8, 5000))
+    _, excess = param_drift(means, centers, WINDOW, 1.0, z=3)
+    assert summarize_excess(excess) < 0.3
+    assert np.mean(np.asarray(excess) > 0) < 0.05
+
+
+def test_param_drift_floor_absorbs_optimizer_jitter():
+    """Movement of a few step sizes (Adam jitter) is under the floor even
+    when the normalizer (a collapsed guide scale) is much smaller."""
+    rng = np.random.default_rng(3)
+    step_size = 1e-3
+    centers = _block_centers()
+    means = np.cumsum(rng.normal(scale=step_size, size=(8, 100)), axis=0)
+    floor = conv.MIN_STEP_COHERENCE * step_size * WINDOW
+    _, no_floor = param_drift(means, centers, WINDOW, 1e-4, z=3)
+    _, with_floor = param_drift(means, centers, WINDOW, 1e-4, z=3,
+                                floor=floor)
+    assert summarize_excess(no_floor) > 1
+    assert summarize_excess(with_floor) == 0
+
+
+def test_param_drift_coherent_movement_beats_floor():
+    step_size = 1e-3
+    centers = _block_centers()
+    means = (centers / WINDOW * 0.5 * step_size * WINDOW)[:, None]  # 50% speed
+    floor = conv.MIN_STEP_COHERENCE * step_size * WINDOW
+    _, excess = param_drift(means, centers, WINDOW, 0.01, z=3, floor=floor)
+    assert summarize_excess(excess) > 1
+
+
+def test_param_drift_accepts_jax_arrays():
+    centers = _block_centers()
+    means = jnp.asarray(np.outer(centers / WINDOW, [1.0, 2.0]))
+    drift, excess = param_drift(means, centers, WINDOW, jnp.ones(2), z=3)
+    np.testing.assert_allclose(np.asarray(drift), [1.0, 2.0], rtol=1e-5)
+
+
+def test_summarize_excess_small_array_uses_max():
+    assert summarize_excess(np.array([0.0, 0.0, 0.7])) == pytest.approx(0.7)
+
+
+def test_summarize_excess_large_array_uses_quantile():
+    excess = np.zeros(10000)
+    excess[:5] = 10.0  # 0.05% of elements in the tail
+    assert summarize_excess(excess) == 0.0
+    excess[:500] = 10.0  # 5%
+    assert summarize_excess(excess) == pytest.approx(10.0)
+
+
+def test_summarize_excess_nonfinite_is_infinite():
+    assert summarize_excess(np.array([0.0, np.nan])) == np.inf
+    assert summarize_excess(np.array([])) == 0.0
+
+
+# -----------------------------------------------------------------------------
+# ConvergenceMonitor decisions
+# -----------------------------------------------------------------------------
+
+_FLAT = {"level": 100.0, "drop": 0.0, "drop_se": 1.0, "t": 0.0}
+_IMPROVING = {"level": 100.0, "drop": 10.0, "drop_se": 1.0, "t": 10.0}
+
+
+@pytest.mark.parametrize("kwargs", [{"step_size_cut": 1.0},
+                                    {"step_size_cut": 0.0},
+                                    {"patience": 0}])
+def test_monitor_rejects_bad_settings(kwargs):
+    with pytest.raises(ValueError):
+        ConvergenceMonitor(1e-3, 1e-6, **kwargs)
+
+
+def test_monitor_cuts_after_patience_stalls():
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    assert [m.end_window(i, _FLAT) for i in range(3)] == \
+        [conv.CONTINUE, conv.CONTINUE, conv.CUT]
+    assert m.step_size == pytest.approx(1e-4)
+    assert m.plateau_count == 0
+    assert m.num_cuts == 1
+
+
+def test_monitor_cut_ignores_moving_parameters():
+    """Above the floor the loss alone decides a cut."""
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=2)
+    moving = {"p": 5.0}
+    m.end_window(0, _FLAT, moving)
+    assert m.end_window(1, _FLAT, moving) == conv.CUT
+
+
+def test_monitor_improvement_resets_patience():
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    m.end_window(0, _FLAT)
+    m.end_window(1, _FLAT)
+    m.end_window(2, _IMPROVING)
+    assert m.plateau_count == 0
+    assert m.end_window(3, _FLAT) == conv.CONTINUE
+
+
+def test_monitor_rising_loss_counts_as_stall():
+    rising = {"level": 100.0, "drop": -50.0, "drop_se": 1.0, "t": -50.0}
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=1)
+    assert m.end_window(0, rising) == conv.CUT
+
+
+def test_monitor_loss_rtol_floor():
+    """A significant but negligible improvement (deterministic loss) is a
+    stall."""
+    tiny = {"level": 1e6, "drop": 0.1, "drop_se": 0.0, "t": np.inf}
+    m = ConvergenceMonitor(1e-3, 1e-3, patience=1, loss_rtol=1e-6)
+    assert m.end_window(0, tiny) == conv.CONVERGED
+    m = ConvergenceMonitor(1e-3, 1e-3, patience=1, loss_rtol=1e-9)
+    assert m.end_window(0, tiny) == conv.CONTINUE
+
+
+def test_monitor_stop_needs_parameters_still_at_floor():
+    m = ConvergenceMonitor(1e-6, 1e-6, patience=2, param_tolerance=0.05)
+    moving = {"a": 0.0, "b": 0.2}
+    for i in range(10):
+        assert m.end_window(i, _FLAT, moving) == conv.CONTINUE
+    assert m.last["worst_param"] == "b"
+    assert m.last["params_moving"]
+    still = {"a": 0.0, "b": 0.01}
+    m.end_window(10, _FLAT, still)
+    assert m.end_window(11, _FLAT, still) == conv.CONVERGED
+    assert m.converged
+
+
+def test_monitor_stop_needs_unskewed_loss_at_floor():
+    """At the floor a skewed window is not a plateau: the run continues (and
+    reports it) rather than converging on a median that hides the mean."""
+    skewed = dict(_FLAT, skew=40.0, mean=500.0)
+    m = ConvergenceMonitor(1e-6, 1e-6, patience=2)
+    assert [m.end_window(i, skewed) for i in range(6)] == [conv.CONTINUE] * 6
+    assert m.last["loss_skewed"] and m.last["loss_skew"] == 40.0
+    assert m.last["loss_mean"] == 500.0
+    assert "loss skewed" in m.describe()
+    assert m.end_window(6, dict(_FLAT, skew=0.3)) == conv.CONTINUE
+    assert m.end_window(7, dict(_FLAT, skew=0.3)) == conv.CONVERGED
+
+
+def test_monitor_skew_never_blocks_a_cut():
+    """A smaller step size does not remove rare penalties, so skew only gates
+    the stop."""
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=2)
+    skewed = dict(_FLAT, skew=40.0)
+    m.end_window(0, skewed)
+    assert m.end_window(1, skewed) == conv.CUT
+
+
+def test_monitor_max_loss_skew_setting():
+    m = ConvergenceMonitor(1e-6, 1e-6, patience=1, max_loss_skew=50.0)
+    assert m.end_window(0, dict(_FLAT, skew=40.0)) == conv.CONVERGED
+    m = ConvergenceMonitor(1e-6, 1e-6, patience=1)
+    assert m.end_window(0, dict(_FLAT, skew=np.nan)) == conv.CONTINUE
+
+
+def test_monitor_goes_through_all_stages():
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=1)
+    decisions = [m.end_window(i, _FLAT, {"p": 0.0}) for i in range(4)]
+    assert decisions == [conv.CUT, conv.CUT, conv.CUT, conv.CONVERGED]
+    assert m.step_size == pytest.approx(1e-6)
+    assert m.num_cuts == 3
+
+
+def test_monitor_cut_clamps_at_floor():
+    m = ConvergenceMonitor(1e-3, 5e-4, patience=1)
+    m.end_window(0, _FLAT)
+    assert m.step_size == pytest.approx(5e-4)
+    assert m.at_floor
+
+
+def test_monitor_no_floor_means_no_cuts():
+    m = ConvergenceMonitor(1e-3, None, patience=1)
+    assert m.at_floor
+    assert m.end_window(0, _FLAT) == conv.CONVERGED
+
+
+def test_monitor_drift_floor():
+    m = ConvergenceMonitor(1e-3, 1e-6)
+    assert m.drift_floor(2000) == pytest.approx(
+        conv.MIN_STEP_COHERENCE * 1e-3 * 2000)
+    assert ConvergenceMonitor(np.nan).drift_floor(2000) == 0.0
+
+
+def test_monitor_state_round_trip():
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    m.end_window(0, _FLAT)
+    m.end_window(1, _FLAT)
+    m.end_window(2, _FLAT)  # cut
+    m.end_window(3, _FLAT)
+    state = m.state_dict()
+
+    m2 = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    m2.load_state_dict(state)
+    assert m2.step_size == pytest.approx(1e-4)
+    assert m2.plateau_count == 1
+    assert m2.num_cuts == 1
+    assert m2.last == m.last
+
+
+def test_monitor_describe():
+    m = ConvergenceMonitor(1e-3, 1e-6)
+    assert "no complete" in m.describe()
+    m.end_window(2000, _FLAT, {"theta_loc": 0.3}, {"theta_loc": 0.4})
+    text = m.describe()
+    assert "theta_loc" in text and "step 2000" in text
+
+
+# -----------------------------------------------------------------------------
+# Synthetic loss traces
+# -----------------------------------------------------------------------------
+
+def _noisy(clean, rel_sd=0.05, abs_sd=0.0, seed=0):
+    rng = np.random.default_rng(seed)
+    sd = rel_sd * np.abs(clean) + abs_sd
+    return clean + sd * rng.normal(size=clean.size)
+
+
+def test_trace_monotone_decay_never_stalls_while_falling():
+    """Exponential decay with noise proportional to the loss (the SVI
+    pattern): improving in every window while it is still falling."""
+    steps = np.arange(20 * WINDOW)
+    trace = _noisy(1e3 + 1e6 * np.exp(-steps / 8000), rel_sd=0.25)
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    decisions = _feed(m, trace[:10 * WINDOW])
+    assert conv.CUT not in decisions
+    assert m.last["loss_improving"]
+
+
+def test_trace_noisy_floor_cuts_after_patience():
+    steps = np.arange(30 * WINDOW)
+    trace = _noisy(1e5 + 1e6 * np.exp(-steps / 2000), rel_sd=0.0,
+                   abs_sd=1e4)
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    decisions = _feed(m, trace)
+    first_cut = decisions.index(conv.CUT)
+    # decay is gone after ~6 windows; the cut follows within a few windows
+    assert 5 <= first_cut <= 12
+
+
+def test_trace_two_phase_plateau_shorter_than_patience():
+    """drop, plateau of two windows, larger drop: no cut on the plateau."""
+    seg = WINDOW
+    drop1 = np.linspace(1e6, 5e5, 2 * seg)
+    plateau = np.full(2 * seg, 5e5)
+    drop2 = np.linspace(5e5, 1e4, 4 * seg)
+    trace = _noisy(np.concatenate([drop1, plateau, drop2]), rel_sd=0.0,
+                   abs_sd=2e3)
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    decisions = _feed(m, trace)
+    assert conv.CUT not in decisions
+
+
+def test_trace_two_phase_plateau_longer_than_patience_cuts():
+    """A plateau as long as the patience span does trigger a cut -- the
+    parameter test, not the loss, is what keeps a slow phase from being
+    called converged."""
+    seg = WINDOW
+    trace = _noisy(np.concatenate([np.linspace(1e6, 5e5, 2 * seg),
+                                   np.full(4 * seg, 5e5)]),
+                   rel_sd=0.0, abs_sd=2e3)
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    assert conv.CUT in _feed(m, trace)
+
+
+def test_trace_deterministic_slow_tail():
+    """A noiseless tail: improving until the per-window drop falls under
+    loss_rtol of the level."""
+    steps = np.arange(40 * WINDOW)
+    trace = 1e5 + 1e3 * np.exp(-steps / 5000)
+    m = ConvergenceMonitor(1e-3, 1e-3, patience=3, loss_rtol=1e-6)
+    decisions = _feed(m, trace)
+    assert decisions[-1] == conv.CONVERGED
+    # drop per window at the stop is below 1e-6 * 1e5 = 0.1
+    assert m.last["loss_drop"] < 0.1
+
+
+def test_trace_resume_matches_uninterrupted():
+    rng = np.random.default_rng(4)
+    trace = 1e4 + 1e5 * np.exp(-np.arange(24 * WINDOW) / 6000) \
+        + 300 * rng.normal(size=24 * WINDOW)
+
+    whole = ConvergenceMonitor(1e-3, 1e-6, patience=2)
+    expected = _feed(whole, trace)
+
+    first = ConvergenceMonitor(1e-3, 1e-6, patience=2)
+    half = 10 * WINDOW
+    got = _feed(first, trace[:half])
+    resumed = ConvergenceMonitor(1e-3, 1e-6, patience=2)
+    resumed.load_state_dict(first.state_dict())
+    got += _feed(resumed, trace[half:])
+    assert got == expected
+    assert resumed.step_size == whole.step_size
+
+
+# -----------------------------------------------------------------------------
+# Recorded traces
+# -----------------------------------------------------------------------------
+
+def test_recorded_real_data_trace_is_still_improving():
+    """Real data, ~200k genotypes, batch 65,536 (4 steps per epoch).  The run
+    was stopped by the old rule; every window, including the last, is still
+    a significant improvement."""
+    trace = np.load(os.path.join(TRACES, "real_svi_200k_genotypes.npy"))
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    decisions = _feed(m, trace.astype(float))
+    assert decisions and set(decisions) == {conv.CONTINUE}
+    assert m.last["loss_t"] > 10
+
+
+def test_recorded_simulation_trace_is_still_improving():
+    """Step-3.5 anchored run 0002: the old rule stopped it at step ~8,250
+    ('converged') while the loss was falling ~20% per 1000 steps."""
+    trace = np.load(os.path.join(TRACES, "sim_svi_anchored_run0002.npy"))
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    decisions = _feed(m, trace.astype(float))
+    assert set(decisions) == {conv.CONTINUE}
+    assert m.last["loss_t"] > 10
+
+
+def test_recorded_traces_are_not_skewed():
+    """Neither recorded clean trace has a window near MAX_LOSS_SKEW."""
+    for name in ("real_svi_200k_genotypes", "sim_svi_anchored_run0002",
+                 "sim_svi_unclipped_converged_run0005"):
+        trace = np.load(os.path.join(TRACES, name + ".npy")).astype(float)
+        for _, losses in _windows(trace):
+            assert loss_trend(losses)["skew"] < 1.0, name
+
+
+def test_recorded_clipped_trace_never_converges():
+    """Congression-calibration baseline run 0003 at step size 1e-6 under
+    elementwise gradient clipping: ~15% of steps carry a ~2.4e5 binding
+    penalty (a steep binding curve measured with SD 1e-3).  The block medians
+    are flat, so the loss test alone called every window a plateau; the skew
+    keeps the monitor from calling it converged."""
+    trace = np.load(os.path.join(
+        TRACES, "sim_svi_clipped_events_run0003.npy")).astype(float)
+    skews = [loss_trend(losses)["skew"] for _, losses in _windows(trace)]
+    assert min(skews) > 5 * conv.MAX_LOSS_SKEW
+
+    m = ConvergenceMonitor(1e-6, 1e-6, patience=2)
+    decisions = _feed(m, trace)
+    assert set(decisions) == {conv.CONTINUE}
+    assert m.last["loss_skewed"] and not m.last["loss_improving"]
+
+    # the loss test alone would have stopped it
+    blind = ConvergenceMonitor(1e-6, 1e-6, patience=2, max_loss_skew=np.inf)
+    assert _feed(blind, trace)[-1] == conv.CONVERGED
+
+
+def test_recorded_unclipped_trace_converges_at_floor():
+    """The same simulation (run 0005) refit without clipping, around its
+    convergence: flat and unskewed, so the loss test stops it."""
+    trace = np.load(os.path.join(
+        TRACES, "sim_svi_unclipped_converged_run0005.npy")).astype(float)
+    m = ConvergenceMonitor(1e-6, 1e-6, patience=3)
+    assert _feed(m, trace)[-1] == conv.CONVERGED
+
+
+# -----------------------------------------------------------------------------
+# Pooled check of a run of stalls
+# -----------------------------------------------------------------------------
+
+def test_pooled_loss_trend_matches_single_window_line():
+    """Pooling windows of one exact line gives that line's drop per window."""
+    trace = 1e6 - 50.0 * np.arange(3 * WINDOW)
+    stats = [loss_trend(trace[i * WINDOW:(i + 1) * WINDOW]) for i in range(3)]
+    pooled = conv.pooled_loss_trend(stats)
+    assert pooled["drop"] == pytest.approx(50.0 * WINDOW, rel=1e-6)
+
+
+def test_pooled_check_keeps_a_slow_noisy_descent():
+    """Each window alone is not significant, but three pooled are."""
+    rng = np.random.default_rng(1)
+    trace = 1e6 - 20.0 * np.arange(12 * WINDOW) + 1.5e5 * rng.normal(size=12 * WINDOW)
+    single = [loss_trend(trace[i:i + WINDOW])["t"]
+              for i in range(0, 12 * WINDOW, WINDOW)]
+    assert np.median(single) < conv.ConvergenceMonitor(1e-3).z
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    assert conv.CUT not in _feed(m, trace)
+
+
+def test_pooled_check_still_cuts_a_real_plateau():
+    rng = np.random.default_rng(2)
+    trace = 1e6 + 1.5e5 * rng.normal(size=6 * WINDOW)
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    decisions = _feed(m, trace)
+    assert decisions[2] == conv.CUT
+    assert abs(m.last["pooled_loss_t"]) < 3
+
+
+def test_pooled_check_blocks_a_stop_on_a_descent_at_the_floor():
+    rng = np.random.default_rng(3)
+    trace = 1e6 - 20.0 * np.arange(9 * WINDOW) + 1.5e5 * rng.normal(size=9 * WINDOW)
+    m = ConvergenceMonitor(1e-6, 1e-6, patience=3)
+    assert conv.CONVERGED not in _feed(m, trace)
+
+
+def test_pooled_stalls_survive_a_resume():
+    rng = np.random.default_rng(5)
+    trace = 1e6 - 20.0 * np.arange(6 * WINDOW) + 1.5e5 * rng.normal(size=6 * WINDOW)
+    whole = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    expected = _feed(whole, trace)
+    first = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    got = _feed(first, trace[:2 * WINDOW])
+    resumed = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    resumed.load_state_dict(first.state_dict())
+    got += _feed(resumed, trace[2 * WINDOW:])
+    assert got == expected
+
+
+def test_recorded_count_likelihood_descent_is_not_cut():
+    """Count-likelihood grid v2, run 0012 (poisson, seed 3, mixture), steps
+    1-22,000 at step size 1e-3. The per-window test called windows
+    16k/18k/20k stalls (t = 1.9, 3.0, 1.0) while the window medians kept
+    falling 1.5-1.9e5 per window; the cut at 20k left the fit at twice the
+    ELBO of its seed-2 twin, in a wrong optimum. Pooled, it is a descent."""
+    trace = np.load(os.path.join(
+        TRACES, "sim_svi_counts_noisy_descent_run0012.npy")).astype(float)
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    decisions = _feed(m, trace)
+    assert conv.CUT not in decisions
+
+
+# -----------------------------------------------------------------------------
+# runaway loss
+# -----------------------------------------------------------------------------
+
+def test_runaway_loss_is_diverged_not_converged():
+    """
+    A loss that runs off toward -inf (an unbounded posterior density) stops
+    the run as diverged, where it used to plateau at ~-8e23 and converge.
+    """
+    rng = np.random.default_rng(0)
+    start = 4e4 + rng.normal(0, 100, WINDOW)
+    runaway = -8e23 * (1 + 0.1 * rng.normal(size=6 * WINDOW))
+    m = ConvergenceMonitor(1e-3, None, patience=3)
+    decisions = _feed(m, np.concatenate([start, runaway]))
+    assert decisions[-1] == conv.DIVERGED
+    assert m.diverged and not m.converged
+    assert m.reference_loss == pytest.approx(4e4, rel=0.05)
+
+
+def test_negative_but_bounded_loss_is_not_runaway():
+    """A loss that settles below zero, far above the runaway line, is fine."""
+    rng = np.random.default_rng(1)
+    trace = np.concatenate([1e4 - np.linspace(0, 1.2e4, 4 * WINDOW),
+                            -2e3 + rng.normal(0, 1, 8 * WINDOW)])
+    m = ConvergenceMonitor(1e-3, None, patience=3)
+    decisions = _feed(m, trace)
+    assert conv.DIVERGED not in decisions
+    assert decisions[-1] == conv.CONVERGED
+
+
+def test_reference_loss_survives_checkpoint():
+    m = ConvergenceMonitor(1e-3, 1e-6)
+    m.end_window(WINDOW, loss_trend(np.full(WINDOW, 5e3)
+                                    + np.random.default_rng(2).normal(0, 1, WINDOW)))
+    resumed = ConvergenceMonitor(1e-3, 1e-6)
+    resumed.load_state_dict(m.state_dict())
+    assert resumed.reference_loss == pytest.approx(m.reference_loss)
+
+
+# -----------------------------------------------------------------------------
+# exact (full-batch) loss
+# -----------------------------------------------------------------------------
+
+# Mini-batch statistics that, alone, would read as a stall (noise hides the
+# descent) or as heavily skewed.
+_NOISY = {"level": 6e7, "mean": 6e7, "drop": 1e5, "drop_se": 5e4, "t": 2.0,
+          "skew": 0.0}
+
+
+def _exact_feed(monitor, exact_losses, stats=_NOISY):
+    return [monitor.end_window(i, dict(stats), exact_loss=e)
+            for i, e in enumerate(exact_losses)]
+
+
+def test_exact_descent_hidden_by_minibatch_noise_is_not_a_stall():
+    """A 1e5-per-window descent at mini-batch SE 5e4 stalls on the noisy
+    test (t = 2 < 3) but is a descent on the exact loss."""
+    noisy = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    assert [noisy.end_window(i, dict(_NOISY)) for i in range(3)][-1] == conv.CUT
+
+    exact = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    decisions = _exact_feed(exact, 6e7 - 1e5 * np.arange(10))
+    assert all(d == conv.CONTINUE for d in decisions)
+    assert exact.step_size == 1e-3
+    assert exact.last["loss_exact"] == pytest.approx(6e7 - 9e5)
+    assert exact.last["loss_drop"] == pytest.approx(1e5)
+    assert exact.last["loss_drop_se"] == pytest.approx(0.0, abs=1e-6)
+
+
+def test_exact_first_window_counts_as_descent():
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=1)
+    assert m.end_window(0, dict(_NOISY), exact_loss=6e7) == conv.CONTINUE
+
+
+def test_exact_flat_loss_cuts_then_converges():
+    m = ConvergenceMonitor(1e-3, 1e-5, patience=2, loss_rtol=1e-6)
+    # rises and falls by less than loss_rtol * |loss| (60 nats): a plateau
+    decisions = _exact_feed(m, [6e7, 6e7 + 10, 6e7 - 5, 6e7 + 3, 6e7, 6e7 - 1,
+                                6e7 + 2, 6e7])
+    assert conv.CUT in decisions
+    assert decisions[-1] == conv.CONVERGED
+    assert m.step_size == pytest.approx(1e-5)
+
+
+def test_exact_jittery_descent_is_improving_and_jitter_alone_is_not():
+    """Single windows rise under the optimizer's jitter, but the line through
+    recent windows descends; jitter about a level has no significant slope."""
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3, loss_rtol=1e-8, z=3.0)
+    series = 1000.0 - 10.0 * np.arange(12) + np.tile([0.0, 3.0, -2.0, 1.0], 3)
+    assert conv.CUT not in _exact_feed(m, series)
+
+    flat = ConvergenceMonitor(1e-3, 1e-6, patience=3, loss_rtol=1e-8, z=3.0)
+    decisions = _exact_feed(flat, [1000.0, 1001.0, 999.0, 1000.5, 999.5,
+                                   1000.0, 1001.0, 999.0])
+    assert conv.CUT in decisions
+
+
+def test_exact_small_steady_drops_below_noise_floor_count():
+    """A deterministic, steady descent of 2 nats per window is improving
+    once the floor is below it, however small."""
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3, loss_rtol=1e-6)
+    assert conv.CUT not in _exact_feed(m, 1000.0 - 2.0 * np.arange(10))
+
+
+def test_exact_ignores_minibatch_skew():
+    skewed = dict(_NOISY, skew=50.0)
+    m = ConvergenceMonitor(1e-6, 1e-6, patience=2)
+    decisions = _exact_feed(m, [100.0, 100.0, 100.0], stats=skewed)
+    assert decisions[-1] == conv.CONVERGED
+    assert not m.last["loss_skewed"]
+
+
+def test_exact_runaway_uses_exact_loss():
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    m.end_window(0, dict(_NOISY), exact_loss=100.0)
+    assert m.end_window(1, dict(_NOISY), exact_loss=-1e7) == conv.DIVERGED
+
+
+def test_exact_state_round_trip():
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    _exact_feed(m, [100.0, 90.0])
+    m2 = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    m2.load_state_dict(m.state_dict())
+    # the restored exact losses join the line: 100, 90, 85 fall 7.5 per window
+    m2.end_window(2, dict(_NOISY), exact_loss=85.0)
+    assert m2.last["loss_drop"] == pytest.approx(7.5)
+
+
+def test_exact_describe():
+    m = ConvergenceMonitor(1e-3, 1e-6, patience=3)
+    _exact_feed(m, [100.0, 90.0])
+    assert "exact loss 90" in m.describe()
diff --git a/tests/tfscreen/tfmodel/inference/test_forward_chunking.py b/tests/tfscreen/tfmodel/inference/test_forward_chunking.py
new file mode 100644
index 00000000..e6cfe6da
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_forward_chunking.py
@@ -0,0 +1,88 @@
+"""
+A posterior file must not depend on how the forward pass is chunked.
+
+get_posteriors / get_map_posteriors run the model over genotype chunks of
+``forward_batch_size``. Library-sized latents have to reach the model whole
+(components slice them with ``batch_idx``); slicing them to the chunk first
+made hill_geno / thermo / categorical_geno read past the sliced arrays for
+every chunk after the first (JAX clamps the index), silently corrupting the
+deterministic sites of any library larger than one chunk.
+"""
+
+import os
+import tempfile
+
+import h5py
+import numpy as np
+import pytest
+
+from tfscreen.tfmodel.inference.run_inference import RunInference
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+
+_SITES = ("theta_growth_pred", "growth_pred", "dk_geno", "activity")
+
+
+def _read(path):
+    with h5py.File(path) as f:
+        return {k: f[k][()] for k in _SITES if k in f}
+
+
+def _fit(orchestrator, guide_type="component"):
+    inference = RunInference(model=orchestrator, seed=1)
+    svi = inference.setup_svi(adam_step_size=1e-3, guide_type=guide_type)
+    state, params, _ = inference.run_optimization(
+        svi=svi, max_num_epochs=1,
+        out_prefix=os.path.join(tempfile.mkdtemp(), "fit"))
+    return svi, state, params
+
+
+def _assert_same(a, b):
+    assert set(a) == set(b) and a
+    for k in a:
+        np.testing.assert_allclose(a[k], b[k], rtol=1e-5, atol=1e-6,
+                                   err_msg=f"site '{k}'")
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("theta", ["hill_geno", "categorical_geno",
+                                   "thermo.O2_C4_K3_U0_a.PK"])
+def test_get_posteriors_independent_of_chunk_size(theta):
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV, theta=theta)
+    assert orchestrator.data.num_genotype > 2
+    svi, state, _ = _fit(orchestrator)
+
+    out = {}
+    for forward_batch_size in (512, 2):
+        prefix = os.path.join(tempfile.mkdtemp(), "p")
+        RunInference(model=orchestrator, seed=7).get_posteriors(
+            svi=svi, svi_state=state, out_prefix=prefix,
+            num_posterior_samples=3, forward_batch_size=forward_batch_size)
+        out[forward_batch_size] = _read(f"{prefix}_posterior.h5")
+
+    _assert_same(out[512], out[2])
+
+
+@pytest.mark.slow
+def test_get_map_posteriors_independent_of_chunk_size():
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     theta="hill_geno")
+    _, _, params = _fit(orchestrator, guide_type="delta")
+
+    out = {}
+    for forward_batch_size in (512, 2):
+        prefix = os.path.join(tempfile.mkdtemp(), "m")
+        RunInference(model=orchestrator, seed=7).get_map_posteriors(
+            params, out_prefix=prefix, forward_batch_size=forward_batch_size)
+        out[forward_batch_size] = _read(f"{prefix}_posterior.h5")
+
+    _assert_same(out[512], out[2])
diff --git a/tests/tfscreen/tfmodel/inference/test_full_batch_loss.py b/tests/tfscreen/tfmodel/inference/test_full_batch_loss.py
new file mode 100644
index 00000000..dc0a5fe1
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_full_batch_loss.py
@@ -0,0 +1,48 @@
+"""RunInference.full_batch_loss on a real (small) model."""
+import os
+
+import numpy as np
+import pytest
+
+from tfscreen.tfmodel.inference.run_inference import RunInference
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+_SMOKE = os.path.join(os.path.dirname(__file__), "..", "..", "..",
+                      "smoke-tests", "test_data")
+
+
+@pytest.fixture(scope="module")
+def ri_and_point():
+    orch = ModelOrchestrator(
+        growth_df=os.path.join(_SMOKE, "growth-smoke.csv"),
+        binding_df=os.path.join(_SMOKE, "binding-smoke.csv"),
+        theta="hill_geno", growth_likelihood="counts", sample_offset="level",
+        batch_size=7)
+    ri = RunInference(orch, 0)
+    svi = ri.setup_svi(guide_type="delta")
+    state = svi.init(ri.get_key(), priors=orch.priors,
+                     data=orch.get_batch(orch.data, orch.get_random_idx()))
+    values = {k[:-len("_auto_loc")]: v
+              for k, v in svi.get_params(state).items()}
+    return ri, values
+
+
+def test_full_batch_loss_does_not_depend_on_chunking(ri_and_point):
+    """Binding data (not sliced by genotype) and the library-sized latents
+    repeat in every chunk; they must be counted once."""
+    ri, values = ri_and_point
+    G = ri.model.data.num_genotype
+    one = ri.full_batch_loss(values, chunk_size=G)
+    for chunk in (7, 13, G - 1):
+        assert ri.full_batch_loss(values, chunk_size=chunk) == \
+            pytest.approx(one, rel=1e-6)
+    assert np.isfinite(one)
+
+
+def test_full_batch_loss_moves_with_the_point(ri_and_point):
+    ri, values = ri_and_point
+    moved = dict(values)
+    key = next(k for k in values if np.size(values[k]) > 1
+               and "offset" in k)
+    moved[key] = values[key] + 0.5
+    assert ri.full_batch_loss(moved) != pytest.approx(ri.full_batch_loss(values))
diff --git a/tests/tfscreen/tfmodel/inference/test_guide_selection.py b/tests/tfscreen/tfmodel/inference/test_guide_selection.py
new file mode 100644
index 00000000..4a42b445
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_guide_selection.py
@@ -0,0 +1,83 @@
+"""
+Guide selection against the real model (smoke data): autoguide refusal for
+batch-dependent latents, and fit -> checkpoint -> restore -> posterior
+round trips for each autoguide.
+"""
+import os
+
+import dill
+import h5py
+import pytest
+
+from tfscreen.tfmodel.inference.run_inference import RunInference
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+
+
+def _orchestrator(**kwargs):
+    return ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=_BINDING_CSV,
+                             batch_size=6, **kwargs)
+
+
+@pytest.mark.parametrize("guide_type", ["delta", "auto_normal",
+                                        "auto_multivariate_normal"])
+def test_autoguide_refused_for_batch_dependent_latent(tmpdir, guide_type):
+    """Fitting is refused before the first step; building the guide is not."""
+    ri = RunInference(_orchestrator(theta_growth_noise="beta"), seed=0)
+    svi = ri.setup_svi(guide_type=guide_type)
+    with pytest.raises(ValueError, match="theta_growth_noise_dist"):
+        ri.run_optimization(svi, max_num_epochs=1,
+                            out_prefix=os.path.join(tmpdir, "refused"))
+
+
+def test_component_guide_allowed_for_batch_dependent_latent(tmpdir):
+    """The component guide slices library-sized params itself."""
+    ri = RunInference(_orchestrator(theta_growth_noise="beta"), seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-3, guide_type="component")
+    ri.run_optimization(svi, max_num_epochs=1, init_param_jitter=0.0,
+                        out_prefix=os.path.join(tmpdir, "component"),
+                        epoch_checkpoint_interval=None)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("guide_type,guide_kwargs", [
+    ("auto_normal", None),
+    ("auto_diagonal_normal", None),
+    ("auto_multivariate_normal", None),
+    ("auto_low_rank_multivariate_normal", {"rank": 2}),
+])
+def test_autoguide_round_trip(tmpdir, guide_type, guide_kwargs):
+    """Fit a few minibatched epochs, restore from the checkpoint, sample."""
+    orchestrator = _orchestrator()
+    num_genotype = orchestrator.data.num_genotype
+    out_prefix = os.path.join(tmpdir, guide_type)
+
+    ri = RunInference(orchestrator, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-3, guide_type=guide_type,
+                       guide_kwargs=guide_kwargs)
+    _, params, _ = ri.run_optimization(svi, max_num_epochs=2,
+                                       out_prefix=out_prefix,
+                                       init_param_jitter=0.0,
+                                       epoch_checkpoint_interval=None)
+
+    with open(f"{out_prefix}_checkpoint.pkl", "rb") as f:
+        assert dill.load(f)["guide_type"] == guide_type
+
+    ri2 = RunInference(_orchestrator(), seed=1)
+    svi2, state2 = ri2.restore_svi_from_checkpoint(f"{out_prefix}_checkpoint.pkl")
+    assert type(svi2.guide) is type(svi.guide)
+    restored = svi2.get_params(state2)
+    assert set(restored) == set(params)
+
+    ri2.get_posteriors(svi2, state2, out_prefix, num_posterior_samples=4,
+                       sampling_batch_size=4, forward_batch_size=4)
+    with h5py.File(f"{out_prefix}_posterior.h5", "r") as hf:
+        assert not any(k.startswith("_") for k in hf)
+        assert hf["dk_geno_offset"].shape == (4, num_genotype)
diff --git a/tests/tfscreen/tfmodel/inference/test_initialization.py b/tests/tfscreen/tfmodel/inference/test_initialization.py
new file mode 100644
index 00000000..89493746
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_initialization.py
@@ -0,0 +1,343 @@
+"""
+Tests for inference/initialization.py: site values <-> guide parameters.
+
+The registry-wide test makes sure every component guide follows the
+``{site}_loc(s)``/``{site}_scale(s)`` convention the pre-MAP hand-off relies
+on, or is on an explicit list of exceptions.
+"""
+
+import jax.numpy as jnp
+import numpy as np
+import numpyro
+import numpyro.distributions as dist
+import pytest
+
+from tfscreen.tfmodel.inference.initialization import (
+    component_guide_init,
+    component_guide_map,
+    site_prior_sds,
+    site_unconstrained_prior_sds,
+    site_values,
+    trace_model_sites,
+)
+from tfscreen.tfmodel.inference.run_inference import RunInference
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+from .test_batch_safety import (
+    _BINDING_CSV,
+    _GROWTH_CSV,
+    _SAFE_VARIANTS,
+    _variant_kwargs,
+    _owned_by,
+)
+
+
+def _model(data=None, priors=None):
+    a = numpyro.sample("a", dist.Normal(1.0, 2.0))
+    numpyro.sample("b", dist.HalfNormal(3.0))
+    with numpyro.plate("p", 4):
+        numpyro.sample("c", dist.Normal(a, 0.5))
+    numpyro.sample("d", dist.Cauchy(0.0, 1.0))
+    numpyro.sample("obs", dist.Normal(a, 1.0), obs=0.3)
+
+
+def _guide(data=None, priors=None):
+    numpyro.sample("a", dist.Normal(numpyro.param("a_loc", 0.0),
+                                    numpyro.param("a_scale", 1.0)))
+    numpyro.sample("b", dist.LogNormal(numpyro.param("b_loc", 0.0),
+                                       numpyro.param("b_scale", 0.5)))
+    with numpyro.plate("p", 4):
+        numpyro.sample("c", dist.Normal(numpyro.param("c_locs", jnp.zeros(4)),
+                                        numpyro.param("c_scales",
+                                                      jnp.ones(4))))
+    # does not follow the convention
+    numpyro.sample("d", dist.Normal(numpyro.param("dee", 0.0), 1.0))
+
+
+def test_trace_model_sites_skips_observed():
+    sites = trace_model_sites(_model, None, None)
+    assert set(sites) == {"a", "b", "c", "d"}
+
+
+def test_component_guide_map():
+    mapping, unmatched, defaults = component_guide_map(_guide, None, None)
+    assert mapping["a"]["kind"] == "normal"
+    assert mapping["a"]["loc"] == "a_loc" and mapping["a"]["scale"] == "a_scale"
+    assert mapping["b"]["kind"] == "lognormal"
+    assert mapping["c"]["loc"] == "c_locs" and mapping["c"]["shape"] == (4,)
+    assert unmatched == ["d"]
+    assert set(defaults) == {"a_loc", "a_scale", "b_loc", "b_scale",
+                             "c_locs", "c_scales", "dee"}
+
+
+def test_site_values_priorities_and_shapes():
+    sites = trace_model_sites(_model, None, None)
+    mapping, _, _ = component_guide_map(_guide, None, None)
+    values = site_values({"b_loc": np.log(2.0),      # guide loc -> exp
+                          "a": 5.0, "a_auto_loc": 6.0,  # MAP key wins
+                          "c": 1.5,                   # broadcast
+                          "d": np.ones(3)},           # wrong shape: skipped
+                         sites, mapping)
+    assert float(values["b"]) == pytest.approx(2.0)
+    assert float(values["a"]) == pytest.approx(6.0)
+    np.testing.assert_allclose(values["c"], 1.5)
+    assert "d" not in values
+
+
+def test_component_guide_init():
+    mapping, _, defaults = component_guide_map(_guide, None, None)
+    params, skipped = component_guide_init(
+        {"a": jnp.array(2.0), "b": jnp.array(0.0), "c": jnp.full(4, 3.0)},
+        mapping, defaults, init_scale=0.2)
+    assert float(params["a_loc"]) == pytest.approx(2.0)
+    np.testing.assert_allclose(params["c_locs"], 3.0)
+    assert skipped == ["b"]          # log of 0 is not a location
+    assert "b_loc" not in params
+    # scales capped, smaller defaults kept
+    assert float(params["a_scale"]) == pytest.approx(0.2)
+    np.testing.assert_allclose(params["c_scales"], 0.2)
+    assert float(params["b_scale"]) == pytest.approx(0.2)
+    params, _ = component_guide_init({}, mapping, defaults, init_scale=None)
+    assert params == {}
+
+
+def _bounded_guide(data=None, priors=None):
+    numpyro.sample("e", dist.Normal(
+        numpyro.param("e_loc", 0.0),
+        numpyro.param("e_scale", 1.0,
+                      constraint=dist.constraints.greater_than(1e-4))))
+
+
+def test_component_guide_init_stays_inside_scale_bound():
+    """
+    A capped scale never starts on its constraint's lower bound.
+
+    With the cap equal to the bound (both 1e-4 for the hyperparameter
+    scales), the param started at the bound, -inf in unconstrained space,
+    and never moved (two-stage grid, 2026-09-29).
+    """
+    from numpyro.distributions.transforms import biject_to
+
+    mapping, _, defaults = component_guide_map(_bounded_guide, None, None)
+    assert mapping["e"]["scale_lower_bound"] == pytest.approx(1e-4)
+    params, _ = component_guide_init({}, mapping, defaults, init_scale=1e-4)
+    assert float(params["e_scale"]) == pytest.approx(2e-4)
+    u = biject_to(dist.constraints.greater_than(1e-4)).inv(params["e_scale"])
+    assert np.isfinite(float(u))
+    # unbounded scales are unchanged
+    mapping, _, defaults = component_guide_map(_guide, None, None)
+    assert mapping["a"]["scale_lower_bound"] is None
+    params, _ = component_guide_init({}, mapping, defaults, init_scale=0.2)
+    assert float(params["a_scale"]) == pytest.approx(0.2)
+
+
+def test_site_prior_sds():
+    sds = site_prior_sds(trace_model_sites(_model, None, None,
+                                           substitutions={"a": 0.0}))
+    assert float(sds["a"]) == pytest.approx(2.0)
+    np.testing.assert_allclose(sds["c"], 0.5)
+    assert float(sds["b"]) == pytest.approx(3.0 * np.sqrt(1 - 2 / np.pi))
+    assert "d" not in sds  # Cauchy: no finite variance
+
+
+def test_site_prior_sds_skip_undefined_variance():
+    """InverseGamma(2, .) with Python floats raises ZeroDivisionError from its
+    variance (the horseshoe slab prior, slab_df = 4); that site is skipped,
+    not the whole model."""
+
+    def model(data=None, priors=None):
+        numpyro.sample("a", dist.Normal(1.0, 2.0))
+        numpyro.sample("c2", dist.InverseGamma(2.0, 2.0))
+
+    sites = trace_model_sites(model, None, None)
+    sds = site_prior_sds(sites)
+    assert "c2" not in sds
+    assert float(sds["a"]) == pytest.approx(2.0)
+    # the unconstrained spread falls back to Monte Carlo (log units)
+    assert np.isfinite(float(site_unconstrained_prior_sds(sites)["c2"]))
+
+
+def test_site_unconstrained_prior_sds():
+    """Prior spread in the units guide locations live in: exact SDs for real
+    sites with a finite variance, a robust Monte Carlo estimate (IQR / 1.349)
+    otherwise -- log units for a positive site, finite for a Cauchy."""
+    sites = trace_model_sites(_model, None, None, substitutions={"a": 0.0})
+    sds = site_unconstrained_prior_sds(sites)
+    assert float(sds["a"]) == pytest.approx(2.0)
+    np.testing.assert_allclose(sds["c"], 0.5)
+    assert sds["c"].shape == (4,)
+    # log|3Z|, Z ~ N(0, 1): IQR / 1.349 = 0.95 (the scale does not matter)
+    assert 0.6 < float(sds["b"]) < 1.4
+    # Cauchy(0, 1): IQR / 1.349 = 1.48
+    assert 0.8 < float(sds["d"]) < 2.5
+    # deterministic for a given seed
+    again = site_unconstrained_prior_sds(sites)
+    assert float(again["b"]) == float(sds["b"])
+
+
+# Guide sites that do not follow the {site}_loc(s) convention, and why.
+_KNOWN_UNMAPPED = {
+    # horseshoe_geno names its guide params activity_lambda_offset_* and
+    # activity_activity_offset_*; renaming them would break its checkpoints.
+    ("activity", "horseshoe_geno"): {"activity_global_scale",
+                                     "activity_local_scale",
+                                     "activity_offset"},
+    # noise latents guided by their priors (no location parameter)
+    ("theta_growth_noise", "logit_normal"): {"theta_growth_noise_epsilon"},
+    ("theta_binding_noise", "beta"): {"theta_binding_noise_dist"},
+}
+
+
+@pytest.mark.parametrize("axis,variant", _SAFE_VARIANTS)
+def test_component_guides_follow_loc_scale_convention(axis, variant):
+    orchestrator = ModelOrchestrator(batch_size=None,
+                                     **_variant_kwargs(axis, variant))
+    ri = RunInference(orchestrator, seed=0)
+    mapping, unmatched, _ = component_guide_map(orchestrator.jax_model_guide,
+                                                orchestrator.priors,
+                                                ri._trace_batch())
+    unmatched = {s for s in unmatched if _owned_by(axis, s)}
+    assert unmatched == _KNOWN_UNMAPPED.get((axis, variant), set())
+    no_scale = [s for s, e in mapping.items()
+                if e["scale"] is None and _owned_by(axis, s)]
+    assert no_scale == []
+
+
+def test_default_model_guide_start_round_trip():
+    """Guesses -> site values -> guide params reproduce the site values."""
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     batch_size=None)
+    ri = RunInference(orchestrator, seed=0)
+    values = ri.site_values(orchestrator.init_params)
+    assert "dk_geno_offset" in values  # keyed by site in the guesses
+    start = ri.component_guide_start(values,
+                                     guesses=orchestrator.init_params,
+                                     init_scale=0.1)
+    np.testing.assert_allclose(start["dk_geno_offset_locs"],
+                               values["dk_geno_offset"], rtol=1e-6)
+    assert float(np.max(start["dk_geno_offset_scales"])) == pytest.approx(0.1)
+    # param-keyed guesses (not sites) are kept
+    assert "condition_growth_k_locs" in start
+
+
+def test_default_guide_start_stays_near_its_point():
+    """
+    SVI starts where the pre-MAP ended, not far above it.
+
+    The starting scale is one number in every site's own units, so a large one
+    is enormous on growth rates (per minute) and ln_cfu levels: at 0.1 the
+    relative-fit grid's SVI started ~1000x above its pre-MAP loss and
+    re-descended into other optima (2026-09-27).  Checked on the count
+    likelihood, the most sensitive to it.
+    """
+    from numpyro.handlers import seed, substitute, trace
+    from numpyro.infer.util import log_density
+
+    from tfscreen.tfmodel.scripts.fit_model_cli import DEFAULT_GUIDE_INIT_SCALE
+
+    orchestrator = ModelOrchestrator(growth_df=_GROWTH_CSV,
+                                     binding_df=_BINDING_CSV,
+                                     batch_size=None,
+                                     growth_likelihood="counts",
+                                     sample_offset="level")
+    ri = RunInference(orchestrator, seed=0)
+    values = ri.site_values(orchestrator.init_params)
+    data = orchestrator.get_batch(orchestrator.data,
+                                  jnp.asarray(orchestrator.get_random_idx()))
+
+    def neg_log_joint(params, key):
+        draw = trace(seed(substitute(orchestrator.jax_model_guide, params),
+                          key)).get_trace(data=data,
+                                          priors=orchestrator.priors)
+        point = {k: s["value"] for k, s in draw.items() if s["type"] == "sample"}
+        lp, _ = log_density(orchestrator.jax_model, (),
+                            dict(data=data, priors=orchestrator.priors), point)
+        return -float(lp)
+
+    def start(scale):
+        return ri.component_guide_start(values,
+                                        guesses=orchestrator.init_params,
+                                        init_scale=scale)
+
+    at_point = neg_log_joint(start(1e-12), 0)
+
+    def excess(scale):
+        params = start(scale)
+        return np.median([neg_log_joint(params, k) for k in range(8)]) - at_point
+
+    assert excess(DEFAULT_GUIDE_INIT_SCALE) < 0.01 * abs(at_point)
+
+    # every guide param starts strictly inside its constraint, so none is
+    # stuck at -inf in unconstrained space
+    from numpyro.distributions.transforms import biject_to
+    guide_trace = trace(seed(orchestrator.jax_model_guide, 0)).get_trace(
+        data=data, priors=orchestrator.priors)
+    params = start(DEFAULT_GUIDE_INIT_SCALE)
+    for name, value in params.items():
+        constraint = guide_trace[name]["kwargs"].get("constraint")
+        if constraint is None:
+            continue
+        u = biject_to(constraint).inv(jnp.asarray(value))
+        assert np.all(np.isfinite(np.asarray(u))), name
+    # the test can fail: the old default started far from the point
+    assert excess(0.1) > abs(at_point)
+
+
+def test_component_guide_map_skips_wrong_shape_and_unbroadcastable_params():
+    """A same-named param of the wrong shape is passed over for the one that
+    matches; a param whose shape fits the site but not the distribution's
+    own (unexpanded) location cannot match it."""
+
+    def guide(data=None, priors=None):
+        # e: a scalar e_loc of zeros would broadcast onto the loc; it is
+        # skipped on shape and e_locs is chosen
+        numpyro.param("e_loc", 0.0)
+        with numpyro.plate("p", 4):
+            numpyro.sample("e", dist.Normal(
+                numpyro.param("e_locs", jnp.zeros(4)),
+                numpyro.param("e_scales", jnp.ones(4))))
+        # f: an expanded scalar Normal; f_locs has the site's shape (4,) but
+        # cannot broadcast to the base distribution's scalar loc
+        numpyro.param("f_locs", jnp.zeros(4))
+        numpyro.sample("f", dist.Normal(numpyro.param("f_loc", 0.0),
+                                        1.0).expand([4]))
+
+    mapping, unmatched, _ = component_guide_map(guide, None, None)
+    assert mapping["e"]["loc"] == "e_locs"
+    assert mapping["e"]["scale"] == "e_scales"
+    assert "f" not in mapping
+    assert "f" in unmatched
+
+
+class _NoSupport(dist.Distribution):
+    """A distribution that cannot report its support."""
+
+    def __init__(self):
+        super().__init__(batch_shape=(), event_shape=())
+
+    @property
+    def support(self):
+        raise NotImplementedError
+
+    def sample(self, key, sample_shape=()):
+        return jnp.zeros(sample_shape + self.batch_shape)
+
+
+def test_site_unconstrained_prior_sds_skips_unusable_sites():
+    """Sites without a support, or whose prior has no spread in unconstrained
+    units, are left out; the others are kept."""
+    from numpyro.distributions.transforms import ExpTransform
+
+    def model(data=None, priors=None):
+        numpyro.sample("a", dist.Normal(1.0, 2.0))
+        numpyro.sample("nosupport", _NoSupport())
+        # exp of a point mass: positive support, every draw identical, so
+        # the unconstrained IQR is 0
+        numpyro.sample("point", dist.TransformedDistribution(
+            dist.Delta(0.0), ExpTransform()))
+
+    sites = trace_model_sites(model, None, None)
+    sds = site_unconstrained_prior_sds(sites)
+    assert set(sds) == {"a"}
+    assert float(sds["a"]) == pytest.approx(2.0)
diff --git a/tests/tfscreen/tfmodel/inference/test_laplace_prior_floor.py b/tests/tfscreen/tfmodel/inference/test_laplace_prior_floor.py
new file mode 100644
index 00000000..2ffec93f
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_laplace_prior_floor.py
@@ -0,0 +1,290 @@
+"""
+The prior SDs behind the Laplace eigenvalue floor
+(``RunInference._laplace_prior_precision``) and the structure traces that
+read a model's sites.
+
+On the PddG pilot fits (2026-10-10) the prior SDs were taken from a trace
+whose latent sites were drawn from the prior. The draws (ln K up to +/-60)
+predicted a non-finite ln_cfu, the count likelihood refused to be built
+("CountNegativeBinomial ... invalid log_mu"), and the floor fell back to a
+flat 1e-3 without saying why it mattered: 16-38 negative eigenvalues became
+directions with SD ~30. The same refusal stopped tfs-summarize-fit's
+trajectory plots.
+"""
+import dataclasses
+import os
+import warnings
+
+import jax.numpy as jnp
+import numpy as np
+import numpyro
+import numpyro.distributions as dist
+import pandas as pd
+import pytest
+from flax import struct
+from numpyro import handlers
+
+from tfscreen.tfmodel.generative.observe.growth_counts import (
+    count_distribution,
+)
+from tfscreen.tfmodel.inference import run_inference as ri_module
+from tfscreen.tfmodel.inference.initialization import (
+    structure_trace,
+    trace_model_sites,
+)
+from tfscreen.tfmodel.inference.run_inference import (
+    LAPLACE_MIN_EIGENVALUE,
+    RunInference,
+)
+
+_PRIOR_SD = 1000.0
+
+
+@pytest.fixture(autouse=True)
+def _run_in_tmp_path(tmp_path, monkeypatch):
+    monkeypatch.chdir(tmp_path)
+
+
+# ---------------------------------------------------------------------------
+# A model whose prior draws break its count observation
+# ---------------------------------------------------------------------------
+
+@struct.dataclass
+class _Data:
+    batch_idx: jnp.ndarray
+    counts: jnp.ndarray
+    num_genotype: int = struct.field(pytree_node=False, default=4)
+
+
+def _wide_count_model(data, priors):
+    """
+    A shared ln K with a very wide prior and one effect per genotype. The
+    predicted log mean read count is exp(|ln K|) + effect, so any prior draw
+    with |ln K| above ~89 overflows float32 and the count distribution refuses
+    the non-finite log_mu, as the PddG fits' ln_cfu did. At the MAP (ln K
+    near 0) everything is finite.
+    """
+    ln_K = numpyro.sample("ln_K", dist.Normal(0.0, _PRIOR_SD))
+    with numpyro.plate("shared_genotype_plate", data.num_genotype, dim=-1):
+        effect = numpyro.sample("geno_p", dist.Normal(0.0, 2.0))
+        log_mu = jnp.exp(jnp.abs(ln_K)) + effect
+        numpyro.sample("growth_obs", count_distribution(log_mu, 5.0, 0.01),
+                       obs=data.counts)
+
+
+class _WideCountModel:
+    def __init__(self, num_genotype=4):
+        self.data = _Data(num_genotype=num_genotype,
+                          batch_idx=jnp.arange(num_genotype),
+                          counts=jnp.full(num_genotype, 3.0))
+        self.priors = {}
+        self.jax_model = _wide_count_model
+        self.jax_model_guide = lambda data, priors: None
+
+    def get_batch(self, data, indices):
+        return _Data(num_genotype=len(indices), batch_idx=jnp.asarray(indices),
+                     counts=data.counts[jnp.asarray(indices)])
+
+    def get_random_idx(self, key=None, num_batches=1):
+        if num_batches == 1:
+            return np.array([0])
+        return np.zeros((num_batches, 1), dtype=int)
+
+
+def _map_params(num_genotype=4):
+    """A MAP point (unconstrained, AutoDelta names) where log_mu is finite."""
+    return {"ln_K_auto_loc": jnp.array(0.3),
+            "geno_p_auto_loc": jnp.linspace(-0.5, 0.5, num_genotype)}
+
+
+def _prior_draw_breaks_observation(model):
+    """The precondition: a validated trace with prior draws fails."""
+    fn = handlers.seed(model.jax_model, 0)
+    try:
+        handlers.trace(fn).get_trace(data=model.data, priors=model.priors)
+    except ValueError as err:
+        return "invalid log_mu" in str(err)
+    return False
+
+
+def test_prior_draw_breaks_the_count_observation():
+    assert _prior_draw_breaks_observation(_WideCountModel())
+
+
+def test_structure_trace_ignores_invalid_observation():
+    model = _WideCountModel()
+    tr = structure_trace(model.jax_model, data=model.data,
+                         priors=model.priors)
+    assert {"ln_K", "geno_p", "growth_obs"} <= set(tr)
+    sites = trace_model_sites(model.jax_model, model.priors, model.data)
+    assert set(sites) == {"ln_K", "geno_p"}
+
+
+def test_trace_model_sites_substitutes_unconstrained_values():
+    model = _WideCountModel()
+    unc = {"ln_K": jnp.array(0.3), "geno_p": jnp.arange(4.0)}
+    sites = trace_model_sites(model.jax_model, model.priors, model.data,
+                              unconstrained=unc)
+    np.testing.assert_allclose(sites["ln_K"]["value"], 0.3)
+    np.testing.assert_allclose(sites["geno_p"]["value"], np.arange(4.0))
+
+
+def test_laplace_prior_precision_uses_prior_sds_not_flat_floor():
+    model = _WideCountModel()
+    ri = RunInference(model, seed=0)
+    mp = _map_params()
+    unc = {k[:-len("_auto_loc")]: v for k, v in mp.items()}
+    prec = ri._laplace_prior_precision(
+        unc, {"priors": model.priors, "data": model.data}, 5)
+    # ravel order: geno_p (4), ln_K (1)
+    np.testing.assert_allclose(prec[:4], 1 / 2.0 ** 2, rtol=1e-6)
+    np.testing.assert_allclose(prec[4], 1 / _PRIOR_SD ** 2, rtol=1e-6)
+
+
+def test_get_laplace_posteriors_floors_at_prior_on_wide_count_model(tmp_path,
+                                                                    capsys):
+    model = _WideCountModel()
+    ri = RunInference(model, seed=0)
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", RuntimeWarning)
+        ri.get_laplace_posteriors(_map_params(), str(tmp_path / "lap"),
+                                  num_posterior_samples=20,
+                                  sampling_batch_size=10)
+    out = capsys.readouterr().out
+    assert "Could not compute prior SDs" not in out
+    assert os.path.exists(tmp_path / "lap_posterior.h5")
+
+
+def test_prior_sd_failure_is_an_error_unless_allowed(tmp_path, monkeypatch,
+                                                     capsys):
+    model = _WideCountModel()
+
+    def broken(*args, **kwargs):
+        raise ValueError("broken prior")
+
+    monkeypatch.setattr(ri_module, "site_unconstrained_prior_sds", broken)
+    ri = RunInference(model, seed=0)
+    with pytest.raises(RuntimeError, match="broken prior"):
+        ri.get_laplace_posteriors(_map_params(), str(tmp_path / "lap"),
+                                  num_posterior_samples=10,
+                                  sampling_batch_size=10)
+    assert not os.path.exists(tmp_path / "lap_posterior.h5")
+
+    ri = RunInference(model, seed=0)
+    with pytest.warns(RuntimeWarning, match="broken prior"):
+        ri.get_laplace_posteriors(_map_params(), str(tmp_path / "lap"),
+                                  num_posterior_samples=10,
+                                  sampling_batch_size=10,
+                                  allow_flat_floor=True)
+    assert "WARNING" in capsys.readouterr().out
+    assert os.path.exists(tmp_path / "lap_posterior.h5")
+
+
+def test_elements_without_prior_sd_are_named(monkeypatch):
+    model = _WideCountModel()
+    real = ri_module.site_unconstrained_prior_sds
+
+    def drop_ln_K(sites, *args, **kwargs):
+        out = real(sites, *args, **kwargs)
+        out.pop("ln_K")
+        return out
+
+    monkeypatch.setattr(ri_module, "site_unconstrained_prior_sds", drop_ln_K)
+    ri = RunInference(model, seed=0)
+    unc = {k[:-len("_auto_loc")]: v for k, v in _map_params().items()}
+    with pytest.warns(RuntimeWarning, match=r"1 of 5 MAP elements.*ln_K \(1\)"):
+        prec = ri._laplace_prior_precision(
+            unc, {"priors": model.priors, "data": model.data}, 5)
+    assert prec[4] == LAPLACE_MIN_EIGENVALUE
+
+
+# ---------------------------------------------------------------------------
+# A count-likelihood thermodynamic model with extreme prior draws
+# ---------------------------------------------------------------------------
+
+_WT_SD = 10.0
+
+_SMOKE = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                      "..", "..", "..", "smoke-tests", "test_data")
+
+
+@pytest.fixture(scope="module")
+def wide_thermo_counts(tmp_path_factory):
+    """
+    The smoke screen fit with counts and thermo.O2_C4_K3_U0_a.PddG, with the
+    wt ln K priors moved to 100 +/- 10 so that prior draws of ln K are
+    extreme (the pilot's draws, through the learned sigma_s, reached +/-60).
+    Linear-space theta overflowed there (K^2 > float32 max above ln K 44)
+    and gave NaN ln_cfu.
+    """
+    from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+
+    growth = os.path.join(_SMOKE, "growth-smoke.csv")
+    df = pd.read_csv(growth)
+    muts = sorted({m for g in df["genotype"].unique() if g != "wt"
+                   for m in g.split("/")})
+    ddG = tmp_path_factory.mktemp("ddG") / "ddG.csv"
+    pd.DataFrame({"mut": muts, "H": 0.5, "HD": -0.5, "L": 0.2,
+                  "LE2": 0.1}).to_csv(ddG, index=False)
+    o = ModelOrchestrator(growth_df=growth,
+                          binding_df=os.path.join(_SMOKE, "binding-smoke.csv"),
+                          batch_size=6, growth_likelihood="counts",
+                          sample_offset="level",
+                          theta="thermo.O2_C4_K3_U0_a.PddG",
+                          thermo_data=str(ddG))
+    theta = dataclasses.replace(o.priors.theta,
+                                theta_ln_K_op_wt_loc=100.0,
+                                theta_ln_K_op_wt_scale=_WT_SD,
+                                theta_ln_K_HL_wt_loc=100.0,
+                                theta_ln_K_HL_wt_scale=_WT_SD,
+                                theta_ln_K_E_wt_loc=100.0,
+                                theta_ln_K_E_wt_scale=_WT_SD)
+    o._priors = dataclasses.replace(o.priors, theta=theta)
+    return o
+
+
+def test_thermo_counts_prior_draw_was_extreme(wide_thermo_counts):
+    o = wide_thermo_counts
+    tr = structure_trace(o.jax_model, data=o.data, priors=o.priors)
+    for name in ("theta_ln_K_op_wt", "theta_ln_K_HL_wt", "theta_ln_K_E_wt"):
+        assert float(np.ravel(tr[name]["value"])[0]) > 60
+
+
+def test_thermo_counts_laplace_prior_precision(wide_thermo_counts):
+    o = wide_thermo_counts
+    ri = RunInference(o, seed=0)
+    svi = ri.setup_svi(guide_type="delta")
+    state = svi.init(ri.get_key(), data=o.data, priors=o.priors)
+    mp = svi.optim.get_params(state.optim_state)
+    unc = {k[:-len("_auto_loc")]: v for k, v in mp.items()
+           if k.endswith("_auto_loc")}
+    D = sum(int(np.size(v)) for v in unc.values())
+    full = ri._unscaled_batch(o.data, jnp.arange(o.data.num_genotype))
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", RuntimeWarning)
+        prec = ri._laplace_prior_precision(unc, {"priors": o.priors,
+                                                 "data": full}, D)
+    assert prec.shape == (D,)
+    assert np.all(np.isfinite(prec)) and np.all(prec > 0)
+    # the moved wt priors give their sites prior precision 1/10^2
+    for name in ("theta_ln_K_op_wt", "theta_ln_K_HL_wt", "theta_ln_K_E_wt"):
+        assert name in unc
+    assert np.sum(np.isclose(prec, 1 / _WT_SD ** 2, rtol=1e-4)) >= 3
+
+
+def test_prediction_traces_survive_extreme_prior_draws(wide_thermo_counts):
+    """
+    predict() reads the model's structure from traces with prior draws
+    (tfs-summarize-fit's trajectory plots go through it); they must not need
+    the count observation to accept those draws.
+    """
+    from tfscreen.tfmodel.analysis.prediction import predict
+
+    o = wide_thermo_counts
+    ri = RunInference(o, seed=0)
+    svi = ri.setup_svi(guide_type="delta")
+    state = svi.init(ri.get_key(), data=o.data, priors=o.priors)
+    params = {k: np.asarray(v) for k, v in svi.get_params(state).items()}
+    out = predict(o, params, predict_sites=["growth_pred"], num_samples=None)
+    df = out if isinstance(out, pd.DataFrame) else next(iter(out.values()))
+    assert len(df) > 0
diff --git a/tests/tfscreen/tfmodel/inference/test_model_params.py b/tests/tfscreen/tfmodel/inference/test_model_params.py
new file mode 100644
index 00000000..6818232f
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_model_params.py
@@ -0,0 +1,355 @@
+"""
+The model's own learned ``pyro.param`` sites (thermo PnnC NN weights) must
+reach every post-fit run of the model.
+
+Regression: the posterior paths kept only sample-site values, so the forward
+passes ran the model with every model-side param at its init value (NN = 0).
+A ``--laplace point`` posterior's theta then differed from the MAP's own
+theta.  The PddG/PnnC ΔΔG scale σ_s was a model-side param too until
+2026-10-11; it is now the sample site ``theta_ddG_sigma``, so it is part of
+the MAP sites and the Laplace, with the per-mutation constants
+``theta_dlnK`` (checked here as well).
+"""
+
+import h5py
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+import pytest
+from numpyro import handlers
+from numpyro.distributions import constraints
+from numpyro.distributions.transforms import biject_to
+
+from tfscreen.tfmodel.inference.model_params import (
+    MODEL_PARAMS_GROUP,
+    hold_model_params,
+    read_model_params,
+    sample_keys,
+)
+
+STRUCTS = ("H", "HD", "L", "LE2")
+GENOTYPES = ["wt", "M42I", "K84L", "M42I/K84L"]
+SIGMA = "theta_ddG_sigma"            # σ_s, a sample site
+NN_B2 = "theta_nn_H_b2"              # a PnnC model-side param
+THETA_SITES = ("theta_ddG", "theta_ln_K_op", "theta_ln_K_HL", "theta_ln_K_E")
+
+
+# ---------------------------------------------------------------------------
+# Small PnnC and PddG problems
+# ---------------------------------------------------------------------------
+
+def _growth_csv(path):
+    rng = np.random.default_rng(0)
+    rows = []
+    for geno in GENOTYPES:
+        parts = geno.split("/") if geno != "wt" else []
+        cols = {}
+        for i in range(3):
+            p = parts[i] if i < len(parts) else None
+            cols[f"wt_aa_{i + 1}"] = p[0] if p else ""
+            cols[f"resid_{i + 1}"] = int(p[1:-1]) if p else ""
+            cols[f"mut_aa_{i + 1}"] = p[-1] if p else ""
+        for rep in [1.0, 2.0]:
+            for conc in [0.0, 100.0]:
+                rows.append({
+                    "sample": f"s-{geno}-{rep}-{conc}", "genotype": geno,
+                    **cols, "num_muts": len(parts), "counts": 1000,
+                    "replicate": rep, "condition_pre": "pre-cond",
+                    "t_pre": 24.0, "condition_sel": "sel+cond", "t_sel": 48.0,
+                    "titrant_name": "iptg", "titrant_conc": conc,
+                    "barcode_i7": "AAAA", "barcode_i5": "TTTT", "seq_run": 1,
+                    "od600": 0.3, "library": "lib", "obs_file": "dummy.csv",
+                    "sample_cfu": 1e7, "sample_cfu_std": 1e5,
+                    "sample_cfu_detectable": True, "adjusted_counts": 1001,
+                    "frequency": 0.1, "cfu": 1e6, "cfu_var": 1e8,
+                    "ln_cfu_var": 0.01,
+                    "ln_cfu": 14.0 + 0.1 * rng.standard_normal(),
+                })
+    pd.DataFrame(rows).to_csv(path, index=False)
+    return str(path)
+
+
+def _binding_csv(path):
+    rows = [{"genotype": g, "titrant_name": "iptg", "titrant_conc": c,
+             "theta_obs": 0.5, "theta_std": 0.05, "tx": 0.5, "pred": 0.5}
+            for g in GENOTYPES for c in [1e-6, 1e-4]]
+    pd.DataFrame(rows).to_csv(path, index=False)
+    return str(path)
+
+
+def _ensemble_h5(path, resnums=(42, 84)):
+    with h5py.File(path, "w") as hf:
+        hf.create_dataset("structure_names",
+                          data=np.array(STRUCTS, dtype=h5py.string_dtype()))
+        for i, name in enumerate(STRUCTS):
+            rng = np.random.RandomState(i)
+            grp = hf.create_group(name)
+            dist_mat = np.abs(rng.randn(2, 2)).astype(np.float32)
+            np.fill_diagonal(dist_mat, 0.0)
+            grp.create_dataset("logP", data=rng.randn(2, 20).astype(np.float32))
+            grp.create_dataset("residue_nums",
+                               data=np.asarray(resnums, dtype=np.int32))
+            grp.create_dataset("dist_matrix", data=dist_mat)
+            grp.create_dataset("n_chains_bearing_mut", data=np.int32(2))
+    return str(path)
+
+
+def _ddG_csv(path):
+    pd.DataFrame([{"mut": "K84L", "H": 0.5, "HD": -0.3, "L": 0.2, "LE2": 1.0},
+                  {"mut": "M42I", "H": -0.4, "HD": 0.8, "L": 0.1, "LE2": -0.6}]
+                 ).to_csv(path, index=False)
+    return str(path)
+
+
+def _orchestrator(tmp_path, kind):
+    from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+    thermo_data = (_ensemble_h5(tmp_path / "ens.h5") if kind == "PnnC"
+                   else _ddG_csv(tmp_path / "ddG.csv"))
+    return ModelOrchestrator(_growth_csv(tmp_path / "growth.csv"),
+                             _binding_csv(tmp_path / "binding.csv"),
+                             theta=f"thermo.O2_C4_K3_U0_a.{kind}",
+                             thermo_data=thermo_data)
+
+
+@pytest.fixture(scope="module", params=["PnnC", "PddG"])
+def fit(request, tmp_path_factory):
+    """
+    A short MAP of a small PnnC or PddG model, with σ_s then moved well
+    away from its fit (x 2.5) and, for PnnC, the model-side W2 and b2 set to
+    nonzero values, so a forward pass that drops either is visibly wrong.
+    """
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    # Two models in one process can collide in jax's trace cache (their
+    # data carry arrays as static pytree fields); start each one clean.
+    jax.clear_caches()
+    tmp = tmp_path_factory.mktemp(request.param)
+    orch = _orchestrator(tmp, request.param)
+    ri = RunInference(orch, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="delta")
+    state, _, _ = ri.run_optimization(svi, max_num_epochs=3,
+                                      out_prefix=str(tmp / "map"),
+                                      checkpoint_interval=100)
+
+    unconstrained = dict(svi.optim.get_params(state.optim_state))
+    rng = np.random.default_rng(1)
+    sigma_key = f"{SIGMA}_auto_loc"           # unconstrained: log σ_s
+    unconstrained[sigma_key] = unconstrained[sigma_key] + np.log(2.5)
+    for k in list(unconstrained):
+        if k.endswith("_W2") or k.endswith("_b2"):
+            unconstrained[k] = jnp.asarray(
+                0.5 * rng.standard_normal(np.shape(unconstrained[k])),
+                dtype=jnp.float32)
+
+    # The MAP's own theta: the model run at every fitted value, sample
+    # sites and model-side params alike.
+    model_side = {k: v for k, v in unconstrained.items()
+                  if not k.endswith("_auto_loc")}
+    assert bool(model_side) == (request.param == "PnnC")
+    sites = RunInference(orch, seed=0)._map_params_to_constrained(unconstrained)
+    full = orch.get_batch(orch.data, jnp.arange(orch.data.num_genotype))
+    tr = handlers.trace(handlers.substitute(
+        handlers.seed(orch.jax_model, 0), data={**sites, **model_side})
+    ).get_trace(priors=orch.priors, data=full)
+    expected = {k: np.asarray(tr[k]["value"])
+                for k in THETA_SITES + ("growth_pred",)}
+    return dict(kind=request.param, orch=orch, tmp=tmp,
+                unconstrained=unconstrained, model_side=model_side,
+                expected=expected, sites=sites)
+
+
+# ---------------------------------------------------------------------------
+# Unit behavior
+# ---------------------------------------------------------------------------
+
+def test_hold_model_params_beats_outer_substitute():
+    import numpyro
+
+    def model():
+        return numpyro.param("w", jnp.zeros(2))
+
+    held = hold_model_params(model, {"w": jnp.ones(2)})
+    out = handlers.substitute(handlers.seed(held, 0), data={"w": -jnp.ones(2)})()
+    np.testing.assert_array_equal(out, [1.0, 1.0])
+    tr = handlers.trace(handlers.seed(held, 0)).get_trace()
+    assert "w" not in tr                      # hidden from outer handlers
+    assert hold_model_params(model, {}) is model
+
+
+def test_read_model_params_sources():
+    group = {MODEL_PARAMS_GROUP: {"a": np.ones(2)}, "x": np.zeros((3, 2))}
+    assert set(read_model_params(group)) == {"a"}
+    assert sample_keys(group) == ["x"]
+    raw = {"a": np.ones(2), "x_auto_loc": np.zeros(2)}
+    assert set(read_model_params(raw, names=["a", "b"])) == {"a"}
+
+
+def test_model_without_params_is_unchanged(tmp_path):
+    """A model with no model-side params writes no group and runs as before."""
+    from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    orch = ModelOrchestrator(_growth_csv(tmp_path / "growth.csv"),
+                             _binding_csv(tmp_path / "binding.csv"),
+                             theta="hill_geno")
+    ri = RunInference(orch, seed=0)
+    svi = ri.setup_svi(guide_type="delta")
+    state = svi.init(jax.random.PRNGKey(0), priors=orch.priors,
+                     data=orch.get_batch(orch.data, jnp.arange(4)))
+    params = svi.optim.get_params(state.optim_state)
+    assert ri.model_params(params) == {}
+    ri.get_map_posteriors(params, out_prefix=str(tmp_path / "p"))
+    assert ri._fixed_model() is orch.jax_model
+    with h5py.File(tmp_path / "p_posterior.h5", "r") as hf:
+        assert MODEL_PARAMS_GROUP not in hf
+
+
+# ---------------------------------------------------------------------------
+# Posterior and prediction paths reproduce the MAP
+# ---------------------------------------------------------------------------
+
+def test_point_posterior_matches_map_theta(fit):
+    """``--laplace point``: the posterior's theta is the MAP's theta."""
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    ri = RunInference(fit["orch"], seed=0)
+    prefix = str(fit["tmp"] / "point")
+    ri.get_map_posteriors(fit["unconstrained"], out_prefix=prefix)
+    with h5py.File(f"{prefix}_posterior.h5", "r") as hf:
+        for site, want in fit["expected"].items():
+            np.testing.assert_allclose(hf[site][0], want, rtol=1e-5,
+                                       atol=1e-5, err_msg=site)
+        stored = read_model_params(hf)
+        np.testing.assert_allclose(hf[SIGMA][0], fit["sites"][SIGMA],
+                                   rtol=1e-5)
+    assert set(stored) == set(fit["model_side"])
+
+
+def test_predict_from_point_posterior_matches_map(fit):
+    """tfs-predict-growth (``predict``) reuses the stored model params."""
+    from tfscreen.tfmodel.analysis.prediction import predict
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    prefix = str(fit["tmp"] / "point_pred")
+    RunInference(fit["orch"], seed=0).get_map_posteriors(
+        fit["unconstrained"], out_prefix=prefix)
+    stored_growth = fit["expected"]["growth_pred"]      # the MAP's own
+    df = predict(fit["orch"], f"{prefix}_posterior.h5",
+                 predict_sites=["growth_pred"], q_to_get=[0.5],
+                 num_samples=None)
+    # every predicted growth value is one of the MAP's own values
+    pred = df["q0.5"].to_numpy()
+    assert pred.size > 0
+    nearest = np.min(np.abs(stored_growth.ravel()[None, :] - pred[:, None]),
+                     axis=1)
+    np.testing.assert_array_less(nearest, 1e-4 * np.maximum(1.0, np.abs(pred)))
+
+
+def test_laplace_holds_model_params(fit):
+    """The full Laplace: σ_s is a site, so it is in the Hessian and varies
+    across draws; the model-side params (PnnC NN) are outside it, held at
+    the MAP, so the prior mean is the same in every draw."""
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    ri = RunInference(fit["orch"], seed=0)
+    prefix = str(fit["tmp"] / "laplace")
+    ri.get_laplace_posteriors(fit["unconstrained"], out_prefix=prefix,
+                              num_posterior_samples=8, sampling_batch_size=4)
+    with h5py.File(f"{prefix}_posterior.h5", "r") as hf:
+        sigma = np.asarray(hf[SIGMA])                    # (n, S)
+        dlnK = np.asarray(hf["theta_dlnK"])               # (n, M, 3)
+        ddG = np.asarray(hf["theta_ddG"])                 # post-hoc (n, M, S)
+        prior_mean = np.asarray(hf["theta_ddG_prior_mean"])
+        assert set(read_model_params(hf)) == set(fit["model_side"])
+    assert sigma.shape == (8, len(STRUCTS))
+    assert (sigma > 0).all()
+    assert np.ptp(sigma, axis=0).min() > 0               # sampled, not held
+    # Δln K is in the Hessian too, and the post-hoc ΔΔG follows each draw.
+    assert dlnK.shape == (8, 2, 3)
+    assert np.ptp(dlnK, axis=0).min() > 0
+    assert ddG.shape == (8, 2, len(STRUCTS))
+    assert np.isfinite(ddG).all()
+    # around the MAP's σ_s (a Laplace in log σ_s; with two mutations its
+    # SD is near the prior's 1.2, so 8 draws pin the mean only loosely)
+    log_err = np.log(sigma).mean(axis=0) - np.log(fit["sites"][SIGMA])
+    np.testing.assert_array_less(np.abs(log_err), 1.5)
+    np.testing.assert_allclose(prior_mean,
+                               np.broadcast_to(prior_mean[0], prior_mean.shape),
+                               atol=1e-5)
+
+
+def test_svi_posterior_uses_fitted_model_params(tmp_path):
+    """Guide sampling: the forward pass sees the store's NN weights."""
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    orch = _orchestrator(tmp_path, "PnnC")
+    ri = RunInference(orch, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-6, guide_type="component")
+    start = {NN_B2: jnp.full((1,), 0.7)}
+    state, params, _ = ri.run_optimization(svi, init_params=start,
+                                           max_num_epochs=1,
+                                           out_prefix=str(tmp_path / "svi"),
+                                           checkpoint_interval=100)
+    b2 = np.asarray(svi.get_params(state)[NN_B2])
+    assert np.all(np.abs(b2 - 0.7) < 0.01)
+    ri.get_posteriors(svi, state, out_prefix=str(tmp_path / "svi"),
+                      num_posterior_samples=6, sampling_batch_size=3)
+    with h5py.File(tmp_path / "svi_posterior.h5", "r") as hf:
+        prior_mean = np.asarray(hf["theta_ddG_prior_mean"])   # (n, M, S)
+        np.testing.assert_allclose(read_model_params(hf)[NN_B2], b2)
+        assert hf[SIGMA].shape == (6, len(STRUCTS))
+    # The H structure's MLP output carries b2 (W2 = 0 at init), the same in
+    # every draw.
+    np.testing.assert_allclose(prior_mean,
+                               np.broadcast_to(prior_mean[0], prior_mean.shape),
+                               atol=1e-5)
+    assert np.abs(prior_mean[0][:, 0]).min() > 0.1
+
+
+def test_map_start_carries_model_params(tmp_path):
+    """``init_params`` (the --init_from / pre-MAP hand-off) starts the
+    model-side params where the earlier fit left them."""
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    orch = _orchestrator(tmp_path, "PnnC")
+    ri = RunInference(orch, seed=0)
+    start = ri.model_params({NN_B2: np.full((1,), 3.0),
+                             "not_a_param": np.ones(2)})
+    assert set(start) == {NN_B2}
+    svi = ri.setup_svi(adam_step_size=1e-6, guide_type="delta")
+    state, params, _ = ri.run_optimization(svi, init_params=start,
+                                           max_num_epochs=1,
+                                           out_prefix=str(tmp_path / "m"),
+                                           checkpoint_interval=100)
+    np.testing.assert_allclose(params[NN_B2], 3.0, rtol=1e-3)
+
+
+def test_pddg_has_no_model_params(tmp_path):
+    """σ_s is a sample site now, so PddG registers no model-side params."""
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+
+    ri = RunInference(_orchestrator(tmp_path, "PddG"), seed=0)
+    assert ri.model_params({"theta_ddG_sigma_s": np.ones(4)}) == {}
+
+
+def test_extract_params_writes_model_params(fit):
+    """tfs-extract-params shows the learned NN weights (PnnC) and writes
+    σ_s as the ddG_sigma table."""
+    from tfscreen.tfmodel.inference.run_inference import RunInference
+    from tfscreen.tfmodel.scripts.extract_params_cli import (
+        _extract_from_checkpoint,
+        _extract_from_posteriors,
+    )
+
+    prefix = str(fit["tmp"] / "extract")
+    RunInference(fit["orch"], seed=0).get_map_posteriors(
+        fit["unconstrained"], out_prefix=prefix)
+    _extract_from_posteriors(fit["orch"], f"{prefix}_posterior.h5",
+                             f"{prefix}_tfs")
+    sig = pd.read_csv(f"{prefix}_tfs_ddG_sigma.csv")
+    assert list(sig["structure"]) == list(STRUCTS)
+    np.testing.assert_allclose(sig["q0.5"], fit["sites"][SIGMA], rtol=1e-5)
+    if fit["model_side"]:
+        df = pd.read_csv(f"{prefix}_tfs_model_params.csv")
+        assert set(df["parameter"]) == set(fit["model_side"])
diff --git a/tests/tfscreen/tfmodel/inference/test_run_inference.py b/tests/tfscreen/tfmodel/inference/test_run_inference.py
index cd0c3114..0d26a687 100644
--- a/tests/tfscreen/tfmodel/inference/test_run_inference.py
+++ b/tests/tfscreen/tfmodel/inference/test_run_inference.py
@@ -6,7 +6,12 @@
 import numpyro
 import numpyro.distributions as dist
 import h5py
-from tfscreen.tfmodel.inference.run_inference import RunInference
+from tfscreen.tfmodel.inference.run_inference import (
+    LAPLACE_MIN_EIGENVALUE,
+    RunInference,
+    laplace_eigenvalue_floor,
+    resolve_guide_type,
+)
 import os
 import dill
 import optax
@@ -16,6 +21,13 @@
 from numpyro.infer.svi import SVIState
 from flax import struct
 
+@pytest.fixture(autouse=True)
+def _run_in_tmp_path(tmp_path, monkeypatch):
+    # Many tests below call run_optimization / _write_checkpoint with the
+    # default out_prefix ("tfs"), which writes checkpoint/loss files into the
+    # cwd. Run every test from its own tmp dir so nothing leaks into the repo.
+    monkeypatch.chdir(tmp_path)
+
 @struct.dataclass
 class MockData:
     num_genotype: int = 10
@@ -59,116 +71,6 @@ def test_setup_svi():
     svi = ri.setup_svi(guide_type="delta")
     assert isinstance(svi.guide, AutoDelta)
     
-def test_run_optimization(tmpdir, mocker):
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    
-    out_prefix = os.path.join(tmpdir, "test")
-    
-    # Mock jax and svi methods to avoid actual execution
-    mocker.patch("jax.jit", side_effect=lambda x: x)
-    # Mock scan_fn or just lax.scan
-    mocker.patch("jax.lax.scan", return_value=("state", jnp.array([1.0, 0.9])))
-    
-    mock_svi = mocker.Mock()
-    mock_svi.init.return_value = "state"
-    mock_svi.update.return_value = ("state", 1.0)
-    mock_svi.get_params.return_value = {"p": jnp.array(1.0)}
-    
-    # Mock get_key to avoid randomness issues in tests
-    mocker.patch.object(ri, "get_key", return_value=jnp.array([0, 42]))
-    
-    state, params, converged = ri.run_optimization(
-        mock_svi, 
-        max_num_epochs=10, 
-        convergence_check_interval=1,
-        checkpoint_interval=1,
-        out_prefix=out_prefix,
-        convergence_tolerance=0.01,
-        patience=1
-    )
-    
-    assert state is not None
-    assert "p" in params
-    assert isinstance(converged, bool)
-    assert os.path.exists(f"{out_prefix}_losses.bin")
-    assert os.path.exists(f"{out_prefix}_losses.txt")
-
-def test_run_optimization_no_patch_scan(mocker):
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    # 228: svi_state is None
-    mocker.patch("jax.jit", side_effect=lambda x: x)
-    mock_svi = mocker.Mock()
-    # Use jnp array instead of string for state
-    state = jnp.array([0.0])
-    mock_svi.init.return_value = state
-    mock_svi.update.return_value = (state, 1.0)
-    mock_svi.get_params.return_value = {"p": jnp.array(1.0)}
-    
-    # 236-241: scan_fn
-    # Don't patch lax.scan, let it run
-    # 228: Call jitter
-    ri.run_optimization(mock_svi, max_num_epochs=2, convergence_check_interval=1, checkpoint_interval=1, init_params={"p": jnp.array(1.0)})
-    assert len(ri._loss_deque) > 0
-
-def test_run_optimization_svi_state_types(tmpdir, mocker):
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    mocker.patch("jax.jit", side_effect=lambda x: x)
-    mock_svi = mocker.Mock()
-    mock_svi.init.return_value = jnp.array([0.0])
-    mock_svi.update.return_value = (jnp.array([0.0]), 1.0)
-    mock_svi.get_params.return_value = {"p": jnp.array(1.0)}
-    
-    # 269: Passing a non-string, non-None state
-    ri.run_optimization(mock_svi, svi_state=jnp.array([1.0]), max_num_epochs=1)
-    
-    # 265-266: Passing an invalid string
-    with pytest.raises(ValueError, match="is not valid"):
-        ri.run_optimization(mock_svi, svi_state="invalid_path_to_svi_state", max_num_epochs=1)
-
-def test_run_optimization_convergence(mocker):
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    
-    mocker.patch("jax.jit", side_effect=lambda x: x)
-    # Return losses that decrease and then stabilize
-    losses = np.concatenate([np.linspace(100, 10, 50), np.ones(50) * 10])
-    mocker.patch("jax.lax.scan", return_value=("state", jnp.array(losses)))
-    
-    mock_svi = mocker.Mock()
-    mock_svi.init.return_value = "state"
-    mock_svi.update.return_value = ("state", 10.0)
-    mock_svi.get_params.return_value = {"p": jnp.array(1.0)}
-    
-    state, params, converged = ri.run_optimization(
-        mock_svi, 
-        max_num_epochs=100, 
-        convergence_check_interval=1,
-        checkpoint_interval=1,
-        convergence_tolerance=1e-1,
-        convergence_window=1,
-        patience=1
-    )
-    # It might not converge in exactly this setup due to how scan returns, 
-    # but it covers the logic in _update_loss_deque and _check_convergence
-    assert converged in [True, False]
-
-def test_run_optimization_nan_explosion(mocker):
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    
-    mocker.patch("jax.jit", side_effect=lambda x: x)
-    mocker.patch("jax.lax.scan", return_value=("state", jnp.array([1.0])))
-    
-    mock_svi = mocker.Mock()
-    mock_svi.init.return_value = "state"
-    mock_svi.get_params.return_value = {"p": jnp.array(np.nan)}
-    
-    with pytest.raises(RuntimeError, match="model exploded"):
-        ri.run_optimization(mock_svi, max_num_epochs=1, convergence_check_interval=1, checkpoint_interval=1)
-
 # =============================================================================
 # get_posteriors — real model + component guide, no Predictive mocking
 # =============================================================================
@@ -414,32 +316,6 @@ def test_concat_genotype_chunks_negative_axis():
     np.testing.assert_array_equal(merged, expected)
 
 
-def test_run_optimization_restore(tmpdir, mocker):
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    out_prefix = os.path.join(tmpdir, "test_restore")
-    
-    # Create a valid checkpoint
-    state = SVIState(None, None, None) if ri.get_key().size > 0 else SVIState(None, None)
-    try:
-        state = SVIState(None, None, None)
-    except:
-        state = SVIState(None, None)
-    
-    ri._write_checkpoint(state, out_prefix)
-    checkpoint_file = f"{out_prefix}_checkpoint.pkl"
-    
-    mocker.patch("jax.jit", side_effect=lambda x: x)
-    mocker.patch("jax.lax.scan", return_value=(state, jnp.array([1.0])))
-    
-    mock_svi = mocker.Mock()
-    mock_svi.init.return_value = state
-    mock_svi.update.return_value = (state, 1.0)
-    mock_svi.get_params.return_value = {"p": jnp.array(1.0)}
-    
-    # Run with checkpoint path
-    ri.run_optimization(mock_svi, svi_state=checkpoint_file, max_num_epochs=1, convergence_check_interval=1, checkpoint_interval=1)
-
 def test_write_params(tmpdir):
     model = MockModel()
     ri = RunInference(model, seed=42)
@@ -472,7 +348,7 @@ def test_write_losses_txt_has_header(tmpdir):
     assert os.path.exists(txt_path)
     with open(txt_path) as fh:
         first_line = fh.readline().strip()
-    assert first_line == "epoch,loss,relative_change"
+    assert first_line == "epoch,loss,step,step_size"
 
 
 def test_write_losses_txt_format(tmpdir):
@@ -485,107 +361,19 @@ def test_write_losses_txt_format(tmpdir):
 
     # Simulate one interval having completed
     ri._current_step = ri._iterations_per_epoch  # epoch 1
-    ri._relative_change = 0.123
-    ri._write_losses(np.array([42.0, 43.0]), out_prefix)
+    ri._step_size = 1e-3
+    ri._write_losses(np.array([42.0, 43.0, 50.0]), out_prefix)
 
     with open(f"{out_prefix}_losses.txt") as fh:
         lines = [l.strip() for l in fh if l.strip()]
 
-    assert lines[0] == "epoch,loss,relative_change"
-    # Second line: epoch=1, loss=last value (43.0), relative_change=0.123
+    assert lines[0] == "epoch,loss,step,step_size"
+    # Second line: epoch=1, loss=median of the block (43.0), step, step size
     parts = lines[1].split(",")
     assert int(parts[0]) == 1
     assert float(parts[1]) == pytest.approx(43.0)
-    assert float(parts[2]) == pytest.approx(0.123)
-
-
-def test_update_loss_deque_all_constant():
-    """Constant losses produce relative_change=0 via zero numerator."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    from collections import deque
-    ri._loss_deque = deque(maxlen=200)
-    ri._update_loss_deque(np.ones(200))
-    assert ri._relative_change == 0.0
-
-
-def test_update_loss_deque_uses_total_improvement():
-    """Metric is normalized by total improvement, not current loss magnitude."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    from collections import deque
-
-    # Deque of size 4; warm-up: old_half=[100,90], new_half=[80,70]
-    ri._loss_deque = deque(maxlen=4)
-    ri._update_loss_deque([100.0, 90.0, 80.0, 70.0])
-
-    # loss_start = mean([100,90]) = 95; loss_best = mean([80,70]) = 75
-    # denom = |95 - 75| = 20; numerator = |95 - 75| = 20 → metric = 1.0
-    assert ri._loss_start == pytest.approx(95.0)
-    assert ri._loss_best == pytest.approx(75.0)
-    assert ri._relative_change == pytest.approx(1.0)
-
-
-def test_update_loss_deque_sign_change():
-    """Metric is robust when the loss function crosses zero."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    from collections import deque
-
-    ri._loss_deque = deque(maxlen=4)
-    ri._update_loss_deque([10.0, 5.0, -5.0, -10.0])
-
-    # loss_start = 7.5; loss_best = -7.5; denom = 15; metric = 15/15 = 1.0
-    assert ri._relative_change == pytest.approx(1.0)
-    assert ri._loss_start == pytest.approx(7.5)
-    assert ri._loss_best == pytest.approx(-7.5)
-
-
-def test_update_loss_deque_loss_start_fixed_after_first_check():
-    """loss_start is captured once at the end of warm-up and never updated."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    from collections import deque
-
-    ri._loss_deque = deque(maxlen=4)
-    ri._update_loss_deque([100.0, 90.0, 80.0, 70.0])
-    first_loss_start = ri._loss_start
-
-    ri._update_loss_deque([60.0, 50.0])
-    assert ri._loss_start == first_loss_start
-
-
-def test_update_loss_deque_warmup_returns_inf():
-    """Returns inf while the deque is not yet full."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    from collections import deque
-    ri._loss_deque = deque(maxlen=10)
-    ri._update_loss_deque([1.0, 2.0, 3.0])
-    assert ri._relative_change == np.inf
-
-
-def test_loss_state_reset_at_run_start(mocker):
-    """_loss_start and _loss_best are reset each time run_optimization begins."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-
-    # Simulate stale state from a previous run
-    ri._loss_start = 999.0
-    ri._loss_best = -999.0
-
-    mocker.patch("jax.jit", side_effect=lambda x: x)
-    mocker.patch("jax.lax.scan", return_value=("state", jnp.array([1.0])))
-    mock_svi = mocker.Mock()
-    mock_svi.init.return_value = "state"
-    mock_svi.update.return_value = ("state", 1.0)
-    mock_svi.get_params.return_value = {"p": jnp.array(1.0)}
-
-    ri.run_optimization(mock_svi, max_num_epochs=1,
-                        convergence_check_interval=1, checkpoint_interval=1)
-
-    # After the run resets state, _loss_start must not retain the stale value
-    assert ri._loss_start != 999.0
+    assert int(parts[2]) == ri._iterations_per_epoch
+    assert float(parts[3]) == pytest.approx(1e-3)
 
 
 def test_get_site_names(mocker):
@@ -651,6 +439,130 @@ def test_setup_svi_invalid_guide_type():
     with pytest.raises(ValueError, match="not recognized"):
         ri.setup_svi(guide_type="bad_guide")
 
+
+# -----------------------------------------------------------------------------
+# Autoguide batch-safety guard in run_optimization
+# -----------------------------------------------------------------------------
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "..", "smoke-tests", "test_data",
+)
+
+
+def _smoke_orchestrator(**kwargs):
+    from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+    return ModelOrchestrator(
+        growth_df=os.path.join(_SMOKE_DATA, "growth-smoke.csv"),
+        binding_df=os.path.join(_SMOKE_DATA, "binding-smoke.csv"),
+        batch_size=6,
+        **kwargs,
+    )
+
+
+def _svi_with_guide(ri, guide):
+    from numpyro.infer import SVI, Trace_ELBO
+    from numpyro.optim import ClippedAdam
+    return SVI(ri.model.jax_model, guide, ClippedAdam(step_size=1e-3),
+               loss=Trace_ELBO())
+
+
+def test_run_optimization_refuses_any_autoguide(tmpdir):
+    """The guard keys off AutoGuide, not a particular guide class."""
+    from numpyro.infer.autoguide import AutoNormal
+    ri = RunInference(_smoke_orchestrator(theta_growth_noise="beta"), seed=0)
+    svi = _svi_with_guide(ri, AutoNormal(ri.model.jax_model))
+    with pytest.raises(ValueError, match="'AutoNormal' cannot fit"):
+        ri.run_optimization(svi, max_num_epochs=1,
+                            out_prefix=os.path.join(tmpdir, "refused"))
+
+
+def test_check_autoguide_reports_dense_guide_size(capsys):
+    from numpyro.infer.autoguide import AutoMultivariateNormal
+    ri = RunInference(_smoke_orchestrator(), seed=0)
+    ri._check_autoguide(AutoMultivariateNormal(ri.model.jax_model))
+    assert "latent dimensions" in capsys.readouterr().out
+
+
+def test_check_autoguide_warns_on_large_dense_guide(monkeypatch):
+    from numpyro.infer.autoguide import AutoMultivariateNormal
+    from tfscreen.tfmodel.inference import run_inference
+    monkeypatch.setattr(run_inference, "_DENSE_GUIDE_WARN_GB", 0.0)
+    ri = RunInference(_smoke_orchestrator(), seed=0)
+    with pytest.warns(UserWarning, match="auto_low_rank_multivariate_normal"):
+        ri._check_autoguide(AutoMultivariateNormal(ri.model.jax_model))
+
+
+@pytest.mark.parametrize("guide_type,cls_name", [
+    ("delta", "AutoDelta"),
+    ("auto_normal", "AutoNormal"),
+    ("auto_diagonal_normal", "AutoDiagonalNormal"),
+    ("auto_multivariate_normal", "AutoMultivariateNormal"),
+    ("auto_low_rank_multivariate_normal", "AutoLowRankMultivariateNormal"),
+])
+def test_setup_svi_builds_autoguide(guide_type, cls_name):
+    from numpyro.infer import autoguide
+    ri = RunInference(MockModel(), seed=42)
+    svi = ri.setup_svi(guide_type=guide_type)
+    assert isinstance(svi.guide, getattr(autoguide, cls_name))
+    assert ri._guide_type == guide_type
+
+
+@pytest.mark.parametrize("alias,canonical", [
+    ("AutoNormal", "auto_normal"),
+    ("AUTO_NORMAL", "auto_normal"),
+    ("AutoDelta", "delta"),
+    ("auto_delta", "delta"),
+    ("autolowrankmultivariatenormal", "auto_low_rank_multivariate_normal"),
+    ("Component", "component"),
+])
+def test_resolve_guide_type_aliases(alias, canonical):
+    assert resolve_guide_type(alias) == canonical
+
+
+def test_setup_svi_forwards_guide_kwargs():
+    ri = RunInference(MockModel(), seed=42)
+    svi = ri.setup_svi(guide_type="auto_low_rank_multivariate_normal",
+                       guide_kwargs={"rank": 3, "init_scale": 0.05})
+    assert svi.guide.rank == 3
+    assert svi.guide._init_scale == 0.05
+    assert ri._guide_kwargs == {"rank": 3, "init_scale": 0.05}
+
+
+@pytest.mark.parametrize("guide_type,kwargs", [
+    ("auto_normal", {"rank": 3}),
+    ("delta", {"init_scale": 0.1}),
+    ("component", {"init_scale": 0.1}),
+])
+def test_setup_svi_rejects_unaccepted_kwargs(guide_type, kwargs):
+    ri = RunInference(MockModel(), seed=42)
+    with pytest.raises(ValueError, match="not accepted"):
+        ri.setup_svi(guide_type=guide_type, guide_kwargs=kwargs)
+
+
+def test_setup_svi_component_rejects_init_values():
+    ri = RunInference(MockModel(), seed=42)
+    with pytest.raises(ValueError, match="init_values"):
+        ri.setup_svi(guide_type="component", init_values={"a": 1.0})
+
+
+def test_setup_svi_init_values_become_init_loc_fn():
+    ri = RunInference(MockModel(), seed=42)
+    values = {"global_p": 1.0}
+    svi = ri.setup_svi(guide_type="auto_normal", init_values=values)
+    assert svi.guide.init_loc_fn.keywords["values"] is values
+
+
+def test_checkpoint_records_guide(tmpdir):
+    ri = RunInference(MockModel(), seed=42)
+    ri.setup_svi(guide_type="auto_normal", guide_kwargs={"init_scale": 0.2})
+    out_prefix = os.path.join(tmpdir, "guide_meta")
+    ri._write_checkpoint({"dummy": np.zeros(2)}, out_prefix)
+    with open(f"{out_prefix}_checkpoint.pkl", "rb") as f:
+        saved = dill.load(f)
+    assert saved["guide_type"] == "auto_normal"
+    assert saved["guide_kwargs"] == {"init_scale": 0.2}
+
 def test_restore_checkpoint_current_step(tmpdir):
     """_restore_checkpoint restores _current_step when present in checkpoint."""
     model = MockModel()
@@ -678,77 +590,6 @@ def test_restore_checkpoint_current_step(tmpdir):
     assert isinstance(restored, SVIState)
 
 
-def test_checkpoint_round_trips_convergence_state(tmpdir):
-    """_write_checkpoint saves loss_start/loss_best; _restore_checkpoint loads them."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    out_prefix = os.path.join(tmpdir, "test_conv")
-
-    ri._loss_start = -12345.0
-    ri._loss_best = -99999.0
-
-    try:
-        state = SVIState(None, None, None)
-    except Exception:
-        state = SVIState(None, None)
-
-    ri._write_checkpoint(state, out_prefix)
-
-    # Fresh instance simulates a restart
-    ri2 = RunInference(model, seed=0)
-    assert ri2._loss_start is None
-    assert ri2._loss_best == np.inf
-
-    ri2._restore_checkpoint(f"{out_prefix}_checkpoint.pkl")
-    assert ri2._loss_start == pytest.approx(-12345.0)
-    assert ri2._loss_best == pytest.approx(-99999.0)
-
-
-def test_checkpoint_missing_convergence_state_uses_defaults(tmpdir):
-    """Old checkpoints without loss_start/loss_best leave defaults intact."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    out_prefix = os.path.join(tmpdir, "test_old")
-
-    try:
-        state = SVIState(None, None, None)
-    except Exception:
-        state = SVIState(None, None)
-
-    import dill as _dill
-    checkpoint_file = f"{out_prefix}_checkpoint.pkl"
-    with open(checkpoint_file, "wb") as f:
-        _dill.dump({'svi_state': state, 'main_key': ri._main_key}, f)
-
-    ri._restore_checkpoint(checkpoint_file)
-    assert ri._loss_start is None   # default — not present in old checkpoint
-    assert ri._loss_best == np.inf  # default
-
-def test_run_optimization_1d_block_idx(mocker):
-    """1D block_idx is reshaped to 2D before passing to fast_scan."""
-
-    class MockModel1D(MockModel):
-        def get_random_idx(self, key=None, num_batches=1):
-            # Always return a 1D array regardless of num_batches
-            return np.array([0])
-
-    model = MockModel1D()
-    ri = RunInference(model, seed=42)
-
-    mocker.patch("jax.jit", side_effect=lambda x: x)
-    mocker.patch("jax.lax.scan", return_value=("state", jnp.array([1.0])))
-
-    mock_svi = mocker.Mock()
-    mock_svi.init.return_value = "state"
-    mock_svi.update.return_value = ("state", 1.0)
-    mock_svi.get_params.return_value = {"p": jnp.array(1.0)}
-
-    # Should not raise; the reshape path is exercised
-    ri.run_optimization(mock_svi, max_num_epochs=1,
-                        convergence_check_interval=1,
-                        checkpoint_interval=1)
-
-
 # =============================================================================
 # _map_params_to_constrained
 # =============================================================================
@@ -803,6 +644,30 @@ def test_map_params_to_constrained_values_finite():
 # get_map_posteriors
 # =============================================================================
 
+def _batch_sized_map_params(model, batch_size):
+    """MAP params whose genotype latent is batch-sized (pre-fix checkpoint)."""
+    ri, map_params = _laplace_map_params(model)
+    aliased = dict(map_params)
+    aliased["geno_p_auto_loc"] = map_params["geno_p_auto_loc"][..., :batch_size]
+    return ri, aliased
+
+
+def test_get_map_posteriors_rejects_batch_sized_latent(tmpdir):
+    """A genotype latent shorter than the library is refused, not clipped."""
+    ri, aliased = _batch_sized_map_params(LaplaceModel(num_genotype=5), 3)
+    with pytest.raises(ValueError, match="mini-batch position"):
+        ri.get_map_posteriors(aliased, out_prefix=str(tmpdir.join("aliased")))
+
+
+def test_get_laplace_posteriors_rejects_batch_sized_latent(tmpdir):
+    """The Laplace path refuses it too, before computing the Hessian."""
+    ri, aliased = _batch_sized_map_params(LaplaceModel(num_genotype=5), 3)
+    with pytest.raises(ValueError, match="mini-batch position"):
+        ri.get_laplace_posteriors(aliased,
+                                  out_prefix=str(tmpdir.join("aliased")),
+                                  num_posterior_samples=2)
+
+
 def test_get_map_posteriors_creates_h5(tmpdir):
     """get_map_posteriors writes an HDF5 posterior file."""
     model = LaplaceModel(num_genotype=4)
@@ -1090,44 +955,6 @@ def test_get_laplace_posteriors_non_auto_loc_keys_ignored(tmpdir):
     assert os.path.exists(f"{out_prefix}_posterior.h5")
 
 
-def test_get_laplace_posteriors_negative_eigenvalues_no_nan(tmpdir, mocker):
-    """An indefinite Hessian (negative eigenvalues) must not produce NaN posteriors.
-
-    Regression test: a MAP solution at a saddle point has negative Hessian
-    eigenvalues.  The old code (jitter + jnp.linalg.inv) left the covariance
-    non-PD in float32, causing jax.random.multivariate_normal to return all-NaN.
-    The fix uses numpy float64 eigendecomposition, clamps negative eigenvalues
-    to 1e-3, and samples via z @ L^T instead of multivariate_normal.
-
-    LaplaceModel(num_genotype=2) has D=3 unconstrained parameters
-    (1 global_p + 2 geno_p), so a 3x3 Hessian is injected.
-    """
-    model = LaplaceModel(num_genotype=2)
-    ri, map_params = _laplace_map_params(model)
-
-    # 3x3 symmetric matrix with one clearly negative eigenvalue (-5).
-    bad_H = jnp.array([[10., 0., 0.],
-                        [ 0., -5., 0.],
-                        [ 0.,  0.,  8.]])
-
-    # jax.hessian(fn)(x): patch so that jax.hessian(pe_fn) returns a callable
-    # that ignores x and returns bad_H regardless of fn/x.
-    mocker.patch("jax.hessian", return_value=lambda x: bad_H)
-
-    out_prefix = str(tmpdir.join("laplace_negeig"))
-    ri.get_laplace_posteriors(
-        map_params=map_params,
-        out_prefix=out_prefix,
-        num_posterior_samples=20,
-        sampling_batch_size=10,
-        forward_batch_size=2,
-    )
-
-    with h5py.File(f"{out_prefix}_posterior.h5", "r") as hf:
-        for k in hf.keys():
-            assert not np.any(np.isnan(hf[k][:])), f"NaN found in posterior key '{k}'"
-
-
 # =============================================================================
 # get_nuts_posteriors
 # =============================================================================
@@ -1396,170 +1223,424 @@ def test_write_epoch_checkpoint_atomic_no_tmp_left(tmpdir):
 
 
 # =============================================================================
-# run_optimization — epoch checkpoint integration
+# compute_hessian_sigmas: takes constrained MAP values (svi.get_params)
 # =============================================================================
 
-def _optimization_mocks(mocker):
-    """Shared mock setup for run_optimization epoch checkpoint tests."""
-    mocker.patch("jax.jit", side_effect=lambda x: x)
-    mocker.patch("jax.lax.scan", return_value=("state", jnp.array([1.0])))
-    mock_svi = mocker.Mock()
-    mock_svi.init.return_value = "state"
-    mock_svi.update.return_value = ("state", 1.0)
-    mock_svi.get_params.return_value = {"p": jnp.array(1.0)}
-    return mock_svi
-
-
-def test_run_optimization_epoch_checkpoint_creates_dir(tmpdir, mocker):
-    """A checkpoints/ directory is created next to out_prefix when interval is set."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    mock_svi = _optimization_mocks(mocker)
-    out_prefix = os.path.join(str(tmpdir), "myrun")
-
-    ri.run_optimization(
-        mock_svi,
-        out_prefix=out_prefix,
-        max_num_epochs=1,
-        convergence_check_interval=1,
-        checkpoint_interval=1,
-        epoch_checkpoint_interval=1,
-    )
-
-    assert os.path.isdir(os.path.join(str(tmpdir), "checkpoints"))
-
-
-def test_run_optimization_epoch_checkpoint_file_written(tmpdir, mocker):
-    """An epoch checkpoint file is written after the first epoch."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    mock_svi = _optimization_mocks(mocker)
-    out_prefix = os.path.join(str(tmpdir), "myrun")
-
-    ri.run_optimization(
-        mock_svi,
-        out_prefix=out_prefix,
-        max_num_epochs=1,
-        convergence_check_interval=1,
-        checkpoint_interval=1,
-        epoch_checkpoint_interval=1,
-    )
-
-    checkpoints_dir = os.path.join(str(tmpdir), "checkpoints")
-    pkl_files = [f for f in os.listdir(checkpoints_dir) if f.endswith(".pkl")]
-    assert len(pkl_files) >= 1
-
-
-def test_run_optimization_epoch_checkpoint_correct_epoch_number(tmpdir, mocker):
-    """The epoch number in the filename matches step // iterations_per_epoch."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    mock_svi = _optimization_mocks(mocker)
-    out_prefix = os.path.join(str(tmpdir), "myrun")
+def test_compute_hessian_sigmas_takes_constrained_values():
+    """
+    run_optimization returns svi.get_params(), which AutoDelta constrains.
+    compute_hessian_sigmas must map those back to unconstrained space; it
+    used to take them as unconstrained, so a positive site was evaluated at
+    exp(value) (the pre-fit's k_scale came out NaN on the count likelihood,
+    2026-09-27). With s ~ LogNormal(1, 0.5), log s is Normal(1, 0.5): at the
+    MAP s = e the unconstrained sigma is 0.5 and the constrained one e / 2.
+    """
+    def model(data, priors):
+        numpyro.sample("s", dist.LogNormal(1.0, 0.5))
+        numpyro.sample("x", dist.Normal(0.0, 2.0))
 
-    ri.run_optimization(
-        mock_svi,
-        out_prefix=out_prefix,
-        max_num_epochs=2,
-        convergence_check_interval=1,
-        checkpoint_interval=1,
-        epoch_checkpoint_interval=1,
-    )
+    m = MockModel(num_genotype=1)
+    m.jax_model = model
+    ri = RunInference(m, seed=0)
 
-    checkpoints_dir = os.path.join(str(tmpdir), "checkpoints")
-    # MockModel: num_genotype=10, batch=1 → _iterations_per_epoch=10.
-    # After epoch 1: step=10, epoch=1 → 0000001_checkpoint.pkl
-    assert os.path.exists(os.path.join(checkpoints_dir, "0000001_checkpoint.pkl"))
+    # confirm what AutoDelta's get_params hands over
+    svi = ri.setup_svi(guide_type="delta",
+                       init_values={"s": float(np.e), "x": 0.0})
+    state = svi.init(ri.get_key(), data=m.data, priors=m.priors)
+    params = svi.get_params(state)
+    assert float(params["s_auto_loc"]) == pytest.approx(np.e, rel=1e-5)
 
+    out = ri.compute_hessian_sigmas(params)
+    assert float(out["s"]["map"]) == pytest.approx(np.e, rel=1e-5)
+    assert float(out["s"]["sigma"]) == pytest.approx(np.e * 0.5, rel=1e-3)
+    assert float(out["x"]["sigma"]) == pytest.approx(2.0, rel=1e-3)
 
-def test_run_optimization_epoch_checkpoint_disabled_when_none(tmpdir, mocker):
-    """No epoch checkpoints dir is created when epoch_checkpoint_interval=None."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    mock_svi = _optimization_mocks(mocker)
-    out_prefix = os.path.join(str(tmpdir), "myrun")
 
-    ri.run_optimization(
-        mock_svi,
-        out_prefix=out_prefix,
-        max_num_epochs=2,
-        convergence_check_interval=1,
-        checkpoint_interval=1,
-        epoch_checkpoint_interval=None,
-    )
+def test_run_nuts_starts_at_given_values(mocker):
+    """
+    run_nuts starts every chain at the given constrained site values and
+    falls back to prior medians (it once handed initialize_model's ParamInfo
+    tuple to init_to_value, which matched no site).
+    """
+    from unittest.mock import MagicMock
+
+    import numpyro.distributions as dist
+
+    ri = RunInference.__new__(RunInference)
+    ri._seed = 0
+    ri.model = MagicMock()
+    ri.model.data.num_genotype = 3
+    nuts = mocker.patch("numpyro.infer.NUTS")
+    mcmc = mocker.patch("numpyro.infer.MCMC")
+    mcmc.return_value.get_extra_fields.return_value = {}
+    ri.run_nuts(num_warmup=1, num_samples=1, init_values={"a": 2.5},
+                dense_mass=True)
+    assert nuts.call_args.kwargs["dense_mass"] is True
+    strategy = nuts.call_args.kwargs["init_strategy"]
+    site = {"type": "sample", "is_observed": False, "name": "a",
+            "fn": dist.Normal(0.0, 1.0), "value": None, "kwargs": {}}
+    assert float(strategy(site)) == 2.5
+    # the full data set goes in as one batch
+    ri.model.get_batch.assert_called_once()
+
+
+def test_report_nuts_diagnostics(capsys):
+    """Worst split R-hat and least n_eff, skipping constant sites."""
+    from unittest.mock import MagicMock
+
+    rng = np.random.default_rng(0)
+    mixed = rng.normal(size=(2, 200))
+    stuck = np.stack([rng.normal(0, 1, 200), rng.normal(5, 1, 200)])
+    mcmc = MagicMock()
+    mcmc.get_samples.return_value = {"good": mixed, "bad": stuck,
+                                     "fixed": np.zeros((2, 200))}
+    RunInference._report_nuts_diagnostics(mcmc)
+    out = capsys.readouterr().out
+    assert "max split R-hat" in out and "(bad)" in out
+    assert "inf" not in out
+
+
+# ---------------------------------------------------------------------------
+# laplace_eigenvalue_floor
+# ---------------------------------------------------------------------------
+
+def test_laplace_eigenvalue_floor_uses_prior_curvature():
+    """A direction flatter than the prior (or negative) gets the prior's curvature."""
+    rng = np.random.default_rng(0)
+    q, _ = np.linalg.qr(rng.normal(size=(3, 3)))
+    prior_precision = np.array([4.0, 100.0, 1.0])
+    curvature = np.einsum("ij,i,ij->j", q, prior_precision, q)
+    eigenvalues = np.array([-50.0, 1e6, 0.5 * curvature[2]])
+    floored, n = laplace_eigenvalue_floor(eigenvalues, q, prior_precision)
+    assert n == 2
+    assert floored[0] == pytest.approx(curvature[0])
+    assert floored[1] == pytest.approx(1e6)          # well-determined: kept
+    assert floored[2] == pytest.approx(curvature[2])
+
+
+def test_laplace_eigenvalue_floor_bounds_leak_into_other_elements():
+    """
+    A negative eigenvalue no longer inflates every element it touches.
 
-    assert not os.path.isdir(os.path.join(str(tmpdir), "checkpoints"))
+    The old flat floor of 1e-3 gave such a direction a variance of 1000, so
+    an element with a 3% share of it (the growth slope m, seed 8 of the
+    two-stage grid) had an SD near 1.
+    """
+    theta = 0.03
+    v = np.array([[np.cos(theta), -np.sin(theta)],
+                  [np.sin(theta), np.cos(theta)]])
+    eigenvalues = np.array([-10.0, 1e6])     # direction 0 mostly element 0
+    prior_precision = np.array([1.0, 1e4])
+    floored, _ = laplace_eigenvalue_floor(eigenvalues, v, prior_precision)
+    cov = v @ np.diag(1.0 / floored) @ v.T
+    old = v @ np.diag(1.0 / np.maximum(eigenvalues, 1e-3)) @ v.T
+    assert np.sqrt(old[1, 1]) > 0.5
+    assert np.sqrt(cov[1, 1]) < 0.05
 
 
-def test_run_optimization_epoch_checkpoint_disabled_when_zero(tmpdir, mocker):
-    """No epoch checkpoints are written when epoch_checkpoint_interval=0."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    mock_svi = _optimization_mocks(mocker)
-    out_prefix = os.path.join(str(tmpdir), "myrun")
+def test_laplace_eigenvalue_floor_minimum():
+    floored, n = laplace_eigenvalue_floor(np.array([-1.0]), np.eye(1),
+                                          np.array([0.0]))
+    assert floored[0] == pytest.approx(LAPLACE_MIN_EIGENVALUE)
+    assert n == 1
 
-    ri.run_optimization(
-        mock_svi,
-        out_prefix=out_prefix,
-        max_num_epochs=2,
-        convergence_check_interval=1,
-        checkpoint_interval=1,
-        epoch_checkpoint_interval=0,
-    )
 
-    assert not os.path.isdir(os.path.join(str(tmpdir), "checkpoints"))
+# =============================================================================
+# Block (per-genotype) Laplace
+# =============================================================================
 
+@struct.dataclass
+class BlockData:
+    batch_idx: jnp.ndarray
+    y: jnp.ndarray            # (num_genotype, 2) observations
+    num_genotype: int = struct.field(pytree_node=False, default=0)
+    coupled: bool = struct.field(pytree_node=False, default=False)
+
+
+def _block_jax_model(data, priors):
+    """Shared mu; per-genotype (a, b) in a library-sized plate, sliced by
+    batch_idx; two observations per genotype that correlate a and b. With
+    ``coupled`` each observation also sees the library mean of a, so the
+    genotype blocks are not separable."""
+    mu = numpyro.sample("mu", dist.Normal(0., 1.))
+    with numpyro.plate("block_genotype_plate", data.num_genotype, dim=-1):
+        a = numpyro.sample("a", dist.Normal(mu, 1.))
+        b = numpyro.sample("b", dist.Normal(0., 1.))
+    a_b, b_b = a[data.batch_idx], b[data.batch_idx]
+    if data.coupled:
+        a_b = a_b + jnp.mean(a)
+    y = data.y[data.batch_idx]
+    with numpyro.plate("obs_genotype_plate", data.batch_idx.shape[0], dim=-1):
+        numpyro.sample("y1", dist.Normal(a_b + b_b, 0.3), obs=y[:, 0])
+        numpyro.sample("y2", dist.Normal(a_b - 2.0 * b_b, 0.2), obs=y[:, 1])
+
+
+class BlockModel:
+    def __init__(self, num_genotype=6, coupled=False, seed=3):
+        rng = np.random.default_rng(seed)
+        self.data = BlockData(num_genotype=num_genotype,
+                              batch_idx=jnp.arange(num_genotype),
+                              y=jnp.asarray(rng.normal(size=(num_genotype, 2))),
+                              coupled=coupled)
+        self.priors = {}
+        self.jax_model = _block_jax_model
+        self.jax_model_guide = lambda data, priors: None
 
-def test_run_optimization_epoch_checkpoint_file_exists_error(tmpdir, mocker):
-    """FileExistsError propagates when an epoch checkpoint already exists."""
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    mock_svi = _optimization_mocks(mocker)
-    out_prefix = os.path.join(str(tmpdir), "myrun")
+    def get_batch(self, data, indices):
+        return data.replace(batch_idx=jnp.asarray(indices))
 
-    ri.run_optimization(
-        mock_svi,
-        out_prefix=out_prefix,
-        max_num_epochs=1,
-        convergence_check_interval=1,
-        checkpoint_interval=1,
-        epoch_checkpoint_interval=1,
-    )
+    def get_random_idx(self, key=None, num_batches=1):
+        if num_batches == 1:
+            return np.array([0])
+        return np.zeros((num_batches, 1), dtype=int)
 
-    # A second run pointing at the same checkpoints/ directory must raise.
-    ri2 = RunInference(MockModel(), seed=7)
-    mock_svi2 = mocker.Mock()
-    mock_svi2.init.return_value = "state"
-    mock_svi2.update.return_value = ("state", 1.0)
-    mock_svi2.get_params.return_value = {"p": jnp.array(1.0)}
-
-    with pytest.raises(FileExistsError, match="checkpoint.pkl"):
-        ri2.run_optimization(
-            mock_svi2,
-            out_prefix=out_prefix,
-            max_num_epochs=1,
-            convergence_check_interval=1,
-            checkpoint_interval=1,
-            epoch_checkpoint_interval=1,
-        )
 
+def _block_setup(model):
+    import jax.flatten_util
+    ri, map_params = _laplace_map_params(model)
+    unc = {k[:-len("_auto_loc")]: jnp.asarray(v) for k, v in map_params.items()
+           if k.endswith("_auto_loc")}
+    flat, unravel = jax.flatten_util.ravel_pytree(unc)
+    G = model.data.num_genotype
+    kw = {"priors": model.priors,
+          "data": ri._unscaled_batch(model.data, jnp.arange(G))}
+    return ri, map_params, unc, flat, unravel, kw
+
+
+def test_block_laplace_factors_match_full_hessian_blocks():
+    """Each block's covariance is the inverse of the matching diagonal block
+    of the full Hessian: given mu, genotypes do not couple."""
+    from numpyro.infer.util import potential_energy
+    model = BlockModel()
+    ri, _, unc, flat, unravel, kw = _block_setup(model)
+    dim_map = ri._get_genotype_dim_map()
+    elem_idx, L = ri.block_laplace_factors(unc, dim_map, model.data, flat,
+                                           unravel, kw)
+    assert elem_idx.shape == (6, 2) and L.shape == (6, 2, 2)
+    pe = lambda f: potential_energy(model.jax_model, [], kw, unravel(f))
+    H = ri._chunked_hessian(pe, flat, 64)
+    for g in range(6):
+        hb = H[np.ix_(elem_idx[g], elem_idx[g])]
+        np.testing.assert_allclose(L[g] @ L[g].T, np.linalg.inv(hb),
+                                   rtol=1e-4, atol=1e-6)
+        # the observations correlate a and b, so the blocks are not diagonal
+        assert abs(hb[0, 1]) > 1.0
+
+
+def test_block_laplace_factors_chunking_matches():
+    model = BlockModel(num_genotype=7)
+    ri, _, unc, flat, unravel, kw = _block_setup(model)
+    dim_map = ri._get_genotype_dim_map()
+    e1, L1 = ri.block_laplace_factors(unc, dim_map, model.data, flat, unravel, kw)
+    e2, L2 = ri.block_laplace_factors(unc, dim_map, model.data, flat, unravel, kw,
+                                      genotype_chunk_size=3)
+    np.testing.assert_array_equal(e1, e2)
+    np.testing.assert_allclose(np.einsum("gij,gkj->gik", L1, L1),
+                               np.einsum("gij,gkj->gik", L2, L2), rtol=1e-5)
+
+
+def test_block_laplace_factors_refuse_coupled_genotypes():
+    model = BlockModel(coupled=True)
+    ri, _, unc, flat, unravel, kw = _block_setup(model)
+    with pytest.raises(ValueError, match="coupled"):
+        ri.block_laplace_factors(unc, ri._get_genotype_dim_map(), model.data,
+                                 flat, unravel, kw)
+
+
+def test_get_laplace_posteriors_block_holds_shared_and_varies_genotypes(tmpdir):
+    model = BlockModel()
+    ri, map_params, *_ = _block_setup(model)
+    out = str(tmpdir.join("block"))
+    ri.get_laplace_posteriors(map_params, out_prefix=out,
+                              num_posterior_samples=400,
+                              sampling_batch_size=100,
+                              forward_batch_size=6,
+                              block_genotypes=True)
+    with h5py.File(f"{out}_posterior.h5", "r") as hf:
+        mu, a = hf["mu"][...], hf["a"][...]
+    np.testing.assert_allclose(mu, float(map_params["mu_auto_loc"]), atol=1e-6)
+    assert a.shape == (400, 6)
+    assert np.all(a.std(axis=0) > 0.01)
+    np.testing.assert_allclose(a.mean(axis=0),
+                               np.asarray(map_params["a_auto_loc"]), atol=0.1)
+
+
+def _arrowhead_covariance(elem_idx, L_blocks, shared_idx, L_shared, response):
+    """Dense covariance implied by the arrowhead factors."""
+    G, B = elem_idx.shape
+    D = G * B + shared_idx.size
+    cov = np.zeros((D, D))
+    css = L_shared @ L_shared.T
+    cov[np.ix_(shared_idx, shared_idx)] = css
+    for g in range(G):
+        Mg = np.asarray(response[g], dtype=float)
+        cov[np.ix_(elem_idx[g], shared_idx)] = -Mg @ css
+        cov[np.ix_(shared_idx, elem_idx[g])] = -(Mg @ css).T
+        for h in range(G):
+            Mh = np.asarray(response[h], dtype=float)
+            block = Mg @ css @ Mh.T
+            if g == h:
+                block = block + L_blocks[g] @ L_blocks[g].T
+            cov[np.ix_(elem_idx[g], elem_idx[h])] = block
+    return cov
+
+
+@pytest.mark.parametrize("chunk", [None, 3])
+def test_arrowhead_laplace_matches_full_laplace(chunk):
+    """Genotype blocks plus their coupling to the shared mu reproduce the
+    inverse of the full Hessian, chunked or not (the chunked sum counts the
+    priors once per chunk and must take the surplus back out)."""
+    from numpyro.infer.util import potential_energy
+    model = BlockModel(num_genotype=7)
+    ri, _, unc, flat, unravel, kw = _block_setup(model)
+    factors = ri.arrowhead_laplace_factors(unc, ri._get_genotype_dim_map(),
+                                           model.data, flat, unravel, kw,
+                                           genotype_chunk_size=chunk)
+    elem_idx, L_blocks, shared_idx, L_shared, response = factors
+    assert shared_idx.size == 1 and response.shape == (7, 2, 1)
+    pe = lambda f: potential_energy(model.jax_model, [], kw, unravel(f))
+    H = ri._chunked_hessian(pe, flat, 64)
+    np.testing.assert_allclose(_arrowhead_covariance(*factors),
+                               np.linalg.inv(H), rtol=1e-4, atol=1e-6)
+
+
+def test_get_laplace_posteriors_block_shared_varies_shared(tmpdir):
+    model = BlockModel()
+    ri, map_params, unc, flat, unravel, kw = _block_setup(model)
+    from numpyro.infer.util import potential_energy
+    pe = lambda f: potential_energy(model.jax_model, [], kw, unravel(f))
+    cov = np.linalg.inv(ri._chunked_hessian(pe, flat, 64))
+    out = str(tmpdir.join("arrow"))
+    ri.get_laplace_posteriors(map_params, out_prefix=out,
+                              num_posterior_samples=4000,
+                              sampling_batch_size=500,
+                              forward_batch_size=6,
+                              block_genotypes=True, block_shared=True)
+    with h5py.File(f"{out}_posterior.h5", "r") as hf:
+        mu, a = hf["mu"][...], hf["a"][...]
+    # ravel_pytree orders sites a, b, mu
+    i_mu, i_a = 12, np.arange(6)
+    assert mu.std() == pytest.approx(np.sqrt(cov[i_mu, i_mu]), rel=0.1)
+    # a follows mu: its draws correlate with mu's as the full Laplace says
+    sd_a = np.sqrt(np.diag(cov)[i_a])
+    want = cov[i_mu, i_a] / (np.sqrt(cov[i_mu, i_mu]) * sd_a)
+    got = [np.corrcoef(mu, a[:, g])[0, 1] for g in range(6)]
+    np.testing.assert_allclose(got, want, atol=0.08)
+
+
+def test_block_shared_needs_block_genotypes(tmpdir):
+    model = BlockModel()
+    ri, map_params, *_ = _block_setup(model)
+    with pytest.raises(ValueError, match="block_genotypes"):
+        ri.get_laplace_posteriors(map_params, out_prefix=str(tmpdir.join("x")),
+                                  block_shared=True)
+
+
+def test_unscaled_batch_removes_minibatch_scale():
+    """A model configured with batch_size below the library weights each
+    growth likelihood by num_genotype / batch_size even at full batch;
+    the Laplace must see each observation once."""
+    orch = _smoke_orchestrator()
+    ri = RunInference(orch, seed=0)
+    G = orch.data.num_genotype
+    data = jax.device_put(orch.data)
+    scaled = orch.get_batch(data, jnp.arange(G))
+    assert float(jnp.max(scaled.growth.scale_vector)) > 1.0
+    unscaled = ri._unscaled_batch(data, jnp.arange(G))
+    np.testing.assert_array_equal(np.asarray(unscaled.growth.scale_vector), 1.0)
 
-def test_run_optimization_epoch_checkpoint_dir_alongside_out_prefix(tmpdir, mocker):
-    """checkpoints/ is placed in the same directory as out_prefix, not the cwd."""
-    subdir = tmpdir.mkdir("subdir")
-    model = MockModel()
-    ri = RunInference(model, seed=42)
-    mock_svi = _optimization_mocks(mocker)
-    out_prefix = os.path.join(str(subdir), "run")
 
-    ri.run_optimization(
-        mock_svi,
-        out_prefix=out_prefix,
-        max_num_epochs=1,
-        convergence_check_interval=1,
-        checkpoint_interval=1,
-        epoch_checkpoint_interval=1,
-    )
+# =============================================================================
+# Laplace edge cases
+# =============================================================================
 
-    assert os.path.isdir(os.path.join(str(subdir), "checkpoints"))
-    assert not os.path.isdir(os.path.join(str(tmpdir), "checkpoints"))
+def test_get_laplace_posteriors_reports_negative_eigenvalues(tmpdir, mocker,
+                                                             capsys):
+    """A saddle-point MAP is named in the log, and the negative direction is
+    floored at the prior's curvature (Normal(0, 1) priors: precision 1)."""
+    model = LaplaceModel(num_genotype=2)
+    ri, map_params = _laplace_map_params(model)
+    bad_H = np.diag([10.0, -5.0, 8.0])
+    mocker.patch.object(ri, "_chunked_hessian", return_value=bad_H)
+    out_prefix = str(tmpdir.join("laplace_saddle"))
+    ri.get_laplace_posteriors(map_params=map_params, out_prefix=out_prefix,
+                              num_posterior_samples=2000,
+                              sampling_batch_size=1000,
+                              forward_batch_size=2)
+    out = capsys.readouterr().out
+    assert "1 negative Hessian eigenvalues" in out
+    assert "min=-5.000e+00" in out
+    assert "1 of 3 eigenvalues raised" in out
+    with h5py.File(f"{out_prefix}_posterior.h5", "r") as hf:
+        geno = hf["geno_p"][...]
+    # ravel order: geno_p[0], geno_p[1], global_p; geno_p[0] has curvature
+    # 10, geno_p[1] the floored -5 -> prior precision 1
+    assert np.std(geno[:, 0]) == pytest.approx(np.sqrt(0.1), rel=0.1)
+    assert np.std(geno[:, 1]) == pytest.approx(1.0, rel=0.1)
+
+
+def test_laplace_prior_precision_falls_back_for_mismatched_shape(monkeypatch):
+    """An element whose prior SD cannot be broadcast onto it gets
+    LAPLACE_MIN_EIGENVALUE; the others get their prior's precision."""
+    import tfscreen.tfmodel.inference.run_inference as ri_mod
+    real = ri_mod.site_unconstrained_prior_sds
+
+    def wrong_shape_for_a(sites):
+        sds = dict(real(sites))
+        sds["a"] = np.ones(4)   # a is library-sized (6)
+        return sds
+
+    monkeypatch.setattr(ri_mod, "site_unconstrained_prior_sds",
+                        wrong_shape_for_a)
+    model = BlockModel()
+    ri, _, unc, flat, unravel, kw = _block_setup(model)
+    D = flat.shape[0]
+    prec = ri._laplace_prior_precision(unc, kw, D)
+    assert prec.shape == (D,) == (13,)
+    # ravel order: a (6), b (6), mu
+    np.testing.assert_allclose(prec[:6], LAPLACE_MIN_EIGENVALUE)
+    np.testing.assert_allclose(prec[6:], 1.0, rtol=1e-4)
+
+
+def test_block_laplace_factors_refuse_model_without_genotype_parameters():
+    model = BlockModel()
+    ri, _, unc, flat, unravel, kw = _block_setup(model)
+    with pytest.raises(ValueError, match="same number of parameters"):
+        ri.block_laplace_factors(unc, {}, model.data, flat, unravel, kw)
+
+
+def test_arrowhead_holds_negative_shared_directions(mocker, capsys):
+    """A negative direction of the Schur complement is held at the MAP: no
+    variance, named in the log and in held_shared_directions."""
+    model = BlockModel()
+    ri, _, unc, flat, unravel, kw = _block_setup(model)
+    real = ri._genotype_hessian_parts
+
+    def negative_shared(*args, **kwargs):
+        elem_idx, blocks, shared_idx, coupling, shared_block = real(*args,
+                                                                    **kwargs)
+        return (elem_idx, blocks, shared_idx, coupling,
+                shared_block - 1e3 * np.eye(shared_block.shape[0]))
+
+    mocker.patch.object(ri, "_genotype_hessian_parts",
+                        side_effect=negative_shared)
+    elem_idx, L_blocks, shared_idx, L_shared, response = \
+        ri.arrowhead_laplace_factors(unc, ri._get_genotype_dim_map(),
+                                     model.data, flat, unravel, kw)
+    out = capsys.readouterr().out
+    assert "1 of 1 directions of the Schur complement negative" in out
+    assert "eigenvalue" in out and "mu[0]" in out
+    np.testing.assert_allclose(L_shared @ L_shared.T, 0.0, atol=1e-12)
+    held = ri.held_shared_directions
+    assert list(held["parameter"]) == ["mu[0]"]
+    assert held["direction"].iloc[0] == 0
+    assert held["eigenvalue"].iloc[0] < 0
+    assert abs(held["loading"].iloc[0]) == pytest.approx(1.0)
+
+
+def test_report_nuts_diagnostics_survives_failure(capsys):
+    from unittest.mock import MagicMock
+    mcmc = MagicMock()
+    mcmc.get_samples.side_effect = RuntimeError("no chains")
+    RunInference._report_nuts_diagnostics(mcmc)
+    assert "NUTS: diagnostics unavailable (no chains)" in capsys.readouterr().out
diff --git a/tests/tfscreen/tfmodel/inference/test_run_optimization.py b/tests/tfscreen/tfmodel/inference/test_run_optimization.py
new file mode 100644
index 00000000..5dacccbb
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_run_optimization.py
@@ -0,0 +1,742 @@
+"""
+RunInference.run_optimization end to end on a small real model: windows,
+step-size cuts, convergence, checkpoints/resume, output files, and the
+parameter normalizers for each guide family.
+"""
+
+import os
+
+import dill
+import numpy as np
+import pytest
+import jax.numpy as jnp
+import numpyro
+import numpyro.distributions as dist
+import optax
+from flax import struct
+from numpyro import handlers
+from numpyro.distributions.distribution import validation_enabled
+
+import jax
+from numpyro.infer import SVI, Trace_ELBO
+from numpyro.optim import Adam, ClippedAdam
+
+from tfscreen.tfmodel.inference.run_inference import (
+    RunInference,
+    adam_optimizer,
+)
+
+NUM_GENOTYPE = 12
+NUM_OBS = 6
+
+
+@pytest.fixture(autouse=True)
+def _run_in_tmp_path(tmp_path, monkeypatch):
+    monkeypatch.chdir(tmp_path)
+
+
+@struct.dataclass
+class ToyData:
+    batch_idx: jnp.ndarray
+    y: jnp.ndarray
+    num_genotype: int = struct.field(pytree_node=False, default=NUM_GENOTYPE)
+
+
+def toy_model(priors, data):
+    mu = numpyro.sample("mu", dist.Normal(0.0, 10.0))
+    sigma = numpyro.sample("sigma", dist.HalfNormal(1.0))
+    with numpyro.plate("toy_genotype_plate", data.num_genotype):
+        theta = numpyro.sample("theta", dist.Normal(mu, 1.0))
+    theta = theta[..., data.batch_idx]
+    y = data.y[data.batch_idx]
+    batch = data.batch_idx.shape[0]
+    with handlers.scale(scale=data.num_genotype / batch):
+        with numpyro.plate("toy_obs_plate", batch):
+            numpyro.sample("y", dist.Normal(theta[:, None], sigma).to_event(1),
+                           obs=y)
+
+
+def toy_guide(priors, data):
+    mu_loc = numpyro.param("mu_loc", 0.0)
+    mu_scale = numpyro.param("mu_scale", 1.0,
+                             constraint=dist.constraints.positive)
+    numpyro.sample("mu", dist.Normal(mu_loc, mu_scale))
+    sigma_loc = numpyro.param("sigma_loc", 0.0)
+    sigma_scale = numpyro.param("sigma_scale", 1.0,
+                                constraint=dist.constraints.positive)
+    numpyro.sample("sigma", dist.LogNormal(sigma_loc, sigma_scale))
+    theta_locs = numpyro.param("theta_locs", jnp.zeros(data.num_genotype))
+    theta_scales = numpyro.param("theta_scales", jnp.ones(data.num_genotype),
+                                 constraint=dist.constraints.positive)
+    with numpyro.plate("toy_genotype_plate", data.num_genotype):
+        numpyro.sample("theta", dist.Normal(theta_locs, theta_scales))
+
+
+class ToyModel:
+    """RunInference-compatible wrapper around toy_model/toy_guide."""
+
+    def __init__(self, batch_size=None, seed=0):
+        rng = np.random.default_rng(seed)
+        theta = 3.0 + rng.normal(size=NUM_GENOTYPE)
+        y = theta[:, None] + 0.5 * rng.normal(size=(NUM_GENOTYPE, NUM_OBS))
+        self.data = ToyData(batch_idx=jnp.arange(NUM_GENOTYPE),
+                            y=jnp.asarray(y))
+        self.priors = {}
+        self.jax_model = toy_model
+        self.jax_model_guide = toy_guide
+        self.init_params = {}
+        self._batch = batch_size or NUM_GENOTYPE
+
+    def get_batch(self, data, idx):
+        return data.replace(batch_idx=jnp.asarray(idx))
+
+    def get_random_idx(self, batch_key=None, num_batches=1):
+        if batch_key is not None:
+            self._rng = np.random.default_rng(batch_key)
+        if not hasattr(self, "_rng"):
+            self._rng = np.random.default_rng(0)
+        idx = np.stack([self._rng.permutation(NUM_GENOTYPE)[:self._batch]
+                        for _ in range(num_batches)])
+        return idx[0] if num_batches == 1 else idx
+
+
+def _optimizer_step_size(svi):
+    """Adam's first step from a fresh state moves each parameter by exactly
+    the step size, whatever the gradient."""
+    params = {"x": jnp.zeros(1)}
+    state = svi.optim.init(params)
+    state = svi.optim.update({"x": jnp.array([0.3])}, state)
+    return abs(float(svi.optim.get_params(state)["x"][0]))
+
+
+def _fit(ri, svi, out_prefix="toy", **kwargs):
+    opts = dict(convergence_window_steps=400, patience=2,
+                max_num_epochs=40000, epoch_checkpoint_interval=None,
+                checkpoint_interval=100)
+    opts.update(kwargs)
+    return ri.run_optimization(svi, out_prefix=out_prefix, **opts)
+
+
+# -----------------------------------------------------------------------------
+# Convergence and step-size cuts
+# -----------------------------------------------------------------------------
+
+def test_component_svi_converges_through_cuts():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    state, params, converged = _fit(ri, svi, final_step_size=1e-4)
+
+    assert converged
+    assert ri._monitor.num_cuts == 2
+    assert ri._monitor.step_size == pytest.approx(1e-4)
+    # the optimizer really runs at the final step size
+    assert _optimizer_step_size(svi) == pytest.approx(1e-4, rel=1e-3)
+    # sensible posterior: mu near the mean of the theta estimates
+    assert float(params["mu_loc"]) == pytest.approx(
+        float(np.mean(params["theta_locs"])), abs=0.5)
+
+    # convergence.csv: a row per window, ending in "converged"
+    with open("toy_convergence.csv") as f:
+        rows = [line.strip().split(",") for line in f]
+    header, rows = rows[0], rows[1:]
+    assert header[0] == "step" and "decision" in header
+    decisions = [r[header.index("decision")] for r in rows]
+    assert decisions.count("cut") == 2
+    assert decisions[-1] == "converged"
+
+    with open("toy_losses.txt") as f:
+        assert f.readline().strip() == "epoch,loss,step,step_size"
+
+
+def test_minibatched_svi_converges():
+    model = ToyModel(batch_size=4)
+    ri = RunInference(model, seed=1)
+    assert ri._iterations_per_epoch == 3
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _, _, converged = _fit(ri, svi, final_step_size=1e-4)
+    assert converged
+
+
+def test_map_converges_and_uses_prior_sds():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="delta")
+    _, params, converged = _fit(ri, svi, final_step_size=1e-4)
+    assert converged
+    assert "mu_auto_loc" in params
+
+
+def test_max_epochs_reports_not_converged(capsys):
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-3, guide_type="component")
+    _, _, converged = _fit(ri, svi, final_step_size=1e-6, max_num_epochs=500)
+    assert not converged
+    assert "without converging" in capsys.readouterr().out
+
+
+def test_no_final_step_size_keeps_step_size():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _, _, converged = _fit(ri, svi)
+    assert converged
+    assert ri._monitor.num_cuts == 0
+
+
+def test_schedule_with_cuts_is_refused():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=optax.constant_schedule(1e-3),
+                       guide_type="component")
+    with pytest.raises(ValueError, match="constant step"):
+        _fit(ri, svi, final_step_size=1e-6)
+
+
+def test_schedule_without_cuts_runs():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=optax.constant_schedule(1e-2),
+                       guide_type="component")
+    _fit(ri, svi, max_num_epochs=1000)
+
+
+# -----------------------------------------------------------------------------
+# Optimizer: gradient clipping off by default
+# -----------------------------------------------------------------------------
+
+def test_setup_svi_defaults_to_unclipped_adam():
+    ri = RunInference(ToyModel(), seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    assert type(svi.optim) is Adam
+    clipped = ri.setup_svi(adam_step_size=1e-2, adam_clip_norm=2.0,
+                           guide_type="component")
+    assert isinstance(clipped.optim, ClippedAdam)
+    assert clipped.optim.clip_norm == 2.0
+
+
+@pytest.mark.parametrize("clip_norm", [None, 2.0])
+def test_step_size_cut_keeps_clip_choice(clip_norm):
+    ri = RunInference(ToyModel(), seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, adam_clip_norm=clip_norm,
+                       guide_type="component")
+    _fit(ri, svi, final_step_size=1e-4)
+    assert ri._monitor.num_cuts > 0
+    if clip_norm is None:
+        assert type(svi.optim) is Adam
+    else:
+        assert isinstance(svi.optim, ClippedAdam)
+        assert svi.optim.clip_norm == clip_norm
+    with open("toy_checkpoint.pkl", "rb") as f:
+        assert dill.load(f)["adam_clip_norm"] == clip_norm
+
+
+def test_clipped_checkpoint_resumes_unclipped():
+    """Adam and ClippedAdam share their state, so a checkpoint written under
+    one resumes under the other."""
+    ri = RunInference(ToyModel(), seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, adam_clip_norm=1.0,
+                       guide_type="component")
+    _fit(ri, svi, max_num_epochs=50)
+    ri2 = RunInference(ToyModel(), seed=0)
+    svi2 = ri2.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _fit(ri2, svi2, svi_state="toy_checkpoint.pkl", max_num_epochs=50)
+    assert ri2._current_step == 100
+
+
+def _wall_model():
+    """A latent pulled toward x = 5 by many observations, and one binding-like
+    observation, measured with SD 1e-3, of a step that is 'on' only for
+    x < 0.  The guide's width is fixed (in the real model it is held up by
+    shared hyperparameters), so the optimizer can only trade the pull against
+    the rare draws that cross the step."""
+
+    def model():
+        x = numpyro.sample("x", dist.Normal(0.0, 10.0))
+        numpyro.sample("pull", dist.Normal(x, 1.0).expand([50]).to_event(1),
+                       obs=jnp.full(50, 5.0))
+        theta = jax.nn.sigmoid(200.0 * (0.0 - x))
+        numpyro.sample("wall", dist.Normal(theta, 1e-3), obs=0.999)
+
+    def guide():
+        loc = numpyro.param("x_loc", -1.0)
+        numpyro.sample("x", dist.Normal(loc, 0.3))
+
+    return model, guide
+
+
+@pytest.mark.parametrize("clip_norm,crosses", [(1.0, True), (None, False)])
+def test_elementwise_clip_walks_through_rare_penalties(clip_norm, crosses):
+    """The failure behind the congression-calibration blow-ups.  Gradients of
+    this loss are far above 1, so ClippedAdam follows each draw's gradient
+    sign: the pull wins every draw that misses the step, and past the step
+    (where it saturates) every draw, so the location is carried through the
+    constraint.  Unclipped Adam balances the expected gradient and keeps the
+    guide on the allowed side, crossing on ~1% of draws."""
+    model, guide = _wall_model()
+    svi = SVI(model, guide, adam_optimizer(1e-2, clip_norm), Trace_ELBO())
+    result = svi.run(jax.random.PRNGKey(0), 3000, progress_bar=False)
+    loc = float(result.params["x_loc"])
+    draws = loc + 0.3 * np.random.default_rng(0).normal(size=20000)
+    p_cross = float(np.mean(draws > 0.0))
+    if crosses:
+        assert loc > 0 and p_cross > 0.5
+    else:
+        assert loc < 0 and p_cross < 0.05
+
+
+# -----------------------------------------------------------------------------
+# Resume, step counter, checkpoints
+# -----------------------------------------------------------------------------
+
+def test_resume_continues_stage_and_step():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    # first cut near step 6,400 (the pooled stall check keeps the descent
+    # that ends there at the large step size)
+    _fit(ri, svi, final_step_size=1e-5, max_num_epochs=7200,
+         checkpoint_interval=10)
+    saved_step = ri._current_step
+    saved_step_size = ri._monitor.step_size
+    assert saved_step_size < 1e-2  # at least one cut happened
+
+    with open("toy_checkpoint.pkl", "rb") as f:
+        ckpt = dill.load(f)
+    assert ckpt["step_size"] == pytest.approx(saved_step_size)
+    assert ckpt["convergence"]["step_size"] == pytest.approx(saved_step_size)
+    rows_before = sum(1 for _ in open("toy_convergence.csv"))
+
+    ri2 = RunInference(ToyModel(), seed=0)
+    svi2 = ri2.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _fit(ri2, svi2, svi_state="toy_checkpoint.pkl", final_step_size=1e-5,
+         max_num_epochs=400)
+    assert ri2._current_step == saved_step + 400
+    assert _optimizer_step_size(svi2) <= saved_step_size * (1 + 1e-3)
+    # resumed runs append to the convergence record
+    assert sum(1 for _ in open("toy_convergence.csv")) > rows_before
+
+
+def test_resume_old_checkpoint_without_convergence_state():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _fit(ri, svi, max_num_epochs=50)
+    with open("toy_checkpoint.pkl", "rb") as f:
+        ckpt = dill.load(f)
+    del ckpt["convergence"], ckpt["step_size"]
+    ckpt["loss_start"], ckpt["loss_best"] = 1.0, 0.5
+    with open("old_checkpoint.pkl", "wb") as f:
+        dill.dump(ckpt, f)
+
+    ri2 = RunInference(ToyModel(), seed=0)
+    svi2 = ri2.setup_svi(adam_step_size=1e-3, guide_type="component")
+    _fit(ri2, svi2, svi_state="old_checkpoint.pkl", max_num_epochs=50)
+    assert ri2._monitor.step_size == pytest.approx(1e-3)
+
+
+def test_resume_migrates_old_convergence_file():
+    """A convergence file written before the loss-skew columns existed is
+    rewritten under the current header on resume; old values keep their
+    columns and new ones are left empty."""
+    ri = RunInference(ToyModel(), seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _fit(ri, svi, max_num_epochs=50)
+    old_cols = [c for c in ri._CONVERGENCE_COLUMNS
+                if c not in ("loss_mean", "loss_skew", "loss_skewed")]
+    with open("toy_convergence.csv", "w") as f:
+        f.write(",".join(old_cols) + "\n")
+        f.write(",".join("7" if c == "step" else "x" for c in old_cols) + "\n")
+
+    ri2 = RunInference(ToyModel(), seed=0)
+    svi2 = ri2.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _fit(ri2, svi2, svi_state="toy_checkpoint.pkl", max_num_epochs=1000)
+
+    import csv
+    with open("toy_convergence.csv", newline="") as f:
+        rows = list(csv.DictReader(f))
+        f.seek(0)
+        header = next(csv.reader(f))
+    assert tuple(header) == ri2._CONVERGENCE_COLUMNS
+    assert rows[0]["step"] == "7" and rows[0]["loss_skew"] == ""
+    assert len(rows) > 1 and rows[-1]["loss_skew"] != ""
+
+
+def test_fresh_run_resets_step_counter():
+    """A pre-MAP followed by SVI on the same RunInference: SVI's counters,
+    loss file and epoch checkpoints start at zero."""
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    ri._current_step = 777
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _fit(ri, svi, max_num_epochs=100)
+    assert ri._current_step == 100
+    with open("toy_losses.txt") as f:
+        lines = f.read().splitlines()
+    assert lines[0] == "epoch,loss,step,step_size"
+    assert int(lines[1].split(",")[2]) < 100
+
+
+def test_invalid_checkpoint_path():
+    ri = RunInference(ToyModel(), seed=0)
+    svi = ri.setup_svi(guide_type="component")
+    with pytest.raises(ValueError, match="is not valid"):
+        _fit(ri, svi, svi_state="missing.pkl", max_num_epochs=1)
+
+
+def test_nan_explosion():
+    # numpyro >= 0.22 validates distribution arguments by default, so svi.init
+    # would refuse the NaN loc before the loop's own NaN check could see it.
+    ri = RunInference(ToyModel(), seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    with validation_enabled(False):
+        with pytest.raises(RuntimeError, match="model exploded"):
+            _fit(ri, svi, init_params={"mu_loc": jnp.array(np.nan)},
+                 init_param_jitter=0.0, max_num_epochs=50)
+
+
+def test_epoch_checkpoints(tmp_path):
+    sub = tmp_path / "sub"
+    sub.mkdir()
+    prefix = str(sub / "run")
+    ri = RunInference(ToyModel(), seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _fit(ri, svi, out_prefix=prefix, max_num_epochs=100,
+         epoch_checkpoint_interval=50)
+    ckpt_dir = sub / "checkpoints"
+    assert ckpt_dir.is_dir()
+    assert not (tmp_path / "checkpoints").exists()
+    names = sorted(os.listdir(ckpt_dir))
+    assert "0000050_checkpoint.pkl" in names
+
+    ri2 = RunInference(ToyModel(), seed=1)
+    svi2 = ri2.setup_svi(adam_step_size=1e-2, guide_type="component")
+    with pytest.raises(FileExistsError, match="checkpoint.pkl"):
+        _fit(ri2, svi2, out_prefix=prefix, max_num_epochs=100,
+             epoch_checkpoint_interval=50)
+
+
+@pytest.mark.parametrize("interval", [None, 0])
+def test_epoch_checkpoints_disabled(interval):
+    ri = RunInference(ToyModel(), seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _fit(ri, svi, max_num_epochs=60, epoch_checkpoint_interval=interval)
+    assert not os.path.isdir("checkpoints")
+
+
+# -----------------------------------------------------------------------------
+# Parameter normalizers
+# -----------------------------------------------------------------------------
+
+def _specs(guide_type, **guide_kwargs):
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(guide_type=guide_type, guide_kwargs=guide_kwargs)
+    idx = model.get_random_idx()
+    state = svi.init(ri.get_key(), priors=model.priors,
+                     data=model.get_batch(model.data, idx))
+    unconstrained = svi.optim.get_params(state.optim_state)
+    specs = ri._param_normalizer_specs(svi, state, unconstrained)
+    return ri, svi, state, specs
+
+
+def test_normalizers_component_guide():
+    _, _, _, specs = _specs("component")
+    assert specs["theta_locs"][:2] == ("scale", "theta_scales")
+    assert specs["mu_loc"][:2] == ("scale", "mu_scale")
+    # LogNormal location (log space) paired with its log-space scale
+    assert specs["sigma_loc"][:2] == ("scale", "sigma_scale")
+    assert specs["theta_scales"] == ("unit",)
+
+    # Floors: PRIOR_SD_FLOOR x the prior SD in the location's own units --
+    # Normal(0, 10) for mu, Normal(mu, 1) for theta, and log units for the
+    # HalfNormal(1) sigma (log|Z| has SD ~1.1; a robust MC estimate)
+    np.testing.assert_allclose(specs["mu_loc"][2], 0.1, rtol=1e-5)
+    np.testing.assert_allclose(specs["theta_locs"][2],
+                               np.full(NUM_GENOTYPE, 0.01), rtol=1e-5)
+    assert 0.005 < float(specs["sigma_loc"][2]) < 0.02
+
+
+def test_normalizer_floors_collapsed_guide_scale():
+    """A guide scale far below the prior SD is not used as the yardstick."""
+    ri, svi, state, specs = _specs("component")
+    constrained = dict(svi.get_params(state))
+    constrained["theta_scales"] = jnp.full(NUM_GENOTYPE, 1e-5)
+    constrained["mu_scale"] = jnp.asarray(0.5)
+    theta = ri._normalizer(specs["theta_locs"], constrained)
+    np.testing.assert_allclose(theta, 0.01, rtol=1e-5)
+    # a scale above its floor is used as is
+    mu = ri._normalizer(specs["mu_loc"], constrained)
+    assert float(mu) == pytest.approx(0.5)
+
+
+def test_normalizers_auto_normal():
+    _, _, _, specs = _specs("auto_normal")
+    assert specs["theta_auto_loc"][:2] == ("scale", "theta_auto_scale")
+    np.testing.assert_allclose(specs["theta_auto_loc"][2],
+                               np.full(NUM_GENOTYPE, 0.01), rtol=1e-5)
+    assert specs["theta_auto_scale"] == ("unit",)
+
+
+def test_normalizers_delta_use_prior_sd():
+    _, _, _, specs = _specs("delta")
+    kind, sd = specs["mu_auto_loc"]
+    assert kind == "prior"
+    assert float(sd) == pytest.approx(10.0)
+    assert specs["theta_auto_loc"][0] == "prior"
+    # positive site: tracked in log units
+    assert specs["sigma_auto_loc"] == ("unit",)
+
+
+@pytest.mark.parametrize("guide_type,kwargs", [
+    ("auto_diagonal_normal", {}),
+    ("auto_multivariate_normal", {}),
+    ("auto_low_rank_multivariate_normal", {"rank": 2}),
+])
+def test_normalizers_auto_continuous(guide_type, kwargs):
+    ri, svi, state, specs = _specs(guide_type, **kwargs)
+    kind, floor = specs["auto_loc"]
+    assert kind == "auto_continuous"
+    assert not any("scale_tril" in k or "cov_factor" in k for k in specs)
+    norm = ri._normalizer(specs["auto_loc"], svi.get_params(state))
+    assert norm.shape == (NUM_GENOTYPE + 2,)
+    assert np.all(np.asarray(norm) > 0)
+
+    # The floor follows the guide's latent layout: mu, sigma (log units),
+    # then the theta block.
+    floor = np.asarray(floor)
+    assert floor.shape == (NUM_GENOTYPE + 2,)
+    unpacked = svi.guide._unpack_latent(jnp.asarray(floor))
+    np.testing.assert_allclose(unpacked["mu"], 0.1, rtol=1e-5)
+    np.testing.assert_allclose(unpacked["theta"], 0.01, rtol=1e-5)
+    assert 0.005 < float(unpacked["sigma"]) < 0.02
+
+
+# -----------------------------------------------------------------------------
+# Starting points
+# -----------------------------------------------------------------------------
+
+def test_site_values_and_component_guide_start():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    guesses = {"mu": 2.5, "theta": np.full(NUM_GENOTYPE, 1.5),
+               "sigma_loc": np.log(0.7)}
+    values = ri.site_values(guesses)
+    assert float(values["mu"]) == pytest.approx(2.5)
+    assert float(values["sigma"]) == pytest.approx(0.7)  # exp of guide loc
+    assert values["theta"].shape == (NUM_GENOTYPE,)
+
+    start = ri.component_guide_start(values, guesses=guesses, init_scale=0.1)
+    assert float(start["mu_loc"]) == pytest.approx(2.5)
+    assert float(start["sigma_loc"]) == pytest.approx(np.log(0.7))
+    np.testing.assert_allclose(start["theta_locs"], 1.5)
+    np.testing.assert_allclose(start["theta_scales"], 0.1)
+    assert float(start["mu_scale"]) == pytest.approx(0.1)
+
+
+def test_site_values_from_map_result():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    values = ri.site_values({"mu_auto_loc": 4.0, "mu": 1.0,
+                             "unknown_auto_loc": 3.0})
+    assert float(values["mu"]) == pytest.approx(4.0)  # MAP key wins
+    assert "unknown" not in values
+
+
+def test_delta_init_values_start_the_map():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(guide_type="delta", init_values={"mu": 7.0})
+    state = svi.init(ri.get_key(), priors=model.priors,
+                     data=model.get_batch(model.data, model.get_random_idx()))
+    params = svi.get_params(state)
+    assert float(params["mu_auto_loc"]) == pytest.approx(7.0)
+    # sites without a value fall back to the prior median (as AutoDelta
+    # does), here given the substituted mu
+    assert float(params["theta_auto_loc"][0]) == pytest.approx(7.0, abs=1.0)
+
+
+# -----------------------------------------------------------------------------
+# Exact (full-batch) loss for MAP windows
+# -----------------------------------------------------------------------------
+
+def _convergence_rows(path="toy_convergence.csv"):
+    with open(path) as f:
+        rows = [line.rstrip("\n").split(",") for line in f]
+    return rows[0], rows[1:]
+
+
+def test_map_windows_use_exact_loss():
+    from numpyro.infer.util import log_density
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="delta")
+    state, params, converged = _fit(ri, svi, final_step_size=1e-4)
+    assert converged
+    header, rows = _convergence_rows()
+    exact = [float(r[header.index("loss_exact")]) for r in rows]
+    assert np.all(np.isfinite(exact))
+    # the last window's exact loss is -log p at the final point
+    values = {k[:-len("_auto_loc")]: v for k, v in params.items()}
+    lp = log_density(model.jax_model, (),
+                     {"data": model.data, "priors": model.priors}, values)[0]
+    assert exact[-1] == pytest.approx(-float(lp), rel=1e-5)
+
+
+def test_svi_windows_keep_minibatch_loss():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="component")
+    _fit(ri, svi, final_step_size=1e-4)
+    header, rows = _convergence_rows()
+    assert all(r[header.index("loss_exact")] in ("", "nan") for r in rows)
+
+
+def test_exact_loss_can_be_turned_off():
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=1e-2, guide_type="delta")
+    _fit(ri, svi, final_step_size=1e-4, exact_loss=False)
+    header, rows = _convergence_rows()
+    assert all(r[header.index("loss_exact")] in ("", "nan") for r in rows)
+
+
+# -----------------------------------------------------------------------------
+# Step size read off a hand-built SVI; runaway (unbounded) loss
+# -----------------------------------------------------------------------------
+
+def test_step_size_read_from_optimizer_when_not_set_up():
+    """An SVI not built by setup_svi gives its step size through the
+    optimizer's ``step_size`` attribute."""
+    model = ToyModel()
+    ri = RunInference(model, seed=0)
+    assert ri._step_size is None
+    optim = Adam(1e-2)
+    optim.step_size = 1e-2
+    svi = SVI(toy_model, toy_guide, optim, Trace_ELBO())
+    _fit(ri, svi, max_num_epochs=50)
+    assert ri._monitor.step_size == pytest.approx(1e-2)
+
+    # no attribute at all: the monitor has no step size
+    ri2 = RunInference(ToyModel(), seed=0)
+    svi2 = SVI(toy_model, toy_guide, Adam(1e-2), Trace_ELBO())
+    _fit(ri2, svi2, max_num_epochs=50)
+    assert np.isnan(ri2._monitor.step_size)
+
+
+def _runaway_model(priors, data):
+    """A wide prior and a factor that grows without bound."""
+    x = numpyro.sample("x", dist.Normal(0.0, 1e3))
+    numpyro.factor("pull", jnp.exp(x))
+
+
+def test_runaway_loss_stops_as_diverged(capsys):
+    model = ToyModel()
+    model.jax_model = _runaway_model
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(adam_step_size=0.05, guide_type="delta")
+    _, params, converged = _fit(ri, svi, final_step_size=1e-4,
+                                max_num_epochs=40000)
+    assert not converged
+    assert ri._monitor.diverged
+    out = capsys.readouterr().out
+    assert "the loss ran away" in out and "Not converged" in out
+    # it stopped at the first window, far short of max_num_epochs
+    assert ri._current_step < 40000
+    assert os.path.isfile("toy_checkpoint.pkl")
+    header, rows = _convergence_rows()
+    assert rows[-1][header.index("decision")] == "diverged"
+
+
+# -----------------------------------------------------------------------------
+# Normalizer edge cases
+# -----------------------------------------------------------------------------
+
+def test_param_transforms_without_init_is_empty():
+    assert RunInference._param_transforms(object()) == {}
+    svi = SVI(toy_model, toy_guide, Adam(1e-2), Trace_ELBO())
+    assert RunInference._param_transforms(svi) == {}
+
+
+def test_auto_continuous_normalizer_without_scale_params():
+    norm = RunInference._normalizer(("auto_continuous", None), {})
+    assert float(norm) == pytest.approx(1.0)
+    norm = RunInference._normalizer(("auto_continuous", jnp.array([0.5, 3.0])),
+                                    {})
+    np.testing.assert_allclose(norm, [1.0, 3.0])
+
+
+def test_posterior_sd_floors_skip_mismatched_shapes():
+    """A location whose shape cannot take its site's prior SD gets no floor
+    (component guide and AutoNormal alike)."""
+    for guide_type, loc in [("component", "theta_locs"),
+                            ("auto_normal", "theta_auto_loc")]:
+        ri, svi, state, _ = _specs(guide_type)
+        unconstrained = svi.optim.get_params(state.optim_state)
+        shapes = {k: jnp.shape(v) for k, v in unconstrained.items()}
+        assert loc in ri._posterior_sd_floors(svi, state, shapes)
+        shapes[loc] = (5,)
+        floors = ri._posterior_sd_floors(svi, state, shapes)
+        assert loc not in floors
+        # the other locations keep theirs
+        assert len(floors) >= 1
+
+
+def test_auto_continuous_floor_zero_for_site_without_prior_sd(monkeypatch):
+    import tfscreen.tfmodel.inference.run_inference as ri_mod
+    real = ri_mod.site_unconstrained_prior_sds
+
+    def drop_mu(sites):
+        sds = dict(real(sites))
+        sds.pop("mu", None)
+        return sds
+
+    monkeypatch.setattr(ri_mod, "site_unconstrained_prior_sds", drop_mu)
+    ri, svi, state, specs = _specs("auto_diagonal_normal")
+    floor = svi.guide._unpack_latent(jnp.asarray(specs["auto_loc"][1]))
+    assert float(floor["mu"]) == 0.0
+    np.testing.assert_allclose(floor["theta"], 0.01, rtol=1e-5)
+
+
+def _odd_guide(priors, data):
+    """toy_guide, but mu's parameters do not follow {site}_loc/_scale."""
+    m = numpyro.param("mu_center", 0.0)
+    s = numpyro.param("mu_width", 1.0, constraint=dist.constraints.positive)
+    numpyro.sample("mu", dist.Normal(m, s))
+    sigma_loc = numpyro.param("sigma_loc", 0.0)
+    sigma_scale = numpyro.param("sigma_scale", 1.0,
+                                constraint=dist.constraints.positive)
+    numpyro.sample("sigma", dist.LogNormal(sigma_loc, sigma_scale))
+    theta_locs = numpyro.param("theta_locs", jnp.zeros(data.num_genotype))
+    theta_scales = numpyro.param("theta_scales", jnp.ones(data.num_genotype),
+                                 constraint=dist.constraints.positive)
+    with numpyro.plate("toy_genotype_plate", data.num_genotype):
+        numpyro.sample("theta", dist.Normal(theta_locs, theta_scales))
+
+
+def test_unpaired_real_location_is_tracked_as_is():
+    model = ToyModel()
+    model.jax_model_guide = _odd_guide
+    ri = RunInference(model, seed=0)
+    svi = ri.setup_svi(guide_type="component")
+    state = svi.init(ri.get_key(), priors=model.priors,
+                     data=model.get_batch(model.data, model.get_random_idx()))
+    specs = ri._param_normalizer_specs(
+        svi, state, svi.optim.get_params(state.optim_state))
+    assert specs["mu_center"] == ("unit",)
+    assert specs["theta_locs"][:2] == ("scale", "theta_scales")
+
+
+def test_component_guide_start_reports_unmatched_and_skipped(capsys):
+    model = ToyModel()
+    model.jax_model_guide = _odd_guide
+    ri = RunInference(model, seed=0)
+    start = ri.component_guide_start({"mu": 2.0, "sigma": -1.0,
+                                      "theta": np.full(NUM_GENOTYPE, 1.5)})
+    out = capsys.readouterr().out
+    assert "without a recognized location parameter" in out and "mu" in out
+    assert "non-positive values for LogNormal-guided sites" in out
+    assert "sigma" in out
+    assert "mu_center" not in start and "sigma_loc" not in start
+    np.testing.assert_allclose(start["theta_locs"], 1.5)
diff --git a/tests/tfscreen/tfmodel/inference/test_staged_map.py b/tests/tfscreen/tfmodel/inference/test_staged_map.py
new file mode 100644
index 00000000..4020af66
--- /dev/null
+++ b/tests/tfscreen/tfmodel/inference/test_staged_map.py
@@ -0,0 +1,238 @@
+"""Tests for tfscreen.tfmodel.inference.staged_map (the staged level-offset MAP)."""
+import os
+from types import SimpleNamespace
+
+import jax.numpy as jnp
+import numpy as np
+import numpyro
+import numpyro.distributions as dist
+import pytest
+from numpyro.handlers import seed, trace
+
+from tfscreen.tfmodel.inference import staged_map as S
+
+OFF = "sample_offset_offset"
+SIG = "sample_offset_sigma"
+
+
+# ---------------------------------------------------------------------------
+# HeldModel
+# ---------------------------------------------------------------------------
+
+class _Toy:
+    """A minimal model object: two latents, an attribute to delegate."""
+
+    data = "the data"
+    priors = "the priors"
+    jax_model_guide = "the guide"
+
+    @staticmethod
+    def jax_model():
+        a = numpyro.sample("a", dist.Normal(0.0, 1.0))
+        b = numpyro.sample("b", dist.Normal(0.0, 1.0).expand([3]).to_event(1))
+        return a, b
+
+
+def test_held_model_conditions_sites():
+    held = S.HeldModel(_Toy(), {"b": np.array([1.0, 2.0, 3.0])})
+    tr = trace(seed(held.jax_model, 0)).get_trace()
+    assert not tr["a"]["is_observed"]
+    assert tr["b"]["is_observed"]
+    assert np.allclose(tr["b"]["value"], [1.0, 2.0, 3.0])
+    # held values become jax arrays (the model indexes them with tracers)
+    assert isinstance(held.held["b"], jnp.ndarray)
+
+
+def test_held_model_delegates():
+    held = S.HeldModel(_Toy(), {"a": 0.0})
+    assert held.data == "the data"
+    assert held.priors == "the priors"
+    assert held.jax_model_guide == "the guide"
+
+
+# ---------------------------------------------------------------------------
+# use_stages
+# ---------------------------------------------------------------------------
+
+def _orch(sample_offset="level", sigma_fixed=0.0, sigma_prior_scale=0.2):
+    so = SimpleNamespace(sigma_fixed=sigma_fixed, sigma_prior_scale=sigma_prior_scale)
+    growth = SimpleNamespace(num_replicate=1, num_time=2, num_condition_pre=1,
+                             num_condition_sel=2, num_titrant_name=1,
+                             num_titrant_conc=3)
+    return SimpleNamespace(settings={"sample_offset": sample_offset},
+                           jax_model=_Toy.jax_model,
+                           priors=SimpleNamespace(growth=SimpleNamespace(sample_offset=so)),
+                           data=SimpleNamespace(growth=growth))
+
+
+@pytest.mark.parametrize("kwargs,expected", [
+    (dict(stage_offsets="auto", analysis_method="map"), True),
+    (dict(stage_offsets="auto", analysis_method="svi"), False),
+    (dict(stage_offsets="auto", analysis_method="map", checkpoint_file="c.pkl"), False),
+    (dict(stage_offsets="auto", analysis_method="map", init_from="p.npz"), False),
+    (dict(stage_offsets="off", analysis_method="map"), False),
+    (dict(stage_offsets="on", analysis_method="map"), True),
+])
+def test_use_stages(kwargs, expected):
+    stage = kwargs.pop("stage_offsets")
+    method = kwargs.pop("analysis_method")
+    assert S.use_stages(stage, _orch(), method, **kwargs) is expected
+
+
+def test_use_stages_auto_needs_level():
+    assert S.use_stages("auto", _orch(sample_offset="zero"), "map") is False
+
+
+@pytest.mark.parametrize("orch,method,kwargs,match", [
+    (_orch(sample_offset="zero"), "map", {}, "level"),
+    (_orch(), "svi", {}, "analysis_method"),
+    (_orch(), "map", {"init_from": "p.npz"}, "init_from"),
+])
+def test_use_stages_on_refuses(orch, method, kwargs, match):
+    with pytest.raises(ValueError, match=match):
+        S.use_stages("on", orch, method, **kwargs)
+
+
+def test_use_stages_bad_choice():
+    with pytest.raises(ValueError, match="stage_offsets"):
+        S.use_stages("maybe", _orch(), "map")
+
+
+def test_offset_sites_sigma_hold():
+    assert S.offset_sites(_orch())[2] == 0.2
+    assert S.offset_sites(_orch(sigma_fixed=0.17))[2] is None
+
+
+# ---------------------------------------------------------------------------
+# run_staged_map, with a fake fit
+# ---------------------------------------------------------------------------
+
+class _FakeRI:
+    def __init__(self, model):
+        self.model = model
+
+    def site_values(self, values):
+        return {k[:-len("_auto_loc")] if k.endswith("_auto_loc") else k: v
+                for k, v in values.items()}
+
+
+class _FakeMap:
+    """Stands in for _run_map: records each call and writes a params file."""
+
+    def __init__(self):
+        self.calls = []
+
+    def __call__(self, ri, init_values=None, checkpoint_file=None,
+                 out_prefix=None, label=None, **kwargs):
+        held = ri.model.held if isinstance(ri.model, S.HeldModel) else {}
+        self.calls.append(dict(held=held, init_values=init_values,
+                               checkpoint_file=checkpoint_file,
+                               out_prefix=out_prefix, kwargs=kwargs))
+        if OFF in held:                       # stage 1: everything but offsets
+            out = {"k_auto_loc": np.array(0.01), "theta_auto_loc": np.ones(4)}
+        elif "k" in held:                     # stage 2: offsets only
+            out = {f"{OFF}_auto_loc": np.linspace(-0.1, 0.1, 6)}
+        else:                                 # stage 3: everything
+            out = {"k_auto_loc": np.array(0.02), f"{OFF}_auto_loc": np.zeros(6)}
+        np.savez(f"{out_prefix}_params.npz", **out)
+        return "state", out, True
+
+
+def _run(tmp_path, orch=None, **kw):
+    fake = _FakeMap()
+    result = S.run_staged_map(orch or _orch(), make_ri=_FakeRI, run_map=fake,
+                              guesses={"k": 0.0}, out_prefix=str(tmp_path / "fit"),
+                              map_kwargs={"adam_step_size": 1e-3, "patience": 3},
+                              **kw)
+    return fake, result
+
+
+def test_three_stages(tmp_path):
+    fake, result = _run(tmp_path)
+    s1, s2, s3 = fake.calls
+    # stage 1: offsets held at 0, learned sigma held at its prior scale
+    assert set(s1["held"]) == {OFF, SIG}
+    assert np.allclose(s1["held"][OFF], 0.0) and s1["held"][OFF].shape == (12,)
+    assert float(s1["held"][SIG]) == pytest.approx(0.2)
+    assert s1["out_prefix"].endswith("fit_stage1")
+    # stage 2: everything from stage 1 held, offsets free
+    assert set(s2["held"]) == {"k", "theta", SIG}
+    assert s2["out_prefix"].endswith("fit_stage2")
+    # stage 3: no holds, starts at stage 1 + stage 2's offsets, small step
+    assert s3["held"] == {}
+    assert s3["out_prefix"].endswith("fit")
+    assert s3["kwargs"]["adam_step_size"] == S.DEFAULT_STAGED_STEP_SIZE
+    assert float(s3["init_values"]["k"]) == 0.01
+    assert np.allclose(s3["init_values"][OFF], np.linspace(-0.1, 0.1, 6))
+    assert result[2] is True
+
+
+def test_fixed_sigma_not_held(tmp_path):
+    fake, _ = _run(tmp_path, orch=_orch(sigma_fixed=0.17))
+    assert set(fake.calls[0]["held"]) == {OFF}
+    assert SIG not in fake.calls[1]["held"]
+
+
+def test_staged_step_size_and_stage_kwargs(tmp_path):
+    fake = _FakeMap()
+    S.run_staged_map(_orch(), _FakeRI, fake, {}, str(tmp_path / "fit"),
+                     map_kwargs={"adam_step_size": 1e-3, "patience": 3},
+                     stage_map_kwargs={"adam_step_size": 1e-2, "patience": 1},
+                     staged_step_size=5e-5)
+    assert fake.calls[0]["kwargs"] == {"adam_step_size": 1e-2, "patience": 1}
+    assert fake.calls[2]["kwargs"] == {"adam_step_size": 5e-5, "patience": 3}
+
+
+def test_finished_stage_reused(tmp_path):
+    _run(tmp_path)
+    os.remove(tmp_path / "fit_params.npz")
+    fake, _ = _run(tmp_path)
+    # stages 1 and 2 are read back; only the joint stage runs
+    assert len(fake.calls) == 1
+    assert fake.calls[0]["held"] == {}
+
+
+def test_interrupted_stage_resumes(tmp_path):
+    open(tmp_path / "fit_stage1_checkpoint.pkl", "w").close()
+    fake, _ = _run(tmp_path)
+    assert fake.calls[0]["checkpoint_file"] == str(tmp_path / "fit_stage1_checkpoint.pkl")
+    assert fake.calls[0]["init_values"] is None
+    assert fake.calls[1]["checkpoint_file"] is None
+
+
+def test_model_params_follow_the_stages(tmp_path):
+    """The model's own learned params (thermo PnnC NN) are not sites:
+    stage 2 holds them at stage 1's values and stage 3 starts from them."""
+    import numpyro
+    from numpyro import handlers
+
+    class _ParamMap(_FakeMap):
+        def __call__(self, ri, init_values=None, checkpoint_file=None,
+                     out_prefix=None, label=None, **kwargs):
+            out = super().__call__(ri, init_values, checkpoint_file,
+                                   out_prefix, label, **kwargs)
+            if out_prefix.endswith("stage1"):     # stage 1 learned NN weights
+                np.savez(f"{out_prefix}_params.npz",
+                         **{k: v for k, v in out[1].items()},
+                         theta_nn_H_b2=np.array([0.5, 2.0]))
+            self.calls[-1]["model"] = ri.model
+            return out
+
+    fake = _ParamMap()
+    S.run_staged_map(_orch(), make_ri=_FakeRI, run_map=fake,
+                     guesses={"k": 0.0}, out_prefix=str(tmp_path / "fit"),
+                     map_kwargs={"adam_step_size": 1e-3})
+    s1, s2, s3 = fake.calls
+    assert "init_params" not in s1["kwargs"]
+    assert "init_params" not in s2["kwargs"]
+    np.testing.assert_allclose(s3["kwargs"]["init_params"]["theta_nn_H_b2"],
+                               [0.5, 2.0])
+
+    # stage 2's model holds them: an outer substitute cannot replace them
+    class _M:
+        def jax_model(self):
+            return numpyro.param("theta_nn_H_b2", np.ones(2))
+    held = S.HeldModel(_M(), {}, params={"theta_nn_H_b2": np.array([0.5, 2.0])})
+    out = handlers.substitute(handlers.seed(held.jax_model, 0),
+                              data={"theta_nn_H_b2": np.zeros(2)})()
+    np.testing.assert_allclose(out, [0.5, 2.0])
diff --git a/tests/tfscreen/tfmodel/scripts/test_configure_and_run.py b/tests/tfscreen/tfmodel/scripts/test_configure_and_run.py
index b72a573e..62bddc6e 100644
--- a/tests/tfscreen/tfmodel/scripts/test_configure_and_run.py
+++ b/tests/tfscreen/tfmodel/scripts/test_configure_and_run.py
@@ -143,14 +143,14 @@ def test_configure_model_passes_base_growth_df_to_orchestrator_and_config(mock_o
     assert written_config["data"]["base_growth"] == "bg.csv"
 
 
-def test_configure_model_requires_transformation_lambda_for_empirical(mock_orchestrator, tmpdir):
-    """transformation_model='empirical' (or 'logit_norm') without transformation_lambda
+def test_configure_model_requires_transformation_lambda_for_mixture(mock_orchestrator, tmpdir):
+    """transformation_model='mixture' without transformation_lambda
     must raise before ModelOrchestrator is even constructed."""
     out_prefix = os.path.join(tmpdir, "test")
     with pytest.raises(ValueError, match="transformation_lambda"):
         configure_model(
             "b.csv", growth_df="g.csv", library_config="lib.yaml",
-            transformation_model="empirical",
+            transformation_model="mixture",
             out_prefix=out_prefix,
         )
 
@@ -169,14 +169,35 @@ def test_configure_model_forwards_transformation_lambda_to_orchestrator(mock_orc
 
     configure_model(
         "b.csv", growth_df="g.csv", library_config="lib.yaml",
-        transformation_model="empirical",
+        transformation_model="mixture",
         transformation_lambda=(0.3572, 0.13),
         out_prefix=out_prefix,
         skip_model_stats=True,
     )
 
     assert mock_orchestrator_class.call_args.kwargs["transformation_lambda"] == (0.3572, 0.13)
-    assert mock_orchestrator_class.call_args.kwargs["transformation"] == "empirical"
+    assert mock_orchestrator_class.call_args.kwargs["transformation"] == "mixture"
+
+
+def test_configure_model_forwards_congression_sets(mock_orchestrator, tmpdir):
+    """--congression_sets reaches the orchestrator as a list of ints; left
+    unset, the orchestrator keeps its own default."""
+    mock_orchestrator_class, _ = mock_orchestrator
+    kw = dict(growth_df="g.csv", library_config="lib.yaml",
+              transformation_model="mixture", transformation_lambda=(0.36, 0.1),
+              skip_model_stats=True)
+    configure_model("b.csv", out_prefix=os.path.join(tmpdir, "a"),
+                    congression_sets=[50, 20, 5], **kw)
+    assert mock_orchestrator_class.call_args.kwargs["congression_sets"] == [50, 20, 5]
+    configure_model("b.csv", out_prefix=os.path.join(tmpdir, "b"), **kw)
+    assert "congression_sets" not in mock_orchestrator_class.call_args.kwargs
+
+
+def test_configure_model_refuses_congression_sets_without_mixture(mock_orchestrator, tmpdir):
+    with pytest.raises(ValueError, match="congression_sets"):
+        configure_model("b.csv", growth_df="g.csv", library_config="lib.yaml",
+                        congression_sets=[50, 20, 5],
+                        out_prefix=os.path.join(tmpdir, "c"), skip_model_stats=True)
 
 
 def test_configure_model_omits_base_growth_when_not_given(mock_orchestrator, tmpdir):
@@ -345,8 +366,10 @@ def test_read_configuration_errors(tmpdir):
         pd.DataFrame({"parameter":["p"], "value":[1]}).to_csv(priors_path, index=False)
         with open(config_path, "w") as f:
              yaml.dump({"data":{"growth":"g", "binding":"b"}, "components":{}, "priors_file": "priors.csv"}, f)
-        with pytest.raises(ValueError, match="guesses_file not specified"):
-            read_configuration(config_path)
+        with patch("tfscreen.tfmodel.configuration_io._update_dataclass",
+                   return_value=MagicMock()):
+            with pytest.raises(ValueError, match="guesses_file not specified"):
+                read_configuration(config_path)
 
         # 5. Missing parameters in guesses
         guesses_path = os.path.join(tmpdir, "guesses.csv")
@@ -357,14 +380,18 @@ def test_read_configuration_errors(tmpdir):
         with patch("tfscreen.tfmodel.configuration_io.ModelOrchestrator") as mock_orchestrator_class:
             mock_orchestrator_inst = mock_orchestrator_class.return_value
             mock_orchestrator_inst.init_params = {"param1": 0, "param2": 0} # param2 missing in CSV
-            with pytest.raises(ValueError, match="Missing initial guesses for parameters"):
-                read_configuration(config_path)
+            with patch("tfscreen.tfmodel.configuration_io._update_dataclass",
+                       return_value=MagicMock()):
+                with pytest.raises(ValueError, match="Missing initial guesses for parameters"):
+                    read_configuration(config_path)
 
         # 6. Guesses file not found on disk
         with open(config_path, "w") as f:
              yaml.dump({"data":{"growth":"g", "binding":"b"}, "components":{}, "priors_file": "priors.csv", "guesses_file": "missing_guesses.csv"}, f)
-        with pytest.raises(FileNotFoundError, match="Guesses file not found"):
-            read_configuration(config_path)
+        with patch("tfscreen.tfmodel.configuration_io._update_dataclass",
+                   return_value=MagicMock()):
+            with pytest.raises(FileNotFoundError, match="Guesses file not found"):
+                read_configuration(config_path)
 
         # 7. Stale guesses file: array parameter has wrong number of values
         guesses_path2 = os.path.join(tmpdir, "guesses_stale.csv")
@@ -425,8 +452,9 @@ def test_read_configuration_unknown_key_error_mentions_label(tmpdir):
 
 
 def test_configure_model_errors(tmpdir):
-    # Missing binding_df
-    with pytest.raises(ValueError, match="binding_df must be provided"):
+    # Neither binding_df nor growth_df
+    with pytest.raises(ValueError,
+                       match="At least one of binding_df and growth_df"):
         configure_model(None)
 
 def test_update_dataclass_recursion():
@@ -445,10 +473,14 @@ class Main:
     assert m2.sub.x == 10.0
     assert m2.y == 20.0
 
-    # Test no update
-    m3 = _update_dataclass(m, "", {"z": 30.0})
+    # No rows: no update
+    m3 = _update_dataclass(m, "", {})
     assert m3 == m
 
+    # A row that names no field is an error, not a silent no-op
+    with pytest.raises(ValueError, match=r"'z'.*tfs-configure-model"):
+        _update_dataclass(m, "", {"z": 30.0})
+
 
 def test_update_dataclass_dict_field_basic():
     """A dict-typed field is populated from `prefix.<sub>` keys in flat_dict."""
@@ -521,16 +553,20 @@ class Comp:
         pinned: Dict[str, float] = field(default_factory=dict)
 
     c = Comp(pinned={"existing": 5.0})
-    updated = _update_dataclass(c, "", {"unrelated": 7.0})
+    updated = _update_dataclass(c, "", {})
     # No update was queued for `pinned` → object is the original
     assert updated is c
     assert updated.pinned == {"existing": 5.0}
+    # A row outside the dict field names nothing and is an error
+    with pytest.raises(ValueError, match="unrelated"):
+        _update_dataclass(c, "", {"unrelated": 7.0})
 
 
-def test_update_dataclass_dict_field_ignores_deeper_nesting():
+def test_update_dataclass_dict_field_rejects_deeper_nesting():
     """
     Keys with more than one '.' beyond the dict-field name are NOT
-    consumed (they are reserved for nested dataclasses).
+    consumed (they are reserved for nested dataclasses), so they name
+    nothing and raise.
     """
     from dataclasses import dataclass, field
     from typing import Dict
@@ -540,10 +576,8 @@ class Comp:
         pinned: Dict[str, float] = field(default_factory=dict)
 
     c = Comp()
-    updated = _update_dataclass(c, "", {"pinned.too.deep": 99.0})
-    # Deep key is ignored → no update applied → original returned
-    assert updated is c
-    assert updated.pinned == {}
+    with pytest.raises(ValueError, match="pinned.too.deep"):
+        _update_dataclass(c, "", {"pinned.too.deep": 99.0})
 
 
 def test_update_dataclass_dict_field_coerces_floats():
@@ -564,7 +598,8 @@ class Comp:
 
 def test_mains(mocker):
     # Test main functions via mocker to cover boilerplate
-    mocker.patch("sys.argv", ["tfs-configure", "b.csv", "--growth_df", "g.csv"])
+    mocker.patch("sys.argv", ["tfs-configure", "--binding_df", "b.csv",
+                              "--growth_df", "g.csv"])
     # Mock the internal call so we don't actually run it
     mock_run = mocker.patch("tfscreen.tfmodel.scripts.configure_model_cli.configure_model", autospec=True)
     from tfscreen.tfmodel.scripts.configure_model_cli import main
@@ -664,3 +699,23 @@ def test_configure_model_raises_on_incompatible(self, mock_orchestrator):
                 condition_growth_model="power",
                 theta_rescale_model="logit",
             )
+
+
+def test_configure_model_forwards_thermo_options(mock_orchestrator, tmpdir):
+    """The thermo_* arguments reach the orchestrator as one thermo_options
+    dict; leaving them all out passes None (the old behaviour)."""
+    mock_orchestrator_class, _ = mock_orchestrator
+    out_prefix = os.path.join(tmpdir, "test")
+
+    configure_model("b.csv", growth_df="g.csv", library_config="lib.yaml",
+                    out_prefix=out_prefix, skip_model_stats=True)
+    assert mock_orchestrator_class.call_args.kwargs["thermo_options"] is None
+
+    configure_model("b.csv", growth_df="g.csv", library_config="lib.yaml",
+                    thermo_ddG_units="kcal/mol", thermo_temperature_K=310.0,
+                    thermo_column_map="HD=HO", thermo_residue_offset=1,
+                    thermo_max_missing_fraction=0.2,
+                    out_prefix=out_prefix, skip_model_stats=True)
+    assert mock_orchestrator_class.call_args.kwargs["thermo_options"] == {
+        "ddG_units": "kcal/mol", "temperature_K": 310.0, "column_map": "HD=HO",
+        "residue_offset": 1, "max_missing_fraction": 0.2}
diff --git a/tests/tfscreen/tfmodel/scripts/test_diagnose_nan_cli.py b/tests/tfscreen/tfmodel/scripts/test_diagnose_nan_cli.py
deleted file mode 100644
index 26d98d65..00000000
--- a/tests/tfscreen/tfmodel/scripts/test_diagnose_nan_cli.py
+++ /dev/null
@@ -1,178 +0,0 @@
-"""Tests for diagnose_nan_cli.py — NaN debugging tool for SVI optimization."""
-import pytest
-import numpy as np
-from unittest.mock import patch, MagicMock
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _make_scalar(value):
-    return np.array(value)
-
-
-def _make_vector(values):
-    return np.array(values)
-
-
-# ---------------------------------------------------------------------------
-# Near-zero filtering logic (pure numpy — no JAX needed)
-# ---------------------------------------------------------------------------
-
-class TestNearZeroFiltering:
-    """The near-zero dict comprehension can be tested with plain numpy arrays."""
-
-    def _filter_near_zero(self, params, threshold):
-        return {k: float(v)
-                for k, v in params.items()
-                if v.ndim == 0 and float(v) < threshold}
-
-    def test_scalar_below_threshold_included(self):
-        params = {"scale_a": _make_scalar(0.005)}
-        result = self._filter_near_zero(params, threshold=0.01)
-        assert "scale_a" in result
-        assert result["scale_a"] == pytest.approx(0.005)
-
-    def test_scalar_above_threshold_excluded(self):
-        params = {"scale_a": _make_scalar(0.05)}
-        result = self._filter_near_zero(params, threshold=0.01)
-        assert "scale_a" not in result
-
-    def test_vector_excluded_regardless_of_values(self):
-        params = {"vec": _make_vector([0.0, 0.0])}
-        result = self._filter_near_zero(params, threshold=1.0)
-        assert "vec" not in result
-
-    def test_mixed_params(self):
-        params = {
-            "bad_scale": _make_scalar(0.001),
-            "good_scale": _make_scalar(0.1),
-            "loc_vec": _make_vector([0.0, 0.001]),
-        }
-        result = self._filter_near_zero(params, threshold=0.01)
-        assert set(result.keys()) == {"bad_scale"}
-
-    def test_exactly_at_threshold_excluded(self):
-        params = {"scale": _make_scalar(0.01)}
-        result = self._filter_near_zero(params, threshold=0.01)
-        assert "scale" not in result
-
-    def test_empty_params(self):
-        assert self._filter_near_zero({}, threshold=0.01) == {}
-
-
-# ---------------------------------------------------------------------------
-# diagnose_nan() — mock all heavy dependencies via module-level patches
-# ---------------------------------------------------------------------------
-
-def _make_diagnose_nan_patches(ri_mock, fake_checkpoint):
-    """Return a list of context-manager patches for diagnose_nan dependencies."""
-    return [
-        patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.jax"),
-        patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.read_configuration",
-              return_value=(MagicMock(), {})),
-        patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.RunInference",
-              return_value=ri_mock),
-        patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.dill") ,
-    ]
-
-
-class TestDiagnoseNanFunction:
-
-    def _run_diagnose(self, tmp_path, seed=0, num_steps=0):
-        """Run diagnose_nan with all heavy deps mocked; return the mocked jax."""
-        ckpt_path = tmp_path / "ckpt.pkl"
-        ckpt_path.write_bytes(b"")
-
-        ri = MagicMock()
-        ri.model.data = MagicMock()
-        ri.model.get_random_idx.return_value = [0]
-        ri.model.get_batch.return_value = MagicMock()
-        ri.get_key.return_value = MagicMock()
-
-        svi = MagicMock()
-        svi.init.return_value = MagicMock()
-        svi.update.return_value = (MagicMock(), 1.0)
-        svi.get_params.return_value = {}
-        ri.setup_svi.return_value = svi
-
-        with patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.jax") as mock_jax, \
-             patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.read_configuration",
-                   return_value=(MagicMock(), {})), \
-             patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.RunInference",
-                   return_value=ri) as MockRI, \
-             patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.dill") as mock_dill:
-
-            mock_jax.device_put.return_value = MagicMock()
-            mock_dill.load.return_value = {"svi_state": {}}
-
-            from tfscreen.tfmodel.scripts.diagnose_nan_cli import diagnose_nan
-            diagnose_nan("cfg.yaml", str(ckpt_path), seed=seed, num_steps=num_steps)
-
-        return mock_jax, MockRI, ri
-
-    def test_sets_jax_debug_nans(self, tmp_path):
-        mock_jax, _, _ = self._run_diagnose(tmp_path)
-        mock_jax.config.update.assert_any_call("jax_debug_nans", True)
-
-    def test_disables_jit(self, tmp_path):
-        mock_jax, _, _ = self._run_diagnose(tmp_path)
-        mock_jax.config.update.assert_any_call("jax_disable_jit", True)
-
-    def test_passes_seed_to_run_inference(self, tmp_path):
-        _, MockRI, _ = self._run_diagnose(tmp_path, seed=42)
-        call_args = MockRI.call_args
-        assert call_args[1]["seed"] == 42
-
-    def test_calls_setup_svi(self, tmp_path):
-        _, _, ri = self._run_diagnose(tmp_path)
-        ri.setup_svi.assert_called_once()
-
-
-# ---------------------------------------------------------------------------
-# main() argument parsing
-# ---------------------------------------------------------------------------
-
-class TestDiagnoseNanMain:
-
-    def test_default_args_routed(self):
-        captured = {}
-
-        def fake_diagnose_nan(config_file, checkpoint_file, seed, num_steps,
-                              near_zero_threshold):
-            captured.update(locals())
-
-        with patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.diagnose_nan",
-                   side_effect=fake_diagnose_nan), \
-             patch("sys.argv", ["tfs-diagnose-nan", "cfg.yaml", "ckpt.pkl"]):
-            from tfscreen.tfmodel.scripts.diagnose_nan_cli import main
-            main()
-
-        assert captured["config_file"] == "cfg.yaml"
-        assert captured["checkpoint_file"] == "ckpt.pkl"
-        assert captured["seed"] == 0
-        assert captured["num_steps"] == 200
-        assert captured["near_zero_threshold"] == pytest.approx(0.01)
-
-    def test_custom_args_routed(self):
-        captured = {}
-
-        def fake_diagnose_nan(config_file, checkpoint_file, seed, num_steps,
-                              near_zero_threshold):
-            captured.update(locals())
-
-        with patch("tfscreen.tfmodel.scripts.diagnose_nan_cli.diagnose_nan",
-                   side_effect=fake_diagnose_nan), \
-             patch("sys.argv", [
-                 "tfs-diagnose-nan", "run.yaml", "run.pkl",
-                 "--seed", "7",
-                 "--num_steps", "50",
-                 "--near_zero_threshold", "0.001",
-             ]):
-            from tfscreen.tfmodel.scripts.diagnose_nan_cli import main
-            main()
-
-        assert captured["seed"] == 7
-        assert captured["num_steps"] == 50
-        assert captured["near_zero_threshold"] == pytest.approx(0.001)
diff --git a/tests/tfscreen/tfmodel/scripts/test_fit_model_cli.py b/tests/tfscreen/tfmodel/scripts/test_fit_model_cli.py
index fcb70140..4d8753d0 100644
--- a/tests/tfscreen/tfmodel/scripts/test_fit_model_cli.py
+++ b/tests/tfscreen/tfmodel/scripts/test_fit_model_cli.py
@@ -3,6 +3,7 @@
 """
 import pytest
 import dill
+import numpy as np
 import jax.numpy as jnp
 from unittest.mock import MagicMock
 
@@ -88,6 +89,9 @@ def _setup(self, mocker, tmp_path):
             ".fit_model_cli.RunInference",
             return_value=MagicMock(_iterations_per_epoch=1),
         )
+        # NUTS starts from a MAP warm-up (pre_map_num_epoch > 0 by default)
+        mocker.patch("tfscreen.tfmodel.scripts.fit_model_cli._run_map",
+                     return_value=(MagicMock(), {}, True))
         fake_samples = {"param": [1.0]}
         run_nuts_mock = mocker.patch(
             "tfscreen.tfmodel.scripts"
@@ -238,6 +242,339 @@ def test_svi_epoch_checkpoint_interval_default_is_1000(self, tmp_path, mocker):
         assert kwargs["epoch_checkpoint_interval"] == 1000
 
 
+# ---------------------------------------------------------------------------
+# guide selection
+# ---------------------------------------------------------------------------
+
+_CLI = "tfscreen.tfmodel.scripts.fit_model_cli"
+
+
+class TestGuideSelection:
+
+    def _patch_run_svi(self, mocker):
+        return mocker.patch(f"{_CLI}._run_svi",
+                            return_value=(MagicMock(), {}, True))
+
+    def test_default_is_component_without_init_values(self, mocker):
+        _patch_common(mocker)
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", seed=1, pre_map_num_epoch=0)
+        kwargs = run_svi_mock.call_args.kwargs
+        assert kwargs["guide_type"] == "component"
+        assert kwargs["guide_kwargs"] == {}
+        assert kwargs["init_values"] is None
+
+    def test_guide_options_resolved_and_forwarded(self, mocker):
+        _patch_common(mocker)
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", seed=1, pre_map_num_epoch=0,
+                  guide_type="AutoLowRankMultivariateNormal",
+                  guide_rank=4, guide_init_scale=0.05)
+        kwargs = run_svi_mock.call_args.kwargs
+        assert kwargs["guide_type"] == "auto_low_rank_multivariate_normal"
+        assert kwargs["guide_kwargs"] == {"rank": 4, "init_scale": 0.05}
+
+    @pytest.mark.parametrize("guide_type,expected", [
+        ("auto_normal", {"init_scale": 1e-4}),
+        ("delta", {}),
+    ])
+    def test_fresh_autoguide_starts_narrow(self, mocker, guide_type,
+                                           expected):
+        """numpyro's default init_scale (0.1) would throw the start away."""
+        _patch_common(mocker)
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", seed=1, pre_map_num_epoch=0,
+                  guide_type=guide_type)
+        assert run_svi_mock.call_args.kwargs["guide_kwargs"] == expected
+
+    @staticmethod
+    def _patch_ri(mocker, guesses=None):
+        """RunInference whose site_values strips ``_auto_loc`` (like the
+        real one does for a MAP result) and keeps site-keyed values."""
+        ri = MagicMock(_iterations_per_epoch=1)
+        ri.site_values.side_effect = lambda values: {
+            (k[:-len("_auto_loc")] if k.endswith("_auto_loc") else k): v
+            for k, v in values.items()}
+        mocker.patch(f"{_CLI}.RunInference", return_value=ri)
+        mocker.patch(f"{_CLI}.read_configuration",
+                     return_value=(MagicMock(), dict(guesses or {})))
+        return ri
+
+    def test_autoguide_starts_from_premap_solution(self, mocker):
+        _patch_common(mocker)
+        self._patch_ri(mocker, guesses={"dk_geno_offset": jnp.zeros(3)})
+        map_params = {"dk_geno_offset_auto_loc": jnp.ones(3),
+                      "theta_hyper_auto_loc": jnp.array(2.0)}
+        run_map_mock = mocker.patch(f"{_CLI}._run_map",
+                                    return_value=(MagicMock(), map_params,
+                                                  True))
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", seed=1, pre_map_num_epoch=5,
+                  guide_type="auto_normal")
+        # the pre-MAP starts from the guesses
+        premap = run_map_mock.call_args.kwargs
+        assert set(premap["init_values"]) == {"dk_geno_offset"}
+        assert premap["max_num_epochs"] == 5
+        init_values = run_svi_mock.call_args.kwargs["init_values"]
+        assert set(init_values) == {"dk_geno_offset", "theta_hyper"}
+        assert float(init_values["theta_hyper"]) == 2.0
+        # the MAP value replaces the guess
+        assert float(init_values["dk_geno_offset"][0]) == 1.0
+
+    def test_autoguide_without_premap_starts_from_guesses(self, mocker):
+        _patch_common(mocker)
+        self._patch_ri(mocker, guesses={"dk_geno_offset": jnp.zeros(3)})
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", seed=1, pre_map_num_epoch=0,
+                  guide_type="auto_normal")
+        kwargs = run_svi_mock.call_args.kwargs
+        assert set(kwargs["init_values"]) == {"dk_geno_offset"}
+        assert kwargs["init_params"] is None
+
+    def test_nuts_starts_from_premap_solution(self, mocker):
+        _patch_common(mocker)
+        self._patch_ri(mocker, guesses={"dk_geno_offset": jnp.zeros(3)})
+        map_params = {"dk_geno_offset_auto_loc": jnp.ones(3)}
+        run_map_mock = mocker.patch(f"{_CLI}._run_map",
+                                    return_value=(MagicMock(), map_params,
+                                                  True))
+        run_nuts_mock = mocker.patch(f"{_CLI}._run_nuts", return_value={})
+        fit_model(config_file="dummy.yaml", seed=1, analysis_method="nuts",
+                  pre_map_num_epoch=5)
+        assert run_map_mock.call_args.kwargs["max_num_epochs"] == 5
+        init_values = run_nuts_mock.call_args.kwargs["init_values"]
+        assert float(init_values["dk_geno_offset"][0]) == 1.0
+
+    def test_nuts_without_premap_starts_from_guesses(self, mocker):
+        _patch_common(mocker)
+        self._patch_ri(mocker, guesses={"dk_geno_offset": jnp.zeros(3)})
+        run_map_mock = mocker.patch(f"{_CLI}._run_map")
+        run_nuts_mock = mocker.patch(f"{_CLI}._run_nuts", return_value={})
+        fit_model(config_file="dummy.yaml", seed=1, analysis_method="nuts",
+                  pre_map_num_epoch=0)
+        run_map_mock.assert_not_called()
+        init_values = run_nuts_mock.call_args.kwargs["init_values"]
+        assert float(init_values["dk_geno_offset"][0]) == 0.0
+
+    @pytest.mark.parametrize("scale,expected", [(None, 1e-4), (0.02, 0.02)])
+    def test_component_guide_starts_from_premap(self, mocker, scale,
+                                                expected):
+        _patch_common(mocker)
+        guesses = {"dk_geno_offset": jnp.zeros(3)}
+        ri = self._patch_ri(mocker, guesses=guesses)
+        ri.component_guide_start.return_value = {"dk_geno_offset_locs": 1}
+        mocker.patch(f"{_CLI}._run_map",
+                     return_value=(MagicMock(),
+                                   {"dk_geno_offset_auto_loc": jnp.ones(3)},
+                                   True))
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", seed=1, pre_map_num_epoch=5,
+                  guide_init_scale=scale)
+        args = ri.component_guide_start.call_args
+        assert float(args.args[0]["dk_geno_offset"][0]) == 1.0
+        assert args.kwargs["guesses"] == guesses
+        assert args.kwargs["init_scale"] == expected
+        kwargs = run_svi_mock.call_args.kwargs
+        assert kwargs["init_params"] == {"dk_geno_offset_locs": 1}
+        assert kwargs["init_values"] is None
+        assert kwargs["guide_kwargs"] == {}
+
+    def test_map_starts_from_guesses(self, mocker):
+        _patch_common(mocker)
+        self._patch_ri(mocker, guesses={"mu": 1.0})
+        run_map_mock = mocker.patch(f"{_CLI}._run_map",
+                                    return_value=(MagicMock(), {}, True))
+        fit_model(config_file="dummy.yaml", seed=1, analysis_method="map")
+        assert run_map_mock.call_args.kwargs["init_values"] == {"mu": 1.0}
+
+    @pytest.mark.parametrize("stage_offsets,staged", [("auto", True),
+                                                       ("off", False)])
+    def test_level_offset_map_is_staged(self, mocker, stage_offsets, staged):
+        _patch_common(mocker)
+        self._patch_ri(mocker, guesses={"mu": 1.0})
+        orch = MagicMock(settings={"sample_offset": "level"})
+        mocker.patch(f"{_CLI}.read_configuration",
+                     return_value=(orch, {"mu": 1.0}))
+        run_map_mock = mocker.patch(f"{_CLI}._run_map",
+                                    return_value=(MagicMock(), {}, True))
+        staged_mock = mocker.patch(f"{_CLI}.run_staged_map",
+                                   return_value=(MagicMock(), {}, True))
+        fit_model(config_file="dummy.yaml", seed=1, analysis_method="map",
+                  stage_offsets=stage_offsets, staged_step_size=2e-4,
+                  epoch_checkpoint_interval=500)
+        assert staged_mock.called is staged
+        assert run_map_mock.called is not staged
+        if staged:
+            kw = staged_mock.call_args.kwargs
+            assert kw["staged_step_size"] == 2e-4
+            assert kw["map_kwargs"]["epoch_checkpoint_interval"] == 500
+            assert kw["stage_map_kwargs"]["epoch_checkpoint_interval"] is None
+            assert kw["guesses"] == {"mu": 1.0}
+
+    def test_map_starts_from_init_from(self, tmp_path, mocker):
+        """A MAP params npz seeds every site it names, over the guesses;
+        guesses for sites it lacks are kept (a level-offset fit started from
+        a zero-offset fit's point)."""
+        npz = tmp_path / "prev_params.npz"
+        np.savez(npz, mu_auto_loc=np.array([2.0, 3.0]),
+                 offset_auto_loc=np.array([0.1]), not_a_site=np.array(9.0))
+        _patch_common(mocker)
+        ri = self._patch_ri(mocker, guesses={"mu": 1.0, "sigma": 0.5})
+        run_map_mock = mocker.patch(f"{_CLI}._run_map",
+                                    return_value=(MagicMock(), {}, True))
+        fit_model(config_file="dummy.yaml", seed=1, analysis_method="map",
+                  init_from=str(npz))
+        start = run_map_mock.call_args.kwargs["init_values"]
+        assert set(start) == {"mu", "sigma", "offset"}
+        np.testing.assert_array_equal(start["mu"], [2.0, 3.0])
+        np.testing.assert_allclose(start["offset"], [0.1], rtol=1e-6)
+        assert start["sigma"] == 0.5
+        # both keys reach site_values, which ranks {site}_auto_loc first
+        seen = ri.site_values.call_args.args[0]
+        assert "mu" in seen and "mu_auto_loc" in seen
+
+    def test_init_from_needs_map_arrays(self, tmp_path, mocker):
+        npz = tmp_path / "other.npz"
+        np.savez(npz, mu=np.array(1.0))
+        _patch_common(mocker)
+        with pytest.raises(ValueError, match="no '\\*_auto_loc' arrays"):
+            fit_model(config_file="dummy.yaml", seed=1, analysis_method="map",
+                      init_from=str(npz))
+
+    def test_init_from_refused_on_resume(self, tmp_path, mocker):
+        ckpt = tmp_path / "ckpt.pkl"
+        with open(ckpt, "wb") as f:
+            dill.dump({"guide_type": "component"}, f)
+        _patch_common(mocker)
+        with pytest.raises(ValueError, match="init_from and checkpoint_file"):
+            fit_model(config_file="dummy.yaml", analysis_method="map",
+                      checkpoint_file=str(ckpt), init_from="x.npz")
+
+    # The model's own learned params (thermo PnnC NN weights) are not
+    # sample sites; the hand-offs must carry them too.
+    SIG = "theta_nn_H_b2"
+
+    def _patch_ri_params(self, mocker, guesses=None):
+        ri = self._patch_ri(mocker, guesses=guesses)
+        ri.model_params.side_effect = lambda p, unconstrained=False: {
+            k: v for k, v in p.items() if k == self.SIG}
+        return ri
+
+    def test_init_from_carries_model_params(self, tmp_path, mocker):
+        npz = tmp_path / "prev_params.npz"
+        np.savez(npz, mu_auto_loc=np.array([2.0]),
+                 theta_nn_H_b2=np.array([0.3, 2.0]))
+        _patch_common(mocker)
+        self._patch_ri_params(mocker, guesses={"mu": 1.0})
+        run_map_mock = mocker.patch(f"{_CLI}._run_map",
+                                    return_value=(MagicMock(), {}, True))
+        fit_model(config_file="dummy.yaml", seed=1, analysis_method="map",
+                  init_from=str(npz))
+        kwargs = run_map_mock.call_args.kwargs
+        np.testing.assert_allclose(kwargs["init_params"][self.SIG], [0.3, 2.0])
+        assert self.SIG not in kwargs["init_values"]
+
+    @pytest.mark.parametrize("guide_type", ["component", "auto_normal"])
+    def test_premap_hands_model_params_to_svi(self, mocker, guide_type):
+        _patch_common(mocker)
+        ri = self._patch_ri_params(mocker,
+                                   guesses={"dk_geno_offset": jnp.zeros(3)})
+        ri.component_guide_start.return_value = {"dk_geno_offset_locs": 1}
+        map_params = {"dk_geno_offset_auto_loc": jnp.ones(3),
+                      self.SIG: jnp.array([0.4, 1.7])}
+        mocker.patch(f"{_CLI}._run_map",
+                     return_value=(MagicMock(), map_params, True))
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", seed=1, pre_map_num_epoch=5,
+                  guide_type=guide_type)
+        init_params = run_svi_mock.call_args.kwargs["init_params"]
+        np.testing.assert_allclose(init_params[self.SIG], [0.4, 1.7])
+        if guide_type == "component":
+            assert init_params["dk_geno_offset_locs"] == 1
+        else:
+            assert set(init_params) == {self.SIG}
+
+    def test_run_svi_autoguide_keeps_only_model_params(self, mocker):
+        ri = MagicMock()
+        ri.model_params.side_effect = lambda p, unconstrained=False: {
+            k: v for k, v in p.items() if k == self.SIG}
+        ri.run_optimization.return_value = (None, {}, True)
+        _run_svi(ri, init_params={self.SIG: 1.0, "x_locs": 2.0},
+                 guide_type="auto_normal", max_num_epochs=0)
+        assert ri.run_optimization.call_args.kwargs["init_params"] == \
+            {self.SIG: 1.0}
+        _run_svi(ri, init_params={"x_locs": 2.0}, guide_type="auto_normal",
+                 max_num_epochs=0)
+        assert ri.run_optimization.call_args.kwargs["init_params"] is None
+
+    def test_resume_skips_start_translation(self, tmp_path, mocker):
+        ckpt = tmp_path / "ckpt.pkl"
+        with open(ckpt, "wb") as f:
+            dill.dump({"guide_type": "component"}, f)
+        _patch_common(mocker)
+        ri = self._patch_ri(mocker)
+        run_map_mock = mocker.patch(f"{_CLI}._run_map")
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", checkpoint_file=str(ckpt))
+        run_map_mock.assert_not_called()
+        ri.site_values.assert_not_called()
+        assert run_svi_mock.call_args.kwargs["checkpoint_file"] == str(ckpt)
+
+    def test_convergence_options_forwarded(self, mocker):
+        _patch_common(mocker)
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", seed=1, pre_map_num_epoch=0,
+                  convergence_window_steps=500, patience=4,
+                  convergence_z=2.5, loss_rtol=1e-7, param_tolerance=0.1,
+                  adam_final_step_size=1e-5, adam_step_size_cut=0.5)
+        kwargs = run_svi_mock.call_args.kwargs
+        assert kwargs["convergence_window_steps"] == 500
+        assert kwargs["patience"] == 4
+        assert kwargs["convergence_z"] == 2.5
+        assert kwargs["loss_rtol"] == 1e-7
+        assert kwargs["param_tolerance"] == 0.1
+        assert kwargs["final_step_size"] == 1e-5
+        assert kwargs["step_size_cut"] == 0.5
+
+    def test_guide_init_scale_must_be_positive(self, mocker):
+        _patch_common(mocker)
+        with pytest.raises(ValueError, match="positive"):
+            fit_model(config_file="dummy.yaml", seed=1, guide_init_scale=0.0)
+
+    @pytest.mark.parametrize("method", ["map", "nuts"])
+    def test_guide_options_rejected_outside_svi(self, method, mocker):
+        _patch_common(mocker)
+        with pytest.raises(ValueError, match="only to analysis_method='svi'"):
+            fit_model(config_file="dummy.yaml", seed=1,
+                      analysis_method=method, guide_type="auto_normal")
+
+    def test_unaccepted_option_rejected_before_fitting(self, mocker):
+        _patch_common(mocker)
+        run_map_mock = mocker.patch(f"{_CLI}._run_map")
+        with pytest.raises(ValueError, match="not accepted"):
+            fit_model(config_file="dummy.yaml", seed=1,
+                      guide_type="auto_normal", guide_rank=3)
+        run_map_mock.assert_not_called()
+
+    def test_resume_with_different_guide_rejected(self, tmp_path, mocker):
+        ckpt = tmp_path / "ckpt.pkl"
+        with open(ckpt, "wb") as f:
+            dill.dump({"guide_type": "component"}, f)
+        _patch_common(mocker)
+        with pytest.raises(ValueError, match="written with guide_type 'component'"):
+            fit_model(config_file="dummy.yaml", checkpoint_file=str(ckpt),
+                      guide_type="auto_normal")
+
+    def test_legacy_checkpoint_resumes_as_component(self, tmp_path, mocker):
+        ckpt = tmp_path / "ckpt.pkl"
+        with open(ckpt, "wb") as f:
+            dill.dump({"svi_state": None}, f)  # no guide metadata
+        _patch_common(mocker)
+        run_svi_mock = self._patch_run_svi(mocker)
+        fit_model(config_file="dummy.yaml", checkpoint_file=str(ckpt))
+        assert run_svi_mock.call_args.kwargs["guide_type"] == "component"
+
+
 # ---------------------------------------------------------------------------
 # _run_svi — convergence message suppression when max_num_epochs=0
 # ---------------------------------------------------------------------------
diff --git a/tests/tfscreen/tfmodel/scripts/test_fit_model_helpers.py b/tests/tfscreen/tfmodel/scripts/test_fit_model_helpers.py
index cb39838b..8972e241 100644
--- a/tests/tfscreen/tfmodel/scripts/test_fit_model_helpers.py
+++ b/tests/tfscreen/tfmodel/scripts/test_fit_model_helpers.py
@@ -4,6 +4,7 @@
 import dill
 
 from tfscreen.tfmodel.scripts.fit_model_cli import (
+    _optimization_kwargs,
     _run_svi,
     _run_map,
     _run_nuts,
@@ -47,7 +48,7 @@ def test_run_svi_flow_converged(mock_run_inference):
         ri,
         init_params=None,
         out_prefix="test_root",
-        max_num_epochs=500,
+        **_optimization_kwargs(max_num_epochs=500, patience=4),
     )
 
     ri.setup_svi.assert_called_once()
@@ -57,10 +58,13 @@ def test_run_svi_flow_converged(mock_run_inference):
         init_params=None,
         out_prefix="test_root",
         svi_state=None,
-        convergence_tolerance=ANY,
-        convergence_window=ANY,
-        patience=ANY,
-        convergence_check_interval=ANY,
+        convergence_window_steps=2000,
+        patience=4,
+        convergence_z=ANY,
+        loss_rtol=ANY,
+        param_tolerance=ANY,
+        final_step_size=1e-6,
+        step_size_cut=0.1,
         checkpoint_interval=ANY,
         max_num_epochs=500,
         init_param_jitter=ANY,
@@ -93,6 +97,28 @@ def test_run_svi_not_converged_stdout(mock_run_inference, capsys):
     captured = capsys.readouterr()
     assert "SVI run has not yet converged." in captured.out
 
+def test_run_svi_autoguide_drops_init_params(mock_run_inference):
+    """An autoguide starts from init_values; component init_params are dropped
+    (only the model's own params, none here, would be kept)."""
+    _, ri = mock_run_inference
+    ri.model_params.return_value = {}
+    _run_svi(ri, init_params={"a_loc": 1.0}, guide_type="auto_normal",
+             init_values={"a": 1.0})
+    ri.setup_svi.assert_called_once_with(adam_step_size=ANY,
+                                         adam_clip_norm=ANY,
+                                         elbo_num_particles=ANY,
+                                         guide_type="auto_normal",
+                                         guide_kwargs=None,
+                                         init_values={"a": 1.0})
+    assert ri.run_optimization.call_args.kwargs["init_params"] is None
+
+
+def test_run_svi_component_guide_keeps_init_params(mock_run_inference):
+    _, ri = mock_run_inference
+    _run_svi(ri, init_params={"a_loc": 1.0}, guide_type="component")
+    assert ri.run_optimization.call_args.kwargs["init_params"] == {"a_loc": 1.0}
+
+
 # =============================================================================
 # Tests for _run_map
 # =============================================================================
@@ -100,31 +126,25 @@ def test_run_svi_not_converged_stdout(mock_run_inference, capsys):
 def test_run_map_flow(mock_run_inference):
     """Test MAP execution flow."""
     _, ri = mock_run_inference
-    init_params = {"p": 10}
+    init_values = {"p": 10}
     state, params, converged = _run_map(
         ri,
-        init_params=init_params,
+        init_values=init_values,
         out_prefix="test_map",
         max_num_epochs=1000,
     )
 
-    # 1. Setup MAP
+    # 1. Setup MAP: an AutoDelta starting at the given site values
     ri.setup_svi.assert_called_once()
+    assert ri.setup_svi.call_args.kwargs["guide_type"] == "delta"
+    assert ri.setup_svi.call_args.kwargs["init_values"] == init_values
 
     # 2. Run Optimization
     ri.run_optimization.assert_called_once_with(
         "mock_svi_obj",
-        init_params=init_params,
         out_prefix="test_map",
         svi_state=None,
-        convergence_tolerance=ANY,
-        convergence_window=ANY,
-        patience=ANY,
-        convergence_check_interval=ANY,
-        checkpoint_interval=ANY,
         max_num_epochs=1000,
-        init_param_jitter=ANY,
-        epoch_checkpoint_interval=ANY
     )
 
     ri.write_params.assert_called_once_with({"p": 1}, out_prefix="test_map")
@@ -136,7 +156,7 @@ def test_run_map_not_converged(mock_run_inference, capsys):
     """Test MAP not converged message."""
     _, ri = mock_run_inference
     ri.run_optimization.return_value = ("state", {"p": 1}, False)
-    _run_map(ri, init_params={"p": 1})
+    _run_map(ri, init_values={"p": 1})
     captured = capsys.readouterr()
     assert "MAP run converged" not in captured.out
     assert "MAP run has not yet converged" in captured.out
@@ -147,8 +167,11 @@ def test_run_map_not_converged(mock_run_inference, capsys):
 # =============================================================================
 
 @pytest.fixture
-def mock_ri_for_nuts(mocker):
+def mock_ri_for_nuts(mocker, tmp_path, monkeypatch):
     """Minimal ri mock for _run_nuts tests."""
+    # _run_nuts writes a real {out_prefix}_checkpoint.pkl even with a mocked
+    # ri; tests using the default out_prefix would drop it in the cwd.
+    monkeypatch.chdir(tmp_path)
     ri = MagicMock()
     mock_mcmc = MagicMock()
     mock_mcmc.get_samples.return_value = {"param": [1.0, 2.0]}
@@ -170,20 +193,32 @@ def test_run_nuts_calls_run_nuts(mock_ri_for_nuts):
         num_samples=20,
         num_chains=2,
         target_accept_prob=0.8,
+        init_values=None,
+        dense_mass=False,
     )
 
 
-def test_run_nuts_calls_get_nuts_posteriors(mock_ri_for_nuts):
+def test_run_nuts_passes_init_values(mock_ri_for_nuts):
+    """_run_nuts hands the starting site values to ri.run_nuts."""
+    ri = mock_ri_for_nuts
+    values = {"dk_geno_offset": 1.0}
+    _run_nuts(ri, init_values=values, nuts_dense_mass=True)
+    assert ri.run_nuts.call_args.kwargs["init_values"] is values
+    assert ri.run_nuts.call_args.kwargs["dense_mass"] is True
+
+
+def test_run_nuts_calls_get_nuts_posteriors(tmp_path, mock_ri_for_nuts):
     """_run_nuts calls ri.get_nuts_posteriors with the samples and forward_batch_size."""
     ri = mock_ri_for_nuts
     expected_samples = {"param": [1.0, 2.0]}
     ri.run_nuts.return_value.get_samples.return_value = expected_samples
+    out_prefix = str(tmp_path / "myroot")
 
-    _run_nuts(ri, out_prefix="myroot", forward_batch_size=64)
+    _run_nuts(ri, out_prefix=out_prefix, forward_batch_size=64)
 
     ri.get_nuts_posteriors.assert_called_once_with(
         expected_samples,
-        out_prefix="myroot",
+        out_prefix=out_prefix,
         forward_batch_size=64,
     )
 
diff --git a/tests/tfscreen/tfmodel/scripts/test_fit_two_stage_cli.py b/tests/tfscreen/tfmodel/scripts/test_fit_two_stage_cli.py
new file mode 100644
index 00000000..037f3745
--- /dev/null
+++ b/tests/tfscreen/tfmodel/scripts/test_fit_two_stage_cli.py
@@ -0,0 +1,537 @@
+"""
+tfs-fit-two-stage: the YAML, the step order, skipping and what each step
+hands the chained CLI functions (mocked here; the chain itself is checked
+end to end in planning/two-stage-fit.md's validation step).
+"""
+import json
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+import yaml
+from unittest.mock import patch
+
+from tfscreen.tfmodel.scripts import fit_two_stage_cli as cli
+
+CONDS = ["kanR+kan", "kanR-kan", "pheS+4CP", "pheS-4CP"]
+
+
+def _growth(path):
+    rows = []
+    for lib, sel, pre in (("kanR", "kanR+kan", "kanR-kan"),
+                          ("kanR", "kanR-kan", "kanR-kan"),
+                          ("pheS", "pheS+4CP", "pheS-4CP"),
+                          ("pheS", "pheS-4CP", "pheS-4CP")):
+        for x in (0.0, 1.0):
+            for r in (1, 2):
+                for t in (100, 150, 200):
+                    s = f"{sel}-{x}-{r}-{t}"
+                    for g in ("wt", "A1B", "C2D", "A1B/C2D"):
+                        rows.append({"sample": s, "library": lib,
+                                     "condition_pre": pre, "condition_sel": sel,
+                                     "titrant_name": "iptg", "titrant_conc": x,
+                                     "replicate": r, "t_sel": t,
+                                     "genotype": g, "counts": 100})
+    pd.DataFrame(rows).to_csv(path, index=False)
+
+
+def _write_yaml(tmp_path, **over):
+    _growth(tmp_path / "growth.csv")
+    (tmp_path / "lib.yaml").write_text("placeholder: 1\n")
+    cfg = {"growth_df": "growth.csv", "library_config": "lib.yaml",
+           "theta_model": "hill_relative", "growth_shares_replicates": True,
+           "set_priors": ["sigma_fixed=0.17"],
+           "two_stage": {"transformation_lambda": [0.38, 0.15],
+                         "jackknife_subsamples": 3,
+                         "num_posterior_samples": 20}}
+    for k, v in over.items():
+        if k == "two_stage":
+            cfg["two_stage"].update(v)
+        elif v is None:
+            cfg.pop(k, None)
+        else:
+            cfg[k] = v
+    p = tmp_path / "two_stage.yaml"
+    p.write_text(yaml.safe_dump(cfg))
+    return str(p)
+
+
+# --- the YAML ---
+
+def test_read_config_resolves_paths_and_defaults(tmp_path):
+    cfg, st = cli.read_two_stage_config(_write_yaml(tmp_path))
+    assert cfg["growth_df"] == str(tmp_path / "growth.csv")
+    assert st["jackknife_subsamples"] == 3
+    assert st["jackknife_drop_frac"] == 0.2
+    assert st["consistency"] is True
+    assert st["transformation_lambda"] == [0.38, 0.15]
+
+
+@pytest.mark.parametrize("over,match", [
+    ({"two_stage": {"transformation_lambda": None}}, "transformation_lambda"),
+    ({"theta_model": "hill_geno"}, "hill_relative"),
+    ({"condition_growth_model": "power"}, "must be linear"),
+    ({"growth_shares_replicates": False}, "growth_shares_replicates"),
+    ({"transformation_model": "mixture"}, "set by the two-stage fit"),
+    ({"not_an_arg": 1}, "Unknown key"),
+    ({"two_stage": {"bogus": 1}}, "Unknown two_stage"),
+    ({"set_priors": ["m_pinned=1"]}, "pins k and m"),
+    ({"growth_df": None}, "needs growth_df"),
+    ({"growth_df": "missing.csv"}, "does not exist"),
+    ({"two_stage": {"fit": {"seed": 3}}}, "cannot be set"),
+    ({"two_stage": {"jackknife_subsamples": 1}}, "at least 2"),
+    ({"binding_df": "growth.csv"}, "growth-only"),
+    ({"theta_binding_noise_model": "genotype_offset"}, "experimental"),
+    ({"theta_growth_noise_model": "beta"}, "experimental"),
+    ({"set_priors": ["theta_growth_log_conc_shift_scale=1"]}, "experimental"),
+    ({"set_priors": ["offset_scale_fixed=0.1"]}, "experimental"),
+    ({"set_priors": ["warp_log_a_scale=1"]}, "experimental"),
+])
+def test_read_config_refuses(tmp_path, over, match):
+    with pytest.raises(ValueError, match=match):
+        cli.read_two_stage_config(_write_yaml(tmp_path, **over))
+
+
+# --- the chain, with the CLI functions mocked ---
+
+class Recorder:
+    """Stand-ins for the chained CLI functions; each writes its outputs."""
+
+    def __init__(self, km=None, x=None):
+        self.calls = []
+        # X by refit directory; anything else (stage 2, consistency) 0.43
+        self.x = x or {"plus": 0.4, "minus": 0.46}
+        self.km = km or {c: (0.01 * (i + 1), (-1) ** i * 0.014)
+                         for i, c in enumerate(CONDS)}
+
+    def configure_model(self, out_prefix, **kw):
+        self.calls.append(("configure", os.getcwd(), kw))
+        open(f"{out_prefix}_config.yaml", "w").write("x: 1\n")
+        pd.DataFrame({"parameter": ["growth.dk_geno.hyper_loc_loc"],
+                      "value": [-3.5]}).to_csv(f"{out_prefix}_priors.csv",
+                                               index=False)
+
+    def fit_model(self, config_file, **kw):
+        self.calls.append(("fit", os.getcwd(), kw))
+        np.savez(cli.PARAMS, dk_geno_hyper_loc_auto_loc=np.array(-2.17),
+                 dk_geno_hyper_scale_auto_loc=np.array(1.53),
+                 dk_geno_hyper_shift_auto_loc=np.array(0.076))
+        open(cli.CHECKPOINT, "w").write("x")
+
+    def extract_params(self, config_file, param_file, out_prefix):
+        self.calls.append(("extract", os.getcwd(), param_file))
+        rng = np.random.default_rng(len(self.calls))
+        k = {c: v[0] + rng.normal(0, 0.001) for c, v in self.km.items()}
+        pd.DataFrame({"condition_rep": list(k), "q0.5": list(k.values())}
+                     ).to_csv(f"{out_prefix}_growth_k.csv", index=False)
+        pd.DataFrame({"condition_rep": list(self.km),
+                      "q0.5": [v[1] for v in self.km.values()]}
+                     ).to_csv(f"{out_prefix}_growth_m.csv", index=False)
+
+    def sample_posterior(self, config_file, checkpoint_file, **kw):
+        self.calls.append(("posterior", os.getcwd(), kw))
+        open(cli.POSTERIOR, "w").write("x")
+
+    def predict_theta(self, config_file, param_file, out_prefix, **kw):
+        self.calls.append(("predict", os.getcwd(), param_file, kw))
+        here = os.path.basename(os.getcwd())
+        x = self.x.get(here, 0.43)
+        rows = [{"genotype": g, "titrant_name": "iptg", "titrant_conc": c,
+                 "q0.025": x - 0.05, "q0.5": x if g != "wt" else 1 - c,
+                 "q0.975": x + 0.05}
+                for g in ("wt", "A1B", "C2D", "A1B/C2D") for c in (0.0, 1.0)]
+        pd.DataFrame(rows).to_csv(f"{out_prefix}.csv", index=False)
+
+    def predict_epistasis(self, config_file, param_file, out_prefix, **kw):
+        self.calls.append(("epistasis", os.getcwd(), param_file, kw))
+        open(f"{out_prefix}.csv", "w").write("x\n")
+
+
+@pytest.fixture
+def rec():
+    r = Recorder()
+    lib = pd.DataFrame({"genotype": ["wt", "A1B", "C2D", "A1B/C2D"],
+                        "in_spiked_origin": [True, False, False, False]})
+    with patch.object(cli, "configure_model", r.configure_model), \
+         patch.object(cli, "fit_model", r.fit_model), \
+         patch.object(cli, "extract_params", r.extract_params), \
+         patch.object(cli, "sample_posterior", r.sample_posterior), \
+         patch.object(cli, "predict_theta", r.predict_theta), \
+         patch.object(cli, "predict_epistasis", r.predict_epistasis), \
+         patch.object(cli, "library_composition_table", return_value=lib):
+        yield r
+
+
+def _calls(rec, kind, where=None):
+    return [c for c in rec.calls if c[0] == kind
+            and (where is None or c[1].endswith(where))]
+
+
+def test_full_run_layout_and_arguments(tmp_path, rec):
+    out = tmp_path / "run"
+    cli.fit_two_stage(_write_yaml(tmp_path, two_stage={"kshift": True}),
+                      out_dir=str(out))
+    for f in ("stage1/km.csv", "jackknife/jk_01/km.csv", "jackknife/k_step.csv",
+              "jackknife/jackknife.json", "stage2/tfs_pred_theta.csv",
+              "kshift/shift.csv", "predict/tfs_pred_theta.csv",
+              "consistency/consistency.csv", "two_stage_config.yaml",
+              "two_stage_provenance.json", "tfs_x.csv",
+              "genotype_reads.csv"):
+        assert (out / f).exists(), f
+
+    # stage 1: only wt and singles, the congression mixture with max
+    s1 = pd.read_csv(out / "stage1" / "tfs_growth.csv")
+    assert set(s1["genotype"]) == {"wt", "A1B", "C2D"}
+    conf1 = _calls(rec, "configure", "stage1")[0][2]
+    assert conf1["transformation_model"] == "mixture"
+    assert conf1["congression_theta_rule"] == "max"
+    assert conf1["transformation_lambda"] == [0.38, 0.15]
+
+    # stage 2: the full table, single, every condition pinned
+    conf2 = _calls(rec, "configure", "stage2")[0][2]
+    assert "transformation_model" not in conf2
+    assert conf2["growth_df"] == str(tmp_path / "growth.csv")
+    pins = pd.read_csv(out / "stage2" / "growth_priors_pinned.csv")
+    assert set(pins["condition_rep"]) == set(CONDS)
+    assert (pins["k_pinned"] == 1).all() and (pins["m_pinned"] == 1).all()
+
+    # jackknife: subsamples of stage 1 with tubes dropped
+    info = json.load(open(out / "jackknife" / "jk_02" / "subsample.json"))
+    assert info["d"] > 0 and info["n"] == s1["sample"].nunique()
+
+    # kshift: three refits from stage 2's MAP at the small step size, the
+    # dk_geno population held at stage 2's MAP; k moved, m kept
+    for v in ("plus", "minus", "zero"):
+        fit = _calls(rec, "fit", f"kshift/{v}")[0][2]
+        assert fit["init_from"] == str(out / "stage2" / cli.PARAMS)
+        assert fit["adam_step_size"] == 1e-5
+        assert fit["adam_final_step_size"] == 1e-6
+        pri = pd.read_csv(out / "kshift" / v / "tfs_configure_priors.csv")
+        held = dict(zip(pri["parameter"], pri["value"].astype(float)))
+        assert held["growth.dk_geno.pinned.hyper_loc"] == pytest.approx(-2.17)
+        assert held["growth.dk_geno.pinned.hyper_scale"] == pytest.approx(1.53)
+        assert held["growth.dk_geno.pinned.hyper_shift"] == pytest.approx(0.076)
+    p = pd.read_csv(out / "kshift" / "plus" / "growth_priors_pinned.csv")
+    m = pd.read_csv(out / "kshift" / "minus" / "growth_priors_pinned.csv")
+    z = pd.read_csv(out / "kshift" / "zero" / "growth_priors_pinned.csv")
+    s2 = pd.read_csv(out / "stage2" / "growth_priors_pinned.csv")
+    assert (p["k_loc"] != m["k_loc"]).all()
+    assert np.allclose(p["m_loc"], m["m_loc"])
+    assert np.allclose(z["k_loc"], s2["k_loc"])
+    assert (out / "stage2" / cli.PRED_MAP).exists()
+    shift = pd.read_csv(out / "kshift" / "shift.csv")
+    assert np.allclose(shift.loc[shift.genotype != "wt", "shift"], -0.03)
+    drift = pd.read_csv(out / "kshift" / "drift.csv").set_index("refit")
+    assert drift.loc["plus", "median_change"] == pytest.approx(-0.03)
+    assert drift.loc["minus", "median_change"] == pytest.approx(0.03)
+    assert drift.loc["zero", "median_change"] == pytest.approx(0.0)
+
+    # predict: stage 2's posterior with the shared shift
+    pred = [c for c in _calls(rec, "predict") if "predict" in c[3].get("out_prefix", "")
+            or c[3].get("shared_shift")]
+    assert pred and pred[0][3]["shared_shift"] == str(out / "kshift" / "shift.csv")
+    assert not _calls(rec, "epistasis")
+
+    # consistency: kanR alone (first library), its conditions only
+    cons = pd.read_csv(out / "consistency" / "growth_priors_pinned.csv")
+    assert set(cons["condition_rep"]) == {"kanR+kan", "kanR-kan"}
+    assert set(pd.read_csv(out / "consistency" / "tfs_growth.csv")["library"]) == {"kanR"}
+
+
+def test_rerun_skips_finished_steps(tmp_path, rec):
+    out = tmp_path / "run"
+    y = _write_yaml(tmp_path)
+    cli.fit_two_stage(y, out_dir=str(out))
+    n = len(rec.calls)
+    cli.fit_two_stage(y, out_dir=str(out))
+    assert len(rec.calls) == n
+
+
+def test_no_consistency_and_epistasis(tmp_path, rec):
+    out = tmp_path / "run"
+    cli.fit_two_stage(_write_yaml(tmp_path, two_stage={"predict_epistasis": True,
+                                                       "kshift": True}),
+                      out_dir=str(out), consistency=False)
+    assert not (out / "consistency").exists()
+    epi = _calls(rec, "epistasis")
+    assert epi and epi[0][3]["scale"] == "add" and epi[0][3]["shared_shift"]
+
+
+def test_one_step_and_one_jackknife_task(tmp_path, rec):
+    out = tmp_path / "run"
+    y = _write_yaml(tmp_path)
+    with pytest.raises(RuntimeError, match="stage 1"):
+        cli.fit_two_stage(y, out_dir=str(out), jackknife_task=1)
+    cli.fit_two_stage(y, out_dir=str(out), step="stage1")
+    assert (out / "stage1" / "km.csv").exists()
+    assert not (out / "stage2").exists()
+    cli.fit_two_stage(y, out_dir=str(out), jackknife_task=2)
+    assert (out / "jackknife" / "jk_02" / "km.csv").exists()
+    assert not (out / "jackknife" / "jk_01").exists()
+    # kshift collects the jackknife and refuses while subsamples are missing
+    cli.fit_two_stage(y, out_dir=str(out), step="stage2")
+    with pytest.raises(RuntimeError, match="not done"):
+        cli.fit_two_stage(y, out_dir=str(out), step="kshift")
+    with pytest.raises(ValueError, match="1-3"):
+        cli.fit_two_stage(y, out_dir=str(out), jackknife_task=4)
+    with pytest.raises(ValueError, match="step must be"):
+        cli.fit_two_stage(y, out_dir=str(out), step="bogus")
+
+
+def test_consistency_with_another_library_and_name(tmp_path, rec):
+    out = tmp_path / "run"
+    y = _write_yaml(tmp_path)
+    cli.fit_two_stage(y, out_dir=str(out))
+    cfg, settings = cli.read_two_stage_config(y)
+    settings["consistency_library"] = "pheS"
+    cli.run_consistency(str(out), cfg, settings, name="consistency_pheS")
+    d = out / "consistency_pheS"
+    assert (d / "consistency.csv").exists()
+    assert set(pd.read_csv(d / "tfs_growth.csv")["library"]) == {"pheS"}
+    cons = pd.read_csv(d / "growth_priors_pinned.csv")
+    assert set(cons["condition_rep"]) == {"pheS+4CP", "pheS-4CP"}
+    # the default run's kanR check is untouched
+    assert set(pd.read_csv(out / "consistency" / "tfs_growth.csv")["library"]) == {"kanR"}
+
+
+def test_kshift_warns_when_the_control_moves(tmp_path, capsys):
+    r = Recorder(x={"plus": 0.4, "minus": 0.46, "zero": 0.5})
+    lib = pd.DataFrame({"genotype": ["wt", "A1B", "C2D", "A1B/C2D"],
+                        "in_spiked_origin": [True, False, False, False]})
+    with patch.object(cli, "configure_model", r.configure_model), \
+         patch.object(cli, "fit_model", r.fit_model), \
+         patch.object(cli, "extract_params", r.extract_params), \
+         patch.object(cli, "sample_posterior", r.sample_posterior), \
+         patch.object(cli, "predict_theta", r.predict_theta), \
+         patch.object(cli, "library_composition_table", return_value=lib):
+        y = _write_yaml(tmp_path)
+        out = tmp_path / "run"
+        for step in ("stage1", "jackknife", "stage2", "kshift"):
+            cli.fit_two_stage(y, out_dir=str(out), step=step)
+    assert "warning: the control refit" in capsys.readouterr().out
+
+
+def test_kshift_settings(tmp_path, rec):
+    y = _write_yaml(tmp_path, two_stage={"kshift_step_size": 1e-4,
+                                         "kshift_control": False,
+                                         "fit": {"adam_step_size": 1e-3}})
+    out = tmp_path / "run"
+    for step in ("stage1", "jackknife", "stage2", "kshift"):
+        cli.fit_two_stage(y, out_dir=str(out), step=step)
+    assert not (out / "kshift" / "zero").exists()
+    fit = _calls(rec, "fit", "kshift/plus")[0][2]
+    assert fit["adam_step_size"] == 1e-4          # the refit's own wins
+    assert _calls(rec, "fit", "stage2")[0][2]["adam_step_size"] == 1e-3
+    assert set(pd.read_csv(out / "kshift" / "drift.csv")["refit"]) == {"plus", "minus"}
+    with pytest.raises(ValueError, match="kshift_step_size"):
+        cli.read_two_stage_config(_write_yaml(tmp_path, two_stage={"kshift_step_size": 0}))
+
+
+def test_read_config_refuses_a_warp_in_growth_priors(tmp_path):
+    pd.DataFrame({"condition_rep": CONDS, "k_loc": 0.01, "m_warp": [1, 0, 0, 0]}
+                 ).to_csv(tmp_path / "gp.csv", index=False)
+    with pytest.raises(ValueError, match="m_warp"):
+        cli.read_two_stage_config(_write_yaml(tmp_path, growth_priors="gp.csv"))
+    pd.DataFrame({"condition_rep": CONDS, "k_loc": 0.01, "m_warp": 0}
+                 ).to_csv(tmp_path / "gp.csv", index=False)
+    cfg, _ = cli.read_two_stage_config(_write_yaml(tmp_path, growth_priors="gp.csv"))
+    assert cfg["growth_priors"] == str(tmp_path / "gp.csv")
+
+
+def test_summary_table(tmp_path, rec):
+    out = tmp_path / "run"
+    cli.fit_two_stage(_write_yaml(tmp_path, two_stage={"kshift": True}),
+                      out_dir=str(out))
+    x = pd.read_csv(out / "tfs_x.csv")
+    assert list(x.columns[:4]) == ["genotype", "num_mutations", "titrant_name",
+                                   "titrant_conc"]
+    assert len(x) == 8                          # 4 genotypes x 2 concentrations
+    g = x.drop_duplicates("genotype").set_index("genotype")
+    # _growth: 100 reads per genotype in each of 48 tubes, 24 per library
+    assert g.loc["A1B", "reads"] == 4800
+    assert g.loc["A1B", "reads_kanR"] == 2400 and g.loc["A1B", "reads_pheS"] == 2400
+    assert g.loc["A1B/C2D", "num_mutations"] == 2 and g.loc["wt", "num_mutations"] == 0
+    assert np.allclose(x.loc[x.genotype != "wt", "k_shift"], -0.03)
+    assert x.loc[x.genotype == "wt", "k_shift"].eq(0).all()
+    assert x.loc[x.genotype == "wt", "inconsistent"].isna().all()
+    assert x.loc[x.genotype == "A1B", "consistency_z"].notna().all()
+
+
+def test_summary_before_consistency_and_without_predict(tmp_path, rec):
+    y = _write_yaml(tmp_path)
+    out = tmp_path / "run"
+    with pytest.raises(RuntimeError, match="predict"):
+        cli.fit_two_stage(y, out_dir=str(out), step="summary")
+    for step in ("stage1", "jackknife", "stage2", "kshift", "predict", "summary"):
+        cli.fit_two_stage(y, out_dir=str(out), step=step)
+    assert pd.read_csv(out / "tfs_x.csv")["inconsistent"].isna().all()
+    cli.fit_two_stage(y, out_dir=str(out), step="consistency")
+    cli.fit_two_stage(y, out_dir=str(out), step="summary")   # rewritten
+    x = pd.read_csv(out / "tfs_x.csv")
+    assert x.loc[x.genotype != "wt", "inconsistent"].notna().all()
+
+
+def test_default_run_has_no_k_shift(tmp_path, rec):
+    """Validation run 2: the shift is off by default, and so is the
+    jackknife that only feeds it."""
+    out = tmp_path / "run"
+    y = _write_yaml(tmp_path, two_stage={"predict_epistasis": True})
+    cli.fit_two_stage(y, out_dir=str(out))
+    assert not (out / "jackknife").exists() and not (out / "kshift").exists()
+    pred = [c for c in _calls(rec, "predict") if "shift_seed" in c[3]]
+    assert pred and pred[0][3]["shared_shift"] is None
+    assert _calls(rec, "epistasis")[0][3]["shared_shift"] is None
+    x = pd.read_csv(out / "tfs_x.csv")
+    assert x["k_shift"].isna().all()
+    assert x.loc[x.genotype != "wt", "inconsistent"].notna().all()
+    # a stale shift table is ignored while kshift is off
+    for step in ("jackknife", "kshift"):
+        cli.fit_two_stage(y, out_dir=str(out), step=step)
+    assert (out / "kshift" / "shift.csv").exists()
+    cli.fit_two_stage(y, out_dir=str(out), step="summary")
+    assert pd.read_csv(out / "tfs_x.csv")["k_shift"].isna().all()
+
+
+# --- resuming, refusals and edge cases of single steps ---
+
+def test_rerun_of_a_half_finished_stage_reuses_its_config_and_fit(tmp_path, rec):
+    out = tmp_path / "run"
+    y = _write_yaml(tmp_path)
+    for step in ("stage1", "stage2"):
+        cli.fit_two_stage(y, out_dir=str(out), step=step)
+    # km.csv gone: stage 1 re-extracts it but neither configures nor fits
+    os.remove(out / "stage1" / "km.csv")
+    n_conf, n_fit = len(_calls(rec, "configure")), len(_calls(rec, "fit"))
+    cli.fit_two_stage(y, out_dir=str(out), step="stage1")
+    assert (out / "stage1" / "km.csv").exists()
+    assert len(_calls(rec, "configure")) == n_conf
+    assert len(_calls(rec, "fit")) == n_fit
+    # stage 2's prediction gone: the posterior is reused, X re-predicted
+    os.remove(out / "stage2" / cli.PRED)
+    n_post, n_pred = len(_calls(rec, "posterior")), len(_calls(rec, "predict"))
+    cli.fit_two_stage(y, out_dir=str(out), step="stage2")
+    assert (out / "stage2" / cli.PRED).exists()
+    assert len(_calls(rec, "posterior")) == n_post
+    assert len(_calls(rec, "predict")) == n_pred + 1
+
+
+class _PinnedRecorder(Recorder):
+    """Every k and m pinned: extraction writes no k/m tables."""
+
+    def configure_model(self, out_prefix, **kw):
+        self.calls.append(("configure", os.getcwd(), kw))
+        open(f"{out_prefix}_config.yaml", "w").write("x: 1\n")
+        rows = []
+        for i, (c, (k, m)) in enumerate(self.km.items()):
+            rows += [("growth.condition_growth.k_loc", k, i, c),
+                     ("growth.condition_growth.m_loc", m, i, c),
+                     ("growth.condition_growth.k_pinned", 1.0, i, c),
+                     ("growth.condition_growth.m_pinned", 1.0, i, c)]
+        pd.DataFrame(rows, columns=["parameter", "value", "flat_index",
+                                    "condition_rep"]
+                     ).to_csv(f"{out_prefix}_priors.csv", index=False)
+
+    def extract_params(self, config_file, param_file, out_prefix):
+        self.calls.append(("extract", os.getcwd(), param_file))
+
+
+def test_km_from_the_map_when_extraction_skips_pinned_k_and_m(tmp_path):
+    r = _PinnedRecorder()
+    lib = pd.DataFrame({"genotype": ["wt", "A1B", "C2D", "A1B/C2D"],
+                        "in_spiked_origin": [True, False, False, False]})
+    with patch.object(cli, "configure_model", r.configure_model), \
+         patch.object(cli, "fit_model", r.fit_model), \
+         patch.object(cli, "extract_params", r.extract_params), \
+         patch.object(cli, "library_composition_table", return_value=lib):
+        out = tmp_path / "run"
+        cli.fit_two_stage(_write_yaml(tmp_path), out_dir=str(out), step="stage1")
+    km = pd.read_csv(out / "stage1" / "km.csv", index_col="condition_rep")
+    assert list(km.index) == CONDS
+    assert np.allclose(km["k"], [r.km[c][0] for c in CONDS])
+    assert np.allclose(km["m"], [r.km[c][1] for c in CONDS])
+
+
+def test_steps_refuse_without_their_inputs(tmp_path, rec):
+    out = tmp_path / "run"
+    y = _write_yaml(tmp_path, two_stage={"kshift": True})
+    with pytest.raises(RuntimeError, match="stage 2 needs stage 1"):
+        cli.fit_two_stage(y, out_dir=str(out), step="stage2")
+    with pytest.raises(RuntimeError, match="consistency check needs stage 2"):
+        cli.fit_two_stage(y, out_dir=str(out), step="consistency")
+    with pytest.raises(RuntimeError, match="needs kshift/shift.csv"):
+        cli.fit_two_stage(y, out_dir=str(out), step="predict")
+    for step in ("stage1", "jackknife"):
+        cli.fit_two_stage(y, out_dir=str(out), step=step)
+    with pytest.raises(RuntimeError, match="start from stage 2's MAP"):
+        cli.fit_two_stage(y, out_dir=str(out), step="kshift")
+    assert not (out / "kshift" / "shift.csv").exists()
+    with pytest.raises(ValueError, match="use it with --step jackknife"):
+        cli.fit_two_stage(y, out_dir=str(out), step="stage2", jackknife_task=1)
+
+
+def test_finished_jackknife_task_and_kshift_are_skipped(tmp_path, rec, capsys):
+    out = tmp_path / "run"
+    y = _write_yaml(tmp_path)
+    for step in ("stage1", "jackknife", "stage2", "kshift"):
+        cli.fit_two_stage(y, out_dir=str(out), step=step)
+    n = len(rec.calls)
+    capsys.readouterr()
+    cli.fit_two_stage(y, out_dir=str(out), jackknife_task=2)
+    cli.fit_two_stage(y, out_dir=str(out), step="kshift")
+    assert len(rec.calls) == n
+    said = capsys.readouterr().out
+    assert "jackknife 2: done, skipped" in said
+    assert "kshift: done, skipped" in said
+
+
+def test_jackknife_summary_refuses_subsamples_of_different_sizes(tmp_path, rec):
+    out = tmp_path / "run"
+    y = _write_yaml(tmp_path)
+    for step in ("stage1", "jackknife"):
+        cli.fit_two_stage(y, out_dir=str(out), step=step)
+    p = out / "jackknife" / "jk_01" / "subsample.json"
+    info = json.load(open(p))
+    info["d"] += 1
+    json.dump(info, open(p, "w"))
+    os.remove(out / "jackknife" / "k_step.csv")
+    _, settings = cli.read_two_stage_config(y)
+    with pytest.raises(RuntimeError, match="differ in tubes kept"):
+        cli.summarize_jackknife(str(out), settings)
+    assert not (out / "jackknife" / "k_step.csv").exists()
+
+
+def _consistency_inputs(out, libraries):
+    (out / "stage1").mkdir(parents=True)
+    (out / "stage2").mkdir()
+    pd.DataFrame({"library": libraries, "condition_pre": "a",
+                  "condition_sel": "b"}
+                 ).to_csv(out / "stage1" / "tfs_growth.csv", index=False)
+    open(out / "stage2" / cli.PRED, "w").write("x\n")
+
+
+def test_consistency_refuses_a_bad_library(tmp_path):
+    y = _write_yaml(tmp_path)
+    cfg, settings = cli.read_two_stage_config(y)
+    out = tmp_path / "two"
+    _consistency_inputs(out, ["kanR", "pheS"])
+    settings["consistency_library"] = "bogus"
+    with pytest.raises(ValueError, match="'bogus' is not one of"):
+        cli.run_consistency(str(out), cfg, settings)
+    out = tmp_path / "one"
+    _consistency_inputs(out, ["kanR", "kanR"])
+    settings["consistency_library"] = None
+    with pytest.raises(ValueError, match="needs two libraries"):
+        cli.run_consistency(str(out), cfg, settings)
+    assert not (out / "consistency").exists()
+
+
+@pytest.mark.filterwarnings("ignore:.*found in sys.modules:RuntimeWarning")
+def test_cli_module_runs_as_script(monkeypatch):
+    import runpy
+    import sys
+    monkeypatch.setattr(sys, "argv", ["tfs-fit-two-stage", "--help"])
+    with pytest.raises(SystemExit) as exc:
+        runpy.run_module("tfscreen.tfmodel.scripts.fit_two_stage_cli",
+                         run_name="__main__")
+    assert exc.value.code == 0
diff --git a/tests/tfscreen/tfmodel/scripts/test_predict_growth_cli.py b/tests/tfscreen/tfmodel/scripts/test_predict_growth_cli.py
index 7069c69b..a29ba636 100644
--- a/tests/tfscreen/tfmodel/scripts/test_predict_growth_cli.py
+++ b/tests/tfscreen/tfmodel/scripts/test_predict_growth_cli.py
@@ -724,13 +724,13 @@ def test_subset_always_includes_binding_and_spiked(self, mock_orchestrator_subse
                            out_prefix=str(tmp_path / "out"),
                            subset_genotypes=True,
                            genotype_batch_size=3,
-                           subset_seed=0)
+                           seed=0)
         genos = set(all_calls[0]["genotypes"])
         assert "wt" in genos      # binding
         assert "A1B" in genos     # spiked
 
     def test_subset_seed_is_deterministic(self, mock_orchestrator_subset, tmp_path):
-        """Same subset_seed → identical sampled block across runs."""
+        """Same seed → identical sampled block across runs."""
         results = []
         for _ in range(2):
             fake_predict, all_calls = self._make_fake_predict(mock_orchestrator_subset)
@@ -740,7 +740,7 @@ def test_subset_seed_is_deterministic(self, mock_orchestrator_subset, tmp_path):
                                out_prefix=str(tmp_path / "out"),
                                subset_genotypes=True,
                                genotype_batch_size=3,
-                               subset_seed=42)
+                               seed=42)
             results.append(sorted(all_calls[0]["genotypes"]))
         assert results[0] == results[1]
 
@@ -756,7 +756,7 @@ def test_subset_different_seeds_can_differ(self, mock_orchestrator_subset, tmp_p
                                out_prefix=str(tmp_path / "out"),
                                subset_genotypes=True,
                                genotype_batch_size=3,
-                               subset_seed=seed)
+                               seed=seed)
             # the non-mandatory member of the block
             extra = set(all_calls[0]["genotypes"]) - {"wt", "A1B"}
             sampled |= extra
@@ -826,7 +826,7 @@ def test_subset_in_training_data_column(self, mock_orchestrator_subset, tmp_path
                            out_prefix=out,
                            subset_genotypes=True,
                            genotype_batch_size=3,
-                           subset_seed=0)
+                           seed=0)
         df = pd.read_csv(f"{out}.csv")
         assert (df["in_training_data"] == 1).all()
 
@@ -899,7 +899,7 @@ def test_backoff_shrinks_until_it_fits(self, mock_orchestrator_subset, tmp_path)
                            out_prefix=out,
                            subset_genotypes=True,
                            genotype_batch_size=6,
-                           subset_seed=0)
+                           seed=0)
         # More than one attempt was made, and the final (successful) call fit.
         assert len(all_calls) > 1
         assert len(all_calls[-1]["genotypes"]) <= 3
@@ -917,7 +917,7 @@ def test_backoff_retains_mandatory_genotypes(self, mock_orchestrator_subset, tmp
                            out_prefix=out,
                            subset_genotypes=True,
                            genotype_batch_size=6,
-                           subset_seed=0)
+                           seed=0)
         # Every attempted call must contain the mandatory anchors.
         for call in all_calls:
             assert "wt" in call["genotypes"]
@@ -937,7 +937,7 @@ def test_backoff_reraises_when_mandatory_alone_ooms(self, mock_orchestrator_subs
                                out_prefix=str(tmp_path / "out"),
                                subset_genotypes=True,
                                genotype_batch_size=6,
-                               subset_seed=0)
+                               seed=0)
         # Last attempt was the mandatory-only block (extra exhausted).
         assert len(all_calls[-1]["genotypes"]) == 2
 
@@ -956,5 +956,5 @@ def fake_predict(**kwargs):
                                out_prefix=str(tmp_path / "out"),
                                subset_genotypes=True,
                                genotype_batch_size=6,
-                               subset_seed=0)
+                               seed=0)
         assert len(all_calls) == 1  # no retries on a non-OOM error
diff --git a/tests/tfscreen/tfmodel/scripts/test_predict_theta_cli.py b/tests/tfscreen/tfmodel/scripts/test_predict_theta_cli.py
index e140e0a3..12139413 100644
--- a/tests/tfscreen/tfmodel/scripts/test_predict_theta_cli.py
+++ b/tests/tfscreen/tfmodel/scripts/test_predict_theta_cli.py
@@ -462,3 +462,64 @@ def fake_curves(**kwargs):
                           out_prefix=str(tmp_path / "out"))
 
         assert extract_calls["q_to_get"] is None
+
+
+# ---------------------------------------------------------------------------
+# X-scale label (relative theta component; roadmap C8)
+# ---------------------------------------------------------------------------
+
+@pytest.mark.parametrize("theta,label", [("hill_relative", "X"),
+                                         ("hill_geno", None)])
+def test_relative_output_is_labelled_X(mock_extract, mock_orchestrator,
+                                       tmp_path, theta, label):
+    mock_orchestrator._theta = theta
+    out = str(tmp_path / "out")
+    with patch("tfscreen.tfmodel.scripts.predict_theta_cli.extract_theta_curves",
+               return_value=pd.DataFrame({"genotype": ["wt"],
+                                          "titrant_name": ["IPTG"],
+                                          "titrant_conc": [0.0],
+                                          "q0.5": [1.0]})):
+        predict_theta("cfg.yaml", "post.h5", out_prefix=out)
+    df = pd.read_csv(f"{out}.csv")
+    if label is None:
+        assert "theta_scale" not in df.columns
+    else:
+        assert (df["theta_scale"] == label).all()
+
+
+# ---------------------------------------------------------------------------
+# Non-finite theta is reported, not written silently
+# ---------------------------------------------------------------------------
+
+def test_nan_theta_is_reported(mock_extract, tmp_path):
+    out = str(tmp_path / "out")
+    df = pd.DataFrame({"genotype": ["wt", "wt", "A1B", "A1B"],
+                       "titrant_name": ["IPTG"] * 4,
+                       "titrant_conc": [0.0, 1.0, 0.0, 1.0],
+                       "q0.5": [0.9, 0.1, np.nan, np.nan]})
+    with patch("tfscreen.tfmodel.scripts.predict_theta_cli.extract_theta_curves",
+               return_value=df):
+        with pytest.warns(RuntimeWarning, match=r"2 of 4 .*1 genotype.*A1B"):
+            predict_theta("cfg.yaml", "post.h5", out_prefix=out)
+    assert pd.read_csv(f"{out}.csv")["q0.5"].isna().sum() == 2
+
+
+def test_finite_theta_is_not_reported(mock_extract, tmp_path):
+    import warnings
+    df = pd.DataFrame({"genotype": ["wt"], "titrant_name": ["IPTG"],
+                       "titrant_conc": [0.0], "q0.5": [1.0]})
+    with patch("tfscreen.tfmodel.scripts.predict_theta_cli.extract_theta_curves",
+               return_value=df), warnings.catch_warnings():
+        warnings.simplefilter("error", RuntimeWarning)
+        predict_theta("cfg.yaml", "post.h5", out_prefix=str(tmp_path / "o"))
+
+
+def test_warn_nonfinite_theta_without_value_columns():
+    """A table with no numeric value column has nothing to check."""
+    import warnings
+    from tfscreen.tfmodel.scripts.predict_theta_cli import warn_nonfinite_theta
+    df = pd.DataFrame({"genotype": ["wt"], "titrant_name": ["iptg"],
+                       "titrant_conc": [np.nan], "in_training_data": [1]})
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        assert warn_nonfinite_theta(df) == 0
diff --git a/tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py b/tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py
index a81f95ba..7c0fa510 100644
--- a/tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py
+++ b/tests/tfscreen/tfmodel/scripts/test_prefit_calibration_cli.py
@@ -857,7 +857,7 @@ def test_overrides_replace_production_components(self):
             "activity": "horseshoe_geno",         # → hierarchical
             "dk_geno": "fixed",              # → hierarchical
             "ln_cfu0": "fixed",              # → hierarchical
-            "transformation": "logit_norm",  # → single
+            "transformation": "mixture",  # → single
             "theta_growth_noise": "beta",    # → zero
             "theta_binding_noise": "beta",   # → zero
             "condition_growth": "linear",    # passthrough
@@ -909,7 +909,7 @@ def test_dk_geno_pinned_carried_through_from_production(self):
             "dk_geno": "pinned",
             "dk_geno_pins_file": "dk_geno_pins.csv",
             "ln_cfu0": "fixed",
-            "transformation": "logit_norm",
+            "transformation": "mixture",
             "theta_growth_noise": "beta",
             "theta_binding_noise": "beta",
             "condition_growth": "linear",
@@ -943,7 +943,7 @@ def test_dk_geno_pins_file_dropped_when_not_pinned(self):
             "dk_geno": "hierarchical_geno",
             "dk_geno_pins_file": "stale_pins.csv",
             "ln_cfu0": "fixed",
-            "transformation": "logit_norm",
+            "transformation": "mixture",
             "theta_growth_noise": "beta",
             "theta_binding_noise": "beta",
             "condition_growth": "linear",
@@ -965,7 +965,7 @@ def test_dk_geno_pins_file_dropped_when_not_pinned(self):
 
     def test_transformation_lambda_dropped_when_forced_to_single(self):
         """
-        Production transformation="empirical" carries a transformation_lambda
+        Production transformation="mixture" carries a transformation_lambda
         (mean, std) pair anchoring its lambda parameter. The calibration
         model always overrides transformation to "single", which has no
         lambda parameter -- ModelOrchestrator raises if transformation_lambda is
@@ -977,7 +977,7 @@ def test_transformation_lambda_dropped_when_forced_to_single(self):
             "activity": "horseshoe_geno",
             "dk_geno": "fixed",
             "ln_cfu0": "fixed",
-            "transformation": "empirical",
+            "transformation": "mixture",
             "transformation_lambda": (0.3572, 0.2592),
             "theta_growth_noise": "beta",
             "theta_binding_noise": "beta",
@@ -1008,7 +1008,7 @@ def test_binding_weight_reset_from_large_production_value(self):
             "activity": "horseshoe_geno",
             "dk_geno": "fixed",
             "ln_cfu0": "fixed",
-            "transformation": "logit_norm",
+            "transformation": "mixture",
             "theta_growth_noise": "beta",
             "theta_binding_noise": "beta",
             "condition_growth": "linear",
@@ -1034,7 +1034,7 @@ def test_does_not_mutate_production_settings(self):
                             "activity": "horseshoe_geno",
                             "dk_geno": "fixed",
                             "ln_cfu0": "fixed",
-                            "transformation": "logit_norm",
+                            "transformation": "mixture",
                             "theta_growth_noise": "beta",
                             "theta_binding_noise": "beta",
                             "spiked_genotypes": None,
@@ -1057,7 +1057,7 @@ def _base_growth_settings(self):
             "activity": "horseshoe_geno",
             "dk_geno": "hierarchical_geno",  # production value; not "pinned"
             "ln_cfu0": "fixed",
-            "transformation": "logit_norm",
+            "transformation": "mixture",
             "theta_growth_noise": "beta",
             "theta_binding_noise": "beta",
             "condition_growth": "linear",
@@ -1514,28 +1514,30 @@ def test_optimizer_kwargs_forwarded(self, tmp_path, mocker):
             seed=1,
             adam_step_size=2.5e-3,
             adam_final_step_size=2.5e-7,
+            adam_step_size_cut=0.5,
             adam_clip_norm=2.0,
             elbo_num_particles=5,
-            convergence_tolerance=1e-4,
-            convergence_window=20,
+            convergence_window_steps=500,
             patience=4,
-            convergence_check_interval=3,
+            convergence_z=2.0,
+            loss_rtol=1e-8,
+            param_tolerance=0.1,
             checkpoint_interval=25,
             max_num_epochs=500,
-            init_param_jitter=0.0,
         )
         kwargs = mock_run_map.call_args.kwargs
         assert kwargs["adam_step_size"] == 2.5e-3
         assert kwargs["adam_final_step_size"] == 2.5e-7
+        assert kwargs["adam_step_size_cut"] == 0.5
         assert kwargs["adam_clip_norm"] == 2.0
         assert kwargs["elbo_num_particles"] == 5
-        assert kwargs["convergence_tolerance"] == 1e-4
-        assert kwargs["convergence_window"] == 20
+        assert kwargs["convergence_window_steps"] == 500
         assert kwargs["patience"] == 4
-        assert kwargs["convergence_check_interval"] == 3
+        assert kwargs["convergence_z"] == 2.0
+        assert kwargs["loss_rtol"] == 1e-8
+        assert kwargs["param_tolerance"] == 0.1
         assert kwargs["checkpoint_interval"] == 25
         assert kwargs["max_num_epochs"] == 500
-        assert kwargs["init_param_jitter"] == 0.0
 
     def test_default_out_prefix_is_prefit(self, tmp_path, mocker):
         cfg, _, _ = self._write_yaml_and_csvs(tmp_path)
@@ -1550,12 +1552,15 @@ def test_custom_out_prefix_is_honored(self, tmp_path, mocker):
                                out_prefix="my_runA")
         assert mock_run_map.call_args.kwargs["out_prefix"] == "my_runA"
 
-    def test_default_init_param_jitter_is_zero(self, tmp_path, mocker):
-        """Pre-fit should be deterministic given a seed; default jitter is 0."""
+    def test_map_starts_from_calibration_guesses(self, tmp_path, mocker):
+        """The calibration MAP starts from the calibration model's guesses
+        (as site values), not from AutoDelta's prior medians."""
         cfg, _, _ = self._write_yaml_and_csvs(tmp_path)
-        _, mock_run_map = self._patch_pipeline(mocker)
+        mock_ri, mock_run_map = self._patch_pipeline(mocker)
+        mock_ri.site_values.return_value = {"site": 1.0}
         run_prefit_calibration(config_file=cfg, seed=1)
-        assert mock_run_map.call_args.kwargs["init_param_jitter"] == 0.0
+        mock_ri.site_values.assert_called_once()
+        assert mock_run_map.call_args.kwargs["init_values"] == {"site": 1.0}
 
     def test_hessian_called_after_map(self, tmp_path, mocker):
         """compute_hessian_sigmas must be called exactly once after MAP."""
@@ -2071,3 +2076,49 @@ def test_main_forwards_checkpoint_file(self, tmp_path):
              patch("sys.argv", ["tfs-prefit-calibration"] + argv):
             main()
         assert mock_run.call_args.kwargs["checkpoint_file"] == "/tmp/ck.pkl"
+
+
+# ---------------------------------------------------------------------------
+# _run_calibration_map
+# ---------------------------------------------------------------------------
+
+def _calibration_map_kwargs(**overrides):
+    kw = dict(init_values={"a": 1.0}, out_prefix="pre", checkpoint_file=None,
+              adam_step_size=1e-3, adam_final_step_size=1e-6,
+              adam_step_size_cut=0.1, adam_clip_norm=None,
+              elbo_num_particles=2, convergence_window_steps=2000,
+              patience=3, convergence_z=3.0, loss_rtol=1e-6,
+              param_tolerance=0.05, checkpoint_interval=10,
+              max_num_epochs=100, epoch_checkpoint_interval=0)
+    kw.update(overrides)
+    return kw
+
+
+@pytest.mark.parametrize("converged,message", [
+    (True, "Calibration MAP run converged."),
+    (False, "Calibration MAP run has not yet converged."),
+])
+def test_run_calibration_map(converged, message, capsys):
+    from tfscreen.tfmodel.scripts.prefit_calibration_cli import (
+        _run_calibration_map,
+    )
+    ri = MagicMock()
+    ri.setup_svi.return_value = "map_obj"
+    ri.run_optimization.return_value = ("state", {"p": 1}, converged)
+
+    out = _run_calibration_map(ri, **_calibration_map_kwargs())
+
+    assert out == ("state", {"p": 1}, converged)
+    ri.setup_svi.assert_called_once_with(adam_step_size=1e-3,
+                                         adam_clip_norm=None,
+                                         elbo_num_particles=2,
+                                         guide_type="delta",
+                                         init_values={"a": 1.0})
+    ri.run_optimization.assert_called_once_with(
+        "map_obj", out_prefix="pre", svi_state=None,
+        convergence_window_steps=2000, patience=3, convergence_z=3.0,
+        loss_rtol=1e-6, param_tolerance=0.05, final_step_size=1e-6,
+        step_size_cut=0.1, checkpoint_interval=10, max_num_epochs=100,
+        epoch_checkpoint_interval=0)
+    ri.write_params.assert_called_once_with({"p": 1}, out_prefix="pre")
+    assert message in capsys.readouterr().out
diff --git a/tests/tfscreen/tfmodel/scripts/test_sample_posterior_cli.py b/tests/tfscreen/tfmodel/scripts/test_sample_posterior_cli.py
index fe653793..88a06a7c 100644
--- a/tests/tfscreen/tfmodel/scripts/test_sample_posterior_cli.py
+++ b/tests/tfscreen/tfmodel/scripts/test_sample_posterior_cli.py
@@ -43,7 +43,8 @@ def _run_nuts(self, tmp_path, ri, forward_batch_size=512):
                 "mcmc_samples": {"activity": np.zeros((10, 4))}
             }
 
-            def fake_nuts_posteriors(samples, out_prefix, forward_batch_size):
+            def fake_nuts_posteriors(samples, out_prefix, forward_batch_size,
+                                     sites_to_save=None):
                 open(h5_src, "w").close()
 
             ri.get_nuts_posteriors.side_effect = fake_nuts_posteriors
@@ -149,6 +150,90 @@ def test_map_checkpoint_calls_get_laplace_posteriors(self, tmp_path):
                              out_prefix=str(tmp_path / "out"))
 
         ri.get_laplace_posteriors.assert_called_once()
+        kw = ri.get_laplace_posteriors.call_args.kwargs
+        assert kw["block_genotypes"] is False
+        assert kw["genotype_chunk_size"] is None
+        assert kw["block_shared"] is False
+
+    def test_laplace_arrowhead_forwarded(self, tmp_path):
+        """--laplace arrowhead asks for the per-genotype Laplace with the
+        shared parameters, with its chunk size."""
+        ri = self._make_map_ri(tmp_path, auto_loc=True)
+        h5_src = str(tmp_path / "out_tmp_posterior_posterior.h5")
+        ckpt_path = str(tmp_path / "map.pkl")
+        open(ckpt_path, "w").close()
+
+        with patch("tfscreen.tfmodel.scripts.sample_posterior_cli.read_configuration",
+                   return_value=(MagicMock(), {})), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.RunInference",
+                   return_value=ri), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.dill") as mock_dill:
+
+            mock_dill.load.return_value = {"svi_state": MagicMock()}
+            ri.get_laplace_posteriors.side_effect = lambda **kw: (
+                open(h5_src, "w").close()
+            )
+
+            from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+            sample_posterior("cfg.yaml", ckpt_path,
+                             out_prefix=str(tmp_path / "out"),
+                             laplace="arrowhead",
+                             genotype_chunk_size=500)
+
+        kw = ri.get_laplace_posteriors.call_args.kwargs
+        assert kw["block_genotypes"] is True
+        assert kw["block_shared"] is True
+        assert kw["genotype_chunk_size"] == 500
+
+    def test_unknown_laplace_refused(self, tmp_path):
+        from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+        with pytest.raises(ValueError, match="laplace must be one of"):
+            sample_posterior("cfg.yaml", str(tmp_path / "x.pkl"),
+                             laplace="dense")
+
+    def test_resolve_laplace(self):
+        from tfscreen.tfmodel.scripts.sample_posterior_cli import resolve_laplace
+        assert resolve_laplace("auto", 100, 1000) == "full"
+        assert resolve_laplace("auto", 1000, 1000) == "full"
+        assert resolve_laplace("auto", 1001, 1000) == "arrowhead"
+        for m in ("full", "arrowhead", "blocks", "point"):
+            assert resolve_laplace(m, 10 ** 9, 1) == m
+        with pytest.raises(ValueError):
+            resolve_laplace("nope", 1, 1)
+
+    def test_auto_picks_arrowhead_above_threshold(self, tmp_path):
+        """--laplace auto on a model above laplace_max_params runs the
+        arrowhead and writes the held directions."""
+        import pandas as pd
+        ri = self._make_map_ri(tmp_path, auto_loc=True)
+        ri.held_shared_directions = pd.DataFrame(
+            {"direction": [0], "eigenvalue": [-1.0],
+             "parameter": ["k[0]"], "loading": [1.0]})
+        h5_src = str(tmp_path / "out_tmp_posterior_posterior.h5")
+        ckpt_path = str(tmp_path / "map.pkl")
+        open(ckpt_path, "w").close()
+
+        with patch("tfscreen.tfmodel.scripts.sample_posterior_cli.read_configuration",
+                   return_value=(MagicMock(), {})), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.RunInference",
+                   return_value=ri), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.dill") as mock_dill:
+
+            mock_dill.load.return_value = {"svi_state": MagicMock()}
+            ri.get_laplace_posteriors.side_effect = lambda **kw: (
+                open(h5_src, "w").close()
+            )
+
+            from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+            sample_posterior("cfg.yaml", ckpt_path,
+                             out_prefix=str(tmp_path / "out"),
+                             laplace_max_params=0)
+
+        kw = ri.get_laplace_posteriors.call_args.kwargs
+        assert kw["block_genotypes"] is True
+        assert kw["block_shared"] is True
+        held = pd.read_csv(tmp_path / "out_held_directions.csv")
+        assert list(held["parameter"]) == ["k[0]"]
 
     def test_map_output_file_renamed(self, tmp_path):
         ri = self._make_map_ri(tmp_path, auto_loc=True)
@@ -173,6 +258,62 @@ def test_map_output_file_renamed(self, tmp_path):
 
         assert os.path.isfile(str(tmp_path / "out.h5"))
 
+    def test_map_point_skips_laplace(self, tmp_path):
+        """map_point writes the MAP point (no Hessian) for a MAP checkpoint."""
+        ri = self._make_map_ri(tmp_path, auto_loc=True)
+        h5_src = str(tmp_path / "out_tmp_posterior_posterior.h5")
+        ckpt_path = str(tmp_path / "map.pkl")
+        open(ckpt_path, "w").close()
+
+        with patch("tfscreen.tfmodel.scripts.sample_posterior_cli.read_configuration",
+                   return_value=(MagicMock(), {})), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.RunInference",
+                   return_value=ri), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.dill") as mock_dill:
+
+            mock_dill.load.return_value = {"svi_state": MagicMock()}
+            ri.get_map_posteriors.side_effect = lambda **kw: (
+                open(h5_src, "w").close()
+            )
+
+            from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+            sample_posterior("cfg.yaml", ckpt_path,
+                             out_prefix=str(tmp_path / "out"), laplace="point")
+
+        ri.get_map_posteriors.assert_called_once()
+        ri.get_laplace_posteriors.assert_not_called()
+        assert ri.get_map_posteriors.call_args.kwargs["map_params"] == \
+            {"global_p_auto_loc": np.array(0.5)}
+        assert os.path.isfile(str(tmp_path / "out.h5"))
+
+    def test_skip_growth_observations_drops_only_those_sites(self, tmp_path):
+        """skip_growth_observations passes a sites_to_save without growth_pred/obs."""
+        ri = self._make_map_ri(tmp_path, auto_loc=True)
+        h5_src = str(tmp_path / "out_tmp_posterior_posterior.h5")
+        ckpt_path = str(tmp_path / "map.pkl")
+        open(ckpt_path, "w").close()
+
+        with patch("tfscreen.tfmodel.scripts.sample_posterior_cli.read_configuration",
+                   return_value=(MagicMock(), {})), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.RunInference",
+                   return_value=ri), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.dill") as mock_dill:
+
+            mock_dill.load.return_value = {"svi_state": MagicMock()}
+            ri.get_map_posteriors.side_effect = lambda **kw: (
+                open(h5_src, "w").close()
+            )
+
+            from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+            sample_posterior("cfg.yaml", ckpt_path,
+                             out_prefix=str(tmp_path / "out"), laplace="point",
+                             skip_growth_observations=True)
+
+        sites = ri.get_map_posteriors.call_args.kwargs["sites_to_save"]
+        assert "growth_pred" not in sites
+        assert "growth_obs" not in sites
+        assert "theta_theta_low" in sites and "ln_cfu0" in sites
+
     def test_map_detection_requires_auto_loc_key(self, tmp_path):
         """Without '_auto_loc' keys the SVI branch must be taken, not MAP."""
         ri = self._make_map_ri(tmp_path, auto_loc=False)  # no _auto_loc key
@@ -251,6 +392,52 @@ def test_svi_output_renamed(self, tmp_path):
         assert os.path.isfile(str(tmp_path / "out.h5"))
 
 
+# ---------------------------------------------------------------------------
+# Routing by recorded guide_type (checkpoints written after guide selection)
+# ---------------------------------------------------------------------------
+
+class TestSamplePosteriorGuideMetadata:
+
+    def _run(self, tmp_path, chk_data):
+        ri = MagicMock()
+        ri.restore_svi_from_checkpoint.return_value = (MagicMock(), MagicMock())
+        # AutoNormal parameters are also named "{site}_auto_loc", so the
+        # legacy name heuristic alone would misroute them to Laplace.
+        ri.setup_svi.return_value.optim.get_params.return_value = {
+            "global_p_auto_loc": np.array(0.5)
+        }
+        h5_src = str(tmp_path / "out_tmp_posterior_posterior.h5")
+        ckpt_path = str(tmp_path / "ckpt.pkl")
+        open(ckpt_path, "w").close()
+        ri.get_posteriors.side_effect = lambda **kw: open(h5_src, "w").close()
+        ri.get_laplace_posteriors.side_effect = lambda **kw: open(h5_src, "w").close()
+
+        with patch("tfscreen.tfmodel.scripts.sample_posterior_cli.read_configuration",
+                   return_value=(MagicMock(), {})), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.RunInference",
+                   return_value=ri), \
+             patch("tfscreen.tfmodel.scripts.sample_posterior_cli.dill") as mock_dill:
+            mock_dill.load.return_value = chk_data
+            from tfscreen.tfmodel.scripts.sample_posterior_cli import sample_posterior
+            sample_posterior("cfg.yaml", ckpt_path, out_prefix=str(tmp_path / "out"))
+        return ri
+
+    @pytest.mark.parametrize("guide_type", ["auto_normal",
+                                            "auto_multivariate_normal",
+                                            "component"])
+    def test_svi_guides_draw_from_guide(self, tmp_path, guide_type):
+        ri = self._run(tmp_path, {"svi_state": MagicMock(),
+                                  "guide_type": guide_type})
+        ri.get_posteriors.assert_called_once()
+        ri.get_laplace_posteriors.assert_not_called()
+
+    def test_delta_checkpoint_goes_to_laplace(self, tmp_path):
+        ri = self._run(tmp_path, {"svi_state": MagicMock(),
+                                  "guide_type": "delta"})
+        ri.get_laplace_posteriors.assert_called_once()
+        ri.get_posteriors.assert_not_called()
+
+
 # ---------------------------------------------------------------------------
 # main() routes to generalized_main
 # ---------------------------------------------------------------------------
diff --git a/tests/tfscreen/tfmodel/scripts/test_setup_grid_cli.py b/tests/tfscreen/tfmodel/scripts/test_setup_grid_cli.py
index cd37358f..28a4edf7 100644
--- a/tests/tfscreen/tfmodel/scripts/test_setup_grid_cli.py
+++ b/tests/tfscreen/tfmodel/scripts/test_setup_grid_cli.py
@@ -1,214 +1,26 @@
-"""Tests for path-rewriting helpers (now in grid_utils, re-exported from setup_grid_cli)."""
+"""Tests for tfs-setup-grid: path helpers and self-contained, movable grids."""
 
+import inspect
 import os
+import shutil
 
+import pandas as pd
+import pytest
 import yaml
 
-import inspect
-
-from tfscreen.util.grid_utils import (
-    relativize_config_paths as _relativize_config_paths,
-    relativize_node as _relativize_node,
-    relativize_template_vars as _relativize_template_vars,
-)
+from tfscreen.util.grid_utils import INPUTS_DIRNAME
+from tfscreen.tfmodel.configuration_io import read_configuration
 from tfscreen.tfmodel.scripts.setup_grid_cli import (
+    setup_grid,
     _cm_kwargs,
     _resolve_cm_paths,
+    _stage_written_config,
     _COMPONENT_AXES,
     _PATH_KEYS,
 )
 from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
 
 
-# ---------------------------------------------------------------------------
-# _relativize_node
-# ---------------------------------------------------------------------------
-
-def test_relativize_node_absolute_existing(tmp_path):
-    """Absolute path to an existing file is rewritten relative to subdir."""
-    f = tmp_path / "data.csv"
-    f.write_text("x")
-    subdir = tmp_path / "run_0001"
-    subdir.mkdir()
-    result = _relativize_node(str(f), str(subdir))
-    assert result == os.path.relpath(str(f), str(subdir))
-
-
-def test_relativize_node_absolute_missing(tmp_path):
-    """Absolute path to a non-existent file is left unchanged."""
-    subdir = tmp_path / "run_0001"
-    subdir.mkdir()
-    missing = str(tmp_path / "ghost.csv")
-    assert _relativize_node(missing, str(subdir)) == missing
-
-
-def test_relativize_node_relative_string(tmp_path):
-    """Relative (non-absolute) strings are never touched."""
-    subdir = tmp_path / "run_0001"
-    subdir.mkdir()
-    assert _relativize_node("data/foo.csv", str(subdir)) == "data/foo.csv"
-
-
-def test_relativize_node_non_string(tmp_path):
-    """Non-string values are returned unchanged."""
-    subdir = str(tmp_path / "run_0001")
-    os.makedirs(subdir)
-    assert _relativize_node(42, subdir) == 42
-    assert _relativize_node(None, subdir) is None
-    assert _relativize_node(True, subdir) is True
-
-
-def test_relativize_node_dict(tmp_path):
-    """Dict values with absolute paths are recursively rewritten."""
-    f = tmp_path / "binding.csv"
-    f.write_text("x")
-    subdir = tmp_path / "run_0001"
-    subdir.mkdir()
-    node = {"binding_df": str(f), "n_samples": 100}
-    result = _relativize_node(node, str(subdir))
-    assert result["binding_df"] == os.path.relpath(str(f), str(subdir))
-    assert result["n_samples"] == 100
-
-
-def test_relativize_node_list(tmp_path):
-    """List values with absolute paths are recursively rewritten."""
-    f = tmp_path / "file.csv"
-    f.write_text("x")
-    subdir = tmp_path / "run_0001"
-    subdir.mkdir()
-    result = _relativize_node([str(f), "unchanged"], str(subdir))
-    assert result[0] == os.path.relpath(str(f), str(subdir))
-    assert result[1] == "unchanged"
-
-
-def test_relativize_node_nested(tmp_path):
-    """Nested dicts/lists are handled recursively."""
-    f = tmp_path / "deep.csv"
-    f.write_text("x")
-    subdir = tmp_path / "run_0001"
-    subdir.mkdir()
-    node = {"outer": {"inner": str(f)}}
-    result = _relativize_node(node, str(subdir))
-    assert result["outer"]["inner"] == os.path.relpath(str(f), str(subdir))
-
-
-# ---------------------------------------------------------------------------
-# _relativize_config_paths
-# ---------------------------------------------------------------------------
-
-def test_relativize_config_paths_data_section(tmp_path):
-    """Absolute paths in the data section are rewritten to relative."""
-    f = tmp_path / "binding.csv"
-    f.write_text("x")
-    subdir = tmp_path / "run_0001"
-    subdir.mkdir()
-    cfg = {"data": {"binding_df": str(f), "n_samples": 100}}
-    yaml_path = subdir / "config.yaml"
-    yaml_path.write_text(yaml.dump(cfg))
-
-    _relativize_config_paths(str(yaml_path), str(subdir))
-
-    with open(yaml_path) as fh:
-        result = yaml.safe_load(fh)
-    assert not os.path.isabs(result["data"]["binding_df"])
-    assert result["data"]["n_samples"] == 100
-
-
-def test_relativize_config_paths_top_level(tmp_path):
-    """Absolute paths outside the data section are also rewritten."""
-    cal = tmp_path / "calibration.json"
-    cal.write_text("{}")
-    subdir = tmp_path / "run_0001"
-    subdir.mkdir()
-    cfg = {"calibration_file": str(cal), "n_samples": 200}
-    yaml_path = subdir / "config.yaml"
-    yaml_path.write_text(yaml.dump(cfg))
-
-    _relativize_config_paths(str(yaml_path), str(subdir))
-
-    with open(yaml_path) as fh:
-        result = yaml.safe_load(fh)
-    assert not os.path.isabs(result["calibration_file"])
-    assert result["n_samples"] == 200
-
-
-def test_relativize_config_paths_no_change(tmp_path):
-    """Config with no absolute paths is not rewritten."""
-    subdir = tmp_path / "run_0001"
-    subdir.mkdir()
-    cfg = {"data": {"binding_df": "../../data/binding.csv"}}
-    yaml_path = subdir / "config.yaml"
-    original = yaml.dump(cfg)
-    yaml_path.write_text(original)
-    mtime_before = yaml_path.stat().st_mtime
-
-    _relativize_config_paths(str(yaml_path), str(subdir))
-
-    assert yaml_path.stat().st_mtime == mtime_before
-
-
-# ---------------------------------------------------------------------------
-# _relativize_template_vars
-# ---------------------------------------------------------------------------
-
-def test_relativize_template_vars_existing_file(tmp_path):
-    """A relative path in a template var that resolves to an existing file is rewritten."""
-    data_dir = tmp_path / "data"
-    data_dir.mkdir()
-    geno = data_dir / "genotypes.csv"
-    geno.write_text("x")
-
-    grid_yaml_dir = str(tmp_path)
-    subdir = str(tmp_path / "grid" / "run_0001")
-    os.makedirs(subdir)
-
-    tmpl_vars = {"genotypes_file": "data/genotypes.csv", "seed": 42}
-    result = _relativize_template_vars(tmpl_vars, grid_yaml_dir, subdir)
-
-    assert not os.path.isabs(result["genotypes_file"])
-    # Path from run subdir should navigate up to tmp_path/data/genotypes.csv
-    resolved = os.path.normpath(os.path.join(subdir, result["genotypes_file"]))
-    assert resolved == str(geno)
-    assert result["seed"] == 42
-
-
-def test_relativize_template_vars_nonexistent(tmp_path):
-    """A string that doesn't point to an existing path is left unchanged."""
-    grid_yaml_dir = str(tmp_path)
-    subdir = str(tmp_path / "run_0001")
-    os.makedirs(subdir)
-
-    tmpl_vars = {"genotypes_file": "data/missing.csv", "label": "control"}
-    result = _relativize_template_vars(tmpl_vars, grid_yaml_dir, subdir)
-    assert result == tmpl_vars
-
-
-def test_relativize_template_vars_absolute_path(tmp_path):
-    """An absolute path in a template var is also rewritten if the file exists."""
-    f = tmp_path / "abs.csv"
-    f.write_text("x")
-    grid_yaml_dir = str(tmp_path)
-    subdir = str(tmp_path / "run_0001")
-    os.makedirs(subdir)
-
-    tmpl_vars = {"genotypes_file": str(f)}
-    result = _relativize_template_vars(tmpl_vars, grid_yaml_dir, subdir)
-    assert not os.path.isabs(result["genotypes_file"])
-    resolved = os.path.normpath(os.path.join(subdir, result["genotypes_file"]))
-    assert resolved == str(f)
-
-
-def test_relativize_template_vars_non_string_passthrough(tmp_path):
-    """Non-string template variable values are returned unchanged."""
-    grid_yaml_dir = str(tmp_path)
-    subdir = str(tmp_path / "run_0001")
-    os.makedirs(subdir)
-
-    tmpl_vars = {"seed": 0, "flag": True, "ratio": 1.5, "none_val": None}
-    result = _relativize_template_vars(tmpl_vars, grid_yaml_dir, subdir)
-    assert result == tmpl_vars
-
-
 # ---------------------------------------------------------------------------
 # _cm_kwargs — component-axis → configure_model parameter translation
 # ---------------------------------------------------------------------------
@@ -285,3 +97,336 @@ def test_path_keys_cover_all_df_params():
     df_params = {p for p in params if p.endswith("_df")}
     missing = df_params - _PATH_KEYS
     assert not missing, f"_PATH_KEYS missing configure_model df args: {sorted(missing)}"
+
+
+# ---------------------------------------------------------------------------
+# End to end: self-contained, movable grid directories
+# ---------------------------------------------------------------------------
+
+_GENOTYPES = ["wt", "A2L", "A2C"]
+
+_LIBRARY = {
+    "reading_frame": 0,
+    "first_amplicon_residue": 1,
+    "wt_seq":      "atggcaaaaccggaatgc",
+    "degen_sites": "...nnt......nnt...",
+    "tiles":       "...111......222...",
+    "tile_combos": ["single-1", "single-2", "double-1-2"],
+    "spiked_seqs": ["..................",
+                    "...ctt............"],
+    "library_mixture": {"single-1": 10, "single-2": 10,
+                        "double-1-2": 100, "spiked": 1},
+}
+
+_TEMPLATE = (
+    "SEED={{ seed }}\n"
+    "GENOTYPES={{ predict_genotypes_file }}\n"
+)
+
+
+def _write_growth(path, ln_cfu=1.0):
+    rows = []
+    for g in _GENOTYPES:
+        for t in (0.0, 10.0):
+            rows.append({"library": "lib", "replicate": 1, "time": t,
+                         "genotype": g, "ln_cfu": ln_cfu, "ln_cfu_std": 0.1,
+                         "condition_pre": "pre-cond",
+                         "condition_sel": "sel+cond",
+                         "t_pre": 1.0, "t_sel": 10.0,
+                         "titrant_name": "iptg", "titrant_conc": 0.0})
+    pd.DataFrame(rows).to_csv(path, index=False)
+
+
+def _write_binding(path):
+    pd.DataFrame([{"genotype": g, "titrant_name": "iptg", "titrant_conc": 0.0,
+                   "theta_obs": 0.5, "theta_std": 0.05}
+                  for g in _GENOTYPES]).to_csv(path, index=False)
+
+
+@pytest.fixture
+def project(tmp_path):
+    """Inputs in <tmp>/data, grid YAML + template in <tmp>/grids."""
+    data = tmp_path / "data"
+    data.mkdir()
+    _write_growth(data / "growth.csv")
+    _write_binding(data / "binding.csv")
+    (data / "library.yaml").write_text(yaml.dump(_LIBRARY))
+    (data / "genotypes.txt").write_text("wt\nA2L\n")
+    grids = tmp_path / "grids"
+    grids.mkdir()
+    (grids / "run.sh").write_text(_TEMPLATE)
+    return tmp_path
+
+
+def _data_block(**extra):
+    variant = {"binding_df": "../data/binding.csv",
+               "growth_df": "../data/growth.csv",
+               "library_config": "../data/library.yaml",
+               "skip_model_stats": True,
+               # synthetic ln_cfu table without read counts
+               "growth_likelihood": "lncfu",
+               "sample_offset_model": "zero"}
+    variant.update(extra)
+    return {"name": "data", "variants": [variant]}
+
+
+def _write_grid(project, cm_blocks, tmpl_blocks=None):
+    grid = {"run_name": "{{ condition_growth }}__seed{{ seed }}",
+            "output_file": "run.sh",
+            "configure_model": cm_blocks,
+            "template": tmpl_blocks if tmpl_blocks is not None else [
+                {"name": "seed", "variants": [{"seed": 0}, {"seed": 1}]},
+                {"name": "predict", "variants": [
+                    {"predict_genotypes_file": "../data/genotypes.txt"}]},
+            ]}
+    path = project / "grids" / "grid.yaml"
+    path.write_text(yaml.dump(grid, sort_keys=False))
+    return str(path)
+
+
+def _default_cm_blocks():
+    return [_data_block(),
+            {"name": "condition_growth",
+             "variants": [{"condition_growth": "linear"}]}]
+
+
+def _config_file_refs(cfg):
+    """Every (key, path) in a written config that names a file, relative to cwd."""
+    refs = [("data." + k, v) for k, v in cfg["data"].items()]
+    for k in ("thermo_data", "presplit_df", "base_growth_df", "dk_geno_pins_file"):
+        if cfg["components"].get(k):
+            refs.append(("components." + k, cfg["components"][k]))
+    if isinstance(cfg.get("library", {}).get("source"), str):
+        refs.append(("library.source", cfg["library"]["source"]))
+    return refs
+
+
+def test_grid_survives_move_and_deleting_inputs(project, monkeypatch):
+    """Move the grid, delete the original inputs: every run still resolves."""
+    grid_yaml = _write_grid(project, _default_cm_blocks())
+    out = project / "grid_out"
+    runs = setup_grid(grid_yaml, out_dir=str(out))
+    assert len(runs) == 2
+
+    moved = project / "elsewhere" / "deeper" / "grid_moved"
+    moved.parent.mkdir(parents=True)
+    shutil.move(str(out), str(moved))
+    shutil.rmtree(project / "data")
+    shutil.rmtree(project / "grids")
+
+    for run in runs:
+        run_dir = moved / run["run"]
+        cfg_path = run_dir / "tfs_configure_config.yaml"
+        cfg = yaml.safe_load(cfg_path.read_text())
+
+        # Data/library paths are read relative to the run directory.
+        refs = _config_file_refs(cfg)
+        assert {k for k, _ in refs} >= {"data.growth", "data.binding",
+                                        "library.source"}
+        for key, path in refs:
+            assert not os.path.isabs(path), key
+            assert path.startswith(os.path.join("..", INPUTS_DIRNAME)), key
+            assert os.path.isfile(run_dir / path), key
+
+        # Per-run outputs sit next to the config.
+        for key in ("priors_file", "guesses_file", "library_file"):
+            assert os.path.isfile(run_dir / cfg[key]), key
+
+        # Rendered template paths resolve too.
+        rendered = (run_dir / "run.sh").read_text()
+        geno_path = rendered.split("GENOTYPES=")[1].strip()
+        assert geno_path == os.path.join("..", INPUTS_DIRNAME, "genotypes.txt")
+        assert (run_dir / geno_path).read_text() == "wt\nA2L\n"
+
+        # No string in the config points outside the grid.
+        assert str(project) not in cfg_path.read_text()
+
+        # And the run actually loads from its own directory.
+        monkeypatch.chdir(run_dir)
+        orchestrator, _ = read_configuration("tfs_configure_config.yaml")
+        assert orchestrator.growth_tm is not None
+
+
+def test_inputs_copied_once(project):
+    """Runs sharing an input share one copy under inputs/."""
+    grid_yaml = _write_grid(project, _default_cm_blocks())
+    out = project / "grid_out"
+    setup_grid(grid_yaml, out_dir=str(out))
+    assert sorted(os.listdir(out / INPUTS_DIRNAME)) == [
+        "binding.csv", "genotypes.txt", "growth.csv", "library.yaml"]
+
+
+def test_same_name_different_files_kept_apart(project):
+    """Two different growth.csv files get distinct copies, each run its own."""
+    other = project / "data" / "other"
+    other.mkdir()
+    _write_growth(other / "growth.csv", ln_cfu=2.0)
+    blocks = [
+        {"name": "data", "variants": [
+            _data_block()["variants"][0],
+            _data_block(growth_df="../data/other/growth.csv")["variants"][0],
+        ]},
+        {"name": "condition_growth", "variants": [{"condition_growth": "linear"}]},
+    ]
+    grid_yaml = _write_grid(project, blocks, tmpl_blocks=[
+        {"name": "seed", "variants": [{"seed": 0}]},
+        {"name": "predict", "variants": [
+            {"predict_genotypes_file": "../data/genotypes.txt"}]}])
+    out = project / "grid_out"
+    runs = setup_grid(grid_yaml, out_dir=str(out))
+    assert len(runs) == 2
+
+    growth_refs = []
+    for run in runs:
+        cfg = yaml.safe_load(
+            (out / run["run"] / "tfs_configure_config.yaml").read_text())
+        growth_refs.append(cfg["data"]["growth"])
+        src = os.path.normpath(os.path.join(
+            project / "grids", run["configure_model"]["growth_df"]))
+        copy = out / run["run"] / cfg["data"]["growth"]
+        assert copy.read_text() == open(src).read()
+    assert sorted(os.path.basename(p) for p in growth_refs) == [
+        "growth.csv", "growth_2.csv"]
+
+
+def test_changed_input_never_overwrites_existing_copy(project):
+    """Re-running setup with a changed input leaves earlier runs' copy intact."""
+    grid_yaml = _write_grid(project, _default_cm_blocks())
+    out = project / "grid_out"
+    setup_grid(grid_yaml, out_dir=str(out))
+    before = (out / INPUTS_DIRNAME / "growth.csv").read_text()
+
+    _write_growth(project / "data" / "growth.csv", ln_cfu=3.0)
+    runs = setup_grid(grid_yaml, out_dir=str(out))
+
+    assert (out / INPUTS_DIRNAME / "growth.csv").read_text() == before
+    cfg = yaml.safe_load(
+        (out / runs[0]["run"] / "tfs_configure_config.yaml").read_text())
+    assert cfg["data"]["growth"] == os.path.join("..", INPUTS_DIRNAME,
+                                                 "growth_2.csv")
+
+
+def test_missing_input_fails_before_writing(project):
+    blocks = [_data_block(presplit_df="../data/missing.csv"),
+              {"name": "condition_growth",
+               "variants": [{"condition_growth": "linear"}]}]
+    out = project / "grid_out"
+    with pytest.raises(FileNotFoundError, match="presplit_df"):
+        setup_grid(_write_grid(project, blocks), out_dir=str(out))
+    assert not out.exists()
+
+
+def test_directory_input_fails_before_writing(project):
+    (project / "data" / "thermo").mkdir()
+    blocks = [_data_block(thermo_data="../data/thermo"),
+              {"name": "condition_growth",
+               "variants": [{"condition_growth": "linear"}]}]
+    out = project / "grid_out"
+    with pytest.raises(ValueError, match="directory"):
+        setup_grid(_write_grid(project, blocks), out_dir=str(out))
+    assert not out.exists()
+
+
+def test_unknown_file_argument_fails_before_writing(project):
+    """A configure_model value outside _PATH_KEYS naming a file is refused."""
+    blocks = [_data_block(),
+              {"name": "extra",
+               "variants": [{"mystery_file": "../data/genotypes.txt"}]}]
+    out = project / "grid_out"
+    with pytest.raises(ValueError, match="mystery_file.*_PATH_KEYS"):
+        setup_grid(_write_grid(project, blocks), out_dir=str(out))
+    assert not out.exists()
+
+
+def test_template_errors_fail_before_writing(project):
+    out = project / "grid_out"
+    grid_yaml = _write_grid(project, _default_cm_blocks(), tmpl_blocks=[
+        {"name": "seed", "variants": [{"seed": 0}]}])  # predict_genotypes_file undefined
+    with pytest.raises(ValueError, match="Undefined template variable"):
+        setup_grid(grid_yaml, out_dir=str(out))
+    assert not out.exists()
+
+    grid_yaml = _write_grid(project, _default_cm_blocks(), tmpl_blocks=[
+        {"name": "seed", "variants": [{"seed": 0}]},
+        {"name": "predict", "variants": [{"predict_genotypes_file": "../data"}]}])
+    with pytest.raises(ValueError, match="directory"):
+        setup_grid(grid_yaml, out_dir=str(out))
+    assert not out.exists()
+
+
+def test_skipped_combination_copies_nothing(project):
+    """An incompatible combination is skipped and leaves no orphan copies."""
+    blocks = [_data_block(),
+              {"name": "combo", "variants": [
+                  {"condition_growth": "power", "theta_rescale": "logit"}]}]
+    out = project / "grid_out"
+    runs = setup_grid(_write_grid(project, blocks), out_dir=str(out))
+    assert runs == []
+    assert not (out / INPUTS_DIRNAME).exists()
+
+
+# ---------------------------------------------------------------------------
+# _stage_written_config
+# ---------------------------------------------------------------------------
+
+def test_stage_written_config_rewrites_every_occurrence(tmp_path):
+    src = str(tmp_path / "lib.yaml")
+    open(src, "w").write("x")
+    cfg = {"data": {"growth": "/g.csv"},
+           "components": {"theta": "hill_geno"},
+           "library": {"source": src},
+           "priors_file": "tfs_configure_priors.csv"}
+    out = _stage_written_config(cfg, {src: "../inputs/lib.yaml"}, str(tmp_path))
+    assert out["library"]["source"] == "../inputs/lib.yaml"
+    assert cfg["library"]["source"] == src  # input not modified
+
+
+def test_stage_written_config_refuses_unknown_outside_path(tmp_path):
+    stray = str(tmp_path / "pins.csv")
+    open(stray, "w").write("x")
+    cfg = {"components": {"dk_geno_pins_file": stray}}
+    with pytest.raises(ValueError, match="dk_geno_pins_file"):
+        _stage_written_config(cfg, {}, str(tmp_path / "run"))
+
+
+def test_stage_written_config_ignores_provenance(tmp_path):
+    # tfs-configure-model records its cwd; that directory exists outside
+    # the run directory but is a record, not an input
+    cfg = {"provenance": {"cwd": str(tmp_path), "command": "tfs-configure-model"},
+           "components": {"theta": "hill_geno"}}
+    out = _stage_written_config(cfg, {}, str(tmp_path / "run"))
+    assert out["provenance"]["cwd"] == str(tmp_path)
+
+
+def test_unstageable_written_config_removes_run_dir(project, monkeypatch):
+    """A written config that cannot be staged fails and leaves no run dir."""
+    from tfscreen.tfmodel.scripts import setup_grid_cli
+
+    def refuse(cfg, path_map, subdir):
+        assert os.path.isdir(subdir)
+        raise ValueError("config names a file outside the grid")
+    monkeypatch.setattr(setup_grid_cli, "_stage_written_config", refuse)
+
+    out = project / "grid_out"
+    with pytest.raises(ValueError, match="outside the grid"):
+        setup_grid(_write_grid(project, _default_cm_blocks()), out_dir=str(out))
+    run_dirs = [p for p in os.listdir(out) if p != INPUTS_DIRNAME]
+    assert run_dirs == []
+
+
+def test_grid_with_subsampled_library_config(project):
+    """A library YAML with a library_genotypes list (relative to the YAML)
+    configures from the original at setup; the snapshot holds only the kept
+    genotypes, and the written config records the subsample but no path
+    outside the grid."""
+    lib = dict(_LIBRARY, library_genotypes="keep.txt")
+    (project / "data" / "library.yaml").write_text(yaml.dump(lib))
+    (project / "data" / "keep.txt").write_text("A2C\nE5A\nA2C/E5D\n")
+    grid_yaml = _write_grid(project, _default_cm_blocks())
+    out = project / "grid_out"
+    runs = setup_grid(grid_yaml, out_dir=str(out))
+    run_dir = out / runs[0]["run"]
+    snap = pd.read_csv(run_dir / "tfs_configure_library.csv")
+    assert set(snap["genotype"]) == {"wt", "A2L", "A2C", "E5A", "A2C/E5D"}
+    cfg = yaml.safe_load((run_dir / "tfs_configure_config.yaml").read_text())
+    assert cfg["library"]["subsample"]["library_genotypes"] == "keep.txt"
diff --git a/tests/tfscreen/tfmodel/scripts/test_subset_genotypes_cli.py b/tests/tfscreen/tfmodel/scripts/test_subset_genotypes_cli.py
deleted file mode 100644
index 31bb03d4..00000000
--- a/tests/tfscreen/tfmodel/scripts/test_subset_genotypes_cli.py
+++ /dev/null
@@ -1,667 +0,0 @@
-"""
-Tests for subset_genotypes_cli.py.
-
-Synthetic data uses genotypes of the form XsiteY (e.g. A1B) to avoid any
-dependency on the real tfscreen test data files.  The fixture `growth_df`
-contains:
-    wt          – 1 genotype, 2 rows
-    Singles:    A1B, A1C (site 1)   B2C, B2D (site 2)   C3D, C3E (site 3)
-    Doubles:    A1B/B2C, A1B/B2D, A1C/B2C, A1C/B2D   (site-1 × site-2)
-                A1B/C3D, A1C/C3D                        (site-1 × site-3)
-                B2C/C3D                                 (site-2 × site-3)
-"""
-
-import os
-
-import pandas as pd
-import pytest
-
-from tfscreen.tfmodel.scripts.subset_genotypes_cli import (
-    _mutations_in_genotype,
-    _reconcile_doubles_and_singles,
-    subset_genotypes,
-)
-
-
-# ---------------------------------------------------------------------------
-# Second synthetic dataset for whitelist-seeding tests
-# ---------------------------------------------------------------------------
-# W1B is the whitelist single.  Its only double partner is W1B/B2C.
-# B2D has no double partner at all (no double contains B2D).
-# The expansion loop would never naturally add W1B/B2C because W1B is not
-# in the reconciled doubles that the loop stumbles across — unless seeding
-# explicitly finds it.
-WHITELIST_SINGLES_SEED = ["W1B"]
-WHITELIST_DOUBLES_SEED = ["W1B/B2C"]  # only double involving W1B
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _build_growth_df(genotypes, rows_per_geno=2):
-    """Return a minimal DataFrame with a 'genotype' column and dummy data."""
-    rows = []
-    for g in genotypes:
-        for _ in range(rows_per_geno):
-            rows.append({"genotype": g, "ln_cfu": 10.0, "replicate": 1})
-    return pd.DataFrame(rows)
-
-
-ALL_SINGLES = ["A1B", "A1C", "B2C", "B2D", "C3D", "C3E"]
-DOUBLES_SITE12 = ["A1B/B2C", "A1B/B2D", "A1C/B2C", "A1C/B2D"]
-DOUBLES_SITE13 = ["A1B/C3D", "A1C/C3D"]
-DOUBLES_SITE23 = ["B2C/C3D"]
-ALL_DOUBLES = DOUBLES_SITE12 + DOUBLES_SITE13 + DOUBLES_SITE23
-
-
-@pytest.fixture
-def growth_csv(tmp_path):
-    """Write the synthetic growth DataFrame to a CSV and return its path."""
-    df = _build_growth_df(["wt"] + ALL_SINGLES + ALL_DOUBLES)
-    path = tmp_path / "growth.csv"
-    df.to_csv(path, index=False)
-    return str(path)
-
-
-@pytest.fixture
-def whitelist_file(tmp_path):
-    """A whitelist containing the two site-1 singles."""
-    p = tmp_path / "whitelist.txt"
-    p.write_text("A1B\nA1C\n")
-    return str(p)
-
-
-@pytest.fixture
-def blacklist_file(tmp_path):
-    p = tmp_path / "blacklist.txt"
-    p.write_text("C3D\nC3E\n")
-    return str(p)
-
-
-def _read_leftout(path):
-    with open(path) as fh:
-        return [l.strip() for l in fh if l.strip() and not l.startswith("#")]
-
-
-def _output_pairs(out_dir):
-    """Return sorted list of (growth_csv, leftout_txt) path pairs."""
-    files = os.listdir(out_dir)
-    growth = sorted(f for f in files if f.endswith("_growth.csv"))
-    leftout = sorted(f for f in files if f.endswith("_leftout.txt"))
-    assert len(growth) == len(leftout)
-    return list(zip(
-        [os.path.join(out_dir, f) for f in growth],
-        [os.path.join(out_dir, f) for f in leftout],
-    ))
-
-
-# ---------------------------------------------------------------------------
-# Unit tests for helpers
-# ---------------------------------------------------------------------------
-
-def test_mutations_in_genotype_wt():
-    assert _mutations_in_genotype("wt") == frozenset()
-
-
-def test_mutations_in_genotype_single():
-    assert _mutations_in_genotype("A1B") == frozenset({"A1B"})
-
-
-def test_mutations_in_genotype_double():
-    assert _mutations_in_genotype("A1B/B2C") == frozenset({"A1B", "B2C"})
-
-
-# ---------------------------------------------------------------------------
-# Unit tests for _reconcile_doubles_and_singles
-# ---------------------------------------------------------------------------
-
-def test_reconcile_keeps_complete_cycles():
-    """Doubles whose both singles are present are kept."""
-    singles = {"A1B", "B2C"}
-    doubles = {"A1B/B2C"}
-    kept, pool = _reconcile_doubles_and_singles(doubles, singles, blacklist=set())
-    assert set(kept) == {"A1B/B2C"}
-    assert pool == {"A1B", "B2C"}
-
-
-def test_reconcile_drops_double_with_missing_single():
-    """Doubles with a missing constituent single are dropped."""
-    singles = {"A1B"}          # B2C is absent
-    doubles = {"A1B/B2C"}
-    kept, pool = _reconcile_doubles_and_singles(doubles, singles, blacklist=set())
-    assert kept == []
-    assert pool == set()
-
-
-def test_reconcile_drops_double_with_blacklisted_single():
-    """Doubles containing a blacklisted single are dropped."""
-    singles = {"A1B", "B2C"}
-    doubles = {"A1B/B2C"}
-    kept, pool = _reconcile_doubles_and_singles(doubles, singles, blacklist={"B2C"})
-    assert kept == []
-    assert pool == set()
-
-
-def test_reconcile_mixed_doubles():
-    """Only doubles with both singles present and not blacklisted are kept."""
-    singles = {"A1B", "A1C", "B2C"}   # B2D absent; C3D absent
-    doubles = {"A1B/B2C", "A1C/B2C", "A1B/B2D", "B2C/C3D"}
-    kept, pool = _reconcile_doubles_and_singles(doubles, singles, blacklist=set())
-    assert set(kept) == {"A1B/B2C", "A1C/B2C"}
-    assert pool == {"A1B", "A1C", "B2C"}
-
-
-def test_reconcile_singles_pool_excludes_isolated_singles():
-    """Singles that don't participate in any kept double are not in the pool."""
-    singles = {"A1B", "A1C", "Z9Z"}   # Z9Z has no double partner
-    doubles = {"A1B/A1C"}
-    kept, pool = _reconcile_doubles_and_singles(doubles, singles, blacklist=set())
-    assert "Z9Z" not in pool
-
-
-# ---------------------------------------------------------------------------
-# Core output-pair tests
-# ---------------------------------------------------------------------------
-
-def test_step_count_and_filenames(growth_csv, tmp_path):
-    """n_steps output pairs are generated (or fewer if double universe is small)."""
-    out = str(tmp_path / "out" / "cv")
-    os.makedirs(os.path.dirname(out))
-    subset_genotypes(growth_csv, n_singles=6, n_steps=5, out_prefix=out, random_seed=0)
-    pairs = _output_pairs(os.path.dirname(out))
-    # 7 doubles total, 5 linspace steps → 5 unique counts (0,1,3,5,7) → 5 pairs
-    assert len(pairs) == 5
-
-
-def test_filenames_encode_counts(growth_csv, tmp_path):
-    """File names contain the correct singles/doubles counts."""
-    out = str(tmp_path / "cv")
-    subset_genotypes(growth_csv, n_singles=6, n_steps=3, out_prefix=out, random_seed=0)
-    files = sorted(os.listdir(tmp_path))
-    growth_files = [f for f in files if "_growth.csv" in f]
-    # Each growth filename must contain hyphenated singles and doubles counts
-    for f in growth_files:
-        assert "-singles_" in f
-        assert "-doubles_" in f
-    # The last step should include all 7 doubles
-    last = sorted(growth_files)[-1]
-    assert "7-doubles" in last
-
-
-def test_wt_always_in_training(growth_csv, tmp_path):
-    """wt genotype appears in every training CSV."""
-    out = str(tmp_path / "cv")
-    subset_genotypes(growth_csv, n_singles=4, n_steps=4, out_prefix=out, random_seed=1)
-    for gcsv, _ in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        assert "wt" in df["genotype"].values
-
-
-def test_first_step_has_zero_doubles(growth_csv, tmp_path):
-    """The first output pair always has 0 doubles in the training set."""
-    out = str(tmp_path / "cv")
-    subset_genotypes(growth_csv, n_singles=6, n_steps=4, out_prefix=out, random_seed=0)
-    pairs = _output_pairs(tmp_path)
-    first_csv, _ = pairs[0]
-    df = pd.read_csv(first_csv)
-    assert all("/" not in g for g in df["genotype"].unique())
-
-
-def test_last_step_has_all_doubles(growth_csv, tmp_path):
-    """The last step includes all constructible doubles; left-out file is empty."""
-    out = str(tmp_path / "cv")
-    subset_genotypes(growth_csv, n_singles=6, n_steps=4, out_prefix=out, random_seed=0)
-    pairs = _output_pairs(tmp_path)
-    last_csv, last_lo = pairs[-1]
-    df = pd.read_csv(last_csv)
-    doubles_in_training = [g for g in df["genotype"].unique() if "/" in g]
-    assert len(doubles_in_training) == len(ALL_DOUBLES)
-    assert _read_leftout(last_lo) == []
-
-
-def test_training_plus_leftout_equals_universe(growth_csv, tmp_path):
-    """For every step, training doubles + left-out doubles = the full double universe."""
-    out = str(tmp_path / "cv")
-    subset_genotypes(growth_csv, n_singles=6, n_steps=5, out_prefix=out, random_seed=0)
-    for gcsv, lout in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        train_doubles = set(g for g in df["genotype"].unique() if "/" in g)
-        leftout = set(_read_leftout(lout))
-        assert train_doubles & leftout == set(), "training and left-out overlap"
-        assert train_doubles | leftout == set(ALL_DOUBLES)
-
-
-def test_only_constructible_doubles_in_training(growth_csv, tmp_path):
-    """Doubles in the training CSV can only come from the selected singles."""
-    out = str(tmp_path / "cv")
-    subset_genotypes(growth_csv, n_singles=4, n_steps=3, out_prefix=out, random_seed=7)
-    for gcsv, _ in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        singles = set(g for g in df["genotype"].unique() if "/" not in g and g != "wt")
-        single_muts = set()
-        for s in singles:
-            single_muts.add(s)
-        for g in df["genotype"].unique():
-            if "/" in g:
-                parts = set(g.split("/"))
-                assert parts <= single_muts, f"{g} uses mutations not in selected singles"
-
-
-# ---------------------------------------------------------------------------
-# Whitelist / blacklist
-# ---------------------------------------------------------------------------
-
-def test_whitelist_singles_always_selected(growth_csv, whitelist_file, tmp_path):
-    """Whitelisted singles appear in every training CSV."""
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=4, n_steps=3,
-        out_prefix=out, whitelist_file=whitelist_file, random_seed=0,
-    )
-    for gcsv, _ in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        genos = set(df["genotype"].unique())
-        assert "A1B" in genos
-        assert "A1C" in genos
-
-
-def test_blacklist_singles_never_selected(growth_csv, blacklist_file, tmp_path):
-    """Blacklisted genotypes never appear in any training CSV."""
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=4, n_steps=3,
-        out_prefix=out, blacklist_file=blacklist_file, random_seed=0,
-    )
-    for gcsv, _ in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        genos = set(df["genotype"].unique())
-        assert "C3D" not in genos
-        assert "C3E" not in genos
-
-
-def test_blacklist_removes_dependent_doubles(growth_csv, blacklist_file, tmp_path):
-    """Doubles that require a blacklisted single are excluded from the universe."""
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=4, n_steps=3,
-        out_prefix=out, blacklist_file=blacklist_file, random_seed=0,
-    )
-    for gcsv, lout in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        all_genos = set(df["genotype"].unique()) | set(_read_leftout(lout))
-        # Any double involving C3D or C3E should not appear at all
-        for g in all_genos:
-            if "/" in g:
-                assert "C3D" not in g.split("/")
-                assert "C3E" not in g.split("/")
-
-
-# ---------------------------------------------------------------------------
-# Reconciliation integration tests
-# ---------------------------------------------------------------------------
-
-@pytest.fixture
-def growth_csv_orphan_doubles(tmp_path):
-    """
-    Dataset where some doubles are missing one constituent single.
-
-    Present singles: A1B, B2C only.
-    Doubles in data:
-        A1B/B2C   — complete cycle (both singles present)  → kept
-        A1C/B2C   — A1C absent                             → dropped
-        A1B/C3D   — C3D absent                             → dropped
-    """
-    genotypes = ["wt", "A1B", "B2C", "A1B/B2C", "A1C/B2C", "A1B/C3D"]
-    df = _build_growth_df(genotypes)
-    path = tmp_path / "orphan_growth.csv"
-    df.to_csv(path, index=False)
-    return str(path)
-
-
-def test_reconciliation_drops_orphan_doubles(growth_csv_orphan_doubles, tmp_path):
-    """Doubles with a missing single never appear in any output file."""
-    out_dir = tmp_path / "out"
-    out_dir.mkdir()
-    out = str(out_dir / "cv")
-    subset_genotypes(
-        growth_csv_orphan_doubles, n_singles=2, n_steps=3, out_prefix=out, random_seed=0
-    )
-    for gcsv, lout in _output_pairs(out_dir):
-        all_genos = (
-            set(pd.read_csv(gcsv)["genotype"].unique()) | set(_read_leftout(lout))
-        )
-        assert "A1C/B2C" not in all_genos
-        assert "A1B/C3D" not in all_genos
-
-
-def test_reconciliation_singles_pool_derived_from_doubles(
-    growth_csv_orphan_doubles, tmp_path
-):
-    """
-    The singles pool is derived from the reconciled doubles, so a single that
-    only appears in a dropped double (here A1C, C3D) is never selected.
-    """
-    out_dir = tmp_path / "out"
-    out_dir.mkdir()
-    out = str(out_dir / "cv")
-    subset_genotypes(
-        growth_csv_orphan_doubles, n_singles=2, n_steps=3, out_prefix=out, random_seed=0
-    )
-    for gcsv, _ in _output_pairs(out_dir):
-        df = pd.read_csv(gcsv)
-        genos = set(df["genotype"].unique())
-        assert "A1C" not in genos
-        assert "C3D" not in genos
-
-
-# ---------------------------------------------------------------------------
-# Deduplication
-# ---------------------------------------------------------------------------
-
-def test_step_deduplication_when_few_doubles(growth_csv, tmp_path):
-    """When n_steps > double universe size, output is deduplicated to unique counts."""
-    out = str(tmp_path / "cv")
-    # 7 doubles, request 20 steps — should collapse to at most 8 unique steps (0..7)
-    subset_genotypes(growth_csv, n_singles=6, n_steps=20, out_prefix=out, random_seed=0)
-    pairs = _output_pairs(tmp_path)
-    assert len(pairs) <= 8
-
-
-# ---------------------------------------------------------------------------
-# Error handling
-# ---------------------------------------------------------------------------
-
-def test_error_whitelist_single_not_in_data(growth_csv, tmp_path):
-    """Raises if a whitelisted single is absent from the growth data."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("NOTREAL\n")
-    with pytest.raises(ValueError, match="Whitelist singles not present"):
-        subset_genotypes(
-            growth_csv, n_singles=3, n_steps=2,
-            out_prefix=str(tmp_path / "cv"), whitelist_file=str(wl),
-        )
-
-
-def test_error_whitelist_double_not_in_data(growth_csv, tmp_path):
-    """Raises if a whitelisted double is absent from the growth data."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("A1B/Z9Z\n")  # Z9Z is not in data
-    with pytest.raises(ValueError, match="Whitelist doubles not present"):
-        subset_genotypes(
-            growth_csv, n_singles=3, n_steps=2,
-            out_prefix=str(tmp_path / "cv"), whitelist_file=str(wl),
-        )
-
-
-def test_error_whitelist_too_many_mutations(growth_csv, tmp_path):
-    """Raises if a whitelisted genotype has more than 2 mutations."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("A1B/B2C/C3D\n")
-    with pytest.raises(ValueError, match="3 mutations"):
-        subset_genotypes(
-            growth_csv, n_singles=3, n_steps=2,
-            out_prefix=str(tmp_path / "cv"), whitelist_file=str(wl),
-        )
-
-
-def test_error_whitelist_double_constituent_single_blacklisted(growth_csv, tmp_path):
-    """Raises if a constituent single of a whitelisted double is blacklisted."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("A1B/B2C\n")
-    bl = tmp_path / "bl.txt"
-    bl.write_text("B2C\n")
-    with pytest.raises(ValueError, match="require singles that are blacklisted"):
-        subset_genotypes(
-            growth_csv, n_singles=3, n_steps=2,
-            out_prefix=str(tmp_path / "cv"),
-            whitelist_file=str(wl), blacklist_file=str(bl),
-        )
-
-
-def test_error_whitelist_blacklist_overlap(growth_csv, tmp_path):
-    """Raises if a genotype appears in both whitelist and blacklist."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("A1B\n")
-    bl = tmp_path / "bl.txt"
-    bl.write_text("A1B\n")
-    with pytest.raises(ValueError, match="both whitelist and blacklist"):
-        subset_genotypes(
-            growth_csv, n_singles=3, n_steps=2,
-            out_prefix=str(tmp_path / "cv"),
-            whitelist_file=str(wl), blacklist_file=str(bl),
-        )
-
-
-def test_whitelist_exceeds_n_singles_uses_whitelist_size(growth_csv, tmp_path):
-    """When whitelist > n_singles, the effective target is len(whitelist), not n_singles."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("A1B\nA1C\nB2C\n")  # 3 whitelist singles
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=2, n_steps=2,
-        out_prefix=out, whitelist_file=str(wl),
-    )
-    for gcsv, _ in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        genos = set(df["genotype"].unique())
-        assert "A1B" in genos
-        assert "A1C" in genos
-        assert "B2C" in genos
-
-
-def test_trim_respects_n_singles(growth_csv, tmp_path):
-    """
-    When seeding balloons selected_singles above n_singles, the trim phase
-    brings it back to exactly n_singles (or whitelist size if larger).
-
-    The full synthetic dataset has 6 singles.  With n_singles=2 and no
-    whitelist, the expansion loop adds one double (2 singles) and stops.
-    With n_singles=2 and whitelist=[A1B] whose seeding pulls in additional
-    partners, the trim must cut back to exactly 2.
-    """
-    wl = tmp_path / "wl.txt"
-    wl.write_text("A1B\n")  # A1B pairs with B2C, B2D, C3D via doubles
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=2, n_steps=3,
-        out_prefix=out, whitelist_file=str(wl), random_seed=0,
-    )
-    for gcsv, _ in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        singles = [g for g in df["genotype"].unique() if "/" not in g and g != "wt"]
-        assert len(singles) == 2
-        assert "A1B" in singles  # whitelist single always present
-
-
-def test_wt_in_whitelist_is_silently_ignored(growth_csv, tmp_path):
-    """wt in the whitelist does not cause an error and is always in training."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("wt\nA1B\n")
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=3, n_steps=2,
-        out_prefix=out, whitelist_file=str(wl),
-    )
-    for gcsv, _ in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        genos = set(df["genotype"].unique())
-        assert "wt" in genos
-        assert "A1B" in genos
-
-
-def test_whitelist_cycles_seeded_before_expansion(tmp_path):
-    """
-    Whitelist singles and all their double partners are always in the
-    training set, even when the random expansion loop would not have
-    reached them.
-
-    Dataset: W1B and B2C are linked by the double W1B/B2C.  A1B/A1C is
-    a separate pair.  With n_singles=2 and whitelist=[W1B], seeding must
-    add B2C (the partner of W1B) before the expansion loop runs — so both
-    W1B and B2C appear in every output.
-    """
-    genotypes = ["wt", "W1B", "B2C", "A1B", "A1C", "W1B/B2C", "A1B/A1C"]
-    df = _build_growth_df(genotypes)
-    csv_path = tmp_path / "g.csv"
-    df.to_csv(csv_path, index=False)
-
-    wl = tmp_path / "wl.txt"
-    wl.write_text("W1B\n")
-
-    out_dir = tmp_path / "out"
-    out_dir.mkdir()
-    subset_genotypes(
-        str(csv_path), n_singles=2, n_steps=3,
-        out_prefix=str(out_dir / "cv"),
-        whitelist_file=str(wl), random_seed=0,
-    )
-    for gcsv, _ in _output_pairs(out_dir):
-        df_out = pd.read_csv(gcsv)
-        genos = set(df_out["genotype"].unique())
-        assert "W1B" in genos, "whitelist single missing"
-        assert "B2C" in genos, "whitelist single's double partner missing"
-
-
-def test_whitelist_double_always_in_training_never_leftout(growth_csv, tmp_path):
-    """A whitelisted double appears in every training CSV and never in any left-out file."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("A1B/B2C\n")
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=4, n_steps=4,
-        out_prefix=out, whitelist_file=str(wl), random_seed=0,
-    )
-    for gcsv, lout in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        assert "A1B/B2C" in df["genotype"].values, "whitelist double missing from training"
-        assert "A1B/B2C" not in _read_leftout(lout), "whitelist double appeared in left-out"
-
-
-def test_whitelist_double_in_first_step(growth_csv, tmp_path):
-    """Whitelisted doubles appear even in step 0 (zero titrated doubles)."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("A1B/B2C\n")
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=4, n_steps=4,
-        out_prefix=out, whitelist_file=str(wl), random_seed=0,
-    )
-    first_csv, _ = _output_pairs(tmp_path)[0]
-    df = pd.read_csv(first_csv)
-    assert "A1B/B2C" in df["genotype"].values
-
-
-def test_whitelist_double_constituent_singles_forced(growth_csv, tmp_path):
-    """Constituent singles of a whitelisted double are always selected."""
-    wl = tmp_path / "wl.txt"
-    wl.write_text("A1B/B2C\n")
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=2, n_steps=3,
-        out_prefix=out, whitelist_file=str(wl), random_seed=0,
-    )
-    for gcsv, _ in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        genos = set(df["genotype"].unique())
-        assert "A1B" in genos, "constituent single A1B missing"
-        assert "B2C" in genos, "constituent single B2C missing"
-
-
-def test_whitelist_double_excluded_from_titration_universe(growth_csv, tmp_path):
-    """
-    The titrated double universe excludes whitelisted doubles.  So for every
-    step, training_doubles ∪ leftout == ALL_DOUBLES − whitelist_doubles.
-    """
-    wl_double = "A1B/B2C"
-    wl = tmp_path / "wl.txt"
-    wl.write_text(f"{wl_double}\n")
-    out = str(tmp_path / "cv")
-    subset_genotypes(
-        growth_csv, n_singles=6, n_steps=4,
-        out_prefix=out, whitelist_file=str(wl), random_seed=0,
-    )
-    expected_universe = set(ALL_DOUBLES) - {wl_double}
-    for gcsv, lout in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        train_doubles = set(g for g in df["genotype"].unique() if "/" in g and g != wl_double)
-        leftout = set(_read_leftout(lout))
-        assert train_doubles | leftout == expected_universe
-
-
-def test_error_missing_genotype_column(tmp_path):
-    """Raises if the growth CSV has no 'genotype' column."""
-    bad = tmp_path / "bad.csv"
-    pd.DataFrame({"ln_cfu": [1.0]}).to_csv(bad, index=False)
-    with pytest.raises(ValueError, match="no 'genotype' column"):
-        subset_genotypes(str(bad), n_singles=2, n_steps=2,
-                           out_prefix=str(tmp_path / "cv"))
-
-
-# ---------------------------------------------------------------------------
-# Smoke test on real test data
-# ---------------------------------------------------------------------------
-
-_REAL_GROWTH = os.path.join(
-    os.path.dirname(__file__),
-    "../../../fixtures/growth.csv",
-)
-
-
-@pytest.mark.slow
-def test_smoke_real_data(tmp_path):
-    """
-    Runs subset_growth_data on the real test growth CSV end-to-end.
-
-    The real data has 965 singles but only 3 doubles (M42I/H74A, M42I/K84L,
-    H74A/K84L).  Reconciliation should automatically restrict the singles pool
-    to {M42I, H74A, K84L} — no whitelist required.
-    """
-    real_path = os.path.normpath(_REAL_GROWTH)
-    if not os.path.exists(real_path):
-        pytest.skip("Real growth CSV not found")
-
-    out = str(tmp_path / "smoke")
-    subset_genotypes(real_path, n_singles=6, n_steps=5, out_prefix=out, random_seed=42)
-
-    pairs = _output_pairs(tmp_path)
-    assert len(pairs) >= 1
-
-    # Every training CSV must contain wt
-    for gcsv, _ in pairs:
-        df = pd.read_csv(gcsv)
-        assert "wt" in df["genotype"].values
-
-    # Last step: all 3 doubles in training, empty left-out
-    last_csv, last_lo = pairs[-1]
-    df = pd.read_csv(last_csv)
-    doubles_in_last = [g for g in df["genotype"].unique() if "/" in g]
-    assert len(doubles_in_last) == 3
-    assert _read_leftout(last_lo) == []
-
-
-@pytest.mark.slow
-def test_smoke_real_data_whitelist(tmp_path):
-    """
-    Whitelist forces specific singles to be included even when not required
-    by reconciliation.  Uses the real test growth CSV.
-    """
-    real_path = os.path.normpath(_REAL_GROWTH)
-    if not os.path.exists(real_path):
-        pytest.skip("Real growth CSV not found")
-
-    wl = tmp_path / "wl.txt"
-    wl.write_text("M42I\nH74A\nK84L\n")
-
-    out = str(tmp_path / "smoke_wl")
-    subset_genotypes(
-        real_path, n_singles=3, n_steps=4,
-        out_prefix=out, whitelist_file=str(wl), random_seed=0,
-    )
-
-    for gcsv, _ in _output_pairs(tmp_path):
-        df = pd.read_csv(gcsv)
-        genos = set(df["genotype"].unique())
-        assert "M42I" in genos
-        assert "H74A" in genos
-        assert "K84L" in genos
diff --git a/tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py b/tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py
index 5a83ccd9..5e7fafc0 100644
--- a/tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py
+++ b/tests/tfscreen/tfmodel/scripts/test_summarize_fit_cli.py
@@ -29,6 +29,8 @@
 )
 
 from tfscreen.tfmodel.scripts.summarize_fit_cli import (
+    _find_fit_config,
+    _find_fit_losses,
     _find_params_or_posterior,
     _find_unique,
     _json_safe,
@@ -198,6 +200,48 @@ def test_warns_and_uses_first_on_multiple_matches(self, tmp_path):
         assert any("Multiple" in str(x.message) for x in w)
 
 
+# ---------------------------------------------------------------------------
+# _find_fit_config / _find_fit_losses (grid run dirs hold other matches)
+# ---------------------------------------------------------------------------
+
+class TestFindFitConfigAndLosses:
+
+    def test_config_ignores_simulate_configs(self, tmp_path):
+        _make_config_yaml(str(tmp_path / "tfs_configure_config.yaml"),
+                          binding_csv_path="b.csv", guesses_name="g.csv")
+        # Simulate configs, one sorting ahead of the tfmodel config.
+        for name in ("a_sim_config.yaml", "tfs_sim_config.yaml"):
+            (tmp_path / name).write_text(yaml.dump({"seed": 1, "growth": {}}))
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            result = _find_fit_config(str(tmp_path))
+        assert result == str(tmp_path / "tfs_configure_config.yaml")
+        assert not any("Multiple" in str(x.message) for x in w)
+
+    def test_config_none_without_tfmodel_config(self, tmp_path):
+        (tmp_path / "tfs_sim_config.yaml").write_text(yaml.dump({"seed": 1}))
+        (tmp_path / "broken_config.yaml").write_text(":\n  - [unclosed")
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            result = _find_fit_config(str(tmp_path))
+        assert result is None
+        assert any("No tfmodel config" in str(x.message) for x in w)
+
+    def test_losses_ignore_prefit_and_premap(self, tmp_path):
+        for name in ("tfs_prefit_losses.txt", "tfs_fit_model_premap_losses.txt",
+                     "tfs_fit_model_losses.txt"):
+            _make_losses_txt(str(tmp_path / name))
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            result = _find_fit_losses(str(tmp_path))
+        assert result == str(tmp_path / "tfs_fit_model_losses.txt")
+        assert not any("Multiple" in str(x.message) for x in w)
+
+    def test_losses_none_when_only_auxiliary(self, tmp_path):
+        _make_losses_txt(str(tmp_path / "tfs_prefit_losses.txt"))
+        assert _find_fit_losses(str(tmp_path)) is None
+
+
 # ---------------------------------------------------------------------------
 # _resolve_path
 # ---------------------------------------------------------------------------
@@ -725,6 +769,28 @@ def test_finds_posterior_h5(self, tmp_path):
         assert kind == "posterior"
         assert path == str(tmp_path / "run_posterior.h5")
 
+    def test_finds_sample_posterior_default_name(self, tmp_path):
+        # tfs-sample-posterior writes {out_prefix}.h5; a prior ground truth
+        # in the same directory is not a posterior
+        (tmp_path / "run1.h5").touch()
+        (tmp_path / "tfs_prior_000_ground_truth.h5").touch()
+        (tmp_path / "run_params.npz").touch()
+        kind, path = _find_params_or_posterior(str(tmp_path))
+        assert kind == "posterior"
+        assert path == str(tmp_path / "run1.h5")
+
+    def test_named_posterior_preferred_over_other_h5(self, tmp_path):
+        (tmp_path / "a.h5").touch()
+        (tmp_path / "z_posterior.h5").touch()
+        kind, path = _find_params_or_posterior(str(tmp_path))
+        assert path == str(tmp_path / "z_posterior.h5")
+
+    def test_ground_truth_only_falls_back_to_params(self, tmp_path):
+        (tmp_path / "x_ground_truth.h5").touch()
+        (tmp_path / "run_params.npz").touch()
+        kind, path = _find_params_or_posterior(str(tmp_path))
+        assert kind == "params"
+
     def test_finds_params_npz(self, tmp_path):
         (tmp_path / "run_params.npz").touch()
         kind, path = _find_params_or_posterior(str(tmp_path))
@@ -1132,9 +1198,16 @@ def test_passes_genotypes_from_binding_df(self, tmp_path):
         _, kwargs = mock_pdf.call_args
         assert set(kwargs["genotypes"]) == {"wt", "A1B", "C2D"}
 
-    def test_passes_none_genotypes_when_binding_df_is_none(self, tmp_path):
+    def test_default_subset_when_binding_df_is_none(self, tmp_path):
+        """Without binding genotypes (a growth-only model) the plots cover a
+        default subset, not every genotype in the library."""
         (tmp_path / "run_posterior.h5").touch()
-        with patch(_PATCH_READ_CONFIG, return_value=(MagicMock(), {})), \
+        orch = MagicMock()
+        orch.growth_tm.tensor_dim_names = ["replicate", "genotype"]
+        orch.growth_tm.tensor_dim_labels = [
+            ["R1"], ["wt", "S1"] + [f"A{i}B" for i in range(30)]]
+        orch.settings = {"spiked_genotypes": ["S1"]}
+        with patch(_PATCH_READ_CONFIG, return_value=(orch, {})), \
              patch(_PATCH_PREDICT_DF, return_value=_MOCK_PRED_DF) as mock_pdf, \
              patch(_PATCH_PLOT_GENO, return_value=MagicMock()):
             _try_plot_trajectories(
@@ -1145,7 +1218,10 @@ def test_passes_none_genotypes_when_binding_df_is_none(self, tmp_path):
                 binding_df=None,
             )
         _, kwargs = mock_pdf.call_args
-        assert kwargs["genotypes"] is None
+        genos = kwargs["genotypes"]
+        assert genos[:2] == ["wt", "S1"]
+        assert len(genos) == 2 + 10
+        assert len(set(genos)) == len(genos)
 
     # ------------------------------------------------------------------
     # CSV output: one CSV per genotype alongside each trajectory PDF
@@ -1674,6 +1750,127 @@ def test_summarize_fit_calls_summarize_params(self, run_dir):
         assert "ref" in df.columns
 
 
+class TestSummarizeThermoTruth:
+    """Thermodynamic models: the scalar K's and the per-mutation / per-pair
+    truth written by tfs-simulate are scored against extracted tables."""
+
+    COMPONENT = "thermo.O2_C12_K5_U0_a.PK"
+    GENOTYPES = ["wt", "H29A", "H29C", "L62A", "H29A/L62A", "H29C/L62A"]
+    SCALAR_KS = ["ln_K_h_l", "ln_K_h_o", "ln_K_l_o"]
+
+    def _simulate(self, run_dir):
+        """Draw truth with the simulator and write it as tfs-simulate does."""
+        import jax
+        from tfscreen.simulate.sim_data_class import build_sim_data
+        from tfscreen.simulate.thermo_to_growth import thermo_to_growth
+
+        lib = pd.DataFrame({"genotype": self.GENOTYPES})
+        sample_df = pd.DataFrame({"titrant_name": "iptg",
+                                  "titrant_conc": [0.0, 0.01, 1.0],
+                                  "condition_pre": "pre",
+                                  "condition_sel": "sel"})
+        growth = {"pre": {"m": 0.0, "b": 0.01}, "sel": {"m": 0.01, "b": 0.01}}
+        sim_data = build_sim_data(lib, sample_df)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            _, _, params, truth = thermo_to_growth(
+                lib["genotype"], sim_data, sample_df, self.COMPONENT,
+                jax.random.PRNGKey(0), growth, rng=np.random.default_rng(0),
+                return_mutation_truth=True)
+        params.to_csv(os.path.join(run_dir, "tfs_sim_parameters.csv"), index=False)
+        truth["mutation"].to_csv(
+            os.path.join(run_dir, "tfs_sim_mutation_parameters.csv"), index=False)
+        truth["pair"].to_csv(
+            os.path.join(run_dir, "tfs_sim_pair_parameters.csv"), index=False)
+        return params, truth["mutation"], truth["pair"]
+
+    @staticmethod
+    def _write_extracted(path, key_df, values):
+        """An extracted table: key columns plus quantiles around ``values``."""
+        df = key_df.copy()
+        for q, shift in (("q0.025", -1.0), ("q0.5", 0.0), ("q0.975", 1.0)):
+            df[q] = np.asarray(values) + shift
+        df.to_csv(path, index=False)
+
+    def _write_fit(self, run_dir, params, mut, pair):
+        for col in self.SCALAR_KS:
+            self._write_extracted(os.path.join(run_dir, f"tfs_params_{col}.csv"),
+                                  params[["genotype"]], params[col])
+        for col in ["ln_K_h_e", "ln_K_l_e"]:
+            self._write_extracted(os.path.join(run_dir, f"tfs_params_{col}.csv"),
+                                  params[["genotype"]].assign(titrant_name="iptg"),
+                                  params[col])
+        for col in [c for c in mut.columns if c.startswith("d_")]:
+            self._write_extracted(os.path.join(run_dir, f"tfs_params_{col}.csv"),
+                                  mut[["mutation"]], mut[col])
+        for col in [c for c in pair.columns if c.startswith("epi_")]:
+            self._write_extracted(os.path.join(run_dir, f"tfs_params_{col}.csv"),
+                                  pair[["pair"]], pair[col])
+
+    def _run(self, tmp_path):
+        out_prefix = str(tmp_path / "summary" / "tfs_summarize")
+        os.makedirs(os.path.dirname(out_prefix), exist_ok=True)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            _summarize_params(str(tmp_path), out_prefix)
+        return out_prefix, [str(x.message) for x in w]
+
+    def test_scalar_Ks_are_scored(self, tmp_path):
+        params, mut, pair = self._simulate(str(tmp_path))
+        self._write_fit(str(tmp_path), params, mut, pair)
+        out_prefix, msgs = self._run(tmp_path)
+
+        truth = params.set_index("genotype")
+        for col in self.SCALAR_KS + ["ln_K_h_e", "ln_K_l_e"]:
+            df = pd.read_csv(f"{out_prefix}_params_{col}.csv")
+            np.testing.assert_allclose(df["ref"], truth.loc[df["genotype"], col],
+                                       rtol=1e-6)
+            # median == truth by construction
+            np.testing.assert_allclose(df["ref"], df["q0.5"], rtol=1e-6)
+            assert os.path.exists(f"{out_prefix}_params_{col}_calibration_curve.csv") \
+                or os.path.exists(f"{out_prefix}_params_{col}.pdf")
+        # dk_geno / activity have no fake extract here; no thermo K is unmatched.
+        unmatched = [m for m in msgs if m.startswith("Expected")
+                     and "ln_K" in m]
+        assert unmatched == []
+
+    def test_mutation_and_pair_truth_are_scored(self, tmp_path):
+        params, mut, pair = self._simulate(str(tmp_path))
+        self._write_fit(str(tmp_path), params, mut, pair)
+        out_prefix, _ = self._run(tmp_path)
+
+        for col in [c for c in mut.columns if c.startswith("d_")]:
+            df = pd.read_csv(f"{out_prefix}_params_{col}.csv")
+            assert df["ref"].notna().all()
+            np.testing.assert_allclose(df["ref"], df["q0.5"], rtol=1e-6)
+        for col in [c for c in pair.columns if c.startswith("epi_")]:
+            df = pd.read_csv(f"{out_prefix}_params_{col}.csv")
+            assert df["ref"].notna().all()
+            np.testing.assert_allclose(df["ref"], df["q0.5"], rtol=1e-6)
+
+    def test_mutation_truth_used_without_single_mutants(self, tmp_path):
+        """The per-mutation file scores d_ tables even when a mutation's single
+        mutant is missing from the per-genotype truth (no sim[m] - sim[wt])."""
+        params, mut, pair = self._simulate(str(tmp_path))
+        self._write_fit(str(tmp_path), params, mut, pair)
+        params[params["genotype"] != "L62A"].to_csv(
+            tmp_path / "tfs_sim_parameters.csv", index=False)
+        out_prefix, _ = self._run(tmp_path)
+        df = pd.read_csv(f"{out_prefix}_params_d_ln_K_h_l.csv")
+        assert df.loc[df["mutation"] == "L62A", "ref"].notna().all()
+
+    def test_pair_labels_matched_after_standardizing(self, tmp_path):
+        params, mut, pair = self._simulate(str(tmp_path))
+        self._write_fit(str(tmp_path), params, mut, pair)
+        # A fit may write the pair in the other order.
+        flipped = pd.read_csv(tmp_path / "tfs_params_epi_ln_K_h_l.csv")
+        flipped["pair"] = ["/".join(p.split("/")[::-1]) for p in flipped["pair"]]
+        flipped.to_csv(tmp_path / "tfs_params_epi_ln_K_h_l.csv", index=False)
+        out_prefix, _ = self._run(tmp_path)
+        df = pd.read_csv(f"{out_prefix}_params_epi_ln_K_h_l.csv")
+        assert df["ref"].notna().all()
+
+
 # ---------------------------------------------------------------------------
 # _summarize_condition_growth_params / _summarize_k_ref / _summarize_growth_params
 # ---------------------------------------------------------------------------
@@ -2314,3 +2511,372 @@ def test_params_calibration_out_prefix_strips_csv_extension(self, run_dir):
                         if "params_activity" in c.kwargs["out_prefix"]]
         assert len(params_calls) == 1
         assert not params_calls[0].kwargs["out_prefix"].endswith(".csv")
+
+
+# ---------------------------------------------------------------------------
+# Tube offsets
+# ---------------------------------------------------------------------------
+
+def _write_offsets(run_dir, pattern):
+    rows = []
+    for cond in ("kanR+kan", "pheS+4CP"):
+        for rep in (1, 2):
+            for t in (0.0, 60.0, 120.0):
+                for i, c in enumerate([0.0, 0.01, 0.1, 1.0]):
+                    rows.append(dict(replicate=rep, library="lib",
+                                     condition_pre="pre", condition_sel=cond,
+                                     titrant_name="iptg", titrant_conc=c,
+                                     t_pre=30.0, t_sel=t,
+                                     value=(2.0 - i if pattern else 0.0)
+                                     + 0.01 * ((rep * 7 + i * 3 + int(t)) % 5)))
+    pd.DataFrame(rows).rename(columns={"value": "q0.5"}).to_csv(
+        os.path.join(run_dir, "run_params_sample_offset_offset.csv"),
+        index=False)
+
+
+class TestTubeOffsets:
+
+    def test_no_offset_file_gives_none(self, run_dir):
+        results = summarize_fit(run_dir)
+        assert results["tube_offsets"] is None
+
+    def test_held_sigma_from_priors_and_flag(self, run_dir):
+        _write_offsets(run_dir, pattern=True)
+        pd.DataFrame({"parameter": ["growth.sample_offset.sigma_fixed"],
+                      "value": [0.17]}).to_csv(
+            os.path.join(run_dir, "test_priors.csv"), index=False)
+        results = summarize_fit(run_dir)
+        s = results["tube_offsets"]
+        assert s["sigma"] == pytest.approx(0.17)
+        assert s["sigma_source"] == "sigma_fixed"
+        assert s["structured"]
+        prefix = os.path.join(run_dir, "summary", "tfs_summarize")
+        for suffix in ("_tube_offsets.csv", "_tube_offset_trends.csv",
+                       "_tube_offsets.pdf"):
+            assert os.path.exists(prefix + suffix)
+        with open(prefix + "_fit_summary.json") as fh:
+            assert json.load(fh)["tube_offsets"]["structured"] is True
+
+    def test_extracted_sigma_wins(self, run_dir):
+        _write_offsets(run_dir, pattern=False)
+        pd.DataFrame({"parameter": ["sample_offset_sigma"],
+                      "q0.5": [0.5]}).to_csv(
+            os.path.join(run_dir, "run_params_sample_offset_sigma.csv"),
+            index=False)
+        s = summarize_fit(run_dir)["tube_offsets"]
+        assert s["sigma"] == pytest.approx(0.5)
+        assert not s["structured"]
+
+
+class TestTubeOffsetHelpers:
+
+    def _priors(self, tmp_path, rows):
+        pd.DataFrame(rows, columns=["parameter", "value"]).to_csv(
+            tmp_path / "priors.csv", index=False)
+        return str(tmp_path / "cfg.yaml")
+
+    def test_held_sigma_none_without_config(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _held_offset_sigma
+        assert _held_offset_sigma(None, "x.yaml") is None
+        assert _held_offset_sigma({"priors_file": "p.csv"}, None) is None
+
+    def test_held_sigma_none_without_priors_file_key(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _held_offset_sigma
+        assert _held_offset_sigma({}, str(tmp_path / "cfg.yaml")) is None
+
+    def test_held_sigma_none_when_priors_missing(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _held_offset_sigma
+        assert _held_offset_sigma({"priors_file": "absent.csv"},
+                                  str(tmp_path / "cfg.yaml")) is None
+
+    def test_held_sigma_none_without_row_or_when_zero(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _held_offset_sigma
+        cfg = self._priors(tmp_path, [["growth.other.value", 1.0]])
+        assert _held_offset_sigma({"priors_file": "priors.csv"}, cfg) is None
+        cfg = self._priors(tmp_path, [["growth.sample_offset.sigma_fixed", 0.0]])
+        assert _held_offset_sigma({"priors_file": "priors.csv"}, cfg) is None
+        cfg = self._priors(tmp_path, [["growth.sample_offset.sigma_fixed", 0.3]])
+        assert _held_offset_sigma({"priors_file": "priors.csv"},
+                                  cfg) == pytest.approx(0.3)
+
+    def test_unwritable_outputs_warn_and_still_return_summary(self, run_dir):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import (
+            _summarize_tube_offsets)
+        _write_offsets(run_dir, pattern=True)
+        out_prefix = os.path.join(run_dir, "no_such_dir", "out")
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            summary = _summarize_tube_offsets(run_dir, out_prefix, None, None)
+        msgs = [str(x.message) for x in w]
+        assert summary["file"] == "run_params_sample_offset_offset.csv"
+        assert summary["sigma_source"] is None
+        assert any(m.startswith(f"Could not write {out_prefix}_tube_offsets.csv")
+                   for m in msgs)
+        assert any(m.startswith(f"Could not write {out_prefix}_tube_offset_trends.csv")
+                   for m in msgs)
+        assert any(m.startswith("Could not plot tube offsets") for m in msgs)
+
+    def test_plot_hides_unused_panels(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _plot_tube_offsets
+        conds = ["a", "b", "c", "d"]
+        tubes = pd.DataFrame({
+            "condition_sel": np.repeat(conds, 2),
+            "titrant_conc": [0.0, 1.0] * 4,
+            "t_sel": [10.0, 20.0] * 4,
+            "offset": np.linspace(-1, 1, 8),
+        })
+        trends = pd.DataFrame({"condition_sel": conds,
+                               "structured": [True, False, False, False]})
+        pdf = str(tmp_path / "t.pdf")
+        import matplotlib.pyplot as plt
+        with patch.object(plt, "close") as close:
+            _plot_tube_offsets(tubes, trends, pdf)
+        fig = close.call_args[0][0]
+        axes = [a for a in fig.axes if a.get_label() != "<colorbar>"]
+        visible = [a for a in axes if a.get_visible()]
+        assert len(visible) == 4
+        assert sum(not a.get_visible() for a in axes) == 2
+        assert visible[0].get_title() == "a (trend)"
+        assert visible[0].get_ylabel() == "offset (ln)"
+        assert os.path.exists(pdf)
+        plt.close(fig)
+
+    def test_summarize_fit_warns_when_tube_offsets_fail(self, run_dir):
+        with patch("tfscreen.tfmodel.scripts.summarize_fit_cli."
+                   "_summarize_tube_offsets", side_effect=RuntimeError("boom")):
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter("always")
+                results = summarize_fit(run_dir)
+        assert results["tube_offsets"] is None
+        assert any("Could not summarize tube offsets: boom" in str(x.message)
+                   for x in w)
+
+
+class TestFindMultiple:
+
+    def test_multiple_tfmodel_configs_warn_and_use_first(self, tmp_path):
+        for name in ("a_config.yaml", "b_config.yaml"):
+            _make_config_yaml(str(tmp_path / name), binding_csv_path="b.csv",
+                              guesses_name="g.csv")
+        with pytest.warns(UserWarning, match="Multiple tfmodel config"):
+            result = _find_fit_config(str(tmp_path))
+        assert result == str(tmp_path / "a_config.yaml")
+
+    def test_multiple_fit_losses_warn_and_use_first(self, tmp_path):
+        for name in ("a_losses.txt", "b_losses.txt"):
+            _make_losses_txt(str(tmp_path / name))
+        with pytest.warns(UserWarning, match="Multiple fit losses"):
+            result = _find_fit_losses(str(tmp_path))
+        assert result == str(tmp_path / "a_losses.txt")
+
+
+# ---------------------------------------------------------------------------
+# Relative (X-scale) fits
+# ---------------------------------------------------------------------------
+
+_REL_CONFIG = {"components": {"theta": "hill_relative",
+                              "theta_gauge_conc": [0.0, 1000.0]}}
+
+
+def _write_wt_ref(path, concs=(0.0, 1000.0), wt=(0.9, 0.1)):
+    rows = [dict(genotype="wt", titrant_name=TITRANT_NAME, titrant_conc=c,
+                 theta_obs=v) for c, v in zip(concs, wt)]
+    rows += [dict(genotype="E4F", titrant_name=TITRANT_NAME, titrant_conc=c,
+                  theta_obs=0.5) for c in TITRANT_CONCS]
+    pd.DataFrame(rows).to_csv(path, index=False)
+
+
+class TestXGauge:
+
+    def test_not_relative_returns_none(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _x_gauge
+        assert _x_gauge({"components": {}}, None, str(tmp_path)) is None
+
+    def test_missing_gauge_or_ref_warns(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _x_gauge
+        cfg = {"components": {"theta": "hill_relative"}}
+        with pytest.warns(UserWarning, match="without a recorded gauge"):
+            assert _x_gauge(cfg, "ref.csv", str(tmp_path)) is None
+        with pytest.warns(UserWarning, match="without a recorded gauge"):
+            assert _x_gauge(_REL_CONFIG, None, str(tmp_path)) is None
+
+    def test_wt_missing_at_gauge_conc_warns(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _x_gauge
+        ref = str(tmp_path / "ref.csv")
+        _write_wt_ref(ref, concs=(0.0, 10.0))
+        with pytest.warns(UserWarning, match="no wt value at the gauge "
+                                             "concentration 1000.0"):
+            assert _x_gauge(_REL_CONFIG, ref, str(tmp_path)) is None
+
+    def test_builds_gauge_with_activity(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _x_gauge
+        ref = str(tmp_path / "ref.csv")
+        _write_wt_ref(ref)
+        pd.DataFrame({"genotype": ["wt", "E4F"], "activity": [1.0, 0.5]}).to_csv(
+            tmp_path / "run_sim_parameters.csv", index=False)
+        g = _x_gauge(_REL_CONFIG, ref, str(tmp_path))
+        assert g["gauge"] == [0.0, 1000.0]
+        assert g["wt"] == {TITRANT_NAME: (pytest.approx(0.9), pytest.approx(0.1))}
+        assert g["activity"] == {"wt": 1.0, "E4F": 0.5}
+
+
+class TestXScaleGrowthRef:
+
+    _gauge = {"wt": {TITRANT_NAME: (0.9, 0.1)}, "activity": {}}
+
+    def test_without_k_m_columns_returns_copy(self):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _x_scale_growth_ref
+        df = pd.DataFrame({"condition_rep": ["a"], "growth_min": [0.1]})
+        out = _x_scale_growth_ref(df, self._gauge)
+        pd.testing.assert_frame_equal(out, df)
+        assert out is not df
+
+    def test_more_than_one_titrant_blanks_k_m(self):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import _x_scale_growth_ref
+        df = pd.DataFrame({"condition_rep": ["a"], "growth_k": [0.01],
+                           "growth_m": [-0.02]})
+        gauge = {"wt": {"IPTG": (0.9, 0.1), "TMG": (0.8, 0.2)}, "activity": {}}
+        with pytest.warns(UserWarning, match="more than one titrant"):
+            out = _x_scale_growth_ref(df, gauge)
+        assert out["growth_k"].isna().all() and out["growth_m"].isna().all()
+        assert df["growth_k"].iloc[0] == 0.01
+
+    def test_condition_growth_params_mapped_to_x(self, tmp_path):
+        from tfscreen.tfmodel.scripts.summarize_fit_cli import (
+            _summarize_condition_growth_params)
+        pd.DataFrame({"condition_rep": ["a"], "growth_k": [0.01],
+                      "growth_m": [-0.02]}).to_csv(
+            tmp_path / "run_sim_growth_parameters.csv", index=False)
+        for name in ("growth_k", "growth_m"):
+            pd.DataFrame({"condition_rep": ["a"], "q0.5": [0.0]}).to_csv(
+                tmp_path / f"run_params_{name}.csv", index=False)
+        out_prefix = str(tmp_path / "out")
+        _summarize_condition_growth_params(str(tmp_path), out_prefix,
+                                           x_gauge=self._gauge)
+        k = pd.read_csv(out_prefix + "_params_growth_k.csv")["ref"].iloc[0]
+        m = pd.read_csv(out_prefix + "_params_growth_m.csv")["ref"].iloc[0]
+        # k_X = k + m theta_wt(c_hi); m_X = m (theta_wt(c_lo) - theta_wt(c_hi))
+        assert k == pytest.approx(0.01 - 0.02 * 0.1)
+        assert m == pytest.approx(-0.02 * 0.8)
+
+
+class TestSummarizeFitRelative:
+
+    def _relative_run(self, run_dir):
+        cfg_path = os.path.join(run_dir, "run_config.yaml")
+        with open(cfg_path) as fh:
+            cfg = yaml.safe_load(fh)
+        cfg["components"] = dict(_REL_CONFIG["components"])
+        with open(cfg_path, "w") as fh:
+            yaml.dump(cfg, fh)
+        _make_pred_csv(os.path.join(run_dir, "run_pred_theta.csv"),
+                       n_training=3, extra_genotypes=["E4F"])
+        ref = os.path.join(run_dir, "ref.csv")
+        _write_wt_ref(ref)
+        return ref
+
+    def test_test_ref_put_on_x_scale(self, run_dir):
+        ref = self._relative_run(run_dir)
+        results = summarize_fit(run_dir, ref_theta_file=ref)
+        assert results["metadata"]["theta_scale"] == "X"
+        df = pd.read_csv(os.path.join(run_dir, "summary",
+                                      "tfs_summarize_theta_corr_test.csv"))
+        e4f = df[df["genotype"] == "E4F"]
+        assert len(e4f) == len(TITRANT_CONCS)
+        # X = (0.5 - 0.1) / (0.9 - 0.1)
+        assert np.allclose(e4f["ref"], 0.5)
+
+    def test_gauge_failure_warns_and_skips_test_stats(self, run_dir):
+        ref = self._relative_run(run_dir)
+        with patch("tfscreen.tfmodel.scripts.summarize_fit_cli._x_gauge",
+                   side_effect=RuntimeError("bad gauge")):
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter("always")
+                results = summarize_fit(run_dir, ref_theta_file=ref)
+        assert any("Could not build the X-scale truth: bad gauge"
+                   in str(x.message) for x in w)
+        assert results["metadata"]["n_theta_test_points"] is None
+
+
+# ---------------------------------------------------------------------------
+# Keyed (per-mutation / per-pair) truth and the ddG_sigma join: failure paths
+# ---------------------------------------------------------------------------
+
+from tfscreen.tfmodel.scripts.summarize_fit_cli import (  # noqa: E402
+    _compute_keyed_obs,
+    _read_keyed_truth,
+    _summarize_ddG_sigma,
+)
+
+
+class TestReadKeyedTruth:
+
+    def test_unreadable_file_warns_and_returns_none(self, tmp_path):
+        (tmp_path / "tfs_sim_mutation_parameters.csv").write_text("")
+        with pytest.warns(UserWarning, match="Could not load"):
+            assert _read_keyed_truth(str(tmp_path), "mutation_parameters",
+                                     "mutation") is None
+
+    def test_missing_key_column_warns_and_returns_none(self, tmp_path):
+        pd.DataFrame({"genotype": ["A1B"], "d_x": [1.0]}).to_csv(
+            tmp_path / "tfs_sim_mutation_parameters.csv", index=False)
+        with pytest.warns(UserWarning, match="has no 'mutation' column"):
+            assert _read_keyed_truth(str(tmp_path), "mutation_parameters",
+                                     "mutation") is None
+
+    def test_absent_file_returns_none_silently(self, tmp_path):
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            assert _read_keyed_truth(str(tmp_path), "pair_parameters",
+                                     "pair") is None
+
+
+def test_compute_keyed_obs_without_key_column_returns_none():
+    truth = pd.DataFrame({"d_x": [1.0]}, index=pd.Index(["A1B"], name="mutation"))
+    params = pd.DataFrame({"genotype": ["A1B"], "q0.5": [0.9]})
+    assert _compute_keyed_obs(params, truth, "d_x", "mutation") is None
+    params = pd.DataFrame({"mutation": ["A1B", "C2D"], "q0.5": [0.9, 0.1]})
+    out = _compute_keyed_obs(params, truth, "d_x", "mutation")
+    assert out[0] == 1.0 and np.isnan(out[1])
+
+
+class TestSummarizeDdGSigmaFailures:
+
+    def _sim(self, run):
+        pd.DataFrame({"structure": ["H"], "ddG_sigma": [1.0]}).to_csv(
+            run / "tfs_sim_ddG_sigma.csv", index=False)
+
+    def test_warns_when_other_ddG_tables_exist_but_sigma_missing(self, tmp_path):
+        self._sim(tmp_path)
+        pd.DataFrame({"mutation": ["A1B"], "q0.5": [0.1]}).to_csv(
+            tmp_path / "tfs_params_ddG_H.csv", index=False)
+        out = tmp_path / "summary"
+        out.mkdir()
+        with pytest.warns(UserWarning,
+                          match=r"Expected \*_params_ddG_sigma.csv"):
+            _summarize_ddG_sigma(str(tmp_path), str(out / "tfs_summarize"))
+        assert not list(out.iterdir())
+
+    def test_unjoinable_truth_warns_and_writes_nothing(self, tmp_path):
+        # Truth without a 'structure' column cannot be joined.
+        pd.DataFrame({"state": ["H"], "ddG_sigma": [1.0]}).to_csv(
+            tmp_path / "tfs_sim_ddG_sigma.csv", index=False)
+        pd.DataFrame({"structure": ["H"], "q0.5": [0.9]}).to_csv(
+            tmp_path / "tfs_params_ddG_sigma.csv", index=False)
+        out = tmp_path / "summary"
+        out.mkdir()
+        with pytest.warns(UserWarning, match="Could not join ddG_sigma truth"):
+            _summarize_ddG_sigma(str(tmp_path), str(out / "tfs_summarize"))
+        assert not list(out.iterdir())
+
+    def test_unwritable_output_warns_and_skips_calibration(self, tmp_path):
+        self._sim(tmp_path)
+        pd.DataFrame({"structure": ["H"], "q0.025": [0.5], "q0.5": [0.9],
+                      "q0.975": [1.3]}).to_csv(
+            tmp_path / "tfs_params_ddG_sigma.csv", index=False)
+        out_prefix = str(tmp_path / "no_such_dir" / "tfs_summarize")
+        with patch("tfscreen.tfmodel.scripts.summarize_fit_cli._try_calibration"
+                   ) as calib:
+            with pytest.warns(UserWarning, match="Could not write"):
+                _summarize_ddG_sigma(str(tmp_path), out_prefix)
+        calib.assert_not_called()
diff --git a/tests/tfscreen/tfmodel/scripts/test_summarize_grid_cli.py b/tests/tfscreen/tfmodel/scripts/test_summarize_grid_cli.py
index 464d6bc7..54fdfb3f 100644
--- a/tests/tfscreen/tfmodel/scripts/test_summarize_grid_cli.py
+++ b/tests/tfscreen/tfmodel/scripts/test_summarize_grid_cli.py
@@ -41,7 +41,7 @@ def _make_fit_summary(subdir, training_rmse=0.05, test_rmse=None,
                       "r_squared": 0.90, "mean_error": 0.001,
                       "pct_success": 1.0, "normalized_rmse": 0.1,
                       "residual_corr": 0.02, "residual_corr_p_value": 0.4,
-                      "bp_p_value": 0.3, "coverage_prob": None}
+                      "bp_p_value": 0.3}
     theta_test = None
     if test_rmse is not None:
         theta_test = dict(theta_training)
@@ -266,3 +266,23 @@ def test_multiple_runs_all_have_fit_summary(self, tmp_path):
         assert len(df) == 3
         rmse_vals = sorted(df["theta_training_rmse"].tolist())
         assert rmse_vals == pytest.approx([0.03, 0.07, 0.12])
+
+
+def test_fit_summary_found_in_summary_subdir(tmp_path):
+    # tfs-summarize-fit's default output is {run_dir}/summary/tfs_summarize
+    sub = tmp_path / "run_0001_a"
+    (sub / "summary").mkdir(parents=True)
+    _make_combo(str(sub))
+    _make_fit_summary(str(sub / "summary"), training_rmse=0.07)
+    df = summarize_grid(str(tmp_path))
+    assert df.loc[0, "theta_training_rmse"] == 0.07
+
+
+def test_simulate_variables_included(tmp_path):
+    sub = tmp_path / "run_0001_a"
+    sub.mkdir()
+    with open(sub / "combo.json", "w") as fh:
+        json.dump({"simulate": {"seed": 3}, "template": {"arm": "map"}}, fh)
+    df = summarize_grid(str(tmp_path))
+    assert df.loc[0, "seed"] == 3
+    assert df.loc[0, "arm"] == "map"
diff --git a/tests/tfscreen/tfmodel/scripts/test_summarize_sbc_cli.py b/tests/tfscreen/tfmodel/scripts/test_summarize_sbc_cli.py
deleted file mode 100644
index 46b16a5c..00000000
--- a/tests/tfscreen/tfmodel/scripts/test_summarize_sbc_cli.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""Tests for summarize_sbc_cli.py — thin CLI wrapper around summarize_sbc."""
-from unittest.mock import patch
-
-
-class TestSummarizeSbcMain:
-
-    def test_main_calls_generalized_main_with_summarize_sbc(self):
-        with patch("tfscreen.tfmodel.scripts.summarize_sbc_cli.generalized_main") as mock_gm:
-            from tfscreen.tfmodel.scripts import summarize_sbc_cli
-            summarize_sbc_cli.main()
-
-        mock_gm.assert_called_once()
-        args, kwargs = mock_gm.call_args
-        assert args[0] is summarize_sbc_cli.summarize_sbc
-
-    def test_summarize_sbc_imported_from_analysis(self):
-        from tfscreen.tfmodel.scripts.summarize_sbc_cli import summarize_sbc
-        from tfscreen.tfmodel.analysis.error_calibration import summarize_sbc as canonical
-        assert summarize_sbc is canonical
-
-    def test_main_passes_manual_arg_types(self):
-        """manual_arg_types must declare sbc_dir and out_prefix as str."""
-        with patch("tfscreen.tfmodel.scripts.summarize_sbc_cli.generalized_main") as mock_gm:
-            from tfscreen.tfmodel.scripts import summarize_sbc_cli
-            summarize_sbc_cli.main()
-
-        _, kwargs = mock_gm.call_args
-        mat = kwargs.get("manual_arg_types", {})
-        assert mat.get("sbc_dir") is str
-        assert mat.get("out_prefix") is str
-
-    def test_main_is_callable(self):
-        from tfscreen.tfmodel.scripts.summarize_sbc_cli import main
-        assert callable(main)
diff --git a/tests/tfscreen/tfmodel/tensors/test_batch.py b/tests/tfscreen/tfmodel/tensors/test_batch.py
index e78f8d1d..ed086b06 100644
--- a/tests/tfscreen/tfmodel/tensors/test_batch.py
+++ b/tests/tfscreen/tfmodel/tensors/test_batch.py
@@ -54,7 +54,6 @@ def full_data_setup():
     map_condition_pre = jnp.arange(total_size, dtype=int)
     map_condition_sel = jnp.arange(total_size, dtype=int)
     good_mask = jnp.ones(total_size, dtype=bool)
-    congression_mask = jnp.ones(total_size, dtype=bool)
     
     # Initial batch metadata
     batch_idx = jnp.arange(total_size, dtype=int)
@@ -69,8 +68,7 @@ def full_data_setup():
         t_sel=t_sel,
         map_condition_pre=map_condition_pre,
         map_condition_sel=map_condition_sel,
-        good_mask=good_mask,
-        congression_mask=congression_mask
+        good_mask=good_mask
     )
     
     return MockDataClass(growth=growth)
diff --git a/tests/tfscreen/tfmodel/tensors/test_batch_consistency.py b/tests/tfscreen/tfmodel/tensors/test_batch_consistency.py
index 16eff816..3b070ab7 100644
--- a/tests/tfscreen/tfmodel/tensors/test_batch_consistency.py
+++ b/tests/tfscreen/tfmodel/tensors/test_batch_consistency.py
@@ -39,7 +39,6 @@ def test_batch_scaling_unbiased():
         t_pre=jnp.zeros((1, 1, 1, total_genotypes)),
         t_sel=jnp.zeros((1, 1, 1, total_genotypes)),
         good_mask=jnp.ones((1, 1, 1, total_genotypes), dtype=bool),
-        congression_mask=jnp.ones(total_genotypes, dtype=bool),
         num_replicate=1,
         num_time=1,
         num_condition_pre=1,
@@ -97,7 +96,6 @@ def test_component_shape_guards():
         t_pre=jnp.zeros((1, 1, 1, batch_size)),
         t_sel=jnp.zeros((1, 1, 1, batch_size)),
         good_mask=jnp.ones((1, 1, 1, batch_size), dtype=bool),
-        congression_mask=jnp.ones(batch_size, dtype=bool),
         num_replicate=1,
         num_time=1,
         num_condition_pre=1,
@@ -157,7 +155,6 @@ def test_num_genotype_preserved():
         t_pre=jnp.zeros((1, 1, 1, total_genotypes)),
         t_sel=jnp.zeros((1, 1, 1, total_genotypes)),
         good_mask=jnp.ones((1, 1, 1, total_genotypes), dtype=bool),
-        congression_mask=jnp.ones(total_genotypes, dtype=bool),
         num_replicate=1,
         num_time=1,
         num_condition_pre=1,
diff --git a/tests/tfscreen/tfmodel/tensors/test_batch_fix.py b/tests/tfscreen/tfmodel/tensors/test_batch_fix.py
index 642833a6..7024ecd4 100644
--- a/tests/tfscreen/tfmodel/tensors/test_batch_fix.py
+++ b/tests/tfscreen/tfmodel/tensors/test_batch_fix.py
@@ -39,8 +39,7 @@ def test_get_batch_metadata_updates():
         t_sel=jnp.zeros((1, 1, 1, 1, 1, 1, total_size)),
         map_condition_pre=jnp.zeros((1, total_size)),
         map_condition_sel=jnp.zeros((1, total_size)),
-        good_mask=jnp.ones((1, total_size), dtype=bool),
-        congression_mask=jnp.ones((total_size,), dtype=bool)
+        good_mask=jnp.ones((1, total_size), dtype=bool)
     )
     
     full_data = MockDataClass(growth=growth)
@@ -60,4 +59,3 @@ def test_get_batch_metadata_updates():
     
     # Verify shape of sliced data (last dim should be new_batch_size)
     assert batch_data.growth.ln_cfu.shape[-1] == new_batch_size
-    assert batch_data.growth.congression_mask.shape[-1] == new_batch_size
diff --git a/tests/tfscreen/tfmodel/test_base_growth.py b/tests/tfscreen/tfmodel/test_base_growth.py
index 383d4e6d..4ae2f87d 100644
--- a/tests/tfscreen/tfmodel/test_base_growth.py
+++ b/tests/tfscreen/tfmodel/test_base_growth.py
@@ -231,7 +231,10 @@ def test_configure_model_to_read_configuration_round_trip(tmp_path,
                     base_growth_df=base_growth_path,
                     library_config=minimal_library_config,
                     out_prefix=out_prefix,
-                    theta_growth_noise_model="zero")
+                    theta_growth_noise_model="zero",
+                    # synthetic ln_cfu table without read counts
+                    growth_likelihood="lncfu",
+                    sample_offset_model="zero")
 
     config_path = f"{out_prefix}_config.yaml"
     orchestrator, init_params = read_configuration(config_path)
diff --git a/tests/tfscreen/tfmodel/test_configuration_io_helpers.py b/tests/tfscreen/tfmodel/test_configuration_io_helpers.py
index 64626398..37721134 100644
--- a/tests/tfscreen/tfmodel/test_configuration_io_helpers.py
+++ b/tests/tfscreen/tfmodel/test_configuration_io_helpers.py
@@ -1,7 +1,8 @@
 """
 Unit tests for the internal helpers in tfscreen.tfmodel.configuration_io.
 
-_update_dataclass is tested in test_configure_and_run.py; this file covers
+_update_dataclass is tested in test_configure_and_run.py and, for priors rows
+that name no prior, here; this file also covers
 _extract_scalars and _gather_dict_field which have no prior test coverage.
 """
 
@@ -18,6 +19,7 @@
     _extract_prior_arrays,
     _assemble_condition_array,
     _read_priors_flat,
+    _static_flag,
     _update_dataclass,
 )
 from tfscreen.tfmodel.generative.components.growth.linear import (
@@ -387,3 +389,151 @@ def test_m_pinned_bool_roundtrips_truthy(self):
         assert not priors.m_pinned
         updated = _update_dataclass(priors, "", {"m_pinned": 1.0})
         assert bool(updated.m_pinned)
+
+# ---------------------------------------------------------------------------
+# Unknown priors rows
+# ---------------------------------------------------------------------------
+
+@dataclasses.dataclass
+class _ThetaPriors:
+    theta_ln_K_h_l_wt_loc: float = 1.84
+    theta_tf_total_M: float = 1e-5
+    max_hill_n: object = None                      # optional, left at None
+    m_is_selection: tuple = (True, False)          # static tuple, kept
+    pinned: dict = dataclasses.field(default_factory=dict)
+
+
+@dataclasses.dataclass
+class _Root:
+    theta: _ThetaPriors = dataclasses.field(default_factory=_ThetaPriors)
+    growth: _GrowthPriors = dataclasses.field(default_factory=_GrowthPriors)
+    binding: object = None                         # absent prior group
+
+
+class TestUnknownPriorRows:
+    """A priors row that names no prior of the model raises, naming it."""
+
+    def test_unknown_row_raises_with_its_name(self):
+        flat = {"theta.theta_ln_K_h_l_wt_loc": 2.0,
+                "theta.theta_op_total_M": 2.5e-8}
+        with pytest.raises(ValueError) as err:
+            _update_dataclass(_Root(), "", flat)
+        msg = str(err.value)
+        assert "theta.theta_op_total_M" in msg
+        assert "tfs-configure-model" in msg
+        assert "theta_ln_K_h_l_wt_loc" not in msg
+
+    def test_every_unknown_row_is_listed(self):
+        flat = {"theta.old_a": 1.0, "growth.condition_growth.old_b": 2.0,
+                "binding.theta_binding_noise.x": 3.0}
+        with pytest.raises(ValueError) as err:
+            _update_dataclass(_Root(), "", flat)
+        for k in flat:
+            assert k in str(err.value)
+
+    def test_known_rows_still_load(self):
+        flat = {
+            "theta.theta_ln_K_h_l_wt_loc": 2.0,          # scalar field
+            "theta.theta_tf_total_M": 2e-5,
+            "theta.max_hill_n": 5.0,                     # field left at None
+            "theta.m_is_selection": "(True, False)",     # static tuple
+            "theta.pinned.hyper_loc": 0.7,               # dict-field entry
+            "growth.condition_growth.k_loc":             # per-condition array
+                jnp.asarray([0.01, 0.02, 0.03]),
+            "growth.condition_growth.k_scale": 0.2,
+        }
+        out = _update_dataclass(_Root(), "", flat)
+        assert out.theta.theta_ln_K_h_l_wt_loc == 2.0
+        assert out.theta.theta_tf_total_M == 2e-5
+        assert out.theta.m_is_selection == (True, False)
+        assert out.theta.pinned == {"hyper_loc": 0.7}
+        assert np.allclose(out.growth.condition_growth.k_loc,
+                           [0.01, 0.02, 0.03])
+        assert out.growth.condition_growth.k_scale == 0.2
+        assert out.binding is None
+
+    def test_every_component_round_trips_its_own_rows(self):
+        """The rows _extract_scalars writes for each component are known."""
+        from tfscreen.tfmodel.generative.registry import model_registry
+
+        checked = 0
+        for group, entry in model_registry.items():
+            modules = entry.values() if isinstance(entry, dict) else [entry]
+            for module in modules:
+                get_priors = getattr(module, "get_priors", None)
+                if get_priors is None:
+                    continue
+                try:
+                    priors = get_priors()
+                except TypeError:
+                    continue
+                flat = _extract_scalars(priors)
+                _update_dataclass(priors, "", flat)
+                checked += 1
+        assert checked > 30
+
+    def test_read_configuration_rejects_a_stale_priors_row(self, tmp_path,
+                                                          mocker):
+        import yaml
+        from tfscreen.tfmodel.configuration_io import read_configuration
+
+        (tmp_path / "config.yaml").write_text(yaml.dump({
+            "data": {"growth": "g.csv", "binding": "b.csv"},
+            "components": {},
+            "priors_file": "priors.csv",
+            "guesses_file": "guesses.csv"}))
+        pd.DataFrame({
+            "parameter": ["theta.theta_ln_K_h_l_wt_loc",
+                          "theta.theta_op_total_M"],
+            "value": [1.84, 2.5e-8]}).to_csv(tmp_path / "priors.csv",
+                                             index=False)
+        pd.DataFrame({"parameter": ["p"], "value": [0.0]}).to_csv(
+            tmp_path / "guesses.csv", index=False)
+
+        orch_cls = mocker.patch(
+            "tfscreen.tfmodel.configuration_io.ModelOrchestrator")
+        orch = orch_cls.return_value
+        orch.priors = _Root()
+        orch.growth_tm = None
+        orch.init_params = {"p": 0.0}
+
+        with pytest.raises(ValueError, match=r"theta\.theta_op_total_M"):
+            read_configuration(str(tmp_path / "config.yaml"))
+    
+    def test_m_pinned_per_condition_rows_become_a_tuple(self):
+        """Indexed per-condition rows arrive as an array; a static field
+        must be hashable, so they become a tuple of bools, or a bool when
+        every condition agrees."""
+        priors = _linear_get_priors()
+        updated = _update_dataclass(
+            priors, "", {"m_pinned": jnp.array([1.0, 0.0, 1.0])})
+        assert updated.m_pinned == (True, False, True)
+        hash(updated.m_pinned)
+        same = _update_dataclass(priors, "", {"m_pinned": jnp.array([1.0, 1.0])})
+        assert same.m_pinned is True
+
+    @pytest.mark.parametrize("value,expected", [
+        (1.0, True), (0.0, False), ("True", True), ("false", False),
+        ("1.0", True), ("(True, False)", (True, False)),
+        ("(False, False)", False), (np.array([0.0, 1.0]), (False, True)),
+    ])
+    def test_static_flag(self, value, expected):
+        assert _static_flag(value) == expected
+
+
+class TestKnownKeysEdgeCases:
+
+    def test_fields_of_a_non_standard_dataclass(self):
+        # an object carrying __dataclass_fields__ itself (not through its
+        # class, so dataclasses.is_dataclass is False) still lists them
+        from types import SimpleNamespace
+        from tfscreen.tfmodel.configuration_io import _dataclass_fields
+        f = dataclasses.field()
+        f.name = "a"
+        obj = SimpleNamespace(__dataclass_fields__={"a": f}, a=1.0)
+        assert not dataclasses.is_dataclass(obj)
+        assert [x.name for x in _dataclass_fields(obj)] == ["a"]
+
+    def test_known_keys_of_a_non_dataclass_is_empty(self):
+        from tfscreen.tfmodel.configuration_io import _known_keys
+        assert _known_keys(object()) == (set(), [])
diff --git a/tests/tfscreen/tfmodel/test_count_likelihood.py b/tests/tfscreen/tfmodel/test_count_likelihood.py
new file mode 100644
index 00000000..676b9599
--- /dev/null
+++ b/tests/tfscreen/tfmodel/test_count_likelihood.py
@@ -0,0 +1,219 @@
+"""
+The read-count growth likelihood (growth_likelihood='counts'; roadmap step 7,
+planning/analysis-roadmap.md), wired through the orchestrator.
+"""
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+import yaml
+from numpyro.handlers import seed, trace
+
+from tfscreen.tfmodel.analysis.prediction import copy_orchestrator
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.inference.batch_safety import (
+    find_orchestrator_batch_dependent_latents,
+    find_orchestrator_batch_order_mismatches,
+)
+from tfscreen.tfmodel.model_orchestrator import (
+    ModelOrchestrator,
+    _add_count_columns,
+)
+from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+_LIBRARY_YAML = os.path.join(_SMOKE_DATA, "library-smoke.yaml")
+
+
+def _orch(**kwargs):
+    return ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=_BINDING_CSV,
+                             batch_size=6, growth_likelihood="counts",
+                             **kwargs)
+
+
+@pytest.fixture(scope="module")
+def counts_model():
+    return _orch(sample_offset="level")
+
+
+def _trace(o):
+    d = o.get_batch(o.data, o.get_random_idx(batch_key=0))
+    return d, trace(seed(o.jax_model, 0)).get_trace(data=d, priors=o.priors)
+
+
+# ---------------------------------------------------------------------------
+# data
+# ---------------------------------------------------------------------------
+
+def test_count_tensors(counts_model):
+    g = counts_model.data.growth
+    assert g.growth_likelihood == "counts"
+    assert g.counts.shape == g.ln_cfu.shape
+    assert g.ln_sample_reads.shape == g.ln_cfu.shape
+    assert g.sample_ln_cfu.shape == g.ln_cfu.shape
+    # Every measured cell's counts match the input file.
+    df = pd.read_csv(_GROWTH_CSV)
+    good = np.asarray(g.good_mask)
+    assert np.sort(np.asarray(g.counts)[good]).tolist() == \
+        np.sort(df["counts"].to_numpy(float)).tolist()
+
+
+def test_lncfu_model_has_no_count_tensors():
+    o = ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=_BINDING_CSV)
+    assert o.data.growth.growth_likelihood == "lncfu"
+    assert o.data.growth.counts is None
+
+
+def test_depth_from_sample_reads_or_derived():
+    df = pd.DataFrame({"counts": [3, 7], "adjusted_counts": [4, 8],
+                       "frequency": [4 / 1000, 8 / 1000],
+                       "sample_cfu": [1e8, 1e8], "sample_cfu_std": [1e6, 1e6]})
+    derived = _add_count_columns(df.copy())
+    assert np.allclose(derived["ln_sample_reads"], np.log(1000.0))
+    assert np.allclose(derived["sample_ln_cfu"], np.log(1e8))
+    given = _add_count_columns(df.assign(sample_reads=990).copy())
+    assert np.allclose(given["ln_sample_reads"], np.log(990.0))
+
+
+@pytest.mark.parametrize("drop,match", [("counts", "'counts' column"),
+                                        ("frequency", "total reads"),
+                                        ("sample_cfu", "total cells")])
+def test_missing_count_columns_refused(drop, match):
+    df = pd.DataFrame({"counts": [3], "adjusted_counts": [4],
+                       "frequency": [0.004], "sample_cfu": [1e8],
+                       "sample_cfu_std": [1e6]}).drop(columns=[drop])
+    with pytest.raises(ValueError, match=match):
+        _add_count_columns(df)
+
+
+@pytest.mark.parametrize("bad", [-1.0, np.nan, np.inf])
+def test_bad_counts_refused(bad):
+    df = pd.DataFrame({"counts": [3.0, bad], "sample_reads": [1000, 1000],
+                       "sample_cfu": [1e8, 1e8],
+                       "sample_cfu_std": [1e6, 1e6]})
+    with pytest.raises(ValueError, match="finite and >= 0"):
+        _add_count_columns(df)
+
+
+@pytest.mark.parametrize("bad", [0.0, -5.0, np.nan])
+def test_bad_sample_reads_refused(bad):
+    df = pd.DataFrame({"counts": [3, 7], "sample_reads": [1000.0, bad],
+                       "sample_cfu": [1e8, 1e8],
+                       "sample_cfu_std": [1e6, 1e6]})
+    with pytest.raises(ValueError, match="total reads must be finite"):
+        _add_count_columns(df)
+
+
+def test_zero_frequency_derived_reads_refused():
+    # adjusted_counts / frequency with frequency 0 is inf: refused, not logged
+    df = pd.DataFrame({"counts": [3], "adjusted_counts": [4],
+                       "frequency": [0.0], "sample_cfu": [1e8],
+                       "sample_cfu_std": [1e6]})
+    with pytest.raises(ValueError, match="total reads must be finite"):
+        _add_count_columns(df)
+
+
+# ---------------------------------------------------------------------------
+# model
+# ---------------------------------------------------------------------------
+
+def test_count_observer_sites(counts_model):
+    _, tr = _trace(counts_model)
+    assert {"growth_phi", "growth_inv_r", "growth_obs"} <= set(tr)
+    assert "growth_nu" not in tr
+    assert tr["growth_obs"]["fn"].base_dist.__class__.__name__ == \
+        "CountNegativeBinomial"
+    assert {"sample_offset_sigma", "sample_offset_offset"} <= set(tr)
+
+
+def test_count_model_is_batch_safe(counts_model):
+    assert find_orchestrator_batch_dependent_latents(counts_model) == {}
+    assert find_orchestrator_batch_order_mismatches(counts_model) == {}
+
+
+def test_counts_refuse_growth_noise():
+    with pytest.raises(ValueError, match="growth_noise"):
+        _orch(growth_noise="normal_kt")
+
+
+def test_unknown_likelihood_refused():
+    with pytest.raises(ValueError, match="growth_likelihood"):
+        ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=_BINDING_CSV,
+                          growth_likelihood="poisson")
+
+
+def test_prediction_copy(counts_model):
+    new = copy_orchestrator(counts_model, genotypes=["wt"])
+    assert new.data.growth.growth_likelihood == "counts"
+    assert new.data.growth.counts is not None
+
+
+# ---------------------------------------------------------------------------
+# configuration
+# ---------------------------------------------------------------------------
+
+def test_configure_round_trip(tmp_path):
+    out_prefix = str(tmp_path / "cl")
+    configure_model(binding_df=_BINDING_CSV, growth_df=_GROWTH_CSV,
+                    library_config=_LIBRARY_YAML, out_prefix=out_prefix,
+                    growth_likelihood="counts", sample_offset_model="level",
+                    skip_model_stats=True)
+    with open(f"{out_prefix}_config.yaml") as fh:
+        config = yaml.safe_load(fh)
+    assert config["components"]["growth_likelihood"] == "counts"
+    assert config["components"]["sample_offset"] == "level"
+
+    o, _ = read_configuration(f"{out_prefix}_config.yaml")
+    assert o.data.growth.growth_likelihood == "counts"
+    assert "growth_obs.phi_loc" in pd.read_csv(f"{out_prefix}_priors.csv")[
+        "parameter"].str.cat(sep=" ")
+
+
+# ---------------------------------------------------------------------------
+# prediction with per-tube offsets
+# ---------------------------------------------------------------------------
+
+@pytest.mark.parametrize("sample_offset", ["level", "normal"])
+def test_predict_on_new_tube_grid(sample_offset):
+    """Per-tube offsets are set to zero on a prediction grid whose tubes are
+    not the training tubes (they used to fail a reshape)."""
+    import jax
+    from numpyro.infer import Predictive
+    from tfscreen.tfmodel.analysis.prediction import predict
+
+    o = ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=_BINDING_CSV,
+                          growth_likelihood="counts",
+                          sample_offset=sample_offset)
+    prior = Predictive(o.jax_model, num_samples=3)(
+        jax.random.PRNGKey(0), data=o.data, priors=o.priors)
+    posterior = {k: v for k, v in prior.items()
+                 if not k.endswith("_obs") and not k.endswith("_pred")}
+    out = predict(o, posterior, t_sel=[0.0, 60.0, 120.0, 240.0],
+                  num_samples=None)
+    assert len(out) > 0
+    assert np.isfinite(out.filter(like="q0.5").to_numpy()).all()
+
+
+def test_configure_defaults_to_counts(tmp_path):
+    """tfs-configure-model defaults to the count likelihood with a per-tube
+    level offset (count-likelihood study, 2026-09-27); ModelOrchestrator
+    keeps lncfu, so a config written before step 7 (no growth_likelihood
+    key) still reads back as the model it was."""
+    out_prefix = str(tmp_path / "d")
+    configure_model(growth_df=_GROWTH_CSV, library_config=_LIBRARY_YAML,
+                    out_prefix=out_prefix, skip_model_stats=True)
+    with open(f"{out_prefix}_config.yaml") as fh:
+        components = yaml.safe_load(fh)["components"]
+    assert components["growth_likelihood"] == "counts"
+    assert components["sample_offset"] == "level"
+    assert components["growth_noise"] == "zero"
+
+    o = ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=_BINDING_CSV)
+    assert o.settings["growth_likelihood"] == "lncfu"
diff --git a/tests/tfscreen/tfmodel/test_data_class.py b/tests/tfscreen/tfmodel/test_data_class.py
index 9050ac3b..c71f93bc 100644
--- a/tests/tfscreen/tfmodel/test_data_class.py
+++ b/tests/tfscreen/tfmodel/test_data_class.py
@@ -43,7 +43,6 @@ def _make_growth_data(num_genotype=4, **overrides):
         t_pre=jnp.zeros(shape),
         t_sel=jnp.zeros(shape),
         good_mask=jnp.ones(shape, dtype=bool),
-        congression_mask=jnp.ones(G, dtype=bool),
         num_replicate=1,
         num_time=1,
         num_condition_pre=1,
diff --git a/tests/tfscreen/tfmodel/test_data_class_jit.py b/tests/tfscreen/tfmodel/test_data_class_jit.py
index abbc6c10..65a1dbfc 100644
--- a/tests/tfscreen/tfmodel/test_data_class_jit.py
+++ b/tests/tfscreen/tfmodel/test_data_class_jit.py
@@ -26,7 +26,6 @@ def create_dummy_data():
             t_pre=jnp.zeros((2, 5, 2, 2, 2, 2, 10)),
             t_sel=jnp.zeros((2, 5, 2, 2, 2, 2, 10)),
             good_mask=jnp.ones((2, 5, 2, 2, 2, 2, 10), dtype=bool),
-            congression_mask=jnp.ones(10, dtype=bool),
             num_replicate=2,
             num_time=5,
             num_condition_pre=2,
diff --git a/tests/tfscreen/tfmodel/test_growth_only.py b/tests/tfscreen/tfmodel/test_growth_only.py
new file mode 100644
index 00000000..c44c10a3
--- /dev/null
+++ b/tests/tfscreen/tfmodel/test_growth_only.py
@@ -0,0 +1,167 @@
+"""
+Growth-only models: growth data with no binding data (roadmap step 1,
+planning/analysis-roadmap.md).
+
+Theta is then inferred from growth alone: no binding tensors, no binding
+likelihood, no theta_binding_noise component and no binding weight.
+"""
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+import yaml
+from numpyro.handlers import seed, trace
+
+from tfscreen.tfmodel.analysis.prediction import copy_orchestrator
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.inference.batch_safety import (
+    find_orchestrator_batch_dependent_latents,
+    find_orchestrator_batch_order_mismatches,
+)
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+from tfscreen.tfmodel.scripts.prefit_calibration_cli import run_prefit_calibration
+from tfscreen.tfmodel.scripts.summarize_fit_cli import _default_trajectory_genotypes
+
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+_LIBRARY_YAML = os.path.join(_SMOKE_DATA, "library-smoke.yaml")
+
+
+@pytest.fixture(scope="module")
+def growth_only():
+    return ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=None,
+                             batch_size=6)
+
+
+def _trace(orchestrator, guide=False):
+    fn = orchestrator.jax_model_guide if guide else orchestrator.jax_model
+    data = orchestrator.get_batch(orchestrator.data,
+                                  orchestrator.get_random_idx(batch_key=0))
+    return trace(seed(fn, 0)).get_trace(data=data, priors=orchestrator.priors)
+
+
+# ---------------------------------------------------------------------------
+# Orchestrator and model
+# ---------------------------------------------------------------------------
+
+def test_no_binding_data(growth_only):
+    assert growth_only.binding_df is None
+    assert growth_only.binding_tm is None
+    assert growth_only.data.binding is None
+    assert growth_only.data.num_binding == 0
+    assert growth_only.priors.binding is None
+    assert "observe_binding" not in growth_only.main_control_kwargs
+    assert "theta_binding_noise" not in growth_only.main_control_kwargs
+
+
+def test_model_and_guide_have_no_binding_sites(growth_only):
+    for guide in (False, True):
+        sites = _trace(growth_only, guide=guide)
+        assert not [k for k in sites if "binding" in k]
+    assert "growth_pred" in _trace(growth_only)
+
+
+def test_growth_only_is_batch_safe(growth_only):
+    assert find_orchestrator_batch_dependent_latents(growth_only) == {}
+    assert find_orchestrator_batch_order_mismatches(growth_only) == {}
+
+
+def test_growth_sites_match_joint_model(growth_only):
+    """Dropping binding removes the binding sites and nothing else."""
+    joint = ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=_BINDING_CSV,
+                              batch_size=6)
+    joint_sites = {k for k in _trace(joint) if "binding" not in k}
+    assert set(_trace(growth_only)) == joint_sites
+
+
+@pytest.mark.parametrize("kwargs,match", [
+    ({"binding_weight": 2.0}, "binding_weight"),
+    ({"theta_binding_noise": "beta"}, "theta_binding_noise"),
+])
+def test_binding_settings_refused_without_binding(kwargs, match):
+    with pytest.raises(ValueError, match=match):
+        ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=None, **kwargs)
+
+
+def test_binding_only_needs_binding():
+    with pytest.raises(ValueError, match="binding_only=True requires"):
+        ModelOrchestrator(None, None, binding_only=True)
+
+
+def test_growth_needed_unless_binding_only():
+    with pytest.raises(ValueError, match="growth_df is required"):
+        ModelOrchestrator(None, _BINDING_CSV)
+
+
+def test_prediction_copy(growth_only):
+    new = copy_orchestrator(growth_only, genotypes=["wt"])
+    assert new.data.binding is None
+    assert new.binding_df is None
+
+
+# ---------------------------------------------------------------------------
+# Configuration round trip
+# ---------------------------------------------------------------------------
+
+@pytest.fixture
+def growth_only_config(tmp_path):
+    out_prefix = str(tmp_path / "go")
+    configure_model(growth_df=_GROWTH_CSV, library_config=_LIBRARY_YAML,
+                    out_prefix=out_prefix, skip_model_stats=True)
+    return f"{out_prefix}_config.yaml", f"{out_prefix}_priors.csv"
+
+
+def test_configure_writes_no_binding(growth_only_config):
+    config_file, priors_file = growth_only_config
+    with open(config_file) as fh:
+        config = yaml.safe_load(fh)
+    assert "binding" not in config["data"]
+    assert config["components"]["binding_only"] is False
+    assert config["components"]["binding_weight"] is None
+
+    # An absent component is left out of the priors CSV, not written as a
+    # "None" row (which came back as NaN).
+    priors = pd.read_csv(priors_file)
+    assert not priors["parameter"].str.startswith("binding").any()
+    assert not (priors["value"].astype(str) == "None").any()
+
+
+def test_configure_round_trip(growth_only_config):
+    config_file, _ = growth_only_config
+    orchestrator, _ = read_configuration(config_file)
+    assert orchestrator.data.binding is None
+    assert orchestrator.priors.binding is None
+
+
+def test_configure_needs_growth_or_binding():
+    with pytest.raises(ValueError, match="At least one of binding_df"):
+        configure_model()
+
+
+def test_prefit_refuses_growth_only(growth_only_config):
+    config_file, _ = growth_only_config
+    with pytest.raises(ValueError, match="growth-only model"):
+        run_prefit_calibration(config_file, seed=0)
+
+
+# ---------------------------------------------------------------------------
+# tfs-summarize-fit's default trajectory subset
+# ---------------------------------------------------------------------------
+
+def test_default_trajectory_genotypes(growth_only):
+    genos = _default_trajectory_genotypes(growth_only, num_random=3, seed=0)
+    assert genos[0] == "wt"
+    assert len(genos) == len(set(genos))
+    # Same seed, same subset.
+    assert genos == _default_trajectory_genotypes(growth_only, num_random=3,
+                                                   seed=0)
+    library = set(np.asarray(growth_only.growth_tm.tensor_dim_labels[
+        growth_only.growth_tm.tensor_dim_names.index("genotype")]).astype(str))
+    assert set(genos) <= library
diff --git a/tests/tfscreen/tfmodel/test_library_file.py b/tests/tfscreen/tfmodel/test_library_file.py
index b973ef36..97a4af55 100644
--- a/tests/tfscreen/tfmodel/test_library_file.py
+++ b/tests/tfscreen/tfmodel/test_library_file.py
@@ -93,7 +93,7 @@ def _masks(orchestrator):
     idx = orchestrator.growth_tm.tensor_dim_names.index("genotype")
     labels = list(orchestrator.growth_tm.tensor_dim_labels[idx])
     return (labels,
-            np.array(orchestrator.data.growth.congression_mask),
+            ~np.array(orchestrator.data.growth.ln_cfu0_spiked_mask),
             np.array(orchestrator.data.growth.ln_cfu0_spiked_mask))
 
 
@@ -158,6 +158,196 @@ def test_legacy_list_still_raises(self):
                               spiked_genotypes=["A2L"])
 
 
+def _bulk_fraction(orchestrator):
+    idx = orchestrator.growth_tm.tensor_dim_names.index("genotype")
+    labels = list(orchestrator.growth_tm.tensor_dim_labels[idx])
+    return dict(zip(labels, np.array(orchestrator.data.growth.bulk_fraction)))
+
+
+class TestBulkFraction:
+    """Congression purity (bulk_fraction) is carried separately from the
+    ln_cfu0 prior class (ln_cfu0_spiked_mask)."""
+
+    def test_from_library_table(self, library_file):
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         library_file=library_file)
+        bf = _bulk_fraction(orchestrator)
+        table = orchestrator.library_df.set_index("genotype")["bulk_fraction"]
+        for g in GENOTYPES:
+            assert bf[g] == pytest.approx(table[g])
+        # wt and A2L are spiked *and* in the bulk; A2C is bulk only.
+        assert 0 < bf["wt"] < 1
+        assert 0 < bf["A2L"] < 1
+        assert bf["A2C"] == pytest.approx(1.0)
+
+    def test_ln_cfu0_class_unchanged(self, library_file):
+        """Spiked-origin genotypes keep the spiked ln_cfu0 class even though
+        they are mostly bulk."""
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         library_file=library_file)
+        labels, _, spiked = _masks(orchestrator)
+        assert spiked[labels.index("wt")] and spiked[labels.index("A2L")]
+        assert not spiked[labels.index("A2C")]
+
+    def test_legacy_spiked_list_is_binary(self):
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         spiked_genotypes=["wt", "A2L"])
+        assert _bulk_fraction(orchestrator) == {"wt": 0.0, "A2L": 0.0,
+                                                "A2C": 1.0}
+
+    def test_no_spike_information_is_all_bulk(self):
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df())
+        assert set(_bulk_fraction(orchestrator).values()) == {1.0}
+
+    def test_unknown_bucket_is_bulk(self, library_file):
+        growth_df = _growth_df(["wt", "A2C", "__unknown__"])
+        orchestrator = ModelOrchestrator(growth_df,
+                                         _binding_df(["wt", "A2C"]),
+                                         library_file=library_file)
+        assert _bulk_fraction(orchestrator)["__unknown__"] == 1.0
+
+    def test_genotype_missing_from_table_raises(self, library_file):
+        table = pd.read_csv(library_file)
+        table[table["genotype"] != "A2C"].to_csv(library_file, index=False)
+        with pytest.raises(ValueError, match="missing from the library"):
+            ModelOrchestrator(_growth_df(), _binding_df(),
+                              library_file=library_file)
+
+    def test_bad_value_raises(self, library_file):
+        table = pd.read_csv(library_file)
+        table.loc[table["genotype"] == "A2C", "bulk_fraction"] = 1.5
+        table.to_csv(library_file, index=False)
+        with pytest.raises(ValueError, match=r"\[0, 1\]"):
+            ModelOrchestrator(_growth_df(), _binding_df(),
+                              library_file=library_file)
+
+    def test_library_sized_under_batching(self, library_file):
+        from tfscreen.tfmodel.tensors.batch import get_batch
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         library_file=library_file)
+        full = np.array(orchestrator.data.growth.bulk_fraction)
+        batch = get_batch(orchestrator.data, np.array([2, 0]))
+        np.testing.assert_array_equal(np.array(batch.growth.bulk_fraction), full)
+
+
+def _labels(orchestrator):
+    idx = orchestrator.growth_tm.tensor_dim_names.index("genotype")
+    return list(orchestrator.growth_tm.tensor_dim_labels[idx])
+
+
+class TestCoresidentSets:
+    """Fixed co-resident sets for the congression mixture (step 3.3b)."""
+
+    def test_shapes_and_strata(self, library_file):
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         library_file=library_file)
+        g = orchestrator.data.growth
+        idx = np.asarray(g.coresident_idx)
+        n = np.asarray(g.coresident_n)
+
+        assert idx.shape == (g.num_genotype, 16, 3)
+        np.testing.assert_array_equal(n, [1] * 12 + [2] * 3 + [3])
+        # Set k has exactly n[k] co-residents; the rest of its slots are -1.
+        filled = (idx >= 0).sum(axis=-1)
+        np.testing.assert_array_equal(filled, np.broadcast_to(n, filled.shape))
+        assert idx.max() < g.num_genotype
+
+    def test_pool_is_bulk_share_of_genotypes_with_data(self, library_file):
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         library_file=library_file)
+        mask = ~np.array(orchestrator.data.growth.ln_cfu0_spiked_mask)
+        pool = orchestrator._coresident_pool(mask)
+        table = orchestrator.library_df.set_index("genotype")
+        weights = np.array([table.loc[g, "pool_fraction"]
+                            * table.loc[g, "bulk_fraction"]
+                            for g in _labels(orchestrator)])
+        np.testing.assert_allclose(pool, weights / weights.sum())
+
+    def test_draws_follow_pool(self, library_file):
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         library_file=library_file,
+                                         congression_sets=[4000])
+        mask = ~np.array(orchestrator.data.growth.ln_cfu0_spiked_mask)
+        pool = orchestrator._coresident_pool(mask)
+        idx = np.asarray(orchestrator.data.growth.coresident_idx)
+        counts = np.bincount(idx[idx >= 0], minlength=len(pool))
+        np.testing.assert_allclose(counts / counts.sum(), pool, atol=0.01)
+
+    def test_unknown_never_drawn(self, library_file):
+        growth_df = _growth_df(["wt", "A2C", "__unknown__"])
+        orchestrator = ModelOrchestrator(growth_df,
+                                         _binding_df(["wt", "A2C"]),
+                                         library_file=library_file,
+                                         congression_sets=[500])
+        unknown = _labels(orchestrator).index("__unknown__")
+        idx = np.asarray(orchestrator.data.growth.coresident_idx)
+        assert not np.any(idx == unknown)
+
+    def test_legacy_pool_uniform_over_non_spiked(self):
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         spiked_genotypes=["wt"])
+        mask = ~np.array(orchestrator.data.growth.ln_cfu0_spiked_mask)
+        pool = orchestrator._coresident_pool(mask)
+        labels = _labels(orchestrator)
+        assert pool[labels.index("wt")] == 0.0
+        np.testing.assert_allclose(pool[[labels.index("A2L"),
+                                         labels.index("A2C")]], [0.5, 0.5])
+
+    def test_empty_pool_gives_empty_sets(self):
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         spiked_genotypes=GENOTYPES)
+        assert np.all(np.asarray(orchestrator.data.growth.coresident_idx) == -1)
+
+    def test_mixture_refuses_empty_pool_with_bulk_genotypes(self,
+                                                            library_file):
+        """Bulk genotypes but nothing drawable: a mixture model cannot build
+        congressed cells, so it refuses; 'single' does not care."""
+        table = pd.read_csv(library_file)
+        table["pool_fraction"] = 0.0
+        table.to_csv(library_file, index=False)
+
+        with pytest.raises(ValueError, match="co-resident pool is empty"):
+            ModelOrchestrator(_growth_df(), _binding_df(),
+                              library_file=library_file,
+                              transformation="mixture")
+        ModelOrchestrator(_growth_df(), _binding_df(),
+                          library_file=library_file, transformation="single")
+
+    def test_seed_reproduces_and_changes_draws(self, library_file):
+        def draw(seed_value):
+            o = ModelOrchestrator(_growth_df(), _binding_df(),
+                                  library_file=library_file,
+                                  congression_sets=[50, 10],
+                                  congression_seed=seed_value)
+            return np.asarray(o.data.growth.coresident_idx)
+
+        np.testing.assert_array_equal(draw(3), draw(3))
+        assert not np.array_equal(draw(3), draw(4))
+
+    def test_settings_record_sets_and_seed(self, library_file):
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         library_file=library_file,
+                                         congression_sets=[5, 2],
+                                         congression_seed=7)
+        assert orchestrator.settings["congression_sets"] == [5, 2]
+        assert orchestrator.settings["congression_seed"] == 7
+
+    @pytest.mark.parametrize("bad", [[], [0, 0], [-1, 2], [1.5], "abc", None])
+    def test_bad_sets_raise(self, bad):
+        with pytest.raises(ValueError, match="congression_sets"):
+            ModelOrchestrator(_growth_df(), _binding_df(),
+                              congression_sets=bad)
+
+    def test_library_sized_under_batching(self, library_file):
+        from tfscreen.tfmodel.tensors.batch import get_batch
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         library_file=library_file)
+        full = np.asarray(orchestrator.data.growth.coresident_idx)
+        batch = get_batch(orchestrator.data, np.array([2, 0]))
+        np.testing.assert_array_equal(np.asarray(batch.growth.coresident_idx),
+                                      full)
+
+
 class TestMutualExclusion:
 
     def test_both_raises(self, library_file):
@@ -211,6 +401,9 @@ def _configure(self, tmp_path, library_config_file, **kwargs):
                         library_config=library_config_file,
                         out_prefix=out_prefix,
                         skip_model_stats=True,
+                        # synthetic ln_cfu table without read counts
+                        growth_likelihood="lncfu",
+                        sample_offset_model="zero",
                         **kwargs)
         return out_prefix
 
@@ -229,6 +422,22 @@ def test_writes_snapshot_and_records_it(self, tmp_path, library_config_file):
         # One source of truth: the derived list is not also written out.
         assert "spiked_genotypes" not in config["components"]
 
+    def test_round_trip_reproduces_coresident_sets(self, tmp_path,
+                                                   library_config_file):
+        out_prefix = self._configure(tmp_path, library_config_file)
+        with open(f"{out_prefix}_config.yaml") as f:
+            config = yaml.safe_load(f)
+        assert config["components"]["congression_sets"] == [12, 3, 1]
+        assert config["components"]["congression_seed"] == 0
+
+        orchestrator, _ = read_configuration(f"{out_prefix}_config.yaml")
+        direct = ModelOrchestrator(_growth_df(), _binding_df(),
+                                   library_file=f"{out_prefix}_library.csv")
+        assert _labels(orchestrator) == _labels(direct)
+        np.testing.assert_array_equal(
+            np.asarray(orchestrator.data.growth.coresident_idx),
+            np.asarray(direct.data.growth.coresident_idx))
+
     def test_round_trip_reproduces_masks(self, tmp_path, library_config_file):
         out_prefix = self._configure(tmp_path, library_config_file)
         orchestrator, _ = read_configuration(f"{out_prefix}_config.yaml")
@@ -299,3 +508,22 @@ def test_config_with_spiked_genotypes_still_loads(self, tmp_path):
 
         reloaded, _ = read_configuration(f"{out_prefix}_config.yaml")
         assert reloaded.settings["spiked_genotypes"] == ["wt", "A2L"]
+
+
+class TestPredictionSubset:
+    """Genotype-subset prediction copies a library_file model's settings."""
+
+    def test_copy_orchestrator_subset_keeps_library_file(self, library_file):
+        from tfscreen.tfmodel.analysis.prediction import copy_orchestrator
+
+        orchestrator = ModelOrchestrator(_growth_df(), _binding_df(),
+                                         library_file=library_file)
+        full = copy_orchestrator(orchestrator)
+        assert sorted(_masks(full)[0]) == sorted(GENOTYPES)
+
+        subset = copy_orchestrator(orchestrator, genotypes=["A2L", "A2C"])
+        assert subset.settings["library_file"] == library_file
+        labels, _, spiked = _masks(subset)
+        # A2L is still in the spiked origin, as in the full model.
+        assert dict(zip(labels, spiked)) == {"A2L": True, "A2C": False}
+
diff --git a/tests/tfscreen/tfmodel/test_model_orchestrator.py b/tests/tfscreen/tfmodel/test_model_orchestrator.py
index 5b992b1d..2c888836 100644
--- a/tests/tfscreen/tfmodel/test_model_orchestrator.py
+++ b/tests/tfscreen/tfmodel/test_model_orchestrator.py
@@ -15,7 +15,6 @@
     _build_growth_tm,
     _build_binding_tm,
     _setup_batching,
-    _check_theta_transformation_compatibility,
     get_batch
 )
 from tfscreen.tfmodel.generative.components.growth.linear import _parse_condition_label
@@ -411,9 +410,10 @@ def test_initialize_classes_logic(mocker):
         "growth_transition": {"instant": MagicMock(), "linear": MagicMock()},
         "ln_cfu0": {"hierarchical": MagicMock()},
         "dk_geno": {"hierarchical_geno": MagicMock()},
-        "activity": {"hierarchical_geno": MagicMock(), "horseshoe_geno": MagicMock()},
+        "activity": {"hierarchical_geno": MagicMock(), "horseshoe_geno": MagicMock(),
+                     "fixed": MagicMock()},
         "theta": {"categorical_geno": MagicMock(), "hill_geno": MagicMock(), "fixed": MagicMock()},
-        "transformation": {"logit_norm": MagicMock(), "single": MagicMock()},
+        "transformation": {"mixture": MagicMock(), "single": MagicMock()},
         "theta_rescale": {"passthrough": MagicMock()},
         "theta_growth_noise": {"zero": MagicMock(), "logit_normal": MagicMock()},
         "theta_binding_noise": {"zero": MagicMock()},
@@ -427,7 +427,7 @@ def test_initialize_classes_logic(mocker):
                 tfscreen.tfmodel.model_orchestrator.model_registry[k][sub_k].get_priors.return_value = {}
                 tfscreen.tfmodel.model_orchestrator.model_registry[k][sub_k].get_guesses.return_value = {}
 
-        model = ModelOrchestrator("g.csv", "b.csv", theta="categorical_geno", transformation="logit_norm", condition_growth="independent", growth_transition="instant")
+        model = ModelOrchestrator("g.csv", "b.csv", theta="categorical_geno", transformation="single", condition_growth="independent", growth_transition="instant")
         assert model._theta == "categorical_geno"
 
 
@@ -489,7 +489,7 @@ def test_initialize_classes_forwards_presplit_to_declaring_component(mocker):
         "dk_geno": {"hierarchical_geno": MagicMock()},
         "activity": {"hierarchical_geno": MagicMock()},
         "theta": {"categorical_geno": MagicMock()},
-        "transformation": {"logit_norm": MagicMock()},
+        "transformation": {"single": MagicMock()},
         "theta_rescale": {"passthrough": MagicMock()},
         "theta_growth_noise": {"zero": MagicMock()},
         "theta_binding_noise": {"zero": MagicMock()},
@@ -508,7 +508,7 @@ def test_initialize_classes_forwards_presplit_to_declaring_component(mocker):
 
         ModelOrchestrator(
             "g.csv", "b.csv",
-            theta="categorical_geno", transformation="logit_norm",
+            theta="categorical_geno", transformation="single",
             condition_growth="independent", growth_transition="instant",
             ln_cfu0="hierarchical", activity="hierarchical_geno",
             theta_growth_noise="zero",
@@ -530,7 +530,7 @@ def test_dk_geno_pinned_requires_pins_file():
     """
     dk_geno='pinned' without dk_geno_pins_file must raise before any data
     I/O happens (mirrors
-    test_model_orchestrator_rejects_categorical_geno_with_empirical:
+    test_model_orchestrator_refuses_retired_transformations:
     "g.csv"/"b.csv" are never touched).
     """
     with pytest.raises(ValueError, match="requires dk_geno_pins_file"):
@@ -667,15 +667,15 @@ def test_transformation_lambda_wrong_length_raises():
     with pytest.raises(ValueError, match="mean, std"):
         ModelOrchestrator(
             "g.csv", "b.csv",
-            transformation="empirical",
+            transformation="mixture",
             transformation_lambda=(0.36,),
         )
 
 
 class _SpyTransformationModule:
     """
-    Thin real-object wrapper around a transformation component (logit_norm/
-    empirical), recording what get_priors/get_guesses were actually called
+    Thin real-object wrapper around a transformation component (mixture),
+    recording what get_priors/get_guesses were actually called
     with.
 
     inspect.signature(component_module.get_priors) must see the real
@@ -723,7 +723,7 @@ def _build_orchestrator_with_transformation_spy(mocker, spy, transformation_lamb
         "dk_geno": {"hierarchical_geno": MagicMock()},
         "activity": {"hierarchical_geno": MagicMock()},
         "theta": {"hill_geno": MagicMock()},
-        "transformation": {"logit_norm": spy},
+        "transformation": {"mixture": spy},
         "theta_rescale": {"passthrough": MagicMock()},
         "theta_growth_noise": {"zero": MagicMock()},
         "theta_binding_noise": {"zero": MagicMock()},
@@ -741,10 +741,11 @@ def _build_orchestrator_with_transformation_spy(mocker, spy, transformation_lamb
 
         return ModelOrchestrator(
             "g.csv", "b.csv",
-            theta="hill_geno", transformation="logit_norm",
+            theta="hill_geno", transformation="mixture",
             transformation_lambda=transformation_lambda,
             condition_growth="independent", growth_transition="instant",
             activity="hierarchical_geno", theta_growth_noise="zero",
+            congression_theta_rule="max",
         )
 
 
@@ -752,9 +753,9 @@ def test_transformation_lambda_wired_into_component_priors_and_guesses(mocker):
     """End-to-end: transformation_lambda=(mean, std) must reach the real component's
     get_priors(lam_mean=..., lam_std=...) and get_guesses(..., lam_mean=...)
     via signature-based dispatch in _initialize_classes."""
-    from tfscreen.tfmodel.generative.components.transformation import logit_norm as real_logit_norm
+    from tfscreen.tfmodel.generative.components.transformation import mixture as real_mixture
 
-    spy = _SpyTransformationModule(real_logit_norm)
+    spy = _SpyTransformationModule(real_mixture)
     model = _build_orchestrator_with_transformation_spy(mocker, spy, (0.3572, 0.13))
 
     assert spy.priors_calls == [(0.3572, 0.13)]
@@ -769,9 +770,9 @@ def test_transformation_lambda_omitted_falls_back_to_placeholder(mocker):
     lam_mean=lam_std=None) rather than being skipped -- it falls back to
     its own placeholder prior rather than ModelOrchestrator refusing to
     build the model."""
-    from tfscreen.tfmodel.generative.components.transformation import logit_norm as real_logit_norm
+    from tfscreen.tfmodel.generative.components.transformation import mixture as real_mixture
 
-    spy = _SpyTransformationModule(real_logit_norm)
+    spy = _SpyTransformationModule(real_mixture)
     model = _build_orchestrator_with_transformation_spy(mocker, spy, None)
 
     assert spy.priors_calls == [(None, None)]
@@ -781,17 +782,16 @@ def test_transformation_lambda_omitted_falls_back_to_placeholder(mocker):
 
 @pytest.mark.parametrize("transformation_key, expected_needs_population", [
     ("single", False),
-    ("logit_norm", False),
-    ("empirical", True),
+    ("mixture", True),
 ])
 def test_transformation_control_kwargs_carry_needs_population_flag(
         mocker, transformation_key, expected_needs_population):
     """
     main_control_kwargs["transformation"] must be a 3-tuple whose third
     element reflects the *real* transformation component's
-    NEEDS_FULL_POPULATION_THETA flag (empirical=True; single/logit_norm=False)
-    — this is the wiring jax_model relies on to decide whether it needs a
-    population-wide theta reference for the congression correction.
+    NEEDS_POPULATION flag (mixture=True; single=False) — this is the wiring
+    jax_model relies on to decide whether it needs library-wide theta,
+    activity and dk_geno for the congression mixture.
     """
     import tfscreen.tfmodel.generative.components.transformation as transformation_pkg
 
@@ -811,14 +811,11 @@ def test_transformation_control_kwargs_carry_needs_population_flag(
         "growth_transition": {"instant": MagicMock()},
         "ln_cfu0": {"hierarchical": MagicMock()},
         "dk_geno": {"hierarchical_geno": MagicMock()},
-        "activity": {"horseshoe_geno": MagicMock()},
-        # Use a mocked "hill_geno" stand-in (not "categorical_geno") so this
-        # wiring test doesn't trip the real theta/transformation compatibility
-        # check exercised separately below.
+        "activity": {"horseshoe_geno": MagicMock(), "fixed": MagicMock()},
         "theta": {"hill_geno": MagicMock()},
         "transformation": {transformation_key: real_transformation_module},
         "theta_rescale": {"passthrough": MagicMock()},
-        "theta_growth_noise": {"logit_normal": MagicMock()},
+        "theta_growth_noise": {"zero": MagicMock(), "logit_normal": MagicMock()},
         "theta_binding_noise": {"zero": MagicMock()},
         "growth_noise": {"zero": MagicMock()},
         "sample_offset": {"zero": MagicMock()},
@@ -846,61 +843,68 @@ def test_transformation_control_kwargs_carry_needs_population_flag(
 
 
 # ---------------------------------------------------------------------------
-# _check_theta_transformation_compatibility
+# Retired transformation components
 # ---------------------------------------------------------------------------
 
-def test_check_theta_transformation_compatibility_rejects_categorical_geno_empirical():
-    """categorical_geno cannot supply a full-population theta reference, so
-    pairing it with transformation='empirical' must raise, not silently
-    reproduce the population-CDF bug."""
-    with pytest.raises(ValueError, match="categorical_geno.*empirical|empirical.*categorical_geno"):
-        _check_theta_transformation_compatibility("categorical_geno", "empirical")
+@pytest.mark.parametrize("name,match", [
+    ("empirical", "replaced by 'mixture'"),
+    ("logit_norm", "has been removed"),
+])
+@pytest.mark.parametrize("binding_only", [False, True])
+def test_model_orchestrator_refuses_retired_transformations(name, match,
+                                                            binding_only):
+    """
+    A config naming a retired transformation fails before any data I/O
+    ("g.csv"/"b.csv" are never touched), with a message saying what
+    replaced it, so an old config cannot silently switch physics.
+    """
+    with pytest.raises(ValueError, match=match):
+        ModelOrchestrator("g.csv", "b.csv", transformation=name,
+                          binding_only=binding_only)
 
 
-@pytest.mark.parametrize("transformation_key", ["single", "logit_norm"])
-def test_check_theta_transformation_compatibility_allows_categorical_geno_elsewhere(
-        transformation_key):
-    """categorical_geno is fine with transformation models that don't need a
-    population-wide theta reference."""
-    _check_theta_transformation_compatibility("categorical_geno", transformation_key)
+# ---------------------------------------------------------------------------
+# Congression theta rule
+# ---------------------------------------------------------------------------
 
+def test_model_orchestrator_refuses_unknown_congression_theta_rule():
+    with pytest.raises(ValueError, match="congression_theta_rule must be one of"):
+        ModelOrchestrator("g.csv", "b.csv", congression_theta_rule="min")
 
-@pytest.mark.parametrize("theta_key", ["hill_geno", "hill_mut", "some_thermo_variant"])
-def test_check_theta_transformation_compatibility_allows_other_theta_with_empirical(
-        theta_key):
-    """Any theta component other than the known-incompatible ones must be
-    allowed with transformation='empirical' (including forward-compatibility
-    with theta components this check has never heard of)."""
-    _check_theta_transformation_compatibility(theta_key, "empirical")
 
+@pytest.mark.parametrize("rule", ["homodimer", "heterodimer"])
+def test_model_orchestrator_partition_rules_need_fixed_activity(rule):
+    """The partition-function rules mix variants' occupancies and require
+    activity 1; refused before any data I/O."""
+    with pytest.raises(ValueError, match="requires TF activity 1"):
+        ModelOrchestrator("g.csv", "b.csv", transformation="mixture",
+                          activity="horseshoe_geno",
+                          congression_theta_rule=rule)
 
-def test_model_orchestrator_rejects_categorical_geno_with_empirical():
-    """
-    End-to-end: constructing a ModelOrchestrator with the incompatible pair
-    must raise before any data loading happens (no growth_df/binding_df I/O
-    needed for this to fire — "g.csv"/"b.csv" are never touched).
-    """
-    with pytest.raises(ValueError, match="categorical_geno"):
-        ModelOrchestrator(
-            "g.csv", "b.csv",
-            theta="categorical_geno",
-            transformation="empirical",
-        )
 
+def test_model_orchestrator_refuses_unknown_congression_dk_rule():
+    with pytest.raises(ValueError, match="congression_dk_rule must be one of"):
+        ModelOrchestrator("g.csv", "b.csv", congression_dk_rule="max")
 
-def test_check_theta_transformation_compatibility_binding_only_skips_check():
-    """
-    In binding-only mode jax_model returns before ever reaching the
-    transformation/congression code path (see generative/model.py's early
-    `if binding_only: ... return`), so the transformation setting is inert
-    and categorical_geno + empirical must be allowed.  Regression test for
-    the smoke-test failure where tfs-configure-model's binding-only pipeline
-    (theta="categorical_geno", transformation left at its "empirical"
-    default) was incorrectly rejected by this check.
-    """
-    _check_theta_transformation_compatibility(
-        "categorical_geno", "empirical", binding_only=True
-    )
+
+@pytest.mark.parametrize("alpha", [None, 0.0, -2.0, float("inf"), "x"])
+def test_model_orchestrator_softmin_needs_positive_finite_alpha(alpha):
+    with pytest.raises(ValueError, match="needs a finite"):
+        ModelOrchestrator("g.csv", "b.csv", congression_dk_rule="softmin",
+                          congression_dk_alpha=alpha)
+
+
+@pytest.mark.parametrize("rule", ["dilution", "min"])
+def test_model_orchestrator_other_dk_rules_refuse_alpha(rule):
+    with pytest.raises(ValueError, match="only used by"):
+        ModelOrchestrator("g.csv", "b.csv", congression_dk_rule=rule,
+                          congression_dk_alpha=10.0)
+
+
+def test_check_congression_dk_alpha_casts():
+    from tfscreen.tfmodel.model_orchestrator import _check_congression_dk_alpha
+    assert _check_congression_dk_alpha("softmin", "100") == 100.0
+    assert _check_congression_dk_alpha("dilution", None) is None
 
 
 def test_model_class_properties(initialized_model_class):
@@ -933,6 +937,13 @@ def test_model_class_properties(initialized_model_class):
     model._thermo_data = None
     model._binding_weight = 1.0
     model._base_growth_df = "bg.csv"
+    model._congression_sets = (12, 3, 1)
+    model._congression_seed = 0
+    model._congression_theta_rule = "homodimer"
+    model._congression_dk_rule = "softmin"
+    model._congression_dk_alpha = 50.0
+    model._growth_likelihood = "lncfu"
+    model._theta_gauge_conc = None
 
     assert ModelOrchestrator.jax_model.fget(model) == "jm"
     assert ModelOrchestrator.jax_model_guide.fget(model) == "jmg"
@@ -940,6 +951,9 @@ def test_model_class_properties(initialized_model_class):
     assert ModelOrchestrator.priors.fget(model) == "p"
     assert ModelOrchestrator.init_params.fget(model) == "ip"
     assert ModelOrchestrator.settings.fget(model)["activity"] == "a"
+    assert ModelOrchestrator.settings.fget(model)["congression_theta_rule"] == "homodimer"
+    assert ModelOrchestrator.settings.fget(model)["congression_dk_rule"] == "softmin"
+    assert ModelOrchestrator.settings.fget(model)["congression_dk_alpha"] == 50.0
     assert ModelOrchestrator.settings.fget(model)["theta"] == "t"
     assert ModelOrchestrator.settings.fget(model)["transformation"] == "tr"
     assert ModelOrchestrator.settings.fget(model)["transformation_lambda"] is None
@@ -964,7 +978,7 @@ def test_extract_parameters_full(initialized_model_class):
     model._condition_growth = "hierarchical"
     model._dk_geno = "hierarchical_geno"
     model._activity = "hierarchical_geno"
-    model._transformation = "logit_norm"
+    model._transformation = "mixture"
     model.growth_tm.df = pd.DataFrame({
         "genotype": ["wt"], "titrant_name": ["T"], 
         "replicate": [1], "condition_pre": ["A"],
@@ -974,7 +988,9 @@ def test_extract_parameters_full(initialized_model_class):
     })
     model.growth_tm.map_groups = {"condition_rep": pd.DataFrame({"replicate":[1], "condition_rep":["A"], "map_condition_rep":[0]})}
     model.growth_tm.tensor_dim_names = ["replicate", "time", "condition_pre", "condition_sel", "titrant_name", "titrant_conc", "genotype"]
-    model.growth_tm.tensor_dim_labels = [[], [], [], [], [], [1.0], []]
+    # the axes ln_cfu0's extraction keys rows by (replicate, condition_pre,
+    # genotype) must carry the table's labels
+    model.growth_tm.tensor_dim_labels = [[1], [], ["A"], [], [], [1.0], ["wt"]]
     
     post = {
         "theta_hill_n": np.zeros((1, 1, 1)), "theta_log_hill_K": np.zeros((1, 1, 1)), 
@@ -1184,11 +1200,11 @@ def _make_model_for_binding_weight(mocker, n_growth, n_binding, binding_weight=N
     return ModelOrchestrator("g.csv", "b.csv", binding_weight=binding_weight)
 
 
-def test_binding_weight_auto_computed(mocker):
-    """binding_weight=None should be resolved to N_growth / N_binding after init."""
-    n_growth, n_binding = 1000, 10
-    model = _make_model_for_binding_weight(mocker, n_growth, n_binding)
-    assert model._binding_weight == pytest.approx(n_growth / n_binding)
+def test_binding_weight_default_is_one(mocker):
+    """binding_weight=None resolves to 1 (each binding row counts once),
+    not N_growth / N_binding (the default until 2026-10-16)."""
+    model = _make_model_for_binding_weight(mocker, 1000, 10)
+    assert model._binding_weight == 1.0
 
 
 def test_binding_weight_explicit_preserved(mocker):
@@ -1207,7 +1223,7 @@ def test_binding_weight_explicit_in_settings(mocker):
     model._dk_geno = "hierarchical_geno"
     model._activity = "horseshoe_geno"
     model._theta = "hill_geno"
-    model._transformation = "empirical"
+    model._transformation = "mixture"
     model._theta_rescale = "passthrough"
     model._theta_growth_noise = "zero"
     model._theta_binding_noise = "zero"
@@ -1222,7 +1238,7 @@ def test_binding_weight_explicit_in_settings(mocker):
 
 
 def test_binding_weight_auto_in_settings(mocker):
-    """Auto-computed weight (not None) should appear in settings after init."""
+    """The resolved default weight (1, not None) appears in settings."""
     n_growth, n_binding = 500, 25
     model = _make_model_for_binding_weight(mocker, n_growth, n_binding)
     model._binding_only = False
@@ -1232,7 +1248,7 @@ def test_binding_weight_auto_in_settings(mocker):
     model._dk_geno = "hierarchical_geno"
     model._activity = "horseshoe_geno"
     model._theta = "hill_geno"
-    model._transformation = "empirical"
+    model._transformation = "mixture"
     model._theta_rescale = "passthrough"
     model._theta_growth_noise = "zero"
     model._theta_binding_noise = "zero"
@@ -1242,7 +1258,7 @@ def test_binding_weight_auto_in_settings(mocker):
     model._thermo_data = None
     model._batch_size = None
     s = ModelOrchestrator.settings.fget(model)
-    assert s["binding_weight"] == pytest.approx(n_growth / n_binding)
+    assert s["binding_weight"] == 1.0
 
 
 def test_binding_weight_applied_to_scale_vector(mocker):
@@ -1446,6 +1462,7 @@ def capturing_get_priors(condition_labels=None, is_selection=None):
         orchestrator.growth_df = growth_df
         orchestrator._data = MagicMock()
         orchestrator._binding_only = False
+        orchestrator._has_binding = True
         orchestrator._batch_size = None
         orchestrator._condition_growth = "linear"
         orchestrator._growth_transition = "instant"
@@ -2361,3 +2378,32 @@ def _populate_side_effect(target_dataclass, sources):
             )
 
         assert model_no_base_growth._priors.growth.base_growth is None
+
+
+# ---------------------------------------------------------------------------
+# hierarchical_factored ln_cfu0 across libraries
+# ---------------------------------------------------------------------------
+
+def _factored_df(libraries):
+    """Two pre-conditions per replicate, from the given libraries."""
+    return pd.DataFrame({
+        "replicate": [1, 1, 2, 2],
+        "condition_pre": ["kanR-kan", "pheS-4CP"] * 2,
+        "library": libraries * 2,
+    })
+
+
+def test_factored_ln_cfu0_refused_across_libraries():
+    from tfscreen.tfmodel.model_orchestrator import _check_factored_ln_cfu0
+    with pytest.raises(ValueError, match="different libraries"):
+        _check_factored_ln_cfu0("hierarchical_factored",
+                                _factored_df(["kanR", "pheS"]))
+
+
+def test_factored_ln_cfu0_allowed_within_one_library():
+    from tfscreen.tfmodel.model_orchestrator import _check_factored_ln_cfu0
+    _check_factored_ln_cfu0("hierarchical_factored", _factored_df(["kanR", "kanR"]))
+    # other ln_cfu0 models and data without a library column are not checked
+    _check_factored_ln_cfu0("hierarchical", _factored_df(["kanR", "pheS"]))
+    _check_factored_ln_cfu0("hierarchical_factored",
+                            _factored_df(["kanR", "pheS"]).drop(columns="library"))
diff --git a/tests/tfscreen/tfmodel/test_model_stats.py b/tests/tfscreen/tfmodel/test_model_stats.py
index 8416ffd4..2cdcde10 100644
--- a/tests/tfscreen/tfmodel/test_model_stats.py
+++ b/tests/tfscreen/tfmodel/test_model_stats.py
@@ -91,7 +91,10 @@ def binding_df():
 # configure_model -- the caller this tool actually runs under -- defaults it to
 # "zero". Pin it so the base fixture reflects the CLI default; the
 # logit_normal case gets its own test.
-BASE_KWARGS = {"batch_size": None, "theta_growth_noise": "zero"}
+# activity is set explicitly: the census checks a learned activity
+# component's scale latents (the default activity is now "fixed").
+BASE_KWARGS = {"batch_size": None, "theta_growth_noise": "zero",
+               "activity": "horseshoe_geno"}
 
 
 @pytest.fixture
@@ -530,7 +533,8 @@ def test_minibatching_does_not_change_counts(growth_df, binding_df):
         ModelOrchestrator(growth_df, binding_df, **BASE_KWARGS))
     batched = count_model_dimensions(
         ModelOrchestrator(growth_df, binding_df, batch_size=2,
-                          theta_growth_noise="zero"))
+                          theta_growth_noise="zero",
+                          activity=BASE_KWARGS["activity"]))
 
     assert full.summary["parameters"]["n_param_total"] == \
         batched.summary["parameters"]["n_param_total"]
diff --git a/tests/tfscreen/tfmodel/test_priors_edit.py b/tests/tfscreen/tfmodel/test_priors_edit.py
new file mode 100644
index 00000000..25ceab67
--- /dev/null
+++ b/tests/tfscreen/tfmodel/test_priors_edit.py
@@ -0,0 +1,419 @@
+"""Tests for tfscreen.tfmodel.priors_edit and the configure flags built on it."""
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from tfscreen.tfmodel import priors_edit as P
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.scripts.configure_model_cli import (
+    check_spikes_in_data,
+    configure_model,
+)
+
+_SMOKE_DATA = os.path.join(os.path.dirname(__file__), "..", "..",
+                           "smoke-tests", "test_data")
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_LIBRARY_YAML = os.path.join(_SMOKE_DATA, "library-smoke.yaml")
+_CONDITIONS = ["kanR+kan", "kanR-kan", "pheS+4CP", "pheS-4CP"]
+
+
+# ---------------------------------------------------------------------------
+# names and overrides
+# ---------------------------------------------------------------------------
+
+def test_parse_prior_overrides():
+    assert P.parse_prior_overrides(["a=1", "b.c = 0.5"]) == {"a": 1.0, "b.c": 0.5}
+    assert P.parse_prior_overrides(None) == {}
+    for bad, match in ((["a"], "name=value"), (["a=x"], "not a number"),
+                       (["a=1", "a=2"], "twice")):
+        with pytest.raises(ValueError, match=match):
+            P.parse_prior_overrides(bad)
+
+
+def test_resolve_prior_name():
+    names = ["growth.sample_offset.sigma_fixed", "theta.theta_n_fixed",
+             "growth.a.scale", "growth.b.scale"]
+    assert P.resolve_prior_name("sigma_fixed", names) == names[0]
+    assert P.resolve_prior_name("sample_offset.sigma_fixed", names) == names[0]
+    assert P.resolve_prior_name("theta.theta_n_fixed", names) == names[1]
+    assert P.resolve_prior_name("a.scale", names) == "growth.a.scale"
+    with pytest.raises(ValueError, match="ambiguous"):
+        P.resolve_prior_name("scale", names)
+    with pytest.raises(ValueError, match="No prior named"):
+        P.resolve_prior_name("nope", names)
+    # a suffix must end at a dot boundary
+    with pytest.raises(ValueError, match="No prior named"):
+        P.resolve_prior_name("fixed", ["theta.theta_n_fixed2"])
+
+
+def test_apply_prior_overrides(tmp_path):
+    path = tmp_path / "p.csv"
+    pd.DataFrame({"parameter": ["x.sigma_fixed", "x.k_loc", "x.k_loc"],
+                  "value": [0.0, 1.0, 2.0],
+                  "flat_index": [np.nan, 0, 1]}).to_csv(path, index=False)
+    out = P.apply_prior_overrides(str(path), {"sigma_fixed": 0.17})
+    assert out == {"x.sigma_fixed": 0.17}
+    df = pd.read_csv(path)
+    assert df.loc[df.parameter == "x.sigma_fixed", "value"].item() == 0.17
+    with pytest.raises(ValueError, match="per-condition"):
+        P.apply_prior_overrides(str(path), {"k_loc": 3.0})
+
+
+# ---------------------------------------------------------------------------
+# growth priors
+# ---------------------------------------------------------------------------
+
+_DEFAULTS = {"k_loc": 0.02, "k_scale": 0.1, "m_loc": 0.0,
+             "m_scale_plus": 0.01, "m_scale_minus": 0.001}
+
+
+def _labels(replicates=True):
+    if replicates:
+        return pd.DataFrame({"replicate": [1, 1, 2, 2],
+                             "condition_rep": ["a", "b", "a", "b"]})
+    return pd.DataFrame({"condition_rep": ["a", "b"]})
+
+
+def test_growth_prior_updates_broadcasts_over_replicates():
+    table = pd.DataFrame({"condition_rep": ["a"], "k_loc": [0.03],
+                          "m_scale": [0.005]})
+    up = P.growth_prior_updates(table, _labels(), _DEFAULTS)
+    pre = P.GROWTH_PRIOR_PREFIX
+    assert np.allclose(up[pre + "k_loc"], [0.03, 0.02, 0.03, 0.02])
+    assert np.allclose(up[pre + "m_scale_plus"], [0.005, 0.01, 0.005, 0.01])
+    assert np.allclose(up[pre + "m_scale_minus"], [0.005, 0.001, 0.005, 0.001])
+    assert pre + "k_scale" not in up
+
+
+def test_growth_prior_updates_per_replicate():
+    table = pd.DataFrame({"replicate": [2], "condition_rep": ["b"],
+                          "k_loc": [0.05]})
+    up = P.growth_prior_updates(table, _labels(), _DEFAULTS)
+    assert np.allclose(up[P.GROWTH_PRIOR_PREFIX + "k_loc"],
+                       [0.02, 0.02, 0.02, 0.05])
+    with pytest.raises(ValueError, match="shares conditions"):
+        P.growth_prior_updates(table, _labels(replicates=False), _DEFAULTS)
+
+
+@pytest.mark.parametrize("table,match", [
+    (pd.DataFrame({"condition_rep": ["c"], "k_loc": [1.0]}), "does not have"),
+    (pd.DataFrame({"condition_rep": ["a"], "q": [1.0]}), "Unknown"),
+    (pd.DataFrame({"condition_rep": ["a", "a"], "k_loc": [1.0, 2.0]}), "more than once"),
+    (pd.DataFrame({"condition_rep": ["a"], "k_scale": [0.0]}), "> 0"),
+    (pd.DataFrame({"k_loc": [1.0]}), "condition_rep"),
+])
+def test_growth_prior_updates_refuses(table, match):
+    with pytest.raises(ValueError, match=match):
+        P.growth_prior_updates(table, _labels(), _DEFAULTS)
+
+
+def test_growth_prior_updates_pin_columns():
+    """0/1 pin columns become per-condition flag arrays; others refused."""
+    defaults = dict(_DEFAULTS, k_pinned=0.0, m_pinned=0.0)
+    table = pd.DataFrame({"condition_rep": ["a"], "k_pinned": [1],
+                          "m_pinned": [1]})
+    up = P.growth_prior_updates(table, _labels(replicates=False), defaults)
+    assert np.array_equal(up[P.GROWTH_PRIOR_PREFIX + "k_pinned"], [1.0, 0.0])
+    assert np.array_equal(up[P.GROWTH_PRIOR_PREFIX + "m_pinned"], [1.0, 0.0])
+    bad = pd.DataFrame({"condition_rep": ["a"], "m_pinned": [0.5]})
+    with pytest.raises(ValueError, match="0 or 1"):
+        P.growth_prior_updates(bad, _labels(replicates=False), defaults)
+
+
+def test_growth_priors_from_wt_rates():
+    rates = pd.DataFrame({
+        "condition_sel": ["kanR+kan", "kanR+kan", "kanR-kan", "kanR-kan"],
+        "titrant_conc": [0.0, 1.0, 0.0, 1.0],
+        "rate_mean": [0.020, 0.012, 0.025, 0.0249],
+        "rate_sd": [0.004, 0.002, 0.0001, 0.0001],
+        "num_replicates": [4, 4, 4, 4]})
+    t = P.growth_priors_from_wt_rates(rates, (0.0, 1.0), sd_floor=0.002)
+    row = t.set_index("condition_rep").loc["kanR+kan"]
+    assert row.k_loc == pytest.approx(0.012)
+    assert row.m_loc == pytest.approx(0.008)
+    assert row.k_scale == pytest.approx(0.002)            # floored
+    assert row.m_scale == pytest.approx(np.hypot(0.002, 0.001))
+    assert t.set_index("condition_rep").loc["kanR-kan"].m_scale == 0.002
+    with pytest.raises(ValueError, match="exactly one row"):
+        P.growth_priors_from_wt_rates(rates, (0.0, 0.5))
+    with pytest.raises(ValueError, match="missing column"):
+        P.growth_priors_from_wt_rates(rates.drop(columns="rate_sd"), (0.0, 1.0))
+
+
+# ---------------------------------------------------------------------------
+# tfs-configure-model
+# ---------------------------------------------------------------------------
+
+def _configure(tmp_path, **kwargs):
+    out_prefix = str(tmp_path / "cfg")
+    configure_model(growth_df=_GROWTH_CSV, library_config=_LIBRARY_YAML,
+                    out_prefix=out_prefix, skip_model_stats=True,
+                    theta_model="hill_relative",
+                    growth_shares_replicates=True, **kwargs)
+    return out_prefix
+
+
+def test_configure_set_priors(tmp_path):
+    prefix = _configure(tmp_path, set_priors=[
+        "sigma_fixed=0.17", "theta_log_hill_n_hyper_scale_fixed=0.5"])
+    o, _ = read_configuration(f"{prefix}_config.yaml")
+    assert float(o.priors.growth.sample_offset.sigma_fixed) == 0.17
+    assert float(o.priors.theta.theta_log_hill_n_hyper_scale_fixed) == 0.5
+
+
+def test_configure_holds_wt_hill_curve(tmp_path):
+    prefix = _configure(tmp_path, set_priors=[
+        "theta_wt_hill_K_fixed=0.0238", "theta_wt_hill_n_fixed=0.753"])
+    o, _ = read_configuration(f"{prefix}_config.yaml")
+    assert float(o.priors.theta.theta_wt_hill_K_fixed) == pytest.approx(0.0238)
+    assert float(o.priors.theta.theta_wt_hill_n_fixed) == pytest.approx(0.753)
+    # free by default
+    (tmp_path / "b").mkdir()
+    o, _ = read_configuration(f"{_configure(tmp_path / 'b')}_config.yaml")
+    assert float(o.priors.theta.theta_wt_hill_K_fixed) == 0.0
+
+
+def test_configure_set_priors_unknown_name(tmp_path):
+    with pytest.raises(ValueError, match="No prior named"):
+        _configure(tmp_path, set_priors=["not_a_prior=1"])
+
+
+def test_configure_growth_priors_and_wt_rates(tmp_path):
+    table = tmp_path / "gp.csv"
+    pd.DataFrame({"condition_rep": _CONDITIONS,
+                  "k_loc": [0.015] * 4, "k_scale": [0.01] * 4,
+                  "m_loc": [0.0] * 4, "m_scale": [0.01] * 4}
+                 ).to_csv(table, index=False)
+    rates = tmp_path / "wt.csv"
+    pd.DataFrame({"condition_sel": ["kanR+kan", "kanR+kan"],
+                  "titrant_conc": [0.0, 1.0], "rate_mean": [0.02, 0.012],
+                  "rate_sd": [0.004, 0.004], "num_replicates": [4, 4]}
+                 ).to_csv(rates, index=False)
+
+    prefix = _configure(tmp_path, growth_priors=str(table),
+                        growth_priors_wt_rates=str(rates))
+    o, _ = read_configuration(f"{prefix}_config.yaml")
+    g = o.priors.growth.condition_growth
+    order = list(P.condition_rep_labels(o)["condition_rep"])
+    k = dict(zip(order, np.asarray(g.k_loc)))
+    m = dict(zip(order, np.asarray(g.m_loc)))
+    assert k["kanR+kan"] == pytest.approx(0.012)   # from the wt rates
+    assert m["kanR+kan"] == pytest.approx(0.008)
+    assert k["pheS+4CP"] == pytest.approx(0.015)   # from the table
+
+
+def test_configure_growth_priors_per_condition_pins(tmp_path):
+    """Pin columns in --growth_priors reach the model as per-condition flags:
+    kanR's k and m clamped (deterministic), pheS's sampled."""
+    import numpyro.handlers as handlers
+    from tfscreen.tfmodel.generative.components.growth.linear import (
+        define_model)
+
+    table = tmp_path / "gp.csv"
+    pins = [1 if c.startswith("kanR") else 0 for c in _CONDITIONS]
+    pd.DataFrame({"condition_rep": _CONDITIONS,
+                  "k_loc": [0.016, 0.017, 0.015, 0.015], "k_scale": [0.01] * 4,
+                  "m_loc": [-0.014, 0.001, 0.0, 0.0], "m_scale": [0.01] * 4,
+                  "k_pinned": pins, "m_pinned": pins}).to_csv(table, index=False)
+    prefix = _configure(tmp_path, growth_priors=str(table))
+    o, _ = read_configuration(f"{prefix}_config.yaml")
+    g = o.priors.growth.condition_growth
+    order = list(P.condition_rep_labels(o)["condition_rep"])
+    expected = tuple(c.startswith("kanR") for c in order)
+    assert g.k_pinned == expected and g.m_pinned == expected
+
+    with handlers.seed(rng_seed=0):
+        tr = handlers.trace(define_model).get_trace(
+            name="condition_growth", data=o.data.growth, priors=g)
+    k = dict(zip(order, np.asarray(tr["condition_growth_k"]["value"])))
+    m = dict(zip(order, np.asarray(tr["condition_growth_m"]["value"])))
+    assert tr["condition_growth_m"]["type"] == "deterministic"
+    assert tr["condition_growth_m_free"]["value"].shape == (2,)
+    assert k["kanR+kan"] == pytest.approx(0.016)
+    assert m["kanR+kan"] == pytest.approx(-0.014)
+    assert m["kanR-kan"] == pytest.approx(0.001)
+
+
+def test_configure_wt_rates_needs_relative_theta(tmp_path):
+    rates = tmp_path / "wt.csv"
+    pd.DataFrame({"condition_sel": ["kanR+kan"], "titrant_conc": [0.0],
+                  "rate_mean": [0.02], "rate_sd": [0.004],
+                  "num_replicates": [4]}).to_csv(rates, index=False)
+    with pytest.raises(ValueError, match="hill_relative"):
+        configure_model(growth_df=_GROWTH_CSV, library_config=_LIBRARY_YAML,
+                        out_prefix=str(tmp_path / "x"), skip_model_stats=True,
+                        growth_priors_wt_rates=str(rates))
+
+
+def test_check_spikes_in_data(tmp_path):
+    comp = pd.DataFrame({"genotype": ["wt", "M42I", "A1V"],
+                         "in_spiked_origin": [True, True, False]})
+    growth = pd.DataFrame({"genotype": ["wt", "A1V"]})
+    with pytest.raises(ValueError, match="M42I"):
+        check_spikes_in_data(comp, growth)
+    check_spikes_in_data(comp, growth, allow_missing=True)
+    check_spikes_in_data(comp, pd.DataFrame({"genotype": ["wt", "M42I"]}))
+
+
+# ---------------------------------------------------------------------------
+# edge cases
+# ---------------------------------------------------------------------------
+
+def _fake_orchestrator(crm):
+    from types import SimpleNamespace
+    return SimpleNamespace(growth_tm=SimpleNamespace(
+        map_groups={"condition_rep": crm}))
+
+
+def test_condition_rep_labels_edge_cases():
+    from types import SimpleNamespace
+    # no growth model
+    assert P.condition_rep_labels(SimpleNamespace(growth_tm=None)) is None
+    # a condition_rep map without any label columns
+    crm = pd.DataFrame({"map_condition_rep": [1, 0], "other": ["x", "y"]})
+    assert P.condition_rep_labels(_fake_orchestrator(crm)) is None
+    # labels come back in map_condition_rep order
+    crm = pd.DataFrame({"map_condition_rep": [1, 0],
+                        "condition_rep": ["b", "a"], "replicate": [1, 1]})
+    out = P.condition_rep_labels(_fake_orchestrator(crm))
+    assert list(out.columns) == ["replicate", "condition_rep"]
+    assert list(out["condition_rep"]) == ["a", "b"]
+
+
+def test_apply_prior_overrides_empty_and_unindexed(tmp_path):
+    path = tmp_path / "p.csv"
+    pd.DataFrame({"parameter": ["x.sigma_fixed", "x.k_loc"],
+                  "value": [0.0, 1.0]}).to_csv(path, index=False)
+    assert P.apply_prior_overrides(str(path), {}) == {}
+    assert P.apply_prior_overrides(str(path), None) == {}
+    # a CSV with only scalar rows (no flat_index column)
+    out = P.apply_prior_overrides(str(path), {"k_loc": 2.5})
+    assert out == {"x.k_loc": 2.5}
+    df = pd.read_csv(path)
+    assert df.loc[df.parameter == "x.k_loc", "value"].item() == 2.5
+    assert df.loc[df.parameter == "x.sigma_fixed", "value"].item() == 0.0
+
+
+def test_apply_prior_overrides_same_prior_twice(tmp_path):
+    path = tmp_path / "p.csv"
+    pd.DataFrame({"parameter": ["x.sigma_fixed"],
+                  "value": [0.0]}).to_csv(path, index=False)
+    with pytest.raises(ValueError, match="set twice"):
+        P.apply_prior_overrides(str(path), {"sigma_fixed": 1.0,
+                                            "x.sigma_fixed": 2.0})
+
+
+def test_growth_prior_updates_needs_growth_model():
+    table = pd.DataFrame({"condition_rep": ["a"], "k_loc": [0.03]})
+    with pytest.raises(ValueError, match="need a growth model"):
+        P.growth_prior_updates(table, None, _DEFAULTS)
+
+
+# ---------------------------------------------------------------------------
+# configure_model_cli._edit_priors refusals
+# ---------------------------------------------------------------------------
+
+def _edit_orch(growth_tm=True):
+    from types import SimpleNamespace
+    crm = pd.DataFrame({"map_condition_rep": [0], "condition_rep": ["kanR+kan"]})
+    tm = SimpleNamespace(map_groups={"condition_rep": crm}) if growth_tm else None
+    return SimpleNamespace(growth_tm=tm,
+                           settings={"theta_gauge_conc": (0.0, 1.0)})
+
+
+def _wt_rates_csv(tmp_path):
+    rates = tmp_path / "wt.csv"
+    pd.DataFrame({"condition_sel": ["kanR+kan", "kanR+kan"],
+                  "titrant_conc": [0.0, 1.0], "rate_mean": [0.02, 0.012],
+                  "rate_sd": [0.004, 0.004], "num_replicates": [4, 4]}
+                 ).to_csv(rates, index=False)
+    return str(rates)
+
+
+def test_edit_priors_needs_growth_data(tmp_path):
+    from tfscreen.tfmodel.scripts.configure_model_cli import _edit_priors
+    with pytest.raises(ValueError, match="need growth data"):
+        _edit_priors(_edit_orch(growth_tm=False), "unused.csv",
+                     growth_priors="unused.csv")
+
+
+def test_edit_priors_needs_linear_growth(tmp_path):
+    from tfscreen.tfmodel.scripts.configure_model_cli import _edit_priors
+    with pytest.raises(ValueError, match="'linear' only"):
+        _edit_priors(_edit_orch(), "unused.csv",
+                     growth_priors_wt_rates=_wt_rates_csv(tmp_path),
+                     condition_growth_model="power")
+
+
+def test_edit_priors_wt_rates_refuse_per_replicate_table(tmp_path):
+    from tfscreen.tfmodel.scripts.configure_model_cli import _edit_priors
+    table = tmp_path / "gp.csv"
+    pd.DataFrame({"replicate": [1], "condition_rep": ["kanR+kan"],
+                  "k_loc": [0.015]}).to_csv(table, index=False)
+    with pytest.raises(ValueError, match="per-replicate"):
+        _edit_priors(_edit_orch(), "unused.csv", growth_priors=str(table),
+                     growth_priors_wt_rates=_wt_rates_csv(tmp_path),
+                     theta_model="hill_relative")
+
+
+def test_edit_priors_needs_growth_prior_rows(tmp_path):
+    from tfscreen.tfmodel.scripts.configure_model_cli import _edit_priors
+    table = tmp_path / "gp.csv"
+    pd.DataFrame({"condition_rep": ["kanR+kan"], "k_loc": [0.015]}
+                 ).to_csv(table, index=False)
+    priors = tmp_path / "priors.csv"
+    pd.DataFrame({"parameter": [P.GROWTH_PRIOR_PREFIX + "k_loc"],
+                  "value": [0.02]}).to_csv(priors, index=False)
+    with pytest.raises(ValueError, match="has no 'growth.condition_growth.k_scale' row"):
+        _edit_priors(_edit_orch(), str(priors), growth_priors=str(table))
+
+
+def test_configure_wt_rates_alone(tmp_path):
+    prefix = _configure(tmp_path, growth_priors_wt_rates=_wt_rates_csv(tmp_path))
+    o, _ = read_configuration(f"{prefix}_config.yaml")
+    g = o.priors.growth.condition_growth
+    order = list(P.condition_rep_labels(o)["condition_rep"])
+    k = dict(zip(order, np.asarray(g.k_loc)))
+    m = dict(zip(order, np.asarray(g.m_loc)))
+    assert k["kanR+kan"] == pytest.approx(0.012)
+    assert m["kanR+kan"] == pytest.approx(0.008)
+    # conditions without wt rates keep the default
+    assert k["pheS+4CP"] != pytest.approx(0.012)
+
+
+@pytest.mark.parametrize("field,site", [("k_titrant_loglinear_scale",
+                                         "condition_growth_k_titrant_slope")])
+def test_configure_titrant_k_traces_on_real_tensors(tmp_path, field, site):
+    import jax
+    from numpyro import handlers
+    prefix = _configure(tmp_path, set_priors=[f"{field}=0.01"])
+    o, _ = read_configuration(f"{prefix}_config.yaml")
+    assert float(getattr(o.priors.growth.condition_growth, field)) == 0.01
+    tr = handlers.trace(handlers.seed(o.jax_model, jax.random.PRNGKey(0))).get_trace(
+        data=o.data, priors=o.priors)
+    assert site in tr
+    n_sel = sum(o.priors.growth.condition_growth.m_is_selection)
+    assert tr[site]["value"].shape[0] == n_sel
+
+
+def test_configure_m_warp_column_traces(tmp_path):
+    import jax
+    import pandas as pd
+    from numpyro import handlers
+    gp = tmp_path / "gp.csv"
+    pd.DataFrame({"condition_rep": ["kanR+kan", "kanR-kan", "pheS+4CP", "pheS-4CP"],
+                  "m_warp": [1, 0, 0, 0]}).to_csv(gp, index=False)
+    prefix = _configure(tmp_path, growth_priors=str(gp))
+    o, _ = read_configuration(f"{prefix}_config.yaml")
+    flags = o.priors.growth.condition_growth.m_warp
+    assert isinstance(flags, tuple) and sum(flags) == 1
+    tr = handlers.trace(handlers.seed(o.jax_model, jax.random.PRNGKey(0))).get_trace(
+        data=o.data, priors=o.priors)
+    assert tr["condition_growth_warp_log_a"]["value"].shape == (1,)
+    bad = tmp_path / "bad.csv"
+    pd.DataFrame({"condition_rep": ["kanR+kan"], "m_warp": [0.5]}).to_csv(bad, index=False)
+    (tmp_path / "x").mkdir()
+    with pytest.raises(ValueError, match="0 or 1"):
+        _configure(tmp_path / "x", growth_priors=str(bad))
diff --git a/tests/tfscreen/tfmodel/test_relative_fit.py b/tests/tfscreen/tfmodel/test_relative_fit.py
new file mode 100644
index 00000000..1350c73a
--- /dev/null
+++ b/tests/tfscreen/tfmodel/test_relative_fit.py
@@ -0,0 +1,241 @@
+"""
+The relative-X fit (theta 'hill_relative'; roadmap step 5,
+planning/analysis-roadmap.md), wired through the orchestrator, configuration,
+prediction and the downstream X-scale checks (C1, C8).
+"""
+import os
+
+import jax
+import numpy as np
+import pandas as pd
+import pytest
+import yaml
+from numpyro.handlers import seed, trace
+from numpyro.infer import Predictive
+
+from tfscreen.analysis.scripts.extract_epistasis_cli import extract_epistasis
+from tfscreen.tfmodel.analysis.extraction import (
+    extract_theta_curves,
+    extract_theta_epistasis,
+)
+from tfscreen.tfmodel.analysis.prediction import copy_orchestrator, predict
+from tfscreen.tfmodel.configuration_io import read_configuration
+from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+from tfscreen.tfmodel.scripts.configure_model_cli import configure_model
+from tfscreen.tfmodel.scripts import summarize_fit_cli
+
+
+_SMOKE_DATA = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    "..", "..", "smoke-tests", "test_data",
+)
+_GROWTH_CSV = os.path.join(_SMOKE_DATA, "growth-smoke.csv")
+_BINDING_CSV = os.path.join(_SMOKE_DATA, "binding-smoke.csv")
+_LIBRARY_YAML = os.path.join(_SMOKE_DATA, "library-smoke.yaml")
+
+_RELATIVE = dict(theta="hill_relative", theta_growth_noise="zero")
+
+
+def _orch(**kwargs):
+    return ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=None,
+                             **{**_RELATIVE, **kwargs})
+
+
+@pytest.fixture(scope="module")
+def relative():
+    return _orch(batch_size=6)
+
+
+@pytest.fixture(scope="module")
+def prior_draws(relative):
+    draws = Predictive(relative.jax_model, num_samples=4)(
+        jax.random.PRNGKey(0), data=relative.data, priors=relative.priors)
+    return {k: v for k, v in draws.items()
+            if not k.endswith("_obs") and not k.endswith("_pred")}
+
+
+# ---------------------------------------------------------------------------
+# gauge and refusals
+# ---------------------------------------------------------------------------
+
+def test_default_gauge_is_measured_range(relative):
+    assert relative.settings["theta_gauge_conc"] == [0.0, 1.0]
+    lc = np.asarray(relative.data.growth.theta_gauge_log_conc)
+    assert lc[0] == pytest.approx(np.log(1e-20))
+    assert lc[1] == pytest.approx(0.0)
+
+
+def test_wt_is_pinned_in_the_model(relative):
+    d = relative.get_batch(relative.data, relative.get_random_idx(batch_key=0))
+    tr = trace(seed(relative.jax_model, 1)).get_trace(data=d,
+                                                     priors=relative.priors)
+    wt = int(relative.data.growth.wt_indexes[0])
+    lo, hi = tr["theta_X_low"]["value"][0, wt], tr["theta_X_high"]["value"][0, wt]
+    K, n = tr["theta_log_hill_K"]["value"][0, wt], tr["theta_hill_n"]["value"][0, wt]
+    lc = np.asarray(relative.data.growth.theta_gauge_log_conc)
+    occ = 1 / (1 + np.exp(-n * (lc - K)))
+    assert lo + (hi - lo) * occ == pytest.approx([1.0, 0.0], abs=1e-5)
+
+
+def test_explicit_gauge():
+    o = _orch(theta_gauge_conc=[0.0, 0.1])
+    assert o.settings["theta_gauge_conc"] == [0.0, 0.1]
+    assert float(o.data.growth.theta_gauge_log_conc[1]) == pytest.approx(np.log(0.1))
+
+
+@pytest.mark.parametrize("gauge", [[1.0, 0.0], [0.1], [-1.0, 1.0]])
+def test_bad_gauge_refused(gauge):
+    with pytest.raises(ValueError, match="theta_gauge_conc"):
+        _orch(theta_gauge_conc=gauge)
+
+
+def test_gauge_refused_for_absolute_theta():
+    with pytest.raises(ValueError, match="theta_gauge_conc"):
+        ModelOrchestrator(growth_df=_GROWTH_CSV, binding_df=None,
+                          theta_growth_noise="zero", theta_gauge_conc=[0, 1])
+
+
+@pytest.mark.parametrize("kwargs,match", [
+    ({"binding_df": _BINDING_CSV}, "binding"),
+    ({"activity": "hierarchical_geno"}, "activity"),
+    ({"theta_rescale": "logit"}, "theta_rescale"),
+    ({"condition_growth": "power"}, "condition_growth"),
+    ({"theta_growth_noise": "logit_normal"}, "theta_growth_noise"),
+    ({"transformation": "mixture", "transformation_lambda": (0.3, 0.05)},
+     "congression_theta_rule"),
+])
+def test_absolute_theta_settings_refused(kwargs, match):
+    with pytest.raises(ValueError, match=match):
+        ModelOrchestrator(growth_df=_GROWTH_CSV,
+                          **{"binding_df": None, **_RELATIVE, **kwargs})
+
+
+def test_mixture_with_max_rule():
+    o = _orch(transformation="mixture", transformation_lambda=(0.3, 0.05),
+              congression_theta_rule="max")
+    d = o.get_batch(o.data, o.get_random_idx(batch_key=0))
+    tr = trace(seed(o.jax_model, 0)).get_trace(data=d, priors=o.priors)
+    assert np.all(np.isfinite(np.asarray(tr["growth_pred"]["value"])))
+
+
+def test_requires_wt(tmp_path):
+    df = pd.read_csv(_GROWTH_CSV)
+    path = tmp_path / "no_wt.csv"
+    df[df["genotype"] != "wt"].to_csv(path, index=False)
+    with pytest.raises(ValueError, match="wt"):
+        ModelOrchestrator(growth_df=str(path), binding_df=None, **_RELATIVE)
+
+
+# ---------------------------------------------------------------------------
+# configuration
+# ---------------------------------------------------------------------------
+
+def test_configure_round_trip(tmp_path):
+    out_prefix = str(tmp_path / "rel")
+    configure_model(growth_df=_GROWTH_CSV, library_config=_LIBRARY_YAML,
+                    out_prefix=out_prefix, theta_model="hill_relative",
+                    theta_gauge_conc=[0.0, 0.1], skip_model_stats=True)
+    with open(f"{out_prefix}_config.yaml") as fh:
+        config = yaml.safe_load(fh)
+    assert config["components"]["theta"] == "hill_relative"
+    assert config["components"]["theta_gauge_conc"] == [0.0, 0.1]
+    o, _ = read_configuration(f"{out_prefix}_config.yaml")
+    assert o.settings["theta_gauge_conc"] == [0.0, 0.1]
+
+
+def test_configure_records_default_gauge(tmp_path):
+    out_prefix = str(tmp_path / "rel")
+    configure_model(growth_df=_GROWTH_CSV, library_config=_LIBRARY_YAML,
+                    out_prefix=out_prefix, theta_model="hill_relative",
+                    skip_model_stats=True)
+    with open(f"{out_prefix}_config.yaml") as fh:
+        config = yaml.safe_load(fh)
+    assert config["components"]["theta_gauge_conc"] == [0.0, 1.0]
+    other = str(tmp_path / "abs")
+    configure_model(growth_df=_GROWTH_CSV, library_config=_LIBRARY_YAML,
+                    out_prefix=other, skip_model_stats=True)
+    with open(f"{other}_config.yaml") as fh:
+        assert yaml.safe_load(fh)["components"]["theta_gauge_conc"] is None
+
+
+# ---------------------------------------------------------------------------
+# prediction and extraction
+# ---------------------------------------------------------------------------
+
+def test_prediction_subset_keeps_wt_for_the_gauge(relative, prior_draws):
+    new = copy_orchestrator(relative, genotypes=["M42I"])
+    assert set(new.growth_tm.tensor_dim_labels[-1]) == {"M42I", "wt"}
+    out = predict(relative, prior_draws, genotypes=["M42I"], num_samples=None)
+    assert set(out["genotype"].astype(str)) == {"M42I"}
+    assert np.isfinite(out.filter(like="q0.5").to_numpy()).all()
+
+
+def test_extract_curves_on_X(relative, prior_draws):
+    out = extract_theta_curves(relative, prior_draws, num_samples=None)
+    wt = out[out["genotype"] == "wt"].set_index("titrant_conc")
+    # every draw pins wt, so every quantile does
+    assert wt.loc[0.0].filter(like="q").to_numpy() == pytest.approx(1.0, abs=1e-4)
+    assert wt.loc[1.0].filter(like="q").to_numpy() == pytest.approx(0.0, abs=1e-4)
+
+
+def test_epistasis_on_X_is_additive_only(relative, prior_draws):
+    with pytest.raises(ValueError, match="scale='add'"):
+        extract_theta_epistasis(relative, prior_draws, scale="logit")
+    out = extract_theta_epistasis(relative, prior_draws, scale="add")
+    assert "in_regime" not in out.columns
+
+
+def test_extract_epistasis_cli_refuses_non_additive_X(tmp_path):
+    path = tmp_path / "x.csv"
+    pd.DataFrame({"genotype": ["wt", "A1B", "C2D", "A1B/C2D"],
+                  "q0.5": [1.0, 0.5, 0.4, 0.1],
+                  "theta_scale": "X"}).to_csv(path, index=False)
+    with pytest.raises(ValueError, match="scale='add'"):
+        extract_epistasis(str(path), out_prefix=str(tmp_path / "ep"),
+                          scale="logit")
+    extract_epistasis(str(path), out_prefix=str(tmp_path / "ep"), scale="add")
+    assert pd.read_csv(tmp_path / "ep.csv")["ep_obs"].iloc[0] == \
+        pytest.approx((0.1 - 0.4) - (0.5 - 1.0))
+
+
+# ---------------------------------------------------------------------------
+# summarize-fit truth on the X scale
+# ---------------------------------------------------------------------------
+
+def _truth_files(tmp_path):
+    theta = pd.DataFrame({
+        "genotype": ["wt"] * 3 + ["A1B"] * 3,
+        "titrant_name": "iptg",
+        "titrant_conc": [0.0, 0.01, 1.0] * 2,
+        "theta": [0.9, 0.5, 0.1, 0.6, 0.4, 0.2]})
+    theta_file = tmp_path / "tfs_sim_genotype_theta.csv"
+    theta.to_csv(theta_file, index=False)
+    pd.DataFrame({"genotype": ["wt", "A1B"], "activity": [1.0, 0.5]}).to_csv(
+        tmp_path / "tfs_sim_parameters.csv", index=False)
+    return str(theta_file)
+
+
+def test_x_gauge_and_ref(tmp_path):
+    theta_file = _truth_files(tmp_path)
+    config = {"components": {"theta": "hill_relative",
+                             "theta_gauge_conc": [0.0, 1.0]}}
+    g = summarize_fit_cli._x_gauge(config, theta_file, str(tmp_path))
+    assert g["wt"] == {"iptg": (0.9, 0.1)}
+    assert g["activity"]["A1B"] == 0.5
+
+    gt = pd.read_csv(theta_file).rename(columns={"theta": "ref"})
+    X = summarize_fit_cli._x_scale_ref(gt, g)
+    assert X[:3] == pytest.approx([1.0, 0.5, 0.0])
+    assert X[3] == pytest.approx((0.5 * 0.6 - 0.1) / 0.8)
+
+    assert summarize_fit_cli._x_gauge(
+        {"components": {"theta": "hill_geno"}}, theta_file, str(tmp_path)) is None
+
+
+def test_x_scale_growth_ref(tmp_path):
+    g = {"wt": {"iptg": (0.9, 0.1)}, "activity": {}}
+    growth = pd.DataFrame({"condition_rep": ["kan"], "growth_k": [0.02],
+                           "growth_m": [-0.01]})
+    out = summarize_fit_cli._x_scale_growth_ref(growth, g)
+    assert out["growth_k"].iloc[0] == pytest.approx(0.02 - 0.01 * 0.1)
+    assert out["growth_m"].iloc[0] == pytest.approx(-0.01 * 0.8)
diff --git a/tests/tfscreen/tfmodel/test_spiked_genotypes.py b/tests/tfscreen/tfmodel/test_spiked_genotypes.py
index 5e097104..1b38b75f 100644
--- a/tests/tfscreen/tfmodel/test_spiked_genotypes.py
+++ b/tests/tfscreen/tfmodel/test_spiked_genotypes.py
@@ -55,8 +55,8 @@ def test_spiked_genotypes_masking(dummy_data):
     genotype_idx = orchestrator.growth_tm.tensor_dim_names.index("genotype")
     genotype_labels = orchestrator.growth_tm.tensor_dim_labels[genotype_idx]
     
-    # Check the congression_mask in the data object
-    mask = np.array(orchestrator.data.growth.congression_mask)
+    # Spiked genotypes are the ones in the spiked ln_cfu0 class
+    mask = ~np.array(orchestrator.data.growth.ln_cfu0_spiked_mask)
     for i, label in enumerate(genotype_labels):
         if label == "A10G":
             assert not mask[i], f"Genotype {label} should be masked (False)"
@@ -93,7 +93,7 @@ def test_spiked_genotypes_single_string(dummy_data):
     genotype_idx = orchestrator.growth_tm.tensor_dim_names.index("genotype")
     genotype_labels = orchestrator.growth_tm.tensor_dim_labels[genotype_idx]
     
-    mask = np.array(orchestrator.data.growth.congression_mask)
+    mask = ~np.array(orchestrator.data.growth.ln_cfu0_spiked_mask)
     for i, label in enumerate(genotype_labels):
         if label == "A10G":
             assert not mask[i]
diff --git a/tests/tfscreen/tfmodel/test_struct_wiring.py b/tests/tfscreen/tfmodel/test_struct_wiring.py
index a342bcbd..7939859b 100644
--- a/tests/tfscreen/tfmodel/test_struct_wiring.py
+++ b/tests/tfscreen/tfmodel/test_struct_wiring.py
@@ -534,3 +534,64 @@ def test_binding_only_minibatch_svi_runs(tmp_path):
     assert svi_state is not None
     assert isinstance(params, dict)
     assert len(params) > 0
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# thermo_options: units, column handling, numbering checks via the orchestrator
+# ──────────────────────────────────────────────────────────────────────────────
+
+def _ddG_frame(genotypes=_GENOTYPES):
+    muts = sorted({p for g in genotypes if g != "wt" for p in g.split("/")})
+    vals = np.arange(len(muts) * len(_MWC_STRUCTURE_NAMES), dtype=float)
+    df = pd.DataFrame(vals.reshape(len(muts), -1), columns=list(_MWC_STRUCTURE_NAMES))
+    df.insert(0, "mut", muts)
+    return df
+
+
+def test_thermo_options_default_none_in_settings(binding_only_ddG_orchestrator):
+    assert binding_only_ddG_orchestrator.settings["thermo_options"] is None
+
+
+def test_thermo_options_kcal_and_messy_columns(tmp_path):
+    """kcal/mol is divided by RT; 'Mutation', a pandas index and extra
+    columns are accepted; struct_names come back in the model's order."""
+    from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+    binding_path = _make_binding_only_csv(tmp_path)
+    df = _ddG_frame()
+    messy = df.rename(columns={"mut": "Mutation"})
+    messy["unfolded"] = 0.0
+    messy = messy[["Mutation", "unfolded"] + list(reversed(_MWC_STRUCTURE_NAMES))]
+    path = str(tmp_path / "messy.csv")
+    messy.to_csv(path)
+    opts = {"ddG_units": "kcal/mol"}
+    orch = ModelOrchestrator(None, binding_path, binding_only=True,
+                             theta="thermo.O2_C12_K5_U0_a.PddG",
+                             thermo_data=path, thermo_options=opts)
+    b = orch.data.binding
+    assert tuple(b.struct_names) == tuple(_MWC_STRUCTURE_NAMES)
+    RT = 1.987204259e-3 * 298.15
+    expected = df.set_index("mut").loc[orch.mut_labels].to_numpy() / RT
+    np.testing.assert_allclose(b.struct_features, expected, rtol=1e-6)
+    assert orch.settings["thermo_options"] == opts
+
+
+def test_thermo_options_bad_value_fails_at_construction(tmp_path):
+    from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+    binding_path = _make_binding_only_csv(tmp_path)
+    with pytest.raises(ValueError, match="ddG_units"):
+        ModelOrchestrator(None, binding_path, binding_only=True,
+                          theta="thermo.O2_C12_K5_U0_a.PddG",
+                          thermo_data=_make_ddG_prior_csv(tmp_path),
+                          thermo_options={"ddG_units": "eV"})
+
+
+def test_wt_mismatch_fails_in_orchestrator(tmp_path):
+    from tfscreen.tfmodel.model_orchestrator import ModelOrchestrator
+    binding_path = _make_binding_only_csv(tmp_path)
+    df = _ddG_frame()
+    df["mut"] = df["mut"].str.replace("M42I", "L42I")
+    path = str(tmp_path / "bad.csv")
+    df.to_csv(path, index=False)
+    with pytest.raises(ValueError, match=r"M42I \(file wt L42\)"):
+        ModelOrchestrator(None, binding_path, binding_only=True,
+                          theta="thermo.O2_C12_K5_U0_a.PddG", thermo_data=path)
diff --git a/tests/tfscreen/tfmodel/test_two_stage.py b/tests/tfscreen/tfmodel/test_two_stage.py
new file mode 100644
index 00000000..a7782e5b
--- /dev/null
+++ b/tests/tfscreen/tfmodel/test_two_stage.py
@@ -0,0 +1,354 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from tfscreen.tfmodel import two_stage as ts
+
+
+# --- stage 1 genotypes ---
+
+def test_stage1_genotypes_keeps_wt_spikes_and_singles():
+    lib = pd.DataFrame({
+        "genotype": ["wt", "A1B", "C2D", "A1B/C2D", "E3F/G4H", "H5I/J6K/L7M"],
+        "in_spiked_origin": [True, True, False, False, True, True]})
+    have = ["wt", "A1B", "C2D", "A1B/C2D", "E3F/G4H", "N8P", "__unknown__"]
+    out = ts.stage1_genotypes(lib, have)
+    # the spiked double comes along; the unspiked double, absent spikes and
+    # __unknown__ do not; a single not in the library table still counts
+    assert out == sorted(["wt", "A1B", "C2D", "E3F/G4H", "N8P"])
+
+
+def test_is_single():
+    assert ts.is_single("A1B")
+    assert not ts.is_single("wt")
+    assert not ts.is_single("A1B/C2D")
+    assert not ts.is_single("__unknown__")
+
+
+# --- jackknife subsamples ---
+
+def _growth(n_rep=3, concs=(0.0, 0.1, 1.0), conds=("kanR+kan", "pheS+4CP"),
+            times=(100, 150, 200), genotypes=("wt", "A1B")):
+    rows = []
+    for c in conds:
+        for x in concs:
+            for r in range(n_rep):
+                for t in times:
+                    sample = f"{c}-{x}-{r}-{t}"
+                    for g in genotypes:
+                        rows.append({"sample": sample, "library": c[:4],
+                                     "condition_sel": c, "titrant_conc": x,
+                                     "replicate": r, "t_sel": t,
+                                     "genotype": g, "counts": 10})
+    return pd.DataFrame(rows)
+
+
+def test_tube_drops_are_stratified_and_leave_one():
+    tubes = ts.tube_table(_growth())
+    drop = ts.choose_tube_drops(tubes, 0.2, seed=1)
+    kept = tubes[~tubes["sample"].isin(drop)]
+    per = tubes.groupby(list(ts.STRATA)).size()
+    kept_per = kept.groupby(list(ts.STRATA)).size()
+    assert (kept_per >= 1).all()
+    # 9 tubes per stratum, 20% -> 2 dropped from each
+    assert ((per - kept_per) == 2).all()
+    assert ts.choose_tube_drops(tubes, 0.2, seed=1) == drop
+    assert ts.choose_tube_drops(tubes, 0.2, seed=2) != drop
+    # a stratum of one tube is never emptied
+    one = tubes.drop_duplicates(list(ts.STRATA))
+    assert ts.choose_tube_drops(one, 0.9, seed=1) == set()
+    with pytest.raises(ValueError):
+        ts.choose_tube_drops(tubes, 1.0, seed=1)
+
+
+def test_jackknife_subsample_drops_whole_tubes():
+    g = _growth()
+    sub, n, d, dropped = ts.jackknife_subsample(g, 0.2, seed=3)
+    assert n == g["sample"].nunique()
+    assert d == len(dropped) == 12
+    assert not sub["sample"].isin(dropped).any()
+    assert len(sub) == len(g) - d * 2      # two genotypes per tube
+    with pytest.raises(ValueError, match="tube column"):
+        ts.tube_table(g.drop(columns="library"))
+
+
+# --- jackknife k and m ---
+
+def _km(k, m, conds=("a", "b", "c")):
+    return pd.DataFrame({"k": k, "m": m}, index=pd.Index(conds,
+                                                         name="condition_rep"))
+
+
+def test_km_table_from_extract_params():
+    k = pd.DataFrame({"condition_rep": ["a", "a", "b"], "q0.5": [1.0, 3.0, 5.0]})
+    m = pd.DataFrame({"condition_rep": ["a", "b"], "q0.5": [0.1, 0.2]})
+    out = ts.km_table(k, m)
+    assert out.loc["a", "k"] == 2.0 and out.loc["b", "m"] == 0.2
+    with pytest.raises(ValueError, match="different conditions"):
+        ts.km_table(k, m.iloc[:1])
+
+
+def test_jackknife_km_scales_and_finds_the_main_direction():
+    rng = np.random.default_rng(0)
+    common = rng.normal(0, 1, 200)
+    est = [_km([0.01 + 0.002 * c, 0.02 + 0.004 * c, 0.03 + 0.001 * e],
+               [-0.014, 0.0, 0.014])
+           for c, e in zip(common, rng.normal(0, 0.01, 200))]
+    out = ts.jackknife_km(est, n=100, d=20)
+    assert out["factor"] == 4.0
+    raw_sd = np.std([e.loc["a", "k"] for e in est])
+    assert out["se"].loc["a", "k_se"] == pytest.approx(2.0 * raw_sd)
+    assert out["se"].loc["a", "m_se"] == pytest.approx(0.0)
+    assert out["k_share"] > 0.99
+    step = out["k_step"]
+    # the step follows the common move (a:b = 1:2) and its largest entry is +
+    assert step["b"] == pytest.approx(2 * step["a"], rel=0.01)
+    assert step["b"] > 0
+    assert np.hypot(step["a"], step["b"]) == pytest.approx(
+        np.sqrt(out["k_cov"].loc["a", "a"] + out["k_cov"].loc["b", "b"]),
+        rel=0.02)
+
+
+def test_jackknife_km_refuses_bad_inputs():
+    a = _km([1, 2, 3], [0, 0, 0])
+    with pytest.raises(ValueError, match="two subsamples"):
+        ts.jackknife_km([a], 10, 2)
+    with pytest.raises(ValueError, match="0 < d < n"):
+        ts.jackknife_km([a, a], 10, 0)
+    with pytest.raises(ValueError, match="different conditions"):
+        ts.jackknife_km([a, _km([1, 2], [0, 0], ("a", "b"))], 10, 2)
+
+
+# --- pinned priors ---
+
+def test_pinned_growth_priors_and_shift_tables():
+    km = _km([0.015, 0.03, 0.005], [-0.014, 0.0, 0.014])
+    t = ts.pinned_growth_priors(km)
+    assert list(t.columns) == ["condition_rep", "k_loc", "k_scale", "m_loc",
+                               "m_scale", "k_pinned", "m_pinned"]
+    assert (t["k_pinned"] == 1).all() and (t["m_pinned"] == 1).all()
+    one = ts.pinned_growth_priors(km, conditions=["a"])
+    assert list(one["condition_rep"]) == ["a"]
+    step = pd.Series({"a": 0.004, "b": 0.008, "c": 0.002})
+    plus, minus = ts.k_shift_priors(km, step)
+    assert np.allclose(plus["k_loc"] - minus["k_loc"], 2 * step.to_numpy())
+    assert np.allclose(plus["m_loc"], km["m"])
+    with pytest.raises(ValueError, match="every condition"):
+        ts.k_shift_priors(km, step.drop("c"))
+
+
+# --- the shift table ---
+
+def test_shift_table_half_difference_and_wt_zero():
+    rows = pd.DataFrame({"genotype": ["wt", "wt", "a", "a"],
+                         "titrant_name": "iptg",
+                         "titrant_conc": [0.0, 1.0, 0.0, 1.0]})
+    plus = rows.assign(**{"q0.5": [1.0, 0.0, 0.50, 0.20]})
+    minus = rows.assign(**{"q0.5": [1.0, 0.0, 0.56, 0.28]})
+    out = ts.shift_table(plus, minus)
+    a = out[out["genotype"] == "a"].set_index("titrant_conc")["shift"]
+    assert a[0.0] == pytest.approx(-0.03) and a[1.0] == pytest.approx(-0.04)
+    assert (out.loc[out["genotype"] == "wt", "shift"] == 0).all()
+    with pytest.raises(ValueError, match="different rows"):
+        ts.shift_table(plus, minus.iloc[:3])
+
+
+# --- chunked growth tables ---
+
+def test_growth_genotypes_and_filter_growth_csv(tmp_path):
+    g = _growth()
+    src = tmp_path / "g.csv"
+    g.to_csv(src, index=False)
+    assert ts.growth_genotypes(str(src), chunksize=7) == {"wt", "A1B"}
+    dst = tmp_path / "o.csv"
+    n = ts.filter_growth_csv(str(src), str(dst), genotypes=["A1B"], chunksize=7)
+    out = pd.read_csv(dst)
+    assert n == len(out) == (g["genotype"] == "A1B").sum()
+    assert set(out["genotype"]) == {"A1B"}
+    n = ts.filter_growth_csv(str(src), str(dst), library="kanR", chunksize=7)
+    assert set(pd.read_csv(dst)["library"]) == {"kanR"}
+    with pytest.raises(ValueError, match="no rows"):
+        ts.filter_growth_csv(str(src), str(dst), library="nope")
+
+
+# --- marker consistency ---
+
+def _pred(x, sd, genotypes, concs=(0.0, 1.0)):
+    rows = [{"genotype": g, "titrant_name": "iptg", "titrant_conc": c,
+             "q0.5": x[g][i], "q0.025": x[g][i] - 1.96 * sd,
+             "q0.975": x[g][i] + 1.96 * sd}
+            for g in genotypes for i, c in enumerate(concs)]
+    return pd.DataFrame(rows)
+
+
+def test_marker_consistency_flags_a_level_offset_after_the_affine_map():
+    rng = np.random.default_rng(0)
+    genos = ["wt"] + [f"A{i}B" for i in range(200)]
+    both = {g: rng.uniform(0, 1, 2) for g in genos}
+    # one library alone: stretched and shifted, small noise, one outlier
+    one = {g: 0.1 + 1.1 * v + rng.normal(0, 0.01, 2) for g, v in both.items()}
+    one["A7B"] = one["A7B"] + 0.3
+    out = ts.marker_consistency(_pred(both, 0.05, genos), _pred(one, 0.05, genos))
+    assert "wt" not in out.index
+    intercept, slope = out.attrs["affine"]
+    assert slope == pytest.approx(1.1, abs=0.02)
+    assert out.loc["A7B", "inconsistent"]
+    assert out["inconsistent"].sum() == 1
+    assert out.loc["A7B", "z"] == pytest.approx(
+        0.3 / np.hypot(0.05, 0.05), rel=0.15)
+
+
+# --- k and m from a MAP when extraction cannot give them ---
+
+def _priors_csv(path, k_pinned, m_pinned):
+    conds = ["a", "b", "c"]
+    rows = [("growth.condition_growth.m_scale", 0.01, None, None)]
+    for i, c in enumerate(conds):
+        rows += [("growth.condition_growth.k_loc", 0.01 * (i + 1), i, c),
+                 ("growth.condition_growth.m_loc", -0.001 * (i + 1), i, c),
+                 ("growth.condition_growth.k_pinned", float(k_pinned[i]), i, c),
+                 ("growth.condition_growth.m_pinned", float(m_pinned[i]), i, c)]
+    pd.DataFrame(rows, columns=["parameter", "value", "flat_index",
+                                "condition_rep"]).to_csv(path, index=False)
+
+
+def test_km_from_map_pinned_free_and_partial(tmp_path):
+    pri, npz = tmp_path / "p.csv", tmp_path / "m.npz"
+    # k pinned everywhere, m free everywhere
+    _priors_csv(pri, [1, 1, 1], [0, 0, 0])
+    np.savez(npz, condition_growth_m_auto_loc=np.array([-0.5, 0.0, 0.5]))
+    out = ts.km_from_map(str(npz), str(pri))
+    assert list(out.index) == ["a", "b", "c"]
+    assert np.allclose(out["k"], [0.01, 0.02, 0.03])
+    assert np.allclose(out["m"], [-0.5, 0.0, 0.5])
+    # m pinned in b only: the free entries come from {site}_free
+    _priors_csv(pri, [1, 1, 1], [0, 1, 0])
+    np.savez(npz, condition_growth_m_free_auto_loc=np.array([7.0, 9.0]))
+    out = ts.km_from_map(str(npz), str(pri))
+    assert np.allclose(out["m"], [7.0, -0.002, 9.0])
+    # a free parameter missing from the MAP is an error
+    _priors_csv(pri, [0, 1, 1], [1, 1, 1])
+    np.savez(npz, other=np.zeros(1))
+    with pytest.raises(ValueError, match="no k"):
+        ts.km_from_map(str(npz), str(pri))
+
+
+# --- the k-shift refits ---
+
+def test_dk_geno_hypers_and_pins(tmp_path):
+    npz = tmp_path / "p.npz"
+    np.savez(npz, dk_geno_hyper_loc_auto_loc=np.array([-2.0]),
+             dk_geno_hyper_scale_auto_loc=np.array(1.5),
+             dk_geno_hyper_shift_auto_loc=np.array(0.07),
+             other_auto_loc=np.zeros(3))
+    held = ts.dk_geno_hypers(str(npz))
+    assert held == pytest.approx({"hyper_loc": -2.0, "hyper_scale": 1.5,
+                                  "hyper_shift": 0.07})
+    pri = tmp_path / "priors.csv"
+    pd.DataFrame({"parameter": ["growth.dk_geno.hyper_loc_loc",
+                                "growth.dk_geno.pinned.hyper_loc"],
+                  "value": ["-3.5", "9"]}).to_csv(pri, index=False)
+    ts.pin_dk_geno_hypers(str(pri), held)
+    ts.pin_dk_geno_hypers(str(pri), held)        # idempotent
+    df = pd.read_csv(pri)
+    assert len(df) == 4
+    rows = dict(zip(df["parameter"], df["value"]))
+    assert rows["growth.dk_geno.pinned.hyper_loc"] == pytest.approx(-2.0)
+    assert rows["growth.dk_geno.hyper_loc_loc"] == pytest.approx(-3.5)
+
+
+def test_dk_geno_hypers_absent(tmp_path):
+    npz = tmp_path / "p.npz"
+    np.savez(npz, other_auto_loc=np.zeros(3))
+    assert ts.dk_geno_hypers(str(npz)) == {}
+    pri = tmp_path / "priors.csv"
+    pd.DataFrame({"parameter": ["a"], "value": [1]}).to_csv(pri, index=False)
+    ts.pin_dk_geno_hypers(str(pri), {})
+    assert len(pd.read_csv(pri)) == 1
+
+
+def test_refit_drift():
+    def pred(x):
+        return pd.DataFrame({"genotype": ["wt", "A1B", "C2D"],
+                             "titrant_name": "iptg", "titrant_conc": 0.0,
+                             "q0.5": [1.0, x, x + 0.1]})
+    out = ts.refit_drift(pred(0.5), {"plus": pred(0.45), "zero": pred(0.5)})
+    out = out.set_index("refit")
+    assert out.loc["plus", "median_change"] == pytest.approx(-0.05)
+    assert out.loc["zero", "median_change"] == pytest.approx(0.0)
+
+
+# --- the summary table ---
+
+def test_genotype_reads_in_chunks(tmp_path):
+    g = pd.DataFrame({"genotype": ["wt", "A1B", "wt", "A1B", "C2D"],
+                      "library": ["kanR", "kanR", "pheS", "pheS", "kanR"],
+                      "counts": [10, 3, 5, 2, 7]})
+    g.to_csv(tmp_path / "g.csv", index=False)
+    r = ts.genotype_reads(str(tmp_path / "g.csv"), chunksize=2)
+    assert r.loc["wt"].tolist() == [15, 10, 5]
+    assert list(r.columns) == ["reads", "reads_kanR", "reads_pheS"]
+    assert r.loc["C2D", "reads_pheS"] == 0
+
+
+def test_num_mutations():
+    assert [ts.num_mutations(g) for g in ("wt", "A1B", "A1B/C2D/E3F")] == [0, 1, 3]
+
+
+def test_x_summary_joins():
+    pred = pd.DataFrame({"genotype": ["wt", "A1B", "A1B"], "titrant_name": "iptg",
+                         "titrant_conc": [0.0, 0.0, 1.0], "q0.025": 0.1,
+                         "q0.5": 0.5, "q0.975": 0.9, "in_training_data": 1,
+                         "theta_scale": "X"})
+    reads = pd.DataFrame({"reads": [9], "reads_kanR": [9]},
+                         index=pd.Index(["A1B"], name="genotype"))
+    cons = pd.DataFrame({"z": [2.5], "inconsistent": [True]},
+                        index=pd.Index(["A1B"], name="genotype"))
+    shift = pred[["genotype", "titrant_name", "titrant_conc"]].assign(shift=[0, -0.03, -0.02])
+    x = ts.x_summary(pred, reads, cons, shift)
+    assert x["reads"].tolist() == [0, 9, 9]
+    assert x["k_shift"].tolist() == [0, -0.03, -0.02]
+    assert x["inconsistent"].iloc[1] == True and pd.isna(x["inconsistent"].iloc[0])
+    assert x.columns[-2:].tolist() == ["consistency_z", "inconsistent"]
+    bare = ts.x_summary(pred, reads)
+    assert bare["k_shift"].isna().all() and bare["consistency_z"].isna().all()
+
+
+def test_flag_reads_strings_and_numbers():
+    assert ts._flag(" True ") and ts._flag("1.0") and ts._flag("1")
+    assert not ts._flag("0") and not ts._flag("false")
+    assert ts._flag(1.0) and not ts._flag(0)
+
+
+def test_km_from_map_scalar_rows_and_defaults(tmp_path):
+    pri, npz = tmp_path / "p.csv", tmp_path / "m.npz"
+    # k_loc per condition; m_loc and m_pinned scalar rows (broadcast);
+    # no k_pinned rows at all (k free everywhere by default)
+    rows = [("growth.condition_growth.m_loc", -0.004, None, None),
+            ("growth.condition_growth.m_pinned", 1.0, None, None)]
+    rows += [("growth.condition_growth.k_loc", 0.01 * (i + 1), i, c)
+             for i, c in enumerate("abc")]
+    pd.DataFrame(rows, columns=["parameter", "value", "flat_index",
+                                "condition_rep"]).to_csv(pri, index=False)
+    np.savez(npz, condition_growth_k_auto_loc=np.array([0.1, 0.2, 0.3]))
+    out = ts.km_from_map(str(npz), str(pri))
+    assert list(out.index) == ["a", "b", "c"]
+    assert np.allclose(out["k"], [0.1, 0.2, 0.3])
+    assert np.allclose(out["m"], -0.004)
+
+
+def test_km_from_map_needs_per_condition_rows(tmp_path):
+    pri, npz = tmp_path / "p.csv", tmp_path / "m.npz"
+    pd.DataFrame({"parameter": ["growth.condition_growth.k_loc"],
+                  "value": [0.01], "flat_index": [None],
+                  "condition_rep": [None]}).to_csv(pri, index=False)
+    np.savez(npz, other=np.zeros(1))
+    with pytest.raises(ValueError, match="no per-condition growth rows"):
+        ts.km_from_map(str(npz), str(pri))
+
+
+def test_marker_consistency_needs_shared_non_wt_rows():
+    both = _pred({"wt": [1.0, 0.0], "A1B": [0.5, 0.4]}, 0.05, ["wt", "A1B"])
+    one = _pred({"wt": [1.0, 0.0], "C2D": [0.5, 0.4]}, 0.05, ["wt", "C2D"])
+    with pytest.raises(ValueError, match="no non-wt rows in common"):
+        ts.marker_consistency(both, one)
diff --git a/tests/tfscreen/util/cli/test_cli_reference.py b/tests/tfscreen/util/cli/test_cli_reference.py
new file mode 100644
index 00000000..dfab65c0
--- /dev/null
+++ b/tests/tfscreen/util/cli/test_cli_reference.py
@@ -0,0 +1,29 @@
+"""docs/source/cli.rst must match what the commands' parsers print."""
+import importlib.util
+import os
+
+ROOT = os.path.join(os.path.dirname(__file__), "..", "..", "..", "..")
+SCRIPT = os.path.join(ROOT, "docs", "scripts", "make_cli_reference.py")
+
+
+def _module():
+    spec = importlib.util.spec_from_file_location("make_cli_reference", SCRIPT)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+def test_every_entry_point_builds_a_parser():
+    mod = _module()
+    for name, target in mod.entry_points():
+        text = mod.help_text(name, target)
+        assert text.startswith(f"usage: {name}"), name
+
+
+def test_cli_reference_is_current():
+    mod = _module()
+    with open(mod.OUT) as fh:
+        current = fh.read()
+    assert current == mod.render(), (
+        "docs/source/cli.rst is stale; run "
+        "python docs/scripts/make_cli_reference.py")
diff --git a/tests/tfscreen/util/cli/test_generalized_main.py b/tests/tfscreen/util/cli/test_generalized_main.py
index 35e2ad89..965e711e 100644
--- a/tests/tfscreen/util/cli/test_generalized_main.py
+++ b/tests/tfscreen/util/cli/test_generalized_main.py
@@ -58,11 +58,15 @@ def target_func(flag=False, inverse=True):
         assert result['flag'] is False
         assert result['inverse'] is True
         
-        # Toggle
-        generalized_main(target_func, argv=['--flag', '--inverse'])
+        # Toggle: a True default is turned off by --no_<name>
+        generalized_main(target_func, argv=['--flag', '--no_inverse'])
         assert result['flag'] is True
         assert result['inverse'] is False
 
+        # --<name> on a True default keeps it on (never inverts)
+        generalized_main(target_func, argv=['--inverse'])
+        assert result['inverse'] is True
+
     def test_manual_overrides(self):
         result = {}
         def target_func(a, b=1):
@@ -113,9 +117,11 @@ def test_bool_int_default_behavior(self):
         def target_func(v: bool = True):
             result['v'] = v
             
-        # Should be store_false
-        generalized_main(target_func, argv=['--v'])
+        generalized_main(target_func, argv=['--no_v'])
         assert result['v'] is False
+
+        generalized_main(target_func, argv=['--v'])
+        assert result['v'] is True
         
         generalized_main(target_func, argv=[])
         assert result['v'] is True
@@ -156,6 +162,72 @@ def target_func2(items=[]):
                          manual_arg_nargs={'items': '+'})
         assert result2['items'] == ['1', '2'] # Stay as strings by default
 
+    def test_none_default_reads_string(self):
+        result = {}
+        def target_func(path=None):
+            result['path'] = path
+
+        generalized_main(target_func, argv=['--path', 'x.csv'])
+        assert result['path'] == 'x.csv'
+        generalized_main(target_func, argv=[])
+        assert result['path'] is None
+
+    def test_returns_none_for_sys_exit(self):
+        # console scripts run sys.exit(main()); a returned value would exit 1
+        def target_func(a=1):
+            return a * 2
+        assert generalized_main(target_func, argv=['--a', '4']) is None
+
+    def test_help_from_docstring(self, capsys):
+        def target_func(data_file, alpha=0.05, quiet=False):
+            """
+            Do a thing to a file.
+
+            Parameters
+            ----------
+            data_file : str
+                The input table.
+            alpha : float
+                Significance
+                level.
+            quiet : bool
+                Print less.
+            """
+
+        with pytest.raises(SystemExit):
+            generalized_main(target_func, argv=['--help'], prog='tfs-thing')
+        out = capsys.readouterr().out
+        assert out.startswith('usage: tfs-thing')
+        assert 'Do a thing to a file.' in out
+        assert 'The input table.' in out
+        assert 'Significance level. (default: 0.05)' in out
+        assert 'Print less.' in out
+        # The parameter section is not repeated in the description
+        assert '----------' not in out
+
+    def test_provenance_written_for_out_prefix(self, tmp_path, capsys):
+        import json
+        def target_func(out_prefix='x'):
+            pass
+
+        prefix = str(tmp_path / 'sub' / 'run')
+        generalized_main(target_func, argv=['--out_prefix', prefix])
+        prov = json.load(open(prefix + '_provenance.json'))
+        assert 'tfscreen_version' in prov
+        assert 'commit' in prov
+        assert '--out_prefix' in prov['command']
+        assert 'tfscreen' in capsys.readouterr().err
+
+    def test_provenance_can_be_skipped(self, tmp_path):
+        def target_func(out_prefix='x'):
+            pass
+
+        prefix = str(tmp_path / 'run')
+        generalized_main(target_func, argv=['--out_prefix', prefix],
+                         write_provenance=False)
+        assert not (tmp_path / 'run_provenance.json').exists()
+
+
 from unittest.mock import patch
 
 class PatchSysArgv:
@@ -168,3 +240,27 @@ def __enter__(self):
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         self.patcher.stop()
+
+
+def test_split_docstring_without_parameters_section():
+    from tfscreen.util.cli.generalized_main import _split_docstring
+    desc, params = _split_docstring("""
+        Do a thing.
+
+        More text, no numpydoc sections.
+        """)
+    assert desc == "Do a thing.\n\nMore text, no numpydoc sections."
+    assert params == {}
+
+
+def test_capture_parser_raises_when_main_skips_generalized_main():
+    import importlib
+    gm = importlib.import_module("tfscreen.util.cli.generalized_main")
+
+    def not_a_cli():
+        return None
+
+    with pytest.raises(RuntimeError, match="did not call generalized_main"):
+        gm.capture_parser(not_a_cli)
+    # The capture flag is reset even after the failure
+    assert gm._CAPTURE_PARSER is False
diff --git a/tests/tfscreen/util/io/test_read_dataframe.py b/tests/tfscreen/util/io/test_read_dataframe.py
index e656c365..45b893a1 100644
--- a/tests/tfscreen/util/io/test_read_dataframe.py
+++ b/tests/tfscreen/util/io/test_read_dataframe.py
@@ -212,4 +212,17 @@ def test_genotype_as_index_not_categorized():
     result = read_dataframe(df, index_column="genotype")
     assert result.index.name == "genotype"
     # Index should be plain strings, not categorical (dtype varies: object or StringDtype)
-    assert not isinstance(result.index.dtype, pd.CategoricalDtype)
\ No newline at end of file
+    assert not isinstance(result.index.dtype, pd.CategoricalDtype)
+
+def test_read_dataframe_compressed_csv_and_tsv(tmp_path):
+    """A .csv.gz or .tsv.bz2 is read by its inner extension."""
+    import pandas as pd
+    from tfscreen.util.io.read_dataframe import read_dataframe
+
+    df = pd.DataFrame({"a": [1, 2], "b": [0.5, 1.5]})
+    gz = tmp_path / "x.csv.gz"
+    df.to_csv(gz, index=False)
+    pd.testing.assert_frame_equal(read_dataframe(str(gz)), df)
+    bz = tmp_path / "x.tsv.bz2"
+    df.to_csv(bz, sep="\t", index=False)
+    pd.testing.assert_frame_equal(read_dataframe(str(bz)), df)
diff --git a/tests/tfscreen/util/test_grid_utils.py b/tests/tfscreen/util/test_grid_utils.py
index 356220ad..db20db86 100644
--- a/tests/tfscreen/util/test_grid_utils.py
+++ b/tests/tfscreen/util/test_grid_utils.py
@@ -1,15 +1,23 @@
 """
-Unit tests for tfscreen.util.grid_utils.
-
-The relativize_node / relativize_config_paths / relativize_template_vars
-functions are already covered in
-tests/tfscreen/tfmodel/scripts/test_setup_grid_cli.py, so this file focuses
-on the remaining helpers: sanitize, make_jinja_env, and make_run_name.
+Unit tests for tfscreen.util.grid_utils: sanitize, make_jinja_env,
+make_run_name and the input-staging helpers (InputStager,
+check_no_outside_paths, stage_template_vars, render_run_template).
 """
 
+import os
+
 import pytest
 
-from tfscreen.util.grid_utils import sanitize, make_jinja_env, make_run_name
+from tfscreen.util.grid_utils import (
+    INPUTS_DIRNAME,
+    InputStager,
+    check_no_outside_paths,
+    make_jinja_env,
+    make_run_name,
+    render_run_template,
+    sanitize,
+    stage_template_vars,
+)
 
 
 # ---------------------------------------------------------------------------
@@ -133,3 +141,119 @@ def test_empty_suffix_returns_prefix_only(self):
     def test_bad_template_raises_value_error(self):
         with pytest.raises(ValueError, match="run_name template error"):
             make_run_name("{% for %}", {}, 0)
+
+
+# ---------------------------------------------------------------------------
+# InputStager
+# ---------------------------------------------------------------------------
+
+def _write(path, text):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    with open(path, "w") as fh:
+        fh.write(text)
+    return str(path)
+
+
+class TestInputStager:
+    def test_copies_once_and_returns_run_relative_path(self, tmp_path):
+        src = _write(tmp_path / "data" / "growth.csv", "g")
+        stager = InputStager(tmp_path / "grid", "tfs-test")
+        rel = stager.stage(src, "x")
+        assert rel == os.path.join("..", INPUTS_DIRNAME, "growth.csv")
+        assert stager.stage(src, "x") == rel
+        assert os.listdir(tmp_path / "grid" / INPUTS_DIRNAME) == ["growth.csv"]
+        run_dir = tmp_path / "grid" / "run_0001"
+        run_dir.mkdir()
+        assert open(os.path.join(run_dir, rel)).read() == "g"
+
+    def test_same_name_different_file_gets_suffix(self, tmp_path):
+        a = _write(tmp_path / "a" / "growth.csv", "a")
+        b = _write(tmp_path / "b" / "growth.csv", "b")
+        stager = InputStager(tmp_path / "grid", "tfs-test")
+        assert stager.stage(a, "x").endswith("growth.csv")
+        rel_b = stager.stage(b, "x")
+        assert rel_b.endswith("growth_2.csv")
+        assert open(tmp_path / "grid" / INPUTS_DIRNAME / "growth_2.csv").read() == "b"
+
+    def test_never_overwrites_changed_existing_copy(self, tmp_path):
+        old = _write(tmp_path / "grid" / INPUTS_DIRNAME / "growth.csv", "old")
+        src = _write(tmp_path / "data" / "growth.csv", "new")
+        rel = InputStager(tmp_path / "grid", "tfs-test").stage(src, "x")
+        assert rel.endswith("growth_2.csv")
+        assert open(old).read() == "old"
+
+    def test_reuses_identical_existing_copy(self, tmp_path):
+        _write(tmp_path / "grid" / INPUTS_DIRNAME / "growth.csv", "same")
+        src = _write(tmp_path / "data" / "growth.csv", "same")
+        rel = InputStager(tmp_path / "grid", "tfs-test").stage(src, "x")
+        assert rel.endswith(os.sep + "growth.csv")
+
+    def test_missing_file_raises(self, tmp_path):
+        with pytest.raises(FileNotFoundError, match="my key"):
+            InputStager(tmp_path, "tfs-test").stage(str(tmp_path / "no.csv"), "my key")
+
+    def test_directory_raises(self, tmp_path):
+        with pytest.raises(ValueError, match="directory"):
+            InputStager(tmp_path, "tfs-test").stage(str(tmp_path), "my key")
+
+    def test_dry_run_copies_nothing(self, tmp_path):
+        src = _write(tmp_path / "data" / "growth.csv", "g")
+        InputStager(tmp_path / "grid", "tfs-test", dry_run=True).stage(src, "x")
+        assert not os.path.exists(tmp_path / "grid")
+
+
+# ---------------------------------------------------------------------------
+# check_no_outside_paths
+# ---------------------------------------------------------------------------
+
+class TestCheckNoOutsidePaths:
+    def test_known_key_allowed(self, tmp_path):
+        f = _write(tmp_path / "g.csv", "x")
+        check_no_outside_paths({"growth_df": f}, [("growth_df",)], str(tmp_path), "")
+
+    def test_unknown_absolute_file_raises(self, tmp_path):
+        f = _write(tmp_path / "g.csv", "x")
+        with pytest.raises(ValueError, match="a.b.*HINT"):
+            check_no_outside_paths({"a": {"b": f}}, [], str(tmp_path), "HINT")
+
+    def test_unknown_relative_file_raises(self, tmp_path):
+        _write(tmp_path / "g.csv", "x")
+        with pytest.raises(ValueError, match="g.csv"):
+            check_no_outside_paths({"a": ["g.csv"]}, [], str(tmp_path), "")
+
+    def test_per_key_source_dirs(self, tmp_path):
+        _write(tmp_path / "base" / "g.csv", "x")
+        cfg = {"a": "g.csv", "b": "g.csv"}
+        dirs = {"a": str(tmp_path), "b": str(tmp_path / "base")}
+        with pytest.raises(ValueError, match="'b'"):
+            check_no_outside_paths(cfg, [], dirs, "")
+
+    def test_non_paths_pass(self, tmp_path):
+        check_no_outside_paths({"a": "linear", "b": 1, "c": [0.1, None]},
+                               [], str(tmp_path), "")
+
+
+# ---------------------------------------------------------------------------
+# stage_template_vars / render_run_template
+# ---------------------------------------------------------------------------
+
+class TestStageTemplateVars:
+    def test_file_values_staged_others_untouched(self, tmp_path):
+        _write(tmp_path / "genos.txt", "wt")
+        stager = InputStager(tmp_path / "grid", "tfs-test")
+        out = stage_template_vars(
+            {"f": "genos.txt", "seed": 1, "label": "x", "here": "."},
+            str(tmp_path), stager)
+        assert out == {"f": os.path.join("..", INPUTS_DIRNAME, "genos.txt"),
+                       "seed": 1, "label": "x", "here": "."}
+
+    def test_directory_value_raises(self, tmp_path):
+        (tmp_path / "d").mkdir()
+        with pytest.raises(ValueError, match="directory"):
+            stage_template_vars({"f": "d"}, str(tmp_path),
+                                InputStager(tmp_path, "tfs-test"))
+
+    def test_render_names_run_on_undefined(self):
+        tmpl = make_jinja_env(strict=True).from_string("{{ missing }}")
+        with pytest.raises(ValueError, match="run_0007"):
+            render_run_template(tmpl, {}, "run_0007")
diff --git a/tests/tfscreen/util/test_provenance.py b/tests/tfscreen/util/test_provenance.py
new file mode 100644
index 00000000..a0c456e1
--- /dev/null
+++ b/tests/tfscreen/util/test_provenance.py
@@ -0,0 +1,94 @@
+import json
+
+from tfscreen.util import provenance
+
+
+def test_git_state_has_commit_in_checkout():
+    state = provenance.git_state()
+    assert set(state) == {"commit", "dirty"}
+    # The test suite runs from a git checkout
+    assert state["commit"] is None or len(state["commit"]) == 40
+
+
+def test_git_state_without_git(monkeypatch):
+    monkeypatch.setattr(provenance, "_git", lambda *a: None)
+    assert provenance.git_state() == {"commit": None, "dirty": None}
+
+
+def test_get_provenance_fields():
+    provenance.set_command(["tfs-x", "a b", "--y"])
+    prov = provenance.get_provenance()
+    for key in ("tfscreen_version", "commit", "dirty", "command",
+                "python", "host", "cwd", "time"):
+        assert key in prov
+    assert prov["command"] == "tfs-x 'a b' --y"
+
+
+def test_format_provenance():
+    prov = {"tfscreen_version": "9.9.9", "commit": "a" * 40,
+            "dirty": True, "command": "tfs-x"}
+    line = provenance.format_provenance(prov)
+    assert line == "tfscreen 9.9.9, commit aaaaaaaaaa (dirty): tfs-x"
+
+    prov.update(commit=None, command=None)
+    assert provenance.format_provenance(prov) == "tfscreen 9.9.9, no git commit"
+
+
+def test_write_provenance(tmp_path):
+    path = provenance.write_provenance(str(tmp_path / "d" / "p.json"),
+                                       {"tfscreen_version": "1"})
+    assert json.load(open(path)) == {"tfscreen_version": "1"}
+
+
+def test_git_returns_none_when_git_cannot_run(monkeypatch):
+    import subprocess
+
+    def boom(*a, **k):
+        raise FileNotFoundError("git")
+    monkeypatch.setattr(provenance.subprocess, "run", boom)
+    assert provenance._git("rev-parse", "HEAD") is None
+
+    def timeout(*a, **k):
+        raise subprocess.TimeoutExpired("git", 10)
+    monkeypatch.setattr(provenance.subprocess, "run", timeout)
+    assert provenance._git("rev-parse", "HEAD") is None
+
+
+def test_git_returns_none_on_nonzero_exit(monkeypatch):
+    import subprocess
+
+    def fail(*a, **k):
+        return subprocess.CompletedProcess(a, 128, stdout="", stderr="no repo")
+    monkeypatch.setattr(provenance.subprocess, "run", fail)
+    assert provenance._git("rev-parse", "HEAD") is None
+
+
+def test_git_returns_stripped_stdout(monkeypatch):
+    import subprocess
+
+    def ok(*a, **k):
+        return subprocess.CompletedProcess(a, 0, stdout="abc123\n", stderr="")
+    monkeypatch.setattr(provenance.subprocess, "run", ok)
+    assert provenance._git("rev-parse", "HEAD") == "abc123"
+
+
+def test_write_provenance_defaults_to_current(tmp_path, monkeypatch):
+    monkeypatch.setattr(provenance, "_GIT_STATE",
+                        {"commit": "b" * 40, "dirty": False})
+    monkeypatch.setattr(provenance, "_COMMAND", None)
+    provenance.set_command(["tfs-y"])
+    path = provenance.write_provenance(str(tmp_path / "p.json"))
+    prov = json.load(open(path))
+    assert prov["commit"] == "b" * 40
+    assert prov["dirty"] is False
+    assert prov["command"] == "tfs-y"
+
+
+def test_print_provenance_defaults_to_current_and_stderr(capsys, monkeypatch):
+    monkeypatch.setattr(provenance, "_GIT_STATE",
+                        {"commit": "c" * 40, "dirty": True})
+    monkeypatch.setattr(provenance, "_COMMAND", None)
+    provenance.set_command(["tfs-z", "--a"])
+    provenance.print_provenance()
+    err = capsys.readouterr().err
+    assert "commit cccccccccc (dirty): tfs-z --a" in err